Computational strategies for flexible multibody systems

Computational strategies forﬂexible multibody systemsTamer M WasfyAdvanced Science and Automation Corp,Hampton VAtamer@Ahmed K NoorCenter for Advanced Engineering Environments,Old Dominion University,Hampton VA;a.k.noor@The status and some recent developments in computational modeling ofﬂexible multibodysystems are summarized.Discussion focuses on a number of aspects ofﬂexible multibodydynamics including:modeling of theﬂexible components,constraint modeling,solution tech-niques,control strategies,coupled problems,design,and experimental studies.The characteris-tics of the three types of reference frames used in modelingﬂexible multibody systems,namely,ﬂoating frame,corotational frame,and inertial frame,are compared.Future directionsof research are identiﬁed.These include new applications such as micro-and nano-mechanicalsystems;techniques and strategies for increasing theﬁdelity and computational efﬁciency ofthe models;and tools that can improve the design process ofﬂexible multibody systems.Thisreview article cites877references.͓DOI:10.1115/1.1590354͔1INTRODUCTIONAﬂexible multibody system͑FMS͒is a group of intercon-nected rigid and deformable components,each of which may undergo large translational and rotational motions.The com-ponents may also come into contact with the surrounding environment or with one another.Typical connections be-tween the components include:revolute,spherical,prismatic and planar joints,lead screws,gears,and cams.The compo-nents can be connected in closed-loop conﬁgurations͑eg, linkages͒and/or open-loop͑or tree͒conﬁgurations͑eg,ma-nipulators͒.The termﬂexible multibody dynamics͑FMD͒refers to the computational strategies that are used for calculating the dy-namic response͑which includes time-histories of motion,de-formation and stress͒of FMS due to externally applied forces,constraints,and/or initial conditions.This type of simulation is referred to as forward dynamics.FMD also comprises inverse dynamics,which predicts the applied forces necessary to generate a desired motion response.FMD is important because it can be used in the analysis,design, and control of many practical systems such as:ground,air, and space transportation vehicles͑such as bicycles,automo-biles,trains,airplanes,and spacecraft͒;manufacturing ma-chines;manipulators and robots;mechanisms;articulated earthbound structures͑such as cranes and draw bridges͒;ar-ticulated space structures͑such as satellites and space sta-tions͒;and bio-dynamical systems͑human body,animals, and insects͒.Motivated by these applications,FMD has been the focus of intensive research for the last thirty years.FMD is used in the design and control of FMS.In design,FMD can be used to calculate the system parameters͑such as di-mensions,conﬁguration,and materials͒that minimize the system cost while satisfying the design safety constraints ͑such as strength,rigidity,and static/dynamic stability͒.FMD is used in control applications for predicting the response of the multibody system to a given control action and for cal-culating the changes in control actions necessary to direct the system towards the desired response͑inverse dynamics͒. FMD can be used in model-based control as an integral part of the controller as well as in controller design for optimiz-ing the controller/FMS parameters.In recent years,considerable effort has been devoted to modeling,design,and control of FMS.The number of pub-lications on the subject has been steadily increasing.Lists and reviews of the many contributions on the subject are given in survey papers on FMD͓1,2͔and on the general area of multibody dynamics,including both rigid andﬂexible multibody systems͓3–7͔.Special survey papers have been published on a number of special aspects of FMD,including: dynamic analysis ofﬂexible manipulators͓8͔,dynamic analysis of elastic linkages͓9–13͔,and dynamics of satellites withﬂexible appendages͓14͔.A number of books on FMD have been published͓15–23͔.In the last few years,there have been a number of conferences,symposia,and special sessions devoted to FMD͓24͔.Two archival journals are devoted to the subjects of rigid andﬂexible multibody dy-Transmitted by Associate Editor V BirmanAppl Mech Rev vol56,no6,November2003©2003American Society of Mechanical Engineers553namics:‘‘Multibody System Dynamics’’published by Klu-wer Academic Publishers,and‘‘Journal of Multibody Dy-namics’’published by Ingenta Journals.There are a number of commercial codes forﬂexible multibody dynamics͑eg, ADAMS from Mechanical Dynamics Inc,DADS from CADSI Inc,MECANO from Samtech,and SimPack from INTEC GmbH͒as well as many research codes developed at universities and research institutions.A survey of multibody dynamics software up to1990with benchmarks was pre-sented in Schiehlen͓25͔.There are two compelling motiva-tions for developing FMD modeling techniques.Theﬁrst motivation is that a number of current problems have not yet been solved to a satisfactory degree͑see Section9͒.The second motivation is that future multibody systems are likely to require more sophisticated models than has heretofore been provided.This is because practical FMD applications are likely to have more stringent requirements of economy, high performance,light weight,high speed/acceleration,and safety.There is a need to broaden awareness among practicing engineers and researchers about the current status and recent developments in various aspects of FMD.The present paper attempts toﬁll this need by classifying and reviewing the FMD literature.Also,future directions for research that have high potential for improving the accuracy and computational efﬁciency of the predictive capabilities of the dynamics and failure of FMS are identiﬁed.Some of these objectives were addressed in the previous review papers.In the present paper, an attempt is made to provide a more comprehensive review of the literature.The following aspects of FMD are ad-dressed in the present paper:•Models of theﬂexible components•Constraints models•Solution techniques,including solution procedures and methods for enhancing the computational procedures and models•Control strategies•Coupled FMD problems•Design of FMS•Experimental studiesThere are many common elements of FMD with structural dynamics,nonlinearﬁnite element method and crashworthi-ness analysis.Some of the studies in these areas,which in-clude techniques that are suitable for modeling FMS,are included in this review.The number of publications on the diverse aspects of FMD is very large.The cited references are selected for illustrating the ideas presented and are not necessarily the only signiﬁcant contributions to the subject. The discussion in this paper is kept,for the most part,on a descriptive level and for all the mathematical details,the reader is referred to the cited literature.2MODELS OF FLEXIBLE COMPONENTS2.1Deformation reference framesIn multibody dynamics,an inertial frame serves as a global reference frame for describing the motion of the multibody system.In addition,intermediate reference frames that are attached to eachﬂexible component and follow the average local rigid body motion͑rotation and translation͒are often used.The motion of the component relative to the interme-diate frame is,approximately,due only to the deformation of the component.This simpliﬁes the calculation of the internal forces because stress and strain measures that are not invari-ant under rigid body motion,such as the Cauchy stress tensor and the small strain tensor,can be used to calculate these forces with respect to the intermediate frame.These tensors result in a linear force displacement relation.Two main types of intermediate frames are used:ﬂoating and corotational frames.Theﬂoating frame follows an average rigid body motion of the entireﬂexible component or substructure.The corotational frame follows an average rigid body motion of an individualﬁnite element within theﬂexible component.In many papers,intermediate frames are not used,instead the global inertial frame is directly used for measuring deforma-tions.In this approach,the motion of an element consists of a combination of rigid body motion and deformation and the two types of motion are not separated.Nonlinearﬁnite strain measures and corresponding energy conjugate stress mea-sures,which are objective and invariant under rigid body motion,are used to calculate the internal forces with respect to the global inertial frame.A comparison between the major characteristics of the three types of frames,namely,ﬂoating, corotational,and inertial frames is given in Table1.The references where the frames wereﬁrst applied to FMS are given in Table2.Theﬂoating frame approach originated out of research on rigid multibody dynamics in the late1960s.It was used for extending rigid multibody dynamics codes to FMS.This was done by superimposing small elastic deformations on the large rigid body motion obtained using the rigid multibody dynamics code.Initial applications of theﬂoating frame ap-proach included:spinningﬂexible beams͑primarily for space structures applications͒,kineto-elastodynamics of mechanisms,andﬂexible manipulators͑see Table2͒.The ﬂoating frame approach was also used to extend modal analysis and experimental modal identiﬁcation techniques to FMS͓52,54,232,256,272͔.This is performed by identifying the mode shapes and frequencies of eachﬂexible component either numerically or experimentally.Theﬁrst n modes ͑where n is determined by the physics of the problem and the by the required accuracy͒are superposed on the rigid body motion of the component represented by the motion of the ﬂoating frame.In Table3,a partial list of publications on the ﬂoating frame approach is organized according to the tech-niques used and developed and according to the type of ap-plication considered.The corotational frame approach was initially developed as a part of the natural mode method proposed by Argyris et al͓562͔.In this approach,the motion of aﬁnite element is divided into a rigid body motion and natural deformation modes.The approach was used for static modeling of struc-tures undergoing large displacements and small ter,Belytschko and Hsieh͓45͔introduced element rigid convected frames or corotational frames,for the dy-554Wasfy and Noor:Computational strategies forﬂexible multibody systems Appl Mech Rev vol56,no6,November2003Table1.Major characteristics of the three types of framesFloating Frame Corotational Frame Inertial FrameFrame deﬁnition Aﬂoating frame is deﬁned for eachﬂexible component.Theﬂoatingframe of a component follows amean rigid body motionof the component͑see Fig.1͒.A corotational frame is deﬁned for eachelement.The corotational frame of anelement follows a mean rigid body motionof the element͑see Fig.2͒.The global inertial referenceframe is used as a referenceframe for all motions͑seeFig.3͒.Reference framefor:a…Deformation Floating frame…for eachﬂexiblecomponent….Corotational frame…for eachﬁnite element….Global inertial reference frame. b…Internal forces Floating frame.Corotational frameÕGlobal inertialreference frame.Global inertial reference frame.Note:In some implementations,the internal force components are transformed from theﬂoating frame to the global inertial reference frame͑eg,͓26͔͒.Note:The element internal forcecomponents areﬁrst calculated relative tothe corotational frame,then they aretransformed from the corotational frame tothe global inertial frame using thecorotational frame rotation matrix.Note:The internal forces arecalculated usingﬁnite strainmeasures which are invariantunder rigid body motion.c…Inertia forces Floating frame.Global inertial reference frame.Global inertial reference frame.Note:In some implementations,theﬂexible motion inertia force components areﬁrst evaluated with respect to the global inertial reference frame andthen are transformed to theﬂoating frame͑eg,͓27,28͔͒.Notes:•In some implementations,the inertia forcecomponents areﬁrst evaluated relative tothe corotational frame and then aretransformed to the inertial frame͑eg,͓29–31͔͒.Note:In spatial problems,for therotational part of the equations ofmotion,the internal and inertiamoments are often calculated rela-tive to a moving material frame.•In spatial problems,for the rotational partof the equations of motion,the internaland inertia moments are often calculatedrelative to a moving material frame.Transformation toglobal inertial frame Eq.͑1͒.Eq.͑1͒.No transformation is necessary. ModelingConsiderationsa…Incorporation of ﬂexibility effects.Theﬂoating frame approach is thenatural way to extend rigid multibodydynamics toﬂexible multibody systems.The corotational frame transformationeliminates the element rigid body motionsuch that a linear deformation theory can beused for the element internal forces.Generalﬁnite strain measuresthat are invariant undersuperposed rigid body motionare used.b…Magnitude of angular velocities No restriction on angular velocitiesmagnitudes.However,when linear modalreduction is used,the angular velocityshould be low or constant because thestiffness of the body varies with theangular velocity due to the centrifugalstiffening effect͓32͔.No restriction on angular velocities magnitudes.In case of very small elasticdeformations and large angular velocities,special care must be taken duringthe solution procedure͑time step size,number of equilibrium iterations,etc͒to avoid the situation where numerical errors from the rigid body motion areof the order of the elastic part of the response.c…Large deﬂections•Moderate deﬂections can be modeled byusing quadratic strain terms.However,large deﬂections cannot be modeledunless the body is sub-structured.Can handle large deﬂections and large strains.•Without the assumption that the strainsand deﬂections are small,the high-orderterms of theﬂexible-rigid body inertialcoupling terms cannot be neglected andthe formulation becomes verycomplicated.d…Foreshortening Foreshortening effect can be modeled byadding quadratic axial-bending straincoupling terms.Naturally included.e…Centrifugal stiffening Centrifugal stiffening can be modeled byadding the stress produced by the axialcentripetal forces and including axial-bending strain coupling terms.Naturally included.f…Mixing rigid and ﬂexible bodies Since theﬂoating frame formulation isbased on rigid multibody dynamicsanalysis methods,both rigid andﬂexiblebodies can be present in the same model inany conﬁguration with no difﬁculty.Most implementations place some restrictions on the conﬁguration of the rigidbodies,such as a closed-loop,must contain at least oneﬂexible body.Appl Mech Rev vol56,no6,November2003Wasfy and Noor:Computational strategies forﬂexible multibody systems555Table1.(continued)Floating Frame Corotational Frame Inertial FrameCharacteristics of the semi-discrete equations of motion •The equations of motion are written suchthat theﬂexible body coordinates arereferred to aﬂoating frame and the rigidbody coordinates are referred to theinertial frame.•The equations of motion are written with respect to the global inertial frame.•In spatial problems with rotational DOFs,the rotational part of the equationsof motion can be written with respect to a body attached nodal frame͑material frame͓͒33–38͔or with respect to the global inertial frame͑spatial frame͓͒35,39͔.a…Inertia forces•The inertia forces involve nonlinearcentrifugal,Coriolis,and tangentialterms because the accelerations aremeasured with respect to a rotatingframe͑theﬂoating frame͒.•The inertia forces are the product of the mass matrix and the vector of nodal accelerations with respect to the global inertial frame.•In spatial problems with rotational DOFs,the rotational equations͑the Euler equations͒include quadratic angular velocity terms.͑These terms vanish in planar problems.͒•The mass matrix has nonlinearﬂexible-rigid body motion coupling terms.The coupling terms are necessary for an accurate prediction of the dynamic response,when the magnitude of the ﬂexible inertia forces is not negligible relative to that of the rigid body inertia forces.•The translational part of the mass matrix is constant.Effects such as coupling betweenﬂexible and rigid body motion,centrifugal and coriolis accelerationare not present because the inertia forces are measured with respect to an inertial frame.•The solution procedure involves the inversion or the LU factorization of the time varying inertia matrices.b…Internal …structural…forces The internal forces are linear for smallstrains and slow rotational velocities.Thelinear part of the stiffness matrix is thesame as that used in classical linear FEM.The nonlinear part of the stiffness matrixaccounts for geometric nonlinearity andcoupling between the axial and bendingdeformations͑centrifugal stiffeningeffect͒.For small strains,the internal forces arelinear with respect to the corotationalframe.The structural forces aretransformed to the global frame using thenonlinear corotational frametransformation.The internal forces are nonlineareven for small strains becausethey are expressed in terms ofnonlinearﬁnite strain and stressmeasures.Constraintsa…Hinge joints Hinge joints require the addition ofalgebraic constraint equations in theabsolute coordinate formulation.Hinge joints͑revolute joints in planar problems and spherical joints in spatial problems͒do not need an extra algebraic equation and can be modeled by letting two bodies share a node.b…General constraints Constraints due to joints,prescribed mo-tion and closed-loops are expressed interms of algebraic equations.These equa-tions must be solved simultaneously withthe governing differential equations of mo-tion.The development of general,stable,and efﬁcient solution procedures for thissystem of differential-algebraic equationsis still an active research area͓40–42͔͑also see Section4.1͒.Constraints due to joints and prescribed motion are expressed in terms of algebraicequations.If an implicit algorithm is used,then a system of differential-algebraicequations͑DAEs͒must be solved.If an explicit solution procedure is used,nospecial algorithm for solving DAEs is needed.Applicability of linear modal reduction •Can be applied.•Can signiﬁcantly reduce thecomputational time.•Appropriate selection of the deformationcomponents modes requires experienceand judgment on the part of the analyst.Not practical because the element vector ofinternal forces is nonlinear in nodalcoordinates since it involves a rotationmatrix.Not practical because the elementvector of internal forces isnonlinear in nodal coordinatessince it involves a nonlinearﬁnite strain measure.•For accuracy,linear modal reductionshould be restricted to bodiesundergoing slow rotation or uniformangular velocity.•Nonlinear modal reduction͓43,44͔canbe used for bodies undergoing fast non-uniform angular velocity in order toinclude the centrifugal stiffening effect.However,a modal reduction must beperformed at each time step.Possibility of using modal identiﬁcation experiments The mode shapes and natural frequenciesused in modal reduction can be obtainedusing experimental modal analysis tech-niques.Thus,there is a direct way to ob-tain the bodyﬂexibility information fromexperiments without numerical modeling.Experimentally identiﬁed modes cannot be directly used in the model.They can,however,be indirectly used to verify the accuracy of the predicted responseand to tune the parameters of the model.556Wasfy and Noor:Computational strategies forﬂexible multibody systems Appl Mech Rev vol56,no6,November2003namic modeling of planar continuum and beam type ele-ments,using a total displacement explicit solution procedure.The approach was applied to spatial beams in Belytschko et al ͓33͔and to curved beams in Belytschko and Glaum ͓452͔.In Belytschko et al ͓468͔and Belytschko et al ͓469͔,the approach was extended to dynamic modeling of shells using a velocity-based incremental solution procedure.Table 4shows a partial list of publications which used corotational frames for developing computational models suitable for modeling FMS.The publications are organized according to the techniques used and developed and according to the type of application considered.The inertial frame approach has its origins in the non-linear ﬁnite element method and continuum mechanics principles.These techniques were applied to the dynamic analysis of continuum bodies undergoing large rotations and large deformations ͑including both large strains and large deﬂections ͒since the early 1970s ͓92,93͔.In Table 5,publi-cations where the inertial frame approach was used for de-veloping computational models suitable for modeling FMS areclassiﬁed.Fig.1FloatingframeFig.2Corotationalframe Fig.3Inertial frameTable 1.(continued)Floating FrameCorotational FrameInertial FrameMost suitable applicationsThe ﬂoating frame formulation along with modal reduction and new recursive solution strategies ͑based on the relative coordinates formulation ͒offer the most efﬁcient method for the simulation of ﬂexible multibody systems undergoing small elastic deformations and slow rotational speeds ͑such as satellites and space structures ͒.The corotational and inertial frame formulations can handle ﬂexible multibody systems undergoing large deﬂections and large high-speed rigid body motion.In addition,if used in conjunction with an explicit solution procedure,then high-speed wave propagation effects ͑for example,due to contact/impact ͒can be accurately modeled.Least suitable applications Multibody problems,which involve large deﬂections.For multibody problems involving small deformations and slow rotational speeds,the solution time is generally an order of magnitude greater than that of typical methods based on the ﬂoating frame approach with modal coordinates.First knownapplication of the approach to FMS.Adopted in the late 1960s to early 1970s to extend rigid multibody dynamics computer codes to ﬂexible multibody systems.Developed by Belytschko and Hsieh ͓45͔.It was ﬁrst applied to beam type FMS in Housner ͓46–48͔.Used in nonlinear,large deformation FEM since the beginning of the 1970s.It was ﬁrst applied to modeling beam type FMS in Simo and Vu-Quoc ͓49,50͔.Appl Mech Rev vol 56,no 6,November 2003Wasfy and Noor:Computational strategies for ﬂexible multibody systems 5572.2Mathematical descriptions of the intermediate reference framesThe relation between the coordinates of a point in the global inertial frame A (x A )and the coordinates of the same point in the intermediate body reference frame B (x B )is given by:x A ϭx o A /B ϩRA /B x B(1)where x oA /Bare the coordinates of the origin of frame B in frame A ,and R A /B is a rotation matrix describing the rotationfrom A to B .The methods used to deﬁne x oA /Band R A /B for the ﬂoating and corotational frames are outlined subse-quently.2.2.1Floating frameThe motion of the ﬂoating frame ͑position and orientation ͒is commonly referred to as the reference motion of the compo-nent.It is only an approximation of the rigid body motion of the component.Thus there are many ways to deﬁne this ref-erence motion.Two formulations are commonly used,namely,ﬁxed axis and moving axis formulations.In the ﬁxed axis formulation,Cartesian and/or rotation coordinates of one,two,or three selected material points ͑usually the joints ͒on the ﬂexible body are used to deﬁne the ﬂoating frame.Experience is needed for appropriate selection of body ﬁxed axes that are consistent with the boundary conditions,be-cause this choice affects the resulting vibrational modes.In the moving axis formulation,also called the body mean axis formulation,the ﬂoating frame follows a mean displacement of the ﬂexible body and thus does not necessarily coincide with any speciﬁc material point.In this case,two deﬁnitions of the ﬂoating frame are used in practice:a ͒the ﬂoating frame is the frame relative to which the kinetic energy of the ﬂexible motion with respect to an observer stationed at the frame is minimum ͑Tisserand frame ͓͒109,122,123͔;and b ͒the ﬂoating frame is the frame relative to which the sum of the squares of the displacements,with respect to an observer stationed at the frame,is minimum ͑Buckens frame ͓͒122͔.2.2.2Corotational frameThe deﬁnition of the corotational frame depends on the type of elements used for modeling the ﬂexible components.For two-node beam elements,the corotational frame is usually deﬁned by the vector connecting the two nodes ͑eg,͓45͔͒.Itcan also be chosen as the mean beam axis ͑ie,the axis that minimizes the total deformation ͓͒450͔.For 3D beam ele-ments,the remaining two axes are chosen as the cross-sectional axes ͓33,87,456͔.In Park et al ͓479͔and Cho et al ͓480͔a relative nodal coordinate approach is used in which a tree representation of the FMS is constructed and beam ele-ment deformations are measured with respect to the adjacent nodal frame along the tree.For shell and continuum elements,there are two methods to deﬁne the corotational frame.In the ﬁrst method,only some of the nodes of the element are used to deﬁne the corotational frame.This type of deﬁnition was used for con-tinuum elements in Belytschko and Hsieh ͓45͔and for shells in Stolarski and Belytschko ͓455,456,468,470,471,563͔,Be-lytschko et al ͓468͔,Rankin and Brogan ͓455͔,Rankin and Nour-Omid ͓456͔,and Belytschko and Leviathan ͓470,471͔.For example,in Belytschko et al ͓468͔the normal Z-axis for a four node quadrilateral shell element is deﬁned as the normal to the two diagonals of the element,the X-axis is perpendicular to the Z-axis and is aligned with the vector connecting nodes 1and 2,and the Y-axis is perpendicular to the Z-and ing some of the element nodes to deﬁne the corotational frame makes the internal forces dependent on the choice of the element local node num-bering,which may introduce artiﬁcial asymmetries in the response ͓460,474,476͔.In the second method,the origin and orientation of the corotational frame are deﬁned as an average position and rotation of all the element nodes.For example,the origin of the corotational frame can be deﬁned as the origin of the natural element coordinate system ͓85,91,460,464,474,476͔.The orientation of the frame can be determined using one of the following techniques:•Polar decomposition of the deformation gradient tensor at the origin of the natural element coordinate system ͓85,91,460,464,476͔•For shell elements,the Z-axis is normal to the surface of the element at the origin of the natural coordinate system.The angle between the X-axis and the ﬁrst element natural axis is equal to the angle between the Y-axis and the sec-ond element natural direction ͓564͔•A least-square minimization procedure to ﬁnd the orienta-Table 2.Initial references for the application of the three types of frames to FMSFloating FrameCorotational FrameInertial FrameSpinning beams:Nonlinear structural dynamics:Nonlinear ﬁnite element method:Meirovitch and Nelson ͓51͔,Likins ͓52,53,55͔,Likins et al ͓54͔,Grotte et al ͓56͔.Belytschko and Hsieh ͓45͔,Belytschko et al ͓33͔,Argyris et al ͓81͔,Argyris ͓82͔,Belytschko and Hughes ͓83͔.Oden ͓92͔,Bathe et al ͓93͔,Bathe and Bolourchi ͓94͔.Kineto-elastodynamics of mechanisms:Dynamics of planar ﬂexible beams:Winfrey ͓57–59͔,Jasinski et al ͓60,61͔,Sadler and Sandor ͓62͔,Erdman et al ͓9,63,64͔,Imam ͓65͔,Imam and Sandor ͓66͔,Viscomi and Ayre ͓67͔,Dubowsky and Maatuk ͓68͔,Dubowsky and Gardner ͓69,70͔,Bahgat and Willmert ͓71͔,Midha et al ͓72,74,75͔,Midha ͓73͔,Nath and Gosh ͓76͔,Huston ͓77͔,Huston and Passarello ͓78͔.Flexible space structures:Simo and Vu-Quoc ͓50͔.Housner ͓46͔,Housner et al ͓47͔.Dynamics of spatial ﬂexible beams:FMS planar beams:Simo ͓95͔,Simo and Vu-Quoc ͓34,49,96,97͔,Iura and Atluri ͓48͔,Cardona and Geradin ͓35͔,Geradin and Cardona ͓98͔,Crespo Da Silva ͓99͔,Jonker ͓100͔.Yang and Sadler ͓84͔,Wasfy ͓85,86͔,Elkaranshawy and Dokainish ͓31͔.FMS spatial beams:Housner ͓46͔,Housner et al ͓47͔,Wu et al ͓87͔,Crisﬁeld ͓88͔,Crisﬁeld and Shi ͓89,90͔,Wasfy and Noor ͓91͔.Flexible manipulators:FMS shells:Book ͓79,80͔.Wasfy and Noor ͓91͔.558Wasfy and Noor:Computational strategies for ﬂexible multibody systemsAppl Mech Rev vol 56,no 6,November 2003。

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job-shopschedulingproblem：作业车间调度问题

Multistage-based genetic algorithm for flexible job-shop scheduling problemHaipeng Zhang, and Mitsuo GenGraduate School of Information, Production & Systems, Waseda UniversityWakamatsu-ku, Kitakyushu 808-0135, JAPANEmail:*******************.jp,*************AbstractFlexible Job-shop Scheduling Problem is expanded from the traditional Job-shop Scheduling Problem, which possesses wider availability of machines for all the operations. Considering thetwo states of the problem, two definitions (total and partial) of flexibility are offered to separatethe different availability information of machines.In this paper, a new multistage operation-based representation is proposed to make the chromosome simpler. By using this approach, all the crossover and mutation methods can beapplied to this optimal strategy. The efficiency has been improved after using the newrepresentation, and also the objective values outperform others.1. IntroductionThe classical Job-shop Scheduling Problem (JSP) concerns determination of a set of jobs on a set of machines so that the makespan is minimized. It is obviously a single criterion combinational optimization and has been proved as a NP-hard problem with several assumptions as follows: each job has a specified processing order through the machines which is fixed and known in advance; processing times are also fixed and known corresponding to the operations for each job; set-up times between operations are either negligible or included in processing times (sequence-independent); each machine is continuously available from time zero; there are no precedence constraints among operations of different jobs; each operation cannot be interrupted; each machine can process at most one operation at a time.The flexible Job-shop Scheduling Problem (f-JSP) extends JSP by assuming that a machine may be capable of performing more than one type of operation (Najid, Dauzere-Peres & Zaidat,2002). That means for any given operation, there must exist at least one machine capable of performing it. In this paper two kinds of flexibility are considered to describe the performance of f-JSP (Kacem, Hammadi &Borne, 2002). First total flexibility: in this case all operations are achievable on all the machines available; second, partial flexibility: in this case some operations are only achievable on part of the available machines.Most of the literature on the shop scheduling problem concentrates on JSP case (Gen & Cheng, 1997; Gen & Cheng, 2000; Blazewicz, Domschke &Pesch,1996). The f-JSP recently captured the interests of many researchers. The first paper that addresses the f-JSP was given by Brucker and Schlie (Brucker & Schlie, 1990), which proposes a polynomial algorithm for solving the f-JSP with two jobs, in which the machines able to perform an operation have the same processing time. For solving the general case with more than two jobs, two types of approaches have been used: hierarchical approaches and integratedapproaches. The first was based on the idea of decomposing the original problem in order to reduce its complexity. Brandimarte (Brandimarte, 1993) was the first to use this decomposition for the f-JSP. He solved the assignment problem using some existing dispatching rules and then focused on the resulting job shop subproblems, which are solved using a tabu search heuristic. Mati proposed a greedy heuristic for simultaneously dealing with the assignment and the sequencing subproblems of the flexible job shop model (Mati, Rezg & Xie, 2001). The advantage of Mati’s heuristic is its ability to take into account the assumption of identical machine. Kacem (Kacem, Hammadi &Borne, 2002) came to use GA to solve f-JSP, and he adapted two approaches to solve jointly the assignment and JSP (with total or partial flexibility). The first one is the approach by localization (AL). It makes it possible to solve the problem of resource allocation and build an ideal assignment mode (assignments schemata), the second one is an evolutionary approach controlled by the assignment model, and applying GA to solve the f-JSP.In this paper, we propose a more efficient method called multistage-based GA to solve f-JSP (including total flexibility and partial flexibility) compared with Kacem’s approach. The considered objective is to minimize the makespan, total workloads of the machines and the maximum workloads of machines. This multi-objective optimization will be done by a multistage-based GA which including K stages (the total number of operations for all the jobs), and m state (total number of machines). Computational experiments will be carried out to evaluate the efficiency of our methods with a large set of representative problem instances based on practical data. The rest of the paper is organized as follows: In Section 2, we describe the assumptions of flexible Job-shop Scheduling Problem in detail, and propose the mathematical model of this problem. In Section 3, one heuristic method is applied to solve this problem. Section 4 introduces the GA methods and describes implementations used for this problem. Then, the experimental results are illustrated and analysed in Section 5. Finally, Section 6 provides conclusion and suggestions for further work on this problem.2. Mathematical modelIn this paper, the flexible Job-shop Scheduling Problem we are treating is to minimize the makespan, and balance the workload for all machines. Before defining the problem concretely we should add several assumptions to the problem.1.There is a set of jobs and a set of machines.2.Each job consists of one fixed sequence of operations.3.Each machine can process at most one operation at a time.4.Each machine becomes available to other operations once the operations which arecurrently assigned to be completed.5.All machines are available at t = 0.6.All jobs can be started at t = 0.7.There are no precedence constraints among operations of different jobs.8.Any operation cannot be interrupted.9.Neither release times nor due dates are specified.The f-JSP we considering here is a problem which including n-jobs operated on m-machines. Some symbols and notations have been defined as follows:i: index of jobs, i = 1, 2, … nJ i : the i th jobn : total number of jobsk : index of operations, k = 1, 2, … K i o ik : the k th operation of job i (or J i )K i : total number of operations in job i (or J i )j : index of machines, j = 1, 2, … m M j : the j th machinem : total number of machinesp ikj : processing time of operation o ik on machine j (or M j ) U : a set of machines with the size mU ik : a set of available machines for the operation o ikF ik t : completion time of operation o ikW j : workloads (total processing time) of machine M jThe objective function can be described as the following three equations. Eq. (1) gives the first objective makespan and also means to minimize the maximum finishing time considering all the operations. Eq. (2) gives the second objective which is to minimize the maximum of workloads for all machines. Eq. (3) gives the objective total workloads.Eq. (2) combining with Eq. (3) give a physical meaning to the f-JSP, which referring to reducing total processing time and dispatching the operations averagely for each machine. Considering both of the two equations, our objective is to balancing the workloads of all machines. Eq. (4) and Eq. (5) give two basic processing constrains.3. Heuristic methodTo demonstrate f-JSP model clearly, we first prepare a simple example. Table 1 gives the data set of an f-JSP including 3 jobs operated on 4 machines. It is obviously a problem with total flexibility because all the machines are available for each operation (U ik =U ). There are several traditional heuristic methods that can be used to make a feasible schedule.In this case, we use the SPT (select the operation with the shortest processing time) as selective strategy to find an optimal solution, and the algorithm is based on the procedure in Figure 1. Before selection we first make some initialization:• starting from a table D presenting the processing times possibilities• on the various machines, create a new table D’ whose size is the same one as the table D ; • create a table S whose size is the same one as the table D (S is going to represent chosen{}{}(5)t. s. (4)min (3)max min (2)max max min (1),,,k ,i ,t j ,k ,i t p t W W W W t t F ik F k i j k i F ik mj j T j mj M F ik K k ni M i ∀≥∀≤+==⎭⎬⎫⎩⎨⎧=++=≤≤≤≤≤≤∑0111111assignments);•initialize all elements of S to 0 (S ikj=0)•recopy D in D’Table 1. Data set of a 3-job 4-machine Problem.procedure: SPT Assignmentinput: dataset table Doutput: best schedule Sbeginfor (i=1; i<=n)for (k=1; k<=K i)min=+∞;pos=1;for (j=1; j<=m)if (p’ikj<min) then {min=p’ikj; pos=j;}S i,k,pos=1(assignment of o ik to the machine M pos);//updating of D’;for (k’=k+1; k’<=K i’)p’i’,k,pos= p’i’,k,pos+ p i,k,pos;for (i’= i +1; i’<=n)for (k’= 1; k’<=K i’)p’i’,k’,pos= p’i’,k’,pos+ p i,k,pos;endendoutput best schedule SendFigure 1. SPT Assignment Procedure.Following this algorithm, we assign o11 to M1, and add the processing time p111=1 to the elements of the first column of D’. (shown in Table 2)Table 2 D’ (for i=1 and k=1).Table 3 D’ (for i=1 and k=2).Secondly, we assign o12 to M4, and add the processing time p124=1 to the elements of the fourth column of D’ shown in Table 3. By following the same method, we obtain assignment S shown in Table 4.Furthermore, we can denote the schedule based on job sequence as:S={(o11, M1), (o12, M4), (o13, M1), (o21, M2), (o22, M2), (o23, M1),(o31, M3), (o32, M4)}= {(o11,M1: 0-1), (o12, M4: 1-2), (o13, M1: 2-5), (o21, M2: 0-1), (o22, M2: 1-4),(o23, M1: 4-6), (o31, M3: 1-3), (o32, M4: 3-4)}Finally we can calculate the solution by Eq.1, Eq. 2 and Eq. 3 as follows:t M = max{F t11, F t12, F t13, F t21, F t22, F t23, F t31, F t32}=max{1, 2, 5, 1, 4, 6, 3, 4}= 6WM= max{(1+3), (1+3), (3+2), (1+1)}=5W T=4+4+5+2=154. Genetic Algorithm ApproachThere are three parts in this section, firstly some traditional representation (Mesghouni, 1999), secondly Imed Kacem’s approach (Kacem, Hammadi & Borne, 2002), and thirdly multistage operation-based representation.4.1 Traditional Representation of GA4.1.1 Parallel Machine Representation (PM-R)The chromosome is a list of machines placed in parallel (see Table 5). For each machine, we associate operations to execute. Each operation is coded by three elements:Operation k , job J i and Sikj t (starting time of operation o ik on the machine M j ).4.1.2 Parallel Jobs Representation (PJ-R)The chromosome is represented by a list of jobs showed in Table 6. Information of each job is shown in the corresponding row where each case is constituted of two terms: machine M j which executes the operation and corresponding starting time t ikj S .4.2 Imed Kacem’s approachImed Kacem proposed Operations Machines Representation (OM-R) approach (Kacem, Hammadi & Borne, 2002), which based on a traditional representation called Schemata Theorem Representation (ST-R). It was firstly introduced in GAs by Holland (Charon, Germinated & Hudry, 1996).In the case of a binary coding, a schemata is a chromosome model where some genes are fixed and the other are free (see the following Figure 2), Positions 4 and 6 are occupied by the symbol:“*”. This symbol indicates that considered genes can take “0” or “1” as value. Thus, chromosome C 1 and C 2 respect the model imposed by the schemata S.Based on the ST-R approach, Kacem expanded it to Operations Machines Representation (OM-R). It consists in representing the schedule in the same assignment tableS . We replace each case S ikj =1 by the couple (F ik t , Fik t ), while the cases S ikj =0 are unchanged. To explain this coding, we present the same schedule introduced before (Table 7). Furthermore, operation based crossover and the other two kinds of mutation (operator ofTable 5. Parallel machine representation.Table 6.Parallel jobs representation.00*1*001Position : 1 2 3 4 5 6 7800110001S =C 1=C 2mutation reducing the effective processing time, operator of mutation balancing work loads of machines) are included in this approach.4.3 Multistage operation-based approachConsidering the GA approach proposed by Imed Kacem, it is complex even when you take allthe objectives in count, because all the crossover and mutation are based on the chromosome which is described as a constructor of table. Therefore, it will spend more CPU-time for finding solutions; hence a multistage operation-based GA approach has been proposed. Figure 3 presents an f-JSP which includes 3 jobs operated on 4 machines, we add another two nodes (starting node and terminal node) in the figure to make it a formal network presentation. Denoting each operation as one stage, and each machine as one state, the problem can be formulated into an 8-stage, 4-state problem.Connected by the dashed arcs a feasible schedule can be obtained as:It is obviously simpler than all the representations prsented before, and certainly can easily combine with almost all kinds of classic crossover and mutation methods. Figure 4 and Figure 5 separately give the encoding and decoding procedure.Figure 3.Example for Multistage Operation-based Representatin (MO-R).43422141ID :1 2 3 4 5 6 7 8V =5. Numerical ExperimentIn this paper, we use the same dataset (showed in Table 8 & Table 9) as in Kacem’s paper tocompare the results. It is especially f-JSP with both partial flexibility (Uik⊆U) and totalflexibility (Uik=U). The symbol “-” in Table 8 shows that the machine is not available for thecorresponding operation.We have used random selections to generate the initial population. Then we applied the multistage operation-based GA (moGA combining one-cut point crossover and local-search mutation) with the following parameters: popSize: 100; p M=0.3; p C=0.6All results can be summarized in Table 10 and Table 11. Values of different approach show the efficiency. It is easy to find the moGA outperform than all the other approach.Table 10.Result Comparisons(8×8).Heuristic method (SPT) ClassicGAKacem'sApproach moGAt M19 16 16 15W T91 77 75 73W M16 14 14 14 Table 11.Result Comparisons (10×10).Heuristic method (SPT) ClassicGAKacem'sApproach moGAt M16 7 7 7 W T59 53 45 43W M16 7 6 56. ConclusionSome GA approaches have been used for solving f-JSP recently. However the efficiency is mainly affected by the complexity of chromosome representation. In this paper, a new multistage operation-based representation of GA (moGA) approach is proposed to solve f-JSP. The proposed algorithm is designed for optimal the 3 objectives including the makespan t M, total workloads of all machines W, and maximum of workloads for all machines W M.By using some numerical example of related works, we demonstrate the efficiency of moGA. The optimal result is better than the other related approaches.ReferencesNajid, N.M., Dauzere-Peres, S. and Zaidat, A. (2002), A modified simulated annealing method for flexible job shop scheduling problem, IEEE International Conference on Systems, Man and Cybernetics, 5: 6.Kacem, I., Hammadi, S. and Borne, P. (2002), Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems, IEEE Transactions on Systems, Man and Cybernetics, Part C, 32(1): 408-419.Gen, M. and Cheng, R. (1997), Genetic Algorithms & Engineering Design, John Wiley & Sons.Gen, M. and Cheng, R. (2000), Genetic Algorithms & Engineering Design, John Wiley & Sons.Blazewicz, J., Domschke, W. and Pesch, E. (1996), The job shop scheduling problem: conventional and new solution techniques, European Journal of Operational Research, 93: 1-33.Brucker, P. and Schlie, R. (1990), Job-shop scheduling with multi-purpose machines, Computing, 45: 369-375.Brandimarte, P. (1993), Routing and scheduling in a flexible job shop by tabu search, Annals of Operations Research, 41: 157-183.Mati, Y., Rezg, N. and Xie, X. (2001), An Integrated Greedy Heuristic for a Flexible Job Shop Scheduling Problem, IEEE International Conference on Systems, Man, and Cybernetics, 4: 2534-2539.Mesghouni, K. (1999), Application des algorithmes évolutionnistes dans les problèmes d’ optimization en ordonnancement de production, Ph.D. dissertation, USTL 2451.Charon, I., Germinated, A. and Hudry, O. (1996), Méthodes d’Optimization Combinatoires, Paris, France: Masson.。

战略柔性一个世界级制造业新的现实外文翻译

中文3845字本科毕业论文（设计）外文翻译原文：Strategic flexibility: a new reality for world-class manufacturing The development of the concept of flexibility has been slow in the manufacturing literature because of the relatively stable market structure and minimal competitive pressure prior to the 1960s. In fact, manufacturing was not considered particularly important in the formulation of business strategy. As the competitiveness problems increased, practitioners and academicians began to recognize that manufacturing strategy was vital in supporting changes of corporate strategy. Consequently, a number of analytical models and empirical studies were developed to enhance manufacturing flexibility. According to Suarez, Cusumano, and Fine (1995), most empirical studies on manufacturing flexibility serve one of the following purposes: (1) to develop taxonomies of flexibility; (2) to investigate the relationship between flexibility and performance; (3) to cover historical and economical analyses of flexibility; and (4) to develop strategic frameworks for flexibility. Carlsson (1989), Sethi and Sethi (1990), Hyun and Ahn (1992), and Upton (1994) are just a few research works that provide further literature reviews on flexibility.Flexibility is often regarded as one of the competitive priorities, along with cost, quality, and innovation. Just as low cost and high quality have already become a requirement for market entry, flexibility might ultimately be the key to enhancing a firm's competitive ability. While uncertainty can be a threat to some firms, it provides opportunity to those with higher degrees of flexibility, either market-oriented or resources-oriented. Firms that are able to deal with uncertainties that their competitors cannot have market-oriented flexibility. By reducing market uncertainties or exerting influence on customer expectations, firms have more strategic choices and can adopt a more proactive approach to competing. Firms with highly flexible production systemshave resource-oriented flexibility and can be more responsive to the changing market.By combining these two concepts, Figure 1 shows the dominant competitive priorities corresponding to the firm's ability to cope with uncertainties. Not only can world-class manufacturing firms adapt to the changing environment swiftly, but they also can influence market demand (e.g., by creating uncertainties or customer expectations that competitors cannot deal with). Both reactive and proactive approaches have proved to be equally important and require different types of flexibility. Instead of focusing on one particular dimension of flexibility, world-class manufacturing firms need a strategic perspective of flexibility - the ability to quickly adjust their competitive objectives to meet new business conditions.In a stable competitive environment like decades ago, a competitive strategy simply involved defining a competitive position and then defending it. Since the competitive environment has changed rapidly and unpredictably, however, new knowledge and capabilities are needed to support any strategy to create a sustainable competitive advantage. Therefore, the goal of the latest developments in manufacturing strategy is to attain strategic flexibility. Competitive advantage commonly refers to the creation of a production-distribution system that has a unique advantage over its competitors. Achieving competitive advantage does not imply that the company must always do better than the competitors in all areas. The key is to do certain things better in most of the areas. Deciding which areas to target is the central issue of competitive petitive advantages traditionally have been accomplished through economies of scale and product and process technology, but these are no longer sufficient.Competitive advantage through economies of scale is best illustrated by mass production. Furthermore, Henry Ford's dictum that "customers can have any color as long as it's black" still convinces many manufacturers that they must choose between standardization at low cost or flexibility at high cost. This has been disproved by Japanese automobile and electronics manufacturers who achieve an optimal balance of product standardization and manufacturing flexibility.The dynamics of today's competitive environment suggest that economies ofscale and product or process technology will be a diminishing source of competitive advantage. As a result, manufacturers are turning their attention to building the skills and knowledge of their workforce. World-class manufacturers also realize that competitive advantage can be created only when the manufacturing strategy is well integrated with other functional strategies, which together support the overall corporate strategy. It implies that changes in strategy are necessary to cope with the changes in competitive environment and in the organization itself. Therefore, there is no "best" manufacturing strategy, and all competitive manufacturers should be ready to shift from one strategy to another as needed. The appropriate strategy depends on a firm's strengths and weaknesses. Two manufacturing firms may develop different strategies yet both compete in the same market with success. In addition, sticking with a single competitive strategy (no matter how successful) often turns out to be problematic when the underlying conditions change.Given the dynamic nature of the marketplace, flexibility has already become the most important competitive priority of the 1990s. Flexibility is usually classified broadly as product or service-related (such as volume, product mix, and modification) and process technology-related (such as changeover, scheduling, and innovation). While these sources of flexibility are essential to provide competitive advantage to manufacturers, they tend to be operational or tactical in nature. To acquire a sustainable competitive advantage, management must develop strategic flexibility, which requires long-term commitment and the development of critical resources. Note that no specific manufacturing techniques or improvement programs are included. Instead, the emphasis is on developing skills such as knowledge, capabilities, and a flexible organizational structure. These are the foundation of strategic flexibility that allow future changes to take place as needed; and, best of all, their unique nature means that no one else can "copy" them easily.Strategic flexibility allows a manufacturing firm to shift from one dominant strategy to another, from one competitive priority to another, but also implies a long-term commitment of resources and a plan of action. Progress, therefore, depends on the current state of the firm's resources and capabilities. Generally, strategicflexibility is attained through a three-step process: awareness, understanding, and implementation.Phase 1: Be aware that only strategic flexibility will provide sustainable competitive advantage over the long run. During the last two decades, quality improvement, automation, and advanced manufacturing techniques, to name a few, have often been perceived as a path to competitive advantage. While they may lead to positive outcomes, a number of empirical studies suggest that many firms found them ineffective. Many manufacturers focused too much on the form or mechanics of such programs while overlooking the development of skills and capabilities needed to support the changes. Resistance to change is greater if management fails to see the necessity for changes. Until management fully recognizes the need for long-term competitive advantage, there is no clear incentive to devote the time, effort, and expense to develop strategic flexibility.Phase 2: Understand that the manufacturing function's performance links directly to corporate performance and survival. Understanding the importance of the manufacturing function and its link to corporate performance provides a focal point for management to think more proactively about building capabilities for the future. The poor performance of many major manufacturing firms during the last two decades was no surprise to many researchers. Companies that develop a clear linkage between business and manufacturing strategies tend to be more successful and profitable. This finding has substantiated the argument that manufacturing is indeed a key competitive variable, especially in those industries where customers are increasingly cost and quality conscious.Phase 3: Formulate and implement strategies that center on the development of skills, manufacturing capabilities, and lean organizational structures. The outdated manufacturing strategy based on mass production is not responsive enough to cope with rapidly changing markets and shortened product life cycles. In addition, production jobs have become more challenging and conceptual, as routine and repetitive tasks are performed by automated equipment. The full benefit of technology can be exploited only when workers understand and control a large part of theproduction process.* Skills and knowledgeA productive work force today must be highly skilled and flexible, characteristics that can only be developed through extensive training and experience in a variety of job assignments. Therefore, the workplace must be reorganized to promote continuous learning, which must become a normal part of work life. Evidence suggests that not many manufacturers, particularly in the United States, give high priority and commit sufficient resources to training their front-line workers. Management needs to realize that maintaining and upgrading the skills of their workforce is central to their competitive strategy. Management must focus on the cultivation of multi-skilled workers and stop treating them as replaceable parts or a cost to be controlled. In an attempt to find out why the improvement of flexibility has been so elusive, Upton (1995) observed that "most managers put too much faith in machines and technology, and too little faith in the day-to-day management of people" (p. 75). The basic theme of a skills development program is to encourage continuous learning throughout the company. Training programs should be developed in ways that are consistent with carefully defined goals and the availability of resources. More important, management should anticipate future skill needs, not just immediate ones.Complex computer-based production systems are likely to prevail. Training people to conceptualize, design, and use new production technology is as crucial as adopting the technology itself. Technology is often perceived as a way to replace workers, it does not mean that human resources are no longer important in achieving competitiveness. Indeed, the only way for manufacturers to maximize their investment in new technology is to upgrade the skill levels of their workforce. As production becomes more challenging and conceptual, because automated equipment performs most of the routine and repetitive tasks, investment in workforce skills development is increasingly vital.* Manufacturing CapabilitiesStrategic flexibility is not just about a flexible workforce; it requires an augmentation of the workforce with advanced process and information technologiesto satisfy customer demands. Advanced process technology, such as flexible manufacturing systems (FMS) and computer-integrated manufacturing (CIM), is crucial for achieving mass customization. An FMS can manufacture assorted products using the same group of machines linked by automated materials handling systems and controlled by a computer system. Automated and preprogrammed workstations are linked for different operations, ensuring that all members of a family of parts can be produced whenever needed. With the installation of FMS, General Electric can deliver a custom-made circuit box in three days instead of three weeks. Likewise, Motorola manufactures custom-designed electronic pagers in less than three hours.Ample evidence suggests that product designs can be significantly simplified if cross-functional design teams are used. A cross-functional design team will help facilitate a modular approach to product design. This approach provides a viable product design strategy to meet changing demand with the advantage of standardization. Modular design is the creation of products from some combination of existing, standardized components; it requires much creativity and communication across the company. Japanese automobile manufacturers have invested heavily in designing parts that can be combined in a number of ways and used interchangeably among several models. Although the modular design will occasionally increase the cost of tools and dies, it facilitates faster introduction of new car models and drastically reduces product development costs.* Organizational TransformationThe ultimate success of strategic flexibility requires a redefinition of traditional organizational functions, including links with suppliers and customers. Deep organizational hierarchies, as found in major manufacturing firms, impede cooperation and communication. In recent years, many corporate restructuring efforts have moved to flatten organizational structures to focus on cross-functional integration and employee participation. Corporate communication is then facilitated by a structure that is free from departmental boundaries and limitations. An ultimate goal is to turn the entire production process into modules and to create a dynamic network of skills and capabilities that allows the rapid integration of resources tocustomize products or services.Mass production of standardized products is no longer a feasible way to meet the challenge of changing market demand and shortened product life cycles. In fact, the usual method of first identifying a fixed competitive priority, such as cost, quality, time, flexibility, or innovation, and then devoting all resources to meet it will no longer provide a sustainable competitive advantage. World-class manufacturers must obtain strategic flexibility to cope with more uncertainties than just changing demand patterns and production volumes. Strategic flexibility is not an improvement program, but is rather the ability to adapt and the readiness for change. The goal of strategic flexibility is to provide more options so that a firm can shift from a current manufacturing strategy to a new one with minimal penalties in cost, time, or performance. True strategic flexibility can be achieved only through the development of skills and manufacturing capabilities, which eventually lead to complete organizational transformation.Source: Lau, R.S.M.“Strategic flexibility: a new reality for world-class manufacturing”. SAM Advanced Management Journal, 1996(3):P11-15.译文：战略柔性：一个世界级制造业新的现实由于20世纪60年代前期相对稳定的市场结构和较小的竞争压力，柔性概念的发展已经逐渐运用到制造业文化中。

元认知策略计划策略英文表达

元认知策略计划策略英文表达Metacognition strategies are essential tools for effective learning and problem-solving. They involve thinking about your thinking, analyzing your approach, and making adjustments to improve your cognitive processes.Let's dive into some of the key elements of metacognition and how you can apply them in daily life.First up, planning strategies. This is where you set clear goals and outline the steps you'll take to achieve them. It's like having a roadmap for your learning journey. For example, if you're preparing for an exam, you might start by identifying the topics you need to focus on and then create a study schedule that breaks down the material into manageable chunks.Another crucial aspect of metacognition is monitoring your progress. This means checking in with yourself regularly to see how you're doing and making adjustments as needed. It's like having a personal trainer who keeps youaccountable and helps you stay on track. If you notice that a certain study technique isn't working for you, you cantry something else.Reflection is another key component of metacognition. After you've completed a task or studied a topic, it's important to take a step back and evaluate your.。

计算机领域会议排名

计算机领域国际会议分类排名现在的会议非常多，在投文章前，大家可以先看看会议的权威性、前几届的录用率，这样首先对自己的文章能不能中有个大概的心理底线。

权威与否可以和同行的同学沟通、或者看录用文章的水平、或者自己平时阅读文献的时候的慢慢累及。

原来有人做过一个国际会议的排名，如下.sg/home/assourav/crank.htm其中的很多会议我们都非常熟悉的。

但是这个排名是大概2000的时候做的，后来没有更新，所以像ISWC 这个会议在其中就看不到。

但是很多悠久的会议上面都有的，如www,SIGIR,VLDB,EMLC,ICTAI这些等等。

这些东西可以作为一个参考。

现在很多学校的同学毕业都要有检索的要求了。

因此很多不在SCI，EI检索范围内的会议投了可能对毕业无用，所以投之前最好查查会议是不是被SCI，EI检索的。

当然这也不绝对，如Web领域最权威的WWW的全文就只是ISTP检索，而不是SCI，EI检索的（可能是ACM出版的原因吧？）。

罗嗦了这么多！祝愿大家能在好的会议上发PAPER，能被SCI，EI检索。

---------------附，会议排名（from .sg/home/assourav/crank.htm）Computer Science Conference RankingsSome conferences accept multiple categories of papers. The rankings below are for the mos t prestigious category of paper at a given conference. All other categories should be treat ed as "unranked".AREA: DatabasesRank 1:SIGMOD: ACM SIGMOD Conf on Management of DataPODS: ACM SIGMOD Conf on Principles of DB SystemsVLDB: Very Large Data BasesICDE: Intl Conf on Data EngineeringICDT: Intl Conf on Database TheoryRank 2:SSD: Intl Symp on Large Spatial DatabasesDEXA: Database and Expert System ApplicationsFODO: Intl Conf on Foundation on Data OrganizationEDBT: Extending DB TechnologyDOOD: Deductive and Object-Oriented DatabasesDASFAA: Database Systems for Advanced ApplicationsCIKM: Intl. Conf on Information and Knowledge ManagementSSDBM: Intl Conf on Scientific and Statistical DB MgmtCoopIS - Conference on Cooperative Information SystemsER - Intl Conf on Conceptual Modeling (ER)Rank 3:COMAD: Intl Conf on Management of DataBNCOD: British National Conference on DatabasesADC: Australasian Database ConferenceADBIS: Symposium on Advances in DB and Information SystemsDaWaK - Data Warehousing and Knowledge DiscoveryRIDE WorkshopIFIP-DS: IFIP-DS ConferenceIFIP-DBSEC - IFIP Workshop on Database SecurityNGDB: Intl Symp on Next Generation DB Systems and AppsADTI: Intl Symp on Advanced DB Technologies and Integration FEWFDB: Far East Workshop on Future DB SystemsMDM - Int. Conf. on Mobile Data Access/Management (MDA/MDM)ICDM - IEEE International Conference on Data MiningVDB - Visual Database SystemsIDEAS - International Database Engineering and Application Symposium Others:ARTDB - Active and Real-Time Database SystemsCODAS: Intl Symp on Cooperative DB Systems for Adv AppsDBPL - Workshop on Database Programming LanguagesEFIS/EFDBS - Engineering Federated Information (Database) Systems KRDB - Knowledge Representation Meets DatabasesNDB - National Database Conference (China)NLDB - Applications of Natural Language to Data BasesFQAS - Flexible Query-Answering SystemsIDC(W) - International Database Conference (HK CS)RTDB - Workshop on Real-Time DatabasesSBBD: Brazilian Symposium on DatabasesWebDB - International Workshop on the Web and DatabasesWAIM: Interational Conference on Web Age Information ManagementDASWIS - Data Semantics in Web Information SystemsDMDW - Design and Management of Data WarehousesDOLAP - International Workshop on Data Warehousing and OLAPDMKD - Workshop on Research Issues in Data Mining and Knowledge DiscoveryKDEX - Knowledge and Data Engineering Exchange WorkshopNRDM - Workshop on Network-Related Data ManagementMobiDE - Workshop on Data Engineering for Wireless and Mobile AccessMDDS - Mobility in Databases and Distributed SystemsMEWS - Mining for Enhanced Web SearchTAKMA - Theory and Applications of Knowledge MAnagementWIDM: International Workshop on Web Information and Data ManagementW2GIS - International Workshop on Web and Wireless Geographical Information Systems CDB - Constraint Databases and ApplicationsDTVE - Workshop on Database Technology for Virtual EnterprisesIWDOM - International Workshop on Distributed Object ManagementOODBS - Workshop on Object-Oriented Database SystemsPDIS: Parallel and Distributed Information SystemsAREA: Artificial Intelligence and Related SubjectsRank 1:AAAI: American Association for AI National ConferenceCVPR: IEEE Conf on Comp Vision and Pattern RecognitionIJCAI: Intl Joint Conf on AIICCV: Intl Conf on Computer VisionICML: Intl Conf on Machine LearningKDD: Knowledge Discovery and Data MiningKR: Intl Conf on Principles of KR & ReasoningNIPS: Neural Information Processing SystemsUAI: Conference on Uncertainty in AIAAMAS: Intl Conf on Autonomous Agents and Multi-Agent Systems (past: ICAA)ACL: Annual Meeting of the ACL (Association of Computational Linguistics)Rank 2:NAACL: North American Chapter of the ACLAID: Intl Conf on AI in DesignAI-ED: World Conference on AI in EducationCAIP: Inttl Conf on Comp. Analysis of Images and PatternsCSSAC: Cognitive Science Society Annual ConferenceECCV: European Conference on Computer VisionEAI: European Conf on AIEML: European Conf on Machine LearningGECCO: Genetic and Evolutionary Computation Conference (used to be GP)IAAI: Innovative Applications in AIICIP: Intl Conf on Image ProcessingICNN/IJCNN: Intl (Joint) Conference on Neural NetworksICPR: Intl Conf on Pattern RecognitionICDAR: International Conference on Document Analysis and RecognitionICTAI: IEEE conference on Tools with AIAMAI: Artificial Intelligence and MathsDAS: International Workshop on Document Analysis SystemsWACV: IEEE Workshop on Apps of Computer VisionCOLING: International Conference on Computational LiguisticsEMNLP: Empirical Methods in Natural Language ProcessingEACL: Annual Meeting of European Association Computational LingusticsCoNLL: Conference on Natural Language LearningDocEng: ACM Symposium on Document EngineeringIEEE/WIC International Joint Conf on Web Intelligence and Intelligent Agent Technology Rank 3:PRICAI: Pacific Rim Intl Conf on AIAAI: Australian National Conf on AIACCV: Asian Conference on Computer VisionAI*IA: Congress of the Italian Assoc for AIANNIE: Artificial Neural Networks in EngineeringANZIIS: Australian/NZ Conf on Intelligent Inf. SystemsCAIA: Conf on AI for ApplicationsCAAI: Canadian Artificial Intelligence ConferenceASADM: Chicago ASA Data Mining Conf: A Hard Look at DMEPIA: Portuguese Conference on Artificial IntelligenceFCKAML: French Conf on Know. Acquisition & Machine LearningICANN: International Conf on Artificial Neural NetworksICCB: International Conference on Case-Based ReasoningICGA: International Conference on Genetic AlgorithmsICONIP: Intl Conf on Neural Information ProcessingIEA/AIE: Intl Conf on Ind. & Eng. Apps of AI & Expert SysICMS: International Conference on Multiagent SystemsICPS: International conference on Planning SystemsIWANN: Intl Work-Conf on Art & Natural Neural NetworksPACES: Pacific Asian Conference on Expert SystemsSCAI: Scandinavian Conference on Artifical IntelligenceSPICIS: Singapore Intl Conf on Intelligent SystemPAKDD: Pacific-Asia Conf on Know. Discovery & Data MiningSMC: IEEE Intl Conf on Systems, Man and CyberneticsPAKDDM: Practical App of Knowledge Discovery & Data MiningWCNN: The World Congress on Neural NetworksWCES: World Congress on Expert SystemsASC: Intl Conf on AI and Soft ComputingPACLIC: Pacific Asia Conference on Language, Information and ComputationICCC: International Conference on Chinese ComputingICADL: International Conference on Asian Digital LibrariesRANLP: Recent Advances in Natural Language ProcessingNLPRS: Natural Language Pacific Rim SymposiumMeta-Heuristics International ConferenceRank 3:ICRA: IEEE Intl Conf on Robotics and AutomationNNSP: Neural Networks for Signal ProcessingICASSP: IEEE Intl Conf on Acoustics, Speech and SPGCCCE: Global Chinese Conference on Computers in EducationICAI: Intl Conf on Artificial IntelligenceAEN: IASTED Intl Conf on AI, Exp Sys & Neural NetworksWMSCI: World Multiconfs on Sys, Cybernetics & InformaticsLREC: Language Resources and Evaluation ConferenceAIMSA: Artificial Intelligence: Methodology, Systems, ApplicationsAISC: Artificial Intelligence and Symbolic ComputationCIA: Cooperative Information AgentsInternational Conference on Computational Intelligence for Modelling, Control and Automation Pattern MatchingECAL: European Conference on Artificial LifeEKAW: Knowledge Acquisition, Modeling and ManagementEMMCVPR: Energy Minimization Methods in Computer Vision and Pattern RecognitionEuroGP: European Conference on Genetic ProgrammingFoIKS: Foundations of Information and Knowledge SystemsIAWTIC: International Conference on Intelligent Agents, Web Technologies and Internet Commer ceICAIL: International Conference on Artificial Intelligence and LawSMIS: International Syposium on Methodologies for Intelligent SystemsIS&N: Intelligence and Services in NetworksJELIA: Logics in Artificial IntelligenceKI: German Conference on Artificial IntelligenceKRDB: Knowledge Representation Meets DatabasesMAAMAW: Modelling Autonomous Agents in a Multi-Agent WorldNC: ICSC Symposium on Neural ComputationPKDD: Principles of Data Mining and Knowledge DiscoverySBIA: Brazilian Symposium on Artificial IntelligenceScale-Space: Scale-Space Theories in Computer VisionXPS: Knowledge-Based SystemsI2CS: Innovative Internet Computing SystemsTARK: Theoretical Aspects of Rationality and Knowledge MeetingMKM: International Workshop on Mathematical Knowledge ManagementACIVS: International Conference on Advanced Concepts For Intelligent Vision Systems ATAL: Agent Theories, Architectures, and LanguagesLACL: International Conference on Logical Aspects of Computational LinguisticsAREA: Hardware and ArchitectureRank 1:ASPLOS: Architectural Support for Prog Lang and OSISCA: ACM/IEEE Symp on Computer ArchitectureICCAD: Intl Conf on Computer-Aided DesignDAC: Design Automation ConfMICRO: Intl Symp on MicroarchitectureHPCA: IEEE Symp on High-Perf Comp ArchitectureRank 2:FCCM: IEEE Symposium on Field Programmable Custom Computing MachinesSUPER: ACM/IEEE Supercomputing ConferenceICS: Intl Conf on SupercomputingISSCC: IEEE Intl Solid-State Circuits ConfHCS: Hot Chips SympVLSI: IEEE Symp VLSI CircuitsCODES+ISSS: Intl Conf on Hardware/Software Codesign & System SynthesisDATE: IEEE/ACM Design, Automation & Test in Europe ConferenceFPL: Field-Programmable Logic and ApplicationsCASES: International Conference on Compilers, Architecture, and Synthesis for Embedded Syste msRank 3:ICA3PP: Algs and Archs for Parall ProcEuroMICRO: New Frontiers of Information TechnologyACS: Australian Supercomputing ConfISC: Information Security ConferenceUnranked:Advanced Research in VLSIInternational Symposium on System SynthesisInternational Symposium on Computer DesignInternational Symposium on Circuits and SystemsAsia Pacific Design Automation ConferenceInternational Symposium on Physical DesignInternational Conference on VLSI DesignCANPC: Communication, Architecture, and Applications for Network-Based Parallel Computing CHARME: Conference on Correct Hardware Design and Verification MethodsCHES: Cryptographic Hardware and Embedded SystemsNDSS: Network and Distributed System Security SymposiumNOSA: Nordic Symposium on Software ArchitectureACAC: Australasian Computer Architecture ConferenceCSCC: WSES/IEEE world multiconference on Circuits, Systems, Communications & Computers ICN: IEEE International Conference on Networking Topology in Computer Science ConferenceAREA: Applications and MediaRank 1:I3DG: ACM-SIGRAPH Interactive 3D GraphicsSIGGRAPH: ACM SIGGRAPH ConferenceACM-MM: ACM Multimedia ConferenceDCC: Data Compression ConfSIGMETRICS: ACM Conf on Meas. & Modelling of Comp SysSIGIR: ACM SIGIR Conf on Information RetrievalPECCS: IFIP Intl Conf on Perf Eval of Comp \& Comm Sys WWW: World-Wide Web ConferenceRank 2:IEEE VisualizationEUROGRAPH: European Graphics ConferenceCGI: Computer Graphics InternationalCANIM: Computer AnimationPG: Pacific GraphicsICME: Intl Conf on MMedia & ExpoNOSSDAV: Network and OS Support for Digital A/VPADS: ACM/IEEE/SCS Workshop on Parallel \& Dist Simulation WSC: Winter Simulation ConferenceASS: IEEE Annual Simulation SymposiumMASCOTS: Symp Model Analysis \& Sim of Comp \& Telecom Sys PT: Perf Tools - Intl Conf on Model Tech \& Tools for CPE NetStore: Network Storage SymposiumMMCN: ACM/SPIE Multimedia Computing and NetworkingJCDL: Joint Conference on Digital LibrariesRank 3:ACM-HPC: ACM Hypertext ConfMMM: Multimedia ModellingDSS: Distributed Simulation SymposiumSCSC: Summer Computer Simulation ConferenceWCSS: World Congress on Systems SimulationESS: European Simulation SymposiumESM: European Simulation MulticonferenceHPCN: High-Performance Computing and NetworkingGeometry Modeling and ProcessingWISEDS-RT: Distributed Simulation and Real-time Applications IEEE Intl Wshop on Dist Int Simul and Real-Time Applications ECIR: European Colloquium on Information RetrievalEd-MediaIMSA: Intl Conf on Internet and MMedia SysUn-ranked:DVAT: IS\&T/SPIE Conf on Dig Video Compression Alg \& TechMME: IEEE Intl Conf. on Multimedia in EducationICMSO: Intl Conf on Modelling, Simulation and OptimisationICMS: IASTED Intl Conf on Modelling and SimulationCOTIM: Conference on Telecommunications and Information MarketsDOA: International Symposium on Distributed Objects and ApplicationsECMAST: European Conference on Multimedia Applications, Services and TechniquesGIS: Workshop on Advances in Geographic Information SystemsIDA: Intelligent Data AnalysisIDMS: Interactive Distributed Multimedia Systems and Telecommunication ServicesIUI: Intelligent User InterfacesMIS: Workshop on Multimedia Information SystemsWECWIS: Workshop on Advanced Issues of E-Commerce and Web/based Information Systems WIDM: Web Information and Data ManagementWOWMOM: Workshop on Wireless Mobile MultimediaWSCG: International Conference in Central Europe on Computer Graphics and Visualization LDTA: Workshop on Language Descriptions, Tools and ApplicationsIPDPSWPIM: International Workshop on Parallel and Distributed Computing Issues in Wireless N etworks and Mobile ComputingIWST: International Workshop on Scheduling and TelecommunicationsAPDCM: Workshop on Advances in Parallel and Distributed Computational ModelsCIMA: International ICSC Congress on Computational Intelligence: Methods and Applications FLA: Fuzzy Logic and Applications MeetingICACSD: International Conference on Application of Concurrency to System DesignICATPN: International conference on application and theory of Petri netsAICCSA: ACS International Conference on Computer Systems and ApplicationsCAGD: International Symposium of Computer Aided Geometric DesignSpanish Symposium on Pattern Recognition and Image AnalysisInternational Workshop on Cluster Infrastructure for Web Server and E-Commerce Applications WSES ISA: Information Science And Applications ConferenceCHT: International Symposium on Advances in Computational Heat TransferIMACS: International Conference on Applications of Computer AlgebraVIPromCom: International Symposium on Video Processing and Multimedia Communications PDMPR: International Workshop on Parallel and Distributed Multimedia Processing & Retrieval International Symposium On Computational And Applied PdesPDCAT: International Conference on Parallel and Distributed Computing, Applications, and Tec hniquesBiennial Computational Techniques and Applications ConferenceSymposium on Advanced Computing in Financial MarketsWCCE: World Conference on Computers in EducationITCOM: SPIE's International Symposium on The Convergence of Information Technologies and Com municationsConference on Commercial Applications for High-Performance ComputingMSA: Metacomputing Systems and Applications WorkshopWPMC : International Symposium on Wireless Personal Multimedia Communications WSC: Online World Conference on Soft Computing in Industrial Applications HERCMA: Hellenic European Research on Computer Mathematics and its Applications PARA: Workshop on Applied Parallel ComputingInternational Computer Science Conference: Active Media TechnologyIW-MMDBMS - Int. Workshop on Multi-Media Data Base Management SystemsAREA: System TechnologyRank 1:SIGCOMM: ACM Conf on Comm Architectures, Protocols & AppsINFOCOM: Annual Joint Conf IEEE Comp & Comm SocSPAA: Symp on Parallel Algms and ArchitecturePODC: ACM Symp on Principles of Distributed ComputingPPoPP: Principles and Practice of Parallel ProgrammingRTSS: Real Time Systems SympSOSP: ACM SIGOPS Symp on OS PrinciplesSOSDI: Usenix Symp on OS Design and ImplementationCCS: ACM Conf on Comp and Communications SecurityIEEE Symposium on Security and PrivacyMOBICOM: ACM Intl Conf on Mobile Computing and NetworkingUSENIX Conf on Internet Tech and SysICNP: Intl Conf on Network ProtocolsPACT: Intl Conf on Parallel Arch and Compil TechRTAS: IEEE Real-Time and Embedded Technology and Applications Symposium ICDCS: IEEE Intl Conf on Distributed Comp SystemsRank 2:CC: Compiler ConstructionIPDPS: Intl Parallel and Dist Processing SympIC3N: Intl Conf on Comp Comm and NetworksICPP: Intl Conf on Parallel ProcessingSRDS: Symp on Reliable Distributed SystemsMPPOI: Massively Par Proc Using Opt InterconnsASAP: Intl Conf on Apps for Specific Array ProcessorsEuro-Par: European Conf. on Parallel ComputingFast Software EncryptionUsenix Security SymposiumEuropean Symposium on Research in Computer SecurityWCW: Web Caching WorkshopLCN: IEEE Annual Conference on Local Computer NetworksIPCCC: IEEE Intl Phoenix Conf on Comp & CommunicationsCCC: Cluster Computing ConferenceICC: Intl Conf on CommWCNC: IEEE Wireless Communications and Networking ConferenceCSFW: IEEE Computer Security Foundations WorkshopRank 3:MPCS: Intl. Conf. on Massively Parallel Computing SystemsGLOBECOM: Global CommICCC: Intl Conf on Comp CommunicationNOMS: IEEE Network Operations and Management SympCONPAR: Intl Conf on Vector and Parallel ProcessingVAPP: Vector and Parallel ProcessingICPADS: Intl Conf. on Parallel and Distributed SystemsPublic Key CryptosystemsAnnual Workshop on Selected Areas in CryptographyAustralasia Conference on Information Security and PrivacyInt. Conf on Inofrm and Comm. SecurityFinancial CryptographyWorkshop on Information HidingSmart Card Research and Advanced Application ConferenceICON: Intl Conf on NetworksNCC: Nat Conf CommIN: IEEE Intell Network WorkshopSoftcomm: Conf on Software in Tcomms and Comp NetworksINET: Internet Society ConfWorkshop on Security and Privacy in E-commerceUn-ranked:PARCO: Parallel ComputingSE: Intl Conf on Systems Engineering (**)PDSECA: workshop on Parallel and Distributed Scientific and Engineering Computing with Appli cationsCACS: Computer Audit, Control and Security ConferenceSREIS: Symposium on Requirements Engineering for Information SecuritySAFECOMP: International Conference on Computer Safety, Reliability and SecurityIREJVM: Workshop on Intermediate Representation Engineering for the Java Virtual Machine EC: ACM Conference on Electronic CommerceEWSPT: European Workshop on Software Process TechnologyHotOS: Workshop on Hot Topics in Operating SystemsHPTS: High Performance Transaction SystemsHybrid SystemsICEIS: International Conference on Enterprise Information SystemsIOPADS: I/O in Parallel and Distributed SystemsIRREGULAR: Workshop on Parallel Algorithms for Irregularly Structured ProblemsKiVS: Kommunikation in Verteilten SystemenLCR: Languages, Compilers, and Run-Time Systems for Scalable ComputersMCS: Multiple Classifier SystemsMSS: Symposium on Mass Storage SystemsNGITS: Next Generation Information Technologies and SystemsOOIS: Object Oriented Information SystemsSCM: System Configuration ManagementSecurity Protocols WorkshopSIGOPS European WorkshopSPDP: Symposium on Parallel and Distributed ProcessingTreDS: Trends in Distributed SystemsUSENIX Technical ConferenceVISUAL: Visual Information and Information SystemsFoDS: Foundations of Distributed Systems: Design and Verification of Protocols conference RV: Post-CAV Workshop on Runtime VerificationICAIS: International ICSC-NAISO Congress on Autonomous Intelligent SystemsITiCSE: Conference on Integrating Technology into Computer Science EducationCSCS: CyberSystems and Computer Science ConferenceAUIC: Australasian User Interface ConferenceITI: Meeting of Researchers in Computer Science, Information Systems Research & Statistics European Conference on Parallel ProcessingRODLICS: Wses International Conference on Robotics, Distance Learning & Intelligent Communic ation SystemsInternational Conference On Multimedia, Internet & Video TechnologiesPaCT: Parallel Computing Technologies workshopPPAM: International Conference on Parallel Processing and Applied MathematicsInternational Conference On Information Networks, Systems And TechnologiesAmiRE: Conference on Autonomous Minirobots for Research and EdutainmentDSN: The International Conference on Dependable Systems and NetworksIHW: Information Hiding WorkshopGTVMT: International Workshop on Graph Transformation and Visual Modeling Techniques AREA: Programming Languages and Software EngineeringRank 1:POPL: ACM-SIGACT Symp on Principles of Prog LangsPLDI: ACM-SIGPLAN Symp on Prog Lang Design & ImplOOPSLA: OO Prog Systems, Langs and ApplicationsICFP: Intl Conf on Function ProgrammingJICSLP/ICLP/ILPS: (Joint) Intl Conf/Symp on Logic ProgICSE: Intl Conf on Software EngineeringFSE: ACM Conf on the Foundations of Software Engineering (inc: ESEC-FSE) FM/FME: Formal Methods, World Congress/EuropeCAV: Computer Aided VerificationRank 2:CP: Intl Conf on Principles & Practice of Constraint ProgTACAS: Tools and Algos for the Const and An of SystemsESOP: European Conf on ProgrammingICCL: IEEE Intl Conf on Computer LanguagesPEPM: Symp on Partial Evalutation and Prog ManipulationSAS: Static Analysis SymposiumRTA: Rewriting Techniques and ApplicationsIWSSD: Intl Workshop on S/W Spec & DesignCAiSE: Intl Conf on Advanced Info System EngineeringSSR: ACM SIGSOFT Working Conf on Software ReusabilitySEKE: Intl Conf on S/E and Knowledge EngineeringICSR: IEEE Intl Conf on Software ReuseASE: Automated Software Engineering ConferencePADL: Practical Aspects of Declarative LanguagesISRE: Requirements EngineeringICECCS: IEEE Intl Conf on Eng. of Complex Computer SystemsIEEE Intl Conf on Formal Engineering MethodsIntl Conf on Integrated Formal MethodsFOSSACS: Foundations of Software Science and Comp StructAPLAS: Asian Symposium on Programming Languages and SystemsMPC: Mathematics of Program ConstructionECOOP: European Conference on Object-Oriented ProgrammingICSM: Intl. Conf on Software MaintenanceHASKELL - Haskell WorkshopRank 3:FASE: Fund Appr to Soft EngAPSEC: Asia-Pacific S/E ConfPAP/PACT: Practical Aspects of PROLOG/Constraint TechALP: Intl Conf on Algebraic and Logic ProgrammingPLILP: Prog, Lang Implentation & Logic ProgrammingLOPSTR: Intl Workshop on Logic Prog Synthesis & TransfICCC: Intl Conf on Compiler ConstructionCOMPSAC: Intl. Computer S/W and Applications ConfTAPSOFT: Intl Joint Conf on Theory & Pract of S/W DevWCRE: SIGSOFT Working Conf on Reverse EngineeringAQSDT: Symp on Assessment of Quality S/W Dev ToolsIFIP Intl Conf on Open Distributed ProcessingIntl Conf of Z UsersIFIP Joint Int'l Conference on Formal Description Techniques and Protocol Specification, Tes ting, And VerificationPSI (Ershov conference)UML: International Conference on the Unified Modeling LanguageUn-ranked:Australian Software Engineering ConferenceIEEE Int. W'shop on Object-oriented Real-time Dependable Sys. (WORDS)IEEE International Symposium on High Assurance Systems EngineeringThe Northern Formal Methods WorkshopsFormal Methods PacificInt. Workshop on Formal Methods for Industrial Critical SystemsJFPLC - International French Speaking Conference on Logic and Constraint ProgrammingL&L - Workshop on Logic and LearningSFP - Scottish Functional Programming WorkshopLCCS - International Workshop on Logic and Complexity in Computer ScienceVLFM - Visual Languages and Formal MethodsNASA LaRC Formal Methods WorkshopPASTE: Workshop on Program Analysis For Software Tools and EngineeringTLCA: Typed Lambda Calculus and ApplicationsFATES - A Satellite workshop on Formal Approaches to Testing of SoftwareWorkshop On Java For High-Performance ComputingDSLSE - Domain-Specific Languages for Software EngineeringFTJP - Workshop on Formal Techniques for Java ProgramsWFLP - International Workshop on Functional and (Constraint) Logic ProgrammingFOOL - International Workshop on Foundations of Object-Oriented LanguagesSREIS - Symposium on Requirements Engineering for Information SecurityHLPP - International workshop on High-level parallel programming and applicationsINAP - International Conference on Applications of PrologMPOOL - Workshop on Multiparadigm Programming with OO LanguagesPADO - Symposium on Programs as Data ObjectsTOOLS: Int'l Conf Technology of Object-Oriented Languages and SystemsAustralasian Conference on Parallel And Real-Time SystemsPASTE: Workshop on Program Analysis For Software Tools and EngineeringAvoCS: Workshop on Automated Verification of Critical SystemsSPIN: Workshop on Model Checking of SoftwareFemSys: Workshop on Formal Design of Safety Critical Embedded SystemsAda-EuropePPDP: Principles and Practice of Declarative ProgrammingAPL ConferenceASM: Workshops on Abstract State MachinesCOORDINATION: Coordination Models and LanguagesDocEng: ACM Symposium on Document EngineeringDSV-IS: Design, Specification, and Verification of Interactive SystemsFMCAD: Formal Methods in Computer-Aided DesignFMLDO: Workshop on Foundations of Models and Languages for Data and ObjectsIFL: Implementation of Functional LanguagesILP: International Workshop on Inductive Logic ProgrammingISSTA: International Symposium on Software Testing and AnalysisITC: International Test ConferenceIWFM: Irish Workshop in Formal MethodsJava GrandeLP: Logic Programming: Japanese ConferenceLPAR: Logic Programming and Automated ReasoningLPE: Workshop on Logic Programming EnvironmentsLPNMR: Logic Programming and Non-monotonic ReasoningPJW: Workshop on Persistence and JavaRCLP: Russian Conference on Logic ProgrammingSTEP: Software Technology and Engineering PracticeTestCom: IFIP International Conference on Testing of Communicating SystemsVL: Visual LanguagesFMPPTA: Workshop on Formal Methods for Parallel Programming Theory and Applications WRS: International Workshop on Reduction Strategies in Rewriting and Programming FATES: A Satellite workshop on Formal Approaches to Testing of Software FORMALWARE: Meeting on Formalware Engineering: Formal Methods for Engineering Software DRE: conference Data Reverse EngineeringSTAREAST: Software Testing Analysis & Review ConferenceConference on Applied Mathematics and Scientific ComputingInternational Testing Computer Software ConferenceLinux Showcase & ConferenceFLOPS: International Symposum on Functional and Logic ProgrammingGCSE: International Conference on Generative and Component-Based Software Engineering JOSES: Java Optimization Strategies for Embedded Systems。

西北工业大学航天学院【硕士课程简介】

02 航天学院序号：课程编号：02M001课程名称：线性系统理论任课教师：周军刘莹莹英文译名：Linear System Theory先修要求：《线性代数》和《矩阵论》中任一门、《复变函数》内容简介：《线性系统理论》是控制类、系统工程类、电类、计算机类、机电类等许多学科专业硕士研究生的一门公共基础理论课，是控制、信息、系统方面系列理论课程的先行课。

《线性系统理论》是最优估计、最优控制、系统辨识、自适应控制等现代控制理论的基础，系统讲述线性系统的运动规律，揭示系统中固有的结构特性，建立系统的结构、参数与性能之间的定性和定量关系，以及为改善系统性能，满足工程指标要求而采取的各类控制器设计方法。

具体的内容包括：线性系统的状态空间描述、状态空间描述与传递函数描述的关系、线性系统的运动分析、能控性、能观性、稳定性理论、线性反馈系统的状态空间综合方法、线性鲁棒性控制基本理论、线性系统的基本代数理论，以及多变量频域设计方法等。

主要参考书：（1）《线性系统理论》阙志宏主编，西安西北工业大学出版社，1995；（2）《现代控制理论引论》周凤歧等，北京国防工业大学出版社，1988；（3）《线性理论》郑大中编著，北京清华大学出版社；（4）《线性系统理论与设计》[美]陈启宗，科学出版社，1988。

序号：课程编号：02M900课程名称：专业英语任课教师：周军英文译名：Professional English先修要求：专业方面的课程内容简介：本课程作为一种基本的专业英语技能，在阅读和学习与本专业的相关的国外文献资料时，发挥着重要的作用。

因此，主要学习和掌握专业外语的基本语法、句法和结构，通过这门课的学习，期望学生能掌握专业英语的特点；扩大专业英语词汇量，尤其关于本专业有关导弹、航天器、无人机等专业知识方面的英语词汇量；提高专业英语（或科技英语）文章的阅读速度；并进行相应专业英语文献的翻译，在此基础上掌握专业英语的写法，为今后从事工程技术和科学研究工作打下稳固的基础。

flexible regression知识点 -回复

flexible regression知识点-回复Flexible regression is a statistical technique that allows for highly adaptable modeling of relationships between variables. Unlike traditional regression models, which assume a linear relationship between the independent and dependent variables, flexible regression models can capture complex nonlinear relationships. In this article, I will discuss the key concepts and applications of flexible regression.1. Introduction to flexible regression:Flexible regression models are a class of regression models that can accommodate nonlinear relationships, interactions, and varying degrees of complexity. These models are particularly useful when the relationship between the independent and dependent variables is not expected to be purely linear. By modeling nonlinearities, flexible regression enables us to better understand the data and make more accurate predictions.2. Types of flexible regression models:There are various types of flexible regression models, each with its own strengths and characteristics:a) Polynomial regression: This approach allows for the inclusion of higher-order polynomial terms to capture nonlinear relationships. By adding squared, cubic, or higher-order terms of the independent variables, polynomial regression curves can bend and flex to fit more complex patterns.b) Splines: Splines are piecewise-defined polynomial functions that divide the predictor space into segments or knots. The segments are connected smoothly, and the splines can be customized to fit the data more effectively than a single global polynomial equation.c) Generalized Additive Models (GAM): GAM extends the concept of linear regression by allowing for the inclusion of smooth functions of the predictors. These smooth functions are represented by splines or other nonparametric functions and can capture complex nonlinear relationships.d) Nonparametric regression: This type of flexible regression does not make any assumptions about the functional form of the relationship between the variables. Nonparametric regression estimates the relationship from the data directly, withoutspecifying a mathematical equation.3. Advantages of flexible regression models:Flexible regression models offer several advantages over traditional linear regression models:a) Improved model fit: By accommodating nonlinear relationships, flexible regression models can provide a better fit to the data, resulting in more accurate predictions and estimates.b) Better interpretation: The ability to capture nonlinear relationships allows for a more nuanced understanding of the data. These models can reveal complex patterns and interactions between variables that may not be evident in linear regression.c) Flexibility in modeling: Flexible regression models can handle a wide range of data types and can adapt to different functional forms. This flexibility allows researchers to explore various hypotheses and choose the most appropriate model for their data.4. Applications of flexible regression models:Flexible regression models find applications in various fields, suchas:a) Economics: In economics, flexible regression models are used to analyze complex relationships between variables, such as estimating the demand for a product or determining the impact of policy changes on economic outcomes.b) Epidemiology: In epidemiology, flexible regression models are used to study the relationship between risk factors and disease outcomes. These models can capture nonlinear effects of risk factors on disease occurrence and identify high-risk groups.c) Finance: Flexible regression models are widely used in finance to model stock returns, predict asset prices, and analyze the relationship between economic variables and financial markets.d) Environmental science: Flexible regression models are used in environmental science to study the impact of environmental factors on ecological systems. These models can capture nonlinear responses and interactions between environmental variables.5. Challenges and considerations:While flexible regression models offer many advantages, there are some challenges and considerations to keep in mind:a) Overfitting: Flexible regression models have a higher risk of overfitting the data, especially when the number of predictors is large compared to the sample size. Overfitting occurs when the model captures the noise or random variation in the data, leading to poor generalization to new data.b) Interpreting complex models: As flexibility increases, the complexity of the model also increases. Interpreting the results of complex models can be challenging and requires expertise in statistical analysis.c) Computational requirements: Some flexible regression models, especially those based on nonparametric approaches, can be computationally intensive and may require substantial computational resources and time.In conclusion, flexible regression models are a powerful tool for modeling nonlinear relationships between variables. By capturingcomplex patterns, interactions, and nonlinearities, these models improve model fit and facilitate better understanding of the data. Despite some challenges, the benefits of flexible regression models make them a valuable tool in a variety of fields.。

在非惯性系中研究动力刚化问题

在非惯性系中研究动力刚化问题梁立孚;王鹏;宋海燕【摘要】Correct understanding of the dynamic stiffening problem is signality for further researching spacecraft dynamics and establishing a rational numerical model of flexible body dynamics. The dynamic stiffening problem was studied using the theory of a mechanical problem in a non-inertial coordinate system. Two kinds of numerical models for the dynamic stiffening problem were established. The physical meaning of the dynamic stiffening problem was clarified. The approach of correct zero-order modeling was explored. There is a substantive difference between the research of this paper and the research of other scholars.%正确认识动力刚化问题,对深入研究航天器动力学和合理建立柔体动力学的数值计算模型意义重大.应用非惯性坐标系中的力学问题的理论来研究动力刚化问题,给出两类研究动力刚化问题的计算模型,明确了动力刚化问题的物理意义,探索了正确处理零次建模的途径.这样处理动力刚化问题,表现出与其他学者的研究有实质性的差异.【期刊名称】《哈尔滨工程大学学报》【年(卷),期】2012(033)008【总页数】5页(P1052-1056)【关键词】动力刚化;非惯性坐标系;柔体;刚体;航天器动力学【作者】梁立孚;王鹏;宋海燕【作者单位】哈尔滨工程大学力学一级学科博士点,黑龙江哈尔滨150001;上海大学应用数学与力学研究所,上海200444;哈尔滨工程大学力学一级学科博士点,黑龙江哈尔滨150001【正文语种】中文【中图分类】O313文献［1］指出，1987 年 Kane［2］对大范围刚体运动槽型弹性梁进行了研究，指出在大范围刚体运动作高速旋转时，零次耦合建模方法得到弹性梁的变形将无限增大的结果，与实际情况相反.为此，Kane对弹性梁的变形作了比较精确的描述(包括了弯曲变形、剪切变形和扭曲变形)，首次提出动力刚化(dynamic stiffening)的概念.这一问题的提出，引起了各国学者的普遍关注.1989年，Banerjee和Kane［3］又对作大范围刚体运动的弹性薄板进行了研究.Haering［4］，Padilla ［5］采用类似方法对弹性梁动力学性质进行了分析.所得到的结果表明，人们在关于柔性多体系统动力学耦合机理的认识上有待深入，对所描述对象数学模型的准确性有待进一步研究.为了适应我国航天事业发展的需要，我国学者也对这一问题进行了广泛的、深入的研究［6-12］.以上研究，多数是数值的、定量的分析方法，少数学者进行解析的分析讨论.正确的进行解析分析对于深刻把握动力刚化的力学实质、建立正确的数值计算模型是有利的.因此，有必要继续研究下去.在文献［1，12］中，通过一个典型的实例进行研究，本文在其基础上，应用非惯性坐标系中的力学问题的理论来研究动力刚化问题，给出两类研究动力刚化问题的计算模型，得到具有明确物理意义的研究结果.从物理和数学方面说明了产生零级耦合建模的不合理现象的原因，并且建议了合理的处理方法，以便避免零级耦合建模中可能发生不合理现象.这样处理动力刚化问题，表现出与其他学者的研究有实质性的差异.1 在非惯性系中典型实例研究设有如图1所示的力学系统，2根无质量杆AB和BC在B点用铰链连接，在铰链处有一个刚度系数为k的扭簧.长度为R的杆AB的另一端固定在铰链A上，并且绕A点以角速度ω(t)在平面中转动.长度为L的杆BC的另一端固定着质量块m.杆AB和BC之间的相对转角为θ(t)，并且在系统的运动过程中，θ(t)可以为有限量，也可以为小量，其初始值为0.图1 非惯性坐标系Fig.1 Non-inertial coordinate system建立固连于杆AB的连体坐标系Bb1b2(如图1)，由于杆的转动，使得该坐标系成为非惯性坐标系.在这个非惯性坐标系中，如前所述θ(t)可以为有限量，也可以为小量.通过运动分析，可得系统的动能为作用在系统上的力矩，除了弹性力矩kθ外，还有惯性力矩.在转角θ(t)为有限量假设的情况下，离心惯性力fcf为引起的力矩为切向惯性力ft为引起的力矩为其外力势能为在建立动能和势能的表达式时，应当注意:以角速度ω转动的转动中心是A点，该点与质量m的距离为，以角速度转动的转动中心是B点，该点与质量m的距离为L.根据广义协变原理，在非惯性坐标系中，只要合理引入惯性力，就可以将相关力学定律表示为与在惯性系中类似的形式［13-15］，因此Lagrange方程可以表示为将动能的表达式和势能的表达式代入Lagrange方程的各项，并且推导如下:将推导结果代入Lagrange方程，可得整理可得这里顺便指出，方程式(13)是以角位移θ为基本变量的动力学方程.mL2为动力学项，kθ为扭簧引起的力矩，mω2RLsin θ为离心惯性力引起的力矩，m(Rcosθ+L)L为切向惯性力引起的力矩.2 进一步典型实例研究建立固连于杆AB的连体坐标系Bb1b2(图1)，由于杆的转动，使得该坐标系成为非惯性坐标系.在这个非惯性坐标系中，假设θ(t)始终为小角，使得sin θ≈θ，cos θ≈1.通过运动分析，可得系统的动能为作用在系统上的力矩，除了弹性力矩kθ外，还有惯性力矩.在θ(t)始终为小角假设的情况下，离心惯性力的计算公式为引起的力矩为切向惯性力的计算公式为引起的力矩为其外力势能为在建立动能和势能的表达式时，应当注意:以角速度ω转动的转动中心是A点，该点与质量m的距离为(R+L)，以角速度转动的转动中心是B点，该点与质量m的距离为L.根据广义协变原理，在非惯性坐标系中，只要合理引入惯性力，就可以将相关力学定律表示为与在惯性系中类似的形式［13-15］，因此Lagrange方程可以表示为将动能的表达式和势能的表达式代入Lagrange方程的各项，并且推导如下:将推导结果代入Lagrange方程，可得进而可得动力刚度项式(26)明确显示，在这个典型实例中，引起动力刚化的原因是离心惯性力的影响.这里顺便指出，方程式(25)是以角位移为基本变量的动力学方程.mL2为动力学项，kθ为扭簧引起的力矩，mω2RLθ为离心惯性力引起的力矩，m(R+L)L为切向惯性力引起的力矩.本节处理问题的过程，与一般文献中所提及的零级耦合建模相似，只是这里是在非惯性坐标系中研究问题的，而一般文献中多数是在惯性坐标系中.以上论述表明，在非惯性系中合理的处理问题，所谓的零次建模也是可行的.这一点也可说明在非惯性坐标系中研究动力刚化问题的优越性.3 典型实例的另一类计算模型研究建立固连于杆AB的连体坐标系Bb1b2(图2)，由于杆的转动，使得该坐标系成为非惯性坐标系.在这个非惯性坐标系中，假设θ(t)始终为小角，使得sin θ≈θ，cos θ≈1 .通过运动分析，可以得系统的动能为图2 θ(t)始终为小角Fig.2 θ(t)always small angle作用在系统上的力矩，除了弹性力矩kθ外，还有惯性力矩.在θ(t)始终为小角假设的情况下，离心惯性力的计算公式为引起的力矩为将离心惯性力作为主动力引起的附加势能为这一结果与文献［12］给出的结果相同.切向惯性力的计算公式为引起的力矩为将切向惯性力作为主动力引起的附加势能为系统的总外力势能为在建立动能和势能的表达式时，应当注意:以角速度ω转动的转动中心是A点，该点与质量m的距离为(R+L)，以角速度转动的转动中心是B点，该点与质量m的距离为L.根据广义协变原理，在非惯性坐标系中，只要合理引入惯性力，就可以将相关力学定律表示为与在惯性系中类似的形式，因此Lagrange方程可表示为将动能的表达式和势能的表达式代入Lagrange方程的各项，并且推导如下:将推导结果代入Lagrange方程，可得进而可得动力刚度项为式(42)明确显示，在这个典型实例中，引起动力刚化的原因是离心惯性力的影响.这里顺便指出，方程式(41)是以角位移为基本变量的动力学方程.mL2为动力学项，kθ为扭簧引起的力矩，mω2(R+L)Lθ为离心惯性力引起的力矩，m(R+L)L为切向惯性力引起的力矩.4 讨论零级建模如果在应用Lagrange方程之前，对势能函数应用泰勒展开并且取一级近似，可得可见，将使得离心惯性力引起的外力势能消失.这从物理方面说明了所谓零级建模不可行的原因.正弦函数和余弦函数的泰勒展开为如果在应用Lagrange方程之前，对势能函数应用泰勒展开.以往的简化是将正弦函数和余弦函数的泰勒展开都取一级近似.考虑到正弦函数的泰勒级数收敛较快，余弦函数的泰勒级数的收敛较慢，因而取正弦函数的泰勒展开的一级近似，取余弦函数的泰勒展开的二次近似，可得势能的表达式:可见，式(46)与式(18)相同，这也可以从一个侧面说明这样处理问题的正确性.将势能的表达式代入Lagrange方程的有关势能的项，并且推导如下:动能的表达式及其相关推导同前.将推导结果代入Lagrange方程，可得整理可得可见，在应用Lagrange方程之前简化，只要合理进行近似计算，也可以得到合理的建模.具体问题具体分析对于科技工作者来说是至关重要的.研究表明，考虑动力刚化的柔体动力学的建模问题，内容丰富，可以分门别类的进行研究.5 结束语本文是在非惯性坐标系中研究动力刚化问题.首先，给出在非惯性坐标系中研究动力刚化典型实例的一类力学模型，应用有限位移理论研究动力刚化问题的典型实例，得到具有明确物理意义的结果.将这类研究退化到小位移理论，表明所谓零次耦合建模方法也是可行的.然后，给出在非惯性坐标系中研究动力刚化典型实例的另一类力学模型.最后，进一步讨论了如何正确地进行所谓零次耦合建模的问题.参考文献:【相关文献】［1］洪嘉振，蒋丽忠.动力刚化与多体系统刚-柔耦合动力学［J］.计算力学学报，1999，16(3):295-301.HONG Jiazhen，JIANG Lizhong.Dynamic stiffening and multibody dynamics with coupled rigid and deformation motions［J］.Chinese Journal of Computational Mechanics，1999，16(3):295-301.［2］KANE T R，RYAN R R，BANER J A K，Dynamics of a cantilever beam attached to a moving base［J］.Journal of Guidance Control and Dynamics，1987，10(2):139-151.［3］BANERJEE A K，KANE T R.Multi-flexible body dynamics capturing movtion-induced stiffnes［J］.Journal of Applied Mechanics，1989，56:887-892.［4］HAERING W J，RYAN R R，SCOTT A.New formulation for flexible beams undergoing large overall plane motion［J］.Journal of Guidance，Control and Dynamics，1994，17(1):76-83.［5］PADILLA C E，VON FLOTOW A H.Nonlinear strain displacement relations and flexible multibody dynamics［J］.Journal of Guidance，Control and Dynamics，1992，15(1):128-136.［6］孔向东，钟万勰，齐朝晖.计及动力刚化项的柔性机械臂几何非线性模型［J］.机械科学与技术，1998，17(5):722-724.KONG Xiangdong，ZHONG Wanxie，QI Chaohui.Geometric nonlinear model of flexible manipulators in consideration of dynamic stiffening terms ［J］.Mechanical Science and Technology，1998，17(5):722-724.［7］金在权，权成七，刘龙哲.弹性旋转梁的动力刚化效应［J］.延边大学学报，2000，26(2):116-118.JIN Zaiquan，QUAN Chengqi，LIU Longzhe.The stiffening effect of the centrifugal force［J］.Journal of Yanbian University，2000，26(2):116-118.［8］杨辉，洪嘉振，余征跃.动力刚化问题的实验研究［J］.力学学报，2004，36(1):119-124.YANG Hui，HONG Jiazhen，YU Zhengyue.Experimental investigation on dynamic stiffening phenomenon［J］.Acta Mechanica Sinica，2004，36(1):119-124.［9］蒋建平，李东旭.大范围运动矩形板动力刚化分析［J］.动力学与控制，2005，3(1):10-14.JIANG Jianping，LI Dongxu.Dynamic analysis of rectangular plate undergoing overall motion［J］.Journal of Dynamics and Control，2005，3(1):10-14.［10］金国光，刘又五，王树新，等.含动力刚化项的一般多柔体系统动力学研究［J］.哈尔滨工业大学学报，2005，37(1):101-103.JIN Guoguang，LIU Youwu，WANG Shuxin，etal.Generally flexible multi-body system dynamics in consideration of dynamic stiffening terms［J］.Journal of Harbin Institute of Technology，2005，37(1):101-103.［11］章定国，朱志远.一类刚柔耦合系统的动力刚化分析［J］.南京理工大学学报，2006，30(1):21-25.ZHANG Dingguo，ZHU Zhiyuan.Dynamic stiffening of rigid-flexible coupling system［J］.Journal of Nanjing University of Science and Technology，2006，30(1):21-25. ［12］李东旭.挠性航天器结构动力学［M］.北京:科学出版社，2010:285-286.［13］爱因斯坦.相对论的意义［M］.郝建纲，刘道军译.上海:科技教育出版社，2005:36-51. ［14］邱吉宝，向树红，张正平.计算结构动力学［M］.合肥:中国科学技术大学出版社，2009:455-463.［15］梁立孚，刘石泉，王振清，等.飞行器结构动力学中的几个问题［M］.西安:西北工业大学出版社等五社联合出版，2010:158-172.。

医学未折叠蛋白元件英语

医学未折叠蛋白元件英语The intricate world of medicine has long been shaped by the fundamental principles of biochemistry and molecular biology. At the heart of this dynamic interplay lies the enigmatic realm of unfolded protein elements, a domain that has captivated the attention of researchers and clinicians alike. These unique protein structures, often referred to as intrinsically disordered proteins or IDPs, have emerged as a pivotal area of study in the pursuit of understanding and addressing various medical conditions.Traditionally, the study of proteins has been dominated by the notion that a protein's function is intrinsically linked to its well-defined three-dimensional structure. However, the discovery of IDPs has challenged this conventional wisdom, revealing a remarkable diversity in the ways proteins can adopt and utilize their structural properties to perform a multitude of crucial biological functions. Unlike their folded counterparts, IDPs lack a stable tertiary structure, existing instead as dynamic and flexible ensembles that can adapt to a wide range of environmental conditions and interactions.This structural flexibility endows IDPs with a remarkable versatility, allowing them to participate in a vast array of cellular processes, from signal transduction and transcriptional regulation to protein-protein interactions and cellular signaling pathways. By eschewing the constraints of a fixed structure, IDPs can engage in a dynamic dance of conformational changes, enabling them to bind to multiple targets and perform diverse roles within the complex tapestry of the living cell.The significance of IDPs in the realm of medicine cannot be overstated. These unfolded protein elements have been implicated in a wide range of pathological conditions, from neurodegenerative disorders to cancer and infectious diseases. In the case of neurodegenerative diseases, such as Alzheimer's and Parkinson's, the aggregation and misfolding of IDPs, such as tau and α-synuclein, have been identified as key contributors to the development and progression of these devastating conditions. Understanding the underlying mechanisms that govern the behavior of these unfolded proteins has become a crucial area of research, as it holds the promise of unlocking new therapeutic avenues and strategies for intervention.Similarly, in the field of oncology, IDPs have emerged as pivotal players in the complex landscape of cancer biology. Many cancer-related proteins, such as p53 and Myc, are intrinsically disordered,and their structural flexibility allows them to engage in a dynamic interplay with a diverse array of cellular partners, ultimately influencing the hallmarks of cancer, including uncontrolled cell growth, evasion of apoptosis, and metastatic potential. By targeting these unfolded protein elements, researchers are exploring novel approaches to cancer treatment, seeking to disrupt the delicate balance that sustains the malignant phenotype.Beyond their role in disease pathogenesis, IDPs have also garnered attention for their potential as therapeutic targets and biomarkers. The unique structural and functional properties of these unfolded proteins offer opportunities for the development of targeted interventions, such as small-molecule inhibitors or allosteric modulators, that can selectively engage and modulate their behavior. Additionally, the presence and patterns of IDP expression in various disease states have been investigated as potential diagnostic and prognostic indicators, paving the way for more personalized and effective clinical management strategies.The study of unfolded protein elements in medicine is not without its challenges, however. The inherent complexity and dynamic nature of IDPs pose significant hurdles in terms of structural characterization, functional elucidation, and therapeutic targeting. Traditional structural biology techniques, designed for well-folded proteins, often struggle to capture the nuances of IDP behavior, necessitatingthe development of specialized methods and analytical tools.Despite these challenges, the scientific community has made remarkable strides in advancing our understanding of IDPs and their implications in human health and disease. Cutting-edge technologies, such as advanced spectroscopic techniques, computational modeling, and single-molecule approaches, have enabled researchers to delve deeper into the intricate world of unfolded protein elements, revealing their intricate roles in cellular processes and their potential as therapeutic targets.As the field of IDP research continues to evolve, the promise of unlocking new frontiers in medicine becomes increasingly tangible. By unraveling the mysteries of these unfolded protein elements, scientists and clinicians alike are poised to unveil innovative diagnostic strategies, develop targeted therapies, and ultimately improve the lives of patients suffering from a wide range of medical conditions. The journey ahead is filled with both challenges and opportunities, but the potential impact of this burgeoning field on the future of healthcare is truly transformative.。

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电力系统英文书籍推荐unication&control in power system 电力系统通讯与控制2.electric power systems: analysis and control 电力系统: 分析与控制3.Electrical Energy System 电能系统4.embedded generation 嵌入式发电5.fundamentals of power system economics 电力系统经济学基础6.Handbook of Electric Power Calculations 电力系统计算手册7.market operations in electric power systems 电力系统市场运行8.POWER QUALITY 电能质量9.Risk assessment of power systems 电力系统风险评估10.Switching Power Supply Design 开关供电设计11.understanding electric power systems 电力系统学习12.understanding Power Quality problems 电能质量问题学习13.electric energy economic methods 电能经济方法14.FACTS Modelling and Simulation in Power Networks 灵活交流输电: 在电网中的仿真与模拟15.HVDC.and.FACTS.Controllers.Applications.of.Static.Converters.in.Power.System s 高压直流和灵活交流控制器在电力系统中应用16.LOAD-FLOW ANALYSIS IN POWER SYSTEMS 电力系统潮流分析17.Operation of Market-oriented Power Systems 市场化电力系统运营18.Power Generation Operation and Control 发电运行和控制19.Power system economics 电力系统经济学20.power system harmonics 电力系统谐波21.Power System Operations and Electricity Markets 电力系统运行和电力市场22.Power System Restructuring and Deregulation 电力系统改制和放松管制(即电力市场)23.voltage stability of electric power systems 电力系统电压稳定24.Transients in Power Systems 电力系统(电磁)暂态25.transient stability of power systems电力系统暂态稳定26.Wind Energy Handbook 风电手册27.distrbuted generation-the power paradigmfor the new millennium分布式发电28.electric power distribution handbook 配电手册29.electric power engineering handbook 电力工程手册30.spatial load forecasting(空间)电力负荷预测31.power transer-principles and applications 电力变压器-原理和应用32.electric power transer engineering 电力系统变压器工程33.wind and solar power system 风电和太阳能发电34.Electric Power Distribution Reliability 配电网可靠性35.Aging power delivery infrastrutures 送电结构36.Renewable and Efficient Electric Power Systems 可再生与高效电力系统37.probabilityconcepts in electric power systems 电力系统概率应用38.Short Circuits in Power Systems 电力系统短路39.VOLTAGE STABILITY ASSESSMENT,PROCEDURES AND GUIDES 电压稳定性评估,措施和导则40.electric systems, dynamics and stability with AI application 电力系统动态和稳定性: 人工智能应用41.electric power system application of optimiztion 电力系统优化应用42.protective relaying theory and application 继电保护理论与应用43.vehicular electric power systems 车辆电力系统44.electric power quality control techniques 电能质量控制技术45.reliability assessment of electric power systems using monte carlo methods 利用蒙特卡罗方法进行电力系统可靠性评估petitive Electricity Markets 竞争性电力市场47.power quality enhancement using customer power devices 用户电力设备与电能质量提高48.power system harmonics: computer modelling and analysis 电力系统谐波:计算机仿真与分析49.Analysis of Faulted Power Systems 故障电力系统分析50.Dynamic and control of large power system 大电力系统动态与控制51.Distributed power generation: planning and evaluation分布式发电(规划与评估)52.AC-DC power system analysis 交直流电力系统分析53.FACTS (flexible AC transmission system) 灵活交流输电系统54.Power system in emergencies 紧急状态下的电力系统55.Power system restoration 电力系统恢复56.Electric power system quality 电能质量57.Energy Management Systems (EMS) 能量管理系统58.Automatic learning techniques in power systems 自学习技术在电力系统中的应用59.Power system protection 1-4 电力系统保护1-4册 (electricity association 培训教程)60 electrical power system protection 电力系统保护61.elements of power system analysis 电力系统分析基础62.AC power system handbook 交流电力系统手册63. Wind turbine operation in electric power systems: advanced modelling 风力发电(机)在电力系统运行64. Power system control and stability 电力系统控制与稳定性( 不是那本stability and control)65. Analysis of subsynchronous resonance in power system 电力系统次同步谐振分析putationalmethods for large sparse power systems: a object orientedapproach 大稀疏电力系统计算方法: 面向对象的途径67. Power system oscillation 电力系统振荡68. Power system restructuring: engineering and economics 电力系统市场化: 工程和经济69. Distribution system modelling and analysis 配电系统建模与分析70. Electric power engineering 电力工程71. Subsynchronous resonance in power systems 电力系统中的次同步谐振72. Computer modelling of electrical power system 电力系统计算机建模73. High Voltage Direct Current Transmission 高压直流输电74. Electricitydistribution network design (2nd)配电网规划设计75. Industrial power distribution 工业配电76. Protection ofelectricity distribution networks 配电网保护77. Energy function analysis for power system stability 电力系统稳定性的能量函数分析78. Power system commission and maintenance practice电力系统试验(调试)与检修(维护)实践79. Statistical techniques for high-voltage engineering 高电压工程中的统计技术80. Digital protection for power system电力系统数字保护81. Power system protection 电力系统(继电)保护82. Voltage quality in electrical power systems 电力系统电压质量83.Electric power applications of fuzzy systems 模糊系统的电力应用84. Artificial intelligence techniques in power system 电力系统中的人工智能技术85. Insulators in high voltages 高压绝缘体86. Electrical safety供电安全87. High voltageengineering and testing 高电压工程与试验88. Reactive power control in electric systems 电力系统无功(功率)控制93. Electric power system电力系统教程94. Computer-Aided Power systems analysis 计算机辅助电力系统分析99. Reliability evaluation of power system 电力系统可靠性评估106. Power system stability handbook 电力系统稳定性手册109. Reliability assessment of large electric power systems 大电力系统可靠性评估112. HVDC power transmission systems 高压直流输电系统128. Electric Machinery and power system fundamentals 电机与电力系统基础(MATLAB 辅助)129. Intelligent system applications in power engineering (EP and ANN) 智能系统在电力工程中应用(进化计算和神经网)130. Thyristor-based FACTS controllers for electrical transmission systems 基于晶闸管的灵活交流输电系统控制器131. The economics of power system reliability and planning 电力系统可靠性与规划的经济学132. Computational Intelligence Applications to Power systems 计算智能在电力系统中的应用133. Environmental Impact of Power Generation 发电的环境影响134. Operation and Maintenance of Large Turbo-Generators 大型涡轮发电机组运行与检修135. Power system simulation 电力系统仿真136. Advanced load dispatch for power systems 电力系统高级调度137. The development of electric power transmission 电力传输进展138. Renewable Energy Sources 可再生发电源139. Power system dynamics andstablity 电力系统动态与稳定性140. Practical electrical network automation and communication systems 电力系统自动化与通信系统实践141. Electrical power and controls 电力与控制142. Deregulation of Electric Utilities 电力企业放松管制(市场改革)143. Computational Auction Mechanisms for restructured power industry operation 电力市场运行的(计算)投标机理144. Finanicial and economic evaluation of projects in the electricity supply industry 电力工程项目的金融与经济评价145. Electricity economics and planning 电力经济与规划146. Computational Methods for electric power systems 电力系统计算方法147. Power system relaying 电力系统继电保护148. Computer relaying for power systems 电力系统计算机保护149. Modern power system planning 现代电力系统规划150. High Voltage Engineering (2nd) 高电压工程151. Operation of restructured power systems 市场化电力系统运行152. Transer and Inductor Design Handbook变压器和电感设计手册(04增强版)153. Modern power system analysis (matlab supported) 现代电力系统分析(03年含MATLAB版)154. Power distribution planning reference book 配电规划参考手册155. Understanding FACTS 理解灵活交流输电系统156. Power system analysis :short-circuit load flow and harmonics 电力系统分析: 短路潮流和谐波157. Power systems electromagnetic transients simulation 电力系统电磁暂态仿真158. Power electronic control in electrical systems 电力系统中的电力电子控制159. Protection devices and systems for high-voltage applications保护装置和系统的高压应用160. Small signal analysis of power systems 电力系统小信号分析161. Electrical power cable engineering 电力线缆工程162. Power System State Estimation: Theory and Implementation 电力系统状态估计: 理论和实现163. Dielectrics in Electric Fields电场中的电介质(绝缘体)164. spacecraft power system 航天器电力系统165. Grid integration of wind energy conversion systems 风能转换系统的电网整合(接入)166. Power loss: the origins of deregulation and restructuring in the American electricutility system网损:美国电力系统放松管制和市场化的根源167. High Voltage Circuit Breakers: Design and Applications 高压断路器:设计与应用168. Power system capacitors 电力系统电容器169. Energy Management Systems & Direct Digitial Control 能量管理系统(EMS)及直接数字控制170. Pricing in Competitive Electricity Market 电力市场电价171. Designing Competitive Electricity Markets 电力市场设计172. Power system dynamics and stability 电力系统动态与稳定性(美国)173. Theory and problems of electric power systems 电力系统的理论和问题174. Insulation coordinationfor power systems 电力系统绝缘配合175. Modal analysis of large interconnected power systems 大互联电力系统的模式分析176. Making competition work in electricity 电力市场竞争177. Power system operation 电力系统运行178. Transmission line reliability and security 输电线路安全可靠性179. Computer analysis of power systems 电力系统计算机分析89. Electical distribution engineering配电网工程90. Power systemplanning电力系统规划91. Uniquepower system problems 电力系统问题92. Tranmission and Distribution ofElectrical Energy 电力系统输配电95. Electric powertransmission system 输电系统96. Reliability Modelling in Electric power systems电力系统可靠性建模97. High voltage engineering in power system 电力系统高电压工程98. Extra High voltage AC transmission engineering 超高压交流输电工程100. Computation of power system transients 电力系统暂态计算101.Piecewise methods and application to power systems 分段法及其在电力系统中应用103. Analysis and protection of electrical power systems 电力系统分析与保护104. Power systems engineering and mathematicas电力系统工程与数学105. Stability of large power systems 大电力系统稳定性107. Power system reliability evaluation电力系统可靠性评估108.Electric power system dynamics 电力系统动态110. Power system analysis and planning 电力系统分析与规划111. Electric transmission line fundamental 输电线(工程)基础113. Transient Processes in electrical power systems 电力系统暂态过程114.Discrete Fourier transation and its applications to power system 离散傅立叶变换及其在电力系统中的应用115. Electrical Transients inpower system 电力系统暂态116. Optimal economic operation of electric power system 电力系统优化经济调度运行117.High power switching 大功率开关118. power plant engineering 电厂工程119. power plant system design 电厂系统设计120. power plant evaluation and design reference guide 电厂评估和设计参考导则121. planning engineering, and construction of electric power generationfacilities 发电设备的规划和建设工程122. Elements electrical power station design 电站设计基础123.Optimal control applications in electric power systems 电力系统最优控制应用124. applied protected relaying应用继电保护125. power station and substation maintenance 电厂与变电站维修126. Power system operation 电力系统运行127. power system reliability,safety and management 电力系统可靠性,安全与管理。

智能控制外文翻译-其他专业

英文文献资料及翻译Intelligent ControlControl technology is 20 years in the last century established a frequency domain method based upon the classical control theory developed, the control technology of industrial production has been widely used. Promote the development of space technology, the 50's emerged to law-based state space control theory now so widely control technology development, resulting in more applications. Since the 60s, with the development of computer technology, many new methods and technology into engineering, product of stage, appears to accelerate the pace of industrial technological upgrading, which control technology presented new challenges, also provide for the development of a conditions for the theory of intelligent control technology application in the form of intelligent control technology.Intelligent control technology is mainly used to resolve those using traditional methods can not solve the control problem of complex systems, such as intelligent robotics systems, computer integrated manufacturing system (CIMS), a complex industrial process control systems, aerospace control systems, socio-economic management systems, transportation systems, communication network systems, environmental protection and energy systems. These complex systems have the following characteristics: 1. Control object of serious uncertainty, the control model or the model structure and unknown parameters of a large range of changes; 2. Control of highly nonlinear characteristics of the object; 3. Control tasks require complex. For example, the intelligent robot systems require a complex task system has its own planning and decision-making capacity, automatic ability to avoid obstacles to reach the destination.Intelligent control technology often play a role through the intelligent control system. In short, intelligent control system is the system with an intelligent behavior, which uses artificial intelligence methods to solve difficult mathematical methods accurately describe the complex, random, flexible control problem, a self-learning, adaptive, self-organizing capabilities. Its main objective is to explore the human brain deal with things closer to the "thinking" mode is to study a kind of mathematical logic, make the machine like human beings, according to a small amount of fuzzy information, some of the reasoning based on the guidelines, "thinking", you can get a very accurate or adequate approximation of the conclusions and control strategies.The intelligent control technology in the engineering machinery products, to solve the traditional control methods can not well adapt to changing challenges of complex objects. Intelligent control technology can change thecontrol strategy to adapt to the object complexity and uncertainty. It is not just rely on mathematical models, and online experience based on knowledge and reasoning to identify and select the best control strategy, the uncertainty for the system to maintain a predetermined quality and expectations.Intelligence and intelligent systems can be characterized in a number of ways and along a number of dimensions. There are certain attributes of intelligent systems, common in many definitions, which are of particular interest to the control community.In the following, several alternative definitions and certain essential characteristics of intelligent systems are first discussed. A brief working definition of intelligent systems that captures their common characteristics is then presented. In more detail, we start with a rather eneral definition of intelligent systems, we discuss levels of intelligence, and we explain the role of control in intelligent systems and outline several alternative definitions. We then discuss adaptation and learning, autonomy and the necessity for efficient computational structures in intelligent systems, to deal with complexity. We conclude with a brief working characterization of intelligent (control) systems.We start with a general characterization of intelligent systems:An intelligent system has the ability to act appropriately in an uncertain environment, where an appropriate action is that which increases the probability of success, and success is the achievement of behavioral subgoals that support the system’s ultimate goal.In order for a man-made intelligent system to act appropriately, it may emulate functions of living creatures and ultimately human mental faculties. An intelligent system can be characterized along a number of dimensions. There are degrees or levels of intelligence that can be measured along the various dimensions of intelligence. At a minimum, intelligence requires the ability to sense the environment, to make decisions and to control action. Higher levels of intelligence may include the ability to recognize objects and events, to represent knowledge in a world model, and to reason about and plan for the future. In advanced forms, intelligence provides the capacity to perceive and understand, to choose wisely, and to act successfully under a large variety of circumstances so as to survive and prosper in a complex and often hostile environment. Intelligence can be observed to grow and evolve, both through growth in computational power and through accumulation of knowledge of how to sense, decide and act in a complex and changing world.The above characterization of an intelligent system is rather general. According to this, a great number of systems can be considered intelligent. In fact, according to this definition, even a thermostat may be considered to be an intelligent system, although of low level of intelligence. It is common, however, to call a system intelligent when in fact it has a rather high level of intelligence.There exist a number of alternative but related definitions of intelligentsystems and in the following we mention several. They provide alternative, but related characterizations of intelligent systems with emphasis on systems with high degrees of intelligence.The following definition emphasizes the fact that the system in question processes information, and it focuses on man-made systems and intelligent machines:A. Machine intelligence is the process of analyzing, organizing and converting data into knowledge; where (machine) knowledge is defined to be the structured information acquired and applied to remove ignorance or uncertainty about a specific task pertaining to the intelligent machine. This definition leads to the principle of increasing precision with decreasing intelligence, which claims that: applying machine intelligence to a database generates a flow of knowledge, lending an analytic form to facilitate modeling of the process.Next, an intelligent system is characterized by its ability to dynamically assign subgoals and control actions in an internal or autonomous fashion:B. Many adaptive or learning control systems can be thought of as designing a control law to meet well-defined control objectives. This activity represents the system’s attempt to organize or order its “knowledge” of its own dynamical behavior, so to meet a control objective. The organization of knowledge can be seen as one important attribute of intelligence. If this organization is done autonomously by the system, then intelligence becomes a property of the system, rather than of the system’s designer. This implies that systems which autonomously (self) -organize controllers with respect to an internally realized organizational principle are intelligent control systems.A procedural characterization of intelligent systems is given next:C. Intelligence is a property of the system that emerges when the procedures of focusing attention, combinatorial search, and generalization are applied to the input information in order to produce the output. One can easily deduce that once a string of the above procedures is defined, the other levels of resolution of the structure of intelligence are growing as a result of the recursion. Having only one level structure leads to a rudimentary intelligence that is implicit in the thermostat, or to a variable-structure sliding mode controller.The concepts of intelligence and control are closely related and the term “Intelligent Control”has a unique and distinguishable meaning. An intelligent system must define and use goals. Control is then required to move the system to these goals and to define such goals. Consequently, any intelligent system will be a control system. Conversely, intelligence is necessary to provide desirable functioning of systems under changing conditions, and it is necessary to achieve a high degree of autonomous behavior in a control system. Since control is an essential part of any intelligent system, the term “Intelligent Control Systems” is sometimes used in engineering literature instead of “Intelligent Systems”or “Intelligent Machines”. The term “Intelligent Control System”simply stresses the control aspect of the intelligent system.Below, one more alternative characterization of intelligent (control) systems is included. According to this view, a control system consists of data structures or objects (the plant models and the control goals) and processing units or methods (the control laws) :D. An intelligent control system is designed so that it can autonomously achieve a high level goal, while its components, control goals, plant models and control laws are not completely defined, either because they were not known at the design time or because they changed unexpectedly.There are several essential properties present in different degrees in intelligent systems. One can perceive them as intelligent system characteristics or dimensions along which different degrees or levels of intelligence can be measured. Below we discuss three such characteristics that appear to be rather fundamental in intelligent control systems.Adaptation and Learning. The ability to adapt to changing conditions is necessary in an intelligent system. Although adaptation does not necessarily require the ability to learn, for systems to be able to adapt to a wide variety of unexpected changes learning is essential. So the ability to learn is an important characteristic of (highly) intelligent systems.Autonomy and Intelligence. Autonomy in setting and achieving goals is an important characteristic of intelligent control systems. When a system has the ability to act appropriately in an uncertain environment for extended periods of time without external intervention, it is considered to be highly autonomous. There are degrees of autonomy; an adaptive control system can be considered as a system of higher autonomy than a control system with fixed controllers, as it can cope with greater uncertainty than a fixed feedback controller. Although for low autonomy no intelligence (or “low”intelligence) is necessary, for high degrees of autonomy, intelligence in the system (or “high” degrees of intelligence) is essential.Structures and Hierarchies. In order to cope with complexity, an intelligent system must have an appropriate functional architecture or structure for efficient analysis and evaluation of control strategies. This structure should be “sparse” and it should provide a mechanism to build levels of abstraction (resolution, granularity) or at least some form of partial ordering so to reduce complexity. [7] An approach to study intelligent machines involving entropy emphasizes such efficient computational structures. Hierarchies (that may be approximate, localized or combined in heterarchies) that are able to adapt, may serve as primary vehicles for such structures to cope with complexity. The term “hierarchies” refers to functional hierarchies, or hierarchies of range and resolution along spatial or temporal dimensions, and it does not necessarily imply hierarchical hardware. Some of these structures may be hardwired in part. To cope with changing circumstances, the ability to learn is essential, so these structures can adapt to significant, unanticipated changes.In view of the above, a working characterization of intelligent systems (orof (highly) intelligent (control) systems or machines) that captures the essential characteristics present in any such system is:An intelligent system must be highly adaptable to significant unanticipated changes, and so learning is essential. It must exhibit high degree of autonomy in dealing with changes. It must be able to deal with significant complexity, and this leads to certain sparse types of functional architectures such as hierarchies.智能控制控制技术是在上世纪20年代建立了以频域法为主的经典控制理论后发展起来的,控制技术首先在工业生产中得到了广泛的应用。

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