Building Chemical Informatics Grid - Indiana University建筑化学信息学网格-印第安那大学

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STEP

STEP (System for Teaching Experimental Psychology) is an project designed to maximize the use of E-Prime, PsyScope, and other experiment generating systems for teaching undergraduate classes in Experimental Psychology. It is directed by Brian MacWhinney in the Department of Psychology at Carnegie Mellon University. Other contributors include Ping Li of the University of Richmond, Chris Schunn of the University of Pittsburgh, and James St. James at Millikin University.The main components of this project use the new E-Prime system, which is available for Windows only.PsyScope, which is free but not supported, works on the Macintosh.An article from BRMIC summarizes the projectSTEP Resources include:∙The e-prime@ mailing list that is used to share ideas and issues regarding the use of E-Prime to build experiments.To subscribe, go to /group/eprime. When you do this for the first time, you will be asked to set up a Google Groups account. This is not a full Gmail account and they only ask for your name and password. After you do that, when you later log on you can go directly to the relevant list and subscribe byselecting "edit my membership" which is an option in the middle of the column on the right of the page.∙SCRIPTS-Classic: Runnable E-Prime scripts that can be used to demonstrate classic experiments in Experimental Psychology. These are accompanied by descriptions of the original articles.∙SCRIPTS-Plus: Additional E-Prime scripts for certain commonly used paradigms. These are not accompanied by descriptions of theoriginal articles. Some of these are recent contributions.∙PST Samples: PST maintains an excellent collection of about 50 short E-Prime scripts designed to illustrate specific programmingtechniques and tasks. However, to access these you will first need to get a logon ID for the PST site.∙SCRIPTS-Student: E-Prime scripts contributed by Brian MacWhinney's students in Cognitive Research Methods in 2002 and 2003.∙MATERIALS: The E-Prime Getting Started Guide and various PowerPoint tutorials from PST, as well as additional technical documents for response boxes, etc.∙UTILITIES: Utilities for working with E-Prime and PsyScope∙COURSE FRAMEWORK: Complete material set for a Lab course based onE-Prime from Chris Schunn at George Mason UniversityThis project was supported by grant 9972437 from the Division of Undergraduate Education of the National Science Foundation. For more information, contact Brian MacWhinney atCMU.BiomedicalInformaticsResearch Network(BIRN)∙Abouto About: Overviewo BIRN Video Introductiono BIRN FAQso Historyo Executive Committeeo Steering Committeeo Presentations/Publicationso Mediao Sponsorso Newso Contact Us∙Capabilitieso Capabilities: Overviewo Current Capabilitieso Developing Capabilities∙Working with BIRNo Working with BIRN:Overviewo FAQo BIRN User Inquiryo Working GroupsData ManagementWorking Group▪Derived Data WorkingGroup▪Genomics WorkingGroup▪InformationIntegration WorkingGroup▪KnowledgeEngineering WorkingGroup▪Operations WorkingGroup▪Security WorkingGroup▪Workflows WorkingGroupo Mailing Lists∙User Storieso User Stories: Overviewo Multi-site fMRI Studies∙Resourceso Resources: Overviewo Tools▪Acquisition,Calibration andQuality AssuranceTools▪Analysis andProcessing Tools▪Atlasing, Images andVisualization Tools▪Data Management andCollaboration Tools▪Ontologies,Standards andTerminologies Tools▪Security Tools▪FederatedInformaticsResearchEnvironment (FIRE)Primer▪fMRI ScannerCalibrationMethods andRecommendations▪fMRI ImagingQualityAssuranceMethods▪Multi-siteCognitiveParadigms forfMRI studies▪FunctionalImagingCalibrationParadigms▪All Toolso Share Your Tools▪Software Licenseo Best Practiceso Supplemental Materials▪FBIRNRecommendations forMulti-Center fMRIStudies:SupplementalMaterial (JMRI cite)▪Supplement I:FBIRN WorkingGroups▪SupplementII: FBIRN Taskpresentation/psychometriccollection▪SupplementIII: FBIRNScanparameters▪SupplementIV: FBIRNQualityAssuranceProgram▪Supplement V:FBIRNInformatics▪References▪Brain MorphometryMulti-site Studies▪BrainMorphometryImagingProtocols▪BrainMorphometryMRI Phantom▪DTI MultisiteProcessing▪Functional MRIMulti-site Studieso Data▪Alphabetical List ofDataCollaboratorso Collaborators: Overviewo Nonhuman Primate ResearchConsortiumo Radiation Oncology PilotProjecto Taub Institute forResearch at ColumbiaUniversityo Clinical TranslationalScience Institutes (CTSIs)o Function BIRN▪Function BIRNPublicationso InternationalNeuroinformaticsCoordinating Facility(INCF)o Cardiovascular ResearchGridAbout: OverviewThe Biomedical Informatics Research Network (BIRN)is a national initiative to advance biomedical research through data sharing and online collaboration. Funded by the National Institute of General Medicine Sciences (NIGMS), a component of the US National Institutes of Health (NIH), BIRN providesdata-sharing infrastructure, software tools, strategies and advisory services – all from a single source.Our work focuses directly on the biomedical research community’s unique, data-intensive sharing and analysis needs, which are particularly evident in fields such as biomedical imaging and genetics.BIRN provides a user-driven,software-based framework forresearch teams to share significant quantities of data –rapidly, securely and privately – across geographic distance and/or incompatible computing systems. Groups may choose whether to share data internally or with external audiences. In either scenario, hardware and data remain under the control of individual user groups.We also offer data-sharing software tools specific to biomedical research, best practices references, expert advice and other resources. BIRN actively is developing new data-sharing tools, authorization capabilities, and knowledge engineering tools to help researchers make sense of new information in unique ways.CollaborationBIRN is a collaborative effort betweenthe NIGMS and a leadership consortium that includes the University of Southern California’s Information Sciences Institute (ISI), University of Chicago, Massachusetts General Hospital, University of California at Irvine, and University of California at Los Angeles. Our interdisciplinary team consists of leading computer scientists, engineers, physicians, biomedical researchers and other experts.Participants represent some of the nation’s premier tech nical and healthcare research universities, hospitals and other institutions. Such groups range from small research groups who receive NIGMS grants to large consortia, such as the Nonhuman Primate Research Consortium (NHPRC).∙About: Overview∙BIRN Video Introduction∙BIRN FAQs∙History∙Executive Committee∙Steering Committee∙Presentations/Publications∙Media∙Sponsors∙News∙Contact Us∙BIRN Collaboration wiki∙BIRN Documentation wiki∙Project Management∙ACCESS Account Management∙Copyright∙Privacy∙Sitemap∙Contact UsBIRN is supported by NIH grants1U24-RR025736, U24-RR021992,U24-RR021760 and by the Collaborative Tools Support Network Award1U24-RR026057-01.。

多晶硅中英文词汇

多晶硅工程中英文词汇参考安全淋浴safety shower安全生产safety production安全数据safety data安全有效运行safe and efficient operation按照工艺要求加工硅芯seed rod processing to process requirements.板坯slab办公室office room半导体级别semiconductor grade半导体级多晶硅polycrystalline silicon for semi-conductor purpose包装packing包装间packaging room报警alarm泵的液压计算pump hydraulic calculations必须的设计标准necessary design criteria必需的公用设施required utilities必需的公用设施和消耗率required utilities and consumption rates避免聚合物在下游工艺过程中在非常条件下进行反应而发生爆炸to avoid explosion of the polymer in downstream processes by reacting it under very controlled conditions 编制安全阀和其它安全装置清单prepare a list of safety valve and other safety devices编制设备说明书prepare equipment specifications编制以下仪表设计数据prepare instrument design data including the following变配电站substation and power distribution station标准编码standard label标准参数standard parameter标准设备规格specification of standard equipment标准设备装置的规格specifications for standard equipment set表面分析surface analysis表面金属total surface metals表压（磅/平方英寸）psig,不断循环continuous cycling不理想to be non-ideal不锈钢stainless steel布袋收尘器bag house, bag house filter部件编码和位置item number with location操作程序operating procedures操作和维修最大负荷表table of maximum operating and maintenance loads操作人员和工程师培训operators and engineers training操作数据operating data操作顺序operating sequence操作条件operating conditions操作要严谨确保安全must be completed in a precise manner for safetyreasons.产出/投入比input and output ratio产量production capacity产品products产品规格product specifications产品检测product examination产品库product storage产品流product stream产品浓度计算product concentration产品质量quality of product常规晶棒regular rod厂房和/或构筑物的特殊要求special requirements for buildings and/or structures厂区管网pipeline network within the plant area厂区设施plant area facilities厂区占地面积occupied area of the plant area超高纯水ultrapure water超高纯水水池ultrapure water bath彻底清洁最终产品completely clean up the final product称量weighing城市管道水水质的分析指数analytical index of water quality for city water pipe network程序procedures充足的技术信息sufficient technical information抽气evacuations出口outlet出炉的多晶硅棒process harvested polysilicon rods初步布置平面图preliminary arrangement plans初始洗涤initial scrubber储放区holding area储罐场tank storage farm储液槽storage tank处理厂treatment plant传达室gate house传导性conductivity传热流体heat transfer fluid串联的池室consecutive bath chamber纯度purity纯度合格的三氯氢硅desired purities of TSC纯度合格的四氯化硅desired purities of STC纯净水purified water纯品储罐pure storage tank纯三氯氢硅pure TCS纯三氯氢硅储罐pure TCS holding tank纯三氯氢硅罐pure-TCS tank纯三氯氢硅加料罐pure TCS feed head tank纯水pure water纯四氯化硅pure STC粗三氯氢硅储罐crude TCS tank催化剂catalysts带旋流器的备用氯化炉standby chlorinator with the cyclone袋装冶金硅tote bag me-Si单晶硅方棒single crystal silicon square ingot单晶硅头尾料nose and tail material of single crystal silicon单晶及硅片加工车间single crystal silicon and silicon wafer processing plant 单晶拉制monocrystal pulling单线图one-line diagram道路road低成本太阳能用硅low-cost silicon for solar battery purpose低能洗涤器low energy imparting scrubber低能中和系统low energy imparting neutralization system低品位三氯氢硅low grade TCS低热值Lower heating value低温低压氯化工艺low temperature and pressure chlorination process低压lower voltage低压氯化法low pressure chlorination process底馏分塔bottom cut tower电极夹具electrode holder电价electricity price电流加热heated by electrical current电气设计electrical design电阻率resistivity调节到合格点to be controlled at a desired point调压阀pressure control valve (PCV)动作action独立的洗涤器装置separate scrubber unit钝化处理passivation treatment。

点云数据转换成实体模型通过基于点的立体像素化立体像素

点云数据转换成实体模型通过基于点的立体像素化立体像素PointCloudDataConversionintoSolidModelsviaPoint-BasedVoxelization1 2 3 4Tommy Hinks ; Hamish Carr ; Linh Truong-Hong ; and Debra F. Laefer, M.ASCEAbstract:Automatedconversionofpointclouddatafromlaserscanninginto formatsappropriateforstructuralengineeringholdsgreatprom- iseforexploitingincreasinglyavailableaeriallyandterrestriallybase dpixelizeddataforawiderangeofsurveying-relatedapplicationsfrom environmental modeling to disaster management. This paper introduces a point-based voxelization method to automatically transform pointclouddataintosolidmodelsforcomputationalmodeling.Thefundamentalvi abilityofthetechniqueisvisuallydemonstratedforbothaerial andterrestrialdata.Foraerialandterrestrialdata,thiswasachievedinl essthan30sfordatasetsupto650,000points.Inallcases,thesolid models converged without any user intervention when processed in a commercial ?nite-element method program. DOI: 10.1061/ASCESU.1943-5428.0000097 2013 American Society of Civil Engineers.CE Database subject headings: Data processing; Surveys; Finite element method; Information management.Author keywords: Terrestrial; Aerial; Laser scanning; LiDAR; Voxelization; Computational modeling; Solid models; Finite element.Introductionexist.Thispaperlaysthegroundworkforkeyadvancementinsucha pipeline. The procedure proposed herein to reconstruct buildingLaser scanning has achieved great prominence within the civil en- facadesfrompointcloud,whichisafundamentalstepforgenerating gineering community in recent years for topics as divergent as city-scale computational models.coastline monitoring Olsen et al. 2009, 2011, airport layout op- timization Parrish and Nowak 2009, and ground-displacementidenti?cation for water-system risk assessment Stewart et al.FacadeReconstruction2009. Additionally, there has been strong motivation to obtainfurther functionality from laser scanning and other remote-sensing Inrecentyears,developmentsinlaser-scanningtechnologyand?ight-data, including three-dimensional 3D volume estimation forpath planning have allowed aerial laser scanning ALS to acquire mining Mukherji 2012, road documentation Dong et al. 2007,pointclouddataquicklyandaccuratelyatacityscale,therebyhaving structuralidenti?cationShanandLee2005;Zhangetal.2012,and thepotentialforreconstructing3Dbuildingsurfacesacrossanentire emergency planning Laefer and Pradhan 2006. Furthermore,city in nearly real time. A number of approaches based on semi- computational responses of city-scale building groups are increas- automaticLangandForstner1996andautomaticHenricssonetal. inglyindemandforheightenedurbanization,disastermanagement,1996techniqueshavebeenproposedtoreconstructbuildingmodelsand microclimate modeling, but input data are typically too ex- from such data sets, but automatically extracting highly detailed, pensive as a result of the need for manual surveying. Additionally, accurate,andcomplexbuildingsstillremainsachallengeHaalaandcurrent software tools for transforming remote-sensing data into Kada 2010. The semiautomatic procedures need human operator computationalmodelshaveoneormoreofthefollowingproblems: intelligence.TheautomaticvisualmodelingofurbanareasfromALS alowdegreeofreliability,aninabilitytocapturepotentiallycritical data tends to extract sample points for an individual building by details,and/oraneedforahighdegreeofhumaninteraction.Todate, applying segmentation techniques and then reconstructing eacha seamless, automated, and robust transformation pipeline frombuilding individually. In such cases, vertical facade surfaces are notremote-sensing data into city-scale computational models does not portrayed in detail, and outlines may be of relatively low accuracy unless ground planes are integrated, which requires either a priori1 informationormanualintervention.Unfortunately,theeffectivenessDoctoralRecipient,SchoolofComputerScienceandInformatics,Univ.of engineering modeling often depends largely on the geometricCollege Dublin, Bel?eld, Dublin 4, Ireland. E-mail: ******************2accuracy and details of the building models?thus the currentSeniorLecturer,SchoolofComputing,FacultyofEngineering,Univ.ofmismatch.Leeds, Leeds LS2 9JT, U.K. E-mail: h.carr@//0>.3Post-doctoral Researcher, Urban Modelling Group, School of Civil, Presently, commercial products are generally semiautomatic StructuralandEnvironmentalEngineering,Univ.CollegeDublin,Bel?eld, Laefer et al. 2011, whereas in the computer graphics and photo- Dublin 4, Ireland. E-mail: linh.truonghong@gmailgrammetry communities, researchers have focused on automated4AssociateProfessor,LeadPI,UrbanModellingGroup,SchoolofCivil,surfacereconstructionfromdenseandregularsamplepointsHoppeStructuralandEnvironmentalEngineering,Univ.CollegeDublin,Bel?eld, 1994; Kazhdan et al. 2006. Unfortunately, ALS data are oftenDublin 4, Ireland corresponding author. E-mail: ******************* sparse and irregular, and may contain major occlusions on vertical Note.ThismanuscriptwassubmittedonNovember16,2011; approvedsurfaces owing to street- and self-shadowing Hinks et al. 2009.on September 10, 2012; published online on September 13, 2012. Discus- Dedicated urban modeling surface-reconstruction approachession period open until October 1, 2013; separate discussions must be generallyusethemajorbuildingplanesChenandChen2007andsubmitted for individual papers. This paper is part of the Journal ofcan be described as either model-driven or data-driven. Model-Surveying Engineering, Vol. 139, No. 2, May 1, 2013ASCE, ISSN0733-9453/2013/2-72?83/$25.00. driven techniques use a ?xed set of geometric primitives that are72 / JOURNALOFSURVEYINGENGINEERING?ASCE / MAY2013J. Surv. Eng. 2013.139:72-83.Downloaded from by East China Inst of Tech on 04/13/13.Copyright ASCE. For personal use only; all rights reserved.Fig. 1. Work?ow of the proposed approach: *Collection and preparation of LiDAR data involve multiple steps outside the scope of this paper’s scienti?ccontribution;thesegenerallyincludeplanning,collection,re gistration,and?ltering;seeTruong-Hong2011andHinks2011forfurther detailsttedtothepointdata.Suchtechniquescanbeeffectivewhenadataset is sparse because the ?tting of geometric primitives does not require complete data. In contrast, data-driven techniques derive surfaces directly from the point data and are capable of modeling arbitrarilyshapedbuildings.Generally,data-drivenapproachesaremore?exiblethanmodel-drivenapproaches,butareoftensensitiveto noise in the input data.For strictly visual representation, model-driven approachescanbeeffective.Forexample,Haalaetal.1998 proposed four dif-ferent primitives and their combinations to automatically derive 3D building geometry of houses from ALS and existing ground planes.Similarly, Maas and Vosselman 1999 introduced an invariantmoment-basedalgorithmfortheparametersofastandardgabled-roofhouse type that allowed for modeling asymmetric elements such as dormers. However, these efforts assume homogeneous point dis-Fig. 2. Octree representationtributions, which is unrealistic. You et al. 2003 also adapted a set of geometric primitives and ?tting strategies to model complex buildings with irregular shapes, but the approach required user interventionandgeneratedonlylimitedwalldetails.Huetal.2004used a combination of linear and nonlinear ?tting primitives to SolidModelingreconstructacomplexbuilding,inwhichaerialimagerywasusedtore?ne the models. To generate building models directly from point cloud data forIncontrast,manydata-driventechniquesoperatingonALSdata engineering simulations [e.g., FEM], there are three dominant reconstruct roof shapes directly from sample points of roof planes. methods:1constructivesolidgeometryCSG,whereobjectsareSubsequently, the remainder of the building is simply extruded represented using Boolean combinations of simpler objects; 2 to the ground level from the roof-shape outlines. Vosselman and boundary representations B-reps, where object surfaces are rep- Dijkman2001usedaHoughtransformforextractionofplanefaces resentedeitherexplicitly orimplicitly;and3spatialsubdivision roofplanesfromtheALSdata,andthen3Dbuildingmodelswere representations,wherean objectdomain is decomposed intocells reconstructed by combining ground planes and the detected roof withsimple topologic and geometric structure, such as regular planes.Hofmannetal.2003introducedamethodtoextractplanar gridsandoctreesGoldman2009;HoffmannandRossignac1996;roof faces by analyzing triangle mesh slopes and orientations from there are many extensive treatises available for in-depth consid-a triangular irregular network structure generated from ALS data. eration of this topic B?hm et al. 1984; Rossignac and Requicha More recently, Dorninger and Pfeifer 2008 used an a-shape ap- 1984, 1999.proach to determine a roof outline from point clouds of the roof Generating solid models automatically from point cloud data projectedontoahorizontalplane.Also,ZhouandNeumann2010 is particularly important because the cost of manually creating created impressive buildings for a large urban area by using a vol- solid models of existing objects is far greater than the associated umetric modeling approach in which roof planes were determined hardware,software,andtrainingcosts.Assuch,spatialsubdivision based on a normal vector obtained from analysis of grid cells be- representations are used extensively for creating solid models of longingtorooflayers.However,thesemodelsarealsoextrudedand buildings in which regular grids or octrees are employed to de- lack vertical-wall details. compose an entire object intononoverlapping 3D regions, com-Therefore, this paper presents an automated approach to con- monly referred to as voxels. Voxels are usually connected andverting point clouds of individual buildings into solid models for described a simple topologic and geometric structure. In grids, structural analysis by means of computational analysis in which avolumeissubdividedintosmallerregionsbyappropriateplanes thepointcloudthatweresemiautomaticallysegmentedfromLight parallel to the coordinate system axes,typically using aCartesian Detection and Ranging LiDAR data become the input Fig. 1. coordinate system. An initial voxel bounding all point data re-Notably, this proposed approach focuses on reconstructing solid cursively divides a volume into eight subvoxels, organized in modelsbyusingvoxelgridswiththecriticalparameteraseitherthe a hierarchical structure Samet 1989. Voxels may be labeled voxel size or the number of voxel grids; for more details on col- white,black,orgraybasedontheirpositionsFig.2.Blackvoxels lecting ALS and terrestrial laser scanning TLS data and on are completely inside the solid, whereas white voxels are com- segmenting point clouds, see Truong-Hong 2011andHinks pletelyoutside.Voxelswithbothblackandwhitechildrenaregray 2011. Hoffmann and Rossignac 1996.JOURNALOFSURVEYINGENGINEERING?ASCE / MAY2013 / 73J. Surv. Eng. 2013.139:72-83.Downloaded from by East China Inst of Tech on 04/13/13. Copyright ASCE. For personal use only; all rights reserved.Fig.3.Voxelgridspanningavolumeina3Dspaceboundedbyx ,x ,y ,y ,andz ,z ,whe reDx,Dy,andDzarevoxelsizes andmin min minN , N , and N are the number of voxels in each directionx y zIn an application of spatial subdivision for surface recon-struction,CurlessandLevoy1996presentedavolumetricmethodforintegratingrangeimagestoreconstruc tanobject’ssurfacebasedon acumulative weighted signed-distancefunction. Unfortunately,the approach is not suited for arbitrary objects. In related work, GuarnieriandPontin2005builtatriangulatedmeshofanobject’ssurfacebycombiningaconsensussurface[asproposedbyWheeleret al. 1998], an octree representation, and the marching-cubesalgorithm Lorensen and Cline 1987. This multifaceted algorithmFig. 4. Point-based voxelization avoids surface reconstruction and canreducetheeffectofthenoiseowingtosurfacesampling,sensoroperates directly on point datameasurements,andregistrationerrors.However,foroptimalresults,themethodrequiresmodi?cationofparametersthatdependheavilyon input-data characteristics such as the voxel size, the threshold value for the angle, and the distance between two consecutive neighbor-range viewpoints. z 2zminN? 1 ?3?zDzThevoxelhaseightlatticeverticesassociatedwithsixrectangular VoxelizationfacesFig.3.Eachinteriorvoxelhas26neighboringvoxels,witheight sharing a vertex,12 sharing an edge,and six sharing a face. Critical to octree/quadree representations for further processing is Conversely,anexteriororinteriorvoxelonahole’sboundaryoften voxelization. This term describes the conversion of any type of has only 17 neighboring voxels four sharing a vertex, eight geometric or volumetric object such as a curve, surface, solid, or sharinganedge,and?vesharingaface.Moreover,mostexisting computedtomographicdataintovolumetricdatastoredina3Darray voxelization techniques operate on surface representations ofof voxels Karabassi et al. 1999. Initially, a voxel grid divides objects, where a signi?cant part of the problem is to identifya bounded 3D region into a set of cells, which are referred to as throughwhichvoxelsthesurfacespass.Suchmethodsarereferredvoxels. The division is typically conducted in the axial directions to as surface-based voxelization Cohen-Or and Kaufman 1995of a Cartesian coordinate system. Before voxelization, three pairs [Fig.4a?c].Incontrast,thepoint-basedvoxelizationinthispaper ofcoordinatevalues??x , x , ?y , y , and ?z , z ? aremin min minoperates directly on the point data and does not require a derived createdalongthethreeaxesX, Y, and Zde?ningaglobalsystemsurface [Fig. 4a?c]. Point-based voxelization is conceptually Fig. 3. The basic idea of a voxelization algorithm is to examine much simpler than surface-based voxelization algorithms, and whethervoxelsbelongtotheobjectofinterestandtoassignavalue whereas the mechanisms are well known, they have not beenof 1 or 0,respectively Karabassi et al. 1999; a further description applied to generating solid modeling of buildings from LiDARof voxel grids is available in Cohen and Kaufman 1990.data.An initial voxel bounding all point cloud data in 3D Euclidean3Asmentionedearlier,eachvoxelisclassi?edasactiveorinactivespaceR is subdivided into subset voxels by grids along the x-, y-, corresponding to binary values based on the sample points within andz-coordinatesinaCartesiancoordinatesystem.Eachvoxelinthethat voxel [Eq. 4]subset is represented by an index v?i, j, k?, where i2?0; N 21 , xj2?0; N 21 , and k2?0; N 21 Fig. 3. With the dimensionsy zactive ifn$TnofindividualvoxelsDx, Dy, Dz,anumberofvoxelsN , N , Nx y zf n?4?valong each direction are given in Eqs. 1?3 inactive ifn,Tnwheretheargumentn5numberofpointsmapping to avoxel,andx 2xmin T 5user-speci?edthresholdvalue.Typically,T 51,whichmeansn nN? 1 ?1?xDxthat voxels containing at least one mapping point are classi?edasactiveandallothersasinactive.Moresophisticateddensity-basedy 2yminclassi?cation functions can be designed. An example is shown inN? 1 ?2?yDyFig. 5.74 / JOURNALOFSURVEYINGENGINEERING?ASCE / MAY2013J. Surv. Eng. 2013.139:72-83.Downloaded from by East China Inst of Tech on 04/13/13. Copyright ASCE. For personal use only; all rights reserved.Fig. 5. Voxelization model of front building of Trinity College, Dublin, Ireland, created by a voxel grid: a input data set of 245,000 ALS points;bvoxelizationmodelwithvoxelsizeDx5Dy5Dz50:25m;cvoxelclassi?cationwiththethresholdT51andvoxelizationmodelwithaboutn5,000 active voxels n is the largest number of points mapping to asingle voxelFig. 6. Solid model componentsProposedConversionofVoxelizedModelsintoSolidModelsTo reconstruct vertical surfaces of building models, a voxel grid is used to divide data points in a bounded 3D region into smallervoxels. Important facade features such as windows and doors are subsequently detected basedon a voxel’s characteristics, where an inactive voxel represents the inside of an opening. Consequently, building models are converted into an appropriate format for com- putational processing.Anobjectisde?nedbyitssurfaceboundary,whichthenmustbeFig. 7. Face orientation as dictated by the right-hand ruleconvertedintoanappropriatesolidrepresentationcompatiblewithcommercialcomputationalpackages.Althoughmanyschemesareavailable,B-repsarehereinadoptedbecauseoftheircompatibilitywith commercial structural-analysis software e.g., ANSYS soft- Keypointsarerepresentedbya3Dcoordinateofasingularpoint.ware Laefer et al. 2011. The proposed method de?nes both the An edge is de?ned as the connection between exactly two keygeometry and topology of an object by a set of nonoverlappingpoints;forexample,theedgee 5fP, Pgistheedgewithstartingij i jandendingpointP.Notably,edgeshaveanorientation;asfaces approximate the boundary of the solid model. This section pointPi jsuch, e 52eThus, the edges e and e would be ?ipped. EdgepresentsabriefdescriptionoftheB-repschemeimplementedintheij ji ij jiproposed approach; for more details, see Goldman 2009. Ge- ?ipping is important when de?ning an orientable face for dis-ometry is de?ned by key singular points, with each point rep- tinguishing the inside from the outside.resenting a speci?c location in space. Topology is de?ned by Similarly, faces represent surfaces of a solid model that areconnections between key points. When used together, they can connections between edges. The faces are further connected de?neasolidmodelFig.6.DatastructuresfordescribingB-reps to form volumes. A face is de?ned as a list of edgesoften capture the incidence relations between a face and its f5fe ,e ,.,e g that involve closed paths. A face01 12 ?n22??n21?bounding edges and an edge and its bounding vertices, whichconsistingofthreekeypointsisatriangle,whereasqu。

prob_all

id page section no title18 1.2.1例题1盒子里的气球29 1.2.1例题2图书馆313 1.2.2例题1钓鱼413 1.2.2例题2照亮的山景515 1.2.2例题3镜子盒619 1.2.3例题1折纸痕720 1.2.3例题2三色多边形820 1.2.3例题3聪明的学生923 1.2.3例题4丢失的数1028 1.2.4例题1月亮之眼1129 1.2.4例题2Yanghee的数表1231 1.2.4例题3原子链1336 1.3.1引例铁轨1438 1.3.1引例小球钟——时间与运动1540 1.3.1引例笑脸1644 1.3.2引例猜猜我想说什么1750 1.3.3引例勇士Ilya的故事1852 1.3.3例题1蚂蚁和瓢虫1954 1.3.3例题2隔三遍历2061 1.3.4引例拯救大兵瑞恩的故事2162 1.3.4引例英雄和公主的故事2264 1.3.4引例电气工程师2367 1.3.5引例爱丽丝和精灵的故事2468 1.3.5例题1电缆2569 1.3.5引例黑白按钮2670 1.3.5例题2煎饼2772 1.3.5引例傻瓜Ivanushka的故事2875 1.3.5例题3士兵排队2976 1.3.5例题4最小可靠交换3080 1.4.1例题1代码等式3181 1.4.1例题2团伙3281 1.4.1例题3银河英雄传说3382 1.4.1例题4可爱的猴子3483 1.4.1例题5蜗牛3589 1.4.2例题1积水3689 1.4.2例题2赛车3790 1.4.2例题3可怜的奶牛3891 1.4.2例题4最轻巧的语言3997 1.4.3例题1马尔可夫链4099 1.4.3例题2促销41102 1.4.3例题3采矿42108 1.4.4例题1火星地图43110 1.4.4例题2最长回文子串44113 1.5.1例题1括号序列45116 1.5.1例题2棋盘分割46117 1.5.1例题3决斗47117 1.5.1例题4“舞蹈家”怀特先生48119 1.5.1例题5积木游戏49123 1.5.2例题1方块消除50123 1.5.2例题2公路巡逻51125 1.5.2例题3并行期望值52126 1.5.2例题4高性能计算机53130 1.5.2例题5模板匹配54131 1.5.2例题6不可分解的编码55133 1.5.2例题7青蛙的烦恼56134 1.5.2例题8排列问题57135 1.5.2例题9最优排序二叉树58138 1.5.2例题10Bugs公司59139 1.5.2例题11迷宫统计60142 1.5.2例题12贪吃的九头龙61150 1.5.3问题1最长公共子序列问题62150 1.5.3例题1排列的LCS问题63151 1.5.3问题2最长上升子序列问题64151 1.5.3问题3最优二分检索树65152 1.5.3问题4任务调度问题66155 1.5.3例题2序列分割67160 1.6.2引例加密网格68162 1.6.2引例最优程序69164 1.6.2引例旋转的玩具70169 1.6.3引例编辑书稿71171 1.6.3引例埃及分数72175 1.6.4引例三角形大战73178 1.6.4例题1L游戏74180 1.6.5例题1带宽75181 1.6.5例题2小木棍76181 1.6.5例题3生日蛋糕77183 1.6.5例题4汽车问题78184 1.6.5例题5Betsy的旅行79189 1.6.6例题1外公的难题80193 1.6.7例题1篮球冠军赛81204 2.1例题1　“麻烦”子82204 2.1例题2沙漠83205 2.1例题3浪人苏比84206 2.1例题4好动的佳佳85207 2.1例题5细菌86208 2.1例题6X行星87220 2.2.1例题1佳佳的困惑88220 2.2.1例题2除法表达式89221 2.2.1例题3数字游戏90221 2.2.1例题4fibonacci质数91222 2.2.1例题5神秘数92225 2.2.2例题1自动取款机93226 2.2.2例题2人类学家的烦恼94226 2.2.2例题3征服者的军营95233 2.2.3例题1仓库问题96233 2.2.3例题2二进制Stirling数97234 2.2.3例题3荒岛野人98240 2.3.2例题1单色三角形99243 2.3.2引例互不攻击的象##245 2.3.3例题1传球游戏##247 2.3.3例题2无聊的排序##252 2.3.3例题3多边形##257 2.3.4例题1装饰栅栏##258 2.3.4例题2Pibonacci数##259 2.3.4例题3巧克力##274 2.4.2例题1绣花##275 2.4.2例题2漆门##275 2.4.2例题3原始基因##276 2.4.2例题4超级翻转##281 2.4.3例题1地图的五着色##282 2.4.3例题2滑雪##283 2.4.3例题3水平可见线段的三角形##287 2.4.4例题1往返路##287 2.4.4例题2连通图编号问题##287 2.4.4例题3跳舞蝇##289 2.4.4例题4参观洞穴##291 2.4.4例题5公主和英雄##293 2.4.4例题6通讯员##295 2.4.4例题7幼儿园小朋友分组##299 2.5.1引例岛国##300 2.5.1引例野餐计划##303 2.5.1引例地震##304 2.5.2引例罗密欧与朱丽叶##306 2.5.2引例出纳员的雇佣##308 2.5.2例题1瘦陀陀与胖陀陀##309 2.5.2例题2新桥##310 2.5.2例题3穿越沙漠##311 2.5.2例题4隐型石头##312 2.5.2例题5双调路径##315 2.5.3引例奶牛的新年晚会##317 2.5.3引例航天计划问题##318 2.5.3引例终极情报网##323 2.5.3例题1圆桌吃饭问题##324 2.5.3例题2数字游戏##324 2.5.3例题3混合图的欧拉回路##325 2.5.3例题4家园##326 2.5.3例题5道路扩容##329 2.5.4引例神奇的魔术师##331 2.5.4引例任务安排##332 2.5.4引例棋盘上的骑士##333 2.5.4引例丘比特的烦恼##333 2.5.4引例魔术球问题##334 2.5.4例题1皇家卫士##336 2.5.4例题2固定分区的内存管理##336 2.5.4例题3玩具兵##338 2.5.4例题4千年盛典##353 3.1.2例题1房间最短路问题##359 3.1.2例题2管道问题##387 3.3.1例题1篱笆问题##388 3.3.1例题2合金制造问题##401 3.3.4例题1点集的直径##402 3.3.4例题2最小外接矩形##404 3.3.4例题3点集分割##410 3.4.1例题1锡刀problem sourceACM/ICPC World Finals 2002. Problem A. Balloons in a BoxACM/ICPC Regional Contest Northeast Europe 2001. Problem G. Library. Author: Elena Kryuchkova, Roman Elizarov ACM/ICPC Regional Contest East Central North America 1999. Problem G. Gone FishingCentral European Olympiad in Informatics 2000. Day 2 Problem 3. Enlightened LandscapeBaltic Olympiad in Informatics 2001. Day 1 Problem 3. MirrorACM/ICPC Regional Contest South Pacific 1992. Problem F. Paper FoldingThird USU personal programming contest, Ekaterinburg, Russia, February 16, 2002. Author:Dmitry Filimonenkov CTSC 2001. Day 1 Problem 3. Clever. Author: Li Zhang(Classic)Balkan Olympiad in Informatics 1998. Day 2 Problem 2. Evil EyesACM/ICPC Regional Contest Asia-Taejon 2000. Problem H. Lost ListsCentral European Olympiad in Informatics 2001. Day 1 Problem 1. ChainACM/ICPC Regional Contest Central European 1997. Problem A. RailsACM/ICPC World Finals 1995. Problem B. Tempus et mobilius Time and MotionInternet Problem Solving Contest 2001. Problem F. A Censored SmileIOI2002 Practice Session Problem 2. String from substringsUSU high school programming contest 2001. Problem G.Ilya Murumetz. Author: Katz O.E.Polish Olympiad in Informatics 2001. Stage II Problem 4. Ants and the ladybugPolish Olympiad in Informatics 1995. Stage III Problem 2. Step Traversing a TreeCTSC 1999. Day 2 Problem 3. RescueACM/ICPC Regional Contest Asia-Shanghai 1999. Problem I. Princess and HeroCentral European Olympiad in Informatics 1996. Day 2 Problem 3. ElectricianInternet Problem Solving Contest 2000. Problem C. TrollsACM/ICPC Regional Contest Northeast Europe 2001. Problem C. Cable Master. Author: Vladimir Pinaev, Roman Elizaro Internet Problem Solving Contest 2000. Problem F. PuzzleUniversity of Duke Programming Contest 1993. Problem C. Stacks of FlapjacksUSU high school programming contest 2001. Problem A. Gaby Ivanushka. Author: Shamgunov N.Central European Olympiad in Informatics 1998. Day 2 Problem 1. SolidersACM/ICPC Regional Contest Central European 2001. Problem E. ExchangesPolish Olympiad in Informatics 1998. Stage II Problem 2. Word EquationsBaltic Olympiad in Informatics 2003. Day 2 Problem 1. The GangsNOI2002 Day 1 Problem 1. Galaxy. Author: Ji LuoPolish Olympiad in Informatics 2003. Stage III Problem 1. Monkey(Classic)Polish Olympiad in Informatics 1999. Stage III Problem 6. WaterCentral European Olympiad in Informatics 2003. Day 1 Problem 3. The RaceOIBH Reminiscene Programming Contest. Problem E. Eat or Not to Eat. Author: Rujia LiuPolish Olympiad in Informatics 1998. Stage III Problem 4. The lightest languageCTSC 2001. Day 2 Problem 2. Markov. Author: Runting ShiPolish Olympiad in Informatics 2000. Stage III Problem 6. PromotionPolish Olympiad in Informatics 2001. Stage III Problem 5. GoldmineBaltic Olympiad in Informatics 2001. Day 2 Problem 2. Mars MapsACM/ICPC Regional Contest Asia-Kanpur 2001. Problem E. Viewer's Prize in F-TVACM/ICPC Regional Contest Northeast Europe 2001. Problem B. Bracket Sequence. Author: Andrew StankevichNOI99 Day 2 Problem 1. ChessPolish Olympiad in Informatics 1999. Stage I Problem 1. MusketeersACM/ICPC Regional Contest Asia-Shanghai 2000. Problem C. Dance Dance RevolutionNOI97 Day 2 Problem 2. GameIOI2003 National Training Team Originals. Author: Cailiang Liu. ModifiedCTSC2000. Day 1 Problem 3. Patrol. Author: Shenjie LiACM/ICPC Regional Contest Asia-Tehran 2001. Problem G. Parallel ExpectationIOI2001 National Training Team Winter Camp. Problem 2. HPC. Author: Xin QiCentral European Olympiad in Informatics 2001. Day 2 Problem 2. PatternsACM/ICPC World Finals 2002. Problem B. Undecodable Codes(Classic)(Classic)CTSC2001. Day 2 Problem 2. Tree. Author: Fan YangCentral European Olympiad in Informatics 2002. Day 1 Problem 1. BugsElite Problemsetters' First Contest. Problem A. Maze Statistics. Author: Derek KismanNOI2002 Day 1 Problem 3. Dragon. Modification of Internet Problem Solving Contest 2001. B. Author: Ziqing Mao (Classic)(Classic)(Classic)(Classic)IOI2002 Day 2 Problem 1. BatchBalkan Olympiad in Informatics 2003. Day 2 Problem 2. Euro. ModifiedCentral European Olympiad in Informatics 1996. Day 1 Problem 1. Encoding GridACM/ICPC Regional Contest Southwestern Europe 1996. Problem A. Optimal ProgramsACM/ICPC Regional Contest Southwestern Europe 1999. Problem E. Color HashACM/ICPC Regional Contest Asia-Kanpur 2001. Problem G. Editing a Book(Classic)ACM/ICPC Regional Contest East Central North America 1999. Problem A. Traingle WarBaltic Olympiad in Informatics 2002. Day 2 Problem 2. L game. Author: Jimmy MårdellNew Zealand Programming Contest 1991. Problem A. BandwidthACM/ICPC Regional Contest Central Europe 1995. Problem H. SticksNOI99 Day 1 Problem 3. CakeIOI94 Day 2 Problem 1. CarUSACO Computing Olympiad4th Shuguang Programming Contest. Author: Rujia LiuBaltic Olympiad in Informatics 1999. Day 1 Problem 3. BasketballUral Collegiate Programming Contest, April 2001, Perm English TourRomanian Open Contest, December 2001. Author: Mugurel Ionut AndreicaIOI99 National Training Team Originals. Author: Fangfang XiaIV Ural State University Collegiate Programming Contest. Problem G. Nikifor's Walk. Author: Dmitry Filimonenkov Internet Problem Solving Contest 2003. Problem H. Hordes of BacteriaCentral European Olympiad in Informatics 2000. Day 1 Problem 1. X-PlanetUSU Open Collegiate Programming Contest March'2001 Senior Session. Problem F. Nikifor. Author: Filimonenkov D. Baltic Olympiad in Informatics 2000. Day 2 Problem 1. DIVRandy Game. Problem D. Number Game. Author: Chong LongACM/ICPC World Finals Warm-up Contest(Oriental) 2002, Problem A. The Fibonacci Primes. Author: Shahriar Manzoor ACM/ICPC Regional Contest Southeastern European 2001. Problem C. Secret NumbersPolish Olympiad in Informatics 1998. Stage III Problem 2. ATM'sACM/ICPC Regional Contest Southwestern Europe 1999. Problem B. The Archeologists' DilemmaCentral European Olympiad in Informatics 2002. Day 1 Problem 2. ConquerTetrahedron Team Contest May 2001. Problem H. Warehouse Problem. Author: D. FilimonenkovACM/ICPC Regional Contest Central Europe 2001. Problem B. Binary Stirling Numbers.NOI2002. Day 2 Problem 1. Savages. Author: Rujia LiuPolish Olympiad in Informatics 1997. Stage III Problem 5. Monochromatic TrianglesACM/ICPC World Finals Warm-up Contest(Oriental) 2002, Problem B. Bishops. Author: Rezaul Alam ChowdhuryIOI2000 National Training Team Originals. Author: Yi GuoACM/ICPC World Finals 2002. Problem H. Silly Sort(Classic)Central European Olympiad in Informatics 2002. Day 1 Problem 3. FenceInternet Problem Solving Contest 2001. Problem G. FibonacciACM/ICPC Regional Contest Beijing 2002. Problem F. ChocolateUral Collegiate Programming Contest '99. Problem H. Cross-Stitch. Author: Zaletsky PIV Ural State University Collegiate Programming Contest. Problem F. Door Painting. Author: Magaz AsanovPolish Olympiad in Informatics 1999. Stage III Problem 5. PrimitivusIOI2003 National Training Team Originals. Author: Zhilei Xu.(Classic)Polish Olympiad in Informatics 2000. Stage I Problem 3. SkiersACM/ICPC Regional Contest Central Europe 2001. Problem H. Horizontally Visible SegmentsCentral European Olympiad in Informatics 2001. Day 1 Problem 3. Round Trip(Classic)Polish Olympiad in Informatics 2001. Stage III Problem 1. Wandering flees TrainersCentral European Olympiad in Informatics 1997. Day 1 Problem 1. CAVACM/ICPC Regional Contest Central Europe 2001. Problem A. Alice and BobPolish Olympiad in Informatics 1996. Stage I Problem 3. MessengersIV Ural State University Collegiate Programming Contest. Problem E. Partition into Groups. Author: Dmitry Filimonenko ACM/ICPC World Finals 2002. Problem E. IslandACM/ICPC Regional Contest East Central North America 2000. Problem A. PicnicUSACO Computing Olympiad US Open 2001. Problem 2. EarthquakeInternet Problem Solving Contest 1999. Problem H. Romeo and JulietACM/ICPC Regional Contest Asia-Tehran 2000. Problem G. Cashier EmploymentACM/ICPC World Finals Warm-up Contest(Occidental) 2002. Problem A. Asterix and Obelix. Author: Rezaul Alam Cho Balkan Olympiad in Informatics 2000. Day 2 Problem 1. BridgeACM/ICPC World Finals 2002. Problem C. Crossing the DesertACM/ICPC Regional Warm-up Contest 2002. Problem D. The Rock. Author: Jimmy MårdellBaltic Olympiad in Informatics 2002. Day 2 Problem 1. Bicriterial routingUSA Computing Olympiad Winter 2002. Problem 2. New Years Party. Author: Hal Burch(Classic)CTSC2001. Day 1 Problem 1. Agent. Authro: Li ZhangACM/ICPC World Finals Warm-up Contest(Occidental) 2002. Problem D. The Grand Dinner. Author: Rezaul Alam Chow UVA Monthly Contest. Problem H. The Eagles' Nest. Author: Monirul HasanACM/ICPC Regional Contest Northwestern Europe 2002. Problem G. Sightseeing tour. Author: Jimmy MårdellCTSC1999. Day 1 Problem 3. HomelandIOI2000 National Training Team Originals. Author: Li ZhangInternet Problem Solving Contest 2001. Problem H. MagicACM/ICPC Regional Contest Asia-Beijing 2002. Problem G. Machine ScheduleBaltic Olympiad in Informatics 2001. Day 2 Problem 1. KnightCTSC2000. Day 2 Problem 1. Cupid. Author: Fan YangOIBH Reminiscene Programming Contest. Problem H. Hanoi Tower Troubls Again! Author: Rujia LiuCentral European Olympiad in Informatics 2002. Day 2 Problem 2. GuardACM/ICPC World Finals 2001. Problem G. Fixed Partition Memory ManagementCTSC2002. Day 1 Problem 3. Toy. Author: Rujia LiuCTSC2003. Day 1 Problem 3. Ceremony. Author: Rujia LiuACM/ICPC Regional Contest Mid-Central North America 1996. Problem B. The doorACM/ICPC Regional Contest Central Europe 1995. Problem D. Pipe(Classic)(Classic)(Classic)ACM/ICPC Regioanl Warmup 2001. Problem F. Smallest Bounding Rectangle. Author: Rezaul Alam ChowdhuryACM/ICPC World Finals Warm-up Contest(Occidental) 2002. Problem K. The Great Divide. Author: Rezaul Alam Chow ACM/ICPC Regional Contest Central Europe 1996. Problem A. Tin Cutterdata submit how2submit n ny y ural1188 y y uva757n ny ny y uva177n y ural1181 y nn ny nn ny ny y uva514n y uva239y ny nn y ural1088 y ny ny nn y uva258n ny ny y ural1184 y ny y uva120n y ural1082 y ny y zju1388y ny ny ny nn ny ny ny y uva10273 y nn ny ny ny nn ny y ural1183 y ny nn ny ny y uva10559 y ny y zju1022y ny nn nn y ural1143n ny ny ny y uva10531y nn nn nn y uva10534(slightly modified) n y uva10304y ny nn ny y uva656n y uva704n ny ny y uva751y ny y uva140y y uva307y ny ny ny ny nn y ural1155n y ural1170y nn y ural1130y ny nn y ural1095y ny y uva10164n y uva10236y yy nn y uva701y nn y ural1107n y zju1385y y uva10413y nn y uva10237y nn nn ny ny ny y zju1363n y ural1035 n y ural1129 y nn nn ny ny y zju1391 y nn ny ny ny y zju1384 y nn y ural1128 n ny yy ny ny y zju1420 n y uva10246 y nn nn y uva10381 y ny nn ny nn y uva10249 y y uva10546 y y zju1992 y ny ny ny y zju1364 y ny ny y uva10276 y nn ny y uva10418 y ny y uva393 y y uva303 n nn nn nn y uva10173 n y uva10256 y y uva308。

应用地球化学元素丰度数据手册-原版

应用地球化学元素丰度数据手册迟清华鄢明才编著地质出版社·北京·1内容提要本书汇编了国内外不同研究者提出的火成岩、沉积岩、变质岩、土壤、水系沉积物、泛滥平原沉积物、浅海沉积物和大陆地壳的化学组成与元素丰度，同时列出了勘查地球化学和环境地球化学研究中常用的中国主要地球化学标准物质的标准值，所提供内容均为地球化学工作者所必须了解的各种重要地质介质的地球化学基础数据。

本书供从事地球化学、岩石学、勘查地球化学、生态环境与农业地球化学、地质样品分析测试、矿产勘查、基础地质等领域的研究者阅读，也可供地球科学其它领域的研究者使用。

图书在版编目（CIP）数据应用地球化学元素丰度数据手册/迟清华，鄢明才编著. －北京：地质出版社，2007.12ISBN 978－7－116－05536－0Ⅰ. 应… Ⅱ. ①迟…②鄢…Ⅲ. 地球化学丰度－化学元素－数据－手册Ⅳ. P595－62中国版本图书馆CIP数据核字（2007）第185917号责任编辑：王永奉陈军中责任校对：李玫出版发行：地质出版社社址邮编：北京市海淀区学院路31号，100083电话：（010）82324508（邮购部）网址：电子邮箱：zbs@传真：（010）82310759印刷：北京地大彩印厂开本：889mm×1194mm 1/16印张：10.25字数：260千字印数：1－3000册版次：2007年12月北京第1版•第1次印刷定价：28.00元书号：ISBN 978－7－116－05536－0（如对本书有建议或意见，敬请致电本社；如本社有印装问题，本社负责调换）2关于应用地球化学元素丰度数据手册（代序）地球化学元素丰度数据，即地壳五个圈内多种元素在各种介质、各种尺度内含量的统计数据。

它是应用地球化学研究解决资源与环境问题上重要的资料。

将这些数据资料汇编在一起将使研究人员节省不少查找文献的劳动与时间。

这本小册子就是按照这样的想法编汇的。

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COREComputerScienceJournalRankingsJournalRankingACMComputingSurveysA+ACMTransactionsonComputerSystemsA+ ACMTransactionsonComputer-HumanInteractionA+ ACMTransactionsonDatabaseSystemsA+ ACMTransactionsonGraphicsA+ ACMTransactionsonInformationSystemsA+ ACMTransactionsonMathematicalSoftwareA+alA+JournalofCryptologyA+JournalofDocumentationA+JournalofInformationScienceA+ JournalofManagementInformationSystemsA+ JournalofParallelandDistributedComputingA+ JournaloftheAmericanSocietyforInformationScienceandTechnologyA+ JournaloftheAssociationforComputingMachineryA+ JournaloftheAssociationforInformationSystemsA+Library&InformationScienceResearchA+LibraryQuarterlyA+LibraryTrendsA+MachineLearningA+MISQuarterlyA+NeuralComputationA+QuantumInformation&ComputationA+ SchoolLibraryMediaResearchA+ SIAMJournalonComputingA+ ACMJournalofExperimentalAlgorithmicsA ACMTransactionsonAlgorithmsA ACMTransactionsonArchitectureandCodeOptimizationA 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工作坊简介

Introduction 工作坊簡介：結合空間資訊、生物資訊及行為資訊的場域資訊學，近年來不但在研究領域上獲得高度注目，在實務與科技的開發應用上也有蓬勃的發展。

臺大智活特別邀請日本京都大學資訊學教育與研究中心場域資訊學門的教授群，透過工作坊的方式，與參加者深入探討與分享他們的研究經驗與成果。

內容包括了跨國合作格網(Language Grid)、文化工程 (Cultural Computing)、自然觀察(Biologging)、使用者參與式研發 ( Participatory production) 以及嵌入式模擬與遊戲設計( Participatory simulation/gaming)等豐富主題。

Program 議程：TIME TOPIC SPEAKER09:00 – 09:40 Registration09:40 – 10:00 Greeting and brief introduction ofField InformaticsProf. Toru Ishida石田亨教授(京都大學)Session 1: Computing for Collective Intelligence and Culture10:00 ‐ 11:00 Service Oriented CollectiveIntelligence (Language Grid)Prof. Toru Ishida石田亨教授(京都大學)11:00 ‐ 12:00 Cultural Computing : ExploringJapanese CultureProf. Naoko Tosa土佐尚子教授(京都大學)12:00 – 13:30 LunchSession 2: Sensing Activities in Field13:30 ‐ 14:30 Biologging Asst. Prof. Junichi Okuyama奥山隼一助理教授(京都大學)14:30 – 15:00 Tea BreakSession 3: Participatory Approaches15:00 ‐ 16:00 Participatory Production by End Usersand Industrial AccumulationProf. Hajime Kita喜多一教授(京都大學)16:00 ‐ 17:00 Participatory simulation/gaming forscience communication: The role ofexperimentation in building futureAsso. Prof. Reiko Hishiyama菱山玲子副教授(早稻田大學)※ 全程採英文演說，現場將不會提供同步中文翻譯。

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These have Web Service as well as other interfaces (or equivalently, protocols).
Solution, Part III: Domain Specific
Tools and Standards -->More Services
Building a Chemical Informatics Grid
Marlon Pierce Community Grids Laboratory Indiana University
Acknowledgments
CICC researchers and developers who contributed to this presentation:
WSDL definitions define how to write clients to talk with databases, applications, etc.
Web Service messaging through SOAP Discovery services such as UDDI, MDS, and so on.
How can I combine application codes, web resources, and databases to solve a particular problem that interests me?
Specifically, how do I build a runtime environment that can connect the distributed services I need to solve an interesting problem?
Many toolkits available
Axis, .NET, gSOAP, SOAP::Lite, etc.
Web Services can be combined with each other into workflows
Workflow==use case scenario More about this later.
High-throughput screening can now test 100,000 compounds a day for activity against a protein target
Maybe tens of thousands of these compounds will show some activity for the protein
NIH Roadmap for Medical Research /
The NIH recognizes chemical and biological information management as critical to medical research.
Analogous situations exist for other domains Astronomy, Geosciences, Biology/Bioinformatics
online database.
What do you do with all of this data?
High-Throughput Screening
Testing perhaps millions of compounds in a corporate collection to see if any show activity against a certain disease protein
The Solution, Part I: Web Services
Web Services provide the means for wrapping databases, applications, web scavengers, etc, with programming interfaces.
Problem: Connecting It Together
The problem is defining an architecture for tying all of these pieces into a distributed computing system. A “Grid”
Application
Chemical Informatics is the application of information technology to problems in chemistry. Example problems: managing data in large scale drug discovery and molecular modeling
Basic Architectures: Servlets/CGI and Web Services
Browser HTTP GET/POST
Web Server
JDBC
DB
Browser
Web Server WSDL
SOAP
WSDL
WSDL
GUI Client WSDL
SOAP
Web Server
Data mining, clustering Quantum chemistry and molecular modeling
Visualization tools Web resources: journal articles, etc. A Chemical Informatics Grid will need to integrate these into a common, loosely coupled, distributed computing environment.
For academic and government researchers, how can I do all of this in an open fashion?
Data and services can come from anywhere
That is, I must avoid proprietary infrastructure.
The chemist needs to intelligently select the 2 - 3 classes of compounds that show the most promise for being drugs to follow-up
Informatics Implications
Building Blocks: Chemical Informatics Resources: Chemical databases maintained by various groups
NIH PubChem, NIH DTP
Application codes (both commercial and open source)
Prof. Geoffrey Fox, Prof. David Wild, Prof. Mookie Baik, Prof. Gary Wiggins, Dr. Jungkee Kim, Dr. Rajarshi Guha, Sima Patel, Smitha Ajay, Xiao Dong
Thanks also to Prof. Peter Murray Rust and the WWMM group at Cambridge University
Glue Tools and Applications
Chemistry Development Kit (CDK) OpenBabel
These are the basis for building interoperable Chemical Informatics Web Services
Federally funded high throughput screening centers.
100-200 HTS assays per year on small molecules. 100,000’s of small molecules analyzed Data published, publicly available through NIH PubChem
File management, resource allocation management, etc.
Condor: job scheduling on computer clusters and collections
SRB: data grid access OGSA-DAI: uniform Grid interface to databases.
Need to be able to store chemical structure and biological data for millions of data points Computational representation of 2D structure
Need to be able to organize thousands of active compounds into meaningful groups Group similar structures together and relate to activity
High-Throughput Screening
Traditionally, small numbers of compounds wproject or therapeutic area
About 10 years ago, technology developed that enabled large numbers of compounds to be assayed quickly