有关交通灯中英文翻译资料

交通灯外文翻译（5篇范文）

交通灯外文翻译（5篇范文）第一篇：交通灯外文翻译Traffic lights and PLCWith economic development, increased the number of vehicles, road congestion is becoming increasingly serious, intelligent traffic lights on the emerged.At present, the world's Intelligent Transportation System will be: a huge structure, management difficulties, such as the maintenance of large inputs.In order to improve the existing traffic conditions, and to overcome the existing shortcomings of intelligent transportation system I designed analog control traffic lights in urban and rural areas of small-scale smart traffic lights.It has small size, intelligence, maintenance into small, easy to install and so on.And other intelligent transportation system compared to the system to adapt to economic and social development, in line with the current status of scientific and technological development.Intelligent traffic lights are a comprehensive use of computer network communication technology, sensor technology to manage the automatic control system of traffic lights.Urban traffic control system is used for urban traffic data monitoring, traffic signal control and traffic management computer system;it is the modern urban traffic control system command and the most important component.In short, how to use the appropriate control method to maximize the use of costly cities to build high-speed roads, trunk road and the ramp to alleviate urban areas with the neighboring state of traffic congestion has become more and more traffic management and urban planning departments need to address the the main problem.Nowadays, traffic lights installed in each crossing, hasbecome the most common and dredge the traffic, the most effective means.The development of the society, people's consumption level unceasing enhancement, private vehicles unceasing increase.And more cars roads are narrow road traffic is clear.So adopting effective method to control the traffic light is imperative.PLC intelligent control principle is the core of the control system, PLC put the things direction or north-south direction according to quantity of vehicles, the corresponding scale what divides class given the green light direction between north and south direction according to certain rules too long.It can realize divides class according to a given the green cars duration scale of maximum car release, reduce crossroads vehicles, ease traffic congestion stagnation, realize the optimal control, so as to improve the efficiency of the traffic control system.The application of PLC is continuously, and drive to the deepening traditional control test new month benefit updates.It is simple in structure, programming and high reliability etc, convenient already widely used in industrial processes and position in the automatic control.Due to use of PLC has the characteristics of environmental adaptable, and its internal timer is very rich in resources, but the current widely used “progressive” lights, especially for precise control more than thecrossway control can be easily realized.So now increasingly applying PLC traffic light system.Meanwhile, PLC itself also has communication networking function, will the same path as part of a LAN signal unified dispatching management, can shorten the traffic wait times, realize scientific management.In real-time detection and automatic control of PLC application system, PLC is often used as a core components.In the 21st century, PLC willhave greater development.Technically, the computer technology can morely new achievements used in programmable controller design and manufacturing, there will be faster, storage and larger capacity, intelligent stronger varieties appear;Look from product size, can further to mini and super-large direction;Look from product compatibility, the variety of our products will be more rich, specification more complete, perfect man-machine interface and complete communication equipment can better adapt to all kinds of industrial control occasion demands;Look from the market, all countries to their production of multiple products with international competition intensifies and break, can appear a few brand monopoly international market situation, can appear international general programming languages;Judging from the development of the network, programmable controller and other industrial control computer networking constitute a large control system is programmable controller technology development direction.The current computer distributed control system DCS has already a lot of programmable controller applications.Along with the development of computer network, the programmable controller as automation control network and international general network will be an important part of the industry and industry, the numerous fields outside play an increasing role.In China the increasing amount of motor vehicles, many big cities like Beijing, Shanghai, nanjing and other ground appeared traffic overload running condition, traffic accidents problem also more and more serious.And because the various special vehicles(such as an ambulance, 119 120 car, police and various special vehicle 110 in emergency situations, by red under limited to traffic bring a lot of inconvenience, even cause traffic accident.And now, most traffic lights at the same moment willappear two or more than two direction at the same time for the green situation, and increase the incidence of the traffic accident.Therefore, design a kind of designed for special vehicles through and not cause any traffic accident, normal traffic control any time only one direction of modern intelligent traffic light green traffic control system is urgently needed.交通灯与PLC 随着经济的发展，车辆的数目不断增加，道路堵车现象日益严重，智能交通灯就应运而生了。

交通灯外文翻译1

Traffic lightsSignal control is a necessary measure to maintain the quality and safety of traffic circulation. Further development of present signal control has great potential to reduce travel times, vehicle and accident costs, and vehicle emissions. The development of detection and computer technology has changed traffic signal control from fixed-time open-loop regulation to adaptive feedback control. Present adaptive control methods, like the British MOV A, Swedish SOS (isolated signals) and British SCOOT (area-wide control), use mathematical optimization and simulation techniques to adjust the signal timing to the observed fluctuations of traffic flow in real time. The optimization is done by changing the green time and cycle lengths of the signals. In area-wide control the offsets between intersections are also changed. Several methods have been developed for determining the optimal cycle length and the minimum delay at an intersection but, based on uncertainty and rigid nature of traffic signal control, the global optimum is not possible to find out.As a result of growing public awareness of the environmental impact of road traffic many authorities are now pursuing policies to:− manage demand and congestion;− influence mode and route choice;− improve priority for buses, trams and other public service vehicles;− provide better and safer facilities for pedestrians, cyclists and other vulnerable road users;− reduce vehicle emissions, noise and visual intrusion; and− improve safety for all road user groups.In adaptive traffic signal control the increase in flexibility increases the number of overlapping green phases in the cycle, thus making the mathematical optimization very complicated and difficult. For that reason, the adaptive signal control in most cases is not based on precise optimization but on the green extension principle. In practice, uniformity is the principle followed in signal control for traffic safety reasons. This sets limitations to the cycle time and phase arrangements. Hence, traffic signal control in practice are based on tailor-made solutions and adjustments made by the traffic planners. The modern programmable signal controllers with a great number of adjustable parameters are well suited to this process. For good results, an experienced planner and fine-tuning in the field is needed. Fuzzy control has proven to be successful in problems where exact mathematical modelling is hard orimpossible but an experienced human can control the process operator. Thus, traffic signal control in particular is a suitable task for fuzzy control. Indeed, one of the oldest examples of the potentials of fuzzy control is a simulation of traffic signal control in an inter-section of two one-way streets. Even in this very simple case the fuzzy control was at least as good as the traditional adaptive control. In general, fuzzy control is found to be superior in complex problems with multiobjective decisions. In traffic signal control several traffic flows compete from the same time and space, and different priorities are often set to different traffic flows or vehicle groups. In addition, the optimization includes several simultaneous criteria, like the average and maximum vehicle and pedestrian delays, maximum queue lengths and percentage of stopped vehicles. So, it is very likely that fuzzy control is very competitive in complicated real intersections where the use of traditional optimization methods is problematic.Fuzzy logic has been introduced and successfully applied to a wide range of automatic control tasks. The main benefit of fuzzy logic is the opportunity to model the ambiguity and the uncertainty of decision-making. Moreover, fuzzy logic has the ability to comprehend linguistic instructions and to generate control strategies based on priori communication. The point in utilizing fuzzy logic in control theory is to model control based on human expert knowledge, rather than to model the process itself. Indeed, fuzzy control has proven to be successful in problems where exact mathematical modelling is hard or impossible but an experienced human operator can control process. In general, fuzzy control is found to be superior in complex problems with multi-objective decisions.At present, there is a multitude of inference systems based on fuzzy technique. Most of them, however, suffer ill-defined foundations; even if they are mostly performing better that classical mathematical method, they still contain black boxes, e.g. de fuzzification, which are very difficult to justify mathematically or logically. For example, fuzzy IF - THEN rules, which are in the core of fuzzy inference systems, are often reported to be generalizations of classical Modus Ponens rule of inference, but literally this not the case; the relation between these rules and any known many-valued logic is complicated and artificial. Moreover, the performance of an expert system should be equivalent to that of human expert: it should give the same results that the expert gives, but warn when the control situation is so vague that an expert is not sure about the right action. The existing fuzzy expert systems veryseldom fulfil this latter condition.Many researches observe, however, that fuzzy inference is based on similarity. Kosko, for example, writes 'Fuzzy membership...represents similarities of objects to imprecisely defined properties'. Taking this remark seriously, we study systematically many-valued equivalence, i.e. fuzzy similarity. It turns out that, starting from the Lukasiewicz well-defined many-valued logic, we are able to construct a method performing fuzzy reasoning such that the inference relies only on experts knowledge and on well-defined logical concepts. Therefore we do not need any artificial defuzzification method (like Center of Gravity) to determine the final output of the inference. Our basic observation is that any fuzzy set generates a fuzzy similarity, and that these similarities can be combined to a fuzzy relation which turns out to a fuzzy similarity, too. We call this induced fuzzy relation total fuzzy similarity. Fuzzy IF - THEN inference systems are, in fact, problems of choice: compare each IF-part of the rule base with an actual input value, find the most similar case and fire the corresponding THEN-part; if it is not unique, use a criteria given by an expert to proceed. Based on the Lukasiewicz welldefined many valued logic, we show how this method can be carried out formally.Hypothesis and Principles of Fuzzy Traffic Signal Control Traffic signal control is used to maximize the efficiency of the existing traffic systems [6]. However, the efficiency of traffic system can even be fuzzy. By providing temporal separation of rights of way to approaching flows, traffic signals exert a profound influence on the efficiency of traffic flow. They can operate to the advantage or disadvantage of the vehicles or pedestrians; depend on how the rights of ways are allocated. Consequently, the proper application, design, installation, operation, and maintenance of traffic signals is critical to the orderly safe and efficient movement of traffic at intersections.In traffic signal control, we can find some kind of uncertainties in many levels. The inputs of traffic signal control are inaccurate, and that means that we cannot handle the traffic of approaches exactly. The control possibilities are complicated, and handling these possibilities are an extremely complex task. Maximizing safety, minimizing environmental aspects and minimizing delays are some of the objectives of control, but it is difficult to handle them together in the traditional traffic signal control. The causeconsequence- relationship is also not possible to explain in traffic signal control. These are typical features of fuzzy control.Fuzzy logic based controllers are designed to capture the key factors forcontrolling a process without requiring many detailed mathematical formulas. Due to this fact, they have many advantages in real time applications. The controllers have a simple computational structure, since they do not require many numerical calculations. The IFTHEN logic of their inference rules does not require much computational time. Also, the controllers can operate on a large range of inputs, since different sets of control rules can be applied to them. If the system related knowledge is represented by simple fuzzy IFTHEN- rules, a fuzzy-based controller can control the system with efficiency and ease. The main goal of traffic signal control is to ensure safety at signalized intersections by keeping conflict traffic flows apart. The optimal performance of the signalized intersections is the combination of time value, environmental effects and traffic safety. Our goal is the optimal system, but we need to decide what attributes and weights will be used to judge optimality.The entire knowledge of the system designer about the process, traffic signal control in this case, to be controlled is stored as rules in the knowledge base. Thus the rules have a basic influence on the closed-loop behaviour of the system and should therefore be acquired thoroughly. The development of rules is time consuming, and designers often have to translate process knowledge into appropriate rules. Sugeno and Nishida mentioned four ways to derive fuzzy control rules:1. operators experience2. control engineer's knowledge3. fuzzy modelling of the operator's control actions4. fuzzy modelling of the process5. crisp modeling of the process6. heuristic design rules7. on-line adaptation of the rules.Usually a combination of some of these methods is necessary to obtain good results. As in conventional control, increased experience in the design of fuzzy controllers leads to decreasing development times.The main goals of FUSICO-research project are theoretical analysis of fuzzy traffic signal control, generalized fuzzy rules for traffic signal control using linguistic variables, validation of fuzzy control principles and calibration of membership functions, and development of a fuzzy adaptive signal controller. The vehicle-actuated control strategies, like SOS, MOV A and LHOVRA, are the control algorithms of the first generation. The fuzzy control algorithm can be one of the algorithms of thesecond generation, the generation of artificial intelligence (AI). The fuzzy control is capable of handling multi-objective, multi-dimensional and complicated traffic situations, like traffic signalling. The typical advantages of fuzzy control are simple process, effective control and better quality.FUSICO-project modelled the experience of policeman. The rule base development was made during the fall 1996. Mr. Kari J. Sane, experienced traffic signal planner, was working at the Helsinki University of Technology at this time. Everyday discussions and working groups helped us to model his experience to our rules.In particular pathological traffic jams or situations where there are very few vehicles in circulation; there first-in-first-out is the only reasonable control strategy. The Algorithm is looking for the most similar IF-part to the actual input value, and the corresponding THEN-part is then fired. Three realistic traffic signal control systems were constructed by means of the Algorithm and a simulation model tested their performance. Similar simulations were made to a non-fuzzy and classical Mamdani style fuzzy inference systems, too. The results with respect to average vehicle and pedestrian delay or average vehicle delay were in most cases better on fuzzy similarity based control than on the other control systems. Comparisons between fuzzy similarity based control and Mamdani style fuzzy control also strength an assumption that, in approximate reasoning, a fundamental concept is many-valued similarity between objects rather than a generalization of classical Modus Ponens rule of inference.The results of this project have indicated that fuzzy signal control is the potential control method for isolated intersections. The comparison results of Pappis-Mamdani control, fuzzy isolated pedestrian crossing and fuzzy two-phase control are good. The results of isolated pedestrian crossing indicate that the fuzzy control provides the effective compromise between the two opposing objectives, minimum pedestrian delay and minimum vehicle delay. The results of two-phase control and Pappis-Mamdani control indicate that the application area of fuzzy control is very wide. The maximum delay improvement was more than 20 %, which means that the efficiency of fuzzy control can be better than the efficiency of traditional vehicle-actuated control.According to these results, we can say that the fuzzy signal control can be multiobjective and more efficient than conventional adaptive signal control nowadays.The biggest benefits can, probably, be achieved in more complicated intersections and environments. The FUSICO-project continues. The aim is to move step by step to more complicated traffic signals and to continue the theoretical work of fuzzy control. The first example will be the public transport priorities.交通灯信号控制是一种必要的措施以确保的质量和安全,交通循环。

单片机交通灯控制器论文中英文对照资料外文翻译文献

中英文对照资料外文翻译文献附件1：外文资料翻译译文基于单片机的十字路口交通灯控制器的设计由于我国经济的快速发展从而导致了汽车数量的猛增，大中型城市的城市交通，正面临着严峻的考验，从而导致交通问题日益严重，其主要表现如下：交通事故频发，对人类生命安全造成极大威胁；交通拥堵严重，导致出行时间增加，能源消耗加大；空气污染和噪声污染程度日益加深等。

日常的交通堵塞成为人们司空见惯而又不得不忍受的问题。

在这种背景下，结合我国城市道路交通的实际情况，开发出真正适合我们自身特点的智能信号灯控制系统已经成为当前的主要任务。

前言在实际应用上，根据对国内外实际交通信号控制应用的考察，平面独立交叉口信号控制基本采用定周期、多时段定周期、半感应、全感应等几种方式。

前两种控制方式完全是基于对平面交叉口既往交通流数据的统计调查，由于交通流存在的变化性和随机性，这两种方式都具有通行效率低、方案易老化的缺陷，而半感应式和全感应式这两种方式是在前两种方式的基础上增加了车辆检测器并根据其提供的信息来调整周期长和绿信比，它对车辆随机到达的适应性较大，可使车辆在停车线前尽可能少停车，达到交通流畅的效果。

在现代化的工业生产中，电流、电压、温度、压力、流量、流速和开关量都是常用的主要被控参数。

例如：在冶金工业、化工生产、电力工程、造纸行业、机械制造和食品加工等诸多领域中，人们都需要对交通进行有序的控制。

采用单片机来对交通进行控制，不仅具有控制方便、组态简单和灵活性大等优点，而且可以大幅度提高被控制量的技术指标，从而能够大大提高产品的质量和数量。

因此，单片机对交通灯的控制问题是一个工业生产中经常会遇到的问题。

在工业生产中，有很多行业有大量的交通灯设备，在现行系统中,大多数的交通控制信号都是用继电器来完成的,但继电器响应时间长,灵敏度低,长期使用之后,故障机会大大增加,而采用单片机控制,其精度远远大于继电器,响应时间短,软件可靠性高,不会因为工作时间缘故而降低其性能,相比而言,本方案具有很高的可行性。

英文交通规则词汇100

交通规则---词汇1001. 交通规则traffic regulation2. 路标guide post3. 里程碑milestone4. 停车标志mark car stop5. 红绿灯traffic light6. 自动红绿灯automatic traffic signal light7. 红灯red light8. 绿灯green light9. 黄灯amber light10. 交通岗traffic post11. 岗亭police box12. 交通警traffic police13. 打手势pantomime14. 单行线single line15. 双白线double white lines16. 双程线dual carriage-way17. 斑马线zebra stripes18. 划路线机traffic line marker19. 交通干线artery traffic20. 车行道carriage-way21. 辅助车道lane auxiliary22. 双车道two-way traffic23. 自行车通行cyclists only24. 单行道one way only25. 窄路narrow road26. 潮湿路滑slippery when wet27. 陡坡steep hill28. 不平整路rough road29. 弯路curve road ; bend road30. 连续弯路winding road31. 之字路double bend road32. 之字公路switch back road33. 下坡危险dangerous down grade34. 道路交叉点road junction35. 十字路cross road36. 左转turn left37. 右转turn right38. 靠左keep left39. 靠右keep right40. 慢驶slow41. 速度speed42. 超速excessive speed43. 速度限制speed limit44. 恢复速度resume speed45. 禁止通行no through traffic46. 此路不通blocked47. 不准驶入no entry48. 不准超越keep in line ; no overhead49. 不准掉头no turns50. 让车道passing bay51. 回路loop52. 安全岛safety island53. 停车处parking place54. 停私人车private car park55. 只停公用车public car only56. 不准停车restricted stop57. 不准滞留restricted waiting58. 临街停车parking on-street59. 街外停车parking off-street60. 街外卸车loading off-street61. 当心行人caution pedestrian crossing62. 当心牲畜caution animals63. 前面狭桥narrow bridge ahead64. 拱桥hump bridge65. 火车栅level crossing66. 修路road works67. 医院hospital68. 儿童children69. 学校school70. 寂静地带silent zone71. 非寂静地带silent zone ends72. 交通管理traffic control73. 人山人海crowded conditions74. 拥挤的人jam-packed with people75. 交通拥挤traffic jam76. 水泄不通overwhelm77. 顺挤extrusion direct78. 冲挤extrusion impact79. 推挤shoved80. 挨身轻推nudging81. 让路give way82. 粗心行人careless pedestrian83. 犯交通罪committing traffic offences84. 执照被记违章endorsed on driving license85. 危险驾驶dangerous driving86. 粗心驾车careless driving87. 无教员而驾驶driving without an instructor88. 无证驾驶driving without license89. 未经车主同意without the owner's consent90. 无第三方保险without third-party insurance91. 未挂学字牌driving without a "L" plate92. 安全第一safety first93. 轻微碰撞slight impact94. 迎面相撞head-on collision95. 相撞collided96. 连环撞a chain collision97. 撞车crash98. 辗过run over99. 肇事逃跑司机hit-run driver100. 冲上人行道drive onto the pavement。

交通灯毕业论文英文资料翻译

毕业论文英文资料翻译系别：专业：班级：姓名：学号：Introduction of Programmable controllers From a simple heritage, these remarkable systems have evolved to not only replace electromechanical devices, but to solve an ever-increasing array of control problems in both process and nonprocess industries. By all indications, these microprocessor powered giants will continue to break new ground in the automated factory into the 1990s.HISTORYIn the 1960s, electromechanical devices were the order of the day ass far as control was concerned. These devices, commonly known as relays, were being used by the thousands to control many sequential-type manufacturing processes andstand-along machines. Many of these relays were in use in the transportation industry, more specifically, the automotive industry. These relays used hundreds of wires and their interconnections to effect a control solution. The performance of a relay was basically reliable - at least as a single device. But the common applications for relay panels called for 300 to 500 or more relays, and the reliability and maintenance issues associated with supporting these panels became a very great challenge. Cost became another issue, for in spite of the low cost of the relay itself, the installed cost of the panel could be quite high. The total cost including purchased parts, wiring, and installation labor, could range from $30~$50 per relay. To make matters worse, the constantly changing needs of a process called for recurring modifications of a control panel. With relays, this was a costly prospect, as it was accomplished by a major rewiring effort on the panel. In addition these changes were sometimes poorly documented, causing a second-shift maintenance nightmare months later. In light of this, it was not uncommon to discard an entire control panel in favor of a new one with the appropriate components wired in a manner suited for the new process. Add to this the unpredictable, and potentially high, cost of maintaining these systems as on high-volume motor vehicle production lines, and it became clear that something was needed to improve the control process – to make it more reliable, easier to troubleshoot, and more adaptable to changing control needs.That something, in the late 1960s, was the first programmable controller. This first ‘revolutionary’ system wan developed as a specific response to the needs of the major automotive manufacturers in the United States. These early controllers, or programmable logic controllers (PLC), represented the first systems that 1 could be used on the factory floor, 2 could have there ‘logic’ changed without extensiverewiring or component changes, and 3 were easy to diagnose and repair when problems occurred.It is interesting to observe the progress that has been made in the past 15 years in the programmable controller area. The pioneer products of the late 1960s must have been confusing and frightening to a great number of people. For example, what happened to the hardwired and electromechanical devices that maintenance personnel were used to repairing with hand tools? They were replaced with ‘computers’ disguised as electronics designed to replace relays. Even the programming tools were designed to appear as relay equivalent presentations. We have the opportunity now to examine the promise, in retrospect, that the programmable controller brought to manufacturing.All programmable controllers consist of the basic functional blocks shown in Fig.10. 1. We’ll examine each block to understand the relationship to the control system. First we look at the center, as it is the heart ( or at least the brain ) of the system. It consists of a microprocessor, logic memory for the storage of the actual control logic, storage or variable memory for use with data that will ordinarily change as a function power for the processor and memory. Next comes the I/O block. This function takes the control level signals for the CPU and converts them to voltage and current levels suitable for connection with factory grade sensors and actuators. The I/O type can range from digital (discrete or on / off), analog (continuously variable), or a variety of special purpose ‘smart’ I/O which are dedicated to a certain ap plication task. The programmer is shown here, but it is normally used only to initially configure and program a system and is not required for the system to operate. It is also used in troubleshooting a system, and can prove to be a valuable tool in pinpointing the exact cause of a problem. The field devices shown here represent the various sensors and actuators connected to the I/O. These are the arms, legs, eyes, and ears of the system, including push buttons, limit switches, proximity switches, photosensors, thermocouples, RTDS, position sensing devices, and bar code reader as input; and pilot lights, display devices, motor starters, DC and AC drives, solenoids, and printers as outputs.No single attempt could cover its rapidly changing scope, but three basic characteristics can be examined to give classify an industrial control device as a programmable controller.(1) Its basic internal operation is to solve logic from the beginning of memory to some specified point, such as end of memory or end of program. Once the end isreached, the operation begins again at the beginning of memory. This scanning process continues from the time power is supplied to the time it it removed.(2) The programming logic is a form of a relay ladder diagram. Normally open, normally closed contacts, and relay coils are used within a format utilizing a left and a right vertical rail. Power flow (symbolic positive electron flow) is used to determine which coil or outputs are energized or deenergized.(3) The machine is designed for the industrial environment from its basic concept; this protection is not added at a later date. The industrial environment includes unreliable AC power, high temperatures (0 to 60 degree Celsius), extremes of humidity, vibrations, RF noise, and other similar parameters.General application areasThe programmable controller is used in a wide variety of control applications today, many of which were not economically possible just a few years ago. This is true for two general reasons: 1 there cost effectiveness (that is, the cost per I/O point) has improved dramatically with the falling prices of microprocessors and related components, and 2 the ability of the controller to solve complex computation and communication tasks has made it possible to use it where a dedicated computer was previously used.Applications for programmable controllers can be categorized in a number of different ways, including general and industrial application categories. But it is important to understand the framework in which controllers are presently understood and used so that the full scope of present and future evolution can be examined. It is through the power of applications that controllers can be seen in their full light. Industrial applications include many in both discrete manufacturing and process industries. Automotive industry applications, the genesis of the programmable controller, continue to provide the largest base of opportunity. Other industries, such as food processing and utilities, provide current development opportunities.There are five general application areas in which programmable controllers are used. A typical installation will use one or more of these integrated to the control system problem. The five general areas are explained briefly below.DescriptionThe AT89C51 is a low-power, high-performance CMOS 8-bit microcomputer with 4K bytes of Flash programmable and erasable read only memory (PEROM). The device is manufactured using Atmel’s high-density nonvolatile memory technology and is compatible with the industry-standard MCS-51 instruction set and pinout. Theon-chip Flash allows the program memory to be reprogrammed in-system or by a conventional nonvolatile memory programmer. By combining a versatile 8-bit CPU with Flash on a monolithic chip, the Atmel AT89C51 is a powerful microcomputer which provides a highly-flexible and cost-effective solution to many embedded control applications.Function characteristicThe AT89C51 provides the following standard features: 4K bytes of Flash, 128 bytes of RAM, 32 I/O lines, two 16-bit timer/counters, a five vector two-level interrupt architecture, a full duplex serial port, on-chip oscillator and clock circuitry. In addition, the AT89C51 is designed with static logic for operation down to zero frequency and supports two software selectable power saving modes. The Idle Mode stops the CPU while allowing the RAM, timer/counters, serial port and interrupt system to continue functioning. The Power-down Mode saves the RAM contents but freezes the oscillator disabling all other chip functions until the next hardware reset. Pin DescriptionVCC：Supply voltage.GND：Ground.Port 0：Port 0 is an 8-bit open-drain bi-directional I/O port. As an output port, each pin can sink eight TTL inputs. When 1s are written to port 0 pins, the pins can be used as highimpedance inputs.Port 0 may also be configured to be the multiplexed loworder address/data bus during accesses to external program and data memory. In this mode P0 has internal pullups.Port 0 also receives the code bytes during Flash programming,and outputs the code bytes during programverification. External pullups are required during programverification.Port 1Port 1 is an 8-bit bi-directional I/O port with internal pullups.The Port 1 output buffers can sink/source four TTL inputs.When 1s are written to Port 1 pins they are pulled high by the internal pullups and can be used as inputs. As inputs,Port 1 pins that are externally being pulled low will source current (IIL) because of the internal pullups.Port 1 also receives the low-order address bytes during Flash programming and verification.Port 2Port 2 is an 8-bit bi-directional I/O port with internal pullups.The Port 2 output buffers can sink/source four TTL inputs.When 1s are written to Port 2 pins they arepulled high by the internal pullups and can be used as inputs. As inputs,Port 2 pins that are externally being pulled low will source current, because of the internal pullups.Port 2 emits the high-order address byte during fetches from external program memory and during accesses to external data memory that use 16-bit addresses. In this application, it uses strong internal pullupswhen emitting 1s. During accesses to external data memory that use 8-bit addresses, Port 2 emits the contents of the P2 Special Function Register.Port 2 also receives the high-order address bits and some control signals during Flash programming and verification.Port 3Port 3 is an 8-bit bi-directional I/O port with internal pullups.The Port 3 output buffers can sink/source four TTL inputs.When 1s are written to Port 3 pins they are pulled high by the internal pullups and can be used as inputs. As inputs,Port 3 pins that are externally being pulled low will source current (IIL) because of the pullups.Port 3 also serves the functions of various special features of the AT89C51 as listed below:Port 3 also receives some control signals for Flash programming and verification. RSTReset input. A high on this pin for two machine cycles while the oscillator is running resets the device.ALE/PROGAddress Latch Enable output pulse for latching the low byte of the address during accesses to external memory. This pin is also the program pulse input (PROG) during Flash programming.In normal operation ALE is emitted at a constant rate of1/6 the oscillator frequency, and may be used for external timing or clocking purposes. Note, however, that one ALE pulse is skipped during each access to external Data Memory.If desired, ALE operation can be disabled by setting bit 0 of SFR location 8EH. With the bit set, ALE is active only during a MOVX or MOVC instruction. Otherwise, the pin is weakly pulled high. Setting the ALE-disable bit has no effect if the microcontroller is in external execution mode.PSENProgram Store Enable is the read strobe to external program memory.When the AT89C51 is executing code from external program memory, PSEN is activated twice each machine cycle, except that two PSEN activations are skipped during each access to external data memory.EA/VPPExternal Access Enable. EA must be strapped to GND in order to enable the device to fetch code from external program memory locations starting at 0000H up to FFFFH. Note, however, that if lock bit 1 is programmed, EA will be internally latched on reset.EA should be strapped to VCC for internal program executions.This pin also receives the 12-volt programming enable voltage(VPP) during Flash programming, for parts that require12-volt VPP.XTAL1Input to the inverting oscillator amplifier and input to the internal clock operating circuit.XTAL2Output from the inverting oscillator amplifier.Oscillator CharacteristicsXTAL1 and XTAL2 are the input and output, respectively,of an inverting amplifier which can be configured for use as an on-chip oscillator, as shown in Figure 1.Either a quartz crystal or ceramic resonator may be used. To drive the device from an external clock source, XTAL2 should be left unconnected while XTAL1 is driven as shown in Figure 2.There are no requirements on the duty cycle of the external clock signal, since the input to the internal clocking circuitry is through adivide-by-two flip-flop, but minimum and maximum voltage high and low time specifications must be observed.Figure 1. Oscillator Connections Figure 2. External Clock Drive Configuration Idle ModeIn idle mode, the CPU puts itself to sleep while all the onchip peripherals remain active. The mode is invoked by software. The content of the on-chip RAM and all the special functions registers remain unchanged during this mode. The idle mode can be terminated by any enabled interrupt or by a hardware reset.It should be noted that when idle is terminated by a hard ware reset, the device normally resumes program execution,from where it left off, up to two machine cycles before the internal reset algorithm takes control. On-chip hardware inhibits access to internal RAM in this event, but access to the port pins is not inhibited. To eliminate the possibility of an unexpected write to a port pin when Idle is terminated by reset, the instruction following the one that invokes Idle should not be one that writes to a port pin or to external memory.Power-down ModeIn the power-down mode, the oscillator is stopped, and the instruction that invokes power-down is the last instruction executed. The on-chip RAM and Special Function Registers retain their values until the power-down mode is terminated. The only exit from power-down is a hardware reset. Reset redefines the SFRs but does not change the on-chip RAM. The reset should not be activated before VCC is restored to its normal operating level and must be held active long enough to allow the oscillator to restart and stabilize.Program Memory Lock BitsOn the chip are three lock bits which can be left unprogrammed (U) or can be programmed (P) to obtain the additional features listed in the table below.When lock bit 1 is programmed, the logic level at the EA pin is sampled and latched during reset. If the device is powered up without a reset, the latch initializes to a random value, and holds that value until reset is activated. It is necessary that the latched value of EA be in agreement with the current logic level at that pin in order for the device to function properly介绍可编程控制器从一个简单的遗产,这显著的系统已经进化到不仅取代机电设备,而是为了解决日益增加的一系列控制问题在这两种过程和nonprocess行业。

单片机交通灯中英文资料对照外文翻译文献

单片机交通灯中英文资料对照外文翻译文献原文题目：DＥSＩGN ＯＦTRAFFIC LIGＨＴＢASEＤON MCUBeｃaｕse ｏf thｅｒａpｉｄｄevelopmeｎt of ｏuｒｅconｏｍｙｒeｓulｔing ｉn thｅｃar number ｏf lａｒge aｎｄmｅdｉum—sized cｉｔiｅs suｒgeｄａnｄthｅurｂaｎtraffｉc, ｉｓfａcing sｅｒioｕs tｅst，leａdiｎg to the tｒａfficｐroblem ｉncrｅasiｎgly serｉoｕs, its ｂasically ａre behａved as follｏｗs: traffic acciｄent frequencｙ,to the ｈuｍan lｉｆe safeｔy enoｒmous tｈreaｔ，Traf ｆｉc congestｉon，rｅｓulｔｉng in seｒｉousｔravel time inｃrｅases，eneｒgy ｃｏnsumｐｔioｎinｃｒeａｓｅ；Aiｒpollｕｔion and ｎoise poｌlｕtｉｏn degｒｅｅoｆｄｅepeｎiｎg，etc.Daily traffic ｊamｓｂecome ｐeｏpｌe cｏmmonｐｌａｃｅand hａｄｔｏendurｅ。

Ｉｎtｈis context, ｉn coｍbinaｔｉoｎwｉth the aｃtｕaｌsｉtｕatioｎｏf uｒban roaｄtｒaffｉｃ, develop ｔruly suitaｂle ｆor our own cｈaraｃteｒiｓticｓｏf iｎｔelｌigent signａｌｃｏntｒol sｙsteｍhas becｏme the ｍａin task.PrefaceIｎpｒａｃｔicaｌapｐlication at hｏmｅａnｄabrｏad，aｃcorｄiｎg ｔo ｔhｅａｃｔuaｌtrafｆic siｇnal control applｉｃation inspeｃtion,pｌaｎar ｉndepeｎdeｎt inｔersｅction signaｌｃoｎtrol basic using set cycｌe, much time set cyclｅ,half inducｔion, wholｅｓｅｎsｏｒetcｉn several wayｓ. The former tｗo coｎｔrol moｄeｉｓcoｍpleｔely basｅdｏn plａnar ｉntｅｒsectioｎalways traffic floｗｄatａof sｔatｉsticａｌｉnvestigａtion，due to trａffｉｃfloｗthe ｅxistｅnｃe of variablｅsexａnd ｒandｏｍｉciｔy，ｔｈe two methods have traffic effｉcｉeｎcy is ｌow，the scheme, tｈeｄeｆｅｃts of agingａnｄh ａlf ｉｎductive anｄall theｉndｕctive ｔｈe ｔｗo ｍｅthods ａre iｎtｈe former tｗｏｗaysｂased oｎincrｅasｅｄvehicleｄｅｔecｔｏｒand aｃcordｉｎg to the inｆormatioｎproｖidｅd to ａdｊuｓtｃyｃle iｓｌｏng and ｇreen ｌeｔter oｆｖeｈicｌe, it than raｎdom arrived adaｐtabilitｙbigｇer，c ａn maｋe veｈiｃleｓｉnｔhｅparking coｒd bｅfore ａｓｆew ｐａrkinｇ，achieve ｔraffｉc fｌｏｗing eｆfectＩｎmoderｎｉndusｔｒｉal ｐroduction，cｕrrenｔ，volｔage,ｔｅｍｐｅraｔｕre，ｐｒessurｅ, ａｎd ｆｌｏｗrate, vｅloｃity，aｎd swiｔch ｑuanｔity are commｏn ｍaｉnlyｃoｎtrｏllｅd pａrameter。

交通灯外文翻译2

Traffic lightsSignal control is a necessary measure to maintain the quality and safety of traffic circulation. Further development of present signal control has great potential to reduce travel times, vehicle and accident costs, and vehicle emissions. The development of detection and computer technology has changed traffic signal control from fixed-time open-loop regulation to adaptive feedback control. Present adaptive control methods, like the British MOV A, Swedish SOS (isolated signals) and British SCOOT (area-wide control), use mathematical optimization and simulation techniques to adjust the signal timing to the observed fluctuations of traffic flow in real time. The optimization is done by changing the green time and cycle lengths of the signals. In area-wide control the offsets between intersections are also changed. Several methods have been developed for determining the optimal cycle length and the minimum delay at an intersection but, based on uncertainty and rigid nature of traffic signal control, the global optimum is not possible to find out.As a result of growing public awareness of the environmental impact of road traffic many authorities are now pursuing policies to:− manage demand and congestion;− influence mode and route choice;− improve priority for buses, trams and other public service vehicles;− provide better and safer facilities for pedestrians, cyclists and other vulnerable road users;− reduce vehicle emissions, noise and visual intrusion; and− improve safety for all road user groups.In adaptive traffic signal control the increase in flexibility increases the number of overlapping green phases in the cycle, thus making the mathematical optimization very complicated and difficult. For that reason, the adaptive signal control in most cases is not based on precise optimization but on the green extension principle. In practice, uniformity is the principle followed in signal control for traffic safety reasons. This sets limitations to the cycle time and phase arrangements. Hence, traffic signal control in practice are based on tailor-made solutions and adjustments made by the traffic planners. The modern programmable signal controllers with a great number of adjustable parameters are well suited to this process. For good results, an experienced planner and fine-tuning in the field is needed. Fuzzy control has proven to be successful in problems where exact mathematical modelling is hard orimpossible but an experienced human can control the process operator. Thus, traffic signal control in particular is a suitable task for fuzzy control. Indeed, one of the oldest examples of the potentials of fuzzy control is a simulation of traffic signal control in an inter-section of two one-way streets. Even in this very simple case the fuzzy control was at least as good as the traditional adaptive control. In general, fuzzy control is found to be superior in complex problems with multiobjective decisions. In traffic signal control several traffic flows compete from the same time and space, and different priorities are often set to different traffic flows or vehicle groups. In addition, the optimization includes several simultaneous criteria, like the average and maximum vehicle and pedestrian delays, maximum queue lengths and percentage of stopped vehicles. So, it is very likely that fuzzy control is very competitive in complicated real intersections where the use of traditional optimization methods is problematic.Fuzzy logic has been introduced and successfully applied to a wide range of automatic control tasks. The main benefit of fuzzy logic is the opportunity to model the ambiguity and the uncertainty of decision-making. Moreover, fuzzy logic has the ability to comprehend linguistic instructions and to generate control strategies based on priori communication. The point in utilizing fuzzy logic in control theory is to model control based on human expert knowledge, rather than to model the process itself. Indeed, fuzzy control has proven to be successful in problems where exact mathematical modelling is hard or impossible but an experienced human operator can control process. In general, fuzzy control is found to be superior in complex problems with multi-objective decisions.At present, there is a multitude of inference systems based on fuzzy technique. Most of them, however, suffer ill-defined foundations; even if they are mostly performing better that classical mathematical method, they still contain black boxes, e.g. de fuzzification, which are very difficult to justify mathematically or logically. For example, fuzzy IF - THEN rules, which are in the core of fuzzy inference systems, are often reported to be generalizations of classical Modus Ponens rule of inference, but literally this not the case; the relation between these rules and any known many-valued logic is complicated and artificial. Moreover, the performance of an expert system should be equivalent to that of human expert: it should give the same results that the expert gives, but warn when the control situation is so vague that an expert is not sure about the right action. The existing fuzzy expert systems veryseldom fulfil this latter condition.Many researches observe, however, that fuzzy inference is based on similarity. Kosko, for example, writes 'Fuzzy membership...represents similarities of objects to imprecisely defined properties'. Taking this remark seriously, we study systematically many-valued equivalence, i.e. fuzzy similarity. It turns out that, starting from the Lukasiewicz well-defined many-valued logic, we are able to construct a method performing fuzzy reasoning such that the inference relies only on experts knowledge and on well-defined logical concepts. Therefore we do not need any artificial defuzzification method (like Center of Gravity) to determine the final output of the inference. Our basic observation is that any fuzzy set generates a fuzzy similarity, and that these similarities can be combined to a fuzzy relation which turns out to a fuzzy similarity, too. We call this induced fuzzy relation total fuzzy similarity. Fuzzy IF - THEN inference systems are, in fact, problems of choice: compare each IF-part of the rule base with an actual input value, find the most similar case and fire the corresponding THEN-part; if it is not unique, use a criteria given by an expert to proceed. Based on the Lukasiewicz welldefined many valued logic, we show how this method can be carried out formally.Hypothesis and Principles of Fuzzy Traffic Signal Control Traffic signal control is used to maximize the efficiency of the existing traffic systems [6]. However, the efficiency of traffic system can even be fuzzy. By providing temporal separation of rights of way to approaching flows, traffic signals exert a profound influence on the efficiency of traffic flow. They can operate to the advantage or disadvantage of the vehicles or pedestrians; depend on how the rights of ways are allocated. Consequently, the proper application, design, installation, operation, and maintenance of traffic signals is critical to the orderly safe and efficient movement of traffic at intersections.In traffic signal control, we can find some kind of uncertainties in many levels. The inputs of traffic signal control are inaccurate, and that means that we cannot handle the traffic of approaches exactly. The control possibilities are complicated, and handling these possibilities are an extremely complex task. Maximizing safety, minimizing environmental aspects and minimizing delays are some of the objectives of control, but it is difficult to handle them together in the traditional traffic signal control. The causeconsequence- relationship is also not possible to explain in traffic signal control. These are typical features of fuzzy control.Fuzzy logic based controllers are designed to capture the key factors forcontrolling a process without requiring many detailed mathematical formulas. Due to this fact, they have many advantages in real time applications. The controllers have a simple computational structure, since they do not require many numerical calculations. The IFTHEN logic of their inference rules does not require much computational time. Also, the controllers can operate on a large range of inputs, since different sets of control rules can be applied to them. If the system related knowledge is represented by simple fuzzy IFTHEN- rules, a fuzzy-based controller can control the system with efficiency and ease. The main goal of traffic signal control is to ensure safety at signalized intersections by keeping conflict traffic flows apart. The optimal performance of the signalized intersections is the combination of time value, environmental effects and traffic safety. Our goal is the optimal system, but we need to decide what attributes and weights will be used to judge optimality.The entire knowledge of the system designer about the process, traffic signal control in this case, to be controlled is stored as rules in the knowledge base. Thus the rules have a basic influence on the closed-loop behaviour of the system and should therefore be acquired thoroughly. The development of rules is time consuming, and designers often have to translate process knowledge into appropriate rules. Sugeno and Nishida mentioned four ways to derive fuzzy control rules:1. operators experience2. control engineer's knowledge3. fuzzy modelling of the operator's control actions4. fuzzy modelling of the process5. crisp modeling of the process6. heuristic design rules7. on-line adaptation of the rules.Usually a combination of some of these methods is necessary to obtain good results. As in conventional control, increased experience in the design of fuzzy controllers leads to decreasing development times.The main goals of FUSICO-research project are theoretical analysis of fuzzy traffic signal control, generalized fuzzy rules for traffic signal control using linguistic variables, validation of fuzzy control principles and calibration of membership functions, and development of a fuzzy adaptive signal controller. The vehicle-actuated control strategies, like SOS, MOV A and LHOVRA, are the control algorithms of the first generation. The fuzzy control algorithm can be one of the algorithms of thesecond generation, the generation of artificial intelligence (AI). The fuzzy control is capable of handling multi-objective, multi-dimensional and complicated traffic situations, like traffic signalling. The typical advantages of fuzzy control are simple process, effective control and better quality.FUSICO-project modelled the experience of policeman. The rule base development was made during the fall 1996. Mr. Kari J. Sane, experienced traffic signal planner, was working at the Helsinki University of Technology at this time. Everyday discussions and working groups helped us to model his experience to our rules.In particular pathological traffic jams or situations where there are very few vehicles in circulation; there first-in-first-out is the only reasonable control strategy. The Algorithm is looking for the most similar IF-part to the actual input value, and the corresponding THEN-part is then fired. Three realistic traffic signal control systems were constructed by means of the Algorithm and a simulation model tested their performance. Similar simulations were made to a non-fuzzy and classical Mamdani style fuzzy inference systems, too. The results with respect to average vehicle and pedestrian delay or average vehicle delay were in most cases better on fuzzy similarity based control than on the other control systems. Comparisons between fuzzy similarity based control and Mamdani style fuzzy control also strength an assumption that, in approximate reasoning, a fundamental concept is many-valued similarity between objects rather than a generalization of classical Modus Ponens rule of inference.The results of this project have indicated that fuzzy signal control is the potential control method for isolated intersections. The comparison results of Pappis-Mamdani control, fuzzy isolated pedestrian crossing and fuzzy two-phase control are good. The results of isolated pedestrian crossing indicate that the fuzzy control provides the effective compromise between the two opposing objectives, minimum pedestrian delay and minimum vehicle delay. The results of two-phase control and Pappis-Mamdani control indicate that the application area of fuzzy control is very wide. The maximum delay improvement was more than 20 %, which means that the efficiency of fuzzy control can be better than the efficiency of traditional vehicle-actuated control.According to these results, we can say that the fuzzy signal control can be multiobjective and more efficient than conventional adaptive signal control nowadays.The biggest benefits can, probably, be achieved in more complicated intersections and environments. The FUSICO-project continues. The aim is to move step by step to more complicated traffic signals and to continue the theoretical work of fuzzy control. The first example will be the public transport priorities.交通灯信号控制是一种必要的措施以确保的质量和安全,交通循环。

交通信号灯英语单词

关于交通信号灯的英语单词的解析
交通信号灯是交通管理系统中非常重要的一部分，其英语单词是“traffic light”，这个单词由三个部分组成：
1. "traffic" - 交通，这个词表明了信号灯的主要用途，即管理交通。

2. "light" - 灯，这个词指的是信号灯的物理属性，即它是一个灯。

通过这两个词的组合，我们可以理解为这是一个用于管理交通的灯。

在日常生活中，我们通常会听到人们说"red light"（红灯）和"green light"（绿灯），这两个词分别代表交通信号灯中的红灯和绿灯。

例如，当我们在十字路口看到红灯时，我们知道需要停车等待，而看到绿灯时，我们知道可以继续前行。

另外，"yellow light"（黄灯）在交通规则中通常代表警告或减速的意思。

例如，在某些路口，当黄灯亮起时，驾驶者需要准备停车或减速，因为有可能出现红灯或行人正在过马路。

总的来说，"traffic light"这个单词是用于描述管理交通的信号灯的专用名词。