Intelligent Transport(智能交通)英文和翻译

湖南科技大学智能控制理论论文

姓名：_____________ 学院：_____________ 班级：_____________ 学号：_____________

Intelligent Traffic Signal Control Using Wireless SensorNe

tworks

Abstract

The growing vehicle population in all developing and developed countries calls for a major change in the existing traffic signaling systems. The most widely used automated system uses simple timer based operation which is inefficient for non-uniform traffic. Adv anced automated systems in testing use image processing techniques or advanced com munication systems in vehicles to communicate with signals and ask for routing. This mig ht not be implementable in developing countries as they prove to be complex and expens ive. The concept proposed in this paper involves use of wireless sensor networks to sens e presence of traffic near junctions and hence route the traffic based on traffic density in t he desired direction. This system does not require any system in vehicles so can be impl emented in any traffic system easily. This system uses wireless sensor networks technol ogy to sense vehicles and a microcontroller based routing algorithm for traffic managem ent.

Keywords:Intelligent traffic signals, intelligent routing, smart signals, wireless sensor

networks.

INTRODUCTION

The traffic density is escalating at an alarming rate in developing countries which c alls for the need of intelligent traffic signals to replace the conventional manual and timer based systems. Experimental systems in existence involve image processing based dens ity identification for routing of traffic which might be inefficient in situations like fog, rain or dust. The other conceptual system which is based on interaction of vehicles with traffic si gnals and each other require hardware modification on each vehicle and cannot be practi cally implemented in countries like India which have almost 100 million vehicles on road

[1]. The system proposed here involves localized traffic routing for each intersection base

d on wireless sensor networks. Th

e proposed system has a central controller at every jun ction which receives data from tiny wireless sensor nodes placed on the road. The senso

r nodes

have sensors that can detect the presence of vehicle and the transmitter wirelessly trans mits the traffic density to the central controller. The controller makes use of the proposed algorithm to find ways to regulate traffic efficiently.

THE NEED FOR AN ALTERNATE SYSTEM

The most prevalent traffic signaling system in developing countries is the timer based system. This system involves a predefined time setting for each road at an int ersection. While this might prove effective for light traffic, heavy traffic requires an adaptiv e system that will work based on the density of traffic on each road. The first system prop osed for adaptive signaling was based on digital image processing techniques. This syste m works based on the captured visual input from the roads and processing them to find w hich road has dense traffic. This system fails during environmental interaction like rain or fog. Also this system in testing does not prove efficient. The advanced system in testing a t Pittsburgh [2] involves signals communicating with each other and also with the vehicles . The proposed system does not require a network between signals and vehicles and is a standalone system at each intersection.

THE PROPOSED SYSTEM

This paper presents the concept of intelligent traffic routing using wireless sensor networks. The primary elements of this system are the sensor nodes or motes consi sting of sensors and a transmitter. The sensors interact with the physical environment wh

ile the transmitter pages the sensor’s data to the central controller. This system involves t he 4 x 2 array of sensor nodes in each road. This signifies 4 levels of traffic and 2 lanes i n each road. The sensors are ultrasonic or IR based optical sensors which transmits stat us based on presence of vehicle near it. The sensor nodes transmit at specified time inter vals via ZigBee protocol to the central controller placed at every intersection. The controll er receives the signal and computes which road and which lane has to be given green sig nal based on the density of traffic. The controller makes use of the discussed algorithm to perform the intelligent traffic routing.

COMPONENTS INVOLVED IN THE SYSTEM

The proposed system involves wireless sensor networks which are comprised of t hree basic components: the sensor nodes or motes, power source and a central controlle r. The motes in turn are comprised of Sensors and transceiver module. The sensors sens e the vehicles at intersections and transceiver transmit the sensor’s data to the central co ntroller through a wireless medium. The Power source provides the power needed for the sensor nodes and is mostly regenerative. The central controller performs all the computa tions for the sensor networks. The controller receives the input from all sensors and proc esses simultaneously to make the required decisions.

A.Sensors

Sensors are hardware devices that produce a measurable response to a change in a physical condition like temperature or pressure. Sensors measure physical data of the parameter to be monitored. The continual analog signal produced by the sensors is digiti

zed by an analog-to-digital converter and sent to controllers for further processing. A sen sor node should be small in size, consume extremely low energy, operate in high volumet ric densities, be autonomous and operate unattended, and be adaptive to the environme nt. As wireless sensor nodes are typically very small electronic devices, they can only be equipped with a limited power source of less than 0.5-2 ampere-hour and 1.2-3.7 volts. S ensors are classified into three categories: passive Omni-directional sensors; passive nar row-beam sensors; and active sensors [3].

The sensors are implemented in this system placed beneath the roads in an intersec tion or on the lane dividers on each road. The sensors are active obstacle detectors that detect the presence of vehicles in their vicinity. The sensors are set in four levels on each road signifying four levels of traffic from starting from the STOP line. The fourth level indi cates high density traffic and signifies higher priority for the road to the controller. The se nsors required for obstacle detection can be either ultrasonic or Infrared LASER based s ensors for better higher efficiency.

B. Motes

A mote, also known as a sensor node is a node in a wireless sensor network that i s capable of performing some processing, gathering sensory information and communica ting with other connected nodes in the network. The main components of a sensor node are a microcontroller, transceiver, external memory, power source and one or more sens ors [3].

C. Need for Motes

The primary responsibility of a Mote is to collect information from the various distrib uted sensors in any area and to transmit the collected information to the central controller for processing. Any type of sensors can be incorporated with these Motes based on the r equirements. It is a completely new paradigm for distributed sensing and it opens up a fa scinating new way to look at sensor networks.

D. Advantages of Motes

The core of a mote is a small, low-cost, low-power controller.

The controller monitors one or more sensors. It is easy to interface all sorts of sensors, including sensors for temperature, light, sound, position, acceleration, vibrat ion, stress, weight, pressure, humidity, etc. with the mote.

The controller connects to the central controller with a radio link. The most comm on radio links allow a mote to transmit at a distance of about 3 to 61 meters. Power cons umption, size and cost are the barriers to longer distances. Since a fundamental concept with motes is tiny size and associated tiny cost, small and low-power radios are normal.

As motes shrink in size and power consumption, it is possible to imagine solar power or even something exotic like vibration power to keep them running. It is hard to imagine something as small and innocuous as a mote sparking a revolution, but that's exactly what they have done.

Motes are also easy to program, either by using serial or Ethernet cable to conne ct

to the programming board or by using Over the Air Programming (OTAP).

E. Transceivers

Sensor nodes often make use of ISM band, which gives free radio, spectrum allocation and global availability. The possible choices of wireless transmission medi a are radio frequency (RF), optical communication and infrared. Lasers require less ener gy, but need line-of-sight for communication and are sensitive to atmospheric conditions. Infrared, like lasers, needs no antenna but it is limited in its broadcasting capacity. Radio

frequency-based communication is the most relevant that fits most of

the WSN applications. WSNs tend to use license-free communication frequencies: 173, 4 33, 868, and 915 MHz; and 2.4 GHz. The functionality of bothtransmitter and receiver are combined into a single deviceknown as a transceiver [3].

To bring about uniqueness in transmitting and receiving toany particular device vari ous protocols/algorithms are devised. The Motes are often are often provided with powerf ul transmitters and receivers collectively known as transceivers for better long range oper ation and also toachieve better quality of transmission/reception in any environmental co nditions.

F. Power Source

The sensor node consumes power for sensing, communicating and data

processing. More energy is required for data communication than any other process. Power is stored either in batteries or capacitors. Batteries, both rechargeable and non-re chargeable, are the main source of power supply for sensor nodes. Current sensors are able to renew their energy from solar sources, temperature differences, or vibration. Two power saving policies used are Dynamic Power Management (DPM) and Dynamic Voltag e Scaling (DVS). DPM conserves power by shutting down parts of the sensor node which are not currently used or active. A DVS scheme varies the power levels within the senso r node depending on the non-deterministic workload. By varying the voltage along with th e frequency, it is possible to obtain quadratic reduction in power consumption.

G. Tmote Sky

Tmote Sky is an ultra low power wireless module for use in sensor networks,

monitoring applications, and rapid application prototyping. Tmote Sky leverages indu stry standards like USB and IEEE802.15.4 to interoperate seamlessly with other devices. By using industry standards, integrating humidity, temperature, and light sensors, and pr oviding flexible interconnection with peripherals, Tmote Sky enables a wide range of mes h network applications [4]. The TMote is one of the most commonly used motes in wirele ss sensor technology. Any type of sensor can be used in combination with this type of mo te.

Tmote Sky features the Chipcon CC2420 radio for wireless communications. The CC2420 is an IEEE 802.15.4 compliant radio providing the PHY and some MAC function s [5]. With sensitivity exceeding the IEEE 802.15.4 specification and low power operation, the CC2420 provides reliable wireless communication. The CC2420 is highly configurabl e for many applications with the default radio settings providing IEEE 802.15.4 complianc e. ZigBee specifications can be implemented using the built-in wireless transmitter in the Tmote Sky.

H. Tmote Key Features

? 250kbps 2.4GHz IEEE 802.15.4 Chipcon Wireless Transceiver

? Interoperability with other IEEE 802.15.4 devices.

? 8MHz Texas Instruments MSP430 microcontroller (10k RAM, 48k Flash Memory) ? Integrated ADC, DAC, Supply Voltage Supervisor, and DMA Controller ? Integrate d onboard antenna with 50m range indoors / 125m range outdoors ? Integrated Humidity , Temperature, and Light sensors ? Ultra low current consumption ? Fast wakeup from

sleep (<6μs)

? Hardware link-layer encryption and authentication ? Programming and data collec tion via USB

? 16-pin expansion support and optional SMA antenna connector

? TinyOS support : mesh networking and communication implementation ? Compli es with FCC Part 15 and Industry Canada regulations ? Environmentally friendly – compl ies with RoHS regulations [4].

I. ZigBee Wireless Technology

ZigBee is a specification for a suite of high level communication protocols using small, low-power digital radios based on an IEEE 802.15.4 standard for personal ar ea networks [6] [7]. ZigBee devices are often used in mesh network form to transmit data over longer distances, passing data through intermediate devices to reach more distant o nes.This allows ZigBee networks to be formed ad-hoc, with no centralized control or high -power transmitter/receiver able to reach all of the devices. Any ZigBee device can be tas ked with running the network. ZigBee is targeted at applications that require a low data ra te, long battery life, and secure networking. ZigBee has a defined rate of 250kbps, best s uited for periodic or intermittent data or a single signal transmission

from a sensor or input device. Applications include wireless light switches, electrical meters with in-home-displays, traffic management systems, and other consumer and ind ustrial equipment that requires short-range wireless transfer of data at relatively low rates . The technology defined by the ZigBee specification is intended to be simpler and less e xpensive than other WPANs, such as Bluetooth.

J. Types of ZigBee Devices ZigBee devices are of three types:

ZigBee Coordinator (ZC): The most capable device, the Coordinator forms the root of the network tree and might bridge to other networks. There is exactly one Zig Bee Coordinator in each network since it is the device that started the network originally. It stores information about the network, including acting as the Trust Center & repository for security keys. The ZigBee Coordinator the central controller is in this system.

ZigBee Router (ZR): In addition to running an application function, a device can act as an intermediate router, passing on data from other devices.

ZigBee End Device (ZED): It contains just enough functionality to talk to the

parent node. It cannot relay data from other devices. This relationship allows the no de to be asleep a significant amount of the time thereby giving long battery life. A ZED re quires the least amount of memory, and therefore can be less expensive to manufacture t han a ZR or ZC.

K. ZigBee Protocols

The protocols build on recent algorithmic research to automatically construct a low-s peed ad-hoc network of nodes. In most large network instances, the network will be a clu ster of clusters. It can also form a mesh or a single cluster. The current ZigBee protocols support beacon and non-beacon enabled networks. In non-beacon-enabled networks, an un-slotted CSMA/CA channel access mechanism is used. In this type of network, ZigBee Routers typically have their receivers continuously active, requiring a more robust power supply. However, this allows for heterogeneous networks in which some devices receive continuously, while others only transmit when an external stimulus is detected. In beacon

-enabled networks, the special network nodes called ZigBee Routers transmit periodic be acons to confirm their presence to other network nodes. Nodes may sleep between beac ons, thus lowering their duty cycle and extending their battery life. Beacon intervals depe nd on data rate; they may range from 15.36ms to 251.65824s at 250 kbps. In general, th e ZigBee protocols minimize the time the radio is on, so as to reduce power use. In beac oning networks, nodes only need to be active while a beacon is being transmitted. In non -beacon-enabled networks, power consumption is decidedly asymmetrical: some devices are always active, while others spend most of their time sleeping.

V. PROPOSED ALGORITHM A. Basic Algorithm

Consider a left side driving system (followed in UK, Australia, India, Malaysia and 72 other countries). This system can be modified for right side driving system (USA, Canada , UAE, Russia etc.) quite easily. Also consider a junction of four roads numbered as node 1, 2, 3 and 4 respectively. Traffic flows from each node to three other nodes with varied densities. Consider road 1 now given green signal in all directions.

1)Free left turn for all roads (free right for right side driving system).

2)Check densities at all other nodes and retrieve data from strip sensors.

3) Compare the data and compute the highest density.

4) Allow the node with highest density for 60sec.

5)Allowed node waits for 1 time slot for its turn again and the process is repeated f rom step 3.

B. Advanced Algorithm

Assume road three is currently given green to all directions. All left turns are always f ree. No signals/sensors for left lane. Each road is given a time slot of maximum 60 secon ds at a time. This time can be varied depending on the situation of implementation. Consi der 4 levels of sensors Ax, Bx, Cx, Dx with A having highest priority and x representing ro

ads 1 to 4. Also consider 3 lanes of traffic: Left (L), Middle (M) and Right(R) correspondin g to the direction of traffic. Since left

turn is free, Left lanes do not require sensors. So sensors form 4x2 arrays with 4 levels of traffic and 2 lanes and are named MAx, RAx, MBx, RBx and so on and totally 32 sensor s are employed.The following flow represents the sequence of operation done by the sign al.

1) Each sensor transmits the status periodically to the controller. 2) Controller recei ves the signals and computes the following

3) The sensors Ax from each road having highest priority are compared. 4) If a sin gle road has traffic till Ax, it is given green signal in the next time slot. 5) If multiple road s have traffic till Ax, the road waiting for the longest duration is given the green.

6) Once a road is given green, its waiting time is reset and its sensor status is negle cted for that time slot

7) If traffic in middle lane, green is given for straight direction, based on traffic, either right side neighbor is given green for right direction, of opposite road is give green for str aight direction.

8) If traffic in right lane, green is given for right, and based on traffic, left side neighb or is given green for straight or opposite is given green for right.

9) Similar smart decisions are incorporated in the signal based on traffic density and directional traffic can be controlled.

C. Implementation and Restrictions

This system can be implemented by just placing the sensor nodes beneath the road or on lane divider and interfacing the central controller to the existing signal lights and co nnecting the sensor nodes to the controller via the proposed wireless protocol. The only r estriction for implementing the system is taking the pedestrians into consideration. This h as to be visualized for junctions with heavy traffic such as highway intersections and amo unt of pedestrians is very less. Also major intersections have underground or overhead fo otpaths to avoid interaction of pedestrians with heavy traffic.

ACKNOWLEDGMENT

The Authors would like to take this opportunity to thank Ms. P. Sasikala, Assistant Pr ofessor, ECE department, Sri Venkateswara College of Engineering, Sriperumbudur, wh o gave the basic insight into the field of Wireless Sensor Networks. We also thank Mrs. G . Padmavathi, Associate Professor, ECE department, Sri Venkateswara College of Engin eering, Sriperumbudur, who with her expertise in the field of networks advised and guide d on practicality of the concept and provided helpful ideas for future modifications. We als o express our gratitude to Dr. S. Ganesh Vaidyanathan, Head of the department of ECE, Sri Venkateswara College of Engineering, Sriperumbudur, who supports us for every inn ovative project and encourages us “think beyond” for better use of technology. And finall y we express our heart filled gratitude to Sri Venkateswara College of Engineering, which has been the knowledge house for our education and introduced us to the field of Engine ering and supports us for working on various academic projects.

Adaptive urban traffic control

Adaptive signal control systems must have a capability to optimise the traffic flow by adjusting the traffic signals based on current traffic. All used traffic signal control methods are based on feed-back algorithms using traffic demand data -varying from years to a co uple of minutes - in the past. Current adaptive systems often operate on the basis of ada ptive green phases and flexible co-ordination in (sub)networks based on measured traffic conditions (e.g., UTOPIA-spot,SCOOT). These methods are still not optimal where traffic demand changes rapidly within a short time interval. The basic premise is that existing si gnal plan generation tools make rational decisions about signal plans under varying condi tions; but almost none of the current available tools behave pro-actively or have meta-rul es that may change behaviour of the controller incorporated into the system. The next log ical step for traffic control is the inclusion of these meta-rules and pro active and goal-orie nted behaviour. The key aspects of improved control, for which contributions from artificia l intelligence and artificial intelligent agents can be expected, include the capability of dea ling with conflicting objectives; the capability of making pro-active decisions on the basis of temporal analysis; the ability of managing, learning, self adjusting and responding to n on-recurrent and unexpected events (Ambrosino et al.., 1994).

What are intelligent agents

Agent technology is a new concept within the artificial intelligence (AI). The agent pa radigm in AI is based upon the notion of reactive, autonomous, internally-motivated entiti es that inhabit dynamic, not necessarily fully predictable environments (Weiss, 1999). Aut onomy is the ability to function as an independent unit over an extended period of time, p

erforming a variety of actions necessary to achieve pre-designated objectives while respo nding to stimuli produced by integrally contained sensors (Ziegler, 1990). Multi-Agent Sys tems can be characterised by the interaction of many agents trying to solve a variety of pr oblems in a co-operative fashion. Besides AI, intelligent agents should have some additio nal attributes to solve problems by itself in real-time; understand information; have goals and intentions; draw distinctions between situations; generalise; synthesise new concept s and / or ideas; model the world they operate in and plan and predict consequences of a ctions and evaluate alternatives. The problem solving component of an intelligent agent c an be a rule-based system but can also be a neural network or a fuzzy expert system. It may be obvious that finding a feasible solution is a necessity for an agent. Often local opt ima in decentralised systems, are not the global optimum. This problem is not easily solv ed. The solution has to be found by tailoring the interaction mechanism or to have a supe rvising agent co-ordinating the optimisation process of the other agents.

Intelligent agents in UTC,a helpful paradigm

Agent technology is applicable in different fields within UTC. The ones most importa nt mentioning are: information agents, agents for traffic simulation and traffic control. Curr ently, most applications of intelligent agents are information agents. They collect informati on via a network. With special designed agents user specific information can be provided . In urban traffic these intelligent agents are useable in delivering information about weath er, traffic jams, public transport, route closures, best routes, etc. to the user via a Person al Travel Assistant. Agent technology can also be used for aggregating data for further di stribution. Agents and multi agent systems are capable of simulating complex systems fo

r traffic simulation. These systems often use one agent for every traffic participant (in a si milar way as object oriented programs often use objects). The application of agents in (Ur ban) Traffic Control is the one that has our prime interest. Here we ultimately want to use agents for pro-active traffic light control with on-line optimisation. Signal plans then will be determined based on predicted and measured detector data and will be tuned with adjoi ning agents. The most promising aspects of agent technology, the flexibility and pro-activ e behaviour, give UTC the possibility of better anticipation of traffic. Current UTC is not th at flexible, it is unable to adjust itself if situations change and can't handle un-programme d situations. Agent technology can also be implemented on several different control layer s. This gives the advantage of being close to current UTC while leaving considerable free dom at the lower (intersection) level.

Designing agent based urban traffic control systems

The ideal system that we strive for is a traffic control system that is based on actuate d traffic controllers and is able to pro actively handle traffic situations and handling the diff erent, sometimes conflicting, aims of traffic controllers. The proposed use of the concept of agents in this research is experimental.

Assumptions and considerations on agent based urban traffic control

There are three aspects where agent based traffic control and -management can im prove current state of the art UTC systems:

- Adaptability. Intelligent agents are able to adapt its behaviour and can learn from e arlier situations.

- Communication. Communication makes it possible for agents to co-operate and tun

e signal plans.

- Pro-active behaviour. Due to the pro active behaviour traffic control systems are abl e to plan ahead.

To be acceptable as replacement unit for current traffic control units, the system sho uld perform the same or better than current systems. The agent based UTC will require o n-line and pro-active reaction on changing traffic patterns. An agent based UTC should b e demand responsive as well as adaptive during all stages and times. New methods for tr affic control and traffic prediction should be developed as current ones do not suffice and cannot be used in agent technology. The adaptability can also be divided in several differ ent time scales where the system may need to handle in a different way (Rogier, 1999): - gradual changes due to changing traffic volumes over a longer period of time, - abr upt changes due to changing traffic volumes over a longer period of time, - abrupt, temporal, changes due to changing traffic volumes over a short period of ti me,

- abrupt, temporal, changes due to prioritised traffic over a short period of time

One way of handling the balance between performance and complexity is the use of a hierarchical system layout. We propose a hierarchy of agents where every agent is res ponsible for its own optimal solution, but may not only be influenced by adjoining agents but also via higher level agents. These agents have the task of solving conflicts between l ower level agents that they can't solve. This represents current traffic control implementat ions and idea's. One final aspect to be mentioned is the robustness of agent based syste ms (if all communication fails the agent runs on, if the agent fails a fixed program can be

十字路口交通信号灯控制系统设计文献综述

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目的地，由计算机软件按最少的路线等的计算。人工线路设计是由驾驶者根据自己的目的地设计起点、终点和途经点等，自动建立路库。线路规划完毕后，显示器能够在电子地图上显示设计线路，并同时显示汽车运行路径和运行方法。 ·信息查询 为用户提供主要目标，如旅游景点、宾馆、医院等数据库，用户能够在电子地图上根据需要进行查询。查询资料可以文字、语言及图像的形式显示，并在电子地图上显示其位置。同时，监测中心要利用监测控制台 对区域内的任意目标所在位置进行查询，车辆信息将以数字形式在控制中心的电子地图上显示出来。 ·话务指挥 指挥中心可以监测区域内车辆运行状况，对被监控车辆进行合理调度。指挥中心也可随时与被跟踪目标通话，实时管理。 ·紧急援助 通过GPS定位和监控管理系统可以对遇有险情或发生事故的车辆进行紧急援助。监控台的电子地图显示求助信息和报警目标，规划最优援助方案，并以报警声光提醒值班人员进行应急处理。 GPS技术在汽车导航和交通管理工程中的研究与应用目前在中国刚刚起步，而国外在这方面的研究早已开始并取得了一定的成果。加拿大卡尔加里大学设计了一种动态定位系统，该系统包括一台联式惯性系统，两台GPS接收机和一台微机，可测定已有道路的线形参数，

机械类毕业设计外文文献翻译

沈阳工业大学工程学院 毕业设计（论文）外文翻译 毕业设计（论文）题目：工具盒盖注塑模具设计 外文题目：Friction , Lubrication of Bearing 译文题目：轴承的摩擦与润滑 系（部）：机械系 专业班级：机械设计制造及其自动化0801 学生姓名：王宝帅 指导教师：魏晓波 2010年10 月15 日

外文文献原文： Friction , Lubrication of Bearing In many of the problem thus far , the student has been asked to disregard or neglect friction . Actually , friction is present to some degree whenever two parts are in contact and move on each other. The term friction refers to the resistance of two or more parts to movement. Friction is harmful or valuable depending upon where it occurs. friction is necessary for fastening devices such as screws and rivets which depend upon friction to hold the fastener and the parts together. Belt drivers, brakes, and tires are additional applications where friction is necessary. The friction of moving parts in a machine is harmful because it reduces the mechanical advantage of the device. The heat produced by friction is lost energy because no work takes place. Also , greater power is required to overcome the increased friction. Heat is destructive in that it causes expansion. Expansion may cause a bearing or sliding surface to fit tighter. If a great enough pressure builds up because made from low temperature materials may melt. There are three types of friction which must be overcome in moving parts: (1)starting, (2)sliding, and(3)rolling. Starting friction is the friction between two solids that tend to resist movement. When two parts are at a state of rest, the surface irregularities of both parts tend to interlock and form a wedging action. To produce motion in these parts, the wedge-shaped peaks and valleys of the stationary surfaces must be made to slide out and over each other. The rougher the two surfaces, the greater is starting friction resulting from their movement . Since there is usually no fixed pattern between the peaks and valleys of two mating parts, the irregularities do not interlock once the parts are in motion but slide over each other. The friction of the two surfaces is known as sliding friction. As shown in figure ,starting friction is always greater than sliding friction . Rolling friction occurs when roller devces are subjected to tremendous stress which cause the parts to change shape or deform. Under these conditions, the material in front of a roller tends to pile up and forces the object to roll slightly uphill. This changing of shape , known as deformation, causes a movement of molecules. As a result ,heat is produced from the added energy required to keep the parts turning and overcome friction. The friction caused by the wedging action of surface irregularities can be overcome

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