Top PDF Engineering design synthesis of sensor and control systems for intelligent vehicles

Engineering design synthesis of sensor and control systems for intelligent vehicles

Engineering design synthesis of sensor and control systems for intelligent vehicles

A formal engineering design synthesis methodology based on evolutionary computation is presented, with special emphasis on dealing with modern engineering design challenges, such as high or vari- able complexity of design solutions, multiple conflicting design objectives, and noisy evaluation results, etc., which are common to encounter when design and optimization of distributed control systems such as intelligent vehicles are considered. The efficacy of the evolutionary design synthesis method is validated through multiple different case studies. In the sensor configuration case study, a variety of novel design solutions are generated using fuzzy fitness functions with different weights and trade-off strategies selected by the human designer to reflect different engineering design trade- offs made on multiple performance measures. In the neural controller evolution case study, it is shown that the various evolved neural network controllers can achieve performances comparable to, if not better than, that of a hand-coded rule-based controller in the same simplified environment. More importantly, this automatic design synthesis method shows great potential to handle more complex design problems with a large number of design variables and multi-modal noise involved, where a good hand-coded solution may be very difficult or even impossible to obtain. In summary, the evolutionary design synthesis methodology appears promising to
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From Sensor to Street – Intelligent Vehicle Control Systems

From Sensor to Street – Intelligent Vehicle Control Systems

The next challenges that engineers have to face include the transition from the current trans- portation system to an intelligent vehicle-based transportation system. Current infrastructure may require upgrades to enable full integration of intelligent vehicles, while remaining useful to conventional vehicles. Engineers will soon be confronted with the economic, environmental, and societal challenges that are inherent with intelligent vehicle design, as current driving paradigms will likely have to change with tech- nology. These transitional challenges will present unique opportunities to increase both the safety and efficiency of road networks. This research delimits at these issues; however, this field presents an excellent opportunity for investiga- tion from multiple angles.
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INTELLIGENT TRAFFIC CONTROL SYSTEM FOR URBAN AREAS USING WIRELESS SENSOR NETWORKS AND GSM WITHOUT DIRECT COMMUNICATION WITH ON ROAD VEHICLES

INTELLIGENT TRAFFIC CONTROL SYSTEM FOR URBAN AREAS USING WIRELESS SENSOR NETWORKS AND GSM WITHOUT DIRECT COMMUNICATION WITH ON ROAD VEHICLES

The continuous increase in the congestion level on public roads, especially at rush hours, is a critical problem in many countries and is becoming a major concern to transportation specialists and decision makers. The existing methods for traffic management, surveillance and control are not adequately efficient in terms of the performance, cost, and the effort needed for maintenance and support. For example, The 2007 Urban Mobility Report estimates total annual cost of congestion for the 75 U.S. urban areas at 89.6 billion dollars, the value of 4.5 billion hours of delay and 6.9 billion gallons of excess fuel consumed. On smaller scale, the traffic- engineering department in Jordan estimates that the total cost due to congestion in the year 2007 was around 150 million USDs [1]. As such, there is a need for efficient solutions to this critical and important problem. Many techniques have been used including, aboveground sensors like video image processing, microwave radar, laser radar, passive infrared, ultrasonic, and passive acoustic array. However, these systems have a high equipment cost and their accuracy depends on environment conditions [2]. Another widely used technique in conventional traffic surveillance systems is based on intrusive and non-intrusive sensors with inductive loop detectors, micro-loop probes, and pneumatic road tubes in addition to video cameras for the efficient management of public roads [3, 4]. However, intrusive sensors may cause disruption of traffic upon installation and repair, and may result in a high installation and maintenance cost. On the other hand, non-intrusive sensors tend to be large size, power hungry, and affected by the road and weather conditions; thus resulting in degraded efficiency in controlling the traffic flow. As such, it is becoming very crucial to device efficient, adaptive and cost-effective traffic control algorithms that facilitate and guarantee fast and smooth traffic flow that utilize new and versatile technologies. An excellent potential candidate to aid on achieving this objective is the Wireless Sensor Network (WSN) [5]. Many studies suggested the use of WSN technology for traffic control [4, 6, 7, 9- 11]. In [7], a dynamic vehicle detection method and a signal control algorithm to control the state of the signal light in a road intersection using the WSN technology was proposed. In [11], energy efficient protocols that can be used to improve traffic safety using WSN were proposed and used to implement an intelligent traffic management system. In [10], Inter-vehicle communication scheme between neighboring vehicles and in the absence of a central base station (BS) was proposed.
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Design Of Synchronic Generator And Control System For Hybrid Vehicles

Design Of Synchronic Generator And Control System For Hybrid Vehicles

In this method the flowchart diagram is used for to define the system states. In each flowchart set of rules and conditions that are associated with each state of the system is defined. To connection each flowchart with next Flowchart, we can use the set of rules those defined for system. This control method is one of the easiest and most accurate method of control which is in simplicity is able to control complex systems and applied appropriate outputs as inputs to the control system. In this method it is possible to draw desired diagram and flowchart and defined the rules relating to each of them, put state of plant into the desired position and generates an input control signal and applied to relevant plant [7-5]. In the following the description of the control that used in this study is presented. Figure 16 shows a schematic of control between components and heart of control systems.
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Intelligent Irrigation Control System by Employing Wireless Sensor Networks

Intelligent Irrigation Control System by Employing Wireless Sensor Networks

Fast growing technology of wireless sensor networks (WSN) is an advantage to the agriculture sector. Modern advances in the development of WSN offer new trends like Precision Agriculture (PA). This paper presents Fuzzy Logic Based Intelligent Irrigation Control System by employing WSN for PA. In the proposed system the irrigation controller normalizes the desired moisture level in the agricultural soil by controlling the water flow of the irrigation pump based on the sensor readings, by switching the pump between ON and OFF states. For the efficient utilization of water in the agricultural irrigation, the proposed methodology offers a system equipped with soil moisture sensors, temperature sensors, precise irrigation equipments, computer-controlled devices, and an intelligent controller using fuzzy logic approach for irrigation of agricultural fields, which simulates or emulates the human being’s intelligence. This leads to effective utilization of various resources like water and electricity and hence becomes a cost effective system for the expected yield. The fuzzy controller designed in this paper efficiently monitors moisture level in soil, leaf wetness, temperature, humidity, and other essential parameters and also controls the irrigation intelligently. The results show that the application is correct and reasonable and enables user to precisely acquire the crop water requirement information. The system can be effectively applied to PA applications such as water-saving agriculture areas, for example, the croplands, the nursery gardens and the greenhouses.
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A Review on Mobile and Sensor Networks Innovations in Intelligent Transportation Systems

A Review on Mobile and Sensor Networks Innovations in Intelligent Transportation Systems

Selvarajah et al. [20] talk about heterogeneous wireless sensor network for transportation system applications. In their paper, WSN applications in ITS, the transport systems and the use middleware to integrate heterogene- ous wireless cooperative objects (WICOs) are discussed. They describe the EMMA project and its hierarchical approach and communication technologies. An overview of wireless sensor networks middleware and compo- nents of the EMMA middleware is also presented. They describe the different hardware platforms which are used as wireless cooperating objects in the prototype applications. Three applications for each hierarchical level and an inter-hierarchical level application are given with results obtained from the experiments in investigating the feasibility of utilizing EMMA middleware in real transport system applications. Their network hierarchy is depicted in Figure 3.
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Systemic Design And Control Of Electric Vehicles Power Chain

Systemic Design And Control Of Electric Vehicles Power Chain

In this paper, we present a systemic design method of electric vehicle (EVs) chain power , taking into account several constraints such as the speed limit, the energy saving, the cost of the power chain and the reliability of the whole system. This method is based firstly on the analytical sizing of the power chain, and secondly on the analytical modeling of the electrical parameters of the electric actuator control. It takes into account the compatibility between the components of the power chain to reach the critical level of performance of the global system. This approach is based on the application of the general theorems relating to the design of electrotechnical devices. The global design model provides results relating to the manufacturing of the electric motor, converter and the mechanical transmission system [1]. These results increases the compatibility of this approach with the optimization procedures of EVs performance such as the speed limit, the autonomy, the production cost etc. This study ends with a validation study of the design approach. Indeed, the simulation of the electrical, mechanical and magnetic behavior on a global control model of this chain fully validates the design approach.
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Design of Intelligent Control Medical Nursing Bed

Design of Intelligent Control Medical Nursing Bed

The Whole Frame The working principle of the intelligent control medical nursing bed: After the MCU programming, the push rod motor is controlled to complete the automatic folding of the[r]

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Intelligent planning and control of multi assembly systems

Intelligent planning and control of multi assembly systems

The issue of combining ABC with simulation models was considered by Spedding and Sun (1999). They argued that using a simulation model made it easier to implement ABC on their case study. The importance of their research lies in the fact that using simulation and ABC together provides a more powerful tool than either of them. This can be used for providing more useful management information. Tomberg et al (2002) produced a study on the use of ABC and process modelling in providing cost information for designers. The result of their study suggested that using ABC alongside process modelling was a step forward towards creating better cost-conscious design. Gupta and Galloway (2003) showed how an ABC management system could be used as an information system to support the decision-making processes of different operations (product planning and design, inventory management, capacity management, etc.). They demonstrated how their system enabled the operations manager to increase the quality of the decision-making process. Ozbayrak et al (2004) applied ABC alongside a mathematical and simulation model on a manufacturing system that used either MRP or JIT (push or pull system) in order to estimate the product costs. Their work showed how valuable ABC was as a tool for providing not only more accurate cost information compared to the traditional cost allocation system but also important management information. They concluded that the manufacturing planning and control strategies greatly affected the manufacturing costs and that the pull system provided lower manufacturing costs. Homburg (2004) argued that since ABC allocated overhead costs proportionally it was a heuristic, therefore he used simulations and mixed-integer programming to analyse the extent of the sub-optimality incurred by ABC-heuristics. Homburg argued that previous research used a simple set of cost drivers, which restricted the potential of ABC as a heuristic. Therefore, he analysed the effects of establishing a cost driver corresponding to a higher cost level (e.g. portfolio-based cost driver). This driver was used to allocate the costs to inflexible overhead resources proportionally, which improved the quality of ABC-heuristics significantly. Although Homburg had reservations about generalising the results since the simulations he used were based on simplified scenarios, he argued that the results provided some insight in to the quality of using ABC for decision making.
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A Survey of Intelligent Traffic Light Control Systems

A Survey of Intelligent Traffic Light Control Systems

Emergencies are handled using GSM technique. The authorized emergency vehicles like Police van, ambulance or fire trucks are given priority to cross the intersection first. This is possible after a request is sent through an SMS as it approaches at the intersection. The signal remains on till it crosses the junction and it is put off to resume the normal functioning by sending another SMS. Such an emergency handling can be seen in figure 3. There is a GSM transmitter at the end of the vehicle and a receiver at the intersection. To raise the priority correct lane number to intersection, need to be transmitted. In case if more than one emergency arises, the first come first serve mechanism is used to set the priority.
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Intelligent Control Systems Using Soft Computing.pdf

Intelligent Control Systems Using Soft Computing.pdf

For many decades, it has been a goal of science and engineering to develop intelligent machines with a large number of simple elements. References to this subject can be found in the scientific literature of the 19th century. During the 1940s, researchers desiring to duplicate the function of the human brain, have developed simple hardware (and later software) models of biological neurons and their interaction systems. McCulloch and Pitts [1] published the first systematic study of the artificial neural network. Four years later, the same authors explored network paradigms for pattern recognition using a single layer perceptron [2]. In the 1950s and 1960s, a group of researchers combined these biological and psychological insights to produce the first artificial neural network (ANN) [3,4]. Initially implemented as electronic circuits, they were later converted into a more flexible medium of computer simulation. However, researchers such as Minsky and Papert [5] later challenged these works. They strongly believed that intelligence systems are essentially symbol processing of the kind readily modeled on the Von Neumann computer. For a variety of reasons, the symbolic–processing approach became the dominant method. Moreover, the perceptron as proposed by Rosenblatt turned out to be more limited than first expected. [4]. Although further investigations in ANN continued during the 1970s by several pioneer researchers such as Grossberg, Kohonen, Widrow, and others, their works received relatively less attention. The primary factors for the recent resurgence of interest in the area of neural networks are the extension of Rosenblatt, Widrow and Hoff’s works dealing with learning in a complex, multi-layer network, Hopfield mathematical foundation for understanding the dynamics of an important class of networks, as well as much faster computers than those of 50s and 60s.
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Accelerated Logical Inference in the Intelligent Control Systems

Accelerated Logical Inference in the Intelligent Control Systems

Abstract. Were considered the features of the intellectual approach to the development of complex objects control systems. Was selected the mathematical apparatus of the logical inference theory as a basis for describing the functioning of the inference machine - the main module of intellectual control system. In order to increase the efficiency of this module was offered the deductive method of logical inference by the disjuncts dividing based on defining literals. Was described the procedure of performing logical inference from the formation of the original data to achieving a breakpoint. Process of logical inference by dividing the disjuncts (clauses) on the basis of defining literals is illustrated by an example. Application of this method will reduce the number of vertex of the decision tree that require processing, and thus significantly improve the performance of intelligent control system of complex technical objects.
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Design of intelligent sensor based on BP neural network and ZigBee wireless sensor network

Design of intelligent sensor based on BP neural network and ZigBee wireless sensor network

Wireless sensor network node of the network Zigbee input feature factor node layer neuron number is the system (variable) number; the output layer neuron node number is the number of target. Hidden layer nodes selected by selecting experience, usually set as input layer node number 75%. If the input layer has 7 nodes, 1 node in the output layer, the hidden layer can temporarily set to 5 nodes, which form a 7-5-1 BP neural network model. In the training, practical but also on the hidden layer nodes of different number 4, 5, 6 respectively, finally determine the network structure of the most reasonable. To determine the initial weights, the initial weights are not completely equal to a set of values. Have demonstrated, even determine the existence of a group of non equal the system error smaller weights, if the initial set of Wji values are equal to each other, they will always remain equal in the learning process. Therefore, in the process, we design a random number generator program, a random number generating a set of a 0.5~+0.5, as the initial weights of the network.
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A Low-Power Interface Design for Intelligent Sensor Nodes Utilized in Wireless Sensor Networks

A Low-Power Interface Design for Intelligent Sensor Nodes Utilized in Wireless Sensor Networks

[32] 35 the internal routing of the signals 36 of PSoC after programming the peripheral blocks Figure 5.8: Schematic diagram for converting UART signal to RS232 signals Figure 5.9: Snaps[r]

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QUALITY CONTROL OF ARCHIVED INTELLIGENT TRANSPORTATION SYSTEMS DATA

QUALITY CONTROL OF ARCHIVED INTELLIGENT TRANSPORTATION SYSTEMS DATA

involved extreme value records (speed > 100 mph, volume > 3,000, occupancy > 100 percent). TransGuide officials have indicated that during evening hours, TRF LCUs sometimes produce records with extremely high values, particularly speeds. The flag 2a trend in Fig. 1 confirms that observation, although it also points to other times of the day when extreme value records are also relatively high (e.g., from 6 – 10 AM). To assess the feasibility of the quality control thresholds (100 mph for speed, 3,000 for volume, and 100 per-cent for occupancy rate), the researchers analyzed the speed, volume, and occupancy data distributions of the 3.4 billion 20-second lane records in the database (actually 2.2 billion in the case of speed data records because not all lane records were from speed-trap detectors). Fig. 2 summarizes the results of the analysis. Fig. 2(a) shows that most records were between 1 and 75 mph (typical of freeway driving conditions), with a rapidly de-creasing trend between 75 and 93 mph, and a few lingering records above 93 mph. Overall, Fig. 2(a) suggests a reasonable upper speed threshold of about 93 mph (which would translate to about 0.01 percent of records exceeding that threshold).
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A Control Structure for Intelligent Emotion-Oriented eCommerce Systems

A Control Structure for Intelligent Emotion-Oriented eCommerce Systems

III. MATLAB-BASED CONTROL STRUCTURE OF EMOTION-ORIENTED ECOMMERCE SYSTEMS A model of emotion should identify events and objects involved in the interaction process in an eCommerce environment. Emotion models would allow the computer to express emotions at the appropriate time. Appraisal theories have influenced the development of computational models of emotion. Fundamentally, our appraisal of a situation causes an emotional or affective response. Emotion energies prepare the individual to respond and have at least four different aspects – feelings, actions, physiological arousal, and motivational programmes [25]. The importance of appraisal to emotions (primary, secondary, reappraisal) was articulated by Lazarus [26] and later elaborated by Smith and Lazarus [27] who categorized six appraisal components related to primary and secondary appraisals. These categories fall within the primary and secondary appraisals and include motivational congruency (evaluation of goals), motivational relevance (commitment), accountability (assignment of responsibility of blame and praise), problem-coping potentiality (resolvability), emotional-focused potentiality (emotional management of situation), and future expectancy (changeability of situation).
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The Value of Intelligent Vehicles for Marketers and Consumers

The Value of Intelligent Vehicles for Marketers and Consumers

This research aims at reducing the discrepancy between average consumer and industry relating vehicle development. It identifies consumer and automotive manufacturer specifications for Intelligent Vehicles and combines them to desired vehicle attributes enabling value co-creation. Consumers profit from a simpler driving task and assistance, whereas manufacturers benefit from enriched marketing data and market intelligence. To collect information, qualitative interviews with experts from the industry and average consumers have been conducted. The obtained insights were used to apply and modify the ‘Drivability model’, which can contribute to product quality and satisfaction as static benchmark in a dynamic environment. The results point out, that manufacturers need to increase customer trust in Artificial Intelligence and must be transparent in their operations. Managers can combine web-visitor data and Intelligent Vehicle data towards valuable targeting information, which is likely to increase sales and profit. Automotive value chain actors should prepare to invest in augmented analytics, because otherwise human resources needed for repetitive analysis might jeopardize the benefits gained by big data. Automotive marketers need to think about proper value communication strategies for intelligent vehicles, if consumers should be convinced that their product is useful and easy to use. The dynamic automotive environment will enable innovative business models and pricing strategies to attract new customer groups and thus, fuel for market share expansion. Consumers actively appreciate connected vehicles with a trustworthy AI as assistant and streaming-based entertainment opportunities. Passively they desire maximized drivability and a technology that is easy to use and easy to learn. They are concerned about whether the cars decisions are ethical and whether their data is secure and confidentially handled.
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Intelligent Recommendation Module for Emergency Vehicles

Intelligent Recommendation Module for Emergency Vehicles

Abstract The traffic congestion and long queuing issues are avoiding emergency vehicles to pass. In addition to increasing number of cars in the street, lack of adequate infrastructure, lack of economic situation in some nations, weather condition, and the purpose of a city can have an impact to the traffic flow. Those reasons may interfere with the enactment of emergency vehicles traveling to their destinations scenes. This paper aim to enhanced moving the emergency vehicles by intelligent recommendation module for emergency vehicles with monitor module and simulating annealing algorithm to provide the appropriate route for emergency vehicles travel along the optimal road it is not necessary to be the shortest one from root node to the goal client. The result shows that the proposed module able to find the optimal path from 5.11s to 8.24s depends on the number of traffic intersections.
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Efficiency of genetic algorithms in intelligent 
		hybrid control systems

Efficiency of genetic algorithms in intelligent hybrid control systems

One of the perspective approaches to the synthesis CO emulators is the use of artificial neural networks (ANN) [19-21]. The CO emulator is called a neuro emulator (NE). As a basis for NE we apply multi- layered perceptron. The task of NE is identification with a desired accuracy of internal dependences and external connections of the CO, i.e. the development of CO identification model [21].

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Engineering Intelligent Racing Concepts Using Design Research Methods

Engineering Intelligent Racing Concepts Using Design Research Methods

DOI: 10.4236/wjet.2018.62026 424 World Journal of Engineering and Technology improves driving ability. Solo drivers explained that to be skilled, one must bea- ble to drive by “ the seat of the pants feel ” . The “ seat of the pants feel ” is driver jargon for one’s driving intuition and ability to naturally respond to feedback from one’s own vehicle, communicated through the driver’s seat pan surface [5]. These natural reactions are the most effective way to navigate unfamiliar Solo tracks; the ability to process information quickly and properly respond instan- taneously is a skill that many drivers have expressed can only be developed through “seat time”—driver jargon for the amount of time spent in the driver’s seat competing in Solo events. Very few drivers possess OBD (on-board diag- nostic) systems to relate brake and throttle position to specific instances on a course as well as gravitational forces on car suspensions. Additionally, OBD sys- tems are thousands of dollars and are unaffordable for most users that compete in Autocross. For those that do make the financial commitment and purchase this software, many have to take classes or workshops, another large financial commitment, to understand the data that the software is presenting.
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