As an efficient business process execution language which supports web services, BPEL4WS is widely supported by the academic and the industrial circles. According to the shortcomings such as number of computer terms, ...
As an efficient business process execution language which supports web services, BPEL4WS is widely supported by the academic and the industrial circles. According to the shortcomings such as number of computer terms, abstract model definition and the complex description of people activity, this paper presents easy-to-use BPEL4WS modeling method and tool which encapsulates computer terms and convert business models to BPEL4WS models directly. In comparison to the other modeling methods, our method cut down the number of modeling elements by more than 85 percent and save the modeling time by 80 percent and accelerate model running by more than 40 percent. Thus it is more appropriate for popularization.
The ACP (Artificial societies, Computational experiments and Parallel execution) approach has provided us an opportunity to look into new methods in addressing transportation problems from new perspectives. In this pa...
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The ACP (Artificial societies, Computational experiments and Parallel execution) approach has provided us an opportunity to look into new methods in addressing transportation problems from new perspectives. In this paper, we present our works and results of applying ACP approach in modeling and analyzing transportation system, especially carrying out computational experiments based on artificial transportation systems. Two aspects in the modeling process are analyzed. The first is growing artificial transportation system from bottom up using agent-based technologies. The second is modeling environment impacts in simple-is-consistent principle. Finally, two computational experiments are carried out on one specific ATS, Jinan ATS, and numerical results are presented to illustrate the applications of our method.
Traffic congestion leads to problems like delays, decreasing flow rate, and higher fuel consumption. Consequently, keeping traffic moving as efficiently as possible is not only important to economy but also important ...
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Traffic congestion leads to problems like delays, decreasing flow rate, and higher fuel consumption. Consequently, keeping traffic moving as efficiently as possible is not only important to economy but also important to environment. Traffic system is a large complex nonlinear stochastic system. Traditional mathematical methods have some limitations when they are applied in traffic control. Thus, computational intelligence (CI) technologies gain more and more attentions. Neural Networks (NNs) is a well developed CI technology with lots of promising applications in traffic signal control (TSC). In this paper, a neural network (NN) based signal controller is designed to control the traffic lights in an urban traffic road network. Scenarios of simulation are conducted under a microscopic traffic simulation software. Several criterions are collected. Results demonstrate that through online reinforcement training the controllers obtain better control effects than the widely used pre-time and actuated methods under various traffic conditions.
With the advantage of simulating the details of a transportation system, the “microsimulation” of a traffic system has long been a hot topic in the intelligent Transportation systems (ITS) research. The Cellular Aut...
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With the advantage of simulating the details of a transportation system, the “microsimulation” of a traffic system has long been a hot topic in the intelligent Transportation systems (ITS) research. The Cellular Automata (CA) and the Multi-Agent System (MAS) modeling are two typical methods for the traffic microsimulation. However, the computing burden for the microsimulation and the optimization based on it is usually very heavy. In recent years the Graphics Processing Units (GPUs) have been applied successfully in many areas for parallel computing. Compared with the traditional CPU cluster, GPU has an obvious advantage of low cost of hardware and electricity consumption. In this paper we build an MAS model for a road network of four signalized intersections and we use a Genetic Algorithm (GA) to optimize the traffic signal timing with the objective of maximizing the number of the vehicles leaving the network in a given period of time. Both the simulation and the optimization are accelerated by GPU and a speedup by a factor of 195 is obtained. In the future we will extend the work to large scale road networks.
This paper presents a generic video vehicle detection approach through multiple background-based features and statistical learning. The main idea is to configure several virtual loops (as detection zones) on the image...
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This paper presents a generic video vehicle detection approach through multiple background-based features and statistical learning. The main idea is to configure several virtual loops (as detection zones) on the image, assuming moving vehicles may cause pixel intensities and local texture to change, and then by identifying such pixel changes to detect vehicles. In this research, multiple pattern classifiers including LDA + Adaboost, SVM, and Random Forests are used to detect vehicles that are passing through virtual loops. We extract fourteen pattern features (related to foreground area, texture change, and luminance and contrast in the local virtual loop zone and the global image) to train pattern classifiers and then detect vehicles. As experimental results illustrate, the proposed approach is quite robust to detect vehicles under complex dynamic environments, and thus is able to improve the accuracy of traffic data collection in all weather for long term.
Wireless sensor networks consist of a large number of sensor nodes that have low power and limited transmission range and can be used in various scenario. The nodes can be deployed in the long and narrow region, such ...
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Wireless sensor networks consist of a large number of sensor nodes that have low power and limited transmission range and can be used in various scenario. The nodes can be deployed in the long and narrow region, such as road, bridge, tunnel and pipeline, to get some interesting information. The linear topology of these network application is different other application and have special feature, such as multi-hop, long delay, long distance and low reliability. This paper introduces the concept of linear wireless sensor networks and discusses the classification of topology and key issue of this network. The application of the linear wireless sensor network, such as road, bridge, tunnel and pipeline is presented. The research challenges are discussed at last in this paper.
Most researches of traffic incident auto-detection are based on the data from fixed detectors, which are limited by costs and position. In order to resolve this problem, existing algorithms of traffic incident automat...
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Most researches of traffic incident auto-detection are based on the data from fixed detectors, which are limited by costs and position. In order to resolve this problem, existing algorithms of traffic incident automatic detection are analyzed and compared, and an algorithm of traffic incident auto-detection are provided based on mobile-detection technology. The traffic data are grouped in 5-min intervals, analyzed by a three-layer BP neural network, and utilized for traffic incident detection. 16 traffic incidents of different locations and different levels are modeled in the simulation experiment based on VISSIM, and detection rate, false alarm rate and average detection time are adopted as indicators to evaluate the algorithm. Finally, the algorithm is proved to be effective and applicable in practice.
This paper presents an adaptive robust dynamic surface control (ARDSC) algorithm for the position control of DC torque motors which are modeled as third-order nonlinear systems with parametric and nonlinear uncertaint...
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The paper presents a target tracking algorithm which is based on the mean shift algorithm and the normalized moment of inertia (NMI) feature, because the result of the moving target tracking in the air is not satisfac...
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A variable structure neural network adaptive robust control (VSNNARC) is proposed for a class of uncertain nonlinear SISO systems. A neural network which is adjusted by using nodes activation and passivation technique...
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