This paper proposes a novel method to evaluate Traffic Signal control System(TSCS) based on Artificial Transportation systems(ATS). Using this method, we can generate travel demand based on individual's activities...
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This paper proposes a novel method to evaluate Traffic Signal control System(TSCS) based on Artificial Transportation systems(ATS). Using this method, we can generate travel demand based on individual's activities and construct artificial traffic scenarios using basic rules. While interacting with ATS, TSCS can be evaluated by observing and analyzing the traffic status that emerged. The cost of the evaluation is decreased as artificial traffic environment can be constructed in the laboratory to substitute real environment and the evaluation can be carried out before the installation on site. Furthermore, as reasonable travel demand can be generated based on individual's activities, the reliability of evaluation results can be guaranteed. Finally, the effectiveness of this method is verified by experiments.
A novel image deblurring method based on high-order non-local range Markov Random Field (NLR-MRF) prior is proposed in the paper. NLR-MRF is an effective statistical framework to model prior knowledge of natural image...
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A novel image deblurring method based on high-order non-local range Markov Random Field (NLR-MRF) prior is proposed in the paper. NLR-MRF is an effective statistical framework to model prior knowledge of natural images which leads to excellent performance in some low-level vision problems. In our work, the framework is extended to image deblurring. To overcome some limitations of maximum a-posteriori (MAP) estimation, we adopt Bayesian minimum mean squared error (MMSE) estimation to perform deblurring. The high-order NLR-MRF prior can be easily integrated into this framework. Then, an efficient Gibbs sampling algorithm is employed to compute MMSE estimation. The proposed method frees the user from determining regularization parameter beforehand, which relies on unknown noise level. Our deblurring method shows superior or comparable results to the state-of-art deblurring methods.
With the fast development of the economy, the urban traffic demands increases rapidly, Bus rapid transit (BRT) system, a new type and high efficient bus operator system and a comprehensive mass transit system between ...
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With the fast development of the economy, the urban traffic demands increases rapidly, Bus rapid transit (BRT) system, a new type and high efficient bus operator system and a comprehensive mass transit system between the metro and regular bus systems, can alleviate traffic congestion, reduce resident traffic cost effectively, and improve transportation quality and efficiency, with its advantages becomes an effective way to improve urban traffic status. In this article, the definition, major elements, advantages, functions and development of BRT are provided, and a new real-time scheduling is given which is going to be applied to the Zhongshan Avenue BRT system in Guangzhou China.
This paper illustrates a compositional deformable model for detecting vehicle and recognizing vehicle-contours. To overcome the difficulties that vehicles in an image have various sizes, shapes, colors and poses, this...
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This paper illustrates a compositional deformable model for detecting vehicle and recognizing vehicle-contours. To overcome the difficulties that vehicles in an image have various sizes, shapes, colors and poses, this model has two main characteristics: first, the model is made up of constituent parts which shared by vehicles. The locality of parts give the model the ability to recognize vehicles with different types (e.g., although vehicles have various sizes and shapes, they are usually composed by roof, windscreen, windows, etc.). Second, the spatial relationships of these parts are represented by Markov Random Field (MRF). The model is deformable to adapt to vehicles of different shapes and poses because of the appropriately changing of combinations of these parts in the MRF. Experimental results with real world images show that this method is effective in vehicle detection and vehicle-contours recognition.
Road traffic load balancing can avoid network congestion and improve traffic efficiency. This paper proposes a method of dynamic route guidance based on Maximum Flow Theory to balance traffic load of road network. A m...
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Road traffic load balancing can avoid network congestion and improve traffic efficiency. This paper proposes a method of dynamic route guidance based on Maximum Flow Theory to balance traffic load of road network. A modified Ford-Fulkerson algorithm is used for searching the optimal route. In addition, the algorithm is implemented by using MapReduce primitives, which introduces Cloud Computing Platform for large-scale traffic network guidance. Computational experimental environment is built by integrating Artificial Transportation systems (ATS) and Hadoop. Results in ATS and performances on Hadoop show that the method proposed can improve the traffic situation effectively.
Radio fingerprint matching localization method promises high localization accuracy but requires extensive infrastructural effort and search operations. This paper proposes a new search strategy for radio fingerprint m...
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Radio fingerprint matching localization method promises high localization accuracy but requires extensive infrastructural effort and search operations. This paper proposes a new search strategy for radio fingerprint matching method, which sharply reduces search operations and shows little effect on localization accuracy. Indoor experiment is conducted to evaluate our method.
It is important for a chemical plant to find a suitable performance appraisal method. In this paper, based on the ACP (artificial system, computational experiment, and parallel execution) theory and the PageRank algor...
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It is important for a chemical plant to find a suitable performance appraisal method. In this paper, based on the ACP (artificial system, computational experiment, and parallel execution) theory and the PageRank algorithm, a new performance appraisal method is proposed. The proposed method comprehensively involves both peoples and routine management rules from the holistic viewpoint. By comparison with the traditional performance appraisal method, the proposed method is more reasonable, more flexible and robuster.
Chemical industry is complex and continuous process industry, and the control and management of the long-term safe operation involves a great deal of information and data on the staffs, management, equipment and techn...
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Chemical industry is complex and continuous process industry, and the control and management of the long-term safe operation involves a great deal of information and data on the staffs, management, equipment and technology. Consequently, making real-time assessment for the control and management by computational experiment is important for the long-term of safety operation of chemical industry. As the requirement for high-performance computing in large-scale complexsystems, traditional CPU computing can not meet the needs of large-scale computing. Because of the high speed and rapid development features, GPU was widely applied to scientific computation, which is suitable for large-scale intensive computation. This paper carried on specific computation experiments using GPU parallel computing, and designed and analyzed matrix inversion, fuzzy logic computation and Monte Carlo computation respectively for staff management and equipment simulation. Finally, compared to CPU serial computing, the maximum speedup ratio, 34, 85 and 39 times of speedup ratio are achieved.
This paper presents a novel closed-loop method for a multilink robotic fish to mimic the C-start maneuver, in which the turning speed and precision are emphasized. The turning speed is maximized by carefully desig...
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This paper presents a novel closed-loop method for a multilink robotic fish to mimic the C-start maneuver, in which the turning speed and precision are emphasized. The turning speed is maximized by carefully designed preparatory stage, and the turning precision is achieved by the feedback of the turning angle from a gyroscope and a new design of the propulsive stage. Different types of C-starts are studied, due to the different sizes of caudal fins, in order to achieve the highest turning angular velocity. All the proposed types of C-starts are experimented and compared using a four-joint robotic fish. The experimental results show a fastest angular velocity up to 200°/s and the distinctions between all these different types.
A novel image deblurring method based on high-order non-local range Markov Random Field (NLR-MRF) prior is proposed in the paper. NLR-MRF provides an effective framework to model the statistical prior of natural image...
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A novel image deblurring method based on high-order non-local range Markov Random Field (NLR-MRF) prior is proposed in the paper. NLR-MRF provides an effective framework to model the statistical prior of natural images and leads to excellent performance in the application of image denoising and inpainting. Moreover, the framework will be extended to image deblurring in our work. Instead of commonly used maximum a-posteriori (MAP) estimation, which has several shortcomings, the high-order NLR-MRF prior is integrated into Bayesian minimum mean squared error (MMSE) estimation framework. Then, an efficient Gibbs sampling algorithm is adopted to compute MMSE estimation. The proposed method frees the user from determining regularization parameter beforehand, which relies on unknown noise level. We perform experiments on synthetic and real-world data to demonstrate the effectiveness of our method. Both quantitatively and qualitatively evaluations show superior or comparable results to the state-of-art deblurring methods.
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