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.
Two humanoid robots are used to play table tennis with each other. For each humanoid robot, three cameras and a computer are equipped to form a stereovision system and a monocular vision system. The stereovision syste...
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Two humanoid robots are used to play table tennis with each other. For each humanoid robot, three cameras and a computer are equipped to form a stereovision system and a monocular vision system. The stereovision system consisting of two smart cameras and a computer measures the 3-dimensional position of the table tennis ball. It adopts parallel processing mode in order to realize hundred frames level measurement per second. A high-speed digital camera and the computer compose the monocular vision system, which measures the pose of the robot relative to the table via a color mark attached on the robot. The two smart cameras in each stereovision system are synchronized via I/O signals. The vision systems for the two robots are synchronized by time verification. Experimental results verify the effectiveness of the designed vision system and the proposed methods.
Learning control has been an active topic of research for several decades, and is of theoretical, as well as practical, significance. Current theories and developments in learning control are discussed. Following ...
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Learning control has been an active topic of research for several decades, and is of theoretical, as well as practical, significance. Current theories and developments in learning control are discussed. Following a brief introduction of the state as well as new progress on learning control, we give a detail review on the models and algorithms of the control policies developed recently which proved to be advantageous over previous approaches through experimental results. The related results and properties are presented. Then, several potentially developmental topics that are valuable to be further investigated are suggested. Finally, the conclusion remark is proposed.
The present work focuses on the node deployment algorithm of Wireless Sensor Networks. The Central Voronoi Tessellation algorithm is employed to optimize the node position. The energy consumption of the whole sensor n...
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The present work focuses on the node deployment algorithm of Wireless Sensor Networks. The Central Voronoi Tessellation algorithm is employed to optimize the node position. The energy consumption of the whole sensor network will be minimized by using this algorithm. Simulation of the proposed algorithm shows the effectiveness of minimizing the energy consumption.
An analytic flying model that can well represent the physical behavior is derived, where the ball's self-rotational velocity changes along with the flying velocity. Based on the least square method, a rebound mode...
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An analytic flying model that can well represent the physical behavior is derived, where the ball's self-rotational velocity changes along with the flying velocity. Based on the least square method, a rebound model that represents the relation between the velocities before and after rebound is established. The initial trajectory is fitted to three second order polynomials of the flying time with the measured positions of the ball. The initial velocities of the ball in the analytic flying model, including the flying velocity and the self-rotational velocity, are computed from the polynomials. The ball's landing position and velocity is predicted with the model. The velocities after rebound are determined with the rebound model. By taking the velocities after rebound as new initial ones, the flying trajectory after rebound is described with the model again. In other words, the ball's trajectory is predicted. Experimental results verify the effectiveness of the proposed method.
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.
Video-based traffic flow monitoring is a fast emerging field based on the continuous development of computer vision. A survey of the state-of-the-art video processing techniques in traffic flow monitoring is presented...
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Video-based traffic flow monitoring is a fast emerging field based on the continuous development of computer vision. A survey of the state-of-the-art video processing techniques in traffic flow monitoring is presented in this paper. Firstly, vehicle detection is the first step of video processing and detection methods are classified into background modeling based methods and non-background modeling based methods. In particular, nighttime detection is more challenging due to bad illumination and sensitivity to light. Then tracking techniques, including 3D model-based, region-based, active contour-based and feature-based tracking, are presented. A variety of algorithms including MeanShift algorithm, Kalman Filter and Particle Filter are applied in tracking process. In addition, shadow detection and vehicles occlusion bring much trouble into vehicle detection, tracking and so on. Based on the aforementioned video processing techniques, discussion on behavior understanding including traffic incident detection is carried out. Finally, key challenges in traffic flow monitoring are discussed.
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