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International Conference on BioMedical Engineering and Informatics (BMEI)

作者： Yujue Wang Bei Wang Xingyu Wang Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

The previous studies on Human-machine Interface (HMI) based on surface electromyogram (SEMG) control seldom considered individual difference. In this paper, we proposed a novel method to distinguish the handedness of subjects, and extract the real-time control signals from SEMG. SEMG signals recorded at left calf, right calf, left shoulder and right shoulder were analyzed. The instantaneous power of SEMG signals were calculated based on Filter technique and short-time Fourier transformation (STFT).The handedness was discriminated to determine the proper threshold for different subjects. The real-time control signals were generated automatically by comparing the instantaneous power and the thresholds preset.

关键词： Muscles Electrocardiography Band pass filters Time frequency analysis Real time systems Robots Wheelchairs

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A novel approach for moving object detection based on improved particle swarm optimization algorithm

A novel approach for moving object detection based on improv...

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International Conference on control and Automation (ICCA)

作者： Jin Yu Xuan Zhou Feng Qian Key Laboratory of Advanced Control and Optimization of Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

In order to extract from the video sequence in a complete and consistent moving target, a novel algorithm for video object segmentation based on improved particle swarm optimization (IPSO) is presented. The algorithm fuses brightness segmentation and color information at `region level', as to make up for conventional `pixel level' approaches. The IPSO is taken into account for spatial segmentation of the video frame, which combines the mixture Gaussian model of temporal framework in achieving better segmentation. Adapting to the real-time video surveillance, the proposed algorithm can speed up the process of image segmentation, and make background modeling accurately to update. Comparisons were performed with other method that the proposed algorithm can detect intact moving objects even when objects appear and disappear suddenly. The experiment across different types of video shows the efficiency and stability of video object segmentation by the novel approach.

关键词： Object detection Particle swarm optimization Image segmentation Object segmentation Data mining Video sequences Fuses Brightness Video surveillance Stability

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Integration of Multi-feature for Moving Target Detection Algorithm

Integration of Multi-feature for Moving Target Detection Alg...

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International Conference on Electrical and control Engineering (ICECE)

作者： Jin Yu Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

Moving object segmentation and marking object area is one of the key technologies of intelligent surveillance. In this paper, combining the background modeling in pixel level, the proposed algorithm presented gradient-based spatial characteristics in order to improve detection accuracy. For the trade-off problem of background modeling, established the velocity characteristics for each property of moving targets, to make online updates of the GMM more adaptable. The results indicate that this technique can improve the efficiency and stability of detection.

关键词： Hidden Markov models Pixel Adaptation model Image segmentation Computational modeling Object detection Computer vision

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Particle swarm optimization algorithm of self-adaptive parameter based on ant system and its application

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Kongzhi Lilun Yu Yingyong/control Theory and Applications 2010年第11期27卷 1479-1488页

作者： Yang, Fan Hu, Chun-Ping Yan, Xue-Feng Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai 200237 China Shanghai Nuclear Engineering Research and Design Institute Shanghai 200233 China

To adjust the inertia weight in particle swarm optimization(PSO), we propose a novel self-adaptive particle swarm optimization algorithm based on ant system(AS-PSO). First, the inertia weight space is divided into several regions;each of them is given the same initial intensity of pheromone trails. The probability for selecting a parameter region for each particle is determined by the intensity of the region pheromone trails and the particle's a priori knowledge of the search space. The evolution search is then performed in spaces of solutions. Finally the trail of the regions is updated according to the information of evolution. Experiments indicate that the promising AS-PSO algorithm realizes the evolution and the self-adaptation of the inertia weight by ant colony algorithm without increasing the function calls in evaluation. Results show that AS-PSO obviously outperforms the original self-adaptive PSO and the APSO-VI, in which the parameter is adjusted according to the velocity information. Furthermore, satisfactory results have been obtained when AS-SPO algorithm is applied to estimate the parameter of complex system models.

关键词： Particle swarm optimization (PSO)

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Early Warning Technology for Cracking Severity Basedon Improved Cultural Differential Algorithm

Early Warning Technology for Cracking Severity Basedon Impro...

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The 8th World Congress on Intelligent control and Automation(第八届智能控制与自动化世界大会 WCICA 2010)

作者： Yunxia Pu Mandan Liu Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai 200237 China

Effective monitoring and early warning for cracking severity are directly related to the ethylene production stability and the overall economic benefits. Elman neural network is used to establish the early warning model of cracking severity, and an improved Cultural Differential Evolution algorithm(ICDE) is proposed to training the model. This hybrid algorithm integrates Cultural algorithm(CA) and Differential Evolution algorithm(DE) together, and increases some measure indicators of evolution direction, to make the algorithm more adaptive. Simulation results show that this method gains accurate warning signals to the cracking working conditions timely and effectively, obtained satisfactory results.

关键词： Cultural differences Artificial neural networks Furnaces Training Next generation networking Predictive models Production

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Estimation of Distribution Algorithm sampling under Gaussian and Cauchy distribution in continuous domain

Estimation of Distribution Algorithm sampling under Gaussian...

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International Conference on control and Automation (ICCA)

作者： Na Luo Feng Qian Automatic Institute Key Laboratory of Advanced Control and Optimization of Chemical Processes Ministry of Education State-Key Laboratory of Chemical Engineering East China University of Science and Technology Shanghai China

Estimation of Distribution Algorithm is a new population based evolutionary optimization method and it generates new population from probability distribution model. Like most evolutionary algorithms, it is easy to trap into local optimums. In order to avoid this shortcoming, Gaussian and Cauchy probability density function are mixed as probability distribution model. For continuous problems, a new estimation of distribution algorithm sampling under the mixed model is presented. New individuals are generated not only from Gaussian distribution but sometimes from Cauchy distribution in order to keep diversity. The selection strategy of Gaussian and Cauchy distribution are also discussed. The new algorithm is tested on five benchmark functions and results are compared with basic and estimation of distribution algorithm with Cauchy mutation.

关键词： Sampling methods Genetic mutations Electronic design automation and methodology Evolutionary computation Laboratories Probability distribution Gaussian distribution Automatic control Chemical processes control engineering education

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Gradient-based immune algorithm for optimization of dynamic environments

Gradient-based immune algorithm for optimization of dynamic ...

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International Conference on Natural Computation (ICNC)

作者： Xuhua Shi Feng Qian Research Institute of Electric Automatic Control NingBo University China Key Laboratory of Advanced Control and Optimization for Chemical Processes Ecust China University of Science and Technology ShangHai China

A novel immune algorithm suitable for dynamic environments (GIDE) is proposed based on a biological immune mechanism. GIDE models the dynamic process of artificial immune response with gradient-based diversity operators. Unlike traditional artificial immune algorithms, which require that randomly generated cells be added to the current population to explore its fitness landscape, GIDE uses a gradient-based diversity operator to speed up optimization in dynamic environments. Other immune algorithms are compared to GIDE by using Moving Peaks Benchmarks. Preliminary experiments showed that GIDE can maintain high population diversity during the search process, while simultaneously speeding up optimization. Thus, GIDE is useful for optimization of dynamic environments.

关键词： Heuristic algorithms optimization Immune system Aerodynamics Algorithm design and analysis Evolutionary computation Vectors

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An immune inspired approach to 802.11 Wireless LANs coverage optimization

An immune inspired approach to 802.11 Wireless LANs coverage...

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International Conference on Information Networking and Automation (ICINA)

作者： Xuan Zhou Rongbin Qi Qian Li Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China School of Electronics and Information University of Tongji Shanghai China

ISBN: (纸本)9781424481040

Coverage optimization is a critical issue in 802.11 Wireless LANs planning problems. In this paper an immune network algorithm named opt-aiNet is studied in order to automatize the planning process by optimizing the BS's (Base Station) sites. Compared the results of opt-aiNet and genetic algorithms(GA) which have been used in radio coverage optimization before, we found that opt-aiNet could find the optimal solution all times while GA falling into local optimum solution sometimes, and opt-aiNet converged more quickly than GA in most cases. Experimental results show that opt-aiNet is an effective way to optimize the 802.11 Wireless LANs planning problems.

关键词： Education Gallium Indexes Computational modeling

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On Line Estimation of Color Values (B*) In Pet Process Using Gaussian Process Regression

On Line Estimation of Color Values (B*) In Pet Process Using...

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The 8th World Congress on Intelligent control and Automation(第八届智能控制与自动化世界大会 WCICA 2010)

作者： Na Luo Feng Qian School of Information Science and Engineering Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 P.R.China

With improvement of PET quality, the color of PET is required to be controlled. But the mechanism model of PET color is not so clear. In this paper, a modeling method of color values (B*) is introduced. It is based on sparse pseudo-input gaussian process. Online correction is added to the modeling based on sliding time window. Comparing to support vector regression, the model using gaussian process gives both prediction and predictive variance. The results show that B* value model using gaussian process is more flexible and can be used in real process.

关键词： Positron emission tomography Color Joints

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Reduced-order multiple models robust direct adaptive control of non-minimum phase system

Reduced-order multiple models robust direct adaptive control...

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World Congress on Intelligent control and Automation (WCICA)

作者： J.-Z. Liu Z.-L. Wang Xin Wang D.-H. Wang Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China Center of Electrical & Electronic Engineering Shanghai Jiaotong University Shanghai China

The performance of robust adaptive control system based on traditional revised algorithm is awful in the presence of unmodeled dynamics and the performance will go bad, even be unstable, when the working condition (including the parameter, reference input, and so on) change. This paper designs the multiple models robust adaptive controller to control the discrete single-variable non-minimum phase system. First, the higher part of the system is deem to be the unmodelled dynamic and the normalization is introduced to convert the system unmodeled dynamics to bounded disturbance as well as the dead zone is introduced in parameter update algorithm. And then, the multiple models robust adaptive controllers are designed based on the lower part of the system and the best controller is chosen based on the switch index to control the system. The last simulation illustrates that the proposed method is preferable even the working condition changes.

关键词： Adaptation model Adaptive control Robustness Heuristic algorithms Switches

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