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检索条件"机构=Key Laboratory of Image and Graphics Intelligent Processing"

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A hybrid multiobjective immune algorithm with region preference for decision makers

A hybrid multiobjective immune algorithm with region prefere...

引用

Congress on Evolutionary Computation

作者： Licheng Jiao Wei Zhang Ruochen Liu Fang Liu Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

Recently, one of the main tools of decision maker (DM) preference incorporation in the multiobjective optimization (MOO) has been using reference points and achievement scalarizing functions (ASF). The core idea of these methods is converting the original multiobjective problem (MOP) into single objective problem by using ASF to find a single preferred point. However, many DMs not only interest in a single point but also a set of efficient points in their preferred region. In this paper, we introduce a hybrid multiobjective immune algorithm (HMIA) for DM. It combines the immune inspired algorithm and region preference based on a novel dominance concept called region-dominance without ASF. The new algorithm can let DMs flexibly decide the number of reference points and accurately determine the preferred region with its simple and effective interactive methods. To exemplify its advantages, simulated results of HMIA are shown with some well-known problems.

关键词： Delta modulation Neodymium Immune system Cloning Encoding Computational complexity

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Quantum-inspired immune clonal clustering algorithm based on watershed

Quantum-inspired immune clonal clustering algorithm based on...

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Congress on Evolutionary Computation

作者： Yangyang Li Nana Wu Jingjing Ma Licheng Jiao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing and State Key Laboratory of Integrated Services Network Xidian University Xi'an China

Based on the concepts and principles of quantum computing, a novel clustering algorithm, called a quantum-inspired immune clonal clustering algorithm based on watershed (QICW), is proposed to deal with the problem of image segmentation. In QICW, antibody is proliferated and divided into a set of subpopulation groups. Antibodies in a subpopulation group are represented by multi-state gene quantum bits. In the antibody's updating, the quantum mutation operator is applied to accelerate convergence. The quantum recombination realizes the information communication between the subpopulation groups so as to avoid premature convergences. In this paper, the segmentation problem is viewed as a combinatorial optimization problem, the original image is partitioned into small blocks by watershed algorithm, and the quantum-inspired immune clonal algorithm is used to search the optimal clustering centre, and make the sequence of maximum affinity function as clustering result, and finally obtain the segmentation result. Experimental results show that the proposed method is effective for texture image and SAR image segmentation, compared with the genetic clustering algorithm based on watershed (W-GAC), and the k-means algorithm based on watershed (W-KM).

关键词： Clustering algorithms image segmentation Error analysis Radiative recombination Feature extraction Wavelet transforms Partitioning algorithms

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Algorithm of Partition based Network Boosting for imbalanced data classification

Algorithm of Partition based Network Boosting for imbalanced...

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International Joint Conference on Neural Networks (IJCNN)

作者： Gou Shuiping Yang Hui Jiao Licheng Zhuang Xiong Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China and Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

Network Boosting (NB) is an ensemble learning method which combines weak learners together based on a network and can learn the target hypothesis asymptotically. NB has higher generalization ability compared to Bagging and AdaBoost. But, when datasets are class-imbalanced, the performance of NB will decrease quickly. In order to solve this problem, we present a Partition based Network Boosting method (PNB) to classify imbalanced data. For PNB method, every classifier node of the classifier network is provided with the same number of training data which are all of same weights. The classifier in the network is built by the balanced training set sampled from the training data according to the weights record of the training data it holds. And then, the weights of the instances of every node classifier are updated based on the classification results of self-node and its neighbor nodes. The classifier network is trained repeatedly in such a way. Weight factor of hypothesis in the training progress is introduced to improve the performance. The final classification is formed by all the hypotheses of the classifier network learned during the training progress so that the label of new instances can be decided by the weight voting. The experimental results on UCI data and imbalanced biomedical data show that the PNB algorithm has better AUC and recall performance compared with NB learning machine.

关键词： Laboratories Impedance matching Breast Boosting Liver

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A new roadmap constitution method based on dividing grid and level-set

A new roadmap constitution method based on dividing grid and...

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International Symposium on Intelligence Information processing and Trusted Computing

作者： Li, Shidong Ding, Mingyue Cai, Chao Institute for Information Engineering of Hubei University for Nationalities Enshi Hubei China Institute for Pattern Recognition and Artificial Intelligence National Lab. for Multi-spectral Information Processing Technologies HUST Wuhan China College of Life Science and Technology Image Processing and Intelligent Control Key Laboratory of Education Ministry of China HUST Wuhan China

ISBN: (纸本)9780769541969

Roadmap methods were widely used in route planning fields, both for robots and unmanned aircrafts. Traditional roadmap is constituted by connecting the vertexes of convex obstacle, which is related to the locations of start and goal of route, so the roadmap can not be reused when anyone of start and goal changed. Absence of reusability reduces the efficiency of route planning by using roadmap. A new roadmap constitution method was proposed in this paper, which is build on dividing the 2D configuration space into multi-scale grids and setting nodes of roadmap on grid boundary using Level-Set method, same method was used to construct the map cells of roadmap between any two nodes lays on same grid . The roadmap constituted with new method can be reused when the start or goal was changed;it is efficient for replanning problem. © 2010 IEEE.

关键词： Reusability

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Control reconfiguration of new strap-on boosted launch vehicle [PRC] with deadlocked swing engines

Control reconfiguration of new strap-on boosted launch vehic...

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International Conference on Modelling, Identification and Control (ICMIC)

作者： Lei Liu Biao Yin Yongji Wang Zhixiang Deng Department of Control Science and Engineering Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China Department of Control Science and Engineering Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

To enhance the reliability of launch vehicle attitude control systems and prevent the control invalidation caused by swing engine deadlock, a control reconfiguration of fault-tolerant control is proposed. Firstly, the coupling identity between the channels is discussed based on the dynamic model of the vehicle. Then, under the assumption of no rolling motion, we study the swing angles reconfiguration method based on the moment balance principle, and then deduce the relationship between the swing angle of the deadlocked engines and the engines still operate well. From the rationality analysis, the safe bound of the deadlocked engine and reconfiguration conditions are proposed under several assumptions. Finally, the numerical simulation of rocket attitude control based on the discussed reconfiguration strategy is given to demonstrate that the variance of the control affect by the deadlocked engine could be rectified by other booster-engines effectively.

关键词： Engines Power capacitors Variable speed drives System recovery

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Control of a rehabilitation robotic exoskeleton based on intentional reaching direction

Control of a rehabilitation robotic exoskeleton based on int...

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International Symposium on Micromechatronics and Human Science (MHS)

作者： Weiguang Huo Jian Huang Yongji Wang Jun Wu Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Technology Huazhong University of Science and Technology Wuhan China

An upper limb robotic exoskeleton of three degrees of freedom (DOF) is designed for the patients who survived stroke and the elderly who have not enough strength to move their limbs freely. Particular attention is paid to the realization of a intention-driven robotic control approach, by which the robotic exoskeleton can assist the user moving his/her arm freely to where he/she intends to go. A force sensing system made of multiple force sensing resisters (FSRs) is embedded in the robotic exoskeleton. A static force model of the upper limb in a relaxed state is obtained when the user wears the exoskeleton. A hybrid model is proposed to describe the behavior modes of human upper limb motion. Filtering technology is designed to infer the intended moving direction of upper limb based on the measured force information and the static force model. The motion intention of user's upper limb can be online estimated using the filter and a mode transition detector. Guided by the inferred intention, an admittance control strategy is assumed to control the motors of each DOF. The effectiveness of proposed robotic system and control approaches is evaluated by experiments.

关键词： Exoskeletons Joints Robot sensing systems

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The research in a plantar pressure measuring system connected with EEG

The research in a plantar pressure measuring system connecte...

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International Conference on Signal processing Proceedings (ICSP)

作者： Li Wei Qiu Hong Huang Yue Chen Xi Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan China Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Department of Control Science and Engineering Huazhong University of Science and Technology China

The paper presents a foot pressure measuring system connected with EEG It can quantify the relationship between human gait cycle and EEG signals. Understanding the relationship will help in the prevention of potential motor dysfunction disease and the development of rehabilitation tools. The whole system is composed of three parts: EEG signal acquisition, foot landing signal acquisition and the synchronization of these two signals. EEG signal can be acquired by Neuroscan EEG system. The plantar pressure measuring system (PPMS) is designed in details. With PVDF piezoelectric film sensor insole, PPMS could measure the plantar pressure precisely, and send a TTL signal as a mark to EEG system synchronously with EEG signal when the foot contacts the ground at beginning of each gait cycle. The experiment results shows that EEG signals are in line with human gait cycle on dynamic response. From that, some potential diseases can be predict, such as dyskinesia, peripheral neuropathy, neurological disorder, musculoskeletal disease and so on. It can also be used to monitor improvements in rehabilitation.

关键词： Electroencephalography Humans Foot Pressure measurement Low pass filters Diseases Piezoelectric films

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Small universal asynchronous spiking neural P systems

Small universal asynchronous spiking neural P systems

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International Conference on Bio-Inspired Computing: Theories and Applications, BICTA

作者： Linqiang Pan Xiangxiang Zeng Xinyi Zhang Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan Hubei China

Looking for small universal computing devices is a natural and well investigated topic in computer science. Recently, this topic started to be considered also in the framework of (synchronized) spiking neural P systems. In this work, it is focused on small universal spiking neural P systems working in a non-synchronized manner. Specifically, it is proved that there is an asynchronous spiking neural P system with 76 neurons that is equivalent to a universal register machine for computing functions. As generator of sets of numbers, a universal asynchronous spiking neural P system with 75 neurons is constructed.

关键词： Variable speed drives Neurons

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Upper limb rehabilitation robot integrated with motion intention recognition and virtual reality environment

Upper limb rehabilitation robot integrated with motion inten...

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Chinese Control Conference (CCC)

作者： Jun Wu Jian Huan Yongji Wang Key Laboratory of Image Processing and Intelligent Control Department of Control Science & Engineering Huazhong University of Science and Technology Wuhan 430074 China

Robotic-assisted therapy is of benefit to the recovery of upper limb motor function for the patients survived stoke. Whereas, there are few emphases on the patients' motion intention during the rehabilitation process. The goal of this study is to combine the control strategies based on patients' motion intention with an upper limb rehabilitation robot to improve the recovery for the patients. In this paper, we propose an integrated robot-assist rehabilitation system, in which a 3 degree-of-freedom (DOF) exoskeletal rehabilitation robot, an EMG-based intention recognition module and a VR game environment are seamlessly combined. According toharacteristics of EMG signals, the wavelet package analysis approach is applied to extract the features of EMG. The node energy is used to construct the feature vector instead of the original coefficients of wavelet package decomposition to resolve the time-invariance problem. Then feature projection results in the singularity problem of with-in scatter matrix during the feature dimension reduction. To overcome the disadvantage of the with-in scatter matrix, this paper uses a recursive algorithm which is proposed in our previous work. The reduced feature vector is recognized by a neural network classifier and the output of the classifier is used for the control inputs. Preliminary experiments are also performed to implement the control of the rehabilitation robotic system by using the proposed EMG reorganization method, together with a dart game realized in the virtual reality environment. Experimental results show that the performance of motion intention recognition is satisfactory and the entire integrated system is feasible.

关键词： Robots Feature extraction Electromyography Classification algorithms Wavelet packets Games

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Semi-supervised Tissue Segmentation of 3D Brain MR images

Semi-supervised Tissue Segmentation of 3D Brain MR Images

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International Conference on Information Visualisation (IV)

作者： Xiangrong Zhang Feng Dong Gordon Clapworthy Youbing Zhao Licheng Jiao Centre of Computer Graphics and Visualisation University of Bedfordshire UK Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an China

Clustering algorithms have been popularly applied in tissue segmentation in MRI. However, traditional clustering algorithms could not take advantage of some prior knowledge of data even when it does exist. In this paper, we propose a new approach to tissue segmentation of 3D brain MRI using semi-supervised spectral clustering. Spectral clustering algorithm is more powerful than traditional clustering algorithms since it models the voxel-to-voxel relationship as opposed to voxel-to-cluster relationships. In the semi-supervised spectral clustering, two types of instance-level constraints: must-link and cannot-link as background prior knowledge are incorporated into spectral clustering, and the self-tuning parameter is applied to avoid the selection of the scaling parameter of spectral clustering. The semi-supervised spectral clustering is an effective tissue segmentation method because of its advantages in (1) better discovery of real data structure since there is no cluster shape restriction, (2) high quality segmentation results as it can obtain the global optimal solutions in the relaxed continuous domain by eigen-decomposition and combines the pairwise constraints information. Experimental results on simulated and real MRI data demonstrate its effectiveness.

关键词： image segmentation Magnetic resonance imaging Three dimensional displays Clustering algorithms Brain models Data models

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