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2021 International Symposium on Micro-NanoMechatronics and Human Science, MHS 2021

作者： Huo, Jun Yang, Bo Ru, Hongge Huang, Jian Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Wuhan430074 China

ISBN: (纸本)9781665407731

It is a positive trend for hemiplegia with wearable robots in rehabilitation training. Recently, wearable Supernumerary Robotic Limb (SRL) is rising to a hot spot. The difficulty in modeling SRL for hemiplegia is how to make the SRL mechanism match the human upper and lower limbs' size and function simultaneously. In this paper, a simplified model of a four Degree of Freedoms (DOFs) universal SRL (USRL) is introduced. Based on workspace similarity, which is evaluated by Jaccard Index (JI), we establish a multi-object optimization (MOO) model. By applying the Particle Swarm Optimization (PSO) algorithm, we have found the optimal SRL mechanism parameters that meet the requirement of the human upper limb and the cane simultaneously. Meanwhile, a few impact factors are discussed briefly in the present proposal. The simulation results show that the USRL has a sound match with the human and could assist the hemiplegia subject as a supplemental limb. © 2021 IEEE.

关键词： Robotics

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Self-organization of Reconfigurable Protocol Stack for Networked Control Systems

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International Journal of Automation and computing 2011年第2期8卷 221-235页

作者： Chun-Jie Zhou Hui Chen Yuan-Qing Qin Yu-Feng Shi Guang-Can Yu Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University Science and Technology Wuhan 430074 PRC

In networked control systems （NCS）,the control performance depends on not only the control algorithm but also the communication protocol *** performance degradation introduced by the heterogeneous and dynamic communication environment has intensified the need for the reconfigurable protocol *** this paper,a novel architecture for the reconfigurable protocol stack is proposed,which is a unified specification of the protocol components and service interfaces supporting both static and dynamic reconfiguration for existing industrial communication *** the architecture,a triple-level self-organization structure is designed to manage the dynamic reconfiguration procedure based on information exchanges inside and outside the protocol ***,the protocol stack can be self-adaptive to various environment and system requirements through the reconfiguration of working mode,routing and scheduling ***,the study on the protocol of dynamic address management is conducted for the system of controller area network （CAN）.The results show the efficiency of our self-organizing architecture for the implementation of a reconfigurable protocol stack.

关键词： Networked control system （NCS） communication network self-organization protocol stack reconfiguration

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Short-Term Residential Load Forecasting via Pooling-Ensemble Model with Smoothing Clustering

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3690-3702页

作者： Xiao, Jiang-Wen Fang, Hongliang Wang, Yan-Wu Huazhong University of Science and Technology Ministry of Education School of Artificial Intelligence and Automation The Key Laboratory of Image Processing and Intelligent Control Wuhan430074 China

Short-term residential load forecasting is essential to demand side response. However, the frequent spikes in the load and the volatile daily load patterns make it difficult to accurately forecast the load. To deal with these problems, this article proposes a smoothing clustering method for daily load clustering and a pooling-ensemble model for one day ahead load forecasting. The whole short-term load forecasting framework in this article contains three steps. Specifically and first, the states of the residents are obtained by clustering the daily load curves with the proposed smoothing clustering method. Second, a weighted mixed Markov model is built to predict the probability distribution of the load state in the next day. Third, multiple predictors in the pooling-ensemble model are selected for different states and the load is forecasted by weighing the results of the multiple predictors based on the predicted states. Results of the case studies and comparison studies on two public datasets verify the advantages of the smoothing clustering method and the pooling-ensemble model. © 2020 IEEE.

关键词： Probability distributions

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Mem Brain: An Easy-to-Use Online Webserver for Transmembrane Protein Structure Prediction

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Nano-Micro Letters 2018年第1期10卷 12-19页

作者： Xi Yin Jing Yang Feng Xiao Yang Yang Hong-Bin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Computer Science Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering

Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels,transporters, receptors. Because it is difficult to determinate the membrane protein's structure by wet-lab experiments,accurate and fast amino acid sequence-based computational methods are highly desired. In this paper, we report an online prediction tool called Mem Brain, whose input is the amino acid sequence. Mem Brain consists of specialized modules for predicting transmembrane helices, residue–residue contacts and relative accessible surface area of a-helical membrane proteins. Mem Brain achieves aprediction accuracy of 97.9% of ATMH, 87.1% of AP,3.2 ± 3.0 of N-score, 3.1 ± 2.8 of C-score. Mem BrainContact obtains 62%/64.1% prediction accuracy on training and independent dataset on top L/5 contact prediction,respectively. And Mem Brain-Rasa achieves Pearson correlation coefficient of 0.733 and its mean absolute error of13.593. These prediction results provide valuable hints for revealing the structure and function of membrane *** Brain web server is free for academic use and available at ***/bioinf/Mem Brain/.

关键词： Transmembrane a-helices Structure prediction Machine learning Contact map prediction Relative accessible surface area

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Center matching scheme for K-means cluster ensembles

Center matching scheme for K-means cluster ensembles

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MIPPR 2009 - Pattern Recognition and Computer Vision: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Zhang, Li Zhou, Weida Wu, Caili Huo, Jieting Zou, Haishuang Jiao, Licheng Institute of Intelligent Information Processing Key Laboratory of Intelligent Perception and Image Understanding Xidian University 710071 China

ISBN: (纸本)9780819478078

In this paper, a center matching scheme is proposed for constructing a consensus function in the k-means cluster ensemble learning. Each k-means clusterer outputs a sequence with k cluster centers. We randomly select a cluster center sequence as a reference one, and then we rearrange the other cluster center sequences according to the reference sequence. Then we label the data using these matched cluster center sequences. Hence we get multiple partitions or clusterings. Finally, multiple clusterings are combined to the best labeling by using combination rules, such as the majority voting rule, the weighted voting rule and the selective weighted voting rule. Experimental results on 7 UCI data sets show that our ensemble methods could improve the clustering results effectively. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Clustering algorithms

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CLUSTERING VIA DIMENSIONAL REDUCTION METHOD FOR THE PROJECTION PURSUIT BASED ON THE ICSA

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Journal of Electronics(China) 2010年第4期27卷 474-479页

作者： Gou Shuiping Feng Jing Jiao Licheng Key Laboratory of Intelligent Perception and Image Understanding for the Ministry of Education Institute of Intelligent Information Processing Xidian University Xi＇an 710071 China

The performance of the classical clustering algorithm is not always satisfied with the high-dimensional datasets, which make clustering method limited in many application. To solve this problem, clustering method with Projection Pursuit dimension reduction based on Immune Clonal Selection Algorithm (ICSA-PP) is proposed in this paper. Projection pursuit strategy can maintain consistent Euclidean distances between points in the low-dimensional embeddings where the ICSA is used to search optimizing projection direction. The proposed algorithm can converge quickly with less iteration to reduce dimension of some high-dimensional datasets, and in which space, K-mean clustering algorithm is used to partition the reduced data. The experiment results on UCI data show that the presented method can search quicker to optimize projection direction than Genetic Algorithm (GA) and it has better clustering results compared with traditional linear dimension reduction method for Principle Component Analysis (PCA).

关键词： Projection Pursuit (PP) Immune Clonal Selection Algorithm (ICSA) Genetic Algorithm (GA) K-means clustering

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Pre-extracting boundary vectors for support vector machine using pseudo-density estimation method

Pre-extracting boundary vectors for support vector machine u...

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MIPPR 2009 - Pattern Recognition and Computer Vision: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Zhang, Li Zhou, Weida Chen, Guirong Zhou, Hongjie Ye, Ning Jiao, Licheng Institute of Intelligent Information Processing Key Laboratory of Intelligent Perception and Image Understanding Xidian University 710071 China

ISBN: (纸本)9780819478078

Support vector machines (SVMs) have become useful and universal learning machines. SVMs construct a decision function by support vectors (SVs) and their corresponding weights. The training phase of SVMs definitely uses all training samples, which leads to a large computational complexity for a large scale sample set. Moreover support vectors could not be found until a quadratic programming (QP) problem is solved. Actually we know only SVs play a role in the decision function. Hence, pseudo density estimation (PDE) is presented to extract a set of boundary vectors (BVs) which may contain SVs. The PDE method is a variant of Parzen window method. Hyperspheres are considered as the window functions. In our method, for each sample we construct a hypersphere with an unfixed radius. The ratio of the number of samples contained in the hypersphere of a sample to the total training samples can be taken as the pseudo density of the corresponding sample. The set of BVs is taken as the training input to SVMs. In doing so, it speeds the training procedure of SVMs. It is convenient for PDE to determine its parameter. The experiments show that SVMs using PDE have the similar generalization performance to SVMs. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Support vector machines

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A WOA-based algorithm for parameter optimization of support vector regression and its application to condition prognostics 36

A WOA-based algorithm for parameter optimization of support ...

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第36届中国控制会议

作者： LI Sai FANG Huajing School of Automation Key Laboratory of Image Processing and Intelligent ControlMinistry of EducationHuazhong University of Science and Technology

ISBN: (纸本)9781538629185

Condition monitoring is very important for system safety and condition-based *** series prediction capabilities of machine learning like support vector regression(SVR) can be utilized for ***,choosing optimal parameters for SVR is an important step in SVR model design,which heavily affects the performance of ***,a whale optimization algorithm(WOA) based algorithm is proposed for SVR parameters *** proposed algorithm has been evaluated through some benchmark ***,the proposed method with moving window technology is used to condition prognostics of the Tennessee Eastman *** and engineering application show that the SVR-WOA method is effective,by noting that the computation time is shortened in some application scenarios.

关键词： Support vector regression Whale optimization algorithm Parameter optimization Condition prognostics

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K-means selective cluster ensembles based on multiple feature subsets

K-means selective cluster ensembles based on multiple featur...

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MIPPR 2009 - Pattern Recognition and Computer Vision: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Zhang, Li Zhou, Weida Zou, Haishuang Huo, Jieting Wu, Caili Jiao, Licheng Institute of Intelligent Information Processing Key Laboratory of Intelligent Perception and Image Understanding Xidian University 710071 China

ISBN: (纸本)9780819478078

Combining multiple clusterers is emerged as a powerful method for improving both the robustness and the stability of unsupervised classification solutions. In this paper, k-means selective cluster ensembles based on multiple feature subsets are proposed. In the ensemble, a random subset of features is used to train an individual k-means clusterer. In the final step, the selective weighted voting scheme is used for finding the best partition. The consensus function is constructed by relabeling all partitions of clusterers and finding the best partition. Experimental results on 4 UCI data sets show that our ensemble method can improve the clustering performance. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Set theory

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A battery management system with two-stage equalization 29

A battery management system with two-stage equalization

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第29届中国控制与决策会议

作者： Shengping Hong Jianhua Jiang Xi Li School of Automation Key Laboratory of Education Ministry for Image Processing and Intelligent ControlHuazhong University of Science & Technology

ISBN: (纸本)9781509046584

This paper presents a lithium-ion battery pack equalization system and method. The batteries are divided into several groups, which are connected in parallel with the bidirectional DC-DC converters respectively, and the output of DC-DC converters ends are connected in series with each other as a DC bus. The scheme divides equalization of the cells into two stages: intra-group equalization and inter-group equalization, and the two stages are respectively realized by battery time-sharing-access structure and stack energy-sharing structure. Then equalization strategy of the distributed battery energy storage system under two stages is proposed, especially the Single Cell Battery Access Timing Algorithm and MPC Algorithm. The simulation results show that the proposed battery management structure and control strategy can realize fast and accurate SOC equalization.

关键词： Battery management system(BMS) cell equalization battery time-sharing-access structure stack energy-sharing structure Single Cell Battery Access Timing Algorithm MPC Algorithm

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