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Comparing EMG Pattern Recognition with and Without Hand and Wrist Movements

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Journal of Bionic Engineering 2022年第3期19卷 700-708页

作者： Lizhi Pan Kai Liu kun zhu Jianmin Li Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education School of Mechanical EngineeringTianjin UniversityTianjin300072China

Electromyography(EMG)pattern recognition has been widely employed for prosthesis *** studies demonstrated that amputees had poorer performances of EMG pattern recognition when compared to able-bodied *** factors,such as the muscle weakness and atrophy of residual limbs,the length of residual limbs,and the decrease of the affected side's motor cortex,had been studied to improve the performance of ***,there was no study on the factor that the absence of joint movements for *** study aimed to investigate whether the hand and wrist joint movements had effects on the EMG pattern *** able-bodied subjects were tested for 11 hand and wrist gestures with two different gesture modalities:hand and wrist joints unconstrained(HAWJU)and constrained(HAWJC).Time-domain(TD)features and Linear Discriminant Analysis(LDA)were employed to compare the classification performance of the two *** to HAWJU,HAWJC significantly reduced the average Classification Accuracy(CA)across all subjects from 95.53 to 85.52%.The experimental results demonstrated that the hand and wrist joint movements had significant effects on EMG pattern *** outcomes provided a new perspective to study the factors affecting EMG pattern recognition.

关键词： Electromyography(EMG) Pattern recognition Classification accuracy(CA) Joint movements

New Second-order Contingent Epiderivatives and Set-valued Optimization Problems

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Acta Mathematicae Applicatae Sinica 2016年第4期32卷 983-994页

作者： Sheng-kun zhu Sheng-jie LI College of Economic Mathematics Southwestern University of Finance and Economics College of Mathematics and Statistics Chongqing University

In this paper, we introduce the concept of second-order compound contingent epiderivative for set-valued maps and discuss its relationship to the second-order contingent epiderivative. Simultaneously, we also investigate some special properties of the second-order compound contingent epiderivative. By virtue of the second-order compound contingent epiderivative, we establish some unified second-order sufficient and necessary optimality conditions for set-valued optimization problems. All results in this paper generalize the corresponding results in the literature.

关键词： set-valued optimization second-order compound contingent epiderivative optimality conditions

Overlap digraph:An effective model for finding good spaced seeds for biological sequence local alignment

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Chinese Science Bulletin 2011年第11期56卷 1100-1107页

作者： CHEN Ke SHE kun zhu QingXin School of Computer University of Electronic Science and Technology of China Chengdu 610054 China

Spaced seeds technology,which was proposed by PatternHunter,has been proven to be more sensitive and faster than continuous seeds,and it is now widely used for bio-sequence local ***,finding optimal spaced seeds is an NP-hard problem.A seed digraph model is proposed to find good spaced seeds,which are very close to optimal,in a very different but effective *** this different approach,some good long spaced seeds which cannot be calculated by normal optimal sensitivity formulas due to their exponential complexity can be found.

关键词：种子技术有向图模型生物序列种用灵敏度长间隔复杂性

Within-Project and Cross-Project Software Defect Prediction Based on Improved Transfer Naive Bayes Algorithm

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Computers, Materials & Continua 2020年第5期63卷 891-910页

作者： kun zhu Nana Zhang Shi Ying Xu Wang School of Computer Science Wuhan UniversityWuhan430072China Department of Computer Science Vrije University AmsterdamAmsterdam1081HVThe Netherlands

With the continuous expansion of software scale,software update and maintenance have become more and more ***,frequent software code updates will make the software more likely to introduce new *** how to predict the defects quickly and accurately on the software change has become an important problem for software *** defect prediction methods often cannot reflect the feature information of the defect comprehensively,and the detection effect is not ideal ***,we propose a novel defect prediction model named ITNB(Improved Transfer Naive Bayes)based on improved transfer Naive Bayesian algorithm in this paper,which mainly considers the following two aspects:(1)Considering that the edge data of the test set may affect the similarity calculation and final prediction result,we remove the edge data of the test set when calculating the data similarity between the training set and the test set;(2)Considering that each feature dimension has different effects on defect prediction,we construct the calculation formula of training data weight based on feature dimension weight and data gravity,and then calculate the prior probability and the conditional probability of training data from the weight information,so as to construct the weighted bayesian classifier for software defect *** evaluate the performance of the ITNB model,we use six datasets from large open source projects,namely Bugzilla,Columba,Mozilla,JDT,Platform and *** compare the ITNB model with the transfer Naive Bayesian(TNB)*** experimental results show that our ITNB model can achieve better results than the TNB model in terms of accurary,precision and pd for within-project and cross-project defect prediction.

关键词： Cross-project defect prediction transfer Naive Bayesian algorithm edge data similarity calculation feature dimension weight

Software Defect Prediction Based on Stacked Contractive Autoencoder and Multi-Objective Optimization

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Computers, Materials & Continua 2020年第10期65卷 279-308页

作者： Nana Zhang kun zhu Shi Ying Xu Wang School of Computer Science Wuhan UniversityWuhan430072China Department of Computer Science Vrije University AmsterdamAmsterdam1081HVThe Netherlands

Software defect prediction plays an important role in software quality ***,the performance of the prediction model is susceptible to the irrelevant and redundant *** addition,previous studies mostly regard software defect prediction as a single objective optimization problem,and multi-objective software defect prediction has not been thoroughly *** the above two reasons,we propose the following solutions in this paper:(1)we leverage an advanced deep neural network-Stacked Contractive AutoEncoder(SCAE)to extract the robust deep semantic features from the original defect features,which has stronger discrimination capacity for different classes(defective or non-defective).(2)we propose a novel multi-objective defect prediction model named SMONGE that utilizes the Multi-Objective NSGAII algorithm to optimize the advanced neural network-Extreme learning machine(ELM)based on state-of-the-art Pareto optimal solutions according to the features extracted by *** mainly consider two *** objective is to maximize the performance of ELM,which refers to the benefit of the SMONGE *** objective is to minimize the output weight norm of ELM,which is related to the cost of the SMONGE *** compare the SCAE with six state-of-the-art feature extraction methods and compare the SMONGE model with multiple baseline models that contain four classic defect predictors and the MONGE model without SCAE across 20 open source software *** experimental results verify that the superiority of SCAE and SMONGE on seven evaluation metrics.

关键词： Software defect prediction deep neural network stacked contractive autoencoder multi-objective optimization extreme learning machine

KAEA: A Novel Three-Stage Ensemble Model for Software Defect Prediction

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Computers, Materials & Continua 2020年第7期64卷 471-499页

作者： Nana Zhang kun zhu Shi Ying Xu Wang School of Computer Science Wuhan UniversityWuhan430072China Department of Computer Science Vrije University AmsterdamAmsterdam1081HVThe Netherlands

Software defect prediction is a research hotspot in the field of software ***,due to the limitations of current machine learning algorithms,we can’t achieve good effect for defect prediction by only using machine learning *** previous studies,some researchers used extreme learning machine(ELM)to conduct defect ***,the initial weights and biases of the ELM are determined randomly,which reduces the prediction performance of *** by the idea of search based software engineering,we propose a novel software defect prediction model named KAEA based on kernel principal component analysis(KPCA),adaptive genetic algorithm,extreme learning machine and Adaboost algorithm,which has three main advantages:(1)KPCA can extract optimal representative features by leveraging a nonlinear mapping function;(2)We leverage adaptive genetic algorithm to optimize the initial weights and biases of ELM,so as to improve the generalization ability and prediction capacity of ELM;(3)We use the Adaboost algorithm to integrate multiple ELM basic predictors optimized by adaptive genetic algorithm into a strong predictor,which can further improve the effect of defect *** effectively evaluate the performance of KAEA,we use eleven datasets from large open source projects,and compare the KAEA with four machine learning basic classifiers,ELM and its three *** experimental results show that KAEA is superior to these baseline models in most cases.

关键词： Software defect prediction KPCA adaptive genetic algorithm extreme learning machine Adaboost

Synergistic catalysis of the N-hydroxyphthalimide on flower-like bimetallic metal-organic frameworks for boosting oxidative desulfurization

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Petroleum Science 2024年第1期21卷 674-682页

作者： Jing He kun zhu Wei Jiang Dong-Ao zhu Lin-Hua zhu Hai-Yan Huang Wen-Shuai zhu Hua-Ming Li Institute for Energy Research Jiangsu UniversityZhenjiang212013JiangsuChina College of Chemistry and Chemical Engineering Hainan Normal UniversityKey Laboratory of Water Pollution Treatment and Resource Reuse of Hainan ProvinceHaikou571158HainanChina

Synergic catalytic effect between active sites and supports greatly determines the catalytic activity for the aerobic oxidative desulfurization of fuel *** this work,Ni-doped Co-based bimetallic metal-organic framework(CoNi-MOF)is fabricated to disperse N-hydroxyphthalimide(NHPI),in which the whole catalyst provides plentiful synergic catalytic effect to improve the performance of oxidative desulfurization(ODS).As a bimetallic MOF,the second metal Ni doping results in the flower-like morphology and the modification of electronic properties,which ensure the exposure of NHPI and strengthen the synergistic effect of the overall *** with the monometallic Co-MOF and naked NHPI,the NHPI@CoNi-MOF triggers the efficient activation of molecular oxygen and improves the ODS performance without an *** sulfur removal of dibenzothiophene-based model oil reaches 96.4%over the NHPI@CoNi-MOF catalyst in 8 h of ***,the catalytic product of this aerobic ODS reaction is sulfone,which is adsorbed on the catalyst surface due to the difference in *** work provides new insight and strategy for the design of a strong synergic catalytic effect between NHPI and bimetallic supports toward high-activity aerobic ODS materials.

关键词： Metal-organic frameworks Doped Bimetallic N-hydroxyphthalimide Aerobic processes Oxidative desulfurization

Lateral photovoltaic effect in the Ni-SiO_(2)-Si structure with bias

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Optoelectronics Letters 2024年第5期20卷 257-264页

作者： LING Xiang zhu Pengfei zhu kun SONG Pei LI Xiong School of Mathematics Physics and StatisticsShanghai University of Engineering ScienceShanghai 201600China School of Electrical Engineering Liupanshui Normal UniversityLiupanshui 553004China School of Railway Telecommunication Hunan Technical College of Railway High-speedHengyang 421002China

We designed a clamping device to study lateral photovoltaic effect (LPE) in Ni-SiO_(2)-Si structure with bias due to the appropriate barrier *** LPE has a prominent sensitivity and linearity with 532 nm wavelength *** transient response time is 450μs and the relaxation time is 2 250μs in the Ni-SiO_(2)-Si structure without *** LPE sensitivity has a significant improvement with *** transient response time is 6μs and the relaxion time is 47μs with-7 V bias,not only improving the LPE sensitivity,but also increasing the response speed with *** research shows that the Schottky barrier structure can improve the sensitivity and linearity of LPE with bias effectively,and thus it can be used in position sensitive sensors.

关键词： LPE structure effect

Finite-time Consensus for Multi-agent Systems via Nonlinear Control Protocols

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International Journal of Automation and computing 2013年第5期10卷 455-462页

作者： Ya-kun zhu Xin-Ping Guan Xiao-Yuan Luo Institute of Electrical Engineering Yanshan University School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University

This paper investigates the fnite-time consensus problem of multi-agent systems with single and double integrator dynamics,*** novel nonlinear protocols are constructed for frst-order and second-order leader-follower multi-agent systems,*** on the fnite-time control technique,the graph theory and Lyapunov direct method,some theoretical results are proposed to ensure that the states of all the follower agents can converge to its leader agent s state in fnite ***,some simulation results are presented to illustrate the efectiveness of our theoretical results.

关键词： Finite-time consensus multi-agent systems nonlinear control protocol leader-follower single and double integratordynamics