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SSRN 2022年

作者： Zhang, Zhixia Wen, Jie Wang, Hui Cai, Xingjuan Cui, Zhihua Zhang, Wensheng School of Computer Science and Technology Taiyuan University of Science and Technology China School of Information Engineering Nanchang Institute of Technology Nanchang330099 China State Key Laboratory of Intelligent Control and Management of Complex System Institute of Automation Chinese Academy of Sciences China State Key Lab. for Novel Software Technology Nanjing University China

In many-objective optimization problems (MaOPs), it is a difficult task for evolutionary algorithms to balance convergence and diversity while rapidly converging to the Pareto front. As the number of objectives increases, the difficulty of evolutionary algorithms increases with individual selection pressure. To address above problems, we propose a many-objective evolution algorithm assisted by an ideal hyperplane (MaOEA-IH). Firstly, the ideal hyperplane as a reference surface to guide the individual to search the Pareto front, which is established from the extremum of each dimension of the objective space. Secondly, a parallel p -norm mating selection strategy is proposed to improve convergence. In addition, two factors are considered in this strategy: 1) different p -norm are used to measure different spatial scales;2) individual selection uncertainty is defined by incorporating a probabilistic perturbation mechanism. Thirdly, the improved shift-based density estimation strategy is used as the evaluation criterion for environmental selection to improve the convergence and diversity of solution sets and enhance selection pressure. To verify the effectiveness of the proposed algorithm, MAOEA-IH is compared with seven excellent algorithms in three benchmark sets (DTLZ, WFG and MaF). The results show that the proposed MAOEA-IH is highly competitive in solving MaOPs. © 2022, The Authors. All rights reserved.

关键词： Geometry

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NiuEM: A Nested-iterative Unsupervised Learning Model for Single-particle Cryo-EM Image Processing

NiuEM: A Nested-iterative Unsupervised Learning Model for Si...

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2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

作者： Hu, Rui Cai, Jiaming Zheng, Wangjie Yang, Yang Shen, Hong-Bin Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Jiao Tong University Department of Bioinformatics and Biostatistics Shanghai200240 China

ISBN: (纸本)9781728162157

Cryo-electron microscopy (cryo-EM) has become a mainstream technology for solving spatial structures of biomacromolecules, while the processing of cryo-EM images is a very challenging task. One of the great challenges is the high noise in the images. A common method is to cluster the images with close projecting angles to get mean images, which are used for 3D reconstruction. However, due to the extremely low signal-to-noise-ratio, common clustering methods often fail to obtain high-quality mean images, leading to poorly reconstructed structures. In this study, we present a new unsupervised learning framework, called NiuEM, to discriminate images captured from different angles and yield cluster-mean images. NiuEM first generates pseudo-lab.ls and then exploits both contrastive loss and cross-entropy loss for training convolutional layers to learn feature representations. Moreover, the pseudo-lab.ls are updated iteratively to enhance the reliability of lab.ls. We assess the performance of NiuEM on four data sets via both visualized and quantitative experiments. Especially, two kinds of metrics are adopted to measure the performance, regarding the clustering quality and the resolution of reconstructed 3D models, respectively. The experimental results show that NiuEM achieves very competitive clustering accuracy in the comparison with the state-of-the-art image clustering methods. Moreover, the cluster mean images yielded by NiuEM lead to better initial 3D models compared with the mainstream reconstruction tools. © 2020 IEEE.

关键词： Image reconstruction

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Sparse Least Square Support Vector Machines based on Random Entropy 36

Sparse Least Square Support Vector Machines based on Random ...

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第36届中国控制会议

作者： Wenlu Ma Han Liu Faculty of Automation and Information Engineering Xi'an University of Technology Shaanxi Key Lab. of Complex System Control and Intelligent Information Processing Faculty of Automation and Information EngineeringXi’an University of Technology

ISBN: (纸本)9781538629185

Least squares support vector machines(LSSVM) has a good performance in small data samples, but can't solve the large-scale sample problems. In this paper, large data set sparse least squares support vector machines model based on stochastic entropy is proposed, and it can be applied to large-scale data samples. Firstly, the large-scale data set is divided into several subsets. Then the entropy method is used to the sparse samples in each subset. Finally, we use sparse samples sets as training samples, and use least squares support vector machine algorithm to train. The results show that the sparse least squares support vector machine model based on entropy can effectively solve the problem of large-scale data.

关键词： Sparsity LSSVM Large-scale data Random Entropy

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Structure Design and Analysis of Reconfigurable Fixture Robot Based on the Auto-body Panels

Structure Design and Analysis of Reconfigurable Fixture Robo...

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IEEE International Conference on Mechatronics and Automation

作者： Jilong Li Jun Liu Xiaofeng Wang Weimin Ge School of Mechanical Engineering Tianjin University of Technology Tianjin Key Laboratory of the Design and Intelligent Control of the Advanced Mechatronical System Xiqing District Tianjin China Tianjin Key Lab. of the Design & Intell. Control Adv. Mechatronical Syst. Tianjin China

The fixture is an important equipment, and is widely used in the auto-body panels to be assembled and welded. However, most fixtures, which installed on the production lines, are special to make the production cost higher and the production efficiency lower. Therefore, the reconfigurability of the fixture becomes a key factor that effects the production efficiency of automobiles. To handle the above problems, the new reconfigurable fixture robot is proposed in this paper, which can meet the handing, assembling and welding requirements of different automobile's sheet parts on the automobile production lines. First, the three-dimensional model and the kinematic equations of the proposed reconfigurable fixture robot are established. Then, the working space of the reconfigurable fixture robot is also obtained. Finally, the modal analysis of the reconfigurable fixture robot is performed to determine the cause of resonances. Reconfigurable fixture robots are capable of gripping the auto-body panels with the different radii of curvature, which can also meet the work scopes on the production lines.

关键词： Fixtures Manipulators Kinematics Modal analysis Clamps

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A novel multi-microgrids system reliability assessment algorithm using parallel computing

A novel multi-microgrids system reliability assessment algor...

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2017 IEEE Conference on Energy Internet and Energy system Integration, EI2 2017

作者： Su, Xin Cheng, Yan Zhang, Xingyou Wei, Chuanzhi State Grid Jinan Power Supply Company Jinan China State Grid Shandong Electric Power Research Institute Jinan China Key Lab. of Power System Intelligent Dispatch and Control Ministry of Education Shandong University China

ISBN: (纸本)9781538614273

Under the condition of multi-microgrids system, due to the location dispersion of microgrids, and the different characteristics of distributed generations and loads, the size of sampling in a reliability assessment algorithm is always huge, which influences computational efficiency. In this paper, based on the operation characteristics of a multi-microgrid system, a reliability evaluation algorithm based on the parallel computing algorithm of the multi-microgrid system is proposed. The algorithm can sample the operating state of each microgrid at the same time and consider the interaction between the various microgrids in a multi-microgrids system. The algorithm can significantly improve the calculation speed of the reliability assessment algorithm of the multi-microgrid system, which is of great significance to the reliability evaluation of the multi-microgrid system in the region. © 2017 IEEE.

关键词： Reliability analysis

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Model-Predictive Planning for Autonomous Vehicles Anticipating Intentions of Vulnerable Road Users by Artificial Neural Networks

Model-Predictive Planning for Autonomous Vehicles Anticipati...

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IEEE Symposium Series on Computational Intelligence

作者： Jan Eilbrecht Maarten Bieshaar Stefan Zernetsch Konrad Doll Bernhard Sick Olaf Stursberg Control and System Theory Lab University of Kassel Germany Intelligent Embedded Systems Lab University of Kassel Germany Faculty of Engineering University of Applied Sciences Aschaffenburg Germany

This article presents a hierarchical path planning framework that allows to generate plans for autonomous vehicles in the presence of vulnerable road users (VRUs), such as pedestrians and cyclists. Contrasting many existing approaches, the hierarchical approach allows not only to resolve emergency situations, but also to consider regular settings. Planning is based on model predictive control (MPC), which allows to make optimal, anticipatory decisions based on forecasts of the intentions of VRUs while explicitly accounting for constraints. The VRU trajectory forecast is based on a polynomial least-squares approximation of the VRU's trajectories in combination with a multilayer perceptron artificial neural network for prediction over a future horizon. The efficacy of the proposed framework is demonstrated for two example scenarios.

关键词： Planning Trajectory Roads Autonomous vehicles Predictive control Predictive models

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Corrigendum to “Influence of Na doping on the magnetic properties of LaFeO 3 powders and dielectric properties of LaFeO 3 ceramics prepared by citric sol-gel method” [Ceram. Int. 43 (2017) 7922–7928]

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Ceramics International 2017年第18期43卷 17247-17247页

作者： Ensi Cao Yanrong Qin Tingting Cui Li Sun Wentao Hao Yongjia Zhang Key Lab. of Advanced Transducers & Intelligent Control System College of Physics & Optoelectronics Taiyuan University of Technology Taiyuan 030024 People's Republic of China

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control method for redundant manipulators with multi-constraints 7

Control method for redundant manipulators with multi-constra...

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7th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2016

作者： Lu, Beibei Guo, Shuli Han, Lina Key Lab. of Complex System Intelligent Control and Decision School of Automation Beijing Institute of Technology Beijing100081 China Mathematical Institute University of Oxford Andrew Wiles Building Radcliffe Observatory Quarter Woodstock Road OxfordOX2 6GG United Kingdom Department of Cardiovascular Internal Medicine Nanlou Branch Chinese PLA General Hospital Beijing100853 China

ISBN: (纸本)9784990534349

When redundant manipulators work in a complex environment, many constraints need to be met. Gradient projection method (GPM) is a typical way to fulfil constraints that does not affect the main task. However it's difficult to fulfil all the restrictions simultaneously. In this paper, the Jacobian matrix for constraints is proposed and an extended Jacobian matrix form equivalent to the GPM is obtained. Then the multi-objective compliance control scheme is applied to the extended Jacobian matrix form to ensure that the main task and multi-constraints are fulfilled simultaneously. At last we test this scheme in a sixdegree-of-freedom (6-DOF) manipulator and the simulation results confirm our conclusions. © 2016, Fuji Technology Press. All rights reserved.

关键词： Redundant manipulators

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Discovering true association between multimodal data sets using structured and sparse canonical correlation analysis: A simulation study

Discovering true association between multimodal data sets us...

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IEEE International Symposium on Biomedical Imaging

作者： Ali-Reza Mohammadi-Nejad Gholam-Ali Hossein-Zadeh Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence (CIPCE) University of Tehran Tehran Iran School of Cognitive Sciences Institute for Research in Fundamental Sciences (IPM) Tehran Iran Henry Ford Health System Medical Image Analysis Lab. Detroit Michigan USA

ISBN: (纸本)9781479923519

Recently, multi-modal data fusion emerged as a comprehensive approach in the field of neuroimaging. As a multivariate approach, canonical correlation analysis (CCA) is frequently used for data fusion. However, the current CCA-based fusion approaches face many problems in the sense of high-dimensionality, multi-colinearity, unimodal feature selection, asymmetric and reshaping of the imaging data into vectors. In this paper, we propose a structured and sparse CCA (ssCCA) technique to overcome the above problems. To investigate the performance of the proposed approach, we compared three multivariate and multi-modal fusion techniques: standard CCA, regularized sparse CCA, and ssCCA, with respect to their ability to detect multi-modal data associations. The results demonstrate that the ssCCA approach has superior recovery performance in the extraction of the true correlated variables relative to the other two fusion approaches.

关键词： Correlation Imaging Laplace equations Data integration Data models Standards Tuning

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High-efficiency, broad-band and wide-angle optical absorption in ultra-thin organic photovoltaic devices

High-efficiency, broad-band and wide-angle optical absorptio...

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作者： Wang, Wenyan Hao, Yuying Cui, Yanxia Tian, Ximin Zhang, Ye Wang, Hua Shi, Fang Wei, Bin Huang, Wei Key Lab. of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan 030024 China Key Laboratory of Interface Science and Engineering in Advanced Materials Taiyuan University of Technology Taiyuan 030024 China Key Laboratory of Advanced Display and System Applications Ministry of Education Shanghai University Yanchang Road 149 Shanghai 200072 China Nanjing 210046 China

Metal nanogratings as one of the promising architectures for effective light trapping in organic photovoltaics (OPVs) have been actively studied over the past decade. Here we designed a novel metal nanowall grating with ultra-small period and ultra-high aspect-ratio as the back electrode of the OPV device. Such grating results in the strong hot spot effect in-between the neighboring nanowalls and the localized surface plasmon effect at the corners of nanowalls. These combined effects make the integrated absorption efficiency of light over the wavelength range from 400 to 650 nm in the active layer for the proposed structure, with respect to the equivalent planar structure, increases by 102% at TM polarization and by 36.5% at the TM/TE hybrid polarization, respectively. Moreover, it is noted that the hot spot effect in the proposed structure is more effective for ultra-thin active layers, which is very favorable for the exciton dissociation and charge collection. Therefore such a nanowall grating is expected to improve the overall performance of OPV devices. © 2014 Optical Society of America.

关键词： Light absorption

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