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2016 4th International Conference on Machinery,Materials and Information Technology Applications(ICMMITA 2016)

作者： Song Zhang Xiai Chen Xiaohong Gu Qiang Wang Ling Wang School of Mechanical and Electrical Engineering China Jiliang University Zhejiang Yalong Educational Equipment Joint-Stock Co. Ltd State Key Lab. of Industrial Control Technology Department of Control Science and EngineeringZhejiang University

The absorption coefficient, refractive index and optical fingerprint characteristics were acquired by terahertz time-domain spectroscopy in the range of 0.4.5THz of the capsule drug ofloxacin. The results show that ofloxacin had multiple absorption peaks with different intensities within the frequency range, and the absorption coefficient spectrum could be used for the identification of ofloxacin. In the range of 0.4.5THz,the absorption spectra of ofloxacin molecules were simulated by Gaussion software. The vibration modes of some absorption peaks in the absorption spectra were analyzed and identified using the density functional theory. At 1.06 and 1.13 THz, the experimental results show that the absorption peaks are consistent with the absorption peaks of 1.07 and 1.18 THz in the theoretical spectrum, which are caused by the vibrational modes in the molecule ofloxacin.

关键词： Terahertz time-domain spectroscopy Absorption spectrum Density functional theory Ofloxacin

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The application of adaptive backstepping sliding mode for hybrid humanoid robot arm trajectory tracking control

The application of adaptive backstepping sliding mode for hy...

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International Conference on Advanced Mechatronic Systems (ICAMechS)

作者： Li Qin Lihuan Liang Fucai Liu Zhenlin Jin Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University YSU Qin-Huangdao China Department of Mechanical Engineering Yanshan University YSU Qin-Huangdao China

This paper presents a methodology of the dynamic analysis and control for a novel hybrid humanoid robot arm. The hybrid humanoid robot arm under consideration consists of a spherical parallel manipulator (SPM) connecting two revolute pairs in series form. The dynamic model of the hybrid humanoid robot arm has been set up based on the lie group and lie algebra combining with the principle of virtual work which can avoid processing of constraint reaction and the division of logic open-chains, as well as a great quantity of differential operation. Aiming at the parameter uncertainties and disturbances, an adaptive backstepping sliding mode controller is developed. Compared with PD control in trajectory tracking simulation, the results show the advanced of the controller.

关键词： Process control Trajectory Actuators Complexity theory TV

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Differential Privacy-preserving Distributed Machine Learning

Differential Privacy-preserving Distributed Machine Learning

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IEEE Annual Conference on Decision and control

作者： Xin Wang Hideaki Ishii Linkang Du Peng Cheng Jiming Chen State Key Lab. of Industrial Control Technology Zhejiang University Hangzhou P. R. China Dept. of Computer Science Tokyo Institute of Technology Yokohama Japan

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

Distributed machine learning (DML) has received widespread attentions, where a shared prediction model is collab.ratively learned by multiple servers. However, since the data used for model training often contains users' sensitive information, DML faces potential risks of privacy disclosure. Particularly, when servers are untrustworthy, it is critical while challenging to guarantee users to obtain privacy preservation that is self-controllab.e and does not weaken in strength during the whole DML process. In this paper, we propose a privacypreserving solution for DML, where privacy protection is achieved through data randomization at the users' side and a modified alternating direction method of multipliers (ADMM) algorithm is designed for servers to mitigate the effect of data perturbation. We prove that this solution provides differential privacy guarantee and preserves the convergence property of a general ADMM paradigm. Also, we provide extensive theoretical analysis about the performance of the trained model. Numerical experiments using standard classification datasets are finally conducted to validate the theoretical results.

关键词： Servers Data privacy Privacy Machine learning Linear programming Convex functions Minimization

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Stochastic predictive control for energy-efficient cooperative wireless cellular networks

Stochastic predictive control for energy-efficient cooperati...

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IEEE International Conference on Communications (ICC)

作者： Shuhuan Wen F. Richard Yu Jinsong Wu Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China Department of Systems and Computer Engineering Carleton University Ottawa ON Canada Bell Laboratories Shanghai P.R. China

Energy efficiency has become an increasingly important aspect of wireless communications. Coordinated multipoint (CoMP) processing may be considered as a promising approach to improve the energy efficiency, extend the cell coverage, and increase the communication capacity in wireless cellular networks. However, the CoMP communication systems may require a large amount of channel state information (CSI) feedbacks which would be constrained by the wireless backhaul networks. This paper proposes a control theoretical approach to improve the energy efficiency in cooperative wireless cellular networks with CoMP communications. In particular, we propose a stochastic predictive control algorithm to achieve energy-efficient CoMP transmissions through optimal base station grouping. Moreover, since the channel state information in CoMP is often outdated/lost due to the feedback delays in the backhaul network, our proposed approach is based on a new discrete time predictive function control model to mitigate the impacts of packet delay or loss of channel state information. Simulation results show the effectiveness of the proposed scheme.

关键词： Base stations Wireless communication Channel state information Delays Predictive models Predictive control Prediction algorithms

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Fuzzy Neural Network algorithm based on the delay compensation force/position control structure of a redundant actuation parallel robot *

Fuzzy Neural Network algorithm based on the delay compensati...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Shuhuan Wen Yanfang Zha Haiyang Yu Luigi Manfredi Xiongfei Li Sen Wang Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China Institute for Medical Science and Technology (IMSaT) University of Dundee United Kingdom

In this paper, a novel control method of the redundant force branch based on the force/position hybrid control structure of Smith predictor compensation is proposed. A fuzzy PI controller is designed based on Smith predictor compensation structure and it is included in the redundant force branch. This method can obtain good tracking and dynamic performance. However, fuzzy control doesn't have self-learning and adaptive ability, so fuzzy neural network (FNN) controller is used in the redundant force branch. The simulation results show that the proposed FNN algorithm based on delay compensation force/position hybrid control structure can improve the adaptability and the control accuracy of driving force of redundant branch.

关键词： Force Fuzzy control PI control Fuzzy neural networks Parallel robots Mathematical model Artificial neural networks

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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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Multi-PID control of Hammerstein systems with input multiplicity

Multi-PID control of Hammerstein systems with input multipli...

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Chinese control Conference (CCC)

作者： Jingjing Du Xinliang Zhang Chunyue Song School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo P. R. China State Key Lab. of Industrial Control Technology Institute of Industrial Process Control Zhejiang University Hangzhou P. R. China

Hammerstein models are popular in describing nonlinear chemical processes. The general control strategy for Hammerstein models is the nonlinearity inversion method, which is unfortunately inapplicable to Hammerstein systems with input multiplicity. To overcome this difficulty, a Multi-PID control approach is proposed. Thus, a complex control problem is solved by a conventional and easy control strategy. A Heat Exchanger process is simulated to demonstrate the effectiveness of the Multi-PID control approach.

关键词： Process control Heating Steady-state Analytical models Predictive models Switches

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Fuzzy Fine Tuning Therapies for Intelligence of High Speed Electronic Packaging Equipment

Fuzzy Fine Tuning Therapies for Intelligence of High Speed E...

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2016 International Conference on Electrical engineering, Mechanical engineering and Automation (ICEEMEA2016)

作者： He Yunbo Hu Yongshan Chen Xin Gao Jian Yang Zhijun Chen Yun Tang Hui Ao Yinhui Zhang Yu Guangdong Provincial Key Lab. of Computer Integrated Manufacturing Systems Key Laboratory of Mechanical Equipment Manufacturing and Control Technology of Ministry of Education School of Electromechanical Engineering Guangdong University of Technology

It's always difficult to satisfy both strict requirements of dynamic performance and settling performance of high-precision and high-acceleration point-to-point motions on electronic packaging equipment, i.e., Die bonders, Wire bonders, Flip-chip bonders, Wafer bumping machines, and so on. Focusing on this difficulty, Fuzzy logic is promoted in this paper to fine tune feed forward coefficient parameters of motion systems on packaging equipment to satisfy multiple targets of dynamic and settling performance requirements. This intelligent fine tuning approach, with achievable multiple motion targets, provides a therapy to automate and optimize motion system performance on packaging equipment, help on development and mass production of semiconductor packaging equipment, and improve machine intelligence.

关键词： Dynamic Performance Settling Performance Electronic Packaging Equipment Fuzzy Logic Fine Tuning Machine Intelligence

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Adaptive backstepping control of hydraulic servo system with input saturation for rolling mill based on multi-model switching

Adaptive backstepping control of hydraulic servo system with...

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Chinese control Conference (CCC)

作者： Yehong Li Yiming Fang Jianxiong Li Shengli Shi Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China National Engineering Research Center for Equipment and Technology of Cold Strip Rolling Qinhuangdao Qinhuangdao China

For the problems of parameters change abruptly and input saturation of the rolling mill hydraulic servo systems, an adaptive backstepping control method based on multi-model switching is presented in this paper. Firstly, multiple models of the rolling mill hydraulic servo systems under different operating modes are established. Secondly, an auxiliary system is introduced to reduce the effects of input saturation, and the adaptive backstepping controllers are designed for all subsystem. With the help of common Lyapunov theory, the proposed method can guarantee stability of the multi-model switched system under arbitrary switching strategy. Finally, the simulation results demonstrate the effectiveness of the proposed control method.

关键词： Servomotors Switches Backstepping Adaptive systems Valves Actuators

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Decision Variable Classification-Based Evolutionary Algorithm for Dynamic Multi-Objective Optimization

SSRN

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SSRN 2023年

作者： Wei, Lixin Yang, Qingshuai Guo, Zeyin Institute of Electrical Engineering Yanshan University Hebei Qinhuangdao066004 China Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Hebei Province Qinhuangdao066004 China

There are many dynamic multi-objective optimization problems (DMOPs) in real life, and such problems require the algorithms to converge quickly to a new Pareto Front and maintain the diversity of solution sets after the environment changes. To achieve these objectives, a dynamic multi-objective evolutionary algorithm (DEA) based on decision variable classification is proposed in this paper. First, the decision variables are classified into two categories using the decision variable clustering method: convergence-related variables and diversity-related variables. Then, the convergence optimization strategy and the diversity optimization strategy are used to optimize these two categories of decision variables, so that the algorithm can obtain excellent convergence and good diversity while fast tracking the Pareto front. In addition, a method for calculating the degree of environmental change, which uses the information after environmental change, is proposed to improve the algorithm’s ability to respond to environmental change. The proposed algorithm is compared with three state-of-the-art dynamic multi-objective optimization evolutionary algorithms on 16 benchmark problems. The experimental results show that the algorithm can effectively solve dynamic multi-objective optimization problems. © 2023, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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