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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Hai-Peng Ren Xuan Wang Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xian University of Technology Xian China

Due to the advantages of strong carrying capacity and fast response speed, hydraulic servo system has been widely used in industrial motion control field. The hydraulic servo system is susceptible to the nonlinearity, parameter uncertainty and unknown disturbance, which makes the high performance position control of the hydraulic servo system become a challenge. In this paper, considering the hydraulic position servo control system with input saturation, a sliding mode variable structure controller with shrinking boundary layer is designed. The proposed method is used to experimentally track three kinds of reference signals based on Programmable Logic controller (PLC), i.e., S7-200PLC, and hydraulic control system provided by Festo company. The experiment results indicate that the proposed method is effective and achieves smaller steady state tracking error as compared with sign function robust item and the conventional PID control.

关键词： Servomotors Cavity resonators Valves Force Touch sensitive screens

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Direct symbol decoding using GA-SVM in chaotic baseband wireless communication system

arXiv

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arXiv 2021年

作者： Yin, Hui-Ping Ren, Hai-Peng Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China

To retrieve the information from the serious distorted received signal is the key challenge of communication signal processing. The chaotic baseband communication promises theoretically to eliminate the inter-symbol interference (ISI), however, it needs complicated calculation, if it is not impossible. In this paper, a genetic algorithm support vector machine (GA-SVM) based symbol detection method is proposed for chaotic baseband wireless communication system (CBWCS), by this way, treating the problem from a different viewpoint, the symbol decoding process is converted to be a binary classification through GA-SVM model. A trained GA-SVM model is used to decode the symbols directly at the receiver, so as to improve the bit error rate (BER) performance of the CBWCS and simplify the symbol detection process by removing the channel identification and the threshold calculation process as compared to that using the calculated threshold to decode symbol in the traditional methods. The simulation results show that the proposed method has better BER performance in both the static and time-varying wireless channels. The experimental results, based on the wireless open-access research platform, indicate that the BER of the proposed GA-SVM based symbol detection approach is superior to the other counterparts under a practical wireless multipath channel. Copyright © 2021, The Authors. All rights reserved.

关键词： Bit error rate

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Optimizing multi-criteria k-shortest paths in graph by a natural routing genotype-based genetic algorithm

Optimizing multi-criteria k-shortest paths in graph by a nat...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Yang Wang Qing Liu Haipeng Ren Xuan Ma Long Liu Wenqing Wang Jing Zhang Department of Information and Control Engineering Xi'an University of technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China

k-shortest path problem (KSP) is a more general form of the classical shortest path problem in graph. Its task is no longer to find the shortest path between two vertices, but to find the shortest k paths. So far, the reported KSP-algorithms only considers finding shortest k paths with regard to a single criterion, while far more application scenarios of the KSP require to determine k paths by taking more criteria into account. This article formulates the KSP as a multi-objective optimization problem. In consideration of the problem's property and genetic algorithm's great success in multi-objective optimization, the genetic algorithm is utilized as the optimization tool. The genotype is directly represented as the form of natural routing. In order to make the related genetic operators risk-free, the conception of gene-bank is introduced. And the crossover and mutation are both implemented based on the introduced gene-bank. The proposed genetic algorithm is tested on an undirected graph of 9-vertex/65-edge. The testing result shows that, our proposed algorithm is valid and outperforms some other swarm intelligence-based algorithms in terms of the obtained paths' quality.

关键词： Genetic algorithms Optimization Genetics Routing Task analysis Encoding Sociology

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Fault diagnosis for small samples based on VMR

Fault diagnosis for small samples based on VMR

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2024 China Automation Congress, CAC 2024

作者： Wu, Xuejin Wu, Yali Hu, Yanghu Department of Information and Control Engineering Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China State Key Laboratoiy of Metal Extrusion and Forging Equipment Technology China National Heavy Machinery Research Institute Co. Ltd Xi'an710016 China

ISBN: (纸本)9798350368604

The scarcity of fault samples presents a significant challenge for fault diagnosis in industrial processes. Conventional methods often fail to deliver satisfactory results when dealing with small sample sizes. In addressing the issue of small sample fault diagnosis in industrial processes, this paper utilizes the Variational Mode Reconstruction(VMR) algorithm to screen the best components decomposed by the Variational Mode Decomposition(VMD) through the envelope entropy then random weighting them and signal reconstruction to achieve data enhancement, to enrich the sample size and to prevent the model overfitting phenomenon due to the scarcity of samples. Meanwhile, this paper integrates Convolutional Neural Network (CNN) and Long Short-term Memory Neural Network (LSTM) to propose a network model of spatio-temporal feature fusion for feature extraction and classification. The experiments demonstrate that this model not only enhances data diversity but also ensures consistency before and after enhancement, effectively mitigating overfitting. Furthermore, it guarantees high recognition and generalization rates in fault diagnosis. © 2024 IEEE.

关键词： Convolutional neural networks

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Modeling and simulation of MHD melt convection and heat transfer in CZ crystal growth

Modeling and simulation of MHD melt convection and heat tran...

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Chinese Automation Congress

作者： Ni Zhang Ding Liu Weichao Huang National and Local Joint Engineering Shaanxi key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China

This paper presents an axisymmetric swirl incompressible thermal lattice Boltzmann model to study the magnetohydrodynamics (MHD) melt flow and heat transfer at high Grashof number and high Reynolds number under external magnetic field in Czochralski (CZ) crystal growth process. The model is built based on the double distribution function lattice Boltzmann equation (DDF-LBE) and is verified by benchmark problem. By using the proposed model, the evolution relationship of melt flow, temperature is constructed under the combined effects of Lorenz force, rotating inertia force and thermal buoyancy force. Furthermore, the flow patterns and the temperature distribution of silicon melt under different magnetic field strengths are simulated. The simulation results show that with the increase of magnetic field strength, the melt velocity is reduced, which suppresses the melt convection and is beneficial for suppressing the fluctuation of melt. And the temperature gradient below the crystal growth interface increases in the case of crystal rotation, which is helpful to increase the crystallization rate. The combination of Cusp magnetic field, crucible rotation and crystal rotation can be used as an effective method in the production of high-quality crystal.

关键词： Modeling Computer simulation Crystal growth Lattice boltzmann method

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A multi-objective particle swarm optimization for multiple knapsack problem with strong constraints

A multi-objective particle swarm optimization for multiple k...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Xuan Ma Ying Yan Qing Liu Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an City China

Aiming at the characteristics of strong constraints of multiple knapsack problem (MKP), a method based on multi-objective particle swarm optimization is proposed in this paper. We take constraint violation as an optimal objective to avoid the troublesome of dealing with unfeasible solutions unsatisfied constraints. For updating effectively the external archive, the degree of constraint violation and the crowding degree of non-dominated particle in Pareto front are taken as two criteria. To decrease the probability of algorithm converging to a pseudo Pareto front, a mutation operator is designed. The experimental results of solving a number of MKP instances demonstrate that the proposed algorithm performs better to the MKP instances with strong constraints.

关键词： Optimization Sociology Statistics Particle swarm optimization Heuristic algorithms complex systems control systems

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A New Intrusion Detection Method Based on Adaptive Feature Extraction

A New Intrusion Detection Method Based on Adaptive Feature E...

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第三十八届中国控制会议

作者： Ya-Li Wu Guo-Ting Li Yu-Long Fu Xiao-Peng Wang School of Automation and Information Engineering Xi'an University of Technology Shaanxi Province Key Laboratory of Complex System Control and Intelligent Information Processing

Compared with traditional machine learning, intrusion detection based on deep learning has the advantage of feature extraction. Auto-encoders, owing to having good feature extraction and dimensionality reduction ability, are known for the superior performance. However, the network structure selection is time-consuming and laborious. The essential features cannot be automatically extracted according to different distribution characteristics of the data set. Moreover, the existing data pre-processing method fails to adapt the model better. Based on the stack sparse auto-encoders(SSAE), we propose a new intrusion detection model named DBSO-SSAE. The algorithm is based on difference brain storm optimization(DBSO) which automatically adapts to selection network framework. Meanwhile, a new normalization strategy is introduced to further improve the accuracy of the model. The simulation results show that DBSO-SSAE greatly reduces the complexity of manual tuning and increases the adaptability of the network model.

关键词： intrusion detection feature extraction SSAE deep learning difference brain storm optimization

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Detection of Weak Signal Amplitude of Winding Deformation Based on Duffing Oscillator

Detection of Weak Signal Amplitude of Winding Deformation Ba...

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第40届中国控制会议

作者： Sheng-min Li Yuan-yuan He Yue-yue Che Xu-xia Sun Academy of Automation & Information Engineering Xi'an University of Technology Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information Processing

In order to solve the problem that it is difficult to detect the weak sinusoidal response signal when the frequency response method is used to detect the transformer winding deformation fault online,this paper proposes a method to detect the amplitude of the response signal by using Duffing ***,in order to solve the influence of the target signal phase shift on the amplitude detection in the actual detection process,a method of first detecting the signal phase using a vibrator array composed of Duffing vibrators and then detecting the amplitude is ***,using this method to process the two sets of measured response signals of online detection,the detection results show that the maximum error between the detected signal amplitude and the standard signal is 5.69%,which meets the requirements of the power industry standard,which proves that the method used in this article is It is feasible in winding deformation detection.

关键词： On-line detection of winding deformation Weak signal detection Duffing vibrator

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Transformer Winding Deformation Diagnosis Based on Signal Distance

Transformer Winding Deformation Diagnosis Based on Signal Di...

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第30届中国过程控制会议（CPCC 2019）

作者： LiShengmin ZhaoDan HeYuanyuan WangXiaoafan School of Automation and Information Engineering Xi'an University of Technology Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information Processing

Aiming at the problem that the correlation coefficient method cannot judge the deformation of the transformer winding when the measured frequency response curve is the same as the reference frequency response curve(frequency response analysis,FRA) and the amplitude is different, this paper proposed a new transformer winding diagnosis method based on signal *** method can accurately determine whether the winding is deformed according to the mutual distance value [0, 1] by calculating the mutual distance between the measured frequency response curve and the reference frequency response *** the simulation experiment and the national standard judgment criterion, the criteria for judging the deformation of the winding by mutual distance were *** simulation results show that the method is sensitive, reliable and universal.

关键词： FRA Frequency response curve Winding deformation signal distance mutual distance

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Reference-point-based Brain Storm Optimization Algorithm for Many-Objective Optimization

Reference-point-based Brain Storm Optimization Algorithm for...

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第32届中国过程控制会议(CPCC2021)

作者： Yali Wu Xiaoxiao Quan Jinjin Qi Yulong Wang School of Automation and Information Engineering Xi'an University of Technology Shaanxi Province Key Laboratory of Complex System Control and Intelligent Information Processing

Brain storm optimization algorithm,as a new type of swarm intelligence optimization algorithm,has attracted the attention of many researchers as soon as it was put ***,it has been applied to various many-objective,multi-modal and practical engineering *** this paper,a brain storm optimization algorithm based on reference points was proposed to solve many-objective optimization ***,a series of reference points are introduced into the target space,and the population is clustered in an adaptive clustering method centered on the reference point poles to guide the evolution of the ***,the idea based on decomposition is combined with the mechanism of brain storm optimization algorithm to solve many-objective optimization ***,a mechanism to update the external archive set based on the strategy of enhanced dominance and weighted boundary intersection is proposed to maintain the convergence and diversity of the *** simulation results on the DTLZ series of test function sets show that the proposed algorithm based on reference points is effective.

关键词： Brain Storm Optimization Algorithm Reference Point Many-objective optimization problems adaptive cluster

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