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A Multi-model Estimation of Distribution Algorithm for Agent Routing Problem in Multi-point Dynamic Task

A Multi-model Estimation of Distribution Algorithm for Agent...

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第37届中国控制会议

作者： Sai Lu Bin Xin Lihua Dou Ling Wang School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Advanced Innovation Center for Intelligent Robots and Systems Department of Automation Tsinghua University

The agent routing problem in multi-point dynamic task（ARP-MPDT） is a multi-task routing problem of a mobile agent. In this problem, there are multiple tasks to be carried out in different locations. As time goes on, the state of each task will change nonlinearly. The agent must go to the task points in turn to perform the tasks, and the execution time of each task is related to the state of the task point when the agent arrives at the point. ARP-MPDT is a typical NP-hard optimization problem. In this paper, we establish the nonlinear ARP-MPDT model. A multi-model estimation of distribution algorithm（EDA） employing node histogram models（NHM） and edge histogram models（EHM） in probability modeling is used to solve the ARP-MPDT. The selection ratio of NHM and EHM probability models is adjusted adaptively. Finally, performance of the algorithm for solving the ARP-MPDT problem is verified by the computational experiments.

关键词： Estimation of distribution algorithm Multi-model Routing Multi-point dynamic task

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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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Identification of protein complex based on a novel multi-objective method

Identification of protein complex based on a novel multi-obj...

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第37届中国控制会议

作者： Zhu Yuan Peng Xiaoyu Wu Chong School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System China University of Geosciences

Protein complexes are the key molecular entities which plays an indispensable role in our life activities. systematic identification of protein complexes is an important application of data mining in bioscience. The existing computation methods of detecting protein complexes are usually based on the topological properties of protein-protein interaction(PPI) network ***, limited by the inherent single structure of the PPI network, the mining of protein complexes may not be fully *** this paper, we propose an original multi-objective optimization strategy based protein complex detection method, which is used Adaptive Multi-Objective Black Hole algorithm, called AMOBH. In the selection of objective function strategy, we integrate the topological structure of PPI network data with semantic similarity based on GO(Gene Ontology) annotation data. Experiments demonstrate that the method we adopt provide more convinced results and higher computational efficiency compared with the existing prediction measures.

关键词： protein-protein interaction networks protein complex Gene Ontology Black hole algorithm

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A multiobjective flexible job shop scheduling method based on deep reinforcement learning 9

A multiobjective flexible job shop scheduling method based o...

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9th International Symposium on Sensors, Mechatronics, and Automation system, ISSMAS 2023

作者： Liu, Weiqi Chen, Hao Zhang, Jianming Wang, Yaozong College of Computer and Cyber Security Fujian Normal University Fujian Fuzhou China Quanzhou Institute of Equipment Manufacturing Haixi Institutes Chinese Academy of Science Fujian Quanzhou China Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System Fujian Quanzhou China

ISBN: (纸本)9781510672802

To address the multiobjective flexible job shop scheduling problem, this paper establishes a Markov decision Process for Multiobjective Flexible Job-Shop Scheduling Problem, aiming to minimize makespan and total machine load. Additionally, a multiobjective optimization algorithm base deep reinforcement learning is proposed. Firstly, a multiobjective decomposition strategy is introduced to decompose the problem into a set of subproblems, and graph neural networks with identical structures are employed to represent the priority dispatching rules for each subproblem. Experimental results demonstrate that this approach exhibits better generalization, higher solution quality, and faster convergence compared to previous methods. © 2024 SPIE.

关键词： deep reinforcement learning flexible job shop scheduling problem multi-objective optimization

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ICB-MOEA/D: An interactive classification-based multi-objective optimization algorithm

ICB-MOEA/D: An interactive classification-based multi-object...

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第37届中国控制会议

作者： Bin Xin Hepeng Li Ling Wang School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Advanced Innovation Center for Intelligent Robots and Systems Department of Automation Tsinghua University

Interactive multi-objective optimization algorithms have developed rapidly in recent years. In this paper, we propose a new classification-based interactive multi-objective optimization algorithm named ICB-MOEA/D to solve the formulated multiobjective optimization problem. ICB-MOEA/D provides several solutions for the decision maker to choose. The decision maker chooses his/her most preferred solution from these solutions and the historical solutions which have been chosen as the current most preferred solution. ICB-MOEA/D records this solution and classifies the objectives according to the updated preference information into four categories: 1) objectives which are expected to be improved;2) objectives which can be sacrificed;3) objectives which are expected to remain basically unchanged;4) objectives which do not matter currently. Accoding to the number of the objectives in the first category, a new single-objective opitimization model or multi-objective optimization model will be built. The single-objective optimization model will be optimized by a classic variant of differential evolution DE/rand/1/bin, and the multi-objective optimization model will be optimized by a popular docomposition-based multi-objective optimizer MOEA/*** the classifications are done automatically by the algorithm, reducing the burden of the decision maker. ICB-MOEA/D was tested on the two-objective instance ZDT1, and the experiment results show the effectiveness of ICB-MOEA/D.

关键词： interactive multi-objective optimization decomposition classification virtual decision maker

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Immovable Cultural Relics Preservation Through 3D Reconstruction Using NeRF 5

Immovable Cultural Relics Preservation Through 3D Reconstruc...

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5th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2023

作者： Lai, Zhiqing Lian, Mingchang Chen, Songhang Chen, Guoqing Lai, Xiaoling Quanzhou Institute of Equipment Manufacturing Haixi Institutes Chinese Academy of Sciences Quanzhou362200 China Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System Quanzhou362200 China Jingwei Space Information Technology Co. Ltd Longyan364030 China

ISBN: (纸本)9781643684840

Urban and rural development poses significant threats to immovable cultural relics, leading to their degradation and potential extinction. To address this issue, 3D reconstruction has emerged as an effective preservation method. However, traditional radar-based techniques require close scanning and extensive manual intervention, limiting their efficiency. This paper explores the application of neural radiance fields (NeRF) for visually reconstructing immovable cultural relics. NeRF employs a fully connected non-convolutional neural network to generate high-quality 3D models from 2D images. The study discusses NeRF's sampling strategy, positional encoding, field architecture, and rendering process. Experimental investigations evaluate the impact of Fourier parameters on reconstruction quality. A case study reconstructing Tianhou Temple using NeRF is presented, comparing it with ContextCapture software. All results demonstrate that NeRF offers an efficient, and cost-saving solution for reconstructing and preserving immovable cultural relics, providing accurate and realistic models with superior structural integrity. © 2024 The Authors.

关键词： Image reconstruction

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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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A Weighting Factors Autotuning Strategy for Model Predictive Torque control of Permanent Magnet Synchronous Motor Based on Adaptive Multi-objective Black Hole Algorithm

A Weighting Factors Autotuning Strategy for Model Predictive...

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第42届中国控制会议

作者： Zixiang Zhou Tao Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems System Integration and Industrial Robotics Laboratory

The rational design of weighting factors in the cost function for finite control set model predictive torque control(FCS-MPTC) has been a matter of great interest in power electronics and electrical drives. In order to solve this problem, a weighting factors autotuning strategy for FCS-MPTC of permanent magnet synchronous motor(PMSM) based on the adaptive multi-objective black hole algorithm(AMOBH) is proposed. In this paper, the design process of the FCS-MPTC algorithm is first analyzed in detail. Then, an AMOBH algorithm that can take into account both population convergence and population diversity is introduced, and based on this algorithm, the design problem of the weighting factors is successfully transformed into a multiobjective optimization problem by means of reconstructing the cost function and designing the motor operation information collected in real time as the objective functions of the multi-objective optimization algorithm. Simulation results show that the proposed method can find a set of weighting factor combinations suitable for different working condition requirements, and these weighting factors can effectively improve the operation performance of the PMSM system.

关键词： Model Predictive Torque control Weighting Factors Multi-objective Optimization Algorithm Permanent Magnet Synchronous Motor

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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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Temperature Rising Model of Tubular Permanent Magnet Linear Synchronous Motor Based on Transfer Learning-Deep Neural Network

Temperature Rising Model of Tubular Permanent Magnet Linear ...

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第42届中国控制会议

作者： YiFei Wang Tao Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems System Integration and Industrial Robotics Laboratory

High temperature rise of permanent magnet linear synchronous motor can lead to irreversible demagnetization of the motor permanent magnet, which can negatively affect the motor performance. To address this problem, a thermal modeling analysis method based on Transfer learning-Deep neural network(TL-DNN) was proposed in this paper. Its specific implementation steps include(1) corresponding to different heat source inputs, the equivalent thermal circuit method and the finite element analysis was adopted based on the structure and main parameters of PMLSM including overall average temperature rise, coil temperature rise and permanent magnet temperature rise from the data sets of the motor;(2) TL-DNN was used to fit the functional relationship between the input source features and the output targets based on the characteristics of the sample data sets. In order to verify the accuracy of the prediction model with small sample data sets, this paper divided the proportion of the data set and compared the results with other classic nonparametric models(random forest, support vector machine, and deep neural network). The results show that the TL-DNN model outperforms other machine learning models and has better robustness and generalization ability when the training datasets are insufficient, and achieves an organic combination of physical field path model and data-driven model, which provides a feasible solution for PMLSM modeling in the case of small samples.

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