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5th International Conference on Computers and Artificial Intelligence technology, CAIT 2024

作者： Xu, Yanchao Lv, Zheng Zhao, Jun Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education School of Control Science and Engineering Dalian China

ISBN: (纸本)9798331530891

With the rapid development of Industry 4.0, resource-constrained industrial devices are often unable to meet different demands of compute-intensive and latency-sensitive tasks in modern factories. To cope with this challenge, offloading tasks from industrial devices to edge servers or a cloud computing center to obtain lower delay is a good solution. Most of the traditional task offloading approaches rely on centralized decision-making systems, which may not be able to efficiently cope with device heterogeneity and the specific needs of different tasks in industrial environments. To address this problem, this study proposes a more comprehensive edge computing network model for Industrial Internet of Things (IIoT). By integrating an action masking technique, we establish a Markov decision model with a constrained action space. Additionally, we propose a task offloading framework based on the Multi-Agent Advantage Actor Critic (MAA2C) algorithm, adopting the Centralized Training Decentralized Execution (CTDE) architecture, aiming to achieve task offloading decision-making in modern factories and reduce the overall system delay. Validated through numerical simulation experiments, our proposed framework not only performs well in reducing system delay but also shows good adaptability in different industrial environments. © 2024 IEEE.

关键词： Action Masking Edge Computing Industrial Internet of Things Multi-agent Advantage Actor Critic Task Offloading

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Modeling of Fluid Catalytic Cracking System Based on Improved ELM-Hammerstein Model

Modeling of Fluid Catalytic Cracking System Based on Improve...

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2023 China Automation Congress, CAC 2023

作者： Zhang, Enhao Zhu, Haibo Zhou, Fan Zhao, Jun Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian China School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798350303759

The complex process mechanism of fluid catalytic cracking (FCC) and the strong nonlinearity of multi-variable coupling bring significant challenges to the model predictive control (MPC) of FCC production process models. This study presents a modeling method of FCC system based on improved extreme learning machine Hammerstein (IELM-Hammerstein). Aiming at the nonlinear characteristics of FCC process, the Hammerstein model's static nonlinear subsystem is constructed using an extreme learning machine (ELM). Considering the instability of the algorithm caused by random weights and thresholds in the ELM, the sparrow search algorithm (SSA) is adopted to calculate the parameters, then one recursive least square (RLS) method is derived from calculating the linear subsystem regression parameters of Hammerstein model. To prove the effectiveness of this method, the comparative experiments with the traditional identification method are carried out, and this method is applied to modeling and control of the fractionating system in a petroleum refinery. The experimental results indicates that the proposed method guarantees the modeling accuracy of the fractionating system, and improves the tracking control performance. © 2023 IEEE.

关键词： Fluid catalytic cracking

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A Novel Principal Component Analysis Algorithm for Fault Detection

A Novel Principal Component Analysis Algorithm for Fault Det...

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第43届中国控制会议

作者： Wang Shenhang Tian Kun Zheng Wei Liu Mengyu National Key Laboratory of Science and Technology on Aerospace Intelligent Control

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In modern industry,fault detection is very important in high-cost and safety-critical *** component analysis(PCA) is an effective data-driven approach which is widely used in this *** PCA is time-invariant,which often lead to false *** order to overcome the false alarms,recursive PCA algorithms have been proposed for adaptive ***,the amount of calculation of the algorithm is very great because of frequently updated *** this paper,a novel recursive PCA method is proposed for solving the question by introducing the standard residual *** standard residual method serves as an indicator of the level of consistency among data,which is used to decide whether or not to update the PCA *** proposed method is applied to fault detection and experiment results demonstrate the effectiveness of the method.

关键词： Principal component analysis fault detection standard residual method

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Bearing Fault Diagnosis Based on an Improved Morphological Filter

Bearing Fault Diagnosis Based on an Improved Morphological F...

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Ren, Xueping Guo, Liangjian Qiu, Fuqiang Liu, Tongtong School of Mechanical Engineering Inner Monglia University of Science and Technology Baotou014010 China Inner Monglia Key Laboratory of Intelligent Diagnosis and Control of Mechatronic System Baotou014010 China

ISBN: (纸本)9783031734069

Extraction of repetitive shock signals from vibration signals is crucial in bearing fault detection. Morphological filtering can extract the signal components based on the geometric features of the signal. In this paper, we propose an improved morphological filter for extracting fault cycle pulses from vibration signals in the time domain. Morlet wavelets are used as the main waveforms of the structural elements, which guide the construction of the structural elements based on their correlation with the defect-induced pulse, the Teager energy operator (TEO) is used to reduce the modulated signal interference. The improved morphological filtering algorithm can obtain fault features from low signal-to-noise ratio signals. The processing results of one simulated signal and two sets of experimental signals verify the effectiveness and robustness of the method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Wiener filtering

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Deep Co-Training Partial Least Squares Model for Semi-Supervised Industrial Soft Sensing

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3363-3371页

作者： Zheng, Junhua Zhou, Le Ye, Lingjian Ge, Zhiqiang Zhejiang University of Science and Technology School of Automation and Electrical Engineering Hangzhou310023 China Huzhou University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou313000 China Southeast University School of Mathematics Nanjing210096 China

Data-driven soft sensing has become quite popular in recent years, which can provide real-time estimations of key variables in industrial processes. While the introduction of deep learning does improve the prediction performance, it is highly restricted to the number of labeled training data, as well as large computational burden and cumbersome parameter tuning procedures. How to break through the bottleneck of data-drive models in terms of limited labeled data and high computational complexity should be one of the main recent focuses in the field of industrial soft sensing. In this article, a deep co-training PLS (deep CT-PLS) model is proposed to extend the ordinary PLS model to the semi-supervised deep form. While the deep model can efficiently extract inherent natures of process data, the co-training strategy makes lots of unlabeled data useful through a two-view cross training and annotation process. In this case, the performance restriction of the deep PLS model can be greatly relieved, with the incorporation of additional unlabeled data, while at the same time the designed model structure keeps in a low computational complexity. Based on the case study on a real industrial production process, the deep CT-PLS model can significantly improve the soft sensing performance. © 2013 IEEE.

关键词： Self-supervised learning

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Fault Diagnosis of Rolling Bearing in Broad Learning System Based on Multi-domain Feature Selection

Fault Diagnosis of Rolling Bearing in Broad Learning System ...

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Ouyang, Bing Zhang, Chao Wu, Le Liu, Guiyi Wu, Yangbiao Xu, Shuai School of Mechanical Engineering Inner Monglia University of Science and Technology Baotou014010 China Inner Monglia Key Laboratory of Intelligent Diagnosis and Control of Mechatronic System Baotou014010 China

ISBN: (纸本)9783031734069

This paper presents a novel solution for bearing fault diagnosis based on a Broad Learning System with multi-domain feature selection. Currently, the diagnosis of bearing faults is beset with numerous challenges, including operation under complex and fluctuating conditions, the subtlety of early fault indicators, noise interference in measurement signals, variations in operational conditions, and a broad spectrum of potential fault types. These factors collectively contribute to the complexity of accurately identifying and classifying bearing faults. The Broad Learning System introduced in this paper, by integrating multi-domain feature selection, enhances the model's capability to extract and utilize relevant features from noisy or incomplete data, potentially increasing diagnostic accuracy in suboptimal sensor environments. By leveraging features across multiple domains, the method based on the Broad Learning System can capture a wider array of fault characteristics, thereby broadening its applicability across various types of bearing faults and operational conditions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Roller bearings

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Knowledge Graph-Based Image Recognition Transfer Learning Method for On-Orbit Service Manipulation

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space(science & technology) 2021年第1期2021卷 164-172页

作者： Ao Chen Yongchun Xie Yong Wang Linfeng Li Beijing Institute of Control Engineering BeijingChina Science and Technology on Space Intelligent Control Laboratory BeijingChina

Visual perception provides state information of current manipulation scene for control system,which plays an important role in on-orbit service *** the development of deep learning,deep convolutional neural networks(CNNs)have achieved many successful applications in the field of visual *** CNNs are only effective for the application condition containing a large number of training data with the same distribution as the test data;however,real space images are difficult to obtain during large-scale ***,deep CNNs can not be directly adopted for image recognition in the task of on-orbit service *** order to solve the problem of few-shot learning mentioned above,this paper proposes a knowledge graph-based image recognition transfer learning method(KGTL),which learns from training dataset containing dense source domain data and sparse target domain data,and can be transferred to the test dataset containing large number of data collected from target *** average recognition precision of the proposed method is 80.5%,and the average recall is 83.5%,which is higher than that of ResNet50-FC;the average precision is 60.2%,and the average recall is 67.5%.The proposed method significantly improves the training efficiency of the network and the generalization performance of the model.

关键词： Service Visual manipulation

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A spintronic memristive circuit on the optimized RBF-MLP neural network

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Chinese Physics B 2022年第11期31卷 272-283页

作者： Yuan Ge Jie Li Wenwu Jiang Lidan Wang Shukai Duan School of Artificial Intelligence Southwest UniversityChongqing 400715China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715China Brain-inspired Computing and Intelligent Control of Chongqing Key Laboratory Chongqing 400715China National&Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715China

A radial basis function network(RBF)has excellent generalization ability and approximation accuracy when its parameters are set ***,when relying only on traditional methods,it is difficult to obtain optimal network parameters and construct a stable model as *** view of this,a novel radial basis neural network(RBF-MLP)is proposed in this *** connecting two networks to work cooperatively,the RBF’s parameters can be adjusted adaptively by the structure of the multi-layer perceptron(MLP)to realize the effect of the backpropagation updating ***,a genetic algorithm is used to optimize the network’s hidden layer to confirm the optimal neurons(basis function)number *** addition,a memristive circuit model is proposed to realize the neural network’s operation based on the characteristics of spin *** is verified that the network can adaptively construct a network model with outstanding robustness and can stably achieve 98.33%accuracy in the processing of the Modified National Institute of Standards and technology(MNIST)dataset classification *** experimental results show that the method has considerable application value.

关键词： radial basis function network(RBF) genetic algorithm spintronic memristor memristive circuit

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Memristor-based multi-synaptic spiking neuron circuit for spiking neural network

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Chinese Physics B 2022年第4期31卷 225-233页

作者： Wenwu Jiang Jie Li Hongbo Liu Xicong Qian Yuan Ge Lidan Wang Shukai Duan College of Artificial Intelligence Southwest UniversityChongqing 400715China National&Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715China Brain-inspired Computing and Intelligent Control of Chongqing Key Laboratory Chongqing 400715China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715China

Spiking neural networks(SNNs) are widely used in many fields because they work closer to biological ***,due to its computational complexity,many SNNs implementations are limited to computer ***,this paper proposes a multi-synaptic circuit(MSC) based on memristor,which realizes the multi-synapse connection between neurons and the multi-delay transmission of pulse *** synapse circuit participates in the calculation of the network while transmitting the pulse signal,and completes the complex calculations on the software with ***,a new spiking neuron circuit based on the leaky integrate-and-fire(LIF) model is designed in this *** amplitude and width of the pulse emitted by the spiking neuron circuit can be adjusted as *** combination of spiking neuron circuit and MSC forms the multi-synaptic spiking neuron(MSSN).The MSSN was simulated in PSPICE and the expected result was obtained,which verified the feasibility of the ***,a small SNN was designed based on the mathematical model of *** the SNN is trained and optimized,it obtains a good accuracy in the classification of the IRIS-dataset,which verifies the practicability of the design in the network.

关键词： memristor multi-synaptic circuit spiking neuron spiking neural network(SNN)

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Hand medical monitoring system based on machine learning and optimal EMG feature set

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Personal and Ubiquitous Computing 2023年第6期27卷 1991-2007页

作者： Yu, Mingchao Li, Gongfa Jiang, Du Jiang, Guozhang Tao, Bo Chen, Disi Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China Precision Manufacturing Research Institute Wuhan University of Science and Technology Wuhan430081 China Research Center of Biologic Manipulator and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China 3D Printing and Intelligent Manufacturing Engineering Institute Wuhan University of Science and Technology Wuhan430081 China School of Computing University of Portsmouth PortsmouthPO1 3HE United Kingdom

Considering that serious hand function damage will greatly affect the daily life of patients, its recovery mainly depends on the regular inspection and manual training of medical staff, and medical monitoring based on bioelectric signals can largely replace manual re-examination as autonomous rehabilitation technology. So, for the rationality of feature selection and the diversity of classifier design in the gesture recognition process based on electromyography (EMG) signals, this paper proposes a hand medical monitoring system based on feature selection method of feature subset average recognition rate and optimal machine learning algorithm selection, which mainly depends on the prediction of hand movement. At the same time, since most experiments are conducted in different non-public proprietary databases, the comparison between various gesture recognition methods can only be analyzed to a certain extent. Therefore, this paper uses the DB1 dataset in the large publicly available NinaPro database and combines with presently well-known 11 time-domain (TD) features and 5 frequency domain (FD) features, then uses the support vector machine (SVM) classifier to comparative analysis total 136 feature combinations under various feature numbers. Under the premise of ensuring the overall recognition rate of electromyography gesture, this method will be able to reduce the number of features in feature set, according to the change of the average remove redundant features, and construct an optimal reduced EMG feature set. Finally, through the four common hand motion classifiers based on machine learning: SVM, back propagation neural network, linear discriminant analysis, and K-nearest neighbor, this paper tests and verifies the separability of the optimal reduced EMG feature set, and based on this, selects the optimal hand motion classifier to build the optimal hand motion recognition system, improve the hand medical monitoring system, and provide technical reference for the c

关键词： Discriminant analysis

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