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Event-triggered-based self-organizing fuzzy neural network control for the municipal solid waste incineration process

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Science China(Technological Sciences) 2023年第4期66卷 1096-1109页

作者： HE HaiJun MENG Xi TANG Jian QIAO JunFei Faculty of Information Technology Beijing University of TechnologyBejing 100124China Beijing Laboratory of Smart Environmental Protection Beijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Bejing 100124China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Bejing 100124China Xi'an Institute of Electromechanical Information Technology Xi'an 710065China

Due to the large uncertainty in the municipal solid waste incineration(MSWI)process,the furnace temperature of the MSWI process is difficult to control and the controller is updated *** improve the accuracy and reduce the number of controller updates,a novel event-triggered control method based correntropy self-organizing TS fuzzy neural network(ETCSTSFNN)is ***,the neurons of the rule layer are grown or pruned adaptively based on activation intensity and control error to meet the dynamic change of the actual operating ***,the performance index is designed based on the correntropy of tracking errors,and the parameters of the controller are adjusted by gradient descent ***,a fixed threshold event-triggered condition is designed to determine whether the current controller is updated or *** stability of the control system is proved based on the Lyapunov stability ***,the furnace temperature control experiments are conducted based on the actual data of a municipal solid waste incineration plant in *** experimental results show that the proposed ET-CSTSFNN controller shows a better control performance,which can reduce the number of the controller update significantly while achieving accurate furnace temperature control compared with other traditional control methods.

关键词： municipal solid waste incineration furnace temperature fuzzy control event-triggered

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A Diagnosis Model for Rehabilitation Training Prescription for Spinal Cord Injury Patients Based on Multi-label Classification 8

A Diagnosis Model for Rehabilitation Training Prescription f...

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8th International Symposium on Computer Science and Intelligent control, ISCSIC 2024

作者： Qie, Botao Guo, Xin Chen, Wei Hebei University of Technology School of Artificial Intelligence Control Engineering Technology Innovation Center of Hebei Province Tianjin China Nation Research Center for Rehabilitation Technical Aids Beijing China

ISBN: (纸本)9798350380286

Spinal Cord Injury (SCI) is a serious complication that occurs after a spinal fracture. It is the key to prescribe rehabilitation training according to the condition of patients. Therefore, this research proposed a diagnostic model for rehabilitation training prescription for SCI patients based on multi-label classification. Firstly, a data set of electronic medical records of 1441 SCI patients was constructed, and the data prediction processing was completed. By effectively utilizing the correlation between labels, the performance of multi-label classification models can be greatly improved, thereby increasing the diagnosis accuracy of SCI rehabilitation training prescription. We improved the classifier chain algorithm by calculating the influence of labels to determine the order of label chains, and then using mutual information to optimize the learning features of the base classifier to predict the rehabilitation prescriptions for patients. Compared with other improved algorithms, the model shows significant improvements in many metrics. Its hamming loss and ranking loss were 0.2104 and 0.3402, and precision, recall, and F1-score were 8 0. 2 9 %, 76.25%, and 76.23% respectively. In clinical treatment, this model can provide valuable diagnostic information for the clinical treatment of SCI patients, assist medical professionals and physicians in better clinical diagnosis, and enhances the reliability of clinical decisions. © 2024 IEEE.

关键词： Electronic health record

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Small Object Detection Algorithm for Unmanned Aerial Vehicle Based on Multifaceted Feature Information Extraction and Fusion 8

Small Object Detection Algorithm for Unmanned Aerial Vehicle...

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8th International Symposium on Computer Science and Intelligent control, ISCSIC 2024

作者： Yin, Shasha Ge, Yueran Tian, Lingling Hebei University of Technology School of Artificial Intelligence and Data Science Tianjin China Hebei University of Technology Control Engineering Technology Innovation Center of Hebei Province Tianjin China

ISBN: (纸本)9798350380286

Unmanned Aerial Vehicle (UAV) image object detection has been widely applied in security and disaster relief and other aspects. For the sake of solving the problem of many small objects, this paper proposes a Multifaceted feature information extraction and fusion algorithm (MFIEF). Firstly, the multi-information feature extraction convolution module (MFECM) is adopted, which retains more small object feature information and surrounding spatial information. Secondly, the optimized FPN structure (OFPN) is used, the fusion of horizontal and vertical information avoids the loss of information, and the addition of lightweight attention convolution module (LACM) reduces the number of parameters while improving attention to key object information. The experimental results on the VisDrone datasets demonstrate that the MFIEF algorithm achieves a obvious improvement in performance compared to the benchmark algorithm. Specifically, the mAP@0.5 value of MFIEF is 9.7 percentage points higher, while the mAP@0.5:0.95 value of MFIEF is 6.7 percentage points higher than the benchmark algorithm. These findings highlight the superior performance of MFIEF in object detection tasks on the VisDrone datasets. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Integrated Enhanced Feature Fusion Module for Photovoltaic Defect Detection Network 43

Integrated Enhanced Feature Fusion Module for Photovoltaic D...

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43rd Chinese control Conference, CCC 2024

作者： Zhou, Ying Xu, Shibo Chen, Haiyong Liu, Kun Hebei University of Technology School of Artificial Intelligence Tianjin300130 China Hebei Control Engineering Research Center Tianjin300130 China

ISBN: (纸本)9789887581581

To address the challenges of high similarity between surface defect features and background features, as well as the variability in geometric shapes in solar cell surface defects, a solar cell surface defect detection network model called EMFAnet is proposed, which combines multi-scale features and attention mechanisms. Firstly, a Three-path Coordinate Attention (TCA) is designed to enhance the detection capability in the interested regions;secondly, to improve the accuracy and precision of defect localization and description, TCA is fused with deformable convolutions to form a Multi-Path Coordinate-Deformable Conv Module (MCD);lastly, to adapt to complex backgrounds and effectively detect defects at various scales, an enhanced multi-scale feature fusion method is designed. Experimental results demonstrate that the proposed approach achieves significantly higher accuracy in identifying solar cell EL defects compared to existing state-of-the-art detection algorithms. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Convolution

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Constrained Canonical Correlation Analysis for fMRI Analysis Utilizing Experimental Paradigm Information 7th

Constrained Canonical Correlation Analysis for fMRI Analysis...

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7th International Conference on Cognitive Systems and Information Processing, ICCSIP 2022

作者： Li, Ming Zhang, Yan Tang, Pengfei Hu, Dewen College of Intelligence Science and Technology National University of Defense Technology Changsha China Xi’an Satellite Control Center Xi’an China

ISBN: (纸本)9789819906161

Data-driven methods have been successfully used in functional magnetic resonance imaging (fMRI) data analysis, utilizing no explicit prior information. However, in analysis of task fMRI, incorporating prior information about paradigms would be useful to improve the power in detecting desired activations. In this paper, we incorporated the experimental paradigm information into canonical correlation analysis (CCA) model and proposed a temporal constrained CCA approach. Comparing to noise and artifact signal, the response BOLD signal from activated regions changes only after stimulus begin. By incorporating the difference before and after stimulus, the spatial patterns which respond well to stimulus-occurrence become manifest, and the activations can be detected more accurately. Comparisons on simulated data indicated that incorporating prior information about paradigm can improve the accuracy of CCA on activation detection. The proposed method obtained more accurate and robust results than conventional CCA and showed improved power in activation detection than pure data-driven methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Chemical activation

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OPS-YOLO: A Lightweight Foreign Objectors Detection Model for Overhead Power System 43

OPS-YOLO: A Lightweight Foreign Objectors Detection Model fo...

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43rd Chinese control Conference, CCC 2024

作者： Liu, Boning Shi, Rui Deng, Heng Zhang, Liguo Beijing University of Technology Faculty of Information Technology Beijing100124 China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing100124 China

ISBN: (纸本)9789887581581

The performance of modern railway transit systems heavily relies on the overhead power system (OPS). However, OPSs built on the ground are vulnerable to foreign objects that can pose a threat to railway safety. In this paper, an improved lightweight detection method called OPS-YOLO, which is based on YOLO-v5, is proposed to detect foreign objects in OPSs. First, the Ghostnet-v2 is improved to compress the lightweight backbone model to slim down the original YOLO-v5 and reduce the complexity and computation. Then, a parameter-free attention mechanism SimAM is introduced to enhance the detection precision of OPS-YOLO. Furthermore, the Focal-EIoU is chosen as the loss function of the OPS-YOLO to improve object border localization precision while accelerating bounding box regression speed. To evaluate the performance of OPS-YOLO, we generate a customized dataset of OPS scenarios by using augmentation methods to expand the existing samples and improve training results. Finally, we extensive experimental tests on the dataset are conducted to verify the proposed method. Results show that the proposed OPS-YOLO improves the mAP by 0.5% compared to the classical YOLO-v5s network. Additionally, OPS-YOLO compresses the model parameters by 37.9% and reduces the model size by 37.5%. It is demonstrated that OPSYOLO can achieve a balance between computational efficiency and detection accuracy, making it suitable for automating OPS supervision and reducing reliance on human inspection. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Convolutional neural networks

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Dissolved oxygen concentration control in wastewater treatment process based on reinforcement learning

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Science China(Technological Sciences) 2023年第9期66卷 2549-2560页

作者： DU ShengLi CHEN PeiXi HAN HongGui QIAO JunFei School of Artificial Intelligence and Automation Beijing University of TechnologyBeijing 100124China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of EducationBeijing 100124China Beijing Laboratory of Smart Environmental Protection Beijing 100124China

In this article, the dissolved oxygen(DO) concentration control problem in wastewater treatment process(WWTP) is *** existing control strategies that control DO concentration at a fixed value, here we develop a different control *** the proposed control framework, an intelligent control method of DO concentration based on reinforcement learning(RL)algorithm is presented to resolve the DO concentration control problem. By using the deep deterministic policy gradient(DDPG)algorithm, the DO concentration of the fifth tank in the activated sludge reactor can be adjusted dynamically. In addition, by designing two different reward functions and by analysing the relationships among effluent quality, energy consumption, and DO concentration, the target of energy-saving and emission-reducing is achieved. The simulation results indicate that the designed control method can reduce energy consumption while ensuring that the effluent quality meet the specified standards.

关键词： dissolved oxygen concentration wastewater treatment process intelligent control reinforcement learning energy-saving and emission-reducing

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Spatial Coordination of Multiple Nonholonomic Agents With Sensory Connectivity Maintenance

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3425-3435页

作者： Chen, Xi Chen, Meimin Zhu, Lijun Chai, Li Wuhan University of Science and Technology Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan430081 China Shandong Huayu University of Technology School of Electrical Engineering Shandong253034 China School of Artificial Intelligence and Automation Wuhan430074 China Zhejiang University College of Control Science and Engineering Hangzhou310027 China

This article aims to propose a general control strategy for coordination of multiple nonholonomic agents in three dimensional space. For real-world applications, since the field sensor equipped on the mobile agents for local information detection and estimation has some limited detecting range, it is necessary to guarantee that the nearby agents must stay within this range of the onboard sensor. This is called sensory connectivity maintenance. A function termed as coordination function with sensory connectivity maintenance (CFSCM) is defined to describe the performances of the coordination as well as the sensory connectivity status between the agents. Then, a general control strategy is designed based on the proposed CFSCM for spatial coordination of multiple nonholonomic agents with sensory connectivity maintenance. Moreover, the applications of the proposed control strategy for formation with omnidirectional sensors and flocking with directional sensors are shown, respectively. Finally, some numerical examples are conducted to validate the theoretical analysis. © 2013 IEEE.

关键词： Maintenance Robot sensing systems Kinematics Vectors Electronic mail Aerospace electronics Three-dimensional displays System dynamics Robot kinematics Multi-agent systems

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Dynamic Memristor based Cyclic Echo State Network for Time Series Prediction 4

Dynamic Memristor based Cyclic Echo State Network for Time S...

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4th International Conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Li, Guangli Xia, Lei Fu, Jun Duan, Shukai Tang, Jianfeng Wang, Lidan Southwest University College of Artificial Intelligence Chongqing China National Local Joint Engineering Research Center of Intelligent Transmission and Control Technology Chongqing China

ISBN: (纸本)9798350394375

Compared with traditional recurrent neural networks, the training cost of echo-state network computing is lower because the neurons in the reservoir are randomly connected. We only need to train the weight of the connection between the reservoir and the output layer. Due to its high accuracy and low training cost for time series forecasting tasks, it is widely used as a time series forecasting model in many fields. It is very important to provide rich reservoir state for echo state network system, which is the key to hardware implementation. In this paper, we propose a cyclic echo state network (DC-ESN) based on dynamic memristors, which generates rich reservoir states through cyclic structure, and can predict complex time series well even with a single memristor. Compared with the traditional echo state network, the size of the reservoir is reduced and the prediction accuracy is greatly improved. Our work can provide an idea for more efficient memristor based echo state network systems in the future to cope with complex time series prediction tasks. © 2024 IEEE.

关键词： Memristors

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M2PCode - - A Mathematical Kit Applied in the Pocket Lab for Engineering Education

M2PCode - - A Mathematical Kit Applied in the Pocket Lab for...

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2023 China Automation Congress, CAC 2023

作者： Zhou, Xingwei Liu, Guo-Ping Hu, Wenshan Lei, Zhongcheng Wuhan University Department of Artificial Intelligence and Automation Wuhan China Center for Control Science and Technology Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9798350303759

This paper introduces a mathematical tool called M2PCode integrated in the pocket lab of M2PLab (Mathematics to Practice Laboratory) for engineering education. The M2PCode allows users to write and execute M codes in the pocket laboratory conveniently, in which case, users do not need to relay on the mathematical calculation software such as Matlab for the lightweight calculation. M2PCode proposed in this paper has the contribution of the lightweight design that enables users to perform simple math operations in the pocket laboratory and reinforces the functionality of the pocket laboratory. Besides, for the reason that M2PCode is as a stand-alone feature in the pocket laboratory of the M2PLab, the operational efficiency is determined by the hardware performance of the pocket laboratory. This paper adopts the Raspberry Pi as the hardware where the M2PLab is deployed. Based on that, the operational efficiency of M2PCode is quiet well. © 2023 IEEE.

关键词： Laboratories

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