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Flare Forecast Model Based on DS-SMOTE and SVM with Optimized Regular Term

Research in Astronomy and Astrophysics 2023年第6期23卷 38-46页

作者： Jie Wan Jun-Feng Fu Ren-Qing Wen Ke Han Meng-Yao Yu Peng E School of Electrical Engineering and Automation Harbin Institute of TechnologyHarbin 150001China Laboratory for Space Environment and Physical Sciences Harbin Institute of TechnologyHarbin 150001China School of Computer and Information Engineering Harbin University of CommerceHarbin 150028China

The research of flare forecast based on the machine learning algorithm is an important content of space *** order to improve the reliability of the data-driven model and weaken the impact of imbalanced data set on its forecast performance,we proposes a resampling method suitable for flare forecasting and a Particle Swarm Optimization(PSO)-based Support Vector Machine(SVM)regular term optimization *** the problem of intra-class imbalance and inter-class imbalance in flare samples,we adopt the density clustering method combined with the Synthetic Minority Over-sampling Technique(SMOTE)oversampling method,and performs the interpolation operation based on Euclidean distance on the basis of analyzing the clustering space in the minority *** the same time,for the problem that the objective function used for strong classification in SVM cannot adapt to the sample noise,In this research,on the basis of adding regularization parameters,the PSO algorithm is used to optimize the hyperparameters,which can maximize the performance of the ***,through a comprehensive comparison test,it is proved that the method designed can be well applied to the flare forecast problem,and the effectiveness of the method is proved.

关键词： Sun:flares Sun:magnetic fields Sun:X-rays gamma-rays (Sun:)sunspots

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Optimized shearing strategy for heavy plate based on contour recognition

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Journal of Iron and Steel Research International 2023年第9期30卷 1821-1833页

作者： Jian-zhao Cao Yu-xia Wang Shao-wen Huang Chang-tao Wang School of Computer Science and Engineering Shenyang Jianzhu UniversityShenyang 110168LiaoningChina Shandong Iron and Steel Co. Ltd.Jinan 271105ShandongChina School of Electrical and Control Engineering Shenyang Jianzhu UniversityShenyang 110168LiaoningChina

The shearing line is the key to improve the quality and efficiency of heavy plates.A model of contour recognition and intelligent shearing strategy for the heavy plate was ***,multi-array binocular vision linear cameras were used to complete the image ***,the total length of the steel plate after cooling was predicted by back propagation neural network algorithm according to the contour ***,using the scanning line and a new camber description method,the shearing strategy including head/tail irregular shape length and rough dividing strategy was *** practical application shows that the model and strategy can effectively solve the problems existing in the shearing process and can effectively improve the yield of steel *** maximum error of detection width,length,camber,and the length of the irregular deformation area at the head/tail of the plate are all less than 5 *** correlation coefficient of the length prediction model based on the back propagation neural network is very *** reverse ratio result of edge cutting failure using the proposed rough dividing strategy is 1/401=0.2%,which is 2%higher than that by human.

关键词： Heavy plate Contour detection Intelligent shearing strategy Length prediction Neural network model

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Improved target detection algorithm based on YOLOv5s 24

Improved target detection algorithm based on YOLOv5s

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6th International Conference on control and computer Vision, ICCCV 2024

作者： Chen, Boyang Han, Chunxiao Wu, Han Tianjin Key Laboratory of Information Sensing & Intelligent Control School of Automation and Electrical Engineering Tianjin University of Technology and Education China

ISBN: (纸本)9798400718045

Target detection is a research hotspot in the field of computer vision and machine learning, and the deep learning model greatly improves the efficiency of target detection and classification by obtaining feature information and classification through multi-layer neural networks, which is the mainstream algorithm for target detection at present. In this paper, YOLOv5s target detection algorithm is improved, and the SIoU loss function is chosen to replace the CIoU loss function used in the original model, while the SimAM attention mechanism is added to the Neck module of the YOLOv5s model, and it is proved through experiments that the improved model improves the average precision (mAP@0.5) by 0.2% over the original model, and the recall rate (Recall) is improved by 0.8%. The faster convergence and improved precision of the model further improves the performance of the model. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Adversarial machine learning

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Research on Automotive Parts Recognition Based on Improved YOLOv5 Algorithm

Research on Automotive Parts Recognition Based on Improved Y...

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2024 International Conference on Remote Sensing, Mapping, and Image Processing, RSMIP 2024

作者： Xu, Yu Hu, Shan Ming, Rui Zhang, Tianzhi School of Automation and Electrical Engineering Tianjin University of Technology and Education Key Laboratory of Information Sensing & Intelligent Control Tianjin China

ISBN: (纸本)9781510680012

With the continuous development of the automotive industry, the target detection and recognition of automotive parts have become crucial factors for automakers to enhance automation levels. In this paper, to ensure improved detection accuracy while meeting real-time detection requirements, we propose an enhanced YOLOv5 model for automotive parts recognition. This method optimizes candidate box parameters using the K-Means algorithm, addresses feature mismatch issues through variational convolution, and optimizes the loss function using GIOU instead of IOU. Experimental results show that the improved YOLOv5 algorithm achieves an average precision of 99.1%, representing a relative improvement of 1.72% over Fast R-CNN and 3.91% over the standard YOLOv5 algorithm. Furthermore, it demonstrates excellent real-time detection performance, meeting the demands of practical application scenarios. © 2024 SPIE.

关键词： Object recognition

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Position Regulation of Industrial Robots via Bounded Integral Terminal Sliding Mode control Algorithm

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IEEE Transactions on Industrial Electronics 2024年第7期71卷 7403-7412页

作者： Cong, Yongzheng Du, Haibo Zhu, Wenwu Chen, Chih-Chiang Hefei University of Technology School of Electrical Engineering and Automation Hefei230009 China Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China National Cheng Kung University Department of Systems and Naval Mechatronic Engineering Tainan70101 Taiwan

For the position regulation problem of six degrees of freedom (6-DOF) industrial robots, a novel bounded finite-time position regulate algorithm is developed and employed to improve the dynamic performance of the industrial robots. First, based on the model feature, a bounded finite-time position regulate law is proposed to overcome input saturation constraints for the considered systems. Rigorous analysis guarantees the finite-time stability of the closed-loop system in the absence of disturbance. Then, to improve the disturbance rejection property of the robot systems in the presence of disturbance, an improved bounded finite-time control law based on the integral terminal sliding mode is constructed. Finally, simulations and experimental results are provided to show the effectiveness of the proposed bounded control law. © 1982-2012 IEEE.

关键词： Asymptotic stability

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Offline Parameter Identification for Medium-Voltage Permanent Magnet Synchronous Machine 8

Offline Parameter Identification for Medium-Voltage Permanen...

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8th International Conference on Power Energy Systems and Applications, ICoPESA 2024

作者： Zhang, Lei Shi, Shengdong Feng, Ming Chen, Yufeng School of Automation and Electrical Engineering Chengdu Technological University Chengdu China Sichuan Institute for Food and Drug Control Chengdu China

ISBN: (纸本)9798350351668

This paper describes a novel offline parameter identification technique, specifically for medium voltage interior permanent magnet synchronous machine driven by Cascaded H-Bridge inverter. Accurate identification of parameters is indispensable to correct decoupling of current loop in vector control. Unlike low voltage permanent magnet synchronous machines commonly driven by the conventional six-switch inverter, the proposed method specifically focuses on medium voltage applications and takes advantage of the topology of cascaded H bridge to tune machine parameters of resistance, d-axis inductance, q-axis inductance and magnetic flux linkage. By properly modifying the topology structure, the accuracy of identification can be improved with issues like voltage drop across semiconductor devices and low duty ratio considered. In the end, the paper presents simulation results using proposed technique, followed by theoretical analysis as proof of the feasibility of the proposed method. © 2024 IEEE.

关键词： Permanent magnets

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Automatic SOC Equalization Strategy of Energy Storage Units with DC Microgrid Bus Voltage Support

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Energy engineering 2024年第2期121卷 439-459页

作者： Jingjing Tian Shenglin Mo Feng Zhao Xiaoqiang Chen School of Automation&Electrical Engineering Lanzhou Jiaotong UniversityLanzhou730070China Key Laboratory of Opto-Technology and Intelligent Control(Lanzhou Jiaotong University) Ministry of EducationLanzhou730070China

In this paper,an improved sag control strategy based on automatic SOC equalization is proposed to solve the problems of slow SOC equalization and excessive bus voltage fluctuation amplitude and offset caused by load and PV power variations in a stand-alone DC *** strategy includes primary and secondary *** them,the primary control suppresses the DC microgrid voltage fluctuation through the I and II section control,and the secondary control aims to correct the P-U curve of the energy storage system and the PV system,thus reducing the steady-state bus voltage *** simulation results demonstrate that the proposed control strategy effectively achieves SOC balancing and enhances the immunity of bus *** proposed strategy improves the voltage fluctuation suppression ability by approximately 39.4%and 43.1%under the PV power and load power fluctuation conditions,***,the steady-state deviation of the bus voltage,△U_(dc) is only 0.01–0.1 V,ensuring stable operation of the DC microgrid in fluctuating power environments.

关键词： Automatic equalization independent DC microgrid improve droop control secondary control state of charge

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Lightweight Tunnel Leakage Water Detection Algorithm Based on YOLOv8n 25

Lightweight Tunnel Leakage Water Detection Algorithm Based o...

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25th IEEE China Conference on System Simulation Technology and its Application, CCSSTA 2024

作者： Wang, Shiwen Mo, Jiaqing Xu, Liang Zheng, Xinqi Xinjiang University School of Computer Science and Technology Urumqi China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin China

ISBN: (纸本)9798350366600

Tunnel leakage is one of the biggest hazards to shield tunnels, this paper proposes a lightweight tunnel water leakage target detection algorithm that improves YOLOv8n for the shortcomings of the existing tunnel water leakage target detection algorithms, such as poor precision and too many model parameters. To make feature fusion richer and reduce the model parameters, this paper proposes to use a bi-directional feature pyramid network on the head network. In addition, this paper uses multi-scale convolutional attention to adapt to the complex features of the leakage area in the tunnel. Finally, use the dilated reparam block to reduce the computational costs and expand the receptive field. The algorithm in this paper not only improves the detection accuracy compared to the original algorithm, but also significantly reduces the model parameters and computational costs. In addition, the superiority of this paper's algorithm is verified through ablation experiments and comparisons with other mainstream target detection algorithms. © 2024 IEEE.

关键词： Target detection Tunnel leakage YOLOv8n

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Event-triggered adaptive neural network control design for stochastic nonlinear systems with output constraint

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International Journal of Adaptive control and Signal Processing 2024年第1期38卷 342-358页

作者： Shen, Fei Wang, Xinjun Pan, Xinxin School of Electrical and Automation Changshu Institute of Technology Suzhou China College of Information Science and Engineering Shandong Normal University Jinan China College of Computer and Information Hohai University Nanjing China

This paper is concerned with the adaptive neural network event-triggered control (ETC) problem for stochastic nonlinear systems with output constraint. The influence of stochastic disturbance inevitably exists in many practical systems, which leads to system instability. Meanwhile, a novel tan type barrier Lyapunov function (Tan-BLF) structure is proposed to deal with the constraint requirements of stochastic systems. In the sense of probability, the output constraints will not be violated during the operation of the system. In addition, the ETC strategy is adopted to reduce the burden of communication. The asymptotic stability of the closed-loop system is guaranteed without violating output constraints. Meanwhile, the tracking error converges to a small region of the origin. Two simulations results demonstrate the effectiveness of theoretical analysis. © 2023 John Wiley & Sons Ltd.

关键词： Lyapunov functions

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Short-term Load Forecasting of an Integrated Energy System Based on STL-CPLE with Multitask Learning

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Protection and control of Modern Power Systems 2024年第6期9卷 71-92页

作者： Suxun Zhu Hengrui Ma Laijun Chen Bo Wang Hongxia Wang Xiaozhu Li Wenzhong Gao the College of Energy and Electrical En-gineering Qinghai UniversityXining 810016China the School of Electrical and Automation Wuhan UniversityWuhan 430072China the Department of Electrical and Computer Engineering University of DenverDenver CO 80208USA Engineering Research Center for Renewa-ble Energy Generation and Grid Connection Technology Ministry of EducationXinjiang UniversityUrumchi 830046China

Multienergy loads in integrated energy sys-tems(IESs)exhibit strong volatility and randomness,and existing multitask sharing methods often encounter nega-tive migration and seesaw problems when addressing complexity and competition among *** line with these considerations,a short-term multienergy load joint prediction method based on seasonal-trend decomposition using LOESS(STL)and convolutional progressive lay-ered extraction(CPLE)is proposed,called ***,STL is applied to model regular and uncertain load information into interpretable trend,seasonal,and re-sidual ***,joint modeling is performed for the same type of components of multienergy loads.A one-dimensional convolutional neural network(1DCNN)is constructed to extract deeper feature *** approach works in concert with the progressive layered extraction sharing method,and convolutional shared and task-specific experts are developed to acquire common and distinctive representations of multienergy loads, re-spectively. Task-specific parameters are gradually sepa-rated through progressive routing. Finally, a subtask network is built to learn temporal dependencies using long short-term memory (LSTM). Simulation validation is performed on the IES dataset at the Tempe campus of Arizona State University, and the experiments show that the STL-CPLE method exhibits higher prediction accu-racy than do the other methods.

关键词： Integrated energy system multienergy load forecasting convolutional progressive layer extrac-tion network seasonal-trend decomposition.

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