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2024 China Automation Congress, CAC 2024

作者： Wu, Ji Wang, Kai Ma, Wenwen Zhang, Jingxin Wang, Shibo China Electric Power Research Institute Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing210096 China State Grid Shandong Electric Power Research Institute Jinan China

ISBN: (纸本)9798350368604

Accurate ultra-short-term photovoltaic (PV) power forecasting is crucial for the real-time scheduling of grid systems. However, the inherent variability of solar energy makes this task extremely challenging. To enhance PV power forecasting, this paper introduces a novel hybrid model named PVTimesNet, designed to harness the strengths of Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) for effective feature extraction. PVTimesNet consists of two parallel branches: the CNN1d branch, which captures correlations between multiple variables and adjacent time steps, and the LSTM branch, which learns the temporal dependencies within the PV power sequences. The features extracted by both branches are then concatenated and passed through a fully connected layer to generate multi-step PV power forecasts. Experimental results demonstrate that for forecast horizons of 1 to 4 hours, the proposed model significantly outperforms individual models and other CNN-LSTM hybrid structures. Additionally, it exhibits superior performance compared to related methods for longer forecast horizons. © 2024 IEEE.

关键词： Hybrid power

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Finite-Time Observer Based Integral Terminal SMC for Converter-Driven Motor systems with Mismatched Disturbances

Finite-Time Observer Based Integral Terminal SMC for Convert...

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2023 China Automation Congress, CAC 2023

作者： Zhang, Zhongding Guo, Zeyu Wang, Zuo Li, Shihua School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Shenzhen Research Institute Southeast University Shenzhen518063 China

ISBN: (纸本)9798350303759

Converter-driven motor systems play crucial roles in contemporary industrial applications owning to the advantages of smooth starting and stepless speed regulation. For such fourth-order systems with both immeasurable states and mismatched disturbances, a finite-time control solution is proposed to address the speed tracking problem in this paper. Firstly, a finite-time observer is constructed to precisely estimate the immeasurable states and mismatched disturbances. Then, based on integral terminal sliding mode control (ITSMC), a continuous finite-time controller is designed for the systems to achieve both rapid response and superior speed tracking performance. Furthermore, a rigorous global finite-time stability analysis for the closed-loop system is presented. Finally, simulation results validate the effectiveness of the proposed control method. As compared with existing methods, the composite ITSMC algorithm design demonstrates reliable finite-time speed tracking performance and exceptional capability in disturbance rejection. © 2023 IEEE.

关键词： Disturbance rejection

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Fault diagnosis of bearings based on deep separable convolutional neural network and spatial dropout

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Chinese Journal of Aeronautics 2022年第10期35卷 301-312页

作者： Jiqiang ZHANG Xiangwei KONG Xueyi LI Zhiyong HU Liu CHENG Mingzhu YU School of Mechanical Engineering and Automation Northeastern UniversityShenyang 110819China Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of EducationNortheastern UniversityShenyang 110819China Liaoning Province Key Laboratory of Multidisciplinary Design Optimization of Complex Equipment Northeastern UniversityShenyang 110819China Angang Steel Company Limited Anshan 114021China

Bearing pitting,one of the common faults in mechanical systems,is a research hotspot in both academia and *** fault diagnosis methods for bearings are based on manual experience with low diagnostic *** study proposes a novel bearing fault diagnosis method based on deep separable convolution and spatial dropout *** separable convolution extracts features from the raw bearing vibration signals,during which a 3×1 convolutional kernel with a one-step size selects effective features by adjusting its *** similarity pruning process of the channel convolution and point convolution can reduce the number of parameters and calculation quantities by evaluating the size of the weights and removing the feature maps of smaller *** spatial dropout regularization method focuses on bearing signal fault features,improving the independence between the bearing signal features and enhancing the robustness of the model.A batch normalization algorithm is added to the convolutional layer for gradient explosion control and network stability *** validate the effectiveness of the proposed method,we collect raw vibration signals from bearings in eight different health *** experimental results show that the proposed method can effectively distinguish different pitting faults in the bearings with a better accuracy than that of other typical deep learning methods.

关键词： Batch normalization Convolutional neural network Fault diagnosis Similarity pruning Spatial dropout

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Distributed adaptive stabilization for stochastic nonlinear systems with unknown boundaries: a novel fixed-time stability in probability

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Nonlinear Dynamics 2025年 1-15页

作者： Zhu, Jiahui Xie, Liping Zhang, Kanjian The Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China Advanced Ocean Institute of Southeast University Nantong China

This paper investigates the adaptive fixed-time stabilization problem for a class of strict-feedback stochastic nonlinear systems with unknown parameter vectors and unbounded but estimable nonlinear boundary functions. By employing distributed Lyapunov functions for state-space partitioning and distinct error transformations, this paper ensures system states converge to zero in a fixed-time regardless of initial conditions and stochastic disturbances. This paper introduces a distributed controller design that uniquely combines adaptive backstepping with the adding one power integrator method, restricting tracking errors to a positive invariant set to avoid the inherent singularity risks in traditional backstepping-based stochastic control. Unlike existing fixed-time control approaches for stochastic systems, which are typically limited to bounded uncertainties or rely on fuzzy logic with high computational overhead, this paper promotes a method handles fully unknown and unbounded nonlinear boundaries by means of adaptive estimation, thereby completely eliminating the redundant terms inherent in fuzzy approximations and enhancing 30–40% computing efficiency. Two simulations evaluate the robustness and efficacy of the proposed method under diverse initial conditions and stochastic disturbances, using settling time within the theoretically derived fixed convergence time to validate its practical applicability.

关键词： Fixed-time control Adaptive control Stochastic nonlinear systems

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Research on Rolling Bearing Fault Diagnosis Based on Classification Generative Adversarial Network

Research on Rolling Bearing Fault Diagnosis Based on Classif...

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UNIfied Conference of International Workshop on Defence Applications of Multi-Agent systems, DAMAS 2023, International Conference on Maintenance Engineering, IncoME-V 2023, International conference on the Efficiency and Performance Engineering Network, TEPEN 2023

作者： Kang, Ma Hongjun, Wang School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing100192 China Beijing International Science and Technology Cooperation Base for Intelligent Sensing and Control of High-End Equipment Beijing100192 China Beijing Key Laboratory of Measurement and Control of Electromechanical System Beijing100192 China

ISBN: (纸本)9783031494123

In practical engineering, it is usually difficult to label monitoring data, and the fault diagnosis accuracy is not high in strong noise environment. To solve the above problems, this paper uses Cwt-CatGAN method to process signals and diagnose faults. In this method, CWT is first used to convert one-dimensional original vibration signals into two-dimensional time–frequency graphs as the input of classification generation admixture network. Then, the CatGAN model is obtained through the adversarial training process, and fake samples similar to the extraction distribution of CWT are generated. Finally, the input samples are clustered into certain categories. In addition, the performance of the Cwt-CatGAN method is verified by using the classical rotating machine data set, the experiment is compared with the unsupervised fault diagnosis model CatAAE. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Failure analysis

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Multi-task Learning Based Single Temporal Remote Sensing Image Change Detection 12

Multi-task Learning Based Single Temporal Remote Sensing Ima...

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12th International Conference on Computing and Pattern Recognition, ICCPR 2023

作者： Yang, Peixuan Cao, Yichao Lu, Xiaobo College of Software Engineering Southeast University JiangSu NanJing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education JiangSu NanJing China School of Automation Southeast University JiangSu NanJing China

ISBN: (纸本)9798400707988

Change Detection is a classic and fundamental task in remote sensing(RS), and has accomplish remarkable achievement on bitemporal image based methods. However, bitemporal images are very expensive and needs professional researchers to align and label the paired images. Furthermore, most bitemporal image based methods are only semantic-aware not instance-aware, which indicates these methods only use paired images' semantic information. Thus, we propose a method that only needs unpaired RS images with semantic labels. In this paper, we introduce multi-task learning into change detection progress, which comprises four major parts: Boundary-Aware Module, Contextual-Aware Module, Segmentation Module, Change Detection Module. At first, we construct Boundary-Aware Module and Contextual-Aware Module to extract sharp boundary and dense contextual features. Then we feed the feature maps to Change Detection Module and Segmentation Module to accomplish the subtasks. The comprehensive experiments on a single temporal image dataset demonstrate the efficiency of our approach over other bitemporal image based methods, and the experimental results shows our approach is competitive with other methods while all methods was trained on bitemporal image dataset. © 2023 ACM.

关键词： Change detection

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A Flexible Antenna Array Design for Microwave Energy Transmission 17th

A Flexible Antenna Array Design for Microwave Energy Transmi...

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17th Annual Conference of China Electrotechnical Society, CES 2022

作者： Chen, Jinghan Guo, Siwei Nie, Yongjie Liu, Siyang Wang, Ping State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing University Chongqing China Yunnan Key Laboratory of Green Energy Electric Power Measurement Digitalization Control and Protection Yunnan Power Grid Co. Ltd. Kunming China

ISBN: (纸本)9789819904075

Microstrip antenna as a kind of receiver front-end equipment of microwave energy transmission system, which plays an important role on the overall transmission efficiency of the system. Conventional solid metal antennas have poor mechanical properties and are susceptible to plastic deformation and cracks under external forces, which will degrade their performance. Gallium-indium alloy (EGaIn), which is liquid at room temperature, has similar electrical properties to copper and can recover itself after bending, which can make up for the shortcoming of solid-state antennas;Polyethylene naphthalate two formic acid glycol ester (PEN) is an organic polymer material, which has similar electrical properties and superior mechanical properties to conventional materials. Based on the above two materials, this paper proposes a microstrip antenna array based on flexible materials, which can endure a wide range of mechanical deformation and has better ductility and mechanical toughness. The simulation results indicate that the proposed antenna array can operate at 2.45 Hz and achieve 90% efficiency of conventional antennas, which can better meet the demand of microwave energy transmission. © 2023, Beijing Paike Culture Commu. Co., Ltd.

关键词： Microstrip antennas

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A Novel Hybrid Transformer-Based Framework for Solar Irradiance Forecasting under Incomplete Data Scenarios

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IEEE Transactions on Industrial Informatics 2024年第6期20卷 8605-8615页

作者： Zhang, Hanjin Li, Bin Su, Shun-Feng Yang, Wankou Xie, Liping Southeast University School of Economics and Management Nanjing210096 China National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106 Taiwan Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Nanjing210096 China Southeast University Shenzhen Research Institute Shenzhen518063 China

Accurate prediction of solar irradiance is crucial for the effective utilization of solar energy. However, in real-world scenarios, complex irradiance patterns and prevalent incomplete data pose challenges to precise forecasting, resulting in additional uncertainties and instability. To address these issues, this study proposes a novel irradiance forecasting model that integrates a Mask-Transformer data imputation module and a prediction module centered around the typical patterns representation mechanism. The Mask-Transformer leverages a mask modeling mechanism to model the context of missing data, facilitating accurate estimation of missing values and reducing noise and uncertainty in the input data. The typical patterns representation mechanism comprises a series decomposition module and a feature fusion module, providing the module with the capability to mitigate nonlinearity and nonstationarity in solar irradiance data. This enhancement leads to improved short-term forecasting performance while maintaining long-term forecasting capabilities. Experimental results on two datasets demonstrate that the proposed model exhibits sufficient robustness and accuracy, making it effective in scenarios with incomplete data. © 2005-2012 IEEE.

关键词： Forecasting

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Tissue Surface-enhanced Raman Spectroscopy Combined with Machine Learning Algorithms for Rapid Detection of Alveolar Echinococcosis 25

Tissue Surface-enhanced Raman Spectroscopy Combined with Mac...

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

作者： Huang, Jiahui Zheng, Xiangxiang Lv, Guodong Li, Xiaojing Wu, Guohua Xu, Liang Tianjin University of Technology Tianjin Key Laboratory of Complex System Control Theory and Application Tianjin China The First Affiliated Hospital of Xinjiang Medical University State Key Laboratory of Pathogenesis Prevention and Treatment of High Incidence Diseases in Central Asia Urumqi China Beijing University of Posts and Telecommunications School of Electronic Engineering Beijing China

ISBN: (纸本)9798350366600

Alveolar echinococcosis (AE) remains a serious worldwide public health problem as a very harmful human tapeworm disease. Rapid and accurate diagnosis of AE is crucial for treatment and postoperative recovery. This study explored the applicability of tissue surface Raman spectroscopy (SERS) combined with machine learning for rapid AE detection. Specifically, liver tissue section samples were obtained by creating an AE mouse model and SERS spectra were acquired. Five kinds of machine learning methods were used to construct AE diagnosis model, among which the linear kernel support vector machine (SVM) model had the best classification result, and the accuracy rate reached 95.7%. Then, the linear SVM model was subjected to feature significance analysis, which demonstrated that the biochemicals corresponding to the spectra of 641,716 and 1372 cm-1 might be potential biomarkers. Gaussian noise is further added to test the noise interference resistance of different machine learning algorithms, and it is found that SVM with Gaussian radial basis function (RBF) kernel function has better noise interference resistance. The results show that tissue SERS spectroscopy combined with SVM algorithm for rapid detection of AE has a promising application. © 2024 IEEE.

关键词： Raman scattering

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A Power Management Circuit with Supercapacitors for Magnetic Field Energy Harvesting system in Power Transmission Lines 6

A Power Management Circuit with Supercapacitors for Magnetic...

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6th International Conference on Power and Energy Technology, ICPET 2024

作者： Gao, Yuan Fan, Libo Han, Rongjie Huang, Jiabin Liu, Ming Sun, Min Power System Wide Area Measurement and Control Key Laboratory of Sichuan Province University of Electronic Science and Technology Chengdu China State Grid Zhejiang Electric Power Company Hangzhou China

ISBN: (纸本)9798350375855

For magnetic energy harvesting systems, power management circuits play a very important role in the system. This paper introduces an energy management circuit for the energy collection system, which can efficiently deposit the collected energy into the load. The energy management circuit is mainly composed of supercapacitor and MCU. The supercapacitor is used as a start-up excitation and energy storage element. A low power MCU is employed to control the synchronous rectifier circuit Three operating modes based on the primary transmission line current are proposed. The system can switch between the three operating modes autonomously. The system is verified through experiments and effectively achieves steady-state operation and efficient energy management. © 2024 IEEE.

关键词： energy management circuits magnetic field energy harvesting sensor node supercapacitor

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