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第41届中国控制会议

作者： Xinyi Xu Qilin Qu Yanwei Wang Ying Zheng Housheng Su China-Belt and Road Joint Laboratory On Measurement and Control Technology School of Artificial Intelligence and AutomationHuazhong University of Science and Technology School of Mechanical & Electrical Engineering Wuhan Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The intelligent bearing diagnosis with big data has been widely *** high performance diagnosis models are constantly being proposed,these models are difficult to apply in some practical application with limited collectable *** this paper,an Improved Metric Space Support Vector Machine with a two-stage learning strategy is proposed to overcome the challenge of few-shot scenarios under target domain ***,an improved triplet loss is designed in the metric network to select features by utilizing the similarity of ***,kernel method is used to model in few-shot scenarios by maximizing the inter-class *** on CWRU dataset verify the superiority and robustness of IMSSVM comparing with popular baseline ***,the effectiveness of IMSSVM is verified with visualizing the feature embeddings by t-distributed stochastic neighbor embedding.

关键词： fault diagnosis few-shot learning metric learning kernel method

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Adaptive Sliding-Mode Fault Tolerant control for Delayed Nonlinear Systems with Actuator Fault 7

Adaptive Sliding-Mode Fault Tolerant Control for Delayed Non...

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7th International Conference on control Science and Systems engineering, ICCSSE 2021

作者： Zhang, Hongxu Hu, Jun Yu, Xiaoyang Chen, Dongyan Harbin University of Science and Technology School of Measurement Control Technology Communication Engineering Department of Mathematics Harbin China Harbin University of Science and Technology Department of Mathematics Harbin China Harbin University of Science and Technology School of Measurement Control Technology Communication Engineering Harbin China

ISBN: (纸本)9781665444057

This paper focuses on the sliding mode fault-tolerant control (SMFTC) problem for a class of delayed nonlinear systems in presence of actuator fault and nonlinear input. Firstly, the augmented strategy involving system state, actuator fault and external disturbance is constructed, and then the fault-tolerant observer is designed accordingly. An SMFTC law ensuring the asymptotical stability of the reduce-order estimation error is provided subsequently. To further illustrate the system performance, a new sliding function with respect to decomposed signal is constructed and the asymptotically stability of sliding motion is analyzed. Moreover, a novel adaptive-based sliding mode controller is presented to steer the system state onto the sliding manifold. Finally, a verification example is considered to demonstrate the usefulness of the proposed SMFTC approach. © 2021 IEEE.

关键词： Sliding mode control

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Threshold Selection for Circuit Board Nodes Based on LSTM Method 6

Threshold Selection for Circuit Board Nodes Based on LSTM Me...

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6th International Conference on Intelligent control, measurement and Signal Processing, ICMSP 2024

作者： Liu, Huimin Lv, Fangxing Liu, Jingjing Xi'an Shiyou University Xi'an China Xi'an Shiyou University School of Electronic Engineering Xi'an China Key Laboratory of Oil and Gas Well Measurement and Control Technology Shaanxi Province Xi'an China

ISBN: (纸本)9798331516550

With the advancement of modern electronic integration technology, the complexity of circuit board composition has significantly increased, making fault detection more challenging. This study proposes a fault diagnosis method based on the current and voltage test values of circuit nodes. By setting up test points on the circuit board, the method determines whether the board is functioning correctly by evaluating if the test values fall within the specified thresholds. Leveraging existing circuit board test data, the study first performs data cleansing, followed by sliding window processing. Long Short-Term Memory (LSTM) networks are then employed to predict the test values for each window, and the 3σ method is used for conditional judgment to derive appropriate thresholds. This approach enhances detection accuracy and the ability to identify subtle anomalies, thereby significantly improving the reliability and precision of circuit board fault detection without damaging the circuit board. © 2024 IEEE.

关键词： Long short-term memory

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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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Dunhuang murals image inpainting method based on fusion classification pre-training model

Dunhuang murals image inpainting method based on fusion clas...

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2024 International Conference on Culture-Oriented Science and technology, CoST 2024

作者： Ren, Hui Zhang, Chengya Sun, Ke Ye, Chenyu Communication University of China School of Information and Communication Engineering Beijing100024 China Ministry of Culture and Tourism Key Laboratory of Audiovisual Technology and Intelligent Control System Beijing100024 China Beijing Key Laboratory of Modern Performing Arts Technology Beijing100024 China University of Pennsylvania School of Engineering and Applied Science PA19104 United States

ISBN: (纸本)9798350380347

Murals are an important part of China's cultural heritage. Because of their age, Dunhuang murals have suffered from discoloration, fading and damage. With the development of computer image restoration technology, digital image processing provides strong support for mural restoration. Although there are many research results in the field of ancient mural images, there is no pre-trained model based on ancient mural images available for migration. There is a method for Dunhuang murals image restoration based on Generative Adversarial Network (GAN) with improved ResNet50 structure as an encoder in the generator. This research result proposes a generative adversarial network model with a parallel double convolutional extraction of deep features for the generator, combined with a ternary heterogeneous joint discriminator. On this basis, this paper proposes a binary classification task to distinguish ancient mural images from modern natural style images, so that the model pre-learns features such as color, texture, and structure of mural images, which are inputted into the restoration network as a priori information, in order to achieve the effect of reducing the probability of incomplete restoration of the model, and better generalization ability. The experimental results show that the method proposed in this paper has an overall improvement in the evaluation indexes such as Peak Signal to Noise Ratio(PSNR) and Structural Similarities(SSIM) when compared with other methods, and the restored images are more in line with human subjective vision, which proves the validity of the method. © 2024 IEEE.

关键词： Generative adversarial networks

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Mainlobe DRFM Jamming Suppression via Blind Source Separation Using Tensor Decomposition 8

Mainlobe DRFM Jamming Suppression via Blind Source Separatio...

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8th International Conference on Signal and Image Processing, ICSIP 2023

作者： Dong, Luxin Wang, Bing Pan, Bunian Kong, Yukai Yu, Xianxiang Cui, Guolong University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Science and Technology on Electronic Information Control Laboratory Chengdu610036 China

ISBN: (纸本)9798350397932

This paper deals with the suppression problem of mainlobe digital radio frequency memory (DRFM) jamming especially for intra-pulse slice forwarding jamming for phased array radar. A novel mainlobe DRFM jamming suppression method resorting to blind source separation based on tensor decomposition (TD-BSS) is proposed to separate the target echo signal and jamming signal. Specifically, the received signals are reconstructed into higher-order tensor form. Further, the separating matrix optimization problem relying on tensor Canonical Polyadic (CP) decomposition principle is formulated and then solved by an enhanced line search alternating least square (ELS-ALS) algorithm. Finally, the performance of the proposed method is demonstrated through numerical simulations, showing its superior to suppress mainlobe DRFM jamming over the exiting method in terms of interrupted sampling and cyclic repeater jamming (ISCRJ) and chopping and interval (C&I) jamming. © 2023 IEEE.

关键词： Blind source separation

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ADAPTIVE DETECTORS WITH ENHANCED SELECTIVITY PROPERTIES IN THE COMPOUND GAUSSAIN CLUTTER WITH GERNERALIZED INVERSE GAUSSIAN TEXTURE

ADAPTIVE DETECTORS WITH ENHANCED SELECTIVITY PROPERTIES IN T...

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IET International Radar Conference 2023, IRC 2023

作者： Wang, Zeyu Wang, Zhirui Sun, Xiaona State Key Laboratory of Networking and Switching Technology School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Beijing Institute of Radio Measurement Beijing China

ISBN: (纸本)9781839539954

In this paper, the problem of adaptive detection of signals in the compound Gaussian clutter when the signal mismatch occurs is discussed. The texture follows the generalized inverse Gaussian distribution which includes inverse Gaussian, inverse gamma and gamma as three special examples. To increase the mismatch rejection performance, the binary hypothesis test is modified and a fictitious signal is introduced under the null hypothesis. Two adaptive mismatch selective detectors are derived according to the maximum a posteriori test and generalized likelihood ratio test. The performance evaluation shows that the two proposed adaptive detectors have stronger mismatch rejection performance than the conventional detector. © The Institution of engineering & technology 2023.

关键词： Clutter (information theory)

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Collision-Free Formation control of Multi-agent System with Low Oscillation 7th

Collision-Free Formation Control of Multi-agent System with ...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Li, Jingyi Han, Haoran Lv, Maolong Sun, Chenyang Cheng, Jian Glassgow School University of Electronic Science and Technology of China Chengdu611731 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China Air Traffic Control and Navigation College Air Force Engineering University Xian710051 China

ISBN: (纸本)9789819733279

In recent years, multi-agent systems are becoming more complex, scalable, adaptive, and integrating machine learning for enhanced capabilities. A popular approach for addressing this challenge involves the utilisation of artificial potential functions (APF). In this article, we address the problem of obstacle avoidance and achieving a predefined formation, particularly promoting smooth trajectory generation in multi-agent formation control. We employ the APF method to enhance the smoothness of the agents’ movement paths during these tasks. Collision damping terms are generated by differentiating the gradient of APF with respect to time. Incorporating generated damping terms in the APF control, we suppress irregular fluctuations during agents’ motion procedures. To ensure stability in the multi-agent system, we design an energy-based Lyapunov function and utilise it for proof. Simulation examples are presented to validate the theoretical results and demonstrate the advantages of the proposed control algorithm. Through these simulations, it becomes evident that the collision-free formation control algorithm enhances the smoothness of the agents’ trajectories. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multi agent systems

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Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting

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Digital communications and Networks 2023年第5期9卷 1221-1229页

作者： Weishan Zhang Xiao Chen Ke He Leiming Chen Liang Xu Xiao Wang Su Yang College of Computer Science and Technology China University of Petroleum(East China)China Sichuan Energy Internet Research Institute Tsinghua UniversityChina School of Computer and Communication Engineering Beijing University of Science and TechnologyChina State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science Fudan UniversityChina

Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power *** deep-learning-based methods can perform well if there are sufficient training data and enough computational ***,there are challenges in building models through centralized shared data due to data privacy concerns and industry *** learning is a new distributed machine learning approach which enables training models across edge devices while data reside *** this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM *** design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting *** evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.

关键词： Photovoltaic power forecasting Federated learning Edge computing CNN-LSTM

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An Acoustic Dataset Construction Method of Chinese Traditional Musical Instrument Guqin

An Acoustic Dataset Construction Method of Chinese Tradition...

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2024 International Conference on Culture-Oriented Science and technology, CoST 2024

作者： Liu, Jingyu Yang, Chen Li, Zijin Jiang, Wei State Key Laboratory of Media Convergence and Communication Communication University of China 100024 China Ministry of Culture and Tourism Communication University of China Key Laboratory of Acoustic Visual Technology and Intelligent Control System 100024 China Communication University of China School of Information and Communication Engineering 100024 China Central Conservatory of Music Ai Music and Music Information Technology Department 100032 China

ISBN: (纸本)9798350380347

With the acceleration of modernization and the rapid development of popular music, the splendid culture of Guqin, a type of Chinese traditional musical instrument which has lasted for nearly 1,000 years, is facing a serious inheritance crisis. Therefore, how to make full use of modern technical means to digitally store and manage the valuable traditional music cultural resources is the current *** response to the aforementioned challenges, this study focuses on the Guqin, an emblematic Chinese traditional musical instrument. By employing advanced audio feature extraction techniques, this research systematically analyzes the diverse playing techniques of Guqin, thereby establishing a comprehensive acoustic dataset. This endeavor not only facilitates the digital preservation and management of the Guqin's musical heritage but also pioneers a standardized framework for the creation of acoustic datasets. This framework serves as a benchmark for the development of similar datasets, thereby promoting the conservation and standardization of resources associated with Chinese traditional musical instruments. This will provide a scientific basis for the inheritance, standardized management and construction of more acoustic datasets of ethnic musical instruments in the future. © 2024 IEEE.

关键词： Musical instruments

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