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arXiv 2020年

作者： Peng, Gang Yang, Jin Khyam, Mohammad Omar Key Laboratory of Image Processing and Intelligent Control Ministry of EducationB School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Automation South East University Nanjing China School of Engineering and Technology Central Queensland University Melbourne Australia

To improve the efficiency of deep reinforcement learning (DRL)-based methods for robot manipulator trajectory planning in random working environments, we present three dense reward functions. These rewards differ from the traditional sparse reward. First, a posture reward function is proposed to speed up the learning process with a more reasonable trajectory by modeling the distance and direction constraints, which can reduce the blindness of exploration. Second, a stride reward function is proposed to improve the stability of the learning process by modeling the distance and movement distance of joint constraints. Finally, in order to further improve learning efficiency, we are inspired by the cognitive process of human behavior and propose a stage incentive mechanism, including a hard stage incentive reward function and a soft stage incentive reward function. Extensive experiments show that the soft stage incentive reward function is able to improve the convergence rate by up to 46.9% with the state-of-the-art DRL methods. The percentage increase in the convergence mean reward was 4.4-15.5% and the percentage decreases with respect to standard deviation were 21.9-63.2%. In the evaluation experiments, the success rate of trajectory planning for a robot manipulator reached 99.6%. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Research on Attitude control of Quadrotor UAV Based on Active Disturbance Rejection control

Research on Attitude Control of Quadrotor UAV Based on Activ...

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Chinese control and Decision Conference, CCDC

作者： Zuling Song Yongji Wang Lei Liu Zhongtao Cheng Ye Yang National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Science and Technology on Aerospace Intelligent Control Laboratory Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

Aiming at enhancing the disturbance suppression ability of attitude control of quadrotor UAV (Unmanned Aerial Vehicle), design an ADRC(Active Disturbance Rejection control) based on quadrotor UAV. Different from the traditional trial adjustment of the parameter b0 in ADRC design, it can be derived according to the characteristics of the quadrotor model in this paper. Firstly, a non-linear kinematic model of a quadrotor UAV is established, and then each module of the ADRC is designed and debugged by modules. Finally, the system is simulated and verified. The experimental results show that ADRC has a stronger disturbance suppression capability than PID control, which greatly improves the stability of the system and enhances the drone's flight control ability in complex environments.

关键词： Attitude control Unmanned aerial vehicles Propellers Aircraft Robust control Atmospheric modeling

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Parity space-based model mismatch detection for linear discrete time-invariant systems with unknown disturbances

Parity space-based model mismatch detection for linear discr...

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Chinese control Conference (CCC)

作者： Yi Tang Dan Ling Hong Zhang Yanewei Wang Ying Zheng the Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Electrical and Information Engineering Zhengzhou University of Light Industry Zhengzhou China School of Mechanical & Electrical Engineering Wuhan Institute of Technology Wuhan China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

Model mismatch is one of the main factors of control performance degradation. In this paper, a new model mismatch detection approach with parity space-based methods is proposed for linear discrete time-invariant (LDTI) systems. This paper establishes a parity space-based residual evaluation function for the detection of model mismatch. The problem of parity space-based model mismatch is formulated on a state space model of a LDTI system. A standard evaluation function and its thresholds for the detection of model mismatch are proposed by providing the parity model-based residuals. It is shown the proposed approach results in an optimal fault detectability without any assumptions on the probability distribution of unknown disturbances. The effectiveness of the proposed methods is demonstrated by the simulation study of the paper machine head box control example.

关键词： Generators System performance Benchmark testing Sensitivity Degradation Fault detection Aerospace electronics

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Artificial intelligence-based methods for renewable power system operation

Nature Reviews Electrical Engineering

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Nature Reviews Electrical Engineering 2024年第3期1卷 163-179页

作者： Yuanzheng Li Fei Hu Juntao Duan Yong Zhao Zhigang Zeng Yizhou Ding Shangyang He Guanghui Wen Hua Geng Zhengguang Wu Hoay Beng Gooi Chenghui Zhang Shengwei Mei Key Laboratory of lmage Information Processing and Intelligent Control of Ministry of Education of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China National Key Laboratory of Multispectral Information Intelligent Processing Technology Huazhong University of Science and Technology Wuhan China China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan China Department of Systems Science School of Mathematics Southeast University Nanjing China Department of Automation Tsinghua University Beijing China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China School of Electrical and Electronics Engineering Nanyang Technological University Singapore Singapore School of Control Science and Engineering Shandong University Jinan China Department of Electrical Engineering Tsinghua University Beijing China

Carbon neutrality goals are driving the increased use of renewable energy (RE). Large-scale use of RE requires accurate energy generation forecasts; optimized power dispatch, which minimizes costs while satisfying operational constraints; effective system control to ensure a stable power supply; and electricity markets that support bidding and trading decisions associated with RE. However, the uncertainties in RE generation make renewable power systems challenging to operate. For example, the intermittent nature of wind power can make it difficult to balance the supply and demand of electricity in real time; therefore, traditional power sources could be needed to meet the demand, which can increase electricity prices. This Review outlines the potential of artificial intelligence-based methods for supporting renewable power system operation. We discuss the ability of machine learning, deep learning and reinforcement learning methods to facilitate power system forecasts, dispatch, control and markets to support the use of RE. We also emphasize the applicability of these techniques to different operational problems. Finally, we discuss potential trends in renewable power system development and approaches to address the associated operational challenges such as the increasingly distributed nature of RE installations, diversification of energy storage systems and growing market complexity.

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Improving magnetic nanothermometry accuracy through mixing-frequency excitation

arXiv

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arXiv 2020年

作者： Guo, Silin Liu, Jay Du, ZhongZhou Liu, Wenzhong School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China Ningbo Chuanshanjia Electrical and Mechanical Co. Ltd. Ningbo315400 China

In this study, we proposed a temperature model of magnetic nanoparticle relaxation and a phase measurement method under a mixing-frequency excitation field, which can improve the temperature accuracy of magnetic nanothermometry. According to the Debye-based magnetization model for magnetic nanoparticles, the phases at the mixing frequencies are used to solve the relaxation phase delay to the magnetic field with the higher frequency. The method could improve the signal-to-noise ratio of the magnetic response signal, and also weaken the phase shift of the detection coils caused by temperature changes. Experimental results show that the method can achieve static temperature measurement error less than 0.1K and dynamic temperature measurement error less than 0.2K. © 2020, CC-BY.

关键词： Temperature measurement

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Pining control Algorithm for Complex Networks

Pining Control Algorithm for Complex Networks

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第三十八届中国控制会议

作者： Bingjun Wang Hui Liu Jiangnqiao Xu Jiaqi Liu School of Artificial Intelligence and Automation & Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology

In this paper, we quote the concept of resistance distance for Pinning control, and discuss the relationship between the upper bound of the minimum eigenvalue of the grounded Laplacian Matrix and the resistance distance of the Pinning control for one single node. Our simulation results in various networks like BA network, ER network, NW network etc. show that the ranking of the minimum eigenvalue of the grounded Laplacian Matrix is highly consistent with the ranking of resistance distance,much better than the degree ranking, which provides us another approach to estimate the node for Pinning control. In the case of controlling multiple(l) nodes, we use the iterative algorithm to calculate the grounded Laplacian Matrix for better combination of the controlled nodes, through which the computation of NP-hard problem of controlling l nodes can be greatly reduced, and the effect of the iterative algorithm are presented by simulation calculations in different networks,and the smaller l the better effect.

关键词： Complex dynamical network grounded Laplacian matrix pinning control spectral properties

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Gram Matrix Based Transistor Open-Circuit Fault Diagnosis Method for Voltage-Source Inverter Fed Vector controlled Induction Motor Drives

Gram Matrix Based Transistor Open-Circuit Fault Diagnosis Me...

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International Conference on Power Electronics and Motion control (IPEMC)

作者： Yang Zhou Feng Wu Jin Zhao School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Imaging Processing and Intelligence Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Computer Science and Engineering Nanyang Technological University

ISBN: (数字)9781728153018

ISBN: (纸本)9781728153025

This paper proposed a novel, real-time and high efficiency diagnostic method for Insulated Gate Bipolar Transistor(IGBT) open-circuit fault in pulse-width-modulated (PWM) voltage- source inverter fed vector-controlled induction motor. Similarity among three phase currents of inverter can be used to detect transistor open-circuit faults according to the topological analysis of inverter operations both in healthy and failure condition. In this paper, Gram-matrix of three phase currents is proposed to extract features, the eigenvalues of the matrix are proposed to measure the similarity and diagnose transistor open-circuit fault. The proposed method is fast and fair-robust, both single and multiple faults on the same leg can be effectively detected and located. Experimental results evaluate efficiency and merits of proposed diagnostic method.

关键词： Fault diagnosis Voltage source inverters Feature extraction Real-time systems Eigenvalues and eigenfunctions Transistors Voltage control

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NiuEM: A Nested-iterative Unsupervised Learning Model for Single-particle Cryo-EM image processing

NiuEM: A Nested-iterative Unsupervised Learning Model for Si...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Rui Hu Jiaming Cai Wangjie Zheng Yang Yang Hong-Bin Shen Shanghai Jiao Tong University and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Cryo-electron microscopy (cryo-EM) has become a mainstream technology for solving spatial structures of biomacromolecules, while the processing of cryo-EM images is a very challenging task. One of the great challenges is the high noise in the images. A common method is to cluster the images with close projecting angles to get mean images, which are used for 3D reconstruction. However, due to the extremely low signal-to-noise-ratio, common clustering methods often fail to obtain high-quality mean images, leading to poorly reconstructed structures. In this study, we present a new unsupervised learning framework, called NiuEM, to discriminate images captured from different angles and yield cluster-mean images. NiuEM first generates pseudo-labels and then exploits both contrastive loss and cross-entropy loss for training convolutional layers to learn feature representations. Moreover, the pseudo-labels are updated iteratively to enhance the reliability of labels. We assess the performance of NiuEM on four data sets via both visualized and quantitative experiments. Especially, two kinds of metrics are adopted to measure the performance, regarding the clustering quality and the resolution of reconstructed 3D models, respectively. The experimental results show that NiuEM achieves very competitive clustering accuracy in the comparison with the state-of-the-art image clustering methods. Moreover, the cluster mean images yielded by NiuEM lead to better initial 3D models compared with the mainstream reconstruction tools.

关键词： Three-dimensional displays Feature extraction image reconstruction Task analysis Solid modeling Electron microscopy Convolutional neural networks

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BoostTree and BoostForest for Ensemble Learning

arXiv

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arXiv 2020年

作者： Zhao, Changming Wu, Dongrui Huang, Jian Yuan, Ye Zhang, Hai-Tao Peng, Ruimin Shi, Zhenhua Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China The Autonomous Intelligence Unmanned Systems Engineering Research Center of Ministry of Education of China The State Key Lab of Digital Manufacturing Equipment and Technology Wuhan China

Bootstrap aggregating (Bagging) and boosting are two popular ensemble learning approaches, which combine multiple base learners to generate a composite model for more accurate and more reliable performance. They have been widely used in biology, engineering, healthcare, etc. This paper proposes BoostForest, which is an ensemble learning approach using BoostTree as base learners and can be used for both classification and regression. BoostTree constructs a tree model by gradient boosting. It increases the randomness (diversity) by drawing the cut-points randomly at node splitting. BoostForest further increases the randomness by bootstrapping the training data in constructing different BoostTrees. BoostForest generally outperformed four classical ensemble learning approaches (Random Forest, Extra-Trees, XGBoost and LightGBM) on 35 classification and regression datasets. Remarkably, BoostForest tunes its parameters by simply sampling them randomly from a parameter pool, which can be easily specified, and its ensemble learning framework can also be used to combine many other base learners. Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)

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Interpretable classification of epileptic EEG signals using ALIF decomposition and attention-augmented cascaded deep neural networks

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Applied Soft Computing 2025年 180卷

作者： Wei Zeng Minglin Zhang Liangmin Shan Yang Chen Zuoyong Li Shaoyi Du School of Physics and Mechanical and Electrical Engineering Longyan University Longyan 364012 PR China School of Mechanical Engineering and Automation Fuzhou University Fuzhou 350116 PR China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou 350121 PR China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an 710049 PR China

Epilepsy is a chronic neurological disorder characterized by recurrent seizures. Accurate diagnosis and effective monitoring require the precise classification of electroencephalogram (EEG) signals. In this study, we introduce a novel approach that combines Adaptive Local Iterative Filtering (ALIF) for signal decomposition with an attention-enhanced cascaded deep neural network (CDNN) architecture. The ALIF algorithm decomposes EEG signals into intrinsic mode functions (IMFs) that capture inherent oscillatory components. These IMFs are processed by the CDNN, which operates in two stages: a feature extraction module and a classification module. In the feature extraction stage, a SEblock channel attention mechanism dynamically prioritizes significant features within the IMFs. The classification stage employs a hybrid CNN-LSTM architecture that effectively captures both spatial and temporal dependencies. To enhance interpretability, the SHapley Additive exPlanations (SHAP) framework is incorporated to provide insights into the model’s decision-making process, while Gradient-weighted Class Activation Mapping (Grad-CAM) visualizes the most discriminative regions in the input data. Rigorously validated using 10-fold cross-validation on the Bonn and EEG Epilepsy databases, the proposed methodology achieved an exceptional classification accuracy of 100%, with sensitivity, specificity, and F1-scores exceeding 99% across various scenarios. The integration of SHAP and Grad-CAM not only elucidates the model’s decision processes but also contributes to a more interpretable and reliable system for epileptic EEG signal classification. This synergistic combination of advanced signal processing, deep learning, and interpretability techniques holds significant potential to enhance epilepsy diagnosis and strengthen trust in clinical decision support systems.

关键词： ALIF decomposition Cascaded deep neural networks Epileptic EEG signals SEBlock attention mechanism

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