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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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Numerical simulation analysis and optimization of modular joint temperature field based on ANSYS

Numerical simulation analysis and optimization of modular jo...

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第33届中国控制与决策会议

作者： Tao Zhang Wenjing Xu Zizhao Huang Zhenqiang Yang Xiyou Chen Bingjie Zhao Hualiang Zhang School of Electrical Engineering Dalian University of Technology Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences CRRC DALIAN R&D CO. LTD. University of Chinese Academy of Sciences

In the low-speed state,temperature change has a significant impact on the working efficiency of a harmonic ***,since modular joints used in collaborative robots arbitrarily arrange nodes and meshes through the Finite Element Method(FEM),a temperature field calculation model of the integrated joints was established in this *** utilizing the ANSYS software,a steady-state temperature field distribution of the modular joint was calculated,and the temperature rise of the harmonic reducer was reduced by changing the thermal *** the results demonstrated that the method was effective,which is very important for the safe operation and performance improvement of modular j oints.

关键词： harmonic reducer modular joint Finite Element Method Temperature Field ANSYS

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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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Short-Term Photovoltaic Power Prediction Modeling Based on AdaBoost Algorithm and Elman 10

Short-Term Photovoltaic Power Prediction Modeling Based on A...

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10th International Conference on Power and Energy systems, ICPES 2020

作者： Sun, Wenxuan Zhang, Tao Tao, Ran Wang, Anna Northeastern University College of Information Science and Engineering Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China School of Electrical Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9781665404945

In recent years, with the rapid expansion of the installed capacity of renewable energy systems, the availability, stability and quality of smart grids have become increasingly important[1]. The application of renewable energy production forecasting has also been rapidly developed, especially in the field of solar photovoltaic (PV)[2][3]. In the example of solar PV output prediction, machine learning and hybrid technologies have been implemented for many applications. In this paper, a high-precision PV system output power prediction model based on improved AdaBoost and Elman is proposed. Multiple model using integrated AdaBoost algorithm fusion with the bat algorithm for the parameters of optimized combination of weak Elman neural network predictor to become a higher prediction precision, the method of strong predictor of the model can according to the weather information, such as temperature, solar radiation and the history of the output of the PV system data, the probability of photovoltaic power generation for 12 hours and deterministic prediction. The prediction accuracy of the model is determined by Root Mean Squared Error (RMSE). Experimental results show that the prediction accuracy of this algorithm is better than that of other benchmark models, and the algorithm can effectively predict the volatility and irregularity of complex time series. © 2020 IEEE.

关键词： Adaptive boosting

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Adaptive Backstepping Sliding Mode control for Fractional Order systems

Adaptive Backstepping Sliding Mode Control for Fractional Or...

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第三十九届中国控制会议

作者： Xiang Chen Shiqi Zheng Feng Liu Xiongliang Zhang Yuan Sun School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation fo Complex Systems School of Electrical and Electronic Engineering The University of Adelaide

This paper concentrates on the control problem of a class of fractional order nonlinear systems. A new adaptive backstepping sliding mode control is proposed, which inherits both the advantages of backstepping and sliding mode ***, a fractional order sliding mode disturbance observer is proposed to estimate unknown external disturbances. Meanwhile, RBF neural network is adopted to approximate unknown nonlinear terms. Finally, an illustrative simulation result based on a practical fractional order permanent magnet synchronous motor model is presented to verify the validity of proposed controller.

关键词： Fractional order systems adaptive sliding mode control disturbance observer backstepping

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Research on PMSM Vector control System Based on Fuzzy PI Parameter Self-Tuning 10

Research on PMSM Vector Control System Based on Fuzzy PI Par...

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10th International Conference on Power and Energy systems, ICPES 2020

作者： Ding, Jianhua Zhang, Tao Tao, Ran Wang, Anna Northeastern University College of Information Science and Engineering Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China School of Electrical Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9781665404945

Based on the analysis of the permanent magnet synchronous motor (PMSM) mathematical model, a PMSM rotor field-oriented double closed-loop vector control system is constructed. Aiming at the defects of slow speed response, large overshoot, severe torque ripple, and poor anti-disturbance ability in the traditional PI controller used in the system speed loop, a fuzzy PI parameter self-tuning controller is proposed. The controller can set the two parameters of proportional and integral online according to fuzzy control rules. The system simulation model is built based on Matlab/Simulink. The simulation results show that the fuzzy PI parameter self-tuning controller has the advantages of fast dynamic response, high steady-state precision and strong anti-disturbance capability. © 2020 IEEE.

关键词： Permanent magnets

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Adaptive control for Stochastic Nonlinear systems With Quantized States

Adaptive Control for Stochastic Nonlinear Systems With Quant...

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第三十九届中国控制会议

作者： Mingyuan Zhao Shiqi Zheng Feng Liu Xiang Chen Yuan Sun School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation fo Complex Systems School of Electrical and Electronic Engineering The University of Adelaide

In this paper, adaptive control problem is investigated for stochastic nonlinear systems with quantized states. Compared with the existing works, the considered system is more general which contains a deterministic nonlinear term and a stochastic disturbance in each channel. In order to handle the high order nonlinearities, we present a new technique which makes the partial derivative of virtual control law become a constant, this makes the adaptive control scheme of state quantization feasible. Then, a new adaptive law is proposed to deal with some completely unknown parameters, which includes an interesting third-order term of the parameters. Finally, simulation results demonstrate the efficacy of the proposed schemes.

关键词： adaptive control backstepping state quantization stochastic system

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Directly wireless communication of human minds via non-invasive brain-computer-metasurface platform

arXiv

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arXiv 2022年

作者： Ma, Qian Gao, Wei Xiao, Qiang Ding, Lingsong Gao, Tianyi Zhou, Yajun Gao, Xinxin Yan, Tao Liu, Che Gu, Ze Kong, Xianghong Abbasi, Qammer H. Li, Lianlin Qiu, Cheng-Wei Li, Yuanqing Cui, Tie Jun Institute of Electromagnetic Space Southeast University Nanjing210096 China School of Automation Science and Engineering South China University of Technology Guangzhou510641 China State Key Laboratory of Millimeter Wave Southeast University Nanjing210096 China Center of Intelligent Metamaterials Pazhou Laboratory Guangzhou510330 China Research Center for Brain-Computer Interface Pazhou Lab Guangzhou510330 China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore University of Glasgow James Watt School of Engineering GlasgowG12 8QQ United Kingdom State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronics Peking University Beijing100871 China

Brain-computer interfaces (BCIs), invasive or non-invasive, have projected unparalleled vision and promise for assisting patients in need to better their interaction with the surroundings. Inspired by the BCI-based rehabilitation technologies for nerve-system impairments and amputation, we propose an electromagnetic brain-computer-metasurface (EBCM) paradigm, regulated by human's cognition by brain signals directly and non-invasively. We experimentally show that our EBCM platform can translate human's mind from evoked potentials of P300-based electroencephalography to digital coding information in the electromagnetic domain non-invasively, which can be further processed and transported by an information metasurface in automated and wireless fashions. Directly wireless communications of the human minds are performed between two EBCM operators with accurate text transmissions. Moreover, several other proof-of-concept mind-control schemes are presented using the same EBCM platform, exhibiting flexibly-customized capabilities of information processing and synthesis like visual-beam scanning, wave modulations, and pattern encoding. © 2022, CC BY.

关键词： Electrophysiology

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Robust Beamforming for IRS-assisted Wireless Communications under Channel Uncertainty

Robust Beamforming for IRS-assisted Wireless Communications ...

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IEEE Conference on Wireless Communications and Networking

作者： Yongchang Deng Yuze Zou Shimin Gong Bin Lyu Dinh Thai Hoang Dusit Niyato School of Intelligent Systems Engineering Sun Yat-sen University China School of Electronic Information and Communications Huazhong University of Science and Technology China Key Laboratory of Ministry of Education in Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications China School of Electrical and Data Engineering University of Technology Sydney Australia School of Computer Science and Engineering Nanyang Technological University Singapore

In this paper, we consider IRS-assisted transmissions from a multi-antenna access point (AP) to a receiver with uncertain channel information. By adjusting the magnitude of reflecting coefficients, the IRS can sustain its operations by harvesting energy from the AP's signal beamforming. Considering channel estimation errors, we model both the AP-IRS channel and the AP-IRS-receiver as a cascaded channel by norm-based uncertainty sets. This allows us to formulate a robust optimization problem to minimize the AP's transmit power, subject to the receiver's worst-case data rate requirement and the IRS's worst-case power budget constraint. Instead of using the alternating optimization (AO) method, we firstly propose a heuristic scheme to decompose the IRS's phase shift optimization and the AP's active beamforming. Based on semidefinite relaxations of the worst-case constraints, we further devise an iterative algorithm to optimize the AP's transmit beamforming and the magnitude of the IRS's reflecting coefficients efficiently by solving a set of semidefinite programs. Simulation results reveal that the AP requires a higher transmit power to deal with the channel uncertainty. Moreover, the negative effect of channel uncertainty can be alleviated by using a larger-size IRS.

关键词： Wireless communication Uncertainty Array signal processing Simulation Transmitting antennas Channel estimation Receivers

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Passivity properties for regulation of DC networks with stochastic load demand ⁎

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IFAC-PapersOnLine 2020年第2期53卷 13113-13118页

作者： Amirreza Silani Michele Cucuzzella Jacquelien M.A. Scherpen Mohammad Javad Yazdanpanah Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Nijenborgh 4 9747 AG Groningen the Netherlands. Control & Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

In this paper we present new (stochastic) passivity properties for Direct Current (DC) power networks, where the unknown and unpredictable load demand is modelled by a stochastic process. More precisely, the considered power network consists of distributed generation units supplying ZIP loads, i.e., nonlinear loads comprised of impedance (Z), current (I) and power (P) components. Differently from the majority of the results in the literature, where each of these components is assumed to be constant, we consider time-varying loads whose dynamics are described by a class of stochastic differential equations. Finally, we prove that an existing distributed control scheme achieving current sharing and (average) voltage regulation ensures the asymptotic stochastic stability of the controlled network.

关键词： DC power networks Nonlinear systems Stochastic modelling Passivity theory

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