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An intelligent impulsive noise mitigation with deep learning method

International Journal of Mechanical System Dynamics 2024年第3期4卷 346-360页

作者： Guo Yang Yuwen Qian Zikun Wang Xiangwei Zhou Wen Wu The School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjingChina The Division of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLouisianaUSA

To enable message transmission among sensors and equipment,power line communication(PLC)is a widely adopted smart ***,due to the occurrence of impulsive noise(IN),reliable transmissions over PLC channels in the smart grid are ***,in this paper,we propose an adaptive noise mitigation scheme to clip the IN with the sliding window-based method,where the altitude of the received signal in the current time slots is obtained by computing the average altitude of signals in the previous and next time *** detect the states of IN and dynamically estimate the power threshold of signals for the IN mitigation scheme,we develop an intelligent algorithm based on the long short-term memory *** prevent the useful signals from being eliminated as IN signals,we propose the accelerated proximal gradient method(APGM)based on tone reservation to reduce the peak-to-average power ratio(PAPR)for the transmitting signals with low computational *** addition,the closed-form expression of the bit error rate(BER)is derived for the proposed sliding window-based IN mitigation scheme according to the probability density function of the *** results demonstrate that the proposed IN mitigation scheme achieves a better BER performance than the conventional IN mitigation *** addition,the APGM aided by IN mitigation can further improve BER performance due to the PAPR reduction.

关键词： impulsive noise deep learning smart grid

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Spectral analysis of bone-conducted speech using modified linear prediction

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International Journal of Speech Technology 2024年第4期27卷 1039-1053页

作者： Ohidujjaman Hasan, Mahmudul Zhang, Shiming Huda, Mohammad Nurul Uddin, Mohammad Shorif Computer Science and Engineering Daffodil International University Dhaka1216 Bangladesh Computer Science and Engineering Comilla University Comilla3506 Bangladesh School of Electrical and Information Northeast Agricultural University Harbin150030 China Computer Science and Engineering United International University Dhaka1212 Bangladesh Computer Science and Engineering Green University of Bangladesh Kanchon1460 Bangladesh Computer Science and Engineering Jahangirnagar University Savar1342 Bangladesh

This paper improves the performance of linear prediction (LP) in precise spectral estimation of bone-conducted (BC) speech. Inherently, BC speech contains a wide spectral dynamic range that causes ill conditioning in the autocorrelation (ACR) method and its variants, where the Levinson–Durbin (L–D) algorithm is commonly implemented. Instead of the conventional LP-based spectral estimation methods, we utilize the covariance-based method, specifically the modified covariance (MC) method, where the orthogonal decomposition algorithm is deployed. In this paper, we derive the MC method from the least squares (LS) technique for BC speech analysis. The MC method reduces the eigenvalue expansion that compresses the spectral dynamic range of the BC speech signal. The effect of spectral dynamic range compression declines the ill-conditioned properties of LP. Through the proposed method using synthetic BC speech, the resulting power spectrum provides more accurate peaks than the conventional methods. The validity of the proposed method is also analyzed by inspecting real BC speech. This study reveals the utmost use of BC speech in speech processing systems. The experimental results demonstrate that the proposed method provides more accurate spectral estimation for synthetic and real BC speeches compared with conventional spectral estimation methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Autocorrelation

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Remaining Life Prediction of PEMFC Using FOGD-KELM Algorithm 5

Remaining Life Prediction of PEMFC Using FOGD-KELM Algorithm

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5th International Conference on Smart Grid and Energy engineering, SGEE 2024

作者： Guo, Sixu Wang, Hongwei Liu, Chunlei Zhang, Qian School of Electrical Engineering Xinjiang University Urumqi China School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798331530686

Aiming at the difficulty of predicting the remaining service life of proton exchange membrane fuel cells (PEMFC), a novel prediction method combining fractional-order gradient learning and nuclear limit learning machine(is proposed. The method utilizes the long memory property of fractional-order calculus with the nonlinear mapping capability of the kernel-limit learning machine to capture the complex dynamics of the PEMFC decay process. Simulation results show that the prediction accuracy improvement of FOGD-KELM over BP neural network ranges from 65% to 75%, and that of GD-KELM ranges from 50% to 70%, further validating its advantages in data fitting and prediction. © 2024 IEEE.

关键词： Neural networks

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HDMTK: Full Integration of Hierarchical Decision-Making and Tactical Knowledge in Multi-Agent Adversarial Games

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IEEE Transactions on Cognitive and Developmental Systems 2024年第3期17卷 465-479页

作者： Li, Wei Hu, Boling Song, Aiguo Huang, Kaizhu Southeast University School of Instrument Science and Engineering Jiangsu Nanjing210096 China Duke Kunshan University Department of Electrical and Computer Engineering Jiangsu Kunshan215316 China

In the field of adversarial games, existing decision-making algorithms primarily rely on reinforcement learning, which can theoretically adapt to diverse scenarios through trial and error. However, these algorithms often face the challenges of low effectiveness and slow convergence in complex wargame environments. Inspired by how human commanders make decisions, this paper proposes a novel method named Fully Integrating Hierarchical Decision-Making and Tactical Knowledge (HDMTK). This method comprises an upper reinforcement learning module and a lower multi-agent reinforcement learning module. To enable agents to efficiently learn the cooperative strategy, in HDMTK, we separate the whole task into explainable subtasks, and design their corresponding subgoals for shaping the online rewards upon tactical knowledge. Experimental results on the wargame simulation platform 'MiaoSuan' show that, compared to the advanced multi-agent reinforcement learning methods, HDMTK exhibits superior performance and faster convergence in the complex scenarios. © 2016 IEEE.

关键词： Adversarial machine learning

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Temporal Dynamics in instrumental Learning: Integrating Entropy Measures and Computational Models 31

Temporal Dynamics in instrumental Learning: Integrating Entr...

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31st National and 9th International Iranian Conference on Biomedical engineering, ICBME 2024

作者： Hassanzadeh, Zakieh Bahrami, Fariba University College of Engineering University of Tehran Human Motor Control and Computational Neuroscience Lab School of Electrical and Computer Engineering Tehran Iran University College of Engineering University of Tehran School of of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798331529710

This research explores the dynamics of decision-making within an instrumental learning framework, combining analyses of response times, entropy measures, and computational modeling. We conducted a study using an RLWM task, involving 27 participants and varying set sizes to examine the relationship between task complexity and cognitive functions. Our methodology incorporates Shannon entropy, Cumulative Residual Entropy (CRE), and Instantaneous CRE to assess uncertainty and mental effort across different task conditions. We evaluate two computational models - RLWM and its enhanced version, ~ p i H - to interpret both choice patterns and reaction times. Our findings indicate that set size significantly impacts response times and entropy measures, with increased complexity leading to higher entropy and slower responses. The temporal analysis of iCRE suggests a dynamic shift in cognitive strategies throughout the task. Model comparisons highlight distinct capabilities of RLWM and piH in capturing various aspects of cognitive processes. This comprehensive approach provides novel viewpoints into how the brain balances complex learning mechanisms with simpler rules for efficient decision-making under limited mental resources, contributing to our understanding of adaptive behavior in complex environments. © 2024 IEEE.

关键词： Adversarial machine learning

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Autonomous Exploration Algorithm for Mobile Robots in Unknown Confined Environment 16

Autonomous Exploration Algorithm for Mobile Robots in Unknow...

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16th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2024

作者： Lyu, Zhixiao Yin, Yue Liu, Qinyang Yang, Tianyao Nanjing Normal University School of Electrical and Automation Engineering China Nanjing University of Information Science & Technology School of Computer Science School of Cyber Science and Engineering China

ISBN: (纸本)9798331540241

For the efficiency and precision of autonomous exploration, this paper proposes an optimized algorithm for mobile robots in unknown confined environment. However, the traditional algorithms frequently lead to excessive navigation distance and waste of exploration time consumption. To address these limitations, frontier point selection is improved by utilizing RRT, accomplishing significant improvement in computational efficiency. The exploration path planning was accomplished through the utilization of an enhanced Dijkstra's algorithm, in conjunction with a topological map. Through experimental comparisons, the proposed algorithm achieves significant advantages over existing frontier-based and RRT-based algorithms when dealing with complex environments. In particular, our system is able to reduce the traveling distance by up to 55.16% in difficult scenarios. © 2024 IEEE.

关键词： Adversarial machine learning

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Risk-Aware Real-Time Task Allocation for Stochastic Multi-Agent Systems under STL Specifications 63

Risk-Aware Real-Time Task Allocation for Stochastic Multi-Ag...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Engelaar, M.H.W. Zhang, Z. Vlahakis, E.E. Dimarogonas, D.V. Lazar, M. Haesaert, S. Netherlands Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden

ISBN: (纸本)9798350316339

This paper addresses the control synthesis of heterogeneous stochastic linear multi-agent systems with realtime allocation of signal temporal logic (STL) specifications. Based on previous work, we decompose specifications into subspecifications on the individual agent level. To leverage the efficiency of task allocation, a heuristic filter evaluates potential task allocation based on STL robustness, and subsequently, an auctioning algorithm determines the definitive allocation of specifications. Finally, a control strategy is synthesized for each agent-specification pair using tube-based model predictive control (MPC), ensuring provable probabilistic satisfaction. We demonstrate the efficacy of the proposed methods using a multi-shuttle scenario that highlights a promising extension to automated driving applications like vehicle routing. © 2024 IEEE.

关键词： Stochastic systems

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Ill-condition enhancement for BC speech using RMC method

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International Journal of Speech Technology 2024年第4期27卷 1085-1092页

This paper improves the ill-condition of bone-conducted (BC) speech signal by reducing the eigenvalue expansion. BC speech commonly contains a large spectral dynamic range that causes ill-condition for the classical linear prediction (LP) methods. In the field of numerical analysis, we often face the situation where an ill-conditioned case occurs in finding the solution. Principally, eigenvalue expansion causes ill-condition in numerical analysis. To mitigate this problem, the regularized least squares (RLS) technique is commonly used. Motivated by the RLS concept, we derive the regularized modified covariance (RMC) method for BC speech analysis in this study. The RMC method reduces eigenvalue expansion by compressing the spectral dynamic range of the speech signal. Thus, the RMC method resolves the ill-conditioned problem of LP. In experiments, we show that the RMC method provides compressed eigenvalue expansion than the conventional methods for BC speech where synthetic and real BC speeches are considered. The performance of the RMC method is affected by the setting of the regularization parameter. In this paper, the regularization parameter in practice is iteratively and rule-based derived. The RMC method with such a setting provides the best performance for BC speech analysis. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Iterative methods

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Construction and implementation of an emotional care robot based on physiological signal and space-time information 7

Construction and implementation of an emotional care robot b...

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2024 7th International Conference on Power Electronics and control engineering, ICPECE 2024

作者： Zhang, Jun Lu, Fei School of Electrical and Information Engineering Liaoning Institute of Science and Technology Benxi117022 China School of Control Science and Engineering Shandong University Jinan250061 China

With the aging of Chinese society and the intensification of empty nest degrees, focusing on the robot's care of human emotions to help people alleviate negative emotions, "IQ, EQ"products will be widely concerned. Through smart bracelets, this project collects physiological signals such as GSR, heart rate, and R-R intervals of users under different states. It combines spatial and temporal information to build an ontology-based multi-domain knowledge model of intelligent home space. Based on the autonomous cognition, personalized selection, and BP neural network dynamic and flexible semantic reasoning of robot service tasks associated with emotion-time-space information, the service robot will provide appropriate, warm, and intimate daily services for users according to their emotional states and establish a system of autonomous learning, mental development, active cognition and skill development of service robots. © Published under licence by IOP Publishing Ltd.

关键词： Physiological models

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Constructive Function Approximation with Local Models 32

Constructive Function Approximation with Local Models

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32nd Mediterranean Conference on control and Automation, MED 2024

作者： Mavridis, Christos N. Johansson, Karl Henrik School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden

ISBN: (纸本)9798350395440

We introduce a constructive function approximation approach as a general tool, particularly useful in adaptive and data-driven methods for perception and control. The key idea is to estimate of a collection of simple local models as opposed to a single and complex regression model trained in the entire input space. We use principles from the Online Deterministic Annealing (ODA) optimization framework to construct an adaptive partition of the input space, which enables the introduction of local function approximation models within each subset of the partition. We show that both the partitioning and the local model training algorithms are stochastic approximation algorithms that operate online, and with the same observations, as part of a two-timescale stochastic approximation scheme. This process constitutes a heuristic method to gradually increase the complexity of the function approximation framework in a task-agnostic manner, giving emphasis to regions of the input space where the regression error is high. As a result this framework has inherent explainability properties, and is suitable for continuous learning applications where regression improvement without retraining from scratch is crucial. Simulation results illustrate the properties of the proposed approach. © 2024 IEEE.

关键词： Heuristic methods

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