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2025 IEEE International Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Wang, Zonghui Fang, Zhihua He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China School of Intelligence Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Department of Electronic Engineering Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

ISBN: (纸本)9798350368741

With advancements in deep learning, speaker verification systems have significantly improved their performance in noisy environments. Researchers typically demonstrate the effectiveness of their improved models by comparing performance on specific datasets, such as the VoxCeleb benchmark. However, in diverse real-world noise conditions, the out-of-domain generalization ability is also a crucial factor in evaluating a model's performance improvement. Research on stable learning indicates that eliminating the spurious correlation between training and testing data can enhance the generalization of the model. Building on this idea, we propose an improved speaker verification system with high generalization based on the extended U-Net (ExU-Net). It uses the sample reweighting method from stable learning to eliminate sample correlations and retains more effective speaker information through sub-pixel convolutions and coordinate attention mechanisms. We validate the effectiveness of this approach through extensive evaluations on VoxCeleb1, VOiCES, and other out-of-domain noise test sets, highlighting its generalization capability and model robustness. © 2025 IEEE.

关键词： noisy environments out-of-domain generalization Speaker verification stable learning

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A Bivariate Control Strategy on Inductive Power Transfer Converter for Multi-Stage Constant Current Charging 48

A Bivariate Control Strategy on Inductive Power Transfer Con...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Ding, Zhaoyi Iam, Io-Wa Ieong, Chi-Fong Lam, Chi-Seng State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau Macau China Institute of Microelectronics University of Macau Macau China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Compared with the conventional constant current-constant voltage (CC-CV) charging profile of Li-ion battery, multi-stage constant current (MSCC) charging profile that composed of different constant current stages can improve the charging rate. This paper proposes a MSCC charging profile for an inductive power transfer (IPT) converter with LCC-S compensation topology. However, the wide load range variation during charging process impacts the transfer efficiency of the IPT converter. Thus, aiming to improve the transfer efficiency throughout the whole MSCC charging process, a bivariate operation approach is proposed with a variable inductance to achieve different constant current stages, while the voltage-controlled semi-active rectifier (VCSAR) is controlled to ensure the converter's efficiency optimization. Finally, simulation study is conducted based on PSIM software simulation to verify the validity and effectiveness of the bivariate control strategy and MSCC charging for the LCC-S compensated IPT converter. © 2022 IEEE.

关键词： Efficiency

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Review of Different Current Control Strategies for LC-coupling Hybrid Active Power Filter 48

Review of Different Current Control Strategies for LC-coupli...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Hong, Qian-Rong Sou, Wai-Kit Chan, Pak-Ian Gong, Cheng Lam, Chi-Seng State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau Macau China Institute of Microelectronics University of Macau Macau China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Compared with traditional active power filter (APF), the dc-link voltage of LC-coupling hybrid active power filter (LC-HAPF) is lower, thus reducing the system cost and power loss. In order to improve the compensation performance of the LC-HAPF, many current controllers have been proposed in recent decade, which are hysteresis current control (HCC), quasi-proportional-resonant control (QPRC), deadbeat current control (DBCC), second-order sliding-mode control (SOSMC), vector proportional-integral current control (VPIC) and improved model predictive control (IMPC). In this paper, the model of these six controllers will be first reviewed and introduced. After that, their corresponding parameters design methodology are given. Finally, simulation results will be given to compare their compensation performance and summarize their characteristics. © 2022 IEEE.

关键词： Electric current control

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High-Performance Multistage Constant Current Charging for Wireless Power Transfer Systems 48

High-Performance Multistage Constant Current Charging for Wi...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Ieong, Chi-Fong Iam, Io-Wa Ding, Zhaoyi Lam, Chi-Seng State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau Macau China Institute of Microelectronics University of Macau Macau China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

In lithium-ion (li-ion) battery charging process, multistage constant-current (MSCC) charging can effectively increase the charging capacity with less charging time compared with the conventional constant current-to-constant voltage (CC-to-CV) charging in wired charging. This paper proposes and designs a MSCC charging strategy for the wireless power transfer system. By using the LCC-S compensation topology for the inductive power transfer (IPT) converter, the current gain is designed with mathematical derivation to achieve load-independent current output with zero phase angle (ZPA) according to the MSCC charging pattern. Phase-shift modulation (PSM) is adopted to control the input DC-link of the WPT system to achieve different current outputs according to different CC stages. Finally, the charging system is simulated to verify the correctness of the design and the feasibility of the proposed WPT system with MSCC charging. The maximum efficiency of the system is 92.4% and the time-averaged efficiency is 91.3%. © 2022 IEEE.

关键词： Lithium-ion batteries

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Learning the continuous-time optimal decision law from discrete-time rewards

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National Science Open 2024年第5期3卷 130-147页

作者： Ci Chen Lihua Xie Kan Xie Frank Leroy Lewis Yilu Liu Shengli Xie School of Automation Guangdong University of TechnologyGuangdong Key Laboratory of IoT Information TechnologyGuangzhou 510006China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangzhou 510006China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou 510006China UTA Research Institute the University of Texas at ArlingtonFort Worth 76118USA Department of Electrical Engineering and Computer Science University of TennesseeKnoxville 37996USA Oak Ridge National Laboratory Oak Ridge 37830USA Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou 510006China

The concept of reward is fundamental in reinforcement learning with a wide range of applications in natural and social *** an interpretable reward for decision-making that largely shapes the system's behavior has always been a challenge in reinforcement *** this work,we explore a discrete-time reward for reinforcement learning in continuous time and action spaces that represent many phenomena captured by applying physical *** find that the discrete-time reward leads to the extraction of the unique continuous-time decision law and improved computational efficiency by dropping the integrator operator that appears in classical results with integral *** apply this finding to solve output-feedback design problems in power *** results reveal that our approach removes an intermediate stage of identifying dynamical *** work suggests that the discrete-time reward is efficient in search of the desired decision law,which provides a computational tool to understand and modify the behavior of large-scale engineering systems using the optimal learned decision.

关键词： continuous-time state and action decision law learning discrete-time reward dynamical systems reinforcement learning

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Real-Time Fault Detection and Diagnosis Method for Industrial Chemical Tennessee Eastman Process

Real-Time Fault Detection and Diagnosis Method for Industria...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Khadija Attouri Majdi Mansouri Mansour Hajji Abdelmalek Kouadri Kais Bouzrara Hazem Nounou Institut Superieur des Sciences Appliques et de Technologie de Kasserine Kairouan University Tunisia The Department of Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Signals and Systems Laboratory Institute of Electrical and Electronics Engineering University M Hamed Bougara of Boumerdes Boumerdes Algeria The Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The accurate detection and diagnosis of faults are critical for maintaining optimal operation and ensuring the reliability of industrial processes. Notably, the topic of online fault detection and diagnosis has recently presented a significant challenge. This work mainly deploys a neural network technique for the comprehensive detection and diagnosis of faults within the Tennessee Eastman Process (TEP) on a low-computational power system, the Raspberry Pi board. The devolved methodology showcases a remarkable level of accuracy (94.50%) in diagnosing the various TEP faults, affirming its robustness and effectiveness. To elevate the practical applicability of the proposed approach, a meticulous investigation into the implementation of the suggested approach on a Raspberry Pi 4 card was undertaken. The successful realization of this implementation not only highlights the adaptability of the approach but also paves the way for its seamless integration into practical industrial applications.

关键词： Accuracy Embedded systems Fault detection Neural networks electrical fault detection Real-time systems Robustness Power system reliability Information technology Chemicals

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Real-Time Fault Detection Scheme for Industrial Chemical Tennessee Eastman Process

Real-Time Fault Detection Scheme for Industrial Chemical Ten...

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International Conference on Control, Decision and Information Technologies (CoDIT)

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The key idea behind this study is to integrate a moving window dynamic PCA (MW-DPCA) methodology for fault detection within the Tennessee Eastman process (TEP) into a low-computational power system, the Raspberry Pi 4 card, for real-time application. Indeed, the paramount importance of real-time fault detection (FD) in intricate industrial processes presents a critical challenge. Various data-driven techniques have been developed to ensure safety, maintain operational stability, and optimize productivity in such processes. Principal Component Analysis (PCA) is a fundamental data-driven technique that utilizes dimensionality reduction to extract the most informative features from high-dimensional data, simplifying analysis and potentially revealing underlying fault patterns. However, PCA primarily focuses on static relationships and may miss crucial temporal dynamics for fault identification. This is where dynamic PCA (DPCA) excels. By incorporating lagged values of variables, DPCA captures the temporal evolution of features, enabling a more comprehensive understanding of process behavior and improving the detection of faults involving dynamic changes. In order to address the stochastic measurements, a moving average filter tool is also employed. The results obtained and the successful realization of this implementation demonstrate the adaptability of the approach and pave the way for its seamless integration into practical industrial applications.

关键词： Fault detection Power system dynamics Power system stability Feature extraction electrical fault detection Real-time systems Stability analysis Robustness Safety Principal component analysis

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Stable Implementation Of Voice Activity Detector using Zero-Phase Zero Frequency Resonator On FPGA

Stable Implementation Of Voice Activity Detector using Zero-...

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Real Time Communications at IIT (RTC), IEEE International Conference and Expo on

作者： Syed Abdul Jabbar Purva Barche Krishna Gurugubelli Syed Azeemuddin Anil Kumar Vuppala Speech processing Laboratory LTRC IIIT-H Hyderabad India CVEST and Department of Electrical and Computer Engineering IIIT-Hyderabad and The University of Mississippi Mississippi USA

Voice activity detection (VAD), is a signal processing technique used to determine whether a given speech signal contains voiced or unvoiced segments. VAD is used in various applications such as Speech Coding, Voice Controlled Systems, speech feature extraction, etc. For example, in Adaptive multi-rate (AMR) speech coding, VAD is used as an efficient way of coding different speech frames at different bit rates. In this paper, we implemented the application of a Zero-Phase Zero Frequency Resonator (ZP-ZFR) as VAD on hardware. ZP-ZFR is an Infinite Impulse Response (IIR) filter that offers the advantage of requiring a lower filter order, making it suitable for hardware implementation. The proposed system is implemented on the TIMIT database using the Nexys Video Artix-7 FPGA board. The hardware design is carried out using Vivado 2021.1, a popular tool for FPGA development. The Hardware Description Language (HDL) used for implementation is Verilog. The proposed system achieves good performance with low complexity. Therefore this work is implemented on hardware, which can be used in various applications.

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A new accelerated gradient-based estimating sequence technique for solving large-scale optimization problems with composite structure

A new accelerated gradient-based estimating sequence techniq...

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IEEE Conference on Decision and Control

作者： Endrit Dosti Sergiy A. Vorobyov Themistoklis Charalambous Department of Signal Processing and Acoustics Aalto University Espoo Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Various problems arising in control and data analysis can be formulated as large-scale convex optimization problems with a composite objective structure. Within the black-box optimization framework, such problems are typically solved by using accelerated first-order methods. The celebrated examples of such methods are the Fast Gradient Method and the Accelerated Multistep Gradient Method, designed by using the estimating sequences framework. In this work, we present a new class of estimating sequences, which are constructed by making use of a tighter lower bound on the objective function together with the gradient mapping technique. Based on the newly introduced estimating sequences, we construct a new method, which is also equipped with an efficient line-search strategy that provides robustness to the imperfect knowledge of the Lipschitz constant. Our proposed method enjoys the accelerated convergence rate, and our theoretical results are corroborated by numerical experiments conducted on real-world datasets. The experimental results also demonstrate the robustness of the initialization of the proposed method to the imperfect knowledge of the strong convexity parameter of the objective function.

关键词： Gradient methods Data analysis Closed box Linear programming Robustness Convex functions Optimization

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A Gradient-Based Algorithm for Joint Beamforming and Reflection Design in Ris-Assisted Mobile Communications

SSRN

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SSRN 2025年

作者： Pitz, Ciro André Matsuo, Marcos Vinicius Seara, Rui Department of Control Automation and Computation Engineering Federal University of Santa Catarina Santa Catarina Blumenau89065-100 Brazil LINSE–Circuits and Signal Processing Laboratory Department of Electrical and Electronics Engineering Federal University of Santa Catarina Santa Catarina Florianópolis88040-900 Brazil

Reconfigurable intelligent surfaces (RIS) have emerged as a transformative technology for shaping the propagation environment in next-generation mobile communication systems, creating the need for efficient optimization strategies. In this context, this paper introduces a gradient-based algorithm for jointly optimizing beamforming and reflection design in RIS-assisted multi-user (MU) multiple-input single-output (MISO) systems. The proposed approach addresses the sum-rate capacity maximization problem while significantly reducing computational complexity. To achieve this, the optimization problem is reformulated by using eigenvalue minimization, thereby reducing computational burden. This reformulation reduces computational demands without compromising the sum-rate performance. The proposed algorithm incorporates both steepest descent and the damped quasi-Newton Broyden–Fletcher–Goldfarb–Shanno (BFGS) approximation, enabling flexible trade-offs between computational efficiency and convergence rate. Simulation results confirm the effectiveness of the proposed algorithm in enhancing the sum-rate capacity. © 2025, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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