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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Ramandeep Narwal Isaac Wong Subhashish Bhattacharya B. Jayant Baliga Douglas C. Hopkins Department of Electrical and Computer Engineering FREEDM Systems Center North Carolina State University Raleigh NC USA

The advent of the SiC Bidirectional FET (BiDFET), a monolithic 1.2 kV bidirectional switch, has rendered the single-stage three-phase AC/DC converter topology a promising approach for implementing AC/DC converters. This topology, which integrates a full-bridge converter with a single-phase to three-phase matrix converter via a high-frequency transformer, is particularly suitable for applications requiring galvanic isolation, buck-boost functionality, and bidirectional power flow. The single-stage design eliminates the need for bulky and unreliable electrolytic capacitors, and utilizes a single magnetic component for power transfer. In the matrix converter, bidirectional switches, which were traditionally implemented using combinations of multiple semiconductor devices such as MOSFETs, IGBTs, and diodes, can now be realized using the single-chip solution, BiDFET. This advancement leads to a lower switch count, compact converter implementation, with lower inductance commutation cells, thereby enhancing the overall efficiency and compactness of the system. The paper presents a unified model of the converter, considering all control parameters, including the duty cycles and phase shift of transformer voltages. Detailed expressions for power transfer, transformer currents, and currents at AC and DC ports are provided. Additionally, the paper outlines the conditions necessary for soft-switching of all switches and the commutation schemes required for the practical implementation of the matrix converter modulation scheme. A hardware prototype of a 10 kW, 480 V RMS, LL / 800 V AC/DC system has been developed, and experimental results are presented to demonstrate its performance.

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Triple Point Masking

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Liu, Jiaming Kong, Linghe Wu, Yue Gong, Maoguo Li, Hao Miao, Qiguang Ma, Wenping Qin, Can The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The School of Computer Science and Technology Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China The School of Electronic Engineering Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China The College of Mathematics Science Inner Mongolia Normal University Hohhot010022 China The School of Artificial Intelligence Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xidian University Xi’an710071 China The Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States

Existing 3D mask learning methods encounter performance bottlenecks under limited data, and our objective is to overcome this limitation. In this paper, we introduce a triple point masking scheme, named TPM, which serves as a scalable plug- and-play framework for MAE pre-training to achieve multi-mask learning for 3D point clouds. Specifically, we augment the baseline methods with two additional mask choices (i.e., medium mask and low mask) as our core insight is that the recovery process of an object can manifest in diverse ways. Previous high-masking schemes focus on capturing the global representation information but lack fine-grained recovery capabilities, so that the generated pre-training weights tend to play a limited role in the fine-tuning process. With the support of the proposed TPM, current methods can exhibit more flexible and accurate completion capabilities, enabling the potential autoencoder in the pre-training stage to consider multiple representations of a single 3D point cloud object. In addition, during the fine-tuning stage, an SVM-guided weight selection module is proposed to fill the encoder parameters for downstream networks with the optimal weight, maximizing linear accuracy and facilitating the acquisition of intricate representations for new objects. Extensive experimental results and theoretical analysis show that five baselines equipped with the proposed TPM achieve comprehensive performance improvements on various downstream tasks. © 2025 IEEE. All rights reserved.

关键词： 3D modeling

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Enhancing Multi-Label ECG Classification via Task-Guided Lead Correlations in Internet of Medical Things

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IEEE Internet of Things Journal 2025年第12期12卷 20544-20555页

作者： Yuan, Xiaoyan Wang, Wei Chen, Junxin Fang, Kai Bashir, Ali Kashif Mondal, Tapas Hu, Xiping Deen, M. Jamal Beijing Institute of Technology School of Medical Technology Beijing100081 China Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Guangdong Shenzhen518172 China Dalian University of Technology School of Software Dalian116621 China Zhejiang A and F University College of Mathematics and Computer Science Hangzhou311300 China Manchester Metropolitan University Department of Computing and Mathematics United Kingdom Chitkara University Institute of Engineering and Technology Centre for Research Impact and Outcome Chitkara University Punjab Rajpura140401 India McMaster University Faculty of Health Science and the Department of Pediatrics Hamilton Canada McMaster University McMaster School of Biomedical Engineering Hamilton Canada McMaster University Electrical and Computer Engineering Department Hamilton Canada

With the rise of the Internet of Things (IoT), wearable devices have enabled real-time health monitoring, particularly through physiological signals like electrocardiograms (ECG). The standard 12-lead ECG records the electrical activity of the heart from multiple perspectives, providing valuable insights into cardiac health. However, existing 12-lead ECG analysis methods often treat leads as channel-level arrangements or rely on spatial adjacency to predefine lead connections, limiting their ability to capture the complex spatial and functional relationships between leads fully. To address this limitation, we propose TGLLNet, a task-driven model that automatically learns inter-lead relationships to improve multi-label ECG classification. TGLLNet adaptively learns lead connectivity patterns and relational strengths, enhancing ECG representation and improving model generalizability across tasks. Specifically, TGLLNet employs a Temporal Graph Construction (ETGC) module to convert ECG signals into temporal graphs and uses a Residual Pyramid Graph Convolution (RPG) module for multi-level graph embeddings, utilizing a Graph Convolutional Network (GCN) with independently learnable adjacency matrices. Combined with a Temporal Context Convolution (TCC) module, TGLLNet captures spatio-temporal dependencies, significantly improving ECG representation. Experimental results on 7 tasks from PTB-XL and CPSC2018 datasets demonstrate that TGLLNet outperforms existing methods, showing superior generalizability across different tasks. Our code is available at https://***/rosemary333/TGLLnet. © 2014 IEEE.

关键词： Graph embeddings

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Constrained Reinforcement Learning for Stochastic Dynamic Optimal Power Flow Control

Constrained Reinforcement Learning for Stochastic Dynamic Op...

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IEEE General Meeting Power& Energy Society

作者： Tong Wu Anna Scaglione Daniel Arnold Department of Electrical and Computer Engineering Cornell Tech Cornell University USA Lawrence Berkeley National Laboratory

Deep Reinforcement Learning (DRL) algorithms have become popular for solving power system control problems. In conventional DRL, the agent is incentivized to explore all policies that can be encoded in a neural network (NN) with the sole objective of maximizing the reward function. In doing so existing DRL algorithms can produce infeasible solutions, i.e. recommend actions that do not satisfy the power flow equations, voltage limits as well as dynamic constraints. Power systems, are safety critical systems and ensuring the aforementioned physical constraints are met is essential. Often the problem remedied by projecting the actions in the feasible set, which is suboptimum. In this paper, we propose a primal-dual approach to learn optimal constrained DRL policies for dynamic optimal power flow problems, aiming at controlling power generations and battery outputs. Case studies on the IEEE standard systems validate the superior performance of dynamically adapting the policy to the environment and the required safety levels.

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On transitioning from PID to ADRC in thermal power plants

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Control Theory and Technology 2021年第1期19卷 3-18页

作者： Zhenlong Wu Zhiqiang Gao Donghai Li YangQuan Chen Yanhong Liu School of Electrical Engineering Zhengzhou UniversityZhengzhou 450001HenanChina Department of Electrical and Computer Engineering Cleveland State UniversityClevelandOH 44115USA State Key Lab of Power Systems Department of Energy and Power EngineeringTsinghua UniversityBeijing 100084China Mechatronics Embedded Systems and Automation(MESA)LabDepartment of Mechanical EngineeringSchool of EngineeringUniversity of CaliforniaMercedCA 95343USA

This paper focuscs on the recent progress in the adoption of active disturbance rejection control(ADRC)in thermal pro-cesses as a viable alternative to proportional-_integral-derivative(PID),especially in coa-fired power *** profound interpretation of this paradigm shift,with backward compatibility,is discussed in detail.A few fundamental issues associated with ADRC's applications in thermal processes are discussed,such as implementation,tuning,and the structural *** and case studies are presented,encompassing coal-fired power plants,gas turbines and nuclear power plants,as well as highlighting results of field *** discussed are future research opportunities brought by ADRC's entry as the baseline control technology in thermal processes.

关键词： Active disturbance rejection control(ADRC) Proportional-integral-derivative(PID) Thermal processes Coal-fired power plant Field applications

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A Graph-Based Self-Calibration Technique for Cable-Driven Robots with Sagging Cable

A Graph-Based Self-Calibration Technique for Cable-Driven Ro...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： M. R. Dindarloo A. S. Mirjalili S. A. Khalilpour R. Khorrambakht Stephan Weiss H. D. Taghirad Advanced Robotics and Automated Systems (ARAS) Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Department of Electrical and Computer Engineering Tandon School of Engineering Control/Robotics Research Laboratory New York University Brooklyn NY Department of Smart Systems Technologies University of Klagenfurt Austria

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

The efficient operation of large-scale Cable-Driven Parallel Robots (CDPRs) relies on precise calibration of kinematic parameters and the simplicity of the calibration process. This paper presents a graph-based self-calibration framework that explicitly addresses cable sag effects and facilitates the calibration procedure for large-scale CDPRs by only relying on internal sensors. A unified factor graph is proposed, incorporating a catenary cable model to capture cable sagging. The factor graph iteratively refines kinematic parameters, including anchor point locations and initial cable length, by considering jointly onboard sensor data and the robot’s kineto-static model. The applicability and accuracy of the proposed technique are demonstrated through Finite Element (FE) simulations, on both large and small-scale CDPRs subjected to significant initialization perturbations.

关键词： Meters Accuracy Perturbation methods Kinematics Robot sensing systems End effectors Software Calibration Sensors Finite element analysis

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Multitask learning for recognizing stress and depression in social media

arXiv

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arXiv 2023年

作者： Ilias, Loukas Askounis, Dimitris Decision Support Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Zografou Athens15780 Greece

Stress and depression are prevalent nowadays across people of all ages due to the quick paces of life. People use social media to express their feelings. Thus, social media constitute a valuable form of information for the early recognition of stress and depression. Although many research works have been introduced targeting the early recognition of stress and depression, there are still limitations. There have been proposed multi-task learning settings, which use depression and emotion (or figurative language) as the primary and auxiliary tasks respectively. However, although stress is inextricably linked with depression, researchers face these two tasks as two separate tasks. To address these limitations, we present the first study, which exploits two different datasets collected under different conditions, and introduce two multitask learning frameworks, which use depression and stress as the main and auxiliary tasks respectively. Specifically, we use a depression dataset and a stressful dataset including stressful posts from ten subreddits of five domains. In terms of the first approach, each post passes through a shared BERT layer, which is updated by both tasks. Next, two separate BERT encoder layers are exploited, which are updated by each task separately. Regarding the second approach, it consists of shared and task-specific layers weighted by attention fusion networks. We conduct a series of experiments and compare our approaches with existing research initiatives, single-task learning, and transfer learning. Experiments show multiple advantages of our approaches over state-of-the-art ones. © 2023, CC BY-NC-ND.

关键词： Social networking (online)

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Validating a Chaos Based PRBG Under Different Chaotic Maps

Validating a Chaos Based PRBG Under Different Chaotic Maps

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2024 Panhellenic Conference on Electronics and Telecommunications, PACET 2024

作者： Moysis, Lazaros Lawnik, Marcin Volos, Christos Baptista, Murilo S. Fragulis, George F. Goudos, Sotirios K. University of Western Macedonia Department of Electrical and Computer Engineering Kozani Greece Aristotle University of Thessaloniki Laboratory of Nonlinear Systems - Circuits and Complexity Physics Department Thessaloniki Greece University of Western Macedonia Department of Mechanical Engineering Kozani Greece Silesian University of Technology Faculty of Applied Mathematics Department of Mathematics Applications and Methods for Artificial Intelligence Gliwice44-100 Poland Supa University of Aberdeen Institute for Complex Systems and Mathematical Biology Department of Physics AberdeenAB24 3UX United Kingdom Aristotle University of Thessaloniki ELEDIA@AUTH Physics Department Thessaloniki Greece

ISBN: (纸本)9798350318845

Chaos-based random bit generators are abundantly used in chaos-based encryption and security applications, as a fast, deterministic source of randomness, to perform actions like permutation and substitution. The chaotic map used as a source of randomness though may heavily affect the bit generator's statistical randomness, key space, and speed. Thus, any given bit generator may not be equally efficient for all the chaotic maps used as a seed. Motivated by this, the present work considers a bit generator that utilizes a common modulo-based hashing technique, and studies its performance under a plethora of different chaotic maps. For each map, the generator is evaluated with respect to randomness, key space, and operations per bit ratio. This analysis can serve as a guideline for random bit generator usage in future chaos-based encryption designs. © 2024 IEEE.

关键词： Cryptography

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PZT Cantilever for Energy Harvesting and Vibration Sensing

PZT Cantilever for Energy Harvesting and Vibration Sensing

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Eric Danson Dong Sam Ha Bradley Department of Electrical and Computer Engineering Multifunctional Integrated Circuits and Systems (MICS) Group Blacksburg VA USA

The proposed two-stage ensemble machine learning model aims to bridge the gap between energy harvesting and vibration sensing applications for lead zirconate titanate (PZT) and similar piezoceramic materials by enabling one device to perform both functions simultaneously. Two PZT cantilever configurations were tested: one without a tip mass for maximum linearity at low frequencies and one with a tip mass for maximum energy output. The highest absolute prediction error on the testing set is 19% and 7%, respectively. While the R2 score remained nearly 1, the PZT cantilever with the tip mass showed an 11% lower mean absolute error (MAE) and 38% lower mean squared error (MSE) compared to the PZT without, suggesting that PZT cantilevers in energy harvesting configurations can be used to predict acceleration with acceptable accuracy.

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Virtual Resistance Control for an Active Battery Management System

Virtual Resistance Control for an Active Battery Management ...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Alastair P. Thurlbeck Ashraf Siddiquee Mithat John Kisacikoglu Yilmaz Sozer Center for Integrated Mobility Sciences National Renewable Energy Laboratory Golden CO Department of Electrical and Computer Engineering The University of Akron Akron Ohio

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

A virtual resistance control scheme is proposed for an active state-of-charge (SOC) balancing system within a behind-the-meter-storage (BTMS) battery pack. The proposed control enables the balancing system converters to simultaneously track a reference current while also regulating a shared 12 V bus. This enables the system to balance the cell SOCs while supporting auxiliary system loads on the 12 V bus. The advantage of the virtual resistance technique lies in its robustness to the actual resistances of the system. Without virtual resistance, the reference current tracking and 12 V bus load sharing among the converters would be dictated by the varying resistances between each converter and the 12 V bus, which are unequal and difficult to measure accurately. However, by applying the virtual resistance technique, these differences are negated such that the expected reference current tracking and equal load sharing are achieved.

关键词： Resistance Current measurement Battery management systems Robustness Power electronics Battery charge measurement Batteries State of charge electrical resistance measurement

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