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IEEJ Transactions on Electronics, Information and Systems 2025年第1期145卷 16-24页

作者： Shouno, Kazuhiro Sato, Lina Fukagawa, Kanta Tanimoto, Hiroshi Muto, Cosy Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan

This paper proposes a measurement technique for an integrated complex filter. The proposed method is based on two measurement methods with integrated circuitry for calibration. It is accomplished by applying square waves to an FUT (Filter Under Test) instead of sinusoidal waves or by superposing transfer characteristics obtained by applying a single sinusoidal wave to an FUT. In both of the above methods, the output signal connected to the external measurement equipment via a 50 Ω coaxial cable and so on. In this case, the output signal of the FUT should be amplified by internal or external amplifiers, which degrades the response. However, it is cancelled out by the proposed calibration circuitry with some analog switches. In addition to the above, for the RC polyphase filter, the source resistance and the load capacitance degrade the measurement results and their effect is discussed. The validity of the proposal is confirmed through computer simulation for a third-order RCPF whose passband is 8–40 MHz fabricated by Phenitec 0.6 µm CMOS process. It is shown that the measurement error is −1.10 dB for the square-wave method and −1.00 dB and −0.43 degrees for the superposition method at 40 MHz. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Capacitance

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Expeditious detection and segmentation of bone mass variation in DEXA images using the hybrid GLCM-AlexNet approach

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Soft Computing 2024年第19期28卷 11633-11646页

作者： Amiya, Gautam Murugan, Pallikonda Rajasekaran Ramaraj, Kottaimalai Govindaraj, Vishnuvarthanan Vasudevan, Muneeswaran Thirumurugan, M. Zhang, Yu-Dong Abdullah, S. Sheik Thiyagarajan, Arunprasath Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Data Science Division School of Computer Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal India Consultant Orthopaedic Surgeon MGR Medical University Tamilnadu Chennai India School of Informatics University of Leicester LeicesterLE1 7RH United Kingdom

Osteoporosis (OP) is an osteometabolic disorder characterized by a lesser bone mineral density (BMD) and the disruption of bone tissue micro—architecture, resulting in a greater bone fragility and higher likelihoods of fractures. OP emerges once the bone mass declines faster than the body's capacity to replenish it, leading to a significant reduction in the strength of bone. OP impacts each and every bone throughout the body and provides no clinical signs until a fracture happens. Aging leads to the reduction in BMD, and the rate of percentage of fractures rises over time, usually causing mortality and morbidity. Numerous BMD evaluation methods are available, and they are used in a variety of settings by considering the location of the fracture. Dual-energy x-ray absorptiometry (DEXA/DXA) is recognized as the gold standard for predicting the fracture, since it is the most advanced, technologically affirmed, and also has excellent performance. According to the findings, most researchers do not endeavour the identification and the segmentation of low bone masses from DEXA images. Medical image segmentation supports in analyzing and visualizing the bone's low bone mass. The envisaged hybrid approach, that integrates GLCM for feature extraction and AlexNet for a low bone mass variation classification, provides segmented images that assist in categorizing bone health as normal, osteopenia, or osteoporosis. The developed algorithm's performance metrics, including Dice Co-efficient, Sensitivity, and Specificity, were 92.35, 90.26, and 92.42%, respectively. The Orthopedicians ascertained the efficacy of the outcomes rendered by the proposed algorithm. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Fracture

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A machine learning-guided design and manufacturing of wearable nanofibrous acoustic energy harvesters

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Nano Research 2024年第10期17卷 9181-9192页

作者： Negar Hosseinzadeh Kouchehbaghi Maryam Yousefzadeh Aliakbar Gharehaghaji Safoora Khosravi Danial Khorsandi Reihaneh Haghniaz Ke Cao Mehmet R.Dokmeci Mohammad Rostami Ali Khademhosseini Yangzhi Zhu Terasaki Institute for Biomedical Innovation Los AngelesCA 91367USA Department of Textile Engineering Amirkabir University of Technology(Tehran Polytechnic)Hafez Avenue*** TehranIran Department of Electrical and Computer Engineering University of British ColumbiaVancouverBC V6T1Z4Canada Chemical Sciences Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Department of Computer Science University of Southern CaliforniaLos AngelesCA 90007USA

Nanofibrous acoustic energy harvesters(NAEHs)have emerged as promising wearable platforms for efficient noise-to-electricity conversion in distributed power energy systems and wearable sound amplifiers for assistive listening ***,their reallife efficacy is hampered by low power output,particularly in the low-frequency range(<1 kHz).This study introduces a novel approach to enhance the performance of NAEHs by applying machine learning(ML)techniques to guide the synthesis of electrospun polyvinylidene fluoride(PVDF)/polyurethane(PU)nanofibers,optimizing their application in wearable *** use a feed-forward neural network along with solving an optimization problem to find the optimal input values of the electrospinning(applied voltage,nozzle-collector distance,electrospinning time,and drum rotation speed)to generate maximum output performance(acoustic-to-electricity conversion efficiency).We first prepared a dataset to train the network to predict the output power given the input variables with high *** introducing the neural network,we fix the network and then solve an optimization problem using a genetic algorithm to search for the input values that lead to the maximum energy harvesting *** ML-guided wearable PVDF/PU NAEH platform can deliver a maximal acoustoelectric power density output of 829μW/cm^(3) within the surrounding noise *** addition,our system can function stably in a broad frequency(0.1-2 kHz)with a high energy conversion efficiency of 66%.Sound recognition analysis reveals a robust correlation exceeding 0.85 among lexically akin terms with varying sound intensities,contrasting with a diminished correlation below 0.27 for words with disparate semantic ***,this work provides a previously unexplored route to utilize ML in advancing wearable NAEHs with excellent practicability.

关键词： wearable electronics acoustic energy harvester machine learning piezoelectric nanogenerator electrospun nanofiber

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Automatic Plane Pose Estimation for Cardiac Left Ventricle Coverage Estimation via Deep Adversarial Regression Network

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4738-4752页

作者： Zhang, Le Bronik, Kevin Piechnik, Stefan K. Lima, Joao A. C. Neubauer, Stefan Petersen, Steffen E. Frangi, Alejandro F. University of Birmingham School of Engineering College of Engineering and Physical Sciences BirminghamB15 2TT United Kingdom University of Oxford Department of Engineering Science OxfordOX1 3PJ United Kingdom Division of Cardiovascular Medicine OxfordOX3 9DU United Kingdom Johns Hopkins University School of Medicine Division of Cardiology BaltimoreMD21287 United States Queen Mary University of London Centre for Cardiovascular Medicine The William Harvey Research Institute LondonE1 1BB United Kingdom Barts Health Nhs Trust Barts Heart Center LondonE1 1BB United Kingdom The University of Manchester Division of Informatics Imaging and Data Science School of Health Sciences The Department of Computer Science School of Engineering ManchesterM13 9PL United Kingdom

Accurate segmentation of the ventricles plays a crucial role in determining cardiac functional parameters such as ventricular volume, ventricular mass, and ejection fraction. However, poor image quality, such as inadequate coverage of the left ventricle (LV) and right ventricle (RV) in cardiac magnetic resonance (CMR) image sequences, can significantly affect the assessment of cardiac function. This study investigates issues related to missing or corrupted imaging planes, which often lead to incomplete ventricle coverage. To address the challenge of estimating ventricle coverage in CMR images regardless of variations in imaging parameters such as device type, magnetic field strength, and protocol execution, we introduce a novel convolutional neural network (CNN) based on adversarial learning. Additionally, we integrate supplementary information (e.g., cross-view image data) as privileged information to enhance the interpretability of our model's predictions and identify potential biases or inaccuracies. This research represents the first attempt to automatically estimate ventricular coverage by identifying missing slices and plane orientations in CMR images using a dataset-agnostic approach. The effectiveness of the proposed model is demonstrated through the evaluation of datasets from three diverse and sizable image acquisition cohorts, demonstrating superior performance compared to existing methods. © 2020 IEEE.

关键词： Image quality

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Data-driven modeling of dislocation mobility from atomistics using physicsinformed machine learning

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npj Computational Materials 2024年第1期10卷 937-948页

作者： Yifeng Tian Soumendu Bagchi Liam Myhill GiacomoPo Enrique Martinez Yen Ting Lin Nithin Mathew Danny Perez Information Sciences Group ComputerComputational and Statistical Sciences Division(CCS-3)Los Alamos National LaboratoryLos Alamos87545 NMUSA Physics and Chemistry of Materials Theoretical Division(T-1)Los Alamos National LaboratoryLos Alamos87545 NMUSA Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeUSA Department of Materials Science and Engineering Clemson UniversityClemson29623 SCUSA Department of Mechanical and Aerospace Engineering University of MiamiMiami33146 FLUSA

Dislocationmobility,which dictates the response of dislocations to an applied stress,is a fundamental property of crystalline materials that governs the evolution of plastic *** approaches for deriving mobility laws rely on phenomenological models of the underlying physics,whose free parameters are in turn fitted to a small number of intuition-driven atomic scale simulations under varying conditions of temperature and *** tedious and time-consuming approach becomes particularly cumbersome for materials with complex dependencies on stress,temperature,and local environment,such as body-centered cubic crystals(BCC)metals and *** this paper,we present a novel,uncertainty quantification-driven active learning paradigm for learning dislocation mobility laws from automated high-throughput large-scale molecular dynamics simulations,using Graph Neural Networks(GNN)with a physics-informed *** demonstrate that this Physics-informed Graph Neural Network(PI-GNN)framework captures the underlying physics more accurately compared to existing phenomenological mobility laws in BCC metals.

关键词： stress dislocation consuming

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Data-Driven Norm-Optimal Control of Discrete Repetitive Processes 63

Data-Driven Norm-Optimal Control of Discrete Repetitive Proc...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Moore, Kevin L. Chu, Bing Rapisarda, Paolo Colorado School of Mines Division of Engineering Design and Society and the Department of Electrical Engineering 1500 Illinois St. GoldenCO80401 United States University of Southampton School of Electronics and Computer Science SO17 1BJ United Kingdom

ISBN: (纸本)9798350316339

We present a data-driven, model-free approach to norm-optimal control of discrete repetitive processes, exploiting Willems' fundamental lemma. The algorithm is described in detail, and a rigorous analysis of its convergence properties is performed. A numerical example is provided to demonstrate the effectiveness of the proposed design methodology. © 2024 IEEE.

关键词： Adversarial machine learning

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Alternating Direction Method of Multipliers-Based Parallel Optimization for Multi-Agent Collision-Free Model Predictive Control

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 4176-4191页

作者： Cheng, Zilong Ma, Jun Wang, Wenxin Zhu, Zicheng de Silva, Clarence W. Lee, Tong Heng The Department of Electrical and Computer Engineering National University of Singapore Singapore119077 Singapore Guangzhou China The Division of Emerging Interdisciplinary Areas The Hong Kong University of Science and Technology Hong Kong HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian Shenzhen China The Department of Mechanical Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

This article investigates the collision-free control problem for multi-agent systems. For such multi-agent systems, it is the typical situation where conventional methods using either the usual centralized model predictive control (MPC), or even the distributed counterpart, would suffer from substantial difficulty in balancing optimality and computational efficiency. Additionally, the nonconvex characteristics that invariably arise in such collision-free control and optimization problems render it difficult to effectively derive a reliable solution (and also to thoroughly analyze the associated convergence properties). To overcome these challenging issues, this work establishes a suitably novel parallel computation framework through an innovative mathematical problem formulation;and then with this framework and formulation, a parallel algorithm based on alternating direction method of multipliers (ADMM) is presented to solve the subproblems arising from the resulting parallel structure. Furthermore, an efficient and intuitive initialization procedure is developed to accelerate the optimization process, and the optimum is thus determined with significantly improved computational efficiency. As supported by rigorous proofs, the convergence of the proposed ADMM iterations for this nonconvex optimization problem is analyzed and discussed in detail. Finally, a simulation with a group of unmanned aerial vehicles (UAVs) serves as an illustrative example here to demonstrate the effectiveness and efficiency of the proposed approach. Also, the simulation results verify significant improvements in accuracy and computational efficiency compared to other baselines, including primal quadratic mixed integer programming (PQ-MIP), nonconvex quadratic mixed integer programming (NC-MIP), and nonconvex quadratically constrained quadratic programming (NC-QCQP). © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

关键词： Computational efficiency

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Implementation and Evaluation of 0.6 µm CMOS Complex RCPF for Accurate Frequency Response Measurement

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and Systems 2025年第1期145卷 25-37页

作者： Sato, Lina Fukagawa, Kanta Shouno, Kazuhiro Tanimoto, Hiroshi Muto, Cosy Moriyama, Seijiro Takahashi, Chikau Yoshino, Michitaka Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan Anagix Corporation 3-22-27 Numama Kanagawa Zushi249-0004 Japan Takamori Corporation Japan Research Center for Micro-Nano Technology Hosei University 3-11-15 Midori-cho Koganei Tokyo184-0003 Japan

This paper describes the implementation and evaluation of an RC polyphase filter (RCPF) and circuitry for measuring its frequency characteristics. The integrated circuit is fabricated on a 0.6 µm CMOS process and its supply voltage is 5 V. The integrated circuit includes a filter under test (FUT), a four-phase square wave generator, several analog switches and differential amplifiers to amplify the high-impedance signals of the RCPF. This FUT is a third-order complex RCPF whose passband is 8 − 40 MHz. This paper describes the design of these circuits in detail. This integrated circuit can select two measurement methods by means of external control signals. The first method uses a four-phase square wave. The frequency characteristics are measured by using an on-chip four-phase square wave generator driven by an external clock generator and by a spectrum analyzer tuned to the fundamental frequency. The second method uses an external network analyzer. Furthermore, this integrated circuit is equipped with a calibration circuit to measure the frequency characteristics of the circuitry except FUT by passing through the input / output signals of the FUT. As a result, the frequency characteristics of the FUT itself can be accurately measured. The effectiveness is confirmed through evaluation of the fabricated integrated circuit. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Differential amplifiers

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Uncertainty-Informed Model Selection Method for Nonlinear System Identification and Interpretable Machine Learning 32

Uncertainty-Informed Model Selection Method for Nonlinear Sy...

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32nd Mediterranean Conference on Control and Automation, MED 2024

作者： Gu, Yuanlin Wei, Hua-Liang University of Stirling Division of Computer Science and Maths StirlingFK9 4LA United Kingdom University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom

ISBN: (纸本)9798350395440

Modeling uncertainty has been an active and important topic in the fields of data-driven modeling and machine learning. Uncertainty ubiquitously exists in any data modeling process, making it challenging to identify the optimal models among many potential candidates. This article proposes an uncertainty-informed method to address the model selection problem. The performance of the proposed method is evaluated on a dataset generated from a complex system model. The experimental results demonstrate the effectiveness of the proposed method and its superiority over conventional approaches. This method has minimal requirements for the length of training data and model types, making it applicable for various modeling frameworks. © 2024 IEEE.

关键词： Machine learning

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Control of Loop Power Controller Applying Hexagonal Modular Multi-level Converter

Control of Loop Power Controller Applying Hexagonal Modular ...

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2022 International Symposium on Power Electronics, Electrical Drives, Automation and Motion, SPEEDAM 2022

作者： Roknuzzaman, Md Hamasaki, Shin-Ichi Nagasaki University Department of Advanced Technology and Science for Sustainable Development 1-14 Bunkyo-machi Nagasaki Japan Nagasaki University Division of Electrical Engineering and Computer Science 1-14 Bunkyo-machi Nagasaki Japan

ISBN: (数字)9781665484596

ISBN: (纸本)9781665484596

A new loop power controller (LPC) applying three-phase AC/AC Hexagonal Modular Multilevel Converter (HMMC) is introduced in which the secondary side is connected to the power source through a loop to establish the primary side as the secondary source. HMMC represents an interesting and emerging topology for applications like wind turbines and has received extensive attention due to its inherent ability to reduce capacitor voltage fluctuation. A circulating current topology and arm current control based on the internal model principle are applied to achieve the capacitor voltage balancing. The operating principle and its control scheme are explained. The performance and effectiveness of the proposed control strategy are verified by both the simulation and experiment. © 2022 IEEE.

关键词： Power control

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