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Constrained neural classifier training method for flaw detection in industrial pipes using particle swarm optimisation

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International Journal of Innovative Computing and Applications 2022年第3期13卷 150-160页

作者： da Silva, Gilvan Farias de Souza, Edmar E.P. de Simas Filho, Eduardo F. Farias, Paulo C.M.A. Albuquerque, Maria C.S. da Silva, Ivan Costa Farias, Cláudia T.T. Digital Systems Laboratory Electrical Engineering Program Federal University of Bahia BA Salvador Brazil Federal Institute of Bahia BA Salvador Brazil

A novel method for constrained training of multi-class artificial neural network classifiers is proposed in this work. The traditional training procedure is usually based on mean square error minimisation and thus, all classes of interest are considered as having the same relevance for system performance. This is not always the case for real-world applications in which the class relevance may be unbalanced. In this paper, cost functions designed to introduce classification performance constraints for specific classes are presented and particle swarm optimisation is used as global optimisation method. The proposed method is applied to a non-destructive evaluation decision support problem using pulsed eddy currents signals. Experimental results obtained from thermally insulated industrial pipes indicate the efficiency of the proposed method in comparison to neural networks trained from the traditional back-propagation algorithm. © 2022 Inderscience Enterprises Ltd.

关键词： Neural networks

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A Novel Zero Common-Mode Voltage Modulation Strategy for Dual Three-Phase PWM Inverter Based on Unified Voltage Modulation 4

A Novel Zero Common-Mode Voltage Modulation Strategy for Dua...

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4th International Conference on Energy, Power and electrical engineering, EPEE 2024

作者： Kan, Jinpeng Meng, Yongqing Li, Changyi Chen, Wenjie Li, Fengjiao Shi, Yixin Xi'an Jiaotong University School of Electrical Engineering Xi'an China Avic Xi'an Flight Automatic Control Research Institute Department of Computer and Electronic Systems Engineering Xi'an China

ISBN: (纸本)9798331518066

Power electronic switching devices and pulse width modulation (PWM) not only improves the performance of motor drive systems, but also brings about common-mode voltage (CMV) issues, that challenging the normal operation of the system. This article proposes a zero common-mode voltage modulation strategy based on unified voltage modulation for a 60° Y-connected six-phase permanent magnet synchronous motor system driven by a dual three-phase inverter. The theoretical basis and zero common-mode implementation scheme of unified modulation are analyzed, and the effectiveness of the proposed zero common-mode voltage modulation strategy is finally verified through simulation. © 2024 IEEE.

关键词： Pulse width modulation

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Computational Moment Control of Ensemble systems 63

Computational Moment Control of Ensemble Systems

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Kuan, Yuan-Hung Zhang, Wei Li, Jr-Shin Washington University in St. Louis Department of Electrical and Systems Engineering St. LouisMO63130 United States

ISBN: (纸本)9798350316339

Finely manipulation of a large population of structurally identical dynamical systems exhibiting different dynamics, referred to as an ensemble system, is a crucial task arising from various emerging applications across diverse disciplines. A significant challenge in controlling this class of systems is the inherent scalability issue, involving computational complexity and efficiency, due to the massive size. To overcome this bottleneck, in this paper, we introduce a moment transform that maps ensemble systems defined on the space of continuous functions to their associated moment systems defined on the space of moment sequences. This transformation enables the approximation of the dynamics of an ensemble system in terms of a finite-dimensional truncated moment system. We leverage this reduction to facilitate control design for ensemble systems by developing an iterative computational optimal control algorithm with convergence guarantees. The efficiency and performance of the proposed algorithm are further demonstrated through its application to practical ensemble control problems encountered in physics and robotics. © 2024 IEEE.

关键词： Optimal control systems

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Prediction of Radiated Electromagnetic Interference Based on Four-module Motor Drive System 6

Prediction of Radiated Electromagnetic Interference Based on...

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6th International Conference on Energy, Power and Grid, ICEPG 2024

作者： Shi, Yixin Chen, Wenjie Li, Fengjiao Kan, Jinpeng Meng, Yongqing Yang, Guang Xi'an Jiaotong University School of Electrical Engineering Xi'an China Avic Xi'an Flight Automatic Control Research Institute Department of Computer and Electronic Systems Engineering Xi'an China

ISBN: (纸本)9798350377798

In recent years, wide bandgap semiconductor devices such as silicon carbide (SiC) and gallium nitride (GaN) have been increasingly applied in electric drive systems, effectively enhancing system power density. However, the high-frequency and high-speed characteristics of SiC devices exacerbate electromagnetic interference (EMI) issues within the system. Additionally, the cables in electric drive systems exhibit significant antenna effects, radiating conducted EMI into the surrounding space, thereby affecting the normal operation of other sensitive equipment. Therefore, it is essential to research motor drive system topologies that can actively suppress EMI and predict radiated interference in motor drive systems. The topology of a four-module motor paired with parallel inverters can achieve active suppression of EMI at the source. This paper conducts a simulation-based prediction of radiated interference for a parallel inverter-driven four-module motor system. It analyzes the coupling mechanisms of common mode and differential mode radiated interference and validates the radiated interference from both DC and AC cables through simulations, predicting the impact on surrounding sensitive equipment in practical systems. © 2024 IEEE.

关键词： Gallium nitride

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A Physical-Based Artificial Neural Networks Compact Modeling Framework for Emerging FETs

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IEEE Transactions on Electron Devices 2024年第1期71卷 223-230页

作者： Yang, Ya-Shu Li, Yiming Kola, Sekhar Reddy Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory The Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Communications Engineering Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan

We report a compact modeling framework based on the Grove-Frohman (GF) model and artificial neural networks (ANNs) for emerging gate-all-around (GAA) MOSFETs. The framework consists of two ANNs;the first ANN constructed with the drain current model not only can capture the main trend of device I - V characteristics but also can predict its variation even when the amount of training data for the ANN is insufficient or outside the range of applied biases. The second one is then designed to improve the model accuracy by further minimizing the errors between the target and the model outputs. We implement the proposed framework to accurately model emerging GAA nanosheet (NS) MOSFETs and complementary FETs (CFETs) without suffering from divergent issues in circuit simulation. In addition, nonphysical behaviors, such as nonzero current at zero bias, do not occur in the modeling framework. Compared to recently reported machine-learning (ML) models, our approach can achieve a similar level of model accuracy with merely 20% amount of the training data. © 1963-2012 IEEE.

关键词： Neural networks

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Photonics Research Interview with Professor Marlan Scully

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Photonics Research 2022年第1期10卷 256-260页

作者： LAN YANG Department of Electrical and Systems Engineering Washington University in St.LouisSt.LouisMissouri 63130USA

Professor Marlan Scully discusses his career in quantum optics with the Editor-in-Chief of Photonics Research,*** Yang.

关键词： optics Photonics quantum

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A deep learning-based model for automated STN localization using local field potentials in Parkinson’s disease

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Soft Computing 2025年第4期29卷 2343-2362页

作者： Hosny, Mohamed Naeem, Mohamed A. Zhu, Minwei Gao, Wenpeng Elshenhab, Ahmed M. Fu, Yili Department of Electrical Engineering Benha Faculty of Engineering Benha University Benha Egypt State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150080 China Department of Mechatronics and Robotics Engineering Faculty of Engineering South Valley University Qena Egypt First Affiliated Hospital Harbin Medical University Harbin150001 China School of Life Science and Technology Harbin Institute of Technology Harbin150080 China Department of Mathematics Faculty of Science Mansoura University Mansoura35516 Egypt

Accurate identification of the subthalamic nucleus (STN) borders is time-consuming, relying heavily on the neurosurgeon expertise in manually interpreting the electrophysiological signals. Local field potentials (LFPs) have garnered imperative attention due to their strong correlation with the STN. However, existing detection models often face challenges with high computational complexity, hyperparameter optimization and lack of explainability, making them unreliable for clinicians. Therefore, this study introduces an explanatory framework using convolutional neural networks (CNN) for detecting the STN region from LFPs. Continuous wavelet transform is employed to convert LFPs signals into scalogram images, which are then processed by sixteen CNN models. We evaluated our framework by examining the impact of various limiting factors on the classification performance, including model size, learning rate (LR), optimizers and data scaling. Deep features are extracted from the top-performing CNN architectures to capture rich representations of the scalograms. These features are then fused and classified using k-nearest neighbour algorithm. Gradient-weighted class activation mapping is used to explain the decisions made by the proposed model. Our approach achieved an accuracy of 99.61%, outperforming individual CNN models for STN localization. The experimental results revealed that CNN models, embedded with additional hyperparameters and layers, generally outperformed smaller models. Besides, low LR significantly enhanced the performance compared to high LR. Moreover, features extracted from untuned networks produced lower performance than tuned networks. The proposed system could revolutionize deep brain stimulation surgery by increasing efficiency and reducing reliance on clinician expertise for STN detection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Wavelet transforms

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Accelerating CNNs for Pneumonia Disease Diagnosis via Heterogeneous FPGA systems 9

Accelerating CNNs for Pneumonia Disease Diagnosis via Hetero...

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9th South-East Europe Design Automation, Computer engineering, Computer Networks and Social Media Conference, SEEDA-CECNSM 2024

作者： Sanida, Theodora Sideris, Argyrios Sanida, Maria Vasiliki Dossis, Michael Dasygenis, Minas University of Western Macedonia Department of Electrical and Computer Engineering Kozani50131 Greece University of Piraeus Department of Digital Systems Piraeus18534 Greece

ISBN: (纸本)9798350342482

Early and accurate diagnosis of pneumonia is critical for effective treatment and improved patient outcomes. This paper introduces a cutting-edge framework for accelerating the diagnosis of pneumonia using Convolutional Neural Networks (CNNs) on heterogeneous Field-programmable Gate Array (FPGA) systems. The framework is designed to leverage FPGAs' parallel processing capabilities and reconfigurability, enabling significant enhancements in speed and efficiency for processing medical images. Our approach involves the development of an optimized CNN model tailored to the features and characteristics typical of pneumonia in radiographic images. This model is then mapped onto an FPGA, utilizing high-level synthesis and custom hardware descriptions to maximize data throughput and minimize latency. We conducted testing using a diverse chest X-ray dataset, comparing our FPGA-based system's performance against traditional computing platforms such as GPUs and CPUs. Results indicate that our system accelerates the diagnostic process over 5 times and achieves a 97.58% accuracy rate comparable to state-of-the-art methods. The proposed framework enhances medical imaging devices' capabilities, leading to quicker diagnoses, thus reducing hospitalization time and improving patient care. © 2024 IEEE.

关键词： Digital storage

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Physical-Layer Security in AmBC-NOMA Networks With Random Eavesdroppers

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IEEE Internet of Things Journal 2025年第12期12卷 18628-18642页

作者： Pei, Xinyue Wang, Xingwei Huang, Min Chen, Yingyang Li, Xiaofan Tsiftsis, Theodoros A. Northeastern University School of Computer Science and Engineering Shenyang110819 China Northeastern University College of Information Science and Engineering Shenyang110819 China Jinan University College of Information Science and Technology Guangzhou510632 China Jinan University School of Intelligent Systems Science and Engineering Zhuhai519070 China University of Thessaly Department of Informatics Telecommunications Lamia35100 Greece University of Nottingham Ningbo China Department of Electrical and Electronic Engineering Ningbo315100 China

In this work, we investigate the physical-layer security (PLS) of ambient backscatter communication nonorthogonal multiple access (AmBC-NOMA) networks where noncolluding eavesdroppers (Eves) are randomly distributed. In the proposed system, a base station (BS) transmits a superimposed signal to a typical NOMA user pair, while a backscatter device (BD) simultaneously transmits its unique signal by reflecting and modulating the BS’s signal. Meanwhile, Eves passively attempt to wiretap the ongoing transmissions. Notably, the number and locations of Eves are unknown, posing a substantial security threat to the system. To address this challenge, the BS injects artificial noise (AN) to mislead the Eves, and a protected zone is employed to create an Eve-exclusion area around the BS. Theoretical expressions for outage probability (OP) and intercept probability (IP) are provided to evaluate the system’s reliability– security tradeoff. Asymptotic behavior at high signal-to-noise ratio (SNR) is further explored, including the derivation of diversity orders for the OP. Numerical results validate the analytical findings through extensive simulations, demonstrating that both the AN injection and protected zone can effectively enhance PLS. Furthermore, analysis and insights of different key parameters, including transmit SNR, reflection efficiency at the BD, power allocation coefficient, power fraction allocated to desired signal, Eve-exclusion area radius, Eve distribution density, and backscattered AN cancellation efficiency, on OP and IP are also provided. © 2014 IEEE.

关键词： Signal to noise ratio

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Introduction

SpringerBriefs in Applied Sciences and Technology

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SpringerBriefs in Applied Sciences and Technology 2022年 1-5页

作者： de Simón-Martín, Miguel Bracco, Stefano Piazza, Giorgio Pagnini, Luisa Carlotta González-Martínez, Alberto Delfino, Federico Department of Electrical Systems and Automation Engineering University of León León Spain Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture University of Genoa Genoa Italy Department of Civil Chemical and Environmental Engineering University of Genoa Genoa Italy

The deployment of Energy Communities can help to increase the provision of sustainable energy from renewable energy sources at urban, industrial, and commercial levels, granting benefits to all the community members. Their effective integration in the form of microgrids (and nanogrids) will undoubtedly help to decarbonize the energy sector and, at the same time, increase energy resiliency. The main purpose of this book is to provide a systematic framework for the calculation of the Levelized Cost of Energy (LCOEn) for Energy Communities proposing a general formulation and its application to three representative real case studies. To the authors’ knowledge, the existing literature does not address this issue yet. This book is intended for researchers, academics and postgraduate students in energy engineering and economics, promoters of energy projects, policymakers and, in general, for anyone interested in energy sustainability and renewable energy integration. In this chapter, we present the context and the aim of the book, its organization and a brief biographical sketch of the authors.` © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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