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IEEE Transactions on Radar Systems 2024年 2卷 752-766页

作者： Dorvash, Masoud Modarres-Hashemi, Mahmoud Alaee-Kerahroodi, Mohammad Department of Electrical and Computer Engineering Isfahan*** Iran Luxembourg City1855 Luxembourg

This article presents a method for designing transmit beampattern in 4-D imaging automotive multiple-input-multiple-output (MIMO) radars, employing the distance between the designed and desired beampatterns as the design metric. Utilizing the lp -norm criteria, we consider a broader range of p values, specifically for p ≥ 2 and 0 © 2023 IEEE.

关键词： Covariance matrix

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Bidirectional Photoelectric Response of Perovskite Oxide-Based Synaptic ReRAM Device

IEEE Electron Device Letters

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IEEE Electron Device Letters 2024年第3期45卷 384-387页

作者： Shrivastava, Saransh Limantoro, Stephen Ekaputra Juliano, Hans Tseng, Tseung-Yuen Hsinchu30010 Taiwan Department of Electrical Engineering and Computer Science Hsinchu30010 Taiwan

Photo sensing capability of an artificial synaptic device make it more valuable for brain-inspired computing systems, which can conquer the von Neumann bottleneck. In this letter, Ba0.7 Sr0.3 TiO3 (BST) based single layered resistive random access memory (ReRAM) device is proposed with its four different photoelectric applications. First, bidirectional photoelectric response (BPR) is achieved via 405 and 633 nm wavelengths stimulation. Second, this novel BPR contributes to the emulation of light potentiation, and light depression behaviors. Third, an important neurological behavior 'experience dependent plasticity (EDP)' is mimicked by frequency modulation of 405 nm light pulse. Fourth, ultra-high (>200%) indices of post-tetanic potentiation and depression (PTP/PTD) are achieved by amplifying the device conductance as a result of two different pulse train stimulations. A convolutional neural network is designed to classify Zalando's article images with a test accuracy of 87.7%. These results provide an opportunity for the development of multi-wavelength dependent multifunctional artificial brain-inspired systems. © 1980-2012 IEEE.

关键词： Neural networks

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Nonlinear coupling of closely spaced modes in atomically thin MoS2 nanoelectromechanical resonators

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Microsystems & Nanoengineering 2024年第6期10卷 581-591页

作者： S M Enamul Hoque Yousuf Steven W.Shaw Philip X.-L.Feng Department of Electrical and Computer Engineering University of FloridaGainesvilleFL 32611USA Department of Mechanical and Civil Engineering Florida Institute of TechnologyMelbourneFL 32901USA Department of Mechanical Engineering Department of Physics and AstronomyMichigan State UniversityEast LansingMI 48423USA

Nanoelectromechanical systems(NEMS)incorporating atomic or molecular layer van der Waals materials can support multimode resonances and exotic nonlinear *** we investigate nonlinear coupling of closely spaced modes in a bilayer(2L)molybdenum disulfide(MoS_(2))nanoelectromechanical *** model the response from a drumhead resonator using equations of two resonant modes with a dispersive coupling term to describe the vibration induced frequency shifts that result from the induced change in *** employ method of averaging to solve the equations of coupled modes and extract an expression for the nonlinear coupling coefficient(λ)in closed *** thermomechanical noise spectral measurements are used to calibrate the vibration amplitude of mode 2(a_(2))in the displacement *** drive mode 2 near its natural frequency and measure the shifted resonance frequency of mode 1(f_(1s))resulting from the dispersive *** model yieldsλ=0.027±0.005 pm^(-2)·μs^(-2) from thermomechanical noise measurement of mode *** model also captures an anomalous frequency shift of the undriven mode 1 due to nonlinear coupling to the driven mode 2 mediated by large dynamic *** study provides a direct means to quantifyingλby measuring the thermomechanical noise in NEMS and will be valuable for understanding nonlinear mode coupling in emerging resonant systems.

关键词： dynamics coupling nonlinear

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Anomaly-Focused Augmentation Method for Industrial Visual Inspection

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IEEE Access 2025年 13卷 89118-89139页

作者： Melvinlara, Melvin A. Hameed, Ibrahim A. Koziorek, Jiri Faculty of Electrical Engineering and Computer Science Department of Cybernetics and Biomedical Engineering Ostrava70800 Czech Republic Department of Mechanical Engineering and Technology Management Ås1433 Norway

In industrial inspection, the detection of surface defects - such as scratches, dents, or other defects - is crucial for ensuring product quality. However, the limited availability of annotated images of such defects poses challenges for developing reliable detection models. This paper proposes a novel image augmentation algorithm to address this limitation by generating synthetic imperfections from a small sample set. Unlike traditional approaches that augment entire images, the proposed approach isolates defects and applies spatial transformations (e.g., rotation, shear) before blending them into background images. This allows for the creation of diverse training datasets. The algorithm employs a 2D binary map to encode the spatial relationship between defect and background regions of images and then applies an AI-driven model to generate new realistic blended images. By leveraging a generator-discriminator framework, the augmentation process is iteratively refined during training to maintain the style and texture of the original imperfections. Experimental results show that the proposed method significantly enhances the performance of defect detection models compared to existing augmentation techniques while preserving the realism of the generated images. © 2013 IEEE.

关键词： Photointerpretation

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Uncertainty quantification in large language models through convex hull analysis

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Discover Artificial Intelligence 2024年第1期4卷 1-14页

作者： Catak, Ferhat Ozgur Kuzlu, Murat Department of Electrical Engineering and Computer Science University of Stavanger Rogaland Norway Department of Engineering Technology Old Dominion University NorfolkVA United States

Uncertainty quantification approaches have been more critical in large language models (LLMs), particularly high-risk applications requiring reliable outputs. However, traditional methods for uncertainty quantification, such as probabilistic models and ensemble techniques, face challenges when applied to the complex and high-dimensional nature of LLM-generated outputs. This study proposes a novel geometric approach to uncertainty quantification using convex hull analysis. The proposed method leverages the spatial properties of response embeddings to measure the dispersion and variability of model outputs. The prompts are categorized into three types, i.e., ’easy’, ’moderate’, and ’confusing’, to generate multiple responses using different LLMs at varying temperature settings. The responses are transformed into high-dimensional embeddings via a BERT model and subsequently projected into a two-dimensional space using Principal Component Analysis (PCA), Isomap, Multidimensional Scaling (MDS). The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is utilized to cluster the embeddings and compute the convex hull for each selected cluster. The experimental results indicate that the uncertainty of the model for LLMs depends on the prompt complexity, the model, and the temperature setting. © The Author(s) 2024.

关键词： Embeddings

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Skin lesion classification using transfer learning

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Soft Computing 2024年第20期28卷 12337-12343页

作者： Nivedhitha, G. Kalpana, P. Sidthik, A. Sheik Rani, V. Anusha Singh, Ajith B. Rajagopal, R. Department of Computer Science and Engineering Sri Krishna College of Technology Tamil Nadu Coimbatore India Department of Electrical and Electronics Engineering VSB College of Engineering and Technical Campus Tamilnadu Coimbatore642109 India Department of Electronics and Instrumentation Engineering Saveetha Engineering College Tamilnadu Chennai India Department of Electrical and Electronics Engineering Sri Krishna College of Technology Tamil Nadu Coimbatore India Department of Electrical and Electronics Engineering Francis Xavier Engineering College Tirunelveli India

This work presents an essential module for the Transfer Learning approach's classification of melanoma skin lesions. Melanoma, a highly lethal form of skin cancer, poses a significant health threat globally. Image analysis plays a crucial role in enhancing the accuracy of malignant skin lesion classification. Although neural networks trained on extensive datasets have emerged as the latest solution, their scalability remains a challenge. This study proposes an efficient method for classifying skin lesions utilizing labelled data from open sources, leveraging EfficientNet as the foundational model to robustly capture discriminative features from diverse visual perspectives. Validation of the proposed algorithms relies on the classifier's capacity to distinguish between classes is measured by the Area Under the Receiver Operating Characteristic (AUC-ROC) curve. AUC-ROC score greater than zero denotes better classification performance. Our proposed model achieves an impressive score of 98.65%. In contrast to existing approaches, our method demonstrates swift and accurate identification and segmentation of melanoma skin lesions, showcasing its efficacy in advancing the field of skin lesion classification. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Dermatology

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FOD-YOLO NET: Fasteners fault and object detection in railway tracks using deep yolo network1

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8123-8137页

作者： Brintha, K. Jawhar, S. Joseph Department of Computer Science and Engineering Arunachala College of Engineering for Women Manavilai Tamil Nadu Nagercoil India Department of Electrical and Electronics Engineering Arunachala College of Engineering for Women Manavilai Tamil Nadu Nagercoil India

Automated railway security systems prevent train collisions with trackside obstructions that cause accidents in high-speed railways. Rail safety is being improved and accident rates reduced through continuous research. A rapid advancement in deep learning has promoted new possibilities for research in this field. In this work, a novel deep learning-based FOD-YOLO net is proposed for detecting the fasteners faults and objects in the railway tracks. There are two basic components in the deep learning-based YOLOv8: the backbone and the head. YOLOv8 utilizes an improved version of the CSPDarknet53 network for detecting objects on the railway track. The head of YOLOv8 consists of EfficientNet with various convolutional layers with squeeze and excitation blocks for detecting any defect in the track fasteners. These layers are liable for detecting the objectness scores, bounding boxes and class probabilities structured with fully connected layers for the objects and faults in tracks. Based on the results from the Yolo network, the alert message is sent to the loco pilot to avoid accidents using fuzzy logic. The experimental fallouts of proposed FOD-YOLO net achieve higher accuracy and yields better evaluation results with 98.14% accuracy, 98.84% precision and 95.94% recall. From the experimental results, the FOD-YOLO net improves the overall accuracy range by 5.44%, 4.72%, 0.73%, and 13.18% better than Fast RCNN, YOLOv5s-VF, YOLO-GD, and 2D-SSA + Deep network respectively. © 2024 – IOS Press. All rights reserved.

关键词： Object detection

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THE EFFICIENCY AND SECURITY OF MULTI-UNMANNED AERIAL VEHICLE COMMUNICATION NETWORKS: FEDERATED LEARNING BASED ON LIGHTWEIGHT BLOCKCHAIN

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International Journal of electrical engineering 2024年第1期31卷 23-31页

作者： Chung, Hao-Tse Wang, Li-Chun Cheng, Shao-Hung Institute of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taiwan Department of Electrical and Electronic Engineering Chung Cheng Institute of Technology NDU Taiwan

This study develops an innovative federated learning framework specifically designed for multi-unmanned aerial vehicles (UAVs), utilizing blockchain technology to achieve lightweight, secure, and efficient data processing. In this architecture, UAVs directly participate in federated learning without the need for a central server. Instead, a series of base stations execute blockchain nodes to process gradient information and maintain the system. We eliminate single points of failure by transferring gradient aggregation from the central server to blockchain nodes, thereby enhancing the system’s stability and reliability. Furthermore, the use of a private Inter Planetary File System (IPFS) reduces storage and communication costs. A Byzantine Fault Tolerance (BFT) consensus mechanism is adopted to improve system efficiency and security. Experiments confirm that our proposed architecture effectively prevents model poisoning attacks, even in scenarios where malicious blockchain nodes are in the majority, thus ensuring learning performance. Finally, a node reputation selection mechanism based on the Multi-Krum algorithm is introduced to increase the likelihood of nodes submitting high-quality gradients, thereby enhancing overall system efficiency. © 2024, Chinese Institute of electrical engineering. All rights reserved.

关键词： Aircraft communication

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Bidding Strategy for Hybrid PV-BESS Plants via Knowledge-data-complementary Learning

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Journal of Modern Power Systems and Clean Energy 2025年第1期13卷 365-378页

作者： Xiaoge Huang Zhenhuan Ding Zhao Liu Tianqiao Zhao Pei Zhang Xiaojun Wang Department of Electrical and Computer Engineering State University of New York at BinghamtonBinghamtonUSA School of Artificial Intelligence Anhui UniversityHefei 230601China School of Electrical Engineering Beijing Jiaotong UniversityBeijing 100044China Brookhaven National Lab UptonUSA School of Electrical Automation and Information Engineering Tianjin UniversityTianjin 300072China

The hybrid photovoltaic(PV)-battery energy storage system(BESS)plant(HPP)can gain revenue by performing energy arbitrage in low-carbon power ***,multiple operational uncertainties challenge the profitability and reliability of HPP in the day-ahead *** paper proposes two coherent models to address these ***,a knowledge-driven penalty-based bidding(PBB)model for HPP is established,considering forecast errors of PV generation,market prices,and under-generation ***,a data-driven dynamic error quantification(DEQ)model is used to capture the variational pattern of the distribution of forecast *** role of the DEQ model is to guide the knowledgedriven bidding ***,the DEQ model aims at the statistical optimum,but the knowledge-driven PBB model aims at the operational *** two models have independent optimizations based on misaligned *** address this,the knowledge-data-complementary learning(KDCL)framework is proposed to align data-driven performance with knowledge-driven objectives,thereby enhancing the overall performance of the bidding strategy.A tailored algorithm is proposed to solve the bidding *** proposed bidding strategy is validated by using data from the National Renewable Energy Laboratory(NREL)and the New York Independent System Operator(NYISO).

关键词： Hybrid power plant(HPP) photovoltaic(PV) battery energy storage system(BESS) optimization uncertainty bidding strategy error quantification knowledge-data-complementary learning

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Distinguishing between Faults and Non-Fault Disturbances in The Power System with an HVDC Link: A Small-Scale Data-Driven Classification Approach

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Transactions of the Korean Institute of electrical Engineers 2023年第9期72卷 1018-1028页

作者： Jang, Jiyeon Kim, Insu Dept. of Electrical Engineering Inha University Korea Republic of Department of Electrical and Computer Engineering Inha University Korea Republic of

In power systems, faults, such as ground faults and short circuits, and non-fault disturbances, such as large load fluctuations and unbalances, occur frequently. However, the power system responses to power faults and non-fault disturbances are different. Therefore, it is essential to accurately distinguish between faults and non-fault disturbances in power systems. Previous studies have collected large-scale data by monitoring real-time parameters of the power system and detecting the occurrence of power system faults. However, this study does not focus only on diagnosing power system faults but also on accurately distinguishing between faults and non-faults disturbances and uses various classification models to train the data and evaluate and analyze the prediction results. Collecting power system parameters when faults and non-faults disturbances occur is not easy. Therefore, this study used DIgSILENT PowerFactory software to simulate faults and non-faults disturbances in the power system and collected 120 small data sets. The data collected for the 120 cases consists of various metrics such as voltage, current, frequency, rotor speed, and HVDC parameters. This study used seven classification models for training and prediction: decision tree, gradient boosting classifier, k-nearest neighbors, logistic regression, naive Bayes classification, and random forest regression. In addition, this study introduced an importance-based data reorganization method to improve the performance of the best-performing classification model and analyzed its effectiveness. Copyright © The Korean Institute of electrical Engineers.

关键词： Classification (of information)

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