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An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

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Big Data Mining and Analytics 2024年第2期7卷 399-418页

作者： Sankaramoorthy Muthubalaji Naresh Kumar Muniyaraj Sarvade Pedda Venkata Subba Rao Kavitha Thandapani Pasupuleti Rama Mohan Thangam Somasundaram Yousef Farhaoui Department of Electrical and Electronics Engineering CMR College of Engineering&TechnologyHyderabad 501401India Department of Electronics and Communication Engineering Vardhaman College of Engineering KacharamShamshabad 501218India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and TechnologyHyderabad 501301India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India Department of Electrical and Electronics Engineering Bharat Institute of Engineering and TechnologyHyderabad 501510India Department of Computer Science and Engineering Amrita School of ComputingAmrita Vishwa VidhyapeethamBengaluru 560035India T-IDMS Department of Computer ScienceFaculty of Sciences and TechniquesMoulay Ismail UniversityErrachidia 52000Morocco

Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial *** is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced *** main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information *** original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI ***,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting ***,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more *** Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis ***,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security *** results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.

关键词： smart grid Machine Learning(ML) big data analytics AdaBelief Exponential Feature Selection(AEFS) Polar Bear Optimization(PBO) Kernel Extreme Neural Network(KENN)

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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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Robust 3D point clouds classification based on declarative defenders

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Neural Computing and Applications 2025年第3期37卷 1209-1221页

作者： Li, Kaidong Zhang, Tianxiao Zhong, Cuncong Zhang, Ziming Wang, Guanghui Department of Electrical Engineering & Computer Science University of Kansas 1520 West 15th St. LawrenceKS66045 United States Department of Electrical & Computer Engineering Worcester Polytechnic Institute 100 Institute Rd. WorcesterMA01609 United States Department of Computer Science Toronto Metropolitan University 350 Victoria Street TorontoONM5B 2K3 Canada

3D point cloud classification requires distinct models from 2D image classification due to the divergent characteristics of the respective input data. While 3D point clouds are unstructured and sparse, 2D images are structured and dense. Bridging the domain gap between these two data types is a non-trivial challenge to enable model interchangeability. Recent research using lattice point classifier (LPC) highlights the feasibility of cross-domain applicability. However, the lattice projection operation in LPC generates 2D images with disconnected projected pixels. In this paper, we explore three distinct algorithms for mapping 3D point clouds into 2D images. Through extensive experiments, we thoroughly examine and analyze their performance and defense mechanisms. Leveraging current large foundation models, we scrutinize the feature disparities between regular 2D images and projected 2D images. The proposed approaches demonstrate superior accuracy and robustness against adversarial attacks. The generative model-based mapping algorithms yield regular 2D images, further minimizing the domain gap from regular 2D classification tasks. The source code is available at https://***/KaidongLi/***. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Generative adversarial networks

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Cybersecurity advancements for medical image transmission: a hybrid optical-based cryptosystem harnessing chaos, DNA sequences, and mandelbrot keys

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Multimedia Tools and Applications 2025年 1-41页

作者： Alalwan, Nasser El-Shafai, Walid Amoon, Mohammed Benjdira, Bilel Department of Computer and Communication Engineering King Saud University Riyadh Saudi Arabia Prince Sultan University Riyadh11586 Saudi Arabia Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh12435 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt

In today's advanced technological age, characterized by innovations like big data processing, cloud computing, and the Internet of Things (IoT), there is a rising utilization of medical multimedia data, especially medical images. These images, integral to the Internet of Healthcare Things (IoHT), necessitate secure transmission due to the increasing risks of unauthorized breaches and tampering. Current security methods, especially for cloud and mobile platforms, often struggle with challenges related to processing capacity, memory use, data size, and energy, making them ill-suited for extensive medical data or resource-limited environments. To address these challenges, this study introduces a novel hybrid cryptosystem, drawing on the unique qualities of the optical Arnold chaotic map, DNA (DeoxyriboNucleic Acid) sequences, and Mandelbrot keys, providing a fortified approach to the secure streaming of medical images. The proposed framework operates via a precise and structured procedure. It begins by applying the optical Arnold chaotic map cipher to each of the three-color channels (R, G, and B) within a medical image. This is followed by overlaying DNA encoding sequences on the resultant encrypted image from the earlier ciphering phase. Leveraging this groundwork, we incorporate an advanced Mandelbrot set-driven shift mechanism specifically designed to create complex confusion patterns within the R, G, and B segments of the encrypted medical imagery. The efficacy of the proposed cryptosystem is rigorously substantiated through an extensive array of simulations supported by a comprehensive security analysis. The results highlight its unparalleled resilience and security capabilities in the realm of medical image encryption, marking a significant leap over previous systems in the literature. Essentially, our work pioneers a solution to a pressing challenge in medical image security, ensuring enhanced protection of delicate health data among the rapidly evolving advanc

关键词： Cybersecurity

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Review of semiconductor devices and other power electronics components at cryogenic temperature

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iEnergy 2024年第2期3卷 95-107页

作者： Yuchuan Liao Abdelrahman Elwakeel Yudi Xiao Rafael Peña Alzola Min Zhang Weijia Yuan Alfonso J.Cruz Feliciano Lukas Graber Department of Electronic&Electrical Engineering University of StrathclydeGlasgow G11XQUK School of Electrical and Computer Engi-neering Georgia Institute of TechnologyAtlanta GAUSA

With the increasing demand for high power density,and to meet extreme working conditions,research has been focused on inves-tigating the performance of power electronics devices at cryogenic *** aim of this paper is to review the performance of power semiconductor devices,passive components,gate drivers,sensors,and eventually power electronics converters at cryogenic *** comparing the physical properties of semiconductor materials and the electrical performance of commercial power semiconductor devices,silicon carbide switches show obvious disadvantages due to the increased on-resistance and switching time at cryogenic *** contrast,silicon and gallium nitride devices exhibit improved performance when tem-perature is *** performance ceiling of power semiconductor devices can be influenced by gate drivers,within which the commercial alternatives show deteriorated performance at cryogenic temperature compared to room ***,options for voltage and current sense in cryogenic environments are *** on the cryogenic performance of the various components afore-discussed,this paper ends by presenting an overview of the published converter,which are either partially or fully tested in a cryogenic environment.

关键词： Cryogenic power electronics metal-oxide-semiconductor field-effect transistor(MOSFET) insulated-gate bipolar transistor(IGBT) gate driver

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Programmable directional color dynamics using plasmonics

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Microsystems & Nanoengineering 2024年第1期10卷 325-334页

作者： Gyurin Kim Doeun Kim Soeun Ko Jang-Hwan Han Juhwan Kim Joo Hwan Ko Young Min Song Hyeon-Ho Jeong School of Electrical Engineering and Computer Science Gwangju Institute of Science and TechnologyGwangju 61005Republic of Korea Department of Semiconductor Engineering Gwangju Institute of Science and TechnologyGwangju 61005Republic of Korea Artificial Intelligence(AI)Graduate School Gwangju Institute of Science and TechnologyGwangju 61005Republic of Korea

Adaptive multicolor filters have emerged as key components for ensuring color accuracy and resolution in outdoor visual ***,the current state of this technology is still in its infancy and largely reliant on liquid crystal devices that require high voltage and bulky structural ***,we present a multicolor nanofilter consisting of multilayered‘active’plasmonic nanocomposites,wherein metallic nanoparticles are embedded within a conductive polymer *** nanocomposites are fabricated with a total thickness below 100 nm using a‘lithography-free’method at the wafer level,and they inherently exhibit three prominent optical modes,accompanying scattering phenomena that produce distinct dichroic reflection and transmission ***,a pivotal achievement is that all these colors are electrically manipulated with an applied external voltage of less than 1 V with 3.5 s of switching speed,encompassing the entire visible ***,this electrically programmable multicolor function enables the effective and dynamic modulation of the color temperature of white light across the warm-to-cool spectrum(3250 K-6250 K).This transformative capability is exceptionally valuable for enhancing the performance of outdoor optical devices that are independent of factors such as the sun’s elevation and prevailing weather conditions.

关键词： directional prevailing exceptional

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Self-supervised cost of transport estimation for multimodal path planning

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IEEE robotics and Automation Letters 2025年第7期10卷 6872-6879页

作者： Gherold, Vincent Mandralis, Ioannis Sihite, Eric Salagame, Adarsh Ramezani, Alireza Gharib, Morteza California Institute of Technology Aerospace Engineering Department PasadenaCA United States Northeastern University Electrical and Computer Engineering Department BostonMA United States

Autonomous robots operating in real environments are often faced with decisions on how best to navigate their surroundings. In this work, we address a particular instance of this problem: how can a robot autonomously decide on the energetically optimal path to follow given a high-level objective and information about the surroundings? To tackle this problem we developed a self-supervised learning method that allows the robot to estimate the cost of transport of its surroundings using only vision inputs. We apply our method to the multi-modal mobility morphobot (M4), a robot that can drive, fly, segway, and crawl through its environment. By deploying our system in the real world, we show that our method accurately assigns different cost of transports to various types of environments e.g. grass vs smooth road. We also highlight the low computational cost of our method, which is deployed on an Nvidia Jetson Orin Nano robotic compute unit. We believe that this work will allow multi-modal robotic platforms to unlock their full potential for navigation and exploration tasks. © 2016 IEEE.

关键词： Motion planning

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Fetal health classification by support vector machine based on feature vector optimization 10

Fetal health classification by support vector machine based ...

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10th International Conference on Artificial Intelligence and robotics, QICAR 2024

作者： Fotouhi sefidi, Mahdiyeh Pouramirarsalani, Shahrzad Rajebi, Saman Department of Electrical Engineering Seraj Higher Education Institute Tabriz Iran Faculty of Electrical and Computer Engineering university of Tabriz Tabriz Iran

ISBN: (纸本)9798350348873

In recent years, diagnosis of fetal health has prevented fetal hypoxia at birth and reduced infant mortality. The acquired data from Cardiotocography (CTG) signals are used to monitor and evaluate the status of the fetus during pregnancy and delivery. Visual analysis of CTG signals using common guidelines is difficult and time-consuming due to the physiological complexity of the fetus. Artificial intelligence-based computer-aided diagnosis (CAD) systems can be useful in objective medical decision-making and digital health. In this paper, an optimized SVM classification method is proposed, which was used to classify UCI fetal health data. The optimization process was based on the genetic algorithm and the FDR relationship, in which the importance of all the features was obtained in the form of coefficients. By applying these coefficients to the feature vector of the data set, the separation of data into different classes is improved. First, the support vector machine (SVM) algorithm was used without optimization for data classification, and the correct classification rate (CCR) was 88.28. Then, by normalizing, whitening and removing outlier data in the pre-processing stage, the accuracy increased to 90.30. By applying the feature value coefficients to the input of the class band model with the aim of optimizing the feature vector, the classification accuracy increased to 93.98. ©2024 IEEE.

关键词： Genetic algorithms

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Implementation of Instantaneous P-Q Theory to Improve Power Quality by Shunt Active Power Filter for Non-Linear Loads 3

Implementation of Instantaneous P-Q Theory to Improve Power ...

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3rd IEEE International Conference on electrical Power and Energy Systems, ICEPES 2024

作者： Kumar, S. Sampath Arya, K. Chandrakala, K. R. M. Vijaya Nadar, Kannan Pauliah Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electrical and Electronics Engineering Coimbatore India Institute of Technology Jigjiga University Department of Electrical and Computer Engineering Ethiopia

ISBN: (纸本)9798350390728

Non-linear loads insert harmonics into the electrical power system. Harmonic frequencies in an electrical network are a foremost cause for introducing issues in the quality of power. Most of the power quality troubles are non-stationary in nature and mostly occur when a non-linear load is connected to the source. The Total Harmonic Distortion (THD) itself will be very high and becomes a sizable challenge to maintain the quality of power as desired. In this work, a three-phase supply feeding to a three-phase non-linear load as a diode bridge rectifier (DBR) with resistive load is considered. In this context, the source side will be injected with harmonics because of the non-linear load which is not advisable. Therefore, various techniques are implemented to filter the harmonics across the source side. Orthodox systems are based on a filtering technique and from distorted source current, the fundamental current component is separated. Shunt Active Power Filter (SAPF) is the most collective device to reduce the source current harmonics. The pulses generated for SAPF are through the Instantaneous p-q theory technique. The SAPF model is connected among source and non-linear load which is analyzed by MATLAB/Simulink® and validated the operation experimentally by DS1104 card of dSPACE system integrated into MATLAB. A power quality analyzer (fluke meter) is used to evaluate the source current harmonics in the hardware setup. © 2024 IEEE.

关键词： Harmonic analysis

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Biphasic Conductive Inks & Organogels for Soft Machines and Bioelectronics 5

Biphasic Conductive Inks & Organogels for Soft Machines and ...

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5th IEEE International Conference on Flexible and Printable Sensors and Systems, FLEPS 2023

作者： Majidi, Carmel Carnegie Mellon University Department of Mechanical Engineering PittsburghPA United States Robotics Institute Carnegie Mellon University Department of Electrical & Computer Engineering PittsburghPA United States

ISBN: (纸本)9781665457330

Over the past decade, there's been tremendous advancements in soft and highly stretchable circuitry for use in epidermal electronics for health monitoring, wearable computing, and soft robotics. As these technologies continue to improve, there is increasing interest in new material architectures that allow these materials to be printable over large areas or to exhibit robust self-healing properties. Here, I present progress on new classes of highly elastic 'biphasic' composites that incorporate gallium indium (EGaIn) liquid metal and silver flakes within a soft polymer matrix. These include printable inks for creating soft, sticker-like, digital circuits and bioelectronic interface using styrene-isoprene-styrene (SIS) thermoplastic elastomer as the polymer binder. I also present recent progress on EGaIn-Ag-organogel composites that exhibit unique combinations of dynamically tunable electrical conductivity and spontaneous mechanical self-healing. These composites can be utilized for applications that range from highly stretchable digital circuits to bioelectronic stickers for electrophysiological monitoring. © 2023 IEEE.

关键词： Timing circuits

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