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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Keerthana, S. Deepika, N. Pooja, E. Nandhini, I. Shanthalakshmi, M. Khanaghavalle, G.R. Sri Venkateswara College Of Engineering Department Of Computer Science And Engineering Pennalur Tamilnadu Sriperumbudur602117 India Rajalakshmi Institute Of Technology Department Of Computer Science And Business Systems Tamilnadu Kuthambakkam600124 India School Of Electrical And Communication Vel Tech Rangarajan Dr. Sakunthala R&d Institute Of Science And Technology Department Of Ece Avadi India

ISBN: (纸本)9798350352689

Deep learning algorithms are becoming more potent and producing human-synthesized, undifferentiated footage is a simple process thanks to advances in computing power. A method of synthesizing human images called deep fakes is built on neural network techniques like GAN. These technologies use deep learning algorithms to overlay target pictures onto the source films, producing realistic-looking deep-fake photos and videos. In this work, we provide a novel deep-learning approach that can reliably discriminate between actual and AI-generated fake videos. In this work, we test our method on a large number of balanced and mixed datasets created by mixing various publicly available datasets, such as Face-Forensic++, Deepfake detection, Celeb DF, and other publicly available datasets as images and videos, to emulate real-time scenarios and improve model performance with real-time data by using LSTM and ResNet. © 2024 IEEE.

关键词： computer vision

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Volt-Var Control for Utility-scale Solar PV Plants to Replace SVCs 4

Volt-Var Control for Utility-scale Solar PV Plants to Replac...

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4th International Conference on High Voltage engineering and Power systems, ICHVEPS 2023

作者： Shimomukai, Ken Maeda, Haruka Fujita, Goro Shibaura Institute of Technology Functional Control Systems Graduate School of Engineering and Science Tokyo Japan Shibaura Institute of Technology Graduate School of Engineering and Science Department of Electrical Engineering and Computer Science Tokyo Japan Shibaura Institute of Technology Department of Electrical Engineering Tokyo Japan

ISBN: (纸本)9798350318678

One way to increase solar photovoltaic penetration in the grid is management of voltage fluctuations. This is because a photovoltaic plant cannot be interconnected to the grid if it causes voltage violations. Voltage violation is where voltage exceeds the acceptable range. Often, grid operators request photovoltaic plant owners to regulate voltage sufficiently with expensive and space-consuming static Var compensators. Unfortunately, this sometimes makes the project less feasible. This paper argues that there are better ways to regulate voltage. We ran a simulation with a 70 MWp photovoltaic plant as an addition to the grid. Without voltage regulation, voltage violations were found to be significant. This paper found that oversizing the inverter sufficiently would remove all voltage violations without deploying a static Var compensator. This is often a cheaper and space-saving solution for voltage management. This paper argues that economics and spatial efficiency of reactive power compensation devices is key to increasing photovoltaic penetration. © 2023 IEEE.

关键词： Reactive power

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A Comparative Analysis of SiC MOSFET Performance in a Traction Inverter 20

A Comparative Analysis of SiC MOSFET Performance in a Tracti...

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20th IEEE Asia Pacific Conference on Circuits and systems and IEEE Asia Pacific Conference on Postgraduate Research in Microelectronics Electronics, APCCAS and PrimeAsia 2024

作者： Wu, Chih-Chiang Rout, Uma Sankar Wang, Yu-Long Lin, Yi-Ling Hu, Jwu-Sheng National Yang Ming Chiao Tung University Department of Electrical Engineering & Computer Science Taiwan Industrial Technology Research Institute Mechanical and Mechatronics Systems Research Laboratories Hsinchu Taiwan

ISBN: (纸本)9798350378771

This study rigorously tests and verifies silicon carbide (SiC) power modules within a developed 800 V electric vehicle traction inverter. SiC traction inverters offer high efficiency, power density, and compact size. The developed SiCbased traction inverter is thoroughly evaluated on the established platform with various switching frequency, achieving a peak drive efficiency exceeding 98% at 6 kHz and a power density of 50 kW/L. © 2024 IEEE.

关键词： Power MOSFET

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Fractional-order binary bat algorithm for feature selection on high-dimensional microarray data

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7453-7467页

作者： Esfandiari, Ahmad Farivar, Faezeh Khaloozadeh, Hamid Department of Computer and Mechatronics Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Systems and Control Faculty of Electrical Engineering K. N. Toosi University of Technology Seyed-Khandan Shariati Ave. Tehran16317-14191 Iran

High-dimensional microarray data suffer from the confounding effects of irrelevant, redundant and noisy genes on the scalability and efficiency of classification algorithms. In order for an effective dimensionality reduction and the selection of informative genes, this paper introduces a novel approach using fractional calculus concepts. This study proposes a modified version of binary the bat algorithm named fractional-order binary bat algorithm (FBBA) able to control the convergence process using more historical memory of bat behaviors. The gene selection technique contains a two-stage hybrid filter/wrapper method which employs a new correlation-based feature clustering (CFC) algorithm in the filter stage and the FBBA in the wrapper stage. The CFC-FBBA is evaluated on ten microarray gene expression datasets by employing the support vector machine classifier with a k-fold Monte Carlo cross validation data partitioning model. Furthermore, the results show that the CFC-FBBA, while minimizing the size of the gene subset, achieves the highest classification accuracy in most cases compared to several state-of-art hybrid techniques. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Classification (of information)

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An Automated Deep Learning Based Muscular Dystrophy Detection and Classification Model

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computers, Materials & Continua 2022年第4期71卷 305-320页

作者： T.Gopalakrishnan Periakaruppan Sudhakaran K.C.Ramya K.Sathesh Kumar Fahd N.Al-Wesabi Manal Abdullah Alohali Anwer Mustafa Hilal School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Engineering SRM TRP Engineering CollegeTiruchirappalli621105India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Science King Khalid UniversityMuhayel AseerKSA Faculty of Computer and IT Sana’a UniversityYemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Muscular Dystrophy (MD) is a group of inherited muscular diseases that are commonly diagnosed with the help of techniques such asmuscle biopsy, clinical presentation, and Muscle Magnetic Resonance Imaging(MRI). Among these techniques, Muscle MRI recommends the diagnosis ofmuscular dystrophy through identification of the patterns that exist in musclefatty replacement. But the patterns overlap among various diseases whereasthere is a lack of knowledge prevalent with regards to disease-specific ***, artificial intelligence techniques can be used in the diagnosis ofmuscular dystrophies, which enables us to analyze, learn, and predict forthe future. In this scenario, the current research article presents an automated muscular dystrophy detection and classification model using SynergicDeep Learning (SDL) method with extreme Gradient Boosting (XGBoost),called SDL-XGBoost. SDL-XGBoost model has been proposed to act as anautomated deep learning (DL) model that examines the muscle MRI dataand diagnose muscular dystrophies. SDL-XGBoost model employs Kapur’sentropy based Region of Interest (RoI) for detection purposes. Besides, SDLbased feature extraction process is applied to derive a useful set of featurevectors. Finally, XGBoost model is employed as a classification approach todetermine proper class labels for muscle MRI data. The researcher conductedextensive set of simulations to showcase the superior performance of SDLXGBoost model. The obtained experimental values highlighted the supremacyof SDL-XGBoost model over other methods in terms of high accuracy being96.18% and 94.25% classification performance upon DMD and BMD respectively. Therefore, SDL-XGBoost model can help physicians in the diagnosis of muscular dystrophies by identifying the patterns of muscle fatty replacementin muscle MRI.

关键词： Muscle magnetic resonance imaging XGBoost synergic deep learning roI detection kapur’s entropy muscular dystrophies

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Extinction Chains Reveal Intermediate Phases Between the Safety and Collapse in Mutualistic Ecosystems

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engineering 2024年第12期43卷 89-98页

作者： Guangwei Wang Xueming Liu Ying Xiao Ye Yuan Linqiang Pan Xiaohong Guan Jianxi Gao Hai-Tao Zhang The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Digital Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Guangdong HUST Industrial Technology Research Institute Huazhong University of Science and TechnologyDongguan 523808China Guangdong Provincial Engineering Technology Center of Autonomous Unmanned Vessels Dongguan 523808China MOE Key Laboratory of Intelligent Networks and Network Security&State Key Laboratory of Manufacturing Systems Xi’an Jiaotong UniversityXi’an 710049China Tsinghua National Laboratory of Information Science and Technology Department of AutomationTsinghua UniversityBeijing 100084China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA

Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities,such as overfishing and deforestation,and the effects of such damage on ecological stability are *** recent advances in experimental and theoretical studies on regime shifts and tipping points,theoretical tools for understanding the extinction chain,which is the sequence of species extinctions resulting from overexploitation,are still lacking,especially for large-scale nonlinear networked *** this study,we developed a mathematical tool to predict regime shifts and extinction chains in ecosystems under multiple exploitation situations and verified it in 26 real-world mutualistic networks of various sizes and *** discovered five phases during the exploitation process:safe,partial extinction,bistable,tristable,and collapse,which enabled the optimal design of restoration strategies for degraded or collapsed *** validated our approach using a 20-year dataset from an eelgrass restoration ***,we also found a specific region in the diagram spanning exploitation rates and competition intensities,where exploiting more species helps increase *** computational tool provides insights into harvesting,fishing,exploitation,or deforestation plans while conserving or restoring the biodiversity of mutualistic ecosystems.

关键词： Complex system Network science Overexploitation Regime shift Metastability

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Sustainable Smart Road: A Unified Cost Estimation Model for Renewable-Powered Smart Roads 4

Sustainable Smart Road: A Unified Cost Estimation Model for ...

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4th IEEE International Conference on electrical, computer, and Energy Technologies, ICECET 2024

作者： Colombo, Cristian Giovanni Longo, Michela Nocera, Leonardo Ota, Ryosuke Zaninelli, Dario Politecnico di Milano Department of Energy Milan Italy Politecnico di Milano Milan Italy Tokyo Metropolitan University Faculty of Systems Design Department of Electrical Engineering and Computer Science Tokyo Japan

ISBN: (纸本)9798350395914

With new stringent policies affecting the transport and generation sectors, new technologies have emerged to drive the electric and technological transition. One of these is the smart road, a highly sensorised system to enable the integration of new vehicles and safety technologies. In this paper, the implementation of a smart road is simulated, first from an energy point of view and then from an economic point of view, by identifying the power demand of components and their cost. Next, the energetic core of the smart road is implemented. The energy core consists of a PV plant capable of powering the infrastructure. In this paper a simulation to test the proposed methodology is implemented. In the case study, a 280.05 kW smart road is powered by an optimally dimensioned PV plant. The production guaranteed by the 283.8 kWp plant will be 389.49 MW h/year and requires 57232. Furthermore, the 234.57 x20AC investment for the plant will represent less than 10% of the total investment, highlighting how renewable integration can be an economic sustainable purpose. Finally, another method to return the investment as quickly as possible will be proposed. © 2024 IEEE.

关键词： PV' Renewable Integration Road Infrastructure Smart Road

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Green Concerns in Federated Learning over 6G

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China Communications 2022年第3期19卷 50-69页

作者： Borui Zhao Qimei Cui Shengyuan Liang Jinli Zhai Yanzhao Hou Xueqing Huang Miao Pan Xiaofeng Tao National Engineering Laboratory for Mobile Network Technologies Beijing University of Posts and TelecommunicationsBeijing100876China Department of Computer Science New York Institute of TechnologyOld WestburyNY11568USA Department of Electrical and Computer Engineering University of HoustonHoustonTX77204USA

As Information,Communications,and Data Technology(ICDT)are deeply integrated,the research of 6G gradually ***,federated learning(FL)as a distributed artificial intelligence(AI)framework is generally believed to be the most promising solution to achieve“Native AI”in *** the adoption of energy as a metric in AI and wireless networks is emerging,most studies still focused on obtaining high levels of accuracy,with little consideration on new features of future networks and their possible impact on energy *** address this issue,this article focuses on green concerns in FL over *** first analyze and summarize major energy consumption challenges caused by technical characteristics of FL and the dynamical heterogeneity of 6G networks,and model the energy consumption in FL over 6G from aspects of computation and *** classify and summarize the basic ways to reduce energy,and present several feasible green designs for FL-based 6G network architecture from three *** to the simulation results,we provide a useful guideline to researchers that different schemes should be used to achieve the minimum energy consumption at a reasonable cost of learning accuracy for different network scenarios and service requirements in FL-based 6G network.

关键词： 6G native AI federated learning radio access network green communications

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Analog parallel processor for broadband multifunctional integrated system based on silicon photonic platform

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Light(science & Applications) 2025年第3期14卷 746-756页

作者： Na Qian Defu Zhou Haowen Shu Ming Zhang Xingjun Wang Daoxin Dai Xiao Deng Weiwen Zou State Key Laboratory of Advanced Optical Communication Systems and Networks Intelligent Microwave Lightwave Integration Innovation Center(imLic)Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghaiChina State Key Laboratory of Advanced Optical Communications System and Networks Department of ElectronicsSchool of Electronics Engineering and Computer SciencePeking UniversityBejingChina State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and EngineeringInternational Research Center for Advanced PhotonicsZhejiang UniversityHangzhouChina

Sharing the hardware platform between diverse information systems to establish full cooperation among different functionalities has attracted substantial ***,broadband multifunctional integrated systems with large operating frequency ranges are challenging due to the bandwidth and computing speed restrictions of electronic ***,we report an analog parallel processor(APP)based on the silicon photonic platform that directly discretizes and parallelizes the broadband signal in the analog *** APP first discretizes the signal with the optical frequency comb and then adopts optical dynamic phase interference to reassign the analog signal into 2N parallel *** photonic analog parallelism,data rate and data volume in each sequence are simultaneously compressed,which mitigates the requirement on each parallel computing ***,the fusion of the outputs from each computing core is equivalent to directly processing broadband *** the proof-of-concept experiment,two-channel analog parallel processing of broadband radar signals and high-speed communication signals is implemented on the single photonic integrated *** bandwidth of broadband radar signal is 6 GHz and the range resolution of 2.69 cm is *** wireless communication rate of 8 Gbit/s is also *** the bandwidth and speed limitations of the single-computing core along with further exploring the multichannel potential of this architecture,we anticipate that the proposed APP will accelerate the development of powerful optoelectronic processors as critical support for applications such as satellite networks and intelligent driving.

关键词： analog parallel processor sharing hardware platform multifunctional integrated systems establish full cooperation different functionalities analog parallel processor app based silicon photonic platform electronic circuitryherewe broadband signal

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Optimal Price-maker Trading Strategy of Wind Power Producer Using Virtual Bidding

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Journal of Modern Power systems and Clean Energy 2022年第3期10卷 766-778页

作者： Dongliang Xiao Mohamed Kareem AlAshery Wei Qiao IEEE Power and Energy Systems Laboratory Department of Electrical and Computer EngineeringUniversity of Nebraska-LincolnLincolnNE 68588-0511USA School of Electric Power South China University of TechnologyGuangzhou 510641China

This paper proposes a stochastic optimization model for generating the optimal price-maker trading strategy for a wind power producer using virtual bidding,which is a kind of financial tool available in most electricity markets of the United *** the proposed model,virtual bidding is used to improve the wind power producer’s market power in the dayahead(DA)market by trading at multiple buses,which are not limited to the locations of the wind *** optimal joint wind power and virtual trading strategy is generated by solving a bi-level nonlinear stochastic optimization *** upperlevel problem maximizes the total expected profit of the wind power and virtual bidding while using the conditional value at risk(CVa R)for risk *** lower-level problem represents the clearing process of the DA *** using the Karush-Kuhn-Tucker(KKT)conditions,duality theory,and big-M method,the bi-level nonlinear stochastic model is firstly transferred into an equivalent single-level stochastic mathematical program with the equilibrium constraints(MPEC)model and then a mixed-integer linear programming(MILP)model,which can be solved by existing commercial *** reduce the computational cost of solving the proposed stochastic optimization model for large systems,a method of reducing the number of buses considered for virtual bidding is proposed to simplify the stochastic MPEC model by reducing its decision variables and constraints related to virtual *** studies are performed to show the effectiveness of the proposed model and the method of reducing the number of buses considered for virtual *** impacts of the transmission limits,wind unit location,risk aversion parameters,wind power volatility,and wind and virtual capacities on the price-maker trading strategy are also studied through case studies.

关键词： Bi-level optimization electricity market risk management stochastic optimization virtual bidding wind power

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