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Federated Learning on Internet of Things:Extensive and Systematic Review

Computers, Materials & Continua 2024年第5期79卷 1795-1834页

作者： Meenakshi Aggarwal Vikas Khullar Sunita Rani Thomas AndréProla Shyama Barna Bhattacharjee Sarowar Morshed Shawon Nitin Goyal Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpuraPunjab140401India department of CSE&IT BPS Mahila VishwavidyalayaKhanpur KalanSonipatHaryana131305India Engineering Research&Innovation Group Universidad Europea del AtlánticoC/Isabel Torres 21Santander39011Spain department of Project Management Universidad Internacional IberoamericanaAreciboPR00613USA department of Project Management Universidade Internacional do CuanzaEstrada Nacional 250Bairro KaluapandaCuito-BiéAngola department of Computer Science and Engineering University of Science and Technology Chittagong(USTC)Chattogram4202Bangladesh Deptartment of eee University of Science and Technology Chittagong(USTC)Chattogram4202Bangladesh department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendragarhHaryana123031India

The proliferation of IoT devices requires innovative approaches to gaining insights while preserving privacy and resources amid unprecedented data ***,FL development for IoT is still in its infancy and needs to be explored in various areas to understand the key challenges for deployment in real-world *** paper systematically reviewed the available literature using the PRISMA guiding *** study aims to provide a detailed overview of the increasing use of FL in IoT networks,including the architecture and challenges.A systematic review approach is used to collect,categorize and analyze FL-IoT-based *** was performed in the Ieee,Elsevier,Arxiv,ACM,and WOS databases and 92 articles were finally *** measures were published in English and with the keywords“FL”and“IoT”.The methodology begins with an overview of recent advances in FL and the IoT,followed by a discussion of how these two technologies can be *** be more specific,we examine and evaluate the capabilities of FL by talking about communication protocols,frameworks and *** then present a comprehensive analysis of the use of FL in a number of key IoT applications,including smart healthcare,smart transportation,smart cities,smart industry,smart finance,and smart *** key findings from this analysis of FL IoT services and applications are also ***,we performed a comparative analysis with FL IID(independent and identical data)and non-ID,traditional centralized deep learning(DL)*** concluded that FL has better performance,especially in terms of privacy protection and resource *** is excellent for preserving privacy becausemodel training takes place on individual devices or edge nodes,eliminating the need for centralized data aggregation,which poses significant privacy *** facilitate development in this rapidly evolving field,the insights presented are intended to help practitioners and re

关键词： Internet of Things federated learning PRISMA framework of FL applications of FL data privacy communication

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RSPSSL:A novel high-fidelity Raman spectral preprocessing scheme to enhance biomedical applications and chemical resolution visualization

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Light(Science & Applications) 2024年第2期13卷 366-386页

作者： Jiaqi Hu Gina Jinna Chen Chenlong Xue Pei Liang Yanqun Xiang Chuanlun Zhang Xiaokeng Chi Guoying Liu Yanfang Ye Dongyu Cui De Zhang Xiaojun yu Hong Dang Wen Zhang Junfan Chen Quan Tang Penglai Guo Ho-Pui Ho Yuchao Li Longqing Cong Perry Ping Shum State Key Laboratory of Optical Fiber and Cable Manufacture Technology Guangdong Key Laboratory of Integrated Optoelectronics IntellisenseDepartment of EEESouthern University of Science and TechnologyShenzhen 518055China College of Optical and Electronic Technology China Jiliang UniversityHangzhou 310018China department of Nasopharyngeal Carcinoma Sun Yat-sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapyGuangzhou 510060China department of Ocean Science and Engineering Southern University of Science and TechnologyShenzhen 518055China department of Nephrology Chaozhou People’s HospitalChaozhou 521011China Clinical Research Design Division Sun Yatsen Memorial HospitalGuangzhouGuangdong 510120China School of Automation Northwestern Polytechnical UniversityXi’anShaanxi 710072China department of Biomedical Engineering The Chinese University of Hong KongHong KongChina Guangdong Provincial Key Laboratory of Nanophotonic Manipulation Institute of NanophotonicsJinan UniversityGuangzhou 511443China

Raman spectroscopy has tremendous potential for material analysis with its molecular fingerprinting capability in many branches of science and *** is also an emerging omics technique for metabolic profiling to shape precision ***,precisely attributing vibration peaks coupled with specific environmental,instrumental,and specimen noise is *** Raman spectral preprocessing to remove statistical bias noise and sample-related errors should provide a powerful tool for valuable information ***,we propose a novel Raman spectral preprocessing scheme based on self-supervised learning(RSPSSL)with high capacity and spectral *** can preprocess arbitrary Raman spectra without further training at a speed of~1900 spectra per second without human *** experimental data preprocessing trial demonstrated its excellent capacity and signal fidelity with an 88%reduction in root mean square error and a 60%reduction in infinite norm(L__(∞))compared to established *** this advantage,it remarkably enhanced various biomedical applications with a 400%accuracy elevation(ΔAUC)in cancer diagnosis,an average 38%(few-shot)and 242%accuracy improvement in paraquat concentration prediction,and unsealed the chemical resolution of biomedical hyperspectral images,especially in the spectral fingerprint *** precisely preprocessed various Raman spectra from different spectroscopy devices,laboratories,and diverse *** scheme will enable biomedical mechanism screening with the label-free volumetric molecular imaging tool on organism and disease metabolomics profiling with a scenario of high throughput,cross-device,various analyte complexity,and diverse applications.

关键词： spectral scheme resolution

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A simple decision tree-based disturbance monitoring system for VSC-based HVDC transmission link integrating a DFIG wind farm

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Protection and Control of Modern Power Systems 2022年第1期7卷 363-381页

作者： Rajesh Babu Damala Rajesh Kumar Patnaik Ashish Ranjan Dash department of eee Centurion University of Technology and ManagementParalakhemundiOdishaIndia&GMR Institute of TechnologyRajamAndhrapradeshIndia department of eee GMR Institute of TechnologyRajamSrikakulamAndhra PradeshIndia department of eee Centurion University of Technology and ManagementParalakhemundiOdishaIndia

Fault detection and classification is a key challenge for the protection of High Voltage DC(HVDC)transmission *** this paper,the Teager-Kaiser Energy Operator(TKEO)algorithm associated with a decision tree-based fault classi-fier is proposed to detect and classify various DC *** Change Identification Filter is applied to the average and differential current components,to detect the first instant of fault occurrence(above threshold)and register a Change Identified Point(CIP).Further,if a CIP is registered for a positive or negative line,only three samples of currents(i.e.,CIP and each side of CIP)are sent to the proposed TKEO algorithm,which produces their respective 8 indices through which the,fault can be detected along with its *** new approach enables quicker detection allowing utility grids to be restored as soon as *** novel approach also reduces computing complexity and the time required to identify faults with *** importance and accuracy of the proposed scheme are also thor-oughly tested and compared with other methods for various faults on HVDC transmission lines.

关键词： Change Identification Filter Differential current DC faults Simple Decision Tree Fault classifier HVDC transmission link Renewable Energy TKEO algorithm

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Facial Expression Recognition Using Enhanced Convolution Neural Network with Attention Mechanism

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Computer Systems Science & Engineering 2022年第4期41卷 415-426页

作者： K.Prabhu S.SathishKumar M.Sivachitra S.Dineshkumar P.Sathiyabama department of EIE Kongu Engineering CollegePerundurai638060TamilnaduIndia department of eee M.Kumarasamy College of EngineeringKarur639113TamilnaduIndia department of eee Kongu Engineering CollegePerundurai638060TamilnaduIndia department of eee CSI Engineering CollegeKetti643215TamilnaduIndia

Facial Expression Recognition(FER)has been an interesting area of research in places where there is human-computer *** psychol-ogy,emotions and behaviors can be analyzed in ***ﬁers used in FER have been perfect on normal faces but have been found to be constrained in occluded ***,Deep Learning Techniques(DLT)have gained popular-ity in applications of real-world problems including recognition of human *** human face reﬂects emotional states and human *** expression is the most natural and powerful way of communicating *** which form communications between the two are termed Human Machine Interaction(HMI)*** can improve HMI systems as human expressions convey useful information to an *** paper proposes a FER scheme called EECNN(Enhanced Convolution Neural Network with Atten-tion mechanism)to recognize seven types of human emotions with satisfying results in its *** EECNN achieved 89.8%accuracy in classi-fying the images.

关键词： Facial expression recognition linear discriminant analysis animal migration optimization regions of interest enhanced convolution neural network with attention mechanism

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Economic Analysis of Demand Response Incorporated Optimal Power Flow

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Intelligent Automation & Soft Computing 2023年第1期35卷 399-413页

作者： Ulagammai Meyyappan S.Joyal Isac department of eee Saveetha Engineering CollegeChennai602105India

Demand Response(DR)is one of the most cost-effective and unfailing techniques used by utilities for consumer load *** research paper presents different DR programs in deregulated *** description and the classification of DR along with their potential benefits and associated cost components are *** addition,most DR measurement indices and their evaluation are also ***,the economic load model incorporated thermal,wind,and energy storage by considering the elasticity market price from its calculated locational marginal pricing(LMP).The various DR programs like direct load control,critical peak pricing,real-time pricing,time of use,and capacity market programs are considered during this *** effect of demand response in electricity prices is highlighted using a simulated study on Ieee 30 bus *** is done by the Shuffled Frog Leap Algorithm(SFLA).Comprehensive performance comparison on voltage deviations,losses,and cost with and without considering DR is also presented in this paper.

关键词： Demand response wind power generation shufﬂed frog leap algorithm optimal powerﬂow

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Rapid Fault Analysis by Deep Learning-Based PMU for Smart Grid System

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Intelligent Automation & Soft Computing 2023年第2期35卷 1581-1594页

作者： J.Shanmugapriya K.Baskaran department of eee Park College of Engineering and TechnologyCoimbatore641659India department of eee Alagappa Chettiar Government College of Engineering and TechnologyKaraikudi630004India

Smart Grids(SG)is a power system development concept that has received signiﬁcant attention *** signiﬁes real-time data for speciﬁc communication *** best capabilities for monitoring and controlling the grid are essential to system *** of the most critical needs for smart-grid execution is fast,precise,and economically synchronized measurements,which are made feasible by Phasor Measurement Units(PMU).PMUs can pro-vide synchronized measurements and measure voltages as well as current phasors *** utilize GPS time-stamping at Coordinated Universal Time(UTC)to capture electric phasors with great accuracy and *** research tends to Deep Learning(DL)advances to design a Residual Network(ResNet)model that can accurately identify and classify defects in grid-connected *** part of fault detection and probe,the proposed strategy uses a ResNet-50 tech-nique to evaluate real-time measurement data from geographically scattered *** a result of its excellent signal classiﬁcation efﬁciency and ability to extract high-quality signal features,its fault diagnosis performance is *** results demonstrate that the proposed method is effective in detecting and classifying faults at sufﬁcient *** proposed approaches classify the fault type with a precision of 98.5%and an accuracy of 99.1%.The long-short-term memory(LSTM),Convolutional Neural Network(CNN),and CNN-LSTM algo-rithms are applied to compare the ***-world data tends to evaluate these networks.

关键词： Smart grid phasor measurement units global positioning system coordinated universal time deep learning residual network–50

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QoS Constrained Network Coding Technique to Data Transmission Using IoT

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Computer Systems Science & Engineering 2022年第11期43卷 531-544页

作者： A.Sathishkumar T.Rammohan S.Sathish Kumar J.Uma K.Srujan Raju Aarti Sangwan M.Sivachitra M.Prabu department of ECE The Kavery Engineering CollegeMecheri636453TamilnaduIndia department of eee Karpagam College of EngineeringCoimbatore641032India department of eee M.Kumarasamy College of EngineeringKarur639113India department of Computer Science and Engineering CMR Technical CampusHyderabad501401India department of CST Manav Rachna UniversityFaridabad121004India department of eee Kongu Engineering CollegePerundurai638060India department of eee Nandha Engineering CollegePerunduraiTamilnadu638052India

The research work presents,constrained network coding technique to ensure the successful data transmission based composite channel cmos technology using dielectric *** charge fragmentation and charge splitting are two components of the filtered switch domino(FSD)*** behavior of selected switching is achieved using generator called conditional pulse generator which is employed in Multi Dynamic Node Domino(MDND)*** FSD and MDND technique need wide area compared to existing single nodekeeper domino *** aim of this research is to minimize dissipation of power and to achieve less consumption of *** proposed research,works by introducing the method namely Interference and throughput aware Optimized Multicast Routing Protocol(IT-OMRP).The main goal of this proposed research method is to introduce the system which can forward the data packets towards the destination securely and *** achieve the bandwidth and throughput in optimized data transmission,proposed multicast tree is selected by Particle Swarm Optimization which will select the most optimal host node as the branches of multi cast *** node selection is done by considering the objectives residual energy,residual bandwidth and *** node selection multi cast routing is done with the concern of interference to ensure the reliable and successful data *** case of transmission range size is higher than the coverage sense range,successful routing is ensured by selecting secondary host forwarders as a backup which will act as intermediate relay *** NS2 simulator is used to evaluate research outcome from which it is proved that the proposed technique tends to have increased packet delivery ratio than the existing work.

关键词： Multicast routing optimal node selection secondary relay nodes probability of interference residual energy bandwidth throughput

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Genetic Algorithm Based 7-Level Step-Up Inverter with Reduced Harmonics and Switching Devices

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Intelligent Automation & Soft Computing 2023年第3期35卷 3081-3097页

作者： T.Anand Kumar M.Kaliamoorthy I.Gerald Christopher Raj department of eee Dr.Mahalingam College of Engineering and TechnologyPollachiTamilnaduIndia department of eee PSNA College of Engineering and TechnologyDindigulTamilnaduIndia

This paper presents a unique voltage-raising topology for a single-phase seven-level inverter with triple output voltage gain using single input source and two switched *** output voltage has been boosted up to three times the value of input voltage by conﬁguring the switched capacitors in series and parallel combinations which eliminates the use of additional step-up converters and *** selective harmonic elimination(SHE)approach is used to remove the lower-order *** optimal switching angles for SHE is determined using the genetic *** switching angles are com-bined with a level-shifted pulse width modulation(PWM)technique for pulse generation,resulting in reduced total harmonic distortion(THD).A detailed com-parison has been made against other relevant seven-level inverter topologies in terms of the number of switches,drivers,diodes,capacitors,and boosting facil-ities to emphasize the beneﬁts of the proposed *** proposed topology is simulated using MATLAB/SIMULINK and an experimental prototype has been developed to validate the *** Digital Signal Processing(DSP)TMS320F2812 board is used to generate the switching pulses for the proposed technique and the experimental results concur with the simulated model outputs.

关键词： Genetic algorithm multilevel inverter pulse width modulation selective harmonic elimination switched capacitor

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Design of Deep Reinforcement Learning Controller Through Data-assisted Model for Robotic Fish Speed Tracking

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Journal of Bionic Engineering 2023年第3期20卷 953-966页

作者： Palmani Duraisamy Manigandan Nagarajan Santhanakrishnan Amirtharajan Rengarajan Robotics Lab School of EEESASTRA Deemed UniversityThanjavurTamil Nadu613401India department of ECE School of EEESASTRA Deemed UniversityThanjavurTamil Nadu613401India

It is common for robotic fish to generate thrust using reactive force generated by the tail’s physical motion, which interacts with the surrounding fluid. The coupling effect of the body strongly correlates with this thrust. However, hydrodynamics cannot be wholly modeled in analytical form. Therefore, data-assisted modeling is necessary for robotic fish. This work presents the first method of its kind using Genetic Algorithm (GA)-based optimization methods for data-assistive modeling for robotic fish applications. To begin, experimental data are collected in real time with the robotic fish that has been designed and fabricated using 3D printing. Then, the model’s influential parameters are estimated using an optimization problem. Further, a model-based deep reinforcement learning (DRL) controller is proposed to track the desired speed through extensive simulation work. In addition to a deep deterministic policy gradient (DDPG), a twin delayed DDPG (TD3) is employed in the training of the RL agent. Unfortunately, due to its local optimization problem, the RL-DDPG controller failed to perform well during training. In contrast, the RL-TD3 controller effectively learns the control policies and overcomes the local optima problem. As a final step, controller performance is evaluated under different disturbance conditions. In contrast to DDPG and GA-tuned proportional-integral controllers, the proposed model with RL-TD3 controller significantly improves the performance.

关键词： Bioinspired robot Data-assisted model Genetic algorithm Reinforcement learning control Robotic fish

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Frequency Control Approach and Load Forecasting Assessment for Wind Systems

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Intelligent Automation & Soft Computing 2023年第1期35卷 971-982页

作者： K.Sukanya P.Vijayakumar department of eee Karpagam College of EngineeringCoimbatore641032India

Frequency deviation has to be controlled in power generation units when there arefluctuations in system *** several renewable energy sources,wind energy forecasting is majorly focused in this work which is a tough task due to its variations and uncontrollable *** there is a mismatch between generation and demand,the frequency deviation may arise from the actual frequency 50 Hz(in India).To mitigate the frequency deviation issue,it is necessary to develop an effective technique for better frequency control in wind energy *** this work,heuristic Fuzzy Logic Based Controller(FLC)is developed for providing an effective frequency control support by modeling the complex behavior of the system to enhance the load forecasting in wind based hybrid power *** control is applied to reduce the frequency deviation due tofluctuations and load prediction information using ANN(Artificial Neural Network)and SVM(Support Vector Machine)learning *** performance analysis of the proposed method is done with different machine learning based *** forecasting assessment is done over various climates with the aim to decrease the prediction errors and to demote the forecasting *** results show that the Mean Absolute Percentage Error(MAPE),Root Mean Square Error(RMSE)and Normalized Mean Absolute Error(NMAE)values are scaled down by 41.1%,9.9%and 23.1%respectively in the proposed method while comparing with existing wavelet and BPN based approach.

关键词： Load forecasting wind power prediction fuzzy logic controller ANN SVM hybrid power systems

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