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Encrypted Finite-Horizon Energy-to-Peak State Estimation for Time-Varying Systems Under Eavesdropping Attacks: Tackling Secrecy Capacity

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 985-996页

作者： Lei Zou Zidong Wang Bo Shen Hongli Dong Guoping Lu College of Information Science and Technology Donghua UniversityShanghai 201620 Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China Department of Computer Science Brunel University LondonUxbridgeMiddlesex UB83PHUK Intelligence Energy Research Institute Northeast Petroleum UniversityDaqing 163318 Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum UniversityDaqing 163318 Sanya Offshore Oil&Gas Research Institute Northeast Petroleum UniversitySanya 572024China School of Electrical Engineering Nantong UniversityNantong 226019China

This paper is concerned with the problem of finitehorizon energy-to-peak state estimation for a class of networked linear time-varying *** to the inherent vulnerability of network-based communication,the measurement signals transmitted over a communication network might be intercepted by potential *** avoid information leakage,by resorting to an artificial-noise-assisted method,we develop a novel encryption-decryption scheme to ensure that the transmitted signal is composed of the raw measurement and an artificial-noise term.A special evaluation index named secrecy capacity is employed to assess the information security of signal transmissions under the developed encryption-decryption *** purpose of the addressed problem is to design an encryptiondecryption scheme and a state estimator such that:1)the desired secrecy capacity is ensured;and 2)the required finite-horizon–l_(2)-l_(∞)performance is *** conditions are established on the existence of the encryption-decryption mechanism and the finite-horizon state ***,simulation results are proposed to show the effectiveness of our proposed encryption-decryption-based state estimation scheme.

关键词： Artificial-noise-assisted technique eavesdropping encryption-decryption scheme energy-to-peak state estimation finitehorizon state estimation

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Improving Tomato Disease Diagnosis Through ResNet50: An Innovative Approach 4

Improving Tomato Disease Diagnosis Through ResNet50: An Inno...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Dhamotharan, R. Raj, J. Relin Francis Krishnan, R. Santhana Kavitha, S. Soundiraraj, N. Kumar, V. Vinoth Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331540364

Tomato crops are vulnerable to numerous diseases that can severely affect both yield and quality. Early and precise identification of these diseases is indispensable for implementing active management and intervention measures. This research introduces an advanced approach to tomato disease detection by employing the ResNet50 architecture, pretrained on the ImageNet dataset. Through transfer learning, the ResNet50 model leverages its pre-learned features to classify tomato plant images affected by diseases such as Tomato Mosaic Virus (ToMV), Bacterial Spot, Blossom End Rot, Early Blight, and Late Blight. The ResNet50 model is fine-tuned with custom classification layers tailored to the specific diseases, allowing it to achieve high accuracy in disease detection. Preliminary results indicate that the ResNet50 model significantly outperforms traditional machine learning approaches, demonstrating its efficacy in accurately diagnosing tomato diseases. This study provides an effective solution for improving disease detection in tomatoes and highlights the transformative role of deep learning in advancing agricultural practices, fostering sustainability, and enhancing productivity. © 2024 IEEE.

关键词： Fruits

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Reviewing metal supported solid oxide fuel cells for efficient electricity generation with biofuels for mobility

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Journal of Energy Chemistry 2025年第4期103卷 106-153页

作者： Fábio C.Antunes João P.J.de Oliveira Ricardo S.de Abreu Thiago Dias Bruno B.N.S.Brandão Josué M.Gonçalves Josimar Ribeiro Julian Hunt Hudson Zanin Gustavo Doubek School of Electrical and Computer Engineering University of CampinasCampinas 13083-852Brazil School of Food Engineering University of CampinasCampinas 13083-852Brazil School of Chemical Engineering University of CampinasCampinas 13083-852Brazil Mackenzie Institute for Research in Graphene and Nanotechnologies(MackGraphe) Mackenzie Presbyterian InstituteSão Paulo 01302-907Brazil Electrochemistry Research and Development Laboratory(LPDE) Department of ChemistryCenter of Exact SciencesFederal University of Espírito SantoCampus GoiabeirasVitória 29075-910Brazil Biological and Environmental Science and Engineering Division King Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia

Metal-Supported Solid Oxide Fuel Cells(MS-SOFCs)hold significant potential for driving the energy *** electrochemical devices represent the most advanced generation of Solid Oxide Fuel Cell(SOFCs)and can pave the way for mass production and wider adoption than Proton Exchange Membrane Fuel Cells(PEMFCs)due to their fuel flexibility,higher power density and the absence of noble metals in the fabrication *** review examines the state-of-the-art of SOFCs and MS-SOFCs,presenting perspectives and research directions for these key technological devices,highlighting novel materials,techniques,architectures,devices,and degradation mechanisms to address current challenges and future *** such as infiltration/impregnation,ex-solution catalyst synthesis,and the use of a pre-catalytic reformer layer are discussed as their impact on efficiency and prolonged *** concepts are also described and connected with well-dispersed nano particles,hindrance of coarsening,and an increased number of Triple Phase Boundaries(TPBs).This review also describes the synergistic use of reformers with MS-SOFCs to compose solutions in energy generation from readily available ***,the End-of-Life(EoL),recycling,and life-cycle assessments(LCAs)of the Fuel Cell Hybrid Electric Vehicles(FCHEVs)were *** comparing Fuel Cell Electric Vehicles(FCEVs)equipped with(PEMFCs)and FCHEVs equipped with MS-SOFCs,both powered with hydrogen(H_(2))generated by different routes were *** review aims to provide valuable insights into these key technological devices,emphasizing the importance of robust research and development to enhance performance and lifespan while reducing costs and environmental impact.

关键词： Reform Metal-supported solid oxide fuel cell Powertrain systems Fuel cell Hybrid electric vehicle Biofuel

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Energy-Optimized Offloading of Delay-Sensitive Tasks in Hybrid Edge-Cloud Computing

Energy-Optimized Offloading of Delay-Sensitive Tasks in Hybr...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Yuan, Haitao Wang, Shen Ma, Yaofei Bi, Jing Yang, Jinhong Zhang, Jia Zhou, MengChu School of Automation Science and Electrical Engineering Beihang University Beijing100191 China College of Computer Beijing University of Technology Beijing100124 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

Currently, a cloud-edge collaborative system combines almost unlimited storage and computing resources where tasks can be migrated to high-performance servers in edge servers or the cloud. However, resource allocation and task offloading present big challenges due to the competition among mobile devices (MDs) for communication and computing resources of edge servers. Therefore, it is significant to properly offload MDs' tasks to edge servers or the cloud. This work proposes a collaborative edge-cloud architecture, including a centralized cloud, edge servers, and MDs. Then, this work jointly considers computing power, task sizes, computing resources, transmission power of MDs, transmission rates, computing power, transmission power, computing resource of edge servers, and computing resource of the cloud. Considering the abovementioned factors, this work designs a mixed-integer non-linear programming problem. To solve it, a Genetic Simulated annealing-based Particle Swarm Optimization (GSPSO) algorithm is proposed to obtain the best solution. Building upon it, this work proposes an energy-minimized task offloading and resource allocation strategy, thereby minimizing the system's energy consumption while ensuring strict task response time limits. Experimental results show that GSPSO reduces the system's energy by 66.34%, 34.65%, and 4.95% more than particle swarm optimization (PSO), self-adaptive PSO, and Tyrannosaurus optimization. © 2024 IEEE.

关键词： Mobile edge computing

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Energy-Efficient and Latency-Optimized Computation Offloading with Improved MOEA for Industrial Internet of Things

Energy-Efficient and Latency-Optimized Computation Offloadin...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Yang, Jinhong Zhang, Jia Zhou, MengChu College of Computer Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

The unprecedented prosperity of the industrial Internet of Things has thoroughly facilitated the transition from traditional manufacturing towards intelligent manufacturing. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm for lower latency and energy consumption for IEs. However, computational offloading and coordinating of multiple IEs with diverse task types and multiple edge nodes in industrial environments poses challenges. To address this challenge, we propose a multi-task approach encompassing scientific and concurrent workflow tasks to achieve energy-efficient and latency-optimized computation offloading. Furthermore, this work designs an improved Quantum Multi-objective Grey wolf optimizer with Manta ray foraging and Associative learning (QMGMA) to optimize multi-task computation offloading. Comprehensive experiments demonstrate the superior efficiency and stability of QMAGA compared to state-of-the-art algorithms in balancing latency and energy consumption. QMAGA improves average inverse generation distance and average spacing by 37% and 31% on average than multi-objective grey wolf optimizer, non-dominated sorting genetic algorithm II, and multi-objective multi-verse optimization, proving the convergence and diversity of its non-dominated solutions. © 2024 IEEE.

关键词： Smart manufacturing

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Software Reliability Assessment Using Hybrid Neuro-Fuzzy Model

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computer Systems science & engineering 2022年第6期41卷 891-902页

作者： Parul Gandhi Mohammad Zubair Khan Ravi Kumar Sharma Omar H.Alhazmi Surbhi Bhatia Chinmay Chakraborty Department of Computer Applications Manav Rachna International Institute of Research and StudiesFaridabad121006India Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinahSaudi Arabia Department of Computer Applications Chandigarh Group of CollegesLandran140307India College of Computer Sciences and Information Technology King Faisal UniversitySaudi Arabia Department of Electronics&Communication Engineering Birla Institute of TechnologyMesra JharkhandIndia

Software reliability is the primary concern of software developmentorganizations, and the exponentially increasing demand for reliable softwarerequires modeling techniques to be developed in the present era. Small unnoticeable drifts in the software can culminate into a disaster. Early removal of theseerrors helps the organization improve and enhance the software’s reliability andsave money, time, and effort. Many soft computing techniques are available toget solutions for critical problems but selecting the appropriate technique is abig challenge. This paper proposed an efficient algorithm that can be used forthe prediction of software reliability. The proposed algorithm is implementedusing a hybrid approach named Neuro-Fuzzy Inference System and has also beenapplied to test data. In this work, a comparison among different techniques of softcomputing has been performed. After testing and training the real time data withthe reliability prediction in terms of mean relative error and mean absolute relativeerror as 0.0060 and 0.0121, respectively, the claim has been verified. The resultsclaim that the proposed algorithm predicts attractive outcomes in terms of meanabsolute relative error plus mean relative error compared to the other existingmodels that justify the reliability prediction of the proposed model. Thus, thisnovel technique intends to make this model as simple as possible to improvethe software reliability.

关键词： Software quality reliability neural networks fuzzy logic neuro-fuzzy inference system

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A Comparative Study of Ensemble Learning Models for Accurate Solar Irradiance Forecasting 8

A Comparative Study of Ensemble Learning Models for Accurate...

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8th IEEE International Conference on Computational System and Information technology for Sustainable Solutions, CSITSS 2024

作者： Chaudhari, Tejaram Joshi, Aditya Bharti, Devkumar Chavan, Pratibha Deshmukh, M.K. Bhalgat, Pritam Trinity College of Engineering & Research Department of Electronics & Telecommunication Engineering Pune India Birla Institute of Technology & Science Pilani Department of Electrical & Electronics Engineering Goa India SCTR's Pune Institute of Computer Department of Electronics & Telecommunication Engineering Pune India

ISBN: (纸本)9798331505462

Accurate estimation of solar radiation is very important in energy planning and the integration of renewable energy infrastructures into power grids for most places where the data may be sparse. This paper investigates advanced machine learning technologies specifically, ensemble methods such as XGBoost, LightGBM, CatBoost, NGBoost, AdaBoost, Gradient Boosting, Random Forest, and SGD Boost - to improve the prediction of solar radiation. It shows the performance of the above-mentioned models to forecast solar irradiance from meteorological factors using data from the Hawaii Space Exploration Analog and Simulation (HI-SEAS) weather station. It has been shown that ensemble learning boosts the forecast accuracy considerably, especially for ensemble learners optimized for categorical data and probabilistic predictions. This shows how machine learning can be potential and support the global shift to sustainable energy with reliable forecasts of solar radiation in data-scarce regions and offering valuable insights for optimizing renewable energy deployment. © 2024 IEEE.

关键词： Solar irradiance

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Keyword extraction method based on complex network

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International Journal of Information and Communication technology 2024年第4期25卷 323-335页

作者： Yang, Zhen Gong, Huajian Ma, Weiyun Yin, Chunlin Li, Jie Liu, Siyang Zhu, Hua Zhao, Na Electric Power Research Institute of Yunnan Power Grid Kunming650217 China School of Computer Science and Technology Xidian University Xian710000 China Key Laboratory in Software Engineering of Yunnan Province School of Software Yunnan University Kunming650091 China

Keyword extraction has a wide range of applications in the field of natural language processing. Many research results on keyword extraction at present. Among them, keyword extraction methods are based on complex networks do not require a lot of data training in advance and are simple to implement. This paper analyses and realises the application of complex network theory in the Chinese news keyword extraction task. We collected and analysed 2,061 news texts from Sina website as experimental data, and found that the number of keywords extracted affected the performance of keyword extraction method. We also compared the performance of our algorithm with other keyword extraction algorithms. The experiments verify the accuracy and effectiveness of complex networks in keyword extraction of Chinese news texts. Based on PyQt and TextRank, a news text keyword analysis platform is constructed to realise the visualisation of text network and the extraction of Chinese news keywords Copyright © 2024 Inderscience Enterprises Ltd.

关键词： complex network keyword extraction text network TextRank

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Tree Detection from Urban Developed Areas in High-Resolution Satellite Images 1

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11th International Conference on Soft Computing for Problem Solving, SocProS 2022

作者： Singh, Pankaj Pratap Garg, Rahul Dev Prasad, Shitala Department of Computer Science and Engineering Central Institute of Technology Kokrajhar Assam Kokrajhar India Geomatics Engineering Group Department of Civil Engineering Indian Institute of Technology Roorkee Uttarakhand Roorkee India Institute for Infocomm Research A*Star Singapore Singapore

ISBN: (数字)9789811965258

ISBN: (纸本)9789811965241

Preserving trees is a challenging area which indeed an automated method to analyze the percentage of trees area in respect of total land area. In this regard, a good level of extraction approach is required for finding trees area. Initially, three image segmentation approaches have implemented for detection of tree areas in urban developed regions, basic color thresholding, automatic thresholding, and region growing segmentation methods. A semi-automatic approach is proposed for detecting tree areas from high-resolution satellite images (HRSI) of urban developed area in this paper. Initially, a pixel-level classifier will train to assign into two-class label {tree and non-tree} to each pixel in a HRSI and later as pixels group. The pixel-level classification is then refined by region growing method in an image to accurately segmentation of tree and non-tree regions. Therefore, this refined segmentation results will show the tree crowns with natural shape. The proposed approach will be trained on an aerial image of different urban developed area. Finally, the outcomes show tree detection results as well as good scalability of this approach. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image segmentation

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Effectual Energy Optimization Stratagems for Wireless Sensor Network Collections Through Fuzzy-Based Inadequate Clustering

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SN computer science 2024年第8期5卷 1022页

作者： S, Kavitha Rao, Deepak Dasaratha Jain, Anupriya Sharma, Seema Pandit, Shraddha V. Pandey, Rajeev Christ University Karanataka Banglore India Indian Institute of Technology Patna India School of Computer Applications Manav Rachna International Institute of Research and Studies(MRIIRS) Haryana Faridabad India Department of Artificial Intelligence and Data Science PES Modern College of Engineering Pune Shivajinagar 411005 India Department of Computer Science & amp Engineering University Institute of Technology Rajiv Gandhi Proudyogiki Vishwavidyalaya Bhopal Madhya Pradesh India

Wireless Sensor Networks (WSNs) are crucial in the burgeoning Internet of Things (IoT) landscape, serving as a backbone technology that enables myriad applications across various industries. Originating as a simple methodology, WSNs have evolved significantly, propelled by rapid advancements in sensor technology and hardware capabilities. These networks play a pivotal role in collecting and transmitting data, which is essential for the infrastructure of most IoT systems. WSNs operate by deploying sensor nodes across diverse locations to gather environmental data. This scalability and adaptability of WSNs were demonstrated in studies where network coverage was expanded to include 100 and 200 nodes. Notably, the implementation of the innovative FLECH (Fuzzy Logic Energy-efficient Clustering Hierarchy) protocol significantly enhanced energy efficiency, reducing consumption by 12.69% in networks with 100 nodes and by 36.85% in those with 200 nodes, compared to the traditional LEACH (Low-Energy Adaptive Clustering Hierarchy) protocol. This work innovatively combines fuzzy logic and Particle Swarm Optimization (PSO) for efficient Cluster Head selection in Wireless Sensor Networks. The evaluation of these protocols involved numerous simulations and communication tests to ascertain the First Node Die (FND) point—indicative of when a network begins to lose efficacy due to energy depletion. Results indicated that the LEACH protocol reached the FND point faster than FLECH, suggesting that FLECH may offer better longevity and durability for IoT applications, aligning with the needs for sustainable and efficient operation in expanding technological ecosystems. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： FLECH IoT LEACH WSN

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