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Dipper Throated Optimization for Detecting Black-Hole Attacks inMANETs

Computers, Materials & Continua 2023年第1期74卷 1905-1921页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and *** more MANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the ***,MANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole *** proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-MLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering *** is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor *** hybridmethod is primarily designed to combat active black-hole *** the LEACH clustering phase,however,can also detect passive black-hole *** effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested *** diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution ***,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.

关键词： Black-hole attack mobile ad-hoc network optimization dipper throated optimization

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Short-term prediction model of module temperature for photovoltaic power forcasting based on support vector machine

Short-term prediction model of module temperature for photov...

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International Conference on Renewable Power Generation, RPG 2015

作者： Sun, Yujing Wang, Fei Mi, Zengaiang Sun, Hongbin Liu, Chun Wang, Bo Lu, Jing Zhen, Zhao Li, Kangping State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Baoding Hebei Province071003 China State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Haidian District Beijing100084 China China Electric Power Research Institute Haidian District Beijing100192 China

ISBN: (纸本)9781785610400

To improve the prediction accuracy of photovoltaic (PV) power generation, the temperature of PV modules and its prediction is very important. A short-term step-wise temperature prediction model for PV module based on Support Vector Machine (SVM) is proposed in this paper. Firstly, the primary impact factors of PV module temperature are determined in terms of the PV module physical characteristics and the correlation coefficient between each factor and module temperature. Secondly, two kinds of prediction models, direct and step-wise models, are built to predict module temperature respectively. For direct model, the prediction model is based on SVM using the historical data. For step-wise one, the prediction models of the primary impact factors are built at the first step and then the mapping models between module temperature and the impact factors are established. In the end, the deviations of two different kinds of prediction models are analyzed and discussed based on actual operation data. The results indicate that the stepwise prediction model has better accuracy than the direct prediction model if other things are equal.

关键词： Forecasting

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Photovoltaic plant Operating Statuses Identification model based on support vector machine using loss quantity of electricity feature parameters

Photovoltaic plant Operating Statuses Identification model b...

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International Conference on Renewable Power Generation, RPG 2015

作者： Li, Kangping Wang, Fei Zhen, Zhao Sun, Yujing Mi, Zengqiang Sun, Hongbin Liu, Chun Wang, Bo Lu, Jing State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Baoding Hebei Province071003 China State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Haidian District Beijing100084 China China Electric Power Research Institute Haidian District Beijing100192 China

ISBN: (纸本)9781785610400

Operating Statuses Identification (OSI) can help operators to find fault timely and minimize the loss. So it is of great significance for optimal operation of photovoltaic (PV) plants. The loss quantity of electricity (LQOE) is defined as that should have been generated but actually not, which is caused by inverter fault, PV modules fault, dust stratification or combinations of them. Firstly, mathematical models of PV cells are proposed to figure out the LQOE of each PV module. Secondly, after analysing the relation between LQOE and operating statuses, an index set of LQOE describing the distinction of different operating statuses are defined including four statistical feature parameters and a user-defined index. Thirdly, Support Vector Classification models for OSI (OSI-SVC) are built with input features extracted from the index set. Lastly, simulations are carried out to verify the effectiveness and evaluate the performance of the OSI-SVC models. The results indicated that the operating statuses can be effectively recognized by the proposed model.

关键词： Support vector machines

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Cloud identification model for sky images based on Otsu

Cloud identification model for sky images based on Otsu

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International Conference on Renewable Power Generation, RPG 2015

作者： Li, Hongping Wang, Fei Ren, Hui Sun, Hongbin Liu, Chun Wang, Bo Lu, Jing Zhen, Zhao State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Baoding Hebei Province071003 China State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Haidian District Beijing100084 China China Electric Power Research Institute Haidian District Beijing100192 China

ISBN: (纸本)9781785610400

Irradiance changes caused by clouds movement are the mam factors, which lead to mutation of photovoltaic power. In order to increase the accuracy of the ultra-short-term photovoltaic power prediction, based on of clouds images, the cloud identification for sky images is one of the primary aspects for improving photovoltaic power accuracy. A cloud identification model for sky images is established based on Otsu. Firstly, a series of sky images are collected by Total Sky Imager (TSI) with the regions of clouds and sky are distinguished. Secondly, the color features (based on RGB values) of pixels in sample images and the spectral and texture features of the whole image are extracted;Finally, we established a cloud identification model for sky images based on Otsu according to the clouds' and sky areas' division and the general characteristics featured by pixels and images. The simulated results show that the model in this paper can identify the clouds in a sky images effectively.

关键词： Pixels

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Speaker's Emotions Recognition Module Based on the GoogleLeNet Neural Network

Speaker's Emotions Recognition Module Based on the GoogleLeN...

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2022 International Conference on Smart Information systems and Technologies, SIST 2022

作者： Tereikovskyi, Ihor Tereikovska, Liudmyla Kryvoruchko, Olena Tereikovskyi, Oleh Tyshchenko, Dmytro Franchuk, Tamara National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of System Programming And Specialized Computer Systems Kyiv Ukraine Kyiv National University Of Construction And Architecture Dept. Of Information Technology For Design And Applied Mathematics Kyiv Ukraine Kyiv National University Of Trade And Economics Dept. Of Software Engineering And Cyber Security Kyiv Ukraine National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of Department Of Computer Engineering Kyiv Ukraine

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

The article is devoted to the development of means for recognition of the emotions of the speaker, based on the neural network analysis of fixed fragments of the voice signal. The possibility of improving recognition tools through the use of a convolutional neural network of the GoogLeNet type is shown. The expediency of training the network on the examples of the TESS database is determined. A procedure for representing a voice signal in the form of a square grayscale image has been developed. Based on the use of the specified database, the modification of the GoogLeNet network was carried out. The architecture and software implementation of the recognition module have been developed. With the help of computer experiments, it has been established that after 150 epochs of training on a fairly limited training sample, the modified GoogLeNet makes it possible to achieve an accuracy of recognition of the speaker's emotions of approximately 0.97 on test examples. The obtained accuracy is comparable to the accuracy of the best modern systems of this purpose and confirms the promise of using neural network models like GoogLeNet for emotion recognition based on voice signal analysis. The need for further research aimed at reducing the resource intensity of the neural network model is shown. © 2022 IEEE.

关键词： Emotion Recognition

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Research on HRI Evaluation system and Diver Safety Equipment

Research on HRI Evaluation System and Diver Safety Equipment

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OCEANS 2024 - Halifax, OCEANS 2024

作者： Lee, Ye Jun Kim, Min-Gyu Shin, Myung-Jun Park, Tae Sung Kim, Jinhyung Kim, Sunghun Lee, Kichoon Lee, Jihun Min, Jeongtack Research Center Pohang Korea Republic of Pusan National University Hospital Pusan National University School of Medicine Department of Rehabilitation Medicine Busan Korea Republic of Institute of Technology Pusan National University Hospital Department of Convergence Medical Busan Korea Republic of Technical Research Center RNWare Co. Ltd Daegu Korea Republic of Gleam-Systems System Development Division Daejeon Korea Republic of Gleam-Systems Modeling and Simulation Division Daejeon Korea Republic of Marine Leisure Equipment R&d Center Pohang Korea Republic of

ISBN: (纸本)9798331540081

Scuba diving is a common aquatic leisure sport that many individuals enjoy in the sea. With the increasing number of people participating, there is a corresponding rise in related incidents and accidents. Typically, such events are caused by inadequate user proficiency with equipment, sudden onset of physical symptoms, failure to respond to external obstacles, and equipment malfunctions. As interest in scuba diving grows, the development of diver safety equipment that enhances convenience and usability through the interaction between divers and equipment is crucial, not only to assist underwater activities but also to preemptively prevent accidents. This paper explores the analysis of divers' physiological conditions and diver equipment evaluation criteria. Building upon this, it proposes a HRI(Human-Robot Interaction) evaluation system to facilitate the research and development of safety equipment for secure scuba diving operations. © 2024 IEEE.

关键词： Human robot interaction

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Space Charge Transport of Al2O3/Silicone Rubber Composites under Thermal Aging

Space Charge Transport of Al2O3/Silicone Rubber Composites u...

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2022 IEEE International Conference on High Voltage Engineering and Applications, ICHVE 2022

作者： Zhang, Yunxiao Yan, Chenning Wu, Kai Zhou, Yuanxiang Zhang, Tianfeng College of Electrical Engineering and Automation Fuzhou University Fujian Fuzhou China Tsinghua University State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Beijing China School of Electrical Engineering Xinjiang University The Wind Solar Storage Division State Key Laboratory of Power System and Generation Equipment Urumqi China Electric Power Research Institute Anhui Electric Power Company of State Grid Hefei China

ISBN: (纸本)9781665407502

Silicone rubber (SIR) has been widely used in the insulation of cable accessories due to its excellent performance. In this paper, pure SIR and nano Al2O3/SIR composites are thermally aged for 0-80 days at 200°C. The effects of long-term thermal aging on the space charge transport are analyzed. The results show that with the increase of thermal aging time, the accumulation of heteropolar charge of pure SIR and nano Al2O3/SIR composites gradually decreases and changes to the accumulation of homopolar charge. Compared with pure SIR, the space charge accumulation of Al2O3/SIR composites decreases significantly and changes little with the increase of thermal aging time. It is believed that when doped nano Al2O3 in SIR, the increase of carrier mobility and the introduction of metal oxides reacting with fallen free radicals to form stable compounds both lead to less space charge accumulation of Al2O3/SIR composites. © 2022 IEEE.

关键词： Aluminum oxide

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Forecasting Nonlinear Nonstationary Processes in Machine Learning Task

Forecasting Nonlinear Nonstationary Processes in Machine Lea...

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IEEE International Conference on Data Stream Mining & Processing (DSMP)

作者： Peter Bidyuk Aleksandr Gozhyj Irina Kalinina Victoria Vysotska Mikhail Vasilev Romanna Malets Department of Mathematical Methods of System Analysis National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Kyiv Ukraine Department of Intelligent Information Systems Petro Mohyla Black Sea National University Nikolaev Ukraine Department of Information Systems And Networks National University "Lvivska politechnika" Lviv Ukraine Programming Department Ivan Franko Lviv National University Lviv Ukraine

ISBN: (数字)9781728132143

ISBN: (纸本)9781728132150

The article discusses the features of the solving the forecasting problems using machine learning techniques. The issues of accounting and correctly processing non-linear non-stationary processes in the problems of modeling and forecasting time series in various areas are considered. The analysis of stages and methods for solving machine learning problems. As an example, consider the problem of predicting currency pairs based on historical data. A comparative analysis of the normalization methods in data clustering is given. For the six currency pairs a short-term forecast is proposed.

关键词： Forecasting Time series analysis Machine learning Load modeling Currencies Predictive models Classification algorithms

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Unit commitment considering demand response behavior based on utility maximization

Unit commitment considering demand response behavior based o...

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2017 IEEE Conference on Energy Internet and Energy system Integration, EI2 2017

作者： Zhang, Yumin Han, Xueshan Xu, Bo Zhang, Li Sun, Donglei Miao, Xiaofeng Jiang, Jiayin Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Haidian District Beijing100084 China Economic and Technology Research Institute of State Grid Shandong Electric Power Company Jinan China State Grid Shandong Electric Power Company Yantai Electric Power Company Yantai264001 China

ISBN: (纸本)9781538614273

The active participation of demand side resources in power grid operation not only benefits the individual participant but also benefits the whole system. The optimal goal can promote by excavating the active behaviors of demand side. The welfare maximization of market participant is the driving force of such active behaviors. Considering the independence of each user's behavior, the consumer's utility function is selected to depict consumer's preference and demand response behavior. In order to coordinate the consumers' maximum welfare with social welfare maximization, consumers' utility function and units' operation cost are integrated into Unit Commitment (UC) model. The objective function is to maximize social welfare, by subtracting units' operation cost from consumers' total utility, and this objective can be acquired. Considering the proposed model should be solved in a distributed manner, the idea of the centralized coordination and the decentralized decision-making is proposed. Based on the Lagrangian relaxation (LR) principle and distributed real-time pricing scheme, a closed-loop feedback mechanism is constructed to reflect the relationship among generator output, price level, and load demand. The ten generating units example can illustrate the effectiveness of the proposed method. © 2017 IEEE.

关键词： Power markets

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Synergism analysis of an urban metabolic system: Model development and a case study for Beijing, China

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Ecological Modelling 2014年 272卷 188-197页

作者： Yan Zhang Hong Liu Brian D. Fath State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Beijing Normal University Xinjiekouwai Street No. 19 Beijing 100875 China Biology Department Towson University Towson MD 21252 USA Advanced Systems Analysis Program International Institute for Applied System Analysis Laxenburg Austria

The efficiency of urban metabolic processes depends on the degree of mutualism of these processes throughout the metabolic system and on the value gained by each compartment within the system. This can be assessed by means of ecological network-based synergism analysis. In this paper, we used material-flow accounting methods to account for the exchanges of resources and wastes among the compartments of an urban system. Using a seven-compartment urban metabolic network model of Beijing, China, as a case study, we examined the degree of synergism of the compartments, determined the nature of the resulting ecological relationships, and determined the flow of utility to each compartment within the system. The results revealed which types of ecological relationship contributed most to the system (here, exploitation) and identified the key compartments that decreased the system's degree of synergism. The results provide theoretical and empirical support for the development of policies designed to promote healthy development of Beijing's urban metabolic system.

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