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Optimal Deep Reinforcement Learning for Intrusion Detection in UAVs

computers, Materials & Continua 2022年第2期70卷 2639-2653页

作者： V.Praveena A.Vijayaraj P.Chinnasamy Ihsan Ali Roobaea Alroobaea Saleh Yahya Alyahyan Muhammad Ahsan Raza Department of Computer Science and Engineering Dr.N.G.P Institute of TechnologyCoimbatore641048India Department of Information Technology Vignan’s Foundation for ScienceTechnology&ResearchGuntur522213India Department of Information Technology Sri Shakthi Institute of Engineering and TechnologyCoimbatore641062India Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala Lumpur50603Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science Community College in DwadmiShaqra University11961Saudi Arabia Department of Information Technology Bahauddin Zakariya UniversityMultan60000Pakistan

In recent years,progressive developments have been observed in recent technologies and the production cost has been continuously *** such scenario,Internet of Things(IoT)network which is comprised of a set of Unmanned Aerial Vehicles(UAV),has received more attention from civilian tomilitary *** network security poses a serious challenge to UAV networks whereas the intrusion detection system(IDS)is found to be an effective process to secure the UAV *** IDSs are not adequate to handle the latest computer networks that possess maximumbandwidth and data *** order to improve the detection performance and reduce the false alarms generated by IDS,several researchers have employed Machine Learning(ML)and Deep Learning(DL)algorithms to address the intrusion detection *** this view,the current research article presents a deep reinforcement learning technique,optimized by BlackWidow Optimization(DRL-BWO)algorithm,for UAV *** addition,DRL involves an improved reinforcement learning-based Deep Belief Network(DBN)for intrusion *** parameter optimization of DRL technique,BWO algorithm is *** helps in improving the intrusion detection performance of UAV *** extensive set of experimental analysis was performed to highlight the supremacy of the proposed *** the simulation values,it is evident that the proposed method is appropriate as it attained high precision,recall,F-measure,and accuracy values such as 0.985,0.993,0.988,and 0.989 respectively.

关键词： Intrusion detection UAV networks reinforcement learning deep learning parameter optimization

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A Hybrid Approach with BRKGA and Data Mining for the Early/Tardy Scheduling Problem

A Hybrid Approach with BRKGA and Data Mining for the Early/T...

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Congress on Evolutionary Computation

作者： Israel Mendonça Tirana Noor Fatyanosa Masayoshi Aritsugi Pedro Henrique González Silva Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan Faculty of Computer Science Brawijaya University Malang Indonesia System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

This paper introduces a novel hybrid genetic algorithm combined with data mining to solve a version of the early/tardy scheduling problem in which no unforced idle time may be inserted in a sequence. The chromosome representation of the problem is based on random keys and we use the Biased Random-Key Genetic Algorithm (BRKGA) to establish an order in which jobs are scheduled. A data mining component gathers data from the evolutionary steps of BRKGA and suggests chromosomes based on past observations. In this way, we address a key challenge in BRKGA - the exploration of solutions near an individual gene - by leveraging data-driven insights to refine and enhance the search space. Comparative analysis reveals that our hybrid algorithm significantly benefits from the pattern recognition capabilities of data mining, leading to improved scheduled efficiency. The results of a series of computational experiments underscore the potential of this hybrid approach that, although requiring slightly longer computational times, is better than the previous baseline BRKGA algorithm in terms of solution quality.

关键词： Processor scheduling Evolutionary computation Scheduling Data mining Biological cells Genetic algorithms

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Comprehensive Overview of Multi-agent systems for Controlling Smart Grids

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CSEE Journal of Power and Energy systems 2022年第1期8卷 115-131页

作者： Om Prakash Mahela Mahdi Khosravy Neeraj Gupta Baseem Khan Hassan Haes Alhelou Rajendra Mahla Nilesh Patel Pierluigi Siano Power System Planning Division Rajasthan Rajya Vidyut Prasaran Nigam Ltd.Jaipur-302005India Media Integrated Communication Laboratory Graduate School of EngineeringOsaka UniversityOsakaJapan Computer Science and Engineering Department Oakland UniversityRochesterUSA Department of Electrical and Computer Engineering Hawassa UniversityHawassa 1530Ethiopia Faculty of Mechanical and Electrical Engineering Tishreen UniversityLattakiaSyria Department of Electrical Engineering National Institute of Technology Kurukshetra Department of Management&Innovation Systems University of Salerno84084 SalernoItaly

Agents are intelligent entities that act flexibly and autonomously and make wise decisions based on their intelligence and experience.A multi-agent system(MAS)contains multiple,intelligent,and interconnected collaborating agents for solving a problem beyond the ability of a single agent.A smart grid(SG)combines advanced intelligent systems,control techniques,and sensing methods with an existing utility power *** controlling smart grids,various control systems with different architectures have already been ***-based control of power system operations has been shown to overcome the limitations of time required for analysis,relaying,and protection;transmission switching;communication protocols;and management of plant *** systems provide an alternative for fast and accurate power network *** paper provides a comprehensive overview of MASs used for the control of smart *** paper provides a wide-spectrum view of the status of smart grids,MAS-based control techniques and their implementation for the control of smart *** of MASs in the control of various aspects of smart grids-including the management of energy,marketing energy,pricing,scheduling energy,reliability,network security,fault handling capability,communication between agents,SG-electrical vehicles,SG-building energy systems,and soft grids—have been critically *** than a hundred publications on the topic of MAS-based control of smart grids have been critically examined,classified,and arranged for fast reference.

关键词： Coordinated control multi-agent systems renewable energy sources smart energy infrastructure smart grid

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GLC++: Source-Free Universal Domain Adaptation through Global-Local Clustering and Contrastive Affinity Learning

arXiv

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arXiv 2024年

作者： Qu, Sanqing Zou, Tianpei Röhrbein, Florian Lu, Cewu Chen, Guang Tao, Dacheng Jiang, Changjun The School of Automotive Engineering Department of Computer Science Tongji University China The Faculty of Computer Science Chemnitz University of Technology Germany The Department of Computer Science Shanghai Jiao Tong University China The School of Computer Science Faculty of Engineering The University of Sydney Australia The Department of Computer Science Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China

Deep neural networks often exhibit sub-optimal performance under covariate and category shifts. Source-Free Domain Adaptation (SFDA) presents a promising solution to this dilemma, yet most SFDA approaches are restricted to closed-set scenarios. In this paper, we explore Source-Free Universal Domain Adaptation (SF-UniDA) aiming to accurately classify "known" data belonging to common categories and segregate them from target-private "unknown" data. We propose a novel Global and Local Clustering (GLC) technique, which comprises an adaptive one-vs-all global clustering algorithm to discern between target classes, complemented by a local k-NN clustering strategy to mitigate negative transfer. Despite the effectiveness, the inherent closed-set source architecture leads to uniform treatment of "unknown" data, impeding the identification of distinct "unknown" categories. To address this, we evolve GLC to GLC++, integrating a contrastive affinity learning strategy. We examine the superiority of GLC and GLC++ across multiple benchmarks and category shift scenarios. Remarkably, in the most challenging open-partial-set scenarios, GLC and GLC++ surpass GATE by 16.7% and 18.6% in H-score on VisDA, respectively. GLC++ enhances the novel category clustering accuracy of GLC by 4.3% in open-set scenarios on Office-Home. Furthermore, the introduced contrastive learning strategy not only enhances GLC but also significantly facilitates existing methodologies. Copyright © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

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Engagement Level Detection Using Facial Extraction and Multi-Stacked Convolutional Neural Network in e-Learning Settings

Engagement Level Detection Using Facial Extraction and Multi...

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Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS)

作者： Fitra Abdurrachman Bachtiar Al'Ravie Mutiar Mahesa Eric Wallace Cooper Computer Science Faculty Intelligent System Laboratory Universitas Brawijaya Malang Indonesia College of Information Science and Engineering Affective Engineering and Arts Laboratory Ritsumeikan University Ibaraki Japan

ISBN: (数字)9798350379914

ISBN: (纸本)9798350379921

Engagement in learning is crucial in maintaining student eagerness as fuel for student learning to achieve learning success. In e-Learning settings, engagement also plays an im-portant role as a factor influencing student learning success as students are regulating themselves during learning one-on-one using computer devices. Failing to detect student engagement may result in unsuccessful learning. In extreme cases, students may disengage and learning purpose cannot be achieved. This research aims to detect user engagement in e-Learning settings by using a deep learning approach with a feature extraction method. The dataset used in this study is the DAiSEE dataset. The dataset is in the form of videos and is extracted into frames and specific facial features. The facial area was extracted using the Mediapipe Library. The features include 1434 3D landmark features, 52 action unit features, one depth estimation feature, and three head pose estimation features. This research also conducts image selection, which selecting frames with an interval of 15 in the train, validation, and test folders. The multi-stacked Convolutional Neural Network model was used to classify four levels of engagements. The results were compared to the existing approach in engagement detection. The untuned proposed model achieved an accuracy of 48.95%, while the tuned proposed model achieved an accuracy of 52.94%. The proposed model is able to compete with state-of-the-art methods using a less complex model. Even so, the model still cannot learn labels perfectly due to a very imbalanced dataset and a possibility of model overfitting. This research is expected to be useful for educational institutions and the e-Learning field in the future.

关键词： Seminars Electronic learning Accuracy Three-dimensional displays Pose estimation Feature extraction Power electronics Convolutional neural networks Videos Overfitting

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Analyzing the Behavioral Aspects of Implementing Green IT in an Academic Environment 8

Analyzing the Behavioral Aspects of Implementing Green IT in...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Syamsuar, Dedy Triando, Fero Ulfa, Maria Antoni, Darius Mailangkay, Adele Witarsyah, Deden School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Universitas Bina Darma Postgradute Department Palembang Indonesia Universitas Bina Darma Information System Study Program Computer Science Faculty Palembang Indonesia Universitas Indo Global Mandiri Faculty of Computer Science Palembang Indonesia School of Industrial Engineering Telkom University Bandung Indonesia

ISBN: (纸本)9798350326093

Environmental awareness has recently emerged as one of the most crucial topics. As a result, various groups advocate for these technologies and research ways to promote their usage in various contexts. This study examines the factors influencing the intention and use of green technology among academics. This study integrates Price Value (PV) and Consideration of future consequence (CFC) to Theory Planned Behavior (TPB) as a theoretical basis. Two hundred five valid replies were gathered and processed through statistical analysis. The results of this study partly support the developed hypotheses. Four hypotheses developed from TPB have presented significant relationships. However, PV and CFC were not. The findings indicate that individuals in this study did not consider CFC or PV of green IT products as critical factors in their decision-making process. Findings also suggest that for implementation success, competent parties must consider the campaign to increase individual awareness and provide financial support regarding environmental policy. © 2023 IEEE.

关键词： Decision making

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Design and Implementation of a QoL Improvement system for Visually Impaired Patients 5

Design and Implementation of a QoL Improvement System for Vi...

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5th International Conference on Advances in Computational Tools for engineering Applications, ACTEA 2023

作者： Abi Zeid Daou, Roy Beaino, Jad Hawat, Johnny Ibrahim, Ramy Hayek, Ali Boercsoek, Josef Olmedo, Jose Javier Serrano Université la Sagesse Faculty of Engineering Mechatronics Department Beirut Lebanon MART Learning Education and Research Center Chananir Lebanon University of Applied Science Hamm-Lippstadt Department Lippstadt 2 Hardware Engineering Hamm59063 Germany University of Kassel Faculty of Electrical Engineering/Computer Science Department of Computer Architecture and System Programming Germany Centro de Tecnología Biomédica Universidad Politécnica de Madrid Lab of Bioinstrumentation and Nanomedicine Spain

ISBN: (纸本)9798350309959

Visually impaired people encounter several challenges in their mobility and navigation. Their daily activities are obstructed due to their inability to adapt or recognize accurately their surroundings, especially outside their house which they are more familiar with. Thus, it becomes the main reason of accidents, falling off, getting lost in unknown areas, etc. Furthermore, one of the sensory systems that helps the body to process data about the external environment is the visual system. Blind people also lose touch with the outside world, develops poor motor habits, which results in postural problems as a result. This project will assist visually impaired people in their daily life and simplify normal tasks through a system combining two previously designed projects, 'Smart Shoes for Blind and Visually Impaired People', and 'Human posture monitoring device'. The multifunctional system is developed with the goal of securing safe movements for visually impaired people as well as maintaining a good back posture by detecting leaning postures (LP). The purpose of the smart shoe is to identify obstacles and protect the user from unintended accidents. A compatible Android application has been developed to alert the user when there is an obstruction or when he is walking on a wet surface. Voice alarms will be used to acoustically alert the user. If the user collapses, a message with their position will be sent right away to a relative. On the other hand, the smart vest will identify the position of the user's back and alert him to maintain a straight posture through the same application as well. As the system is dealing with human health, some safety measurements would be taken into consideration to implement a safe electrical system in order to reduce error and to increase accuracy. © 2023 IEEE.

关键词： Accidents

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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Pilot Study on Aerial Wireless Superdirectional Sound Sources Utilizing Parametric Array Loudspeakers via Local 5G Networks

Pilot Study on Aerial Wireless Superdirectional Sound Source...

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International Symposium on Antennas and Propagation (ISAP)

作者： Yohei Iihoshi Kan Okubo Department of Electrical Engineering and Computer Science Faculty of System Design Tokyo Metropolitan University Hino Tokyo

This study presents a pilot study on the potential future use of aerial sound sources utilizing parametric array loudspeakers (PALs) in drones via fifth-generation (5G) networks. Integrating superdirectional loudspeakers with high-speed wireless communication is essential for projecting sound from the air to specific locations using drones. Herein, as an experimental approach, we have evaluated the performance of wireless communication, specifically local 5G, for realizing aerial sound sources. The results demonstrated the high performance of the local 5G connection intended for PALs in the air.

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A track donation system using blockchain 2

A track donation system using blockchain

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2nd IEEE International Conference on Electronic engineering, ICEEM 2021

作者： Shaheen, Eisa Hamed, Mohamed Abdl Zaghloul, Walaa Al Mostafa, Eman El Sharkawy, Amr Mahmoud, Asmaa Labeb, Amira Al Enany, Marwa O Attiya, Gamal Ministry of Higher Education Higher Institute of Computer Science and Information System Computer Science Department Egypt Menoufia University Faculty of Electronic Engineering Computer Science and Engineering Department Egypt

ISBN: (纸本)9781665418423

Charities face a difficult funding environment, mainly because charities are not highly transparent. Day over day It became more difficult to know whether donations are reaching the right place, or taking another course, such as financing suspicious campaigns, terrorist projects, etc. As a result, donors lose faith in these organizations, leading to a halt to interactions with charitable organizations. This makes it more difficult for charities to raise money at a great cost. This research proposed a solution to the mentioned problem using Blockchain technology, a decentralized database that provides security, transparency, and lower funding cost by eliminating the presence of third parties between donors and charities. A new track donation model was proposed which introduced several new participants that control the process of donation and remove suspicion of the charity. All donations can be tracked in Blockchain, allowing donors to know where and how their money is being used. The proposed model was implemented using Hyperledger composer then it was tested to obtain a live full track of a donation process. All donations are made using smart contracts, which allow donors to know exactly when and how their donations will be received. The Blockchain track donation system results in time-saving, reducing the cost of donation, and reduces the risk of reaching suspicious campaign donations or terrorist projects. A website was created as a facilitator for users for ease of use of the proposed system. © 2021 IEEE.

关键词： Smart contract

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