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Intelligent Energy Management Energy Storage systems Using Machine Learning

Intelligent Energy Management Energy Storage Systems Using M...

引用

Intelligent systems for Cybersecurity (ISCS), International Conference on

作者： Chinnaraj Govindasamy T.R Nisha Dayana Siddhika Dilip Adkar D. Shyam Prakash A. Shilpa Gupta Sanjeev Kumar Trivedi Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Department of Computer Science Vels Institute of Science Technology and Advance Studies Chennai School of Computer Science Engineering and Applications D Y Patil International University Akurdi Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Department of Electrical and Electronics Engineering Aurora's Technological Research Institute Uppal Hyderabad Department of Electronics Faculty of Engineering and Technology Khwaja Moinuddin chishti Language University Lucknow

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

A nevertheless-emerging generation called cloud computing permits customers to pay for services on a usage-based foundation. Internet-primarily based IT offerings are supplied through cloud computing, at the same time as virtualization enables the availability of PC sources. The muse of cloud computing is the information center, which is made up of networked computers, cables, electricity components, and different components that host and shop corporate records. With cloud facts centers, high overall performance has continually been the most critical concern, yet it compromises strength utilization. The key hassle is to lessen power consumption at the same time as preserving provider nice and performance a good way to stability device performance and strength intake. A detailed grasp of strength use styles within the cloud environment is needed for our suggested technique. We look at power consumption tendencies and reveal how, through the use of the right optimization standards based on our strength intake models, we can keep more strength in cloud records facilities. All through the prediction section, tablet optimization, which has a 97 percent accuracy rate, permits this era to provide extra correct future price forecasts.

关键词： Performance evaluation Cloud computing Power demand Electricity Stability criteria Machine learning Load management

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Interference-Limits Aware Power and Receiver Optimization for 5G NR-U and Wi-Fi in the 6 GHz Shared-Spectrum

Interference-Limits Aware Power and Receiver Optimization fo...

引用

IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Rasika Nilaweera Kalahe-Wattege Fernando Beltran Jairo Gutierrez Information Systems The University of Auckland Auckland New Zealand Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand

ISBN: (数字)9798331540906

ISBN: (纸本)9798331540913

Adjacent Channel Interference (ACI) presents a significant concern for densely deployed Wi-Fi networks in the $\mathbf{6 G H z}$ spectrum. Coexisting Wi-Fi and 5G may be exposed to ACI due to the limitations in the devices’ receiver filtering capabilities or nearby user devices’ high transmit power. Since users of 5G and Wi-Fi networks operate in close proximity in frequency, space, and time, harmful ACI between 5 G and $\mathrm{Wi}-\mathrm{Fi}$ is unavoidable. This study investigates interference-limits aware approaches for enhancing throughput in distributed 5G NR-U-enabled femtocells and interference mitigation in Wi-Fi6E/7 network. Users act rationally and non-cooperatively to optimize their own utility. We have developed a non-cooperative Stackelberg game model to maximize throughput for 5G and mitigate ACI in $\mathrm{Wi-Fi}$ user devices. Our analysis explores the conditions necessary for the existence of a Stackelberg equilibrium within this context. The effectiveness of the proposed model is further demonstrated through simulation results.

关键词： Performance evaluation 5G mobile communication Radio transmitters Receivers Games Throughput Resource management Femtocells Wireless fidelity Optimization

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Feature and Performance Comparison of FaaS Platforms 14

Feature and Performance Comparison of FaaS Platforms

引用

14th IEEE International Conference on Software engineering and Service science, ICSESS 2023

作者： Ma, Penghui Shi, Peichang Yi, Guodong College of Computer Science National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing Changsha410073 China College of Computer Science National University of Defense Technology Key Laboratory of Software Engineering for Complex Systems Changsha410073 China Xiangjiang Lab Changsha410073 China School of Advanced Interdisciplinary Studies Hunan University of Technology and Business Changsha410073 China

ISBN: (纸本)9798350336269

With serverless computing offering more efficient and cost-effective application deployment, the diversity of serverless platforms presents challenges to users, including platform lock-in and costly migration. Moreover, due to the black box nature of function computing, traditional performance benchmarking methods are not applicable, necessitating new studies. This article presents a detailed comparison of six major public cloud function computing platforms and introduces a benchmarking framework for function computing performance. This framework aims to help users make comprehensive comparisons and select the most suitable platform for their specific needs. © 2023 IEEE.

关键词： Benchmarking

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FedVAE: Trajectory privacy preserving based on Federated Variational AutoEncoder

FedVAE: Trajectory privacy preserving based on Federated Var...

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IEEE Conference on Vehicular Technology (VTC)

作者： Yuchen Jiang Ying Wu Shiyao Zhang James J.Q. Yu Department of Computer Science and Engineering Southern University of Science and Technology China Research Institute for Trustworthy Autonomous Systems Southern University of Science and Technology China Department of Computer Science University of York United Kingdom

The use of trajectory data with abundant spatial-temporal information is pivotal in Intelligent Transport systems (ITS) and various traffic system tasks. Location-Based Services (LBS) capitalize on this trajectory data to offer users personalized services tailored to their location information. However, this trajectory data contains sensitive information about users’ movement patterns and habits, necessitating confidentiality and protection from unknown collectors. To address this challenge, privacy-preserving methods like K-anonymity and Differential Privacy have been proposed to safeguard private information in the dataset. Despite their effectiveness, these methods can impact the original features by introducing perturbations or generating unrealistic trajectory data, leading to suboptimal performance in downstream tasks. To overcome these limitations, we propose a Federated Variational AutoEncoder (FedVAE) approach, which effectively generates a new trajectory dataset while preserving the confidentiality of private information and retaining the structure of the original features. In addition, FedVAE leverages Variational AutoEncoder (VAE) to maintain the original feature space and generate new trajectory data, and incorporates Federated Learning (FL) during the training stage, ensuring that users’ data remains locally stored to protect their personal information. The results demonstrate its superior performance compared to other existing methods, affirming FedVAE as a promising solution for enhancing data privacy and utility in location-based applications.

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On the Feasibility of a 3D-Printable Conductive Filament-Based Antenna Array for Conformal Applications

On the Feasibility of a 3D-Printable Conductive Filament-Bas...

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Antennas and Propagation & USNC/URSI National Radio science Meeting

作者： Hemachandra Konduru Benjamin Callies Dipankar Mitra Sayan Roy Shuvashis Dey Rahul Gomes Dept. of Computer Science and Computer Engineering University of Wisconsin La Crosse WI USA Dept. of Physics and Astronomy University of Wisconsin La Crosse WI USA Electrical and Computer Engineering Division of Energy Matter and Systems University of Missouri Kansas City MO USA Dept. of Electrical and Computer Engineering North Dakota State University Fargo ND USA Dept. of Computer Science University of Wisconsin Eau Claire WI USA

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

NASA is actively addressing communication challenges, particularly in developing phased array antenna systems (PAAS) within the 2-2.5 GHz frequency band. The initial stages involve designing individual flexible antenna elements and non-phased antenna arrays. To address the issues of conformal phased array antennas, this work presents design enhancements for a 3D-printable microstrip patch antenna using a conductive Electrifi filament. The modifications, informed by recent research, are evaluated for the antenna's return loss and surface current distributions. The adopted modifications demonstrated superior performances in terms of gain and input reflection (S 11 ). The investigation extends to a 1x4 antenna array based on the modified patch antenna element. Notably, the conformal antennas and the antenna array exhibit low return loss alongside high gain and directivity, suggesting substantial potential for efficient performance in conformal surface applications.

关键词： Phased arrays Microstrip antenna arrays Electric potential Patch antennas NASA Resonant frequency Microstrip antennas

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An Algorithmic Approach to Intrusion Detection in Ad Hoc Wireless Networks Based on Artificial Intelligence

An Algorithmic Approach to Intrusion Detection in Ad Hoc Wir...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Sonekar, Shrikant V. Kshirsagar, Pravin R. Unhelkar, Bhuvan Chakrabarti, Prasun Maram, Balajee Upreti, Kamal J D College of Engineering & Management Department of Computer Science & Engineering Nagpur India J D College of Engineering & Management Department of Electronics & Telecommunication Engineering Nagpur India Muma College of Business University of South Florida Department of Infornation Systems and Decision Sciences Sarasata United States Sir Padampat Singhania University Department of Computer Science & Engineering Rajasthan Udaipur3313606 India Sr University Department of Computer Science and Artificial Intelligence Telangana Warangal506371 India Christ University Department of Computer Science Delhi NCR Ghaziabad India

ISBN: (纸本)9798331527495

The self-configured, autonomous, and framework-free modes of communication that mobile adhoc networks (MANETs) offer have revolutionized our culture. As a result, efforts have been made to explore ways to maximize the potential of MANETs through increased and improved utilization. Standards for AI have been developed thanks to the most recent release of new machine learning technologies. Different security-related issues from malware assaults affect mobile ad hoc networks (MANETs). Any node operates as a router to move data without centralized control, making nodes more vulnerable to threats from other nodes or attackers because of their brief existence. Because of this, MANET needs particular security policies to detect the incorrect entrance of misbehaving nodes. If all nodes are self-assured and correctly collaborate, the networks function better. The paper presents a practical artificial intelligence algorithm-based security system that uses AdaBoost and DT algorithms to recognize and identify packet falling nodes, classify information packets as normal or abnormal, and detect insider threats in real-time. The results showed that DT performed better than AdaBoost, with a 98% accurate prediction rate. Consequently, DT is better able to recognize damaging attacks in MANETs. © 2025 IEEE.

关键词： computer viruses

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An Intrusion Detection System for IoT Using KNN and Decision-Tree Based Classification

An Intrusion Detection System for IoT Using KNN and Decision...

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2021 International Conference of Modern Trends in Information and Communication Technology Industry, MTICTI 2021

作者： Abdaljabar, Zainab Hussam Ucan, Osman Nuri Ali Alheeti, Khattab M. University of Altinbaş Institute of Graduate Studies Department of Electrical and Computer Engineering Turkey University of Anbar College of Computer Science and Information Technology Department of Computer Networking Systems Iraq

ISBN: (纸本)9781665414449

The Internet of Things (IoT) has grown rapidly in recent years, intending to affect everything from everyday life to large industrial systems. Regrettably, this has attracted the attention of hackers, who have turned the Internet of Things into a target for malicious activity, exposing end nodes to attack. IoT devices' sheer volume and diversity make protecting the IoT infrastructure with a traditional intrusion detection system difficult. So to protect IoT devices, the data flow was investigated in an IoT context to protect these devices from hackers. We used two machine learning classifiers in this work: KNN (K-Nearest Neighbors) and DT (Decision Tree). We calculated the Error Rate, Accuracy, Precision, Recall, and F1 score for each method. When we combined these two classifiers, we obtained outstanding results (100 %). We have a high rate of detection of attacks. The findings are summarized. © 2021 IEEE.

关键词： Decision trees

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Delay-Tolerant Multi-Agent DRL for Trajectory Planning and Transmission Control in UAV-Assisted Wireless Networks

Delay-Tolerant Multi-Agent DRL for Trajectory Planning and T...

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IEEE Conference on Vehicular Technology (VTC)

作者： Zesong Fan Shimin Gong Yusi Long Lanhua Li Bo Gu Nguyen Cong Luong School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University China Faculty of Computer Science Phenikaa University Vietnam

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

This paper exploits multiple unmanned aerial vehicles (UAVs) to assist energy transfer, data uploading, and transmission in wireless networks, aiming to maximize the network's energy efficiency (EE). The inherent challenge of inaccessible or energy-intensive real-time information exchanges among UAVs results in undesirable delays in acquiring global network information. Such delayed information significantly hinders the transmission control and trajectory planning of the UAV s in multi-UAV-assisted wireless networks. To address this challenge, we propose a delay-tolerant multi-agent deep reinforcement learning (DT-MADRL) algorithm to jointly optimize the UAVs' trajectories and transmission control strategies based on randomly delayed information. In particular, we integrate a delay penalty term in the reward function that forces each UAV to have more regular information exchanges with the base station (BS). This ensures that each UAV can understand the real-time network environment, thereby reducing information delay and fostering more effective multi-agent collaboration. The simulation results reveal that our proposed algorithm reduces the UAVs' average information delay by 68% and improves overall EE by 28% compared to traditional MADRL algorithms.

关键词： Vehicular and wireless technologies Trajectory planning Wireless networks Simulation Collaboration Autonomous aerial vehicles Real-time systems

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EMVGAN: Emotion-Aware Music-Video Common Representation Learning via Generative Adversarial Networks 5

EMVGAN: Emotion-Aware Music-Video Common Representation Lear...

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5th International Joint Workshop on Multimedia Artworks Analysis and Attractiveness Computing in Multimedia, MMArt-ACM 2022

作者： Tsai, Yu-Chih Pan, Tse-Yu Kao, Ting-Yang Yang, Yi-Hsuan Hu, Min-Chun Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Department of Computer Science National Tsing Hua University Taiwan Institue of Information Systems and Applications National Tsing Hua University Taipei Taiwan Research Center for IT Innovation Academia Sinica Taiwan

ISBN: (纸本)9781450392402

Music can enhance our emotional reactions to videos and images, while videos and images can enrich our emotional response to music. Cross-modality retrieval technology can be used to recommend appropriate music for a given video and vice versa. However, the heterogeneity gap caused by the inconsistent distribution between different data modalities complicates learning the common representation space from different modalities. Accordingly, we propose an emotion-Aware music-video cross-modal generative adversarial network (EMVGAN) model to build an affective common embedding space to bridge the heterogeneity gap among different data modalities. The evaluation results revealed that the proposed EMVGAN model can learn affective common representations with convincing performance while outperforming other existing models. Furthermore, the satisfactory performance of the proposed network encouraged us to undertake the music-video bidirectional retrieval task. © 2022 ACM.

关键词： Generative adversarial networks

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Automated Defect Detection in Electronic Components using Convolutional Neural Networks 9

Automated Defect Detection in Electronic Components using Co...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Shobanadevi, A. Kottu, Sreekanth Kumar, K. R. Senthil Amudha, K. Praveena, K. Venkatesh, R. School of Computing Srm Institute of Science And Technology Department of Data Science And Business Systems Tamil Nadu Chennai600026 India Mallareddy University Department of Computer Science & Engineering Telangana Hyderabad500043 India R.M.K. Engineering College Department of Mechanical Engineering Tamil Nadu Kavaraipettai601206 India Department of Science And Humanities-Physics Tamil Nadu Kavaraipettai601206 India Mohan Babu University Erstwhile SreeVidyanikethan Engineering College Department of Electronics And Communication Engineering Andhra Pradesh 517102 India Department of Physics Tamil Nadu Dindigul624622 India

ISBN: (纸本)9798350365092

This exploration paper explores the operation of convolutional neural networks(CNNs) in automating the discovery of blights in electronic factors. With the rapid-fire advancement of technology, the demand for high- quality electronic factors has surged, emphasizing the need for effective disfigurement discovery styles. using the capabilities of CNNs, this study aims to develop a robust and accurate automated system able to relate colorful types of blights to electronic factors. By training the CNN model on a different dataset of disfigurement images, the system learns to fete subtle variations and anomalies reflective of faults, thereby enhancing examination delicacy and effectiveness. The proposed approach offers promising results in streamlining disfigurement discovery processes, reducing homemade intervention, and ameliorating overall quality control in electronics manufacturing. Results indicate the effectiveness and trustability of CNNs in automating disfigurement discovery, paving the way for enhanced productivity and product trustability in electronic diligence. © 2024 IEEE.

关键词： Quality control

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