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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： Chintureena Thingom Mohan. S E. Afreen Banu E. Saranya M. Jasmin K. Anuradha Department of Computer Science & Engineering Alliance School of Advanced Computing Alliance University Bengaluru Karnataka India Department of Electrical And Electronics Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Department of Computer Engineering Shah & Anchor Kutchhi Engineering College Mumbai Maharashtra India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Mallareddy Engineering College (Autonomous) Hyderabad Telangana India

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

Accurate load forecasting is essential for energy system demand response, energy distribution, and energy waste reduction. This work investigates artificial intelligence-based energy load estimation methods, focussing on Gradient Boosting Machines (GBM) to improve forecast accuracy. The study will focus on GBM use. The research project begins with extensive data preprocessing. This solution includes imputation for missing data and data inclusion to fix dataset imbalances. To improve model performance, autoencoder-based feature selection reduces data dimensionality while keeping crucial information. We also use a correlation matrix to find and remove duplicate characteristics. After that, the Gradient Boosting Machine classifier is used for regression jobs to manage nonlinear relationships and reduce prediction mistakes. This improves forecast accuracy. The studies show that the suggested strategy enhances load forecasting accuracy compared to standard models. This study shows that AI-driven methods can improve demand responsiveness. This can help smart grids and telecoms manage energy more efficiently and sustainably.

关键词： Accuracy Load forecasting Predictive models Feature extraction Boosting Demand response Data models Imputation Smart grids Load modeling

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Adjustable Current Limiting Function with a Monolithically Integrated SiC Circuit Breaker Device

Adjustable Current Limiting Function with a Monolithically I...

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作者： Takamori, Taro Wada, Keiji Boettcher, Norman Erlbacher, Tobias Saito, Wataru Nishizawa, Shin-Ichi Tokyo Metropolitan University Department of Electrical Engineering and Computer Science Tokyo192-0397 Japan Fraunhofer Institute for Integrated Systems and Device Technology IISB Erlangen91058 Germany Kyushu University Research Institute for Applied Mechanics Fukuoka816-8580 Japan

This article proposes a current limiting function for a self-sensing and self-triggering monolithically integrated SiC circuit breaker device. The proposed function provides the device not only with a fast-response current breaking operation but also with the current limiting operation, including supplying a constant current to the load and preventing the detection of inrush currents. This function is suitable for replacing mechanical conductors in initial charging circuits within rectifiers and safety equipment in case of overcurrent phenomena. The proposed method achieves certain constant currents through a simple variation of the gate-drive circuit with the adjustable parameters of an additional MOSFET. The mechanism of the current limiting function is verified using TCAD simulations based on certain conditions. Consequently, experimental results verified that the circuit breaker device with the current limiting function reduces the inrush current by up to 76.9% using a 500 VDC distribution circuit system. © 1972-2012 IEEE.

关键词： Silicon carbide

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Leverage from Blockchain in Commodity Exchange: Asset-Backed Token with Ethereum Blockchain Network and Smart Contract 4th

Leverage from Blockchain in Commodity Exchange: Asset-Backed...

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4th International Conference on Smart Trends for Computing and Communications, SmartCom 2020

作者： Richard Heryadi, Yaya Lukas Trisetyarso, Agung Information Systems Department School of Information Systems Bina Nusantara University Jakarta11840 Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara Univesity Jakarta11840 Indonesia Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta12930 Indonesia

ISBN: (纸本)9789811552236

Ethereum is one of the most popular blockchain platforms with a high number of adoption in the blockchain world today. Ethereum token (ERC-20) can tokenize any real-world object while it is also possible to exchange the token across the network participants. This utility from the token drives many blockchain enthusiasts to create decentralized applications. ERC-20 token could be used to collect project funds, decision voting, or even can be used to provide investment platforms for people that want to hedge the volatility of the currency with buying an asset that is back to back with the crypto-token. This research shows the main characteristics of asset-backed token and its implementation on a complete end-to-end "digitalization assets system" (assets buy and assets redemption). The result shows that asset-backed token needs support from government and other legalizing parties to verify the legitimacy of the asset that backing the token itself. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Phase- and Angle-Sensitive Terahertz Hot-Electron Bolometric Plasmonic Detectors Based on Fets with Graphene Channel and Composite H-BN/Black-P/H-BN Gate Layer

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International Journal of High Speed Electronics and systems 2024年第4期33卷

作者： Ryzhii, Victor Tang, Chao Otsuji, Taiichi Shur, Michael S. Ryzhii, Maxim Mitin, Vladimir Research Institute For Electrical Communication Tohoku University Miyagi Sendai980-8577 Japan Department Of Electrical Computer And Systems Engineering Rensselaer Polytechnic Institute TroyNY United States Department Of Computer Science And Engineering University Of Aizu Aizu-Wakamatsu Fukushima965-8580 Japan Department Of Electrical Engineering University At Buffalo Suny BuffaloNY United States

In this paper, we propose and analyze the terahertz (THz) bolometric vector detectors based on the graphene-channel field-effect transistors (GC-FET) with the black-P gate barrier layer or with the composite b-BN/black-P/b-BN gate layer. The phase difference between the signal received by the FET source and drain substantially affects the plasmonic resonances. This results in a resonant variation of the detector response on the incoming THz signal phase shift and the THz radiation angle of incidence. © 2024 World Scientific Publishing Company.

关键词： Graphene

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On the Convergence of Gossip Learning in the Presence of Node Inaccessibility

On the Convergence of Gossip Learning in the Presence of Nod...

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IEEE International Conference on Communications (ICC)

作者： Tian Liu Yue Cui Xueyang Hu Yecheng Xu Bo Liu Zhejiang Laboratory Hangzhou China Department of Computer Science and Computer Information Systems Auburn University at Montgomery Montgomery USA Department of Computer Science and Software Engineering Auburn University Auburn USA Institute of Rural Development Zhejiang Academy of Agricultural Sciences Hangzhou China DBAPPSecurity Co. Ltd. Hangzhou China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Gossip learning (GL), as a decentralized alternative to federated learning (FL), is more suitable for resourceconstrained wireless networks, such as Flying Ad-Hoc Networks (FANETs) that are formed by unmanned aerial vehicles (UAVs). GL can significantly enhance the efficiency and extend the battery life of UAV networks. Despite the advantages, the performance of GL is strongly affected by data distribution, communication speed, and network connectivity. However, how these factors influence the GL convergence is still unclear. Existing work studied the convergence of GL based on a virtual quantity for the sake of convenience, which failed to reflect the real state of the network when some nodes are inaccessible. In this paper, we formulate and investigate the impact of inaccessible nodes to GL under a dynamic network topology. We first decompose the weight divergence by whether the node is accessible or not. Then, we investigate the GL convergence under the dynamic of node accessibility and theoretically provide how the number of inaccessible nodes, data non-i.i.d.-ness, and duration of inaccessibility affect the convergence. Extensive experiments are carried out in practical settings to comprehensively verify the correctness of our theoretical findings.

关键词： Training Accuracy Network topology Federated learning Wireless networks Autonomous aerial vehicles Ad hoc networks

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Exploiting the Data Gap: Utilizing Non-ignorable Missingness to Manipulate Model Learning

arXiv

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arXiv 2024年

作者： Koyuncu, Deniz Gittens, Alex Yener, Bülent Yung, Moti Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Department of Computer Science Rensselaer Polytechnic Institute TroyNY12180 United States Google LLC New YorkNY10011 United States Department of Computer Science Columbia University New YorkNY10027 United States

Missing data is commonly encountered in practice, and when the missingness is non-ignorable, effective remediation depends on knowledge of the missingness mechanism. Learning the underlying missingness mechanism from the data is not possible in general, so adversaries can exploit this fact by maliciously engineering non-ignorable missingness mechanisms. Such Adversarial Missingness (AM) attacks have only recently been motivated and introduced, and then successfully tailored to mislead causal structure learning algorithms into hiding specific cause- and-effect relationships. However, existing AM attacks assume the modeler (victim) uses full-information maximum likelihood methods to handle the missing data, and are of limited applicability when the modeler uses different remediation strategies. In this work we focus on associational learning in the context of AM attacks. We consider (i) complete case analysis, (ii) mean imputation, and (iii) regression-based imputation as alternative strategies used by the modeler. Instead of combinatorially searching for missing entries, we propose a novel probabilistic approximation by deriving the asymptotic forms of these methods used for handling the missing entries. We then formulate the learning of the adversarial missingness mechanism as a bi-level optimization problem. Experiments on generalized linear models show that AM attacks can be used to change the p-values of features from significant to insignificant in real datasets, such as the California-housing dataset, while using relatively moderate amounts of missingness ( Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Comparison with Detection of Bacteria from Gram Stained Smears Images by Various Object Detectors

Comparison with Detection of Bacteria from Gram Stained Smea...

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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Tsubasa Tanaka Kouichi Hirata Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Department of Artificial Intelligence Kyushu Institute of Technology Iizuka Japan

ISBN: (数字)9798350377903

ISBN: (纸本)9798350377910

In this paper, we detect bacteria from Gram stained smears images by the object detectors of SSD, M2Det, RT-DETR and YOLOv8. Then, we show that SSD and M2Det are not appropriate for detecting bacteria. Also we show that RT-DETR with pre-training and the variation of YOLOv8 shifting convolution layers are more appropriate to detect bacteria than standard YOLOv8.

关键词： Convolution Detectors Bacteria Informatics Standards

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MarineDet: Towards Open-Marine Object Detection

arXiv

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arXiv 2023年

作者： Liang, Haixin Zheng, Ziqiang Ma, Zeyu Yeung, Sai-Kit The Division of Integrative Systems and Design Hong Kong University of Science and Technology Hong Kong The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The School of Computer Science and Engineering University of Electronic Science and Technology of China China

Marine object detection has gained prominence in marine research, driven by the pressing need to unravel oceanic mysteries and enhance our understanding of invaluable marine ecosystems. There is a profound requirement to efficiently and accurately identify and localize diverse and unseen marine entities within underwater imagery. The open-marine object detection (OMOD for short) is required to detect diverse and unseen marine objects, performing categorization and localization simultaneously. To achieve OMOD, we present MarineDet. We formulate a joint visual-text semantic space through pre-training and then perform marine-specific training to achieve in-air-to-marine knowledge transfer. Considering there is no specific dataset designed for OMOD, we construct a MarineDet dataset consisting of 821 marine-relative object categories to promote and measure OMOD performance. The experimental results demonstrate the superior performance of MarineDet over existing generalist and specialist object detection algorithms. To the best of our knowledge, we are the first to present OMOD, which holds a more valuable and practical setting for marine ecosystem monitoring and management. Our research not only pushes the boundaries of marine understanding but also offers a standard pipeline for OMOD. © 2023, CC BY-SA.

关键词： Object detection

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A Late Fusion Framework with Multiple Optimization Methods for Media Interestingness

arXiv

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arXiv 2022年

作者： Shoukat, Maria Ahmad, Khubaib Said, Naina Ahmad, Nasir Hasanuzzaman, Mohammed Ahmad, Kashif Department of Computer Systems Engineering University of Engineering and Technology Peshawar Pakistan Department of Computer Science Munster Technological University Cork Ireland

The recent advancement in Multimedia Analytical, computer Vision (CV), and Artificial Intelligence (AI) algorithms resulted in several interesting tools allowing an automatic analysis and retrieval of multimedia content of users' interests. However, retrieving the content of interest generally involves analysis and extraction of semantic features, such as emotions and interestingness-level. The extraction of such meaningful information is a complex task and generally, the performance of individual algorithms is very low. One way to enhance the performance of the individual algorithms is to combine the predictive capabilities of multiple algorithms using fusion schemes. This allows the individual algorithms to complement each other, leading to improved performance. This paper proposes several fusion methods for the media interestingness score prediction task introduced in CLEF Fusion 2022. The proposed methods include both a naive fusion scheme, where all the inducers are treated equally and a merit-based fusion scheme where multiple weight optimization methods are employed to assign weights to the individual inducers. In total, we used six optimization methods including a Particle Swarm Optimization (PSO), a Genetic Algorithm (GA), Nelder-Mead, Trust Region Constrained (TRC), and Limited-memory Broyden-Fletcher-Goldfarb-Shanno Algorithm (LBFGSA), and Truncated Newton Algorithm (TNA). Overall better results are obtained with PSO and TNA achieving 0.109 mean average precision @10. The task is complex and generally, scores are low. We believe the presented analysis will provide a baseline for future research in the domain. © 2022, CC BY.

关键词： Genetic algorithms

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Model Predictive Control for Automated Network Assurance in Intent-Based Networking enabled Service Function Chains

Model Predictive Control for Automated Network Assurance in ...

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IEEE Symposium on Network Operations and Management

作者： Marios Avgeris Aris Leivadeas Nikolaos Athanasopoulos Ioannis Lambadaris Matthias Falkner Department of Systems and Computer Engineering Carleton University Ottawa Canada Department of Software and IT Engineering Ácole de technologie supérieure Montreal Canada School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast UK Cisco Systems Ottawa Canada

Recent trends in Network Function Virtualization (NFV) combined with Internet of Things (IoT) and 5G applications have reshaped the network service offering. In particular, Service Function Chains (SFCs) can associate network functions with physical and virtual resources towards providing a complete network service. Concurrently, the management of a continuously expanding network and the fulfillment of the applications’ requirements pave the way for autonomic network solutions. Intent Based Networking (IBN) is a novel paradigm that aims to achieve the automatic orchestration of network services and the assurance of their performance. Accordingly, in this paper, we propose a novel automated network assurance model, based on Model Predictive Control, to guarantee the Quality of Service (QoS) and security requirements of multi-tenant and IBN-enabled SFCs. In this context, corrective decisions are proactively taken, in the form of incoming intent relocations among the SFCs. The results reveal that our model can assure with high probability the application requirements and minimize QoS violations.

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