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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Avgeris, Marios Leivadeas, Aris Lambadaris, Ioannis Carleton University Dept. of Systems and Computer Eng. Ottawa Canada Dept. of Software Engineering and It Montréal Canada

ISBN: (纸本)9781665477161

Modern networking paradigms like Service Function Chaining (SFC) allow for services to be broken down to a series of ordered and interconnected Virtualized Network Functions (VNFs) that can be hosted in generic servers in EdgeCloud datacenters. Nonetheless, a critical issue arises, when a hardware or software failure occurs and the VNFs of an SFC need to be repositioned, allowing to autonomously bring the system back to its normal operation, a process called self-healing. In this paper, a distributed methodology is proposed that aims to address this challenge, considering the requirements of all involved actors. Specifically, a Reinforcement Learning (RL) based algorithm is proposed that allows to iteratively optimize and determine an SFC healing solution upon a datacenter failure. As a second stage, a revenue-driven resource allocation mechanism is integrated, to resolve the contention for resources in an already functional datacenter that potentially occurs due to the repositioning. Various simulation scenarios prove the efficiency of our proposed resilient healing mechanism. © 2023 IEEE.

关键词： Resource allocation

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Blockchain Based Digital Forensic Data for Unbreakable Trust and Security

Blockchain Based Digital Forensic Data for Unbreakable Trust...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Imrankhan, M. Viji, M. Sathishkumar, R. Gnanaprakash, T. Roshan, R. Karthik Saravanan, G. Manakula Vinayagar Institute of Technology Dept. of Cse Puducherry India Sri Manakula Vinayagar Engineering College Dept. of Computer Science and Business Systems Puducherry India

ISBN: (纸本)9798331510022

This can be one of the most magnificent advantages realized from applying blockchain technology in a forensic investigation context, namely, a transformative approach to address challenges embedded within legal and criminal justice systems. For example, smart contracts, being self-executing agreements with the automated inclusion of predefined rules, may even automate and secure the process of tracking evidence, managing chain of custody, and access control. This smart contract will ensure that forensic operations are transparent and of sound integrity. Every transaction and action performed on the blockchain is recorded there, instantaneously bringing down tampering to the lowest possibility and any chance for human mistakes. Regarding decentralized nature and cryptography-based security combined with immutability, blockchain is a technology known to have safeguards over sensitive data. Once recorded on the blockchain, the information thereupon becomes completely un-changeable, assuring the reliability and authenticity of evidence. This will be especially important in the management of an unforgeable chain of custody and in ensuring that forensic evidence remains unchanged and can be relied on with integrity throughout the court process.[6] Critical data can then be maintained tamper-proof for management and storage by developing Ethereum-based smart contracts on Remix IDE using Solidity.[15]Each transaction will be time-stamped and safely logged on the Ethereum blockchain, thus developing an unchallenged ledger that makes any investigation that much more trustworthy. In a nutshell, the application of blockchain in forensic activities builds greater control and efficiencies, reduces possible errors and conflicts, and provides transparency, integrity, and an unchangeable, permanent record for forensic processes. The invention thereby significantly upgrades the general integrity and confidence in legal and criminal justice inquiries. © 2024 IEEE.

关键词： Ethereum

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Thermal Model Validation of a Power Electronics Module for Digital Twin Application

Thermal Model Validation of a Power Electronics Module for D...

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2024 Energy Conversion Congress and Expo Europe, ECCE Europe 2024

作者： Zoller, Svenja Cabre-Piqueras, Claudia Schilder, Boris Filba-Martinez, Alber Acker, Lukas Frankfurt University of Applied Science Faculty of Computer Science and Engineering Frankfurt Germany Power Systems Dept. Barcelona Spain

ISBN: (纸本)9798350364446

Thermal stress is a critical failure factor for power electronics components. Hence, when developing a precise digital twin for a power system, it is imperative that thermal models provide a reliable portrayal of the component physics. In that regard, manufacturers offer simplified thermal models that enable designers and users to estimate its behaviour. Nevertheless, the accuracy of these models remains unknown, thus compromising the overall certainty of the digital twin. Therefore, it is of interest to discern whether these simplified models are sufficient for the precision required by the digital twin, and to assess the need of developing a more accurate model. Hence, this paper focuses on validating the theoretical thermal model of a power electronics switch module setup by using the deviation of the module mean time to failure (MTTF) as the acceptance parameter. To achieve this, a novel experimental validation methodology is proposed to retrieve an experimental thermal model and compare it to the theoretical one. This validation is performed considering the discrete thermal impedances and introducing the study of the coupling effects within the module. © 2024 IEEE.

关键词： Insulated gate bipolar transistors (IGBT)

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A HIGH-PRECISION 3D LASER MEASUREMENT METHOD BASED ON ESTIMATION OF TRUE VALUES BY HIGH-DENSITY MEASUREMENTS AND CALCULATION OF DIFFERENCES BETWEEN MEASUREMENT DATA OF TWO TIMES

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AIJ Journal of Technology and Design 2022年第68期28卷 515-520页

作者： Kakizaki, Ken'ichi Mukai, Tomohisa Araki, Shunsuke Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Dept. of Structural Engineering Building Research Institute

3D laser measurement has a problem that the measurement precision is not sufficient for damage analysis of buildings. In addition, when there is an unevenness in the measurement target, it is difficult to distinguish whether it is an initial shape or damage. In this paper, we propose a method to reduce the measurement error by processing the high-density laser measurement data with statistical methods. In addition, we propose a method to detect the damaged area by comparing the initial shape data of the measured object with the newly measured data. © 2022 Architectural Institute of Japan. All rights reserved.

关键词： Measurement errors

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A Systematic Literature Review of the Application of Agile Applied to Large-Scale, Safety-Critical, Cyber-Physical systems

A Systematic Literature Review of the Application of Agile A...

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2024 IEEE International Conference on Data and Software engineering, ICoDSE 2024

作者： Yeman, Robin J. Malaiya, Yashwant K. Dept. Systems Engineering Colorado State University Fort Collins United States Dept. Computer Science Colorado State University Fort Collins United States

ISBN: (纸本)9798331506407

The application of Agile methodologies to large-scale, safety-critical cyber-physical systems (LS/SC/CPS) has shown significant interest over the last 5 years. Although there has been limited research into each of the individual attributes for development projects that are large-scale, safety-critical, or cyber-physical systems, there is a scarcity of research when all three attributes exist simultaneously in a development endeavor. This systematic literature review explores the extent of Agile adoption in projects where all three attributes are present. It begins by examining the extent of Agile adoption by domain, identifying the drivers for change, reviewing the challenges encountered, and investigating the augmentations used to address these challenges. The findings indicate that there has been growth in adoption across the medical, aerospace, automotive, and robotics industries, with limited adoption in energy and transportation. Key drivers include speed, adaptability, and cost. Several challenges have been identified, with regulatory/compliance requirements being the most significant. Multiple augmentations to Agile have been applied to overcome the identified challenges, ranging from incorporating safety frameworks and specialized artifacts to leveraging modeling and simulation, as well as reimagining programmatics. This review highlights industry efforts to apply Agile to LS/SC/CPS, while identifying several gaps in the literature. The largest gap is empirical validation in real-world settings. Future research should focus on improving the existing Agile scaling frameworks with guidance and resources to help achieve the benefits of Agile in LS/SC/CPS development while ensuring regulatory and safety compliance. © 2024 IEEE.

关键词： Resource valuation

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GENAIR: GENERATIVE AI FOR RESILIENT URBAN AIR MOBILITY WITH VTOLS IN DISASTER EVACUATION

GENAIR: GENERATIVE AI FOR RESILIENT URBAN AIR MOBILITY WITH ...

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2024 Winter Simulation Conference, WSC 2024

作者： Alsaheal, Abdullah Hoy, William Haro, Priscila Rehman, Asad Celik, Nurcin Dept. of Industrial and Systems Engineering University of Miami Coral GablesFL United States Dept. of Mathematics and Computer Science Saint Louis University Madrid Spain

ISBN: (纸本)9798331534202

Coastal regions face growing threats, making timely and safe evacuation paramount. Current plans rely heavily on congested ground transportation, leading to delays and heightened stress levels. The emerging field of Urban Air Mobility (UAM), utilizing Vertical Take-Off and Landing Vehicles (VTOLs), promises to alleviate these issues by providing solutions for quick evacuation strategies. Here, we aim to leverage Generative Adversarial Networks (GANs) to expand limited datasets with synthetic data specific to disaster scenarios, evacuation routes, airspace considerations, and the impact of real-time weather events, enabling robust simulation of UAM deployment in disaster evacuations. We identified two applicable scenarios: i) UAM for Extreme Weather Emergency Evacuation and ii) Hospital Evacuations using VTOLs as use cases and illustrated their impact. This research seeks to pave the way for optimized, data-driven evacuation planning with UAM and VTOLs, ultimately enhancing the safety and efficiency of evacuations in the face of extreme events. © 2024 IEEE.

关键词： Air mobility

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Enhancing Performance in Hybrid Satellite-Terrestrial Networks: A Novel Joint NOMA-NC Approach

Enhancing Performance in Hybrid Satellite-Terrestrial Networ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Karavolos, Michail Moraitis, Nektarios Tsipi, Lefteris Vouyioukas, Demosthenes University of the Aegean Dept. of Information and Communication Systems Engineering Samos83200 Greece National Technical University of Athens School of Electrical and Computer Engineering Zografou15773 Greece University of Piraeus Dept. of Digital Systems Piraeus18534 Greece

ISBN: (纸本)9798350351255

An appealing alternative towards dealing with mobility and coverage challenges in future wireless networks, are hybrid satellite-terrestrial networks (HSTNs). The combination of network coding (NC) and non-orthogonal multiple access (NOMA) techniques, have recently gained popularity due to their performance gains in improving the quality of a wireless transmission and exploiting the accessible spectrum. This paper introduces a joint NOMA-NC approach, integrated to an HSTN comprising a satellite, a terrestrial base station (BS), and multiple ground-based mobile terminals (MTs). The suggested method facilitates a cluster of end-users to be concurrently served through NOMA from a satellite using optimal power allocation, whereas the BS utilizes random linear NC to augment the MTs' reception in the event of errors. The simulation results confirm the effectiveness of the proposed methodology in improving system sum throughput, demonstrating substantial enhancements compared with both OMA with NC, and standalone NOMA and OMA approaches. The resilience of the suggested method is proved, improving the sum throughput even at low power transmissions and in the worst case channel conditions. © 2024 IEEE.

关键词： Network coding

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Inflation Prediction: A Comparative Study of ARIMA and LSTM Models Across Different Temporal Resolutions 3

Inflation Prediction: A Comparative Study of ARIMA and LSTM ...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Lakshmi Narayanaa, T. Skandarsini, R.R. Ida, S. Jhansi Sabapathy, S. Rathana Nanthitha, P. RMK Engineering College Dept. of Computer Science and Business Systems Thiruvallur India Panimalar Engineering College Dept. of Information Technology Chennai India

ISBN: (纸本)9798350343632

This research study focuses on the prediction of inflation rates using two distinct models, Autoregressive integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM), tailored to accommodate the temporal resolutions of monthly and quarterly inflation data. While a comprehensive evaluation across varying time horizons was not conducted, the results suggest that the ARIMA model excels in short-term forecasting, as evidenced by its impressive performance on a 2021 holdout dataset. Conversely, the LSTM model displays potential for medium-term predictions. This study underscores the significance of aligning forecasting models with data characteristics, emphasizes the importance of temporal resolution, and discusses potential avenues for improvement, such as multivariate modeling or alternative techniques like Prophet. The findings have broad implications for economists, policymakers, and analysts seeking precise inflation forecasts, shedding light on the nuanced interplay between models and data in the domain of inflation prediction. © 2023 IEEE.

关键词： ARIMA Model Inflation Prediction LSTM Model Short-Term Forecasting Temporal Resolution Time Series Forecasting

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Enhancing Credit Card Fraud Detection through LSTM-Based Sequential Analysis with Early Stopping 2

Enhancing Credit Card Fraud Detection through LSTM-Based Seq...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Jhansi Ida, S. Balasubadra, K. Skandarsini, R.R. Lakshmi Narayanaa, T. RMK Engineering College Dept. of Computer Science and Business Systems Thiruvallur India RMD Engineering College Department of Information Technology Thiruvallur India

ISBN: (纸本)9798350365269

There is still a serious problem with credit card fraud in the world of financial transactions, requiring innovative solutions to effectively detect and halt illicit behavior. This research presents a unique method of detecting credit card fraud by strategically implementing early pausing in a Long Short-Term Memory (LSTM) neural network. Leveraging the sequential dependencies inherent in transaction data, the LSTM model demonstrates remarkable efficacy in discerning fraudulent from legitimate transactions. The early stopping mechanism ensures that the model's training halts at the optimal epoch, preventing over fitting and enhancing generalization. Our thorough analysis highlights the model's versatility by demonstrating a dynamic compromise between recall and accuracy throughout training. Practical implications include enhanced fraud detection accuracy, reduced false alarms, and real-time decision support for financial institutions. The model's adaptability and generalization make it a valuable asset in the ongoing battle against credit card fraud, contributing to a more secure financial environment. Future research directions may explore additional features and alternative architectures to further advance fraud detection capabilities. © 2024 IEEE.

关键词： Long short-term memory

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TCN-MLP: A Hybrid Model for Short-Term Electrical Load Forecasting 1

TCN-MLP: A Hybrid Model for Short-Term Electrical Load Forec...

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1st International Conference of Intelligent Methods, systems and Applications, IMSA 2023

作者： Farid, Karim S. Ali, A.A. Salem, Sameh A. Mohamed, Amr E. Egyptian Electricity Holding Company Dept. Information Systems & Communications Cairo Egypt Helwan University - Faculty of Engineering Dept. Electrical Power Cairo Egypt Helwan University - Faculty of Engineering Dept. Computer & Systems Engineering Cairo Egypt

ISBN: (纸本)9798350335569

Using artificial intelligence (AI) to anticipate electrical consumption is one of the used applications of this technology. The analysis and training of AI models are done using data related to electricity consumption, including consumption itself, temperatures, working hours, the number of inhabitants, etc. AI models can help to reduce electricity expenses and consumption, which helps in identifying peak use periods and creating more efficient energy plans. Another use of artificial intelligence is to advise users on how to use less energy in a more efficient manner. Based on such insight, this study suggests a hybrid model based on a temporal convolutional network and multilayer perceptron (TCN-MLP), to forecast hourly power consumption. The results show that the recommended model can account for the patterns in the historical data relating to hourly power usage. TCN-MLP hybrid model has proved to be better than competing approaches in the coefficient of determination (R2) and the mean absolute percentage error (MAPE%) metrics. © 2023 IEEE.

关键词： Electric power utilization

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