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Aging Analysis Framework of Windows-Based systems through Differential-Analysis of system Snapshots

computers, Materials & Continua 2022年第12期73卷 5091-5102页

作者： Eun-Tae Jang Sung Hoon Baek Ki-Woong Park SysCore Lab Sejong UniversitySeoul05006Korea Department of Computer System Engineering Jungwon UniversityChungcheongbuk-do28024Korea

When aWindows-based system is used for an exceedingly long time,its performance degrades,and the error occurrence rate tends to *** is generally called system *** investigate the reasons for system aging,various studies have been conducted within the range of the operating system kernel to the user ***,finding an accurate reason for system performance degradation remains challenging research *** this study,system monitoring was conducted by dividing a system into‘before software installation,’‘after software installation,’and‘after software removal.’We confirmed that when a software installed in a system is removed,various system elements,such as storage and memory,are not restored to the level prior to the software ***,we established a hypothesis regarding the performance degradation of a computer system owing to repeated software installation/removal operations,investigated the correlation between system aging and repeated software installation/removal operations,and proposed a system aging analysis framework for analyzing the reason behind system *** the proposed system aging analysis framework,we aim to forcibly age a Windows-based system by repeating the software installation/removal operation by utilizing the system forced aging *** framework identifies the elements affecting system performance through a differential data analysis of the system time-series data extracted by the system performance extraction and system component snapshot ***,the aging analysis framework presented in this study is expected to be effectively utilized as an index for studying system aging.

关键词： system aging virtual machine system analysis

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Transformer Internal and Inrush Current Fault Detection Using Machine Learning

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Intelligent Automation & Soft Computing 2023年第4期36卷 153-168页

作者： R.Vidhya P.Vanaja Ranjan N.R.Shanker Department of Electronics and Communication Engineering Loyola-ICAM College of Engineering and TechnologyChennai600034India Embedded System Technologies Department of Electrical and Electronics EngineeringCollege of EngineeringGuindyChennai 600025India Department of Computer Science and Engineering Aalim Muhammed Salegh College of EngineeringChennai 600055India

Preventive maintenance in the transformer is performed through a dif-ferential relay protection system,and it protects the transformer from internal and external ***,the Current Transformer(CT)in the differential protec-tion system mal-operates during inrush *** saturates due to magnetizing inrush currents and causes false tripping of the differential ***,iden-tiﬁcation of tripping in protection relay either due to inrush current or internal faults needs to be *** the above problem,continuous monitoring of transformer breather and CT terminals with thermal camera helps detect the trip-ping in relay due to inrush or internal *** transformer’s internal fault leads to high breathing process in the transformer breather,never for inrush *** inrush currents,CT temperature is *** monitoring of breather and CT of the transformer through thermal imaging and radiometric pix-els detect the causes of CT saturation and differentiates *** wavelet threshold image analytics(HWT-IA)based radiometric pixels analysis of the transformer breather and CT after de-noising provides an accurate result of about 95%for identiﬁcation of the false tripping of differential protection system of transformer.

关键词： Wavelet threshold inrush transformer breather current transformer thermal image

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Adaptive Evolutionary Reinforcement Learning Algorithm with Early Termination Strategy 23

Adaptive Evolutionary Reinforcement Learning Algorithm with ...

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23rd International Conference on Autonomous Agents and Multiagent systems, AAMAS 2024

作者： Wu, Xiaoqiang Zhu, Qingling Lin, Qiuzhen Chen, Weineng Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science and Engineering South China University of Technology Guangzhou China

Evolutionary reinforcement learning algorithms (ERLs), which combine evolutionary algorithms (EAs) with reinforcement learning (RL), have demonstrated significant success in enhancing RL performance. However, most ERLs rely heavily on Gaussian mutation operators to generate new individuals. When the standard deviation is too large or small, this approach will result in the production of poor or highly similar offspring. Such outcomes can be detrimental to the learning process of the RL agent, as too many poor or similar experiences are generated by these individuals. In order to alleviate these issues, this paper proposes an Adaptive Evolutionary Reinforcement Learning (AERL) method that adaptively adjusts both the standard deviation and the evaluation process. By tracking the performance of new individuals, AERL maintains the mutation strength within a suitable range without the need for additional gradient computations. Moreover, the proposed AERL approach early terminates unnecessary evaluations and discards experiences arising from poor individuals, thereby resulting in enhanced learning efficiency. Empirical assessments conducted on a variety of continuous control problems demonstrate the effectiveness of the AERL method. © 2024 International Foundation for Autonomous Agents and Multiagent systems.

关键词： Reinforcement learning

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A Case Study of Big Data Processing in the Cloud Environments with Insights into Advantages, Tools, and Techniques 14

A Case Study of Big Data Processing in the Cloud Environment...

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14th International Conference on Cloud Computing, Data Science and engineering, Confluence 2024

作者： Chowdary, Pullela Harish Thapar, Puneet Alyas, Arsalan Ahmed Singh, Urvashi Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India Information System Technologies Wilmington University United States

ISBN: (纸本)9798350344837

Every single day, users are increasing as the world has explored technology where data is producing more and more big data and cloud technologies are the reason behind the scene because big data and cloud computing are the booming pieces of knowledge currently with AI helpful for users and for other professional and practical purposes. Nowadays integration of technologies also been an emerging topic it helps in the reduction of disadvantages of particular technology to provide better solutions. This paper talks about what, and why big data and cloud technology in daily lives and where big data in cloud technology is used. This paper examines recently proposed frameworks/models where big data and cloud technologies are utilized together and reviewed in a detailed way from the year 2020 to 2023. What Advantages can be there in utilizing both big data and cloud together is also discussed. This study explains the most used big data analytical tools and widely used cloud platforms and performed a simple case study on two datasets (world data and disease symptoms dataset) to draw some insights into which Rapid Miner and Power BI are used where RF and Gradient Boosted Trees got more accuracy than other classification algorithms with 0.7 accuracy while using disease symptoms dataset and some visualizations are drawn with use of world data. © 2024 IEEE.

关键词： Trees (mathematics)

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Artificial Bee Colony with Cuckoo Search for Solving Service Composition

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Intelligent Automation & Soft Computing 2023年第3期35卷 3385-3402页

作者： Fadl Dahan Abdulelah Alwabel Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Sciences Faculty of Computing and Information Technology AlturbahTaiz UniversityTaiz9674Yemen Department of Computer Sciences College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

In recent years,cloud computing has provided a Software As A Service(SaaS)platform where the software can be reused and applied to fulﬁll compli-cated user demands according to speciﬁc Quality of Services(QoS)*** user requirements are formulated as a workﬂow consisting of a set of ***,many services may satisfy the functionality of each task;thus,searching for the composition of the optimal service while maximizing the QoS is formulated as an NP-hard *** work will introduce a hybrid Artiﬁcial Bee Colony(ABC)with a Cuckoo Search(CS)algorithm to untangle service composition *** ABC is a well-known metaheuristic algorithm that can be applied when dealing with different NP-hard problems with an outstanding record of ***,the ABC suffers from a slow convergence ***,the CS is used to overcome the ABC’s limitations by allowing the abandoned bees to enhance their search and override the local *** proposed hybrid algorithm has been tested on 19 datasets and then compared with two standard algorithms(ABC and CS)and three state-of-the-art swarm-based composition *** addition,extensive parameter study experiments were conducted to set up the proposed algorithm’s *** results indicate that the proposed algorithm outperforms the standard algorithms in the three comparison criteria(bestﬁtness value,averageﬁtness value,and average execution time)overall datasets in 30 different ***,the proposed algorithm also exhibits better performance than the state–of–the–art algorithms in the three comparison criteria over 30 different runs.

关键词： Cloud computing web service composition artiﬁcial bee colony cuckoo search

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Inverter-fed Based Electrical Machine Stator Winding Moisture Condensation Detection

Inverter-fed Based Electrical Machine Stator Winding Moistur...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Laborda, Diego F. Zapico, Marcos Orviz Tiemblo, Javier G-A. Lee, Sang Bin Shaikh, Muhammad Faizan Reigosa, David University of Oviedo Dept. of Elect. Computer & System Engineering Gijón Spain Korea University Dept. of Electrical Engineering Seoul Korea Republic of

ISBN: (纸本)9798350376067

Presence of moisture in the stator windings of electrical machines increases the risk of insulation failure. Integrating humidity transducers into the system results in additional costs and fails to provide direct measurements of moisture levels within the stator windings. This paper proposes the use of the capacitive leakage currents generated by a conventional inverter commutation (i.e., square-wave pulses) whenever the machine is off to identify winding moisture condensation offline. This method helps prevent insulation failure during service without additional hardware and can be triggered automatically and remotely. It will be demonstrated that the presence of moisture condensation influences both the magnitude of the capacitive leakage current and its resonance frequency;both of them will serve as reliable indicators of moisture presence in the stator windings. © 2024 IEEE.

关键词： Stators

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Aspect-Level Sentiment Analysis Using Cross Attention Mechanism 7

Aspect-Level Sentiment Analysis Using Cross Attention Mechan...

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7th IEEE International Conference on Big Data and Artificial Intelligence, BDAI 2024

作者： Han, Lei Jiao, Liangbo Shi, Zhan Xu, Mengxi Nanjing Institute of Technology School of Computer Engineering Nanjing China Nanjing Institute of Technology Jiangsu Province Engineering Research Center of IntelliSense Technology and System Nanjing China

ISBN: (纸本)9798350352009

Aspect-level sentiment analysis is a challenging task that involves classifying emotions towards specific aspects mentioned in a sentence. Despite notable progress in previous research, this task remains highly complex. The key focus is on effectively encoding semantics and capturing the interaction between aspect items and context. To address these challenges, this paper proposes a novel sentiment classification method called ALSA-CA. By leveraging the BERT model for semantic encoding and incorporating a cross-attention mechanism to model aspect-item-context interactions, our approach demonstrates improved performance. Additionally, we introduce a positional weighting method that considers the relative positions of words and aspect items within sentences. Through extensive experiments conducted on three public datasets, we demonstrate the effectiveness of our method compared to current state-of-the-art techniques. © 2024 IEEE.

关键词： Semantics

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Edge-Computing-Enabled Deep Learning Approach for Low-Light Satellite Image Enhancement

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2024年 17卷 4071-4083页

作者： Bui, Trong-An Lee, Pei-Jun Liang, Chun-Sheng Hsu, Pei-Hsiang Shiu, Shiuan-Hal Tsai, Chen-Kai National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei10607 Taiwan Liscotech System Company Ltd. Department of Research and Development Taipei100213 Taiwan

Edge computing enables rapid data processing and decision-making on satellite payloads. Deploying deep learning-based techniques for low-light image enhancement improves early detection and tracking accuracy on satellite platforms, but it faces challenges due to limited computational resources. This article proposes an edge-computing-enabled inference model specifically designed onboard satellites. The proposed model follows an encoder-decoder architecture to generate the illumination map with low multiplication matrix complexity, 25.52 GMac of $1920 \times 1200$ image size. To reduce nanosatellite hardware consumption with a single-precision floating-point format, the edge-computing-enabled inference model proposes a quantized convolution that computes signed values. The proposed inference model is deployed on Arm Cortex-M3 microcontrollers onboard satellite payload (86.74 times faster than normal convolution model) but also has a similar quality with the low-light enhanced in full-precision computing of lightweight training model by using the peak signal-to-noise ratio (average of 28.94) and structural similarity index (average of 0.85) metrics. © 2008-2012 IEEE.

关键词： Image enhancement

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Forecasting Mental Stress Using Machine Learning Algorithms

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computers, Materials & Continua 2022年第9期72卷 4945-4966页

作者： Elias Hossain Abdulwahab Alazeb Naif Almudawi Sultan Almakdi Mohammed Alshehri M.Gazi Golam Faruque Wahidur Rahman Department of Software Engineering Daffodil UniversityDhakaBangladesh Collage of Computer Science and Information System University of NajranNajranSaudi Arabia Department of Computer Science and Engineering Khwaja Yunus Ali UniversitySirajganj 6751Bangladesh

Depression is a crippling affliction and affects millions of individuals around the *** general,the physicians screen patients for mental health disorders on a regular basis and treat patients in collaboration with psychologists and other mental health experts,which results in lower costs and improved patient ***,this strategy can necessitate a lot of buy-in from a large number of people,as well as additional training and logistical ***,utilizing the machine learning algorithms,patients with depression based on information generally present in a medical file were analyzed and *** methodology of this proposed study is divided into six parts:Proposed Research Architecture(PRA),Data Pre-processing Approach(DPA),Research Hypothesis Testing(RHT),Concentrated Algorithm Pipeline(CAP),Loss Optimization Stratagem(LOS),and Model Deployment Architecture(MDA).The Null Hypothesis and Alternative Hypothesis are applied to test the *** addition,Ensemble Learning Approach(ELA)and Frequent Model Retraining(FMR)have been utilized for optimizing the loss ***,the Features Importance Interpretation is also delineated in this *** forecasts could help individuals connect with expert mental health specialists more quickly and *** to the findings,71%of people with depression and 80%of those who do not have depression can be appropriately *** study obtained 91%and 92%accuracy through the Random Forest(RF)and Extra Tree *** after applying the Receiver operating characteristic(ROC)curve,79%accuracy was found on top of RF,81%found on Extra Tree,and 82%recorded for the eXtreme Gradient Boosting(XGBoost)***,several factors are identified in terms of predicting depression through statistical data *** the additional effort is needed to develop a more accurate model,this model can be adjustable in the healthcare sector for diagnosing depression.

关键词： Depression analysis mental health machine learning model deployment XGBoost ROC-AUC

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Optimal planning of a green hydrogen fueling station

Optimal planning of a green hydrogen fueling station

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Coelho, António Soares, Filipe Iria, José Inesc Tec Center for Power and Energy System Porto Portugal Australian National University College of Engineering and Computer Science Canberra Australia

ISBN: (纸本)9789531842976

As the global community transitions towards decarbonization and sustainable energy, green hydrogen is emerging as a key clean energy carrier. This paper addresses the role of hydrogen in transportation, emphasizing the European Union's additionality principle for renewable energy sources in green hydrogen production. It introduces a model for optimally designing hydrogen fueling stations, considering electrolyzers, hydrogen storage, fuel cells, PV systems, and batteries. This model also considers the participation in electricity (energy and secondary reserve), hydrogen, and oxygen markets, and it is evaluated under different additionality policy scenarios. Results indicate that stricter additionality policies reduce the internal rate of return. However, participation in secondary reserve markets significantly boosts operational revenues and compensates for higher investment costs. © 2024 IEEE.

关键词： Clean energy

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