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39th Danubia-Adria Symposium on Advances in Experimental Mechanics, DAS 2023

作者： Maślak, Pawel Czmochowski, Jerzy Smolcnicki, Tadeusz Wroclaw University of Science and Technology Faculty of Mechanical Engineering Department of Machine and Vehicle Design and Research Wybrzeże Wyspiańskiego 27 Wroclaw50-370 Poland

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Performance of Microgrid systems on Multiple Dynamic Loads Penetration 10

Performance of Microgrid Systems on Multiple Dynamic Loads P...

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10th IEEE International Conference on Power systems, ICPS 2023

作者： Rumky, Tajrin Jahan Ahmed, Tofael Ahmed, Moudud Ahmad, Shameem Rahman, M.M. Advanced Power System Laboratory Department of Electrical & Electronic Engineering Chittagong4349 Bangladesh Southern University Bangladesh Department of Electrical and Electronic Engineering Chattogram Bangladesh Bangabandhu Sheikh Mujibur Rahman Science and Technology University Department of Electrical and Electronic Engineering Gopalganj8100 Bangladesh Faculty of Engineering Department of Electrical & Electronic Engineering Dhaka1229 Bangladesh East Delta University Department of Electrical and Electronic Engineering Chattogram Bangladesh

ISBN: (纸本)9798350318739

Microgrids (MGs) have emerged as a promising solution to enhance the reliability and sustainability of modern power distribution systems. In recent years, there has been a growing interest in interconnecting multiple microgrids to create more resilient and efficient energy networks. However, the performance of interconnected microgrids under various dynamic load scenarios remains a critical research area. In this paper, problems like low-frequency oscillations (LFOs) and the dynamic performance of multiple induction motors (IMs) in microgrids instead of a single motor with the same rating under a wide range of operating conditions are looked at. A single microgrid and an interconnected microgrid are two alternative model configurations designed in Matlab/Simulink that are considered in this study's comparison of non-linear dynamic simulations. The findings show that, compared to a local microgrid system, the performance of an interconnected microgrid system is more susceptible to being impacted by several parallel IM loads. Furthermore, it has been discovered that large IMs have lower LFO damping capabilities than parallel multiple small IMs. © 2023 IEEE.

关键词： Induction motors

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Assessing Sarcopenia-Prone Risk through Daily Activity of Gait With AI-Powered Wearable IoT Sensors

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IEEE Internet of Things Journal 2025年第12期12卷 20301-20313页

作者： Zhang, Hongyu Wang, Keer Cheng, Clio Yuen Man Chen, Meng Lai, King Wai Chiu Or, Calvin Kalun Hu, Yong Vellaisamy, Arul Lenus Roy Lam, Cindy Lo Kuen Xi, Ning Lou, Vivian W. Q. Li, Wen Jung City University of Hong Kong Department of Mechanical Engineering Hong Kong Hong Kong The University of Hong Kong Department of Social Work and Social Administration Sau Po Centre on Ageing Hong Kong Hong Kong City University of Hong Kong Centre for Robotics and Automation Department of Biomedical Engineering Hong Kong Hong Kong The University of Hong Kong Department of Industrial and Manufacturing System Engineering Hong Kong Hong Kong The University of Hong Kong Li Ka Shing Faculty of Medicine Department of Orthpedics and Traumatology Hong Kong Hong Kong Hong Kong Metropolitan University School of Science and Technology Hong Kong Hong Kong The University of Hong Kong Li Ka-Shing Faculty of Medicine Department of Family Medicine and Primary Care Hong Kong Hong Kong

Sarcopenia is a progressive condition characterized by age-related losses in muscle mass and strength, and irreversible in its advanced stages. While sarcopenia negatively impacts daily living, accurately, quickly and economically assessing its effects can be challenging due to individual variability in activity levels. This study introduced a novel approach for assessing the risk of sarcopenia-prone using machine learning and wearable IoT (Internet of Things) sensors. A total of 53 community-dwelling older adults aged 65+ underwent gait analysis using dual sensors. Nineteen gait features were extracted from each cycle and used to train classification algorithms to categorize participants as healthy, risk level 1, risk level 2, or risk level 3 for sarcopenia. Binary classification of healthy vs. sarcopenic-prone achieved 97.41% accuracy on average, while four-class classification averaged 94.67%. Notably, the research discovered worsening gait symmetry with increasing sarcopenia-prone severity. These results indicate IoT sensor-assessed gait may serve as a sensitive indicator for daily sarcopenia-prone screening. Accurate assessment of sarcopenia-prone individuals can be achieved through only a 4-meter walking test, significantly reducing the burden for older adults. This approach offers a cost-effective, convenient, and accurate method for early sarcopenia risk detection and intervention, potentially improving quality of life for older adults. This system could also aid in creating widely applicable monitoring products for assessing sarcopenia risk, supporting IoT, and thereby enabling early identification and intervention for individuals at risk of this condition. © 2014 IEEE.

关键词： Risk assessment

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Enhancing energy efficiency in shipping container house: A novel approach using hybrid louver systems

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Energy and Built Environment 2024年

作者： Ibrahim, Adnan Alsukkar, Muna Dong, Yifang Saleem, Farrukh Hamza, Amir Hu, Peng Hu, MaoBin Department of Thermal Science and Energy Engineering University of Science and Technology of China Hefei230027 China Department of Architectural Engineering Faculty of Engineering The Hashemite University P.O. Box 330127 Zarqa13133 Jordan Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China CAS Key Laboratory of Mechanical Behavior and Design of Materials Department of Modern Mechanics CAS Center for Excellence in Complex System Mechanics University of Science and Technology of China Hefei China School of Engineering Science University of Science and Technology of China Hefei230026 China

Reusing shipping containers for residential purposes offers a promising approach to address global energy consumption challenges from economic and environmental perspectives. This study parametrically designed hybrid louver shadings by combining fixed vertical triangular slats with variable-depth horizontal rectangular slats, offering a novel approach to shading prefabricated buildings. The energy consumption, daylighting performance, and visual comfort were assessed for various shading configurations. Results indicated that implementing the proposed hybrid louvers, featuring fixed vertical triangular slats and variable-depth horizontal rectangular slats, significantly reduced Energy Use Intensity (EUI) to 133.95 kWh/m2. Moreover, Useful daylight illuminance (UDI), Daylight Autonomy (DA), and Glare Autonomy (GA) values improved to 95.38%, 89.97%, and 91.16%, respectively. The study highlighted the potential of the proposed shading system to significantly reduce overall energy consumption across various ASHRAE climate zones, including Miami (1A), Guangzhou (2A), Melbourne (3C), Esperance (3C), San Diego (3A), and Milan (4A). Notably, energy reductions of up to 50.2% were projected for climates such as Miami (1A) and San Diego (3A). Furthermore, container buildings in warm climate zones exhibited a significantly lower EUI range of 76.58 to 91.95 kWh/m². This study underscores the transformative potential of hybrid louver systems in promoting the widespread adoption of sustainable residential architecture, contributing to global sustainability efforts. © 2024 Southwest Jiatong University

关键词： Daylighting

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Enhanced Congestion Control Model Based on Message Prioritization and Scheduling Mechanism in Vehicle-to-Infrastructure (V2I) 3

Enhanced Congestion Control Model Based on Message Prioritiz...

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3rd International Conference on Emerging Electrical Energy, Electronics and Computing Technologies 2021, ICE4CT 2021

作者： Ismail, Noraini Hossain, Mohammad Asif Md.noor, Rafidah Wahab, Ainuddin Wahid Abdul Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Darul Iman Kuala Nerus21030 Malaysia

Vehicular Ad Hoc Networks (VANETs) is an origination of Mobile Ad-Hoc Network (MANET), where road vehicles will distribute messages and provide safety alerts to notice hazardous circumstances to the drivers. One of its greatest challenges is ensuring Quality of Service (QoS) owing to channel sharing, high traffic and topology changes in VANETs. These challenges cause of degradation of network performance. Congestion control should be properly considered to improve the network performance and transmission message over VANETs. However, there seem significant limitations to most of the other current congestion control mechanisms. In this paper, an enhanced congestion control model based on prioritization and scheduling-based strategy is proposed. This strategy assigns priorities of the safety messages and non-safety messages based of the message type and network condition. Further, safety message can be classified into event-driven messages and beacon messages. At that point, all messages transmit into the appropriate queue based on their priority and schedule all messages in each queue. However, when new message enqueue, reschedule in each queue may cause additional unwanted delay to be processed cause of inefficient scheduling technique. Thus, an improved dynamic scheduling algorithm also proposed to schedule the messages in this circumstance. © Published under licence by IOP Publishing Ltd.

关键词： Vehicular ad hoc networks

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A Machine Learning Model for Foreign Exchange Prediction 2nd

A Machine Learning Model for Foreign Exchange Prediction

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： San, Tan Jun Ajibade, Samuel-Soma M. Ajibade, Temiloluwa Iyanuoluwa Jasser, Muhammed Basheer Ayodele, Olamide Emmanuel Oyewopo, Abdulgafar Olorunleke Adediran, Anthonia Oluwatosin Majeed, Anwar P. P. Abdul Luo, Yang Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Petaling Jaya Malaysia Department of Computer Engineering Istanbul Ticaret University Istanbul Turkey Department of Business Management Scholar School System Birmingham United Kingdom School of Engineering and Technology Sunway University Selangor Darul Ehsan Petaling Jaya47500 Malaysia Department of Banking and Finance Ekiti State University Ekiti State Ado Ekiti Nigeria Department of Business Administration Kwara State University Malete Nigeria Department of Real Estate Faculty of Built Environment Universiti Malaya Kuala Lumpur Malaysia Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

The rise of deep learning algorithms has sparked interest in the financial domain. The foreign exchange market (FOREX) is the globally leading financial market with millions of active stakeholders. Research in deep learning for forecasting price movements has shown its efficacy in financial markets. Notably, LSTM have empirically proven the best performance among other machine learning techniques. However, studies have shown the lack of deep learning hybrid comparisons to fortify empirical claims, especially in the FX market characterized for its high volatility and variance among currency pairs. This study develops a generalized LSTM model to forecast the major pairs and conducts a comparative analysis with a secondary hybrid deep learning model to assess and solidify the robustness of deep learning methods in forex forecasting. The empirical scenario simulates a classification problem employing cross-validation techniques to optimize the models’ generalization rigorously. Evaluation of the model utilized the accuracy, precision, recall, and f1-score metrics. Results indicated the performance of the models from best to worst were LSTM, SRNN and followed by CNN indicating the importance of testing deep learning approaches in different empirical setups. Conclusively, this study highlights the need challenges in forex forecasting and raises the need for more robust assessments on machine learning models forecasting capabilities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis

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Computers, Materials & Continua 2024年第6期79卷 4963-4977页

作者： Iftikhar Naseer Tehreem Masood Sheeraz Akram Zulfiqar Ali Awais Ahmad Shafiq Ur Rehman Arfan Jaffar Faculty of Computer Sciences&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Information System Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Lung cancer is a leading cause of global mortality *** detection of pulmonary tumors can significantly enhance the survival rate of ***,various Computer-Aided Diagnostic(CAD)methods have been developed to enhance the detection of pulmonary nodules with high ***,the existing method-ologies cannot obtain a high level of specificity and *** present study introduces a novel model for Lung Cancer Segmentation and Classification(LCSC),which incorporates two improved architectures,namely the improved U-Net architecture and the improved AlexNet *** LCSC model comprises two distinct *** first stage involves the utilization of an improved U-Net architecture to segment candidate nodules extracted from the lung ***,an improved AlexNet architecture is employed to classify lung *** the first stage,the proposed model demonstrates a dice accuracy of 0.855,a precision of 0.933,and a recall of 0.789 for the segmentation of candidate *** suggested improved AlexNet architecture attains 97.06%accuracy,a true positive rate of 96.36%,a true negative rate of 97.77%,a positive predictive value of 97.74%,and a negative predictive value of 96.41%for classifying pulmonary cancer as either benign or *** proposed LCSC model is tested and evaluated employing the publically available dataset furnished by the Lung Image Database Consortium and Image Database Resource Initiative(LIDC-IDRI).This proposed technique exhibits remarkable performance compared to the existing methods by using various evaluation parameters.

关键词： Lung cancer segmentation AlexNet U-Net classification

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Statistical Analysis of Digital Financial technology Adoption Research

Statistical Analysis of Digital Financial Technology Adoptio...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Ajibade, Temiloluwa Iyanuoluwa Ayodele, Olamide Emmanuel Ajibade, Samuel-Soma M. Goles, Nanet A. Dayupay, Johnry P. Adediran, Anthonia Oluwatosin Scholar School System Department of Business Management Birmingham United Kingdom Ekiti State University Department of Banking and Finance Ekiti State Ado Ekiti Nigeria Sunway University School of Engineering and Technology Department of Computing and Information Systems Selangor Malaysia Cebu Technological University Moalboal Cebu Philippines Federal University of Uberlandia Faculty of Architecture and Urban Design Uberlandia Brazil

ISBN: (纸本)9798350358155

Digital financial technology adoption (DFTA) studies published in Scopus between 2019 and 2022 were examined in this study. After that, we assessed relevant literature, did bibliometric studies, and examined publication and citation patterns. The statistics showed 2643 citations and 176 publications. DFTA research has gained popularity when time-varying TP and TC values increased gradually. This topic's most popular sources are "articles"and "Sustainability (Switzerland)". A search of DFTA research stakeholders revealed several authors, organizations, and nations. Most famously associated with the University of Johannesburg in South Africa, David Mhlanga has produced more publications than anyone else. The National Natural science Foundation and other Chinese donors who dominate DFTA research funding contributed to researchers' and affiliations' output. Cluster and hotspot analysis revealed two themes, "Digital financial technology adoption"and "Sustainable Banking Inclusion,"and three keywords, Digital financial technology adoption, monetary inclusion, and finance. According to the examined literature, DFTA's effects on international financial stability and GDP growth are understudied. Future research should examine DFTA's environmental impact, long-term profitability, and commercial strategies for low-income and rural communities. © 2024 IEEE.

关键词： Decentralized finance

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ResNet50-Based Deep Learning Model for Lung Disease Detection

ResNet50-Based Deep Learning Model for Lung Disease Detectio...

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Multi-Agent systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Janhavi R. Hajbe Sneha K. Patle Shreya V. Talvekar Tanvi W. Khonde Prajyot R. Yesankar Department of Computer Science & Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

By enabling a highly accurate examination of the chest x-ray, deep learning, for example, is changing the methods of recognizing lung disorders. In order to classify lung diseases, such as bacterial pneumonia, viral pneumonia, TB, and COVID-19, this work describes the use of ResNet50, a kind of CNN and a deep learning technique. We prove the potential of deep learning to discern these diseases from the two conventional approaches and most importantly, the patterns one could implicitly observe. The article also discusses the challenges that deep learning may pose on to the healthcare industry including data privacy, availability of data and the issue of overfitting. The paper links with this part by looking at the future of this area of specialization, which includes; multimodal fusion, operational AI, real-time identification, personalized care, data issues, and concerns, and ethics. But if deep learning is successful in addressing all the above-discussed challenges then it can bring the situation to the real world where the identification of lung diseases happens in a previous phase which in turn results in better treatment procedures and higher patient outcomes.

关键词： Deep learning COVID-19 Technological innovation Pneumonia Accuracy Pulmonary diseases Medical services X-ray imaging systematic literature review Residual neural networks

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A HybridManufacturing ProcessMonitoringMethod Using Stacked Gated Recurrent Unit and Random Forest

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Intelligent Automation & Soft Computing 2024年第2期39卷 233-254页

作者： Chao-Lung Yang Atinkut Atinafu Yilma Bereket Haile Woldegiorgis Hendrik Tampubolon Hendri Sutrisno Department of Industrial Management National Taiwan University of Science and TechnologyTaipeiTaiwanChina Faculty of Mechanical and Industrial Engineering Bahir Dar Institute of Technology(BiT)Bahir DarUniversityBahir Dar6000Ethiopia Department of Computer Science and Information Engineering National Taiwan University of Science and TechnologyTaipeiTaiwanChina Department of Information System Faculty of Engineering and Computer ScienceKridaWacana Christian UniversityJakartaIndonesia Department of Information Management National Dong Hwa UniversityHualienTaiwanChina

This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing *** real-time production process monitoring is critical in today’s smart *** more robust the monitoring model,the more reliable a process is to be under *** the past,many researchers have developed real-time monitoring methods to detect process shifts ***,thesemethods have limitations in detecting process shifts as quickly as possible and handling various data volumes and *** this paper,a robust monitoring model combining Gated Recurrent Unit(GRU)and Random Forest(RF)with Real-Time Contrast(RTC)called GRU-RF-RTC was proposed to detect process shifts *** effectiveness of the proposed GRU-RF-RTC model is first evaluated using multivariate normal and nonnormal distribution ***,to prove the applicability of the proposed model in a realmanufacturing setting,the model was evaluated using real-world normal and non-normal *** results demonstrate that the proposed GRU-RF-RTC outperforms other methods in detecting process shifts quickly with the lowest average out-of-control run length(ARL1)in all synthesis and real-world problems under normal and non-normal *** experiment results on real-world problems highlight the significance of the proposed GRU-RF-RTC model in modern manufacturing process monitoring *** result reveals that the proposed method improves the shift detection capability by 42.14%in normal and 43.64%in gamma distribution problems.

关键词： Smart manufacturing process monitoring quality control gated recurrent unit neural network random forest

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