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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Ming-Yi Lin Sheng-Hsien Hsieh Ching-Han Chen Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan Bachelor's Program in Artificial Intelligence and Mechatronics National Pingtung University of Science and Technology Pingtung Taiwan Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan

Researchers emphasize the importance of hardware accelerators for mathematical morphology. If there are any issues, the hardware architecture may need to be redesigned. Thus, we propose a novel, reconfigurable hardware architecture with pipeline techniques for acceleration. With the order control register, there is no need to modify the architecture even if different results are expected. Furthermore, the hardware architecture caters to specific purposes instead of requiring a purpose-built hardware design.

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Dynamic Numerical Studies of a Rotorcraft Entering and Exiting Rectangular Tunnel

Dynamic Numerical Studies of a Rotorcraft Entering and Exiti...

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IEEE International Conference on e-Business Engineering (ICEBE)

作者： Uzu-Kuei Hsu Hong-Bin Chang Yao-Yu Tsai Chang-Hsien Tai Bachelor Program in Artificial Intelligence and Mechatronics National Pingtung University of Science and Technology Kaohsiung City Taiwan SCERDC General Research Service Center National Pingtung University of Science and Technology Kaohsiung City Taiwan Dept. of Vehicle Engineering National Pingtung University of Science and Technology Kaohsiung City Taiwan ROC

In recent years, the utilization of multi-rotor drones has seen a steady rise. Unmanned aerial vehicles (UAV) have found diverse applications, including aerial photography, transportation, military operations, and agriculture. The advent of these compact flying machines offers humanity a range of convenient and versatile choices across various sectors. Although unmanned aerial vehicles hold significant practical value, their stable flight is contingent on favorable weather conditions. Consequently, it becomes crucial to investigate the influence of environmental airflow on aircraft stability. This is particularly important for small machines like rotary-wing aircraft operating in confined spaces, where understanding the aerodynamic effects of the flow field is crucial to avert potential crashes. This study delves into the variations in the flow field during the entry and exit of a single rotor into a tunnel. Utilizing dynamic simulations, it examines how tunnels impact the rotor’s flow field and analyzes thrust curves to understand dynamic rotor behavior. The study also explores the effect of vortex generation on the rotor and employs a simple square tunnel to assess how tunnel size and flight speed influence the rotor’s flow field during flight. The findings hold potential as reference points to enhance flight stability during tunnel flights.

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Constructing a Minimized Connected Dominating Set Based on the Decentralized Nomination Algorithm in Mobile Ad Hoc Networks

SSRN

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SSRN 2023年

作者： Hsieh, Hui-Ching Chiang, Mao-Lun Lin, Tzu-Ling Liao, Lu-Wen Chen, Ze-Yu Department of New Media Communication Hsing Wu University New Taipei City Taiwan Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung City Taiwan Department of Business Management National Taichung University of Science and Technology Taichung404336 Taiwan Department of Intelligent Production Engineering National Taichung University of Science and Technology Taiwan Department of Information and Communication Engineering Chaoyang University of Technology Taichung City Taiwan

Inspired by the backbone architecture of wired networks, in this study, we applied the concept of a virtual backbone to the routing scheme in a mobile ad hoc network (MANET) to develop an effective routing method. Using connected dominating set (CDS)-based methods to construct a virtual backbone network can effectively improve routing performance in a MANET. The CDS virtual backbone network we developed in the present study can respond quickly and identify routing paths through these CDS members as the network topology changes. Meanwhile, the nodes that are not CDS members can enter the power-saving mode to minimize unnecessary power consumption. Wireless nodes in a MANET can move freely within the network;therefore, CDS virtual backbones are easy to deconstruct. Researchers have developed centralized and decentralized CDS algorithms to reconstruct network topologies. When previously developed centralized CDS algorithms are applied, the system requires the information of all nodes in the network to reconstruct the virtual backbone. Therefore, the information exchange costs of such algorithms before refactoring are high. By contrast, decentralized CDS algorithms can use local information to reconstruct virtual backbones quickly. Yet, decentralized algorithms produce larger CDSs than centralized algorithms. To improve upon the performance of previously developed decentralized algorithms, we developed and herein present a new decentralized CDS algorithm called APP-CDS that can quickly construct a virtual backbone with the smallest possible CDS for use in MANET environments. © 2023, The Authors. All rights reserved.

关键词： Routing algorithms

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ECG signals-based security and steganography approaches in WBANs: A comprehensive survey and taxonomy

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Sustainable Computing: Informatics and Systems 2024年 41卷

作者： Masdari, Mohammad Band, Shahab S. Qasem, Sultan Noman Sayed, Biju Theruvil Pai, Hao-Ting Department of Computer Engineering Urmia Branch Islamic Azad University Urmia Iran Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Taiwan Riyadh11432 Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz6803 Yemen Department of Computer Science Dhofar University PO Box 2509 SalalahPCode 211 Oman Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan

Wireless Body Area Networks (WBANs) are integral components of e-healthcare systems, responsible for monitoring patients' physiological states through intelligent implantable or wearable sensor nodes. These nodes collect medical data, which is then transmitted to remote healthcare facilities for thorough evaluation. Securing medical data within WBANs is paramount due to its central role in preserving patient privacy and confidentiality. Notably, Electrocardiogram (ECG) signals have recently gained prominence as pivotal elements within diverse security frameworks. Incorporating ECG signals strategically enhances the security and reliability of WBANs and broader e-healthcare systems, instilling greater trustworthiness. This survey article provides an in-depth exploration of contemporary ECG-based security schemes, adding to the scholarly discourse. The imperative to categorize these security paradigms revolves around their use of ECG signals. This categorization identifies three key domains: the first involves schemes that utilize ECG signals for cryptographic operations, encompassing key generation, agreement, management, and authentication. The second category employs steganography-based techniques, using ECG signals to conceal patients' sensitive medical data. The third category focuses on enhancing ECG signal security during data transmission. Each category is meticulously elaborated, detailing architectural foundations, notable contributions, and intrinsic security services. Furthermore, each section presents a comprehensive overview of the attributes characterizing ECG-based security frameworks. This includes insights into employed datasets, simulation environments, evaluation metrics, and inherent advantages and limitations. Expanding on this, a thorough analysis of distinctive attributes underpinning these security frameworks concludes by shedding light on potential directions for future research. © 2023 Elsevier Inc.

关键词： Authentication

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Energy consumption of high-rise double skin façade buildings, a machine learning analysis

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Journal of Building Engineering 2024年 89卷

作者： Rezaie, Maedeh Kariminia, Shahab Band, Shahab S. Ameri, Rasoul Farokhi, Maryam Pai, Hao-Ting Gocer, Ozgur Rismanchi, Behzad Shooshtarian, Salman Department of Architecture Advancement in Architecture and Urban Planning Research Center Najafabad Branch Islamic Azad University Najafabad Iran Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Taiwan Department of Information Management National Yunlin University of Science and Technology Taiwan Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan School of Architecture Design and Planning The University of Sydney SydneyNSW Australia Renewable Energy and Energy Efficiency Group Department of Infrastructure Engineering Faculty of Engineering and Information Technology The University of Melbourne Australia School of Property Construction and Project Management RMIT University Melbourne Australia

Most energy exchanges take place through the building skin. The skin characteristics play a decisive role in the extent of these exchanges, but they are somewhat more varied in the double skin façade (DSF). Among these characteristics, cavity segmentation has a noticeable effect on the implementation of the DSF in different directions during the hot and cold seasons. The aim of this study was to investigate the role of DSF segmentation in energy consumption and natural ventilation of high-rise buildings in hot and dry climates. This study used DesignBuilder software to study sixty-four segmentation component scenarios in an eight-story residential building in Isfahan, Iran. Moreover, a proposed hybrid model utilizing the hybridization of the Hunger Game Search and Gradient Boosting (HGS-GB) algorithm was employed to estimate energy consumption in various scenarios involving lighting, heating, cooling, and total scenarios. The available outcomes revealed that the HGS-GB model had a better performance in comparison with other individual models, such as Gradient Boosting (GB), Random Forest (RF), and K-nearest neighbors (KNN). The R2 values for lighting, heating, cooling, and total energy estimation were 0.9993, 0.9958, 0.9991, and 0.9922, respectively. The findings of this study suggest the significance of DSF segmentation in energy consumption and natural ventilation in high-rise buildings in hot and dry climates. © 2024 Elsevier Ltd

关键词： Forestry

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Review Integration of smartphone technology and artificial intelligence for advanced ophthalmic care:A systematic review

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Advances in Ophthalmology Practice and Research 2024年第3期4卷 120-127页

作者： Kai Jin Yingyu Li Hongkang Wu Yih Chung Tham Victor Koh Yitian Zhao Ryo Kawasaki Andrzej Grzybowski Juan Ye Eye Center The Second Affiliated Hospital of Zhejiang University School of MedicineZhejiang Provincial Key Laboratory of OphthalmologyZhejiang Provincial Clinical Research Center for Eye DiseasesZhejiang Provincial Engineering Institute on Eye DiseasesHangzhouChina Centre for Innovation and Precision Eye Health National University of SingaporeSingapore Department of Ophthalmology National University of SingaporeSingapore Singapore Eye Research Institute Singapore National Eye CentreSingapore Ophthalmology and Visual Science Academic Clinical Program Duke-NUS Medical SchoolSingapore Cixi Institute of Biomedical Engineering Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingboChina Ningbo Eye Hospital NingboChina Zhejiang International Scientific and Technological Cooperative Base of Biomedical Materials and Technology Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingboChina Zhejiang Engineering Research Center for Biomedical Materials Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingboChina Division of Public Health Department of Social MedicineGraduate School of MedicineOsaka UniversityOsakaJapan Artificial Intelligence Center for Medical Research and Application Osaka University HospitalOsakaJapan Institute for Research in Ophthalmology Foundation for Ophthalmology DevelopmentPoznanPoland

Background:The convergence of smartphone technology and artificial intelligence(AI)has revolutionized the landscape of ophthalmic care,offering unprecedented opportunities for diagnosis,monitoring,and management of ocular ***,there is a lack of systematic studies on discussing the integration of smart-phone and AI in this *** text:This review includes 52 studies,and explores the integration of smartphones and AI in ophthalmology,delineating its collective impact on screening methodologies,disease detection,telemedicine initiatives,and patient *** collective findings from the curated studies indicate promising performance of the smartphone-based AI screening for various ocular diseases which encompass major retinal diseases,glaucoma,cataract,visual impairment in children and ocular surface ***,the utilization of smartphone-based imaging modalities,coupled with AI algorithms,is able to provide timely,efficient and cost-effective screening for ocular *** modality can also facilitate patient self-monitoring,remote patient monitoring and enhancing accessibility to eye care services,particularly in underserved *** involving data pri-vacy,algorithm validation,regulatory frameworks and issues of trust are still need to be ***,evaluation on real-world implementation is imperative as well,and real-world prospective studies are currently ***:Smartphone ocular imaging merged with AI enables earlier,precise diagnoses,personalized treat-ments,and enhanced service accessibility in eye *** is crucial to navigate ethical and data security challenges while responsibly leveraging these innovations,promising a potential revolution in care access and global eye health equity.

关键词： artificial intelligence Smartphone imaging Ophthalmology Telemedicine Ocular disease management

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Explainable machine learning models for estimating daily dissolved oxygen concentration of the Tualatin River

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Engineering applications of Computational Fluid Mechanics 2024年第1期18卷

作者： Li, Shuguang Qasem, Sultan Noman Band, Shahab S. Ameri, Rasoul Pai, Hao-Ting Mehdizadeh, Saeid School of Computer Science and Technology Shandong Technology and Business University Yantai China Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz Yemen Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management National Yunlin University of Science and Technology Douliu Taiwan Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan Water Engineering Department Urmia University Urmia Iran

Monitoring the quality of river water is of fundamental importance and needs to be taken into consideration when it comes to the research into the hydrological field. In this context, the concentration of the dissolved oxygen (DO) is one of the most significant indicators of the quality of river water. The current study aimed to estimate the minimum, maximum, and mean DO concentrations (DO min, DO max, DO mean) at a gauging station located on Tualatin River, United States. To that end, four machine learning models, such as support vector regression (SVR), multi-layer perceptron (MLP), random forest (RF), and gradient boosting (GB) were established. Root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), and Nash-Sutcliffe efficiency (NSE) metrics were employed to better assess the accuracies of these models. The modeling results demonstrated that the SVR and MLP surpassed the RF and GB models. Despite this, the SVR was concluded to be the best-performing method when used to estimate the DO min, DO max, and DO mean. The best error statistics in the testing phase were related to the SVR model with full (four) inputs to estimate DO mean concentration (RMSE = 0.663 mg/l, MAE = 0.508 mg/l, R = 0.945, NSE = 0.875). Finally, the explainability of the superior models (i.e. SVR models) was conducted using SHapley Additive exPlanations (SHAP) for the first time to estimate DO concentration. In fact, evaluating the explainability of machine learning models can provide useful information about the impact of each of the input estimators used in the procedure of models development. It was concluded that the specific conductance (SC) and followed by water temperature (WT) could provide the most contributions for estimating the DO min, DO max, and DO mean concentrations. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： dissolved oxygen concentration estimation Explainable machine learning SHapley additive explanations

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An adaptive replica configuration mechanism based on predictive file popularity and queue balance in mobile edge computing environment

Research Square

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Research Square 2021年

作者： Chiang, Mao-Lun Hsieh, Hui-Ching Chang, Ting-Yi Lin, Wei-Ling Chen, Hong-Wei Department of Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taiwan Department of New Media Communication Hsing Wu University New Taipei City Taiwan Department of Industrial Education and Technology National Changhua University of Education Changhua City Taiwan Department of Information Management Chaoyang University of Technology Taichung City Taiwan

In the current era of the Internet of Things (IoT), various devices can provide more services by connecting to the Internet. However, the explosive growth of connected devices will cause the cloud core overload and significant network delays. To overcome these problems, the Mobile Edge Computing (MEC) network is proposed to provide most of the computing and storage near the radio access network to reduce the traffic of the core cloud network and provide lower latency for the terminal. Mobile edge computing can work with third parties to develop multiple services, such as mobile big data analysis and context-aware services. However, when there is a large amount of popular data accessed in a short period, the system must generate many replicas, which will not only reduce access efficiency but also cause additional traffic overhead. To improve the above problems, an Adaptive Replica Configuration Mechanism (ARCM) is proposed in this paper to predict the popularity of the file and make a replica to the low-blocking node. This method spreads the subsequent access workload by copying the popular file in advance to improve the overall performance of the system. © 2021, CC BY.

关键词： Mobile edge computing

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Functional neuroplasticity of facilitation and interference effects on inhibitory control following 3-month physical exercise in aging

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Scientific Reports 2024年第1期14卷 1-13页

作者： Wu, Hong-Yi Huang, Chih-Mao Hsu, Ai-Ling Chen, Chiao-Nan Wu, Changwei W. Chen, Jyh-Horng Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Bachelor Program in Artificial Intelligence Chang Gung University Taoyuan Taiwan Department of Psychiatry Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan Department of Physical Therapy and Assistive Technology National Yang Ming Chiao Tung University Taipei Taiwan Graduate Institute of Mind Brain and Consciousness Taipei Medical University New Taipei Taiwan Research Center of Sleep Medicine Taipei Medical University Hospital Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan

Preservation of executive function, like inhibition, closely links to the quality of life in senior adults. Although neuroimaging literature has shown enhanced inhibitory function followed by aerobic exercise, current evidence implies inconsistent neuroplasticity patterns along different time durations of exercise. Hence, we conducted a 12-week exercise intervention on 12 young and 14 senior volunteers and repeatedly measured the inhibitory functionality of distinct aspects (facilitation and interference effects) using the numerical Stroop task and functional Magnetic Resonance Imaging. Results showcased improved accuracy and reduced reaction times (RT) after 12-week exercise, attributed to frontoparietal and default mode network effects. In young adults, the first phase (0 to six weeks) exercise increased the activation of the right superior medial frontal gyrus, associated with reduced RT in interference, but in the second intervention phase (six to twelve weeks), the decreased activation of the left superior medial frontal gyrus positively correlated with reduced RT in facilitation. In senior adults, the first six-week intervention led to reduced activations of the inferior frontal gyrus, inferior parietal gyrus, and default mode network regions, associated with the reduced RT in interference. Still, in the second intervention phase, only the visual area exhibited increased activity, associated with reduced RT in interference. Except for the distinctive brain plasticity between the two phases of exercise intervention, the between-group comparison also presented that the old group gained more cognitive benefits within the first six weeks of exercise intervention;however, the cognitive improvements in the young group occurred after six weeks of intervention. Limited by the sample size, these preliminary findings corroborated the benefits of aerobic exercise on the inhibitory functions, implying an age × exercise interaction on the brain plasticity for both facilita

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Flame and smoke detection using Kafka on edge devices

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Internet of Things 2024年 27卷

作者： Yu-Wei Chan Jung-Chun Liu Endah Kristiani Kai-Yu Lien Chao-Tung Yang Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung City Taiwan ROC Department of Computer Science Tunghai University Taichung City 407224 Taiwan ROC Department of Informatics Krida Wacana Christian University Jakarta 11470 Indonesia Research Center for Smart Sustainable Circular Economy Tunghai University Taichung City 407224 Taiwan ROC

This paper presents object detection methods to accurately identify the sources of flame and smoke in vast circumstances. Aerial drones collected the data, analyzed the recognition outputs in real time on an edge device, and then transferred them to the back-end for data processing and warnings using Kafka. To detect flame and smoke occurrences, the models were compared using various convolutional neural networks (CNN). Several factors considered include streaming speed, accuracy, portability, efficiency, and power consumption on edge devices. This work conducted training comparisons of YOLOV4, YOLOV5, YOLOV7, YOLOV8, and Faster RCNN. The inference performance was then evaluated on an edge computing device. The findings showed an accuracy of 0.91 and 0.87, while maintaining a processing speed of roughly 1 frame per second on the Nvidia Jetson NX without acceleration.

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