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Artificial Intelligence, computer, Data Sciences and Applications (ACDSA), International Conference on

作者： Soualihou Ngnamsie Soualihou Jeong-Dong Kim Candra Zonyfar Hyun Lee Taek Lee Jung-Been Lee Dept. of Computer Science and Electronic Convergence Sun Moon University Asan Republic of Korea Dept. of Computer Science and Engineering Sun Moon University Asan Republic of Korea Division of Computer Science and Engineering Sun Moon University Asan Republic of Korea

Chronic diseases present a significant challenge in healthcare, often requiring ongoing medical attention and posing limitations on patients' daily activities. Diagnosis of such diseases is hindered by the absence of specific symptoms making them hard to detect and or prevent. Addressing this issue, researchers have turned to computational approaches, analyzing patients' medical records to predict the presence or absence of chronic diseases with promising results. However, there is potential for further improvement. This paper introduces an ensemble classifier, ELVot-CroDiP, designed to enhance chronic disease prediction. ELVot-CroDiP harnesses the collective strengths of various machine learning algorithms through a majority voting mechanism, thereby boosting predictive accuracy. The model’s performance was evaluated using precision, recall, f1-score, and accuracy across five medical datasets, including heart disease, chronic kidney disease, diabetes, and heart stroke. Comparative analysis shows that ELVot-CroDiP offers superior performance in predicting chronic diseases compared to existing models, as evidenced by its higher scores in the mentioned evaluation metrics for each dataset tested.

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Optimizing Traffic Signal Control to Enhance Transportation Efficiency and Maximize Pedestrian Benefits in the Road Network

Optimizing Traffic Signal Control to Enhance Transportation ...

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International Conference on Intelligent Transportation

作者： Vijayalakshmi K Kumarasamy Abhilasha Jairam Saroj Joseph Duhamel Yu Liang Dalei Wu Michael P. Hunter Mina Sartipi Department of Computer Science and Engineering University of Tennessee at Chattanooga USA Buildings and Transportation Sciences Division Oak Ridge National Laboratory USA School of Civil and Environmental Engineering Georgia Institute of Technology USA

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Increasing urban mobility requirements demand efficient transportation system strategies for both vehicular and pedestrian movement. This study enhances the Decentralized Graph-based Multi-Agent Reinforcement Learning (DGMARL) approach, originally tailored for vehicular traffic signal timing, to incorporate pedestrian traffic dynamics. The improved algorithm considers crucial metrics such as Eco_PI, assesses vehicle fuel consumption by factoring in stops and delays, and addresses pedestrian waiting time, crucial for system efficiency while acknowledging driver waiting time impact. Utilizing Digital Twin simulation along the MLK Smart Corridor in Chattanooga, Tennessee, the algorithm's performance is compared for various pedestrian control scenarios. To evaluate the effectiveness of DGMARL, this study compared DGMARL-enabled signal management with automated pedestrian traffic detection and an actuated signal management system (real-word baseline) with pedestrian recall, which predetermingly enforces a pedestrian phase every cycle. Findings indicate substantial improvements with DGMARL, showing a 28.29% enhancement in vehicle Eco_PI, a 60.55 % reduction in pedestrian waiting time, and a 55.74% decrease in driver stop delay, on average, compared to the baseline actuated signal timing plan.

关键词： Adaptation models Pedestrians Heuristic algorithms Reinforcement learning Digital twins Delays Fuels Vehicle dynamics Vehicles Testing

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Optimal Resource Allocation for NOMA-Assisted Backscatter Communication in 6G Internet of Vehicles Networks

Optimal Resource Allocation for NOMA-Assisted Backscatter Co...

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International Conference on Information Networking

作者： Van Dat Tuong Dongwan Kim Eui-Jik Kim Jeongyeup Paek Sungrae Cho School of Computer Science and Engineering Chung-Ang University Seoul Republic of Korea Department of Electronic Engineering Dong-A University Busan Republic of Korea Division of Software Hallym University Chuncheon Republic of Korea

ISBN: (数字)9798350330946

ISBN: (纸本)9798350330953

Non-orthogonal multiple access (NOMA) is considered as an emerging technology to improve the spectral efficiency performance for 6G networks. Ambient backscatter communication (BackCom) is another promising solution owing to its advantages in increasing spectral and power efficiency. The investigation of integrating NOMA with BackCom is conducted to enhance the connectivity of the emerging 5G low-powered Internet-of-Vehicles (IoVs) and future 6G transportation systems. This study proposes an optimal resource allocation scheme for the NOMA-assisted BackCom IoV network. Specifically, multiple Road-Side Units (RSUs) are considered sending superimposed signals to the subscribed IoVs in the downlink NOMA. BackCom tags are considered to transmit data symbols to nearby IoVs by reflecting the signals of RSUs. Thus, we aim to maximize the total data rate performance of the NOMA-assisted BackCom IoV network. A joint problem that simultaneously optimizes the IoV clustering, IoV power allocation, and reflection coefficients of BackCom tags is formulated. To solve the problems efficiently, multiple subproblems are decomposed that can be solved using CVX optimization tool. To validate the performance of the proposed method, we compare it with those of state-of-the-art benchmark schemes. Numerical results will be provided to show the superiority of the proposed framework.

关键词： 6G mobile communication NOMA Transportation Symbols Benchmark testing Reflection coefficient Resource management

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Highly ordered inkjet-printed quantum-dot thin films enable efficient and stable QLEDs with EQE exceeding 23%

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eScience 2024年第3期4卷 152-162页

作者： Changting Wei Bo Xu Meng Zhang Zhenhuang Su Jiawei Gu Wenrui Guo Xingyu Gao Wenming Su Zheng Cui Seokwoo Jeon Zhiyong Fan Haibo Zeng MIIT Key Laboratory of Advanced Display Materials and Devices Institute of Optoelectronics&NanomaterialsSchool of Materials Science and EngineeringNanjing University of Science and TechnologyNanjing 210094China Ningbo Nato New Material Technology Co. LtdNingbo 315600China Shanghai Synchrotron Radiation Facility(SSRF) Zhangjiang LaboratoryShanghai Advanced Research InstituteChinese Academy of SciencesShanghai 201204China School of Mechanical Engineering Jiangsu UniversityZhenjiang 212013China Printable Electronics Research Center Nano Devices and Materials DivisionSuzhou Institute of Nano-Tech and Nano-BionicsChinese Academy of SciencesSuzhou 215123China Department of Materials Science and Engineering Korea UniversitySeoul 02841South Korea Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong Kong 999077China

Inkjet-printed quantum dot light-emitting diodes(QLEDs)are emerging as a promising technology for next-generation ***,the progress in fabricating QLEDs using inkjet printing technique has been slower compared to spin-coated devices,particularly in terms of efficiency and *** key to achieving high performance QLEDs lies in creating a highly ordered and uniform inkjet-printed quantum dot(QD)thin *** this study,we present a highly effective strategy to significantly improve the quality of inkjet-printed CdZnSe/CdZnS/ZnS QD thin films through a pressure-assisted thermal annealing(PTA)*** from this PTA process,a high quality QD thin film with ordered packing,low surface roughness,high photoluminescence and excellent electrical property is *** mechanism behind the PTA process and its profound impact on device performance have been thoroughly investigated and ***,a record high external quantum efficiency(EQE)of 23.08%with an impressive operational lifetime(T50)of up to 343,342h@100cdm−2,and a record EQE of 22.43%with T50 exceeding to 1,500,463h@100cdm−2 are achieved in inkjet-printed red and green CdZnSe-based QLEDs,*** work highlights the PTA process as an important approach to realize highly efficient and stable inkjet-printed QLEDs,thus advancing QLED technology to practical applications.

关键词： Inkjet printing Quantum dot light-emitting diode High efficiency Thermal annealing Highly ordered

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Fault Detection in Solar Power System with Internet of Things using Multi resolution Sinusoidal Neural Network - Snow Geese Optimization Approach 4

Fault Detection in Solar Power System with Internet of Thing...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Kumar, Pramod Purushothaman, K.E. Arunmozhi, R.V. Jayachitra, S. Rani, D. Leela Kaliappan, S. Ganga Institute of Technology and Management Department of Computer Science and Engineering Haryana Kablana124001 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Tamil Nadu Chennai600062 India Karpagam Academy of Higher Education Department of Electrical and Electronic Engineering Tamil Nadu Coimbatore641021 India S.A. Engineering College Department of Chemistry Tamil Nadu Chennai600077 India Department of Electronics and Communication Engineering Andhra Pradesh Tirupati517102 India Lovely Professional University Division of Research and Development Delhi Punjab Jalandhar144411 India

ISBN: (纸本)9798331540364

Fault detection in power systems, including Photovoltaic (PV) systems, using Internet of Things (IoT) involves deploying sensors to monitor key parameters and analyzing the data identify anomalies. The accuracy of these systems can be compromised by issues such as noisy or low-quality sensor data, which may lead to false positives or missed faults. Additionally, the complexity of managing large volumes of data from numerous IoT devices can strain the system, further affecting detection accuracy. To overcome these drawbacks, this paper proposes an efficient approach for fault uncovering in solar power system grounded on IoT. The data are collected from solar power generation dataset. Subsequently, the data are nourished to preprocessing. In preprocessing segment removes the absent values in the data utilizing Reverse Lognormal Kalman Filter (RLKF). The consequence after the preprocessing data is transported to the Multiresolution Sinusoidal Neural Network (MSNN). The line-line faults, ground faults, line-ground faults and partial shading are successfully predicted and classified by using MSNN. The Snow Geese Optimization (SGO) is rummage-sale to optimize the weight structure of MSNN. The projected MSNN-SGO is utilized within the MATLAB platform. Performance metrics counting precision, accuracy, Root Mean Squared Error (RMSE), F1-score, and Mean Squared Error (MSE) were examined in order to determine the proposed method. The proposed DAGCN-COA technique yields 14.89%, 16.89%, and 18.23% higher precision, 16.65%, 18.85%, and 17.89% higher accuracy, 22.36%, 15.42% and 28.27% lower RMSE when analysed the existing methods. The proposed MSNN-SGO technique is associated with the existing methods for instance spatial-temporal recurrent graph neural network (STRGNN), convolutional neural network (CNN), and long short-term memory network (LSTMN), respectively. © 2024 IEEE.

关键词： Mean square error

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Performance Comparison of Interpolation Methods for Enriching WiFi Fingerprint Database 24

Performance Comparison of Interpolation Methods for Enrichin...

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9th International Conference on Cloud Computing and Internet of Things, CCIOT 2024

作者： Vu-Thanh, Trung He, Jing Duong-Bao, Ninh Thi, Luong Nguyen Lee, Seon-Woo Nguyen-Huu, Khanh College of Computer Science and Electronic Engineering Hunan University Changsha China Faculty of Mathematics and Informatics Dalat University Dalat Viet Nam Faculty of Information Technology Dalat University Dalat Viet Nam Division of Software Hallym University Chuncheon Korea Republic of Department of Electronics and Telecommunications Dalat University Dalat Viet Nam

ISBN: (纸本)9798400717161

Recently, WiFi Fingerprinting-based indoor positioning systems have gained significant attention due to the low cost of infrastructure deployment. In the data collection process, a higher density of reference points may lead to better positioning results. However, it makes this process time-consuming and laborious. To solve this problem, this study applies and compares different interpolation methods to enrich the fingerprint database without increasing the cost of the collection process. To evaluate the performance of the chosen methods, five interpolation methods are implemented in a multi-condition WiFi Fingerprinting dataset. Moreover, to check the robustness of the methods, different numbers of reference points are considered when creating the new interpolated fingerprint databases. According to the results, inverse distance weighting (IDW) is the most suitable interpolation method with low complexity and stable performance. The IDW-based database achieves an average interpolation error of less than 6 dBm compared to the original database. When applying this interpolated database to perform positioning tasks, it helps reduce the collection cost by 30% with slightly lower positioning results compared to the original database. This work demonstrates the feasibility of using interpolation to ease the data collection process of WiFi Fingerprinting, especially in complex and big areas such as airports or hospitals. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Wi-Fi

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Combining Tags of Various Lengths Benefits Peptide Identification in Bottom-up Proteomics

Combining Tags of Various Lengths Benefits Peptide Identific...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Lai, Shengzhi Li, Ning Yu, Weichuan The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong The Hong Kong University of Science and Technology Division of Life Science Hong Kong Hkust Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian Shenzhen China

ISBN: (纸本)9798350337488

Peptide identification provides key information for protein inference in bottom-up proteomics. Post-translational modifications (PTMs) are essential to understand cellular activities at the protein level. In current database search methods for peptide identification, precursor mass is a critical parameter to narrow down the search space. However, true peptides may be excluded from the search space if precursor masses are modified by PTMs. Thus, many researchers use peptide sequence segments called tags which are invariant to PTMs in database search. Shorter tags are more sensitive but less accurate, whereas longer tags are more accurate but less frequent. Current methods use tags of fixed lengths, ignoring the effect of different tag lengths. To address this issue, we propose to combine tags of various lengths to improve tag-based peptide identification methods. Using combined tags, true peptides are included in the search space in more cases, resulting in at least 35% and 49% more peptide identifications and PTM results compared to benchmark methods using the same quality control parameters. © 2023 IEEE.

关键词： bottom-up proteomics peptide identification post-translational modification tag-based database search

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WiFi-based Positioning System with k-means Clustering and Outlier Removal: Evidence from Multiple Datasets 23

WiFi-based Positioning System with k-means Clustering and Ou...

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12th International Symposium on Information and Communication Technology, SOICT 2023

作者： Pham-Hai, Dang Duong-Bao, Ninh He, Jing Nguyen Thi, Luong Lee, Seon-Woo Nguyen-Huu, Khanh Department of Electronics and Telecommunications Dalat University Dalat Viet Nam College of Computer Science and Electronic Engineering Hunan University Changsha China Faculty of Information Technology Dalat University Dalat Viet Nam Division of Software Hallym University Chuncheon Korea Republic of

ISBN: (纸本)9798400708916

Indoor positioning systems are receiving much attention due to their practical applications in our daily lives. WiFi Fingerprinting is the technique that is commonly used for indoor positioning due to its utilization of the WiFi infrastructure which is ubiquitously deployed in modern buildings. However, this technique suffers from the computational burden when determining the user's position in big areas that consist of lots of fingerprints and reference points. To reduce the search space as well as computing time, the clustering algorithm can be applied. In this paper, we evaluate the efficacy of k-means clustering in the WiFi Fingerprinting-based positioning system. Moreover, k-means clustering with outlier removal is applied in several public datasets, in which the WiFi data was collected in different environmental conditions, to show how much the clustering algorithm can help in reducing the computing time as well as improving the positioning accuracy. From the experimental results, compared to popular deterministic-based algorithms, the clustering algorithm can significantly reduce the computing time by up to 79.07%. Nevertheless, it cannot surpass the positioning accuracy of the compared algorithms. © 2023 ACM.

关键词： Indoor positioning systems

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Real-time deep learning-assisted mechano-acoustic system for respiratory diagnosis and multifunctional classification

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npj Flexible electronics 2024年第1期8卷 192-203页

作者： Hee Kyu Lee Sang Uk Park Sunga Kong Heyin Ryu Hyun Bin Kim SangHoon Lee Danbee Kang Sun Hye Shin Ki Jun Yu Juhee Cho Joohoon Kang Il Yong Chun Hye Yun Park Sang Min Won Department of Electrical and Computer Engineering Sungkyunkwan UniversitySuwonRepublic of Korea Department of Clinical Research Design and Evaluation SAIHSTSungkyunkwan UniversitySeoulRepublic of Korea Patient-Centered Outcomes Research Institute Samsung Medical CenterSeoulRepublic of Korea Center for Clinical Epidemiology Samsung Medical CenterSeoulRepublic of Korea Division of Pulmonary and Critical Care Medicine Department of MedicineSamsung Medical CenterSungkyunkwan University School of MedicineSeoulRepublic of Korea School of Electrical and Electronic Engineering Yonsei UniversitySeoulRepublic of Korea School of Advanced Materials Science and Engineering Sungkyunkwan UniversitySuwonRepublic of Korea Center for Neuroscience Imaging Research Institute for Basic ScienceSuwonRepublic of Korea

Epidermally mounted sensors using triaxial accelerometers have been previously used to monitor physiological processes with the implementation of machine learning(ML)algorithm *** findings from these previous studies have established a strong foundation for the analysis of highresolution,intricate signals,typically through frequency domain *** this study we integrate a wireless mechano-acoustic sensor with a multi-modal deep learning system for the real-time analysis of signals emitted by the laryngeal prominence area of the thyroid cartilage at frequency ranges up to 1 *** interface provides real-time data visualization and communication with the ML server,creating a system that assesses severity of chronic obstructive pulmonary disease and analyzes the user’s speech patterns.

关键词： system diagnosis mounted

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Mitigation of Cyberattacks through Battery Storage for Stable Microgrid Operation

Mitigation of Cyberattacks through Battery Storage for Stabl...

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2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2022

作者： Zografopoulos, Ioannis Karamichailidis, Panagiotis Procopiou, Andreas T. Teng, Fei Konstantopoulos, George C. Konstantinou, Charalambos Cemse Division Saudi Arabia Watts Battery Corp. Palo AltoCA United States Imperial College Dept. of Electrical and Electronic Engineering London United Kingdom University of Patras Dept. of Electrical and Computer Engineering Greece

ISBN: (数字)9781665432542

ISBN: (纸本)9781665432542

In this paper, we present a mitigation methodology that leverages battery energy storage system (BESS) resources in coordination with microgrid (MG) ancillary services to maintain power system operations during cyberattacks. The control of MG agents is achieved in a distributed fashion, and once a misbehaving agent is detected, the MG,′s mode supervisory controller (MSC) isolates the compromised agent and initiates self-healing procedures to support the power demand and restore the compromised agent. Our results demonstrate the practicality of the proposed attack mitigation strategy and how grid resilience can be improved using BESS synergies. Simulations are performed on a modified version of the Canadian urban benchmark distribution model. © 2022 IEEE.

关键词： Electric power distribution

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