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2023 International Conference on electrical engineering and Informatics, ICEEI 2023

作者： Muharram, Arief Purnama Sajid, Fahmi School of Electrical Engineering and Informatics Institut Teknologi Bandung Program of Informatics Bandung Indonesia

ISBN: (纸本)9798350381290

Diabetes mellitus, particularly type-2 diabetes, remains a prevalent health issue, raising concerns due to its associated risk of complications. Among these, cardiovascular complications pose a significant threat, exhibiting high morbidity and mortality rates. Health screening plays a pivotal role in stratifying the risk levels of diabetes patients, facilitating proactive measures to prevent the progression of complications. As such, the primary objective of this study is to develop a predictive model system for assessing cardiovascular risk in diabetes patients. Our study used the Cardiovascular Disease dataset and conducts experiments with various supervised machine learning algorithms, such as Naive Bayes, decision tree, random forest, AdaBoost, and XG- Boost. The results reveal that ensemble learning algorithms based on boosting, particularly AdaBoost and XGBoost, outperform other supervised machine learning methods. However, even with the best performance achieved using the dataset, the accuracy stands at 0.71, and the F -1 score is 0.69, which is still acceptable for screening purposes. Although these results provide valuable insights, indicating individuals at higher risk for cardiovascular complications in diabetes, further improvements are needed to enhance early prevention strategies. © 2023 IEEE.

关键词： Decision trees

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Visualization of Methamphetamine-Contaminated Fingermarks on Glass Surfaces by Field Emission Scanning Electron Microscope for Forensic Investigation

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Journal of Forensic Science and Medicine 2023年第1期9卷 6-11页

作者： Sarah Aliah Amir Sarifudin Kah Haw Chang Bee Ee Khoo Ahmad Fahmi Lim Abdullah Forensic Science Program School of Health SciencesHealth CampusUniversiti Sains MalaysiaKelantanMalaysia Mechatronic Engineering Program School of Electrical and Electronic EngineeringEngineering CampusUniversiti Sains MalaysiaPenangMalaysia

Background:Fingermark is an individual’s primary identification *** is helpful in determining individuals involved in illegal activities and is frequently encountered in clandestine *** forensic investigation,the critical question to be answered is whether a fingermark was left on a surface before or after the initiation of an unlawful *** and Objectives:This study aimed to investigate the visualization of methamphetamine-contaminated fingermarks on glass surfaces and estimate the immediacy of their *** and Methods:In this study,the prior-deposition contaminated fingermarks,i.e.,fingermarks deposited a surface priorly contaminated by methamphetamine,and the postdeposition contaminated fingermarks,i.e.,fingermarks deposited on a clean surface but subsequently contaminated with methamphetamine were visualized and compared using Field Emission Scanning Electron Microscope(FESEM).Results:Under FESEM,the latent fingermarks and the crystalline structure of methamphetamine were clearly *** postdeposition contaminated fingermarks appeared in smudge conditions in all the three replicate samples,where the ridge and nonridge areas could not be *** the contrary,the prior-deposition contaminated fingermark demonstrated distinct separations between ridges and ***,the application of fingerprint powders reduced the possibility to determine the immediacy of ***:To conclude,both prior-deposition contaminated fingermarks and postdeposition contaminated fingermarks can be discriminated,providing information on the instance when a fingermark was left on a surface.

关键词： Fingermark methamphetamine scanning electron microscope visualization powder

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Individual Health Profiling Using Multimodal Sensors on Smart Health Care 17

Individual Health Profiling Using Multimodal Sensors on Smar...

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17th International Conference on Telecommunication Systems, Services, and Applications, TSSA 2023

作者： Ikawati, Vidya Saepudin, Didin Susanto, Bambang Munandar, Ahmad Nurba, Hardy Purnama Prihatmanto, Ary Setijadi Doctoral Program of School of Electrical and Informatics Engineering Institute of Technology Bandung Bandung Indonesia Universitas Sangga Buana Bandung Indonesia Universitas Sangga Buana Faculty of Engineering Department of Electrical Engineering Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798350309164

Health Service, also referred to as Health Care System, plays a pivotal role in maintaining and enhancing health by preventing illnesses and injuries. In this context, electronic health records are essential for both inpatients and outpatients, serving as a crucial tool for healthcare providers and policymakers. However, the potential of these records extends beyond individual care to support broader health policy and research initiatives. Personal Smart Health Care, an individual-based digital health technology, bridges the gap between personal health management and medical records, enabling comprehensive healthcare improvements. Smart healthcare, driven by technologies like IoT, mobile applications, cloud computing, big data, 5G, artificial intelligence, and biotechnology, offers a holistic approach to healthcare, emphasizing diagnosis, treatment, disease prevention, risk monitoring, and virtual assistance. An intelligent framework for smart healthcare includes four layers: The instrumentation layer, information system layer, business intelligence layer, and gamification layer, each contributing to enhanced patient outcomes, streamlined processes, and improved patient care. The gamification layer incentivizes healthy behaviors and fosters patient engagement, especially in healthcare scenarios. This comprehensive framework underscores the transformative potential of smart healthcare, where technology-driven solutions can revolutionize the healthcare landscape, improving patient care, health management, and the overall well-being of individuals and populations. © 2023 IEEE.

关键词： Big data

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Seafloor Imaging Maps by Incorporating Side Scan Sonar Technology on Hybrid Autonomous Underwater Glider 6

Seafloor Imaging Maps by Incorporating Side Scan Sonar Techn...

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6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023

作者： Atmaja, Ardian Budi Kusuma Hidayat, Egi Muhammad Idris Riyanto Trilaksono, Bambang Siregar, Simon Habibullah, Natsir School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Institut Teknologi Bandung Telkom University Doctoral Program of Electrical Engineering Department Computer Engineering Faculty of Applied Science Indonesia

ISBN: (纸本)9798350358346

This research aims to construct a two-dimensional image to represent an underwater geometry map with a Side Scan Sonar (SSS) mounted on a Hybrid Autonomous Underwater Glider (HAUG). Building the underwater map has two stages of the process. The first stage is preprocessing which includes Time-varying gains (TVG), slant range correction, and ground range correction. The second is HAUG navigation, the movement of HAUG orientation to the angle and distance of SSS readings and plotting the geometry of the grid map. With these two stages, a two-dimensional grid map will be formed. Meanwhile, the 3D plot utilizes slant range and ground range correction to obtain the surface height of the bottom of the pool. From the experiments conducted to conduct mapping in the pool, two-dimensional and three-dimensional visual forms of the pool floor and pool walls were produced. The results of the estimated measurement of the width of the pool obtained 14.05 meters and the height of the pool 3.199 meters with an SSS beam angle of 58 degrees. © 2023 IEEE.

关键词： Geometry

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Designed harmonic step filter automatic control system to improve power quality and electric efficiency

Designed harmonic step filter automatic control system to im...

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2024 International Conference on Power Electronics and Energy System, ICPEES 2024

作者： Pasaribu, Faisal Irsan Sara, Ira Devi Tarmizi, Tarmizi Nasaruddin, Nasaruddin Doctoral Program School of Engineering Universitas Syiah Kuala Aceh23111 Indonesia Department Electrical and Computer Engineering Universitas Syiah Kuala Aceh23111 Indonesia

Good power quality and high-power efficiency are important aspects of power system management. Harmonic, as an unwanted frequency component in electrical signals, can interfere with power quality and reduce electrical efficiency. A passive harmonic filter is a device used to reduce or filter harmonics in electrical power systems. This research proposes technology Internet of Things with an automatic control system that moves the step filter to solve the harmonic problem, with the aim of improving the quality of power and electrical efficiency. The step filter functions to identify and isolate the harmonic, thus enabling appropriate controls to suppress the impact of the harmonic. The proposed system uses sophisticated control algorithms using sensors that can read current, voltage, frequency, and power factor to adjust the step filter parameters dynamically, following changes in load conditions and harmonizing. Simulation of the control network using the proteus software is subsequently undertaken in further research to design the control device. Using an automatic control set in the control simulation results reduced the need for manual intervention and let the system adjust the step filter automatically in response to changing network conditions. This showed that this method worked to lower harmonic distortion, increases the power factor through the filter used, and make the system more energy efficient overall. The study emphasizes the importance of applying sophisticated control strategies to manage harmonics efficiently, which ultimately paves the way to a cleaner and more reliable power grid. © Published under licence by IOP Publishing Ltd.

关键词： Power quality

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Mobility-Aware Federated Learning-Based Proactive UAVs Placement in Emerging Cellular Networks

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 1305-1318页

作者： Manzoor, Sanaullah Shakir, Muhammad Zeeshan Hasna, Mazen O. Qaraqe, Khalid A. University of the West of Scotland School of Computing Engineering and Physical Sciences PaisleyG72 0LH United Kingdom Qatar University College of Engineering Doha Qatar Texas A and M University Electrical and Computer Engineering Program Doha Qatar

With the vast proliferation of smart mobile devices, there is an ever-increasing demand for higher data rates and seamless connectivity throughout. Current 5th generation and beyond (B5G) cellular networks struggle to eradicate outage zones and ensure seamless connectivity. One promising solution to this problem is the use of unmanned aerial vehicles (UAVs) to assist the traditional ground network and provide connectivity in places where there are no small base stations or faulty ones as a result of some natural disasters such as flooding. In this paper, we propose a novel users' mobility-aware & users' demand-aware federated learning-based proactive UAV placement (MFPUP) framework to assist the existing ground communication network and minimise overall network outages. Our MFPUP framework utilises the federated learning-based mobility prediction model that recommends the potential outage areas to deploy UAVs using user-UAV association techniques such as the optimum association approach (OAP) and the greedy association approach (GAP). In order to validate the performance of the proposed MFPUP scheme we carried out extensive simulations. The proposed LSTM-based mobility model outperforms the DNN model with 92.88% prediction accuracy. Further, our results show that the proposed MFPUP framework associates the optimal number of users to UAVs while also improving 1.25 times users' downlink rates as compared other UAVs placement schemes. © 2023 CCBY.

关键词： Aircraft accidents

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Label Space-Induced Pseudo Label Refinement for Multi-Source Black-Box Domain Adaptation

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IEEE Transactions on Image Processing 2025年 34卷 3181-3193页

作者： Yoo, Chaehwa Liu, Xiaofeng Xing, Fangxu Woo, Jonghye Kang, Je-Won Chungbuk National University School of Electrical Engineering Cheongju28644 Korea Republic of Massachusetts General Hospital Harvard Medical School Gordon Center for Medical Imaging Department of Radiology BostonMA02114 United States Yale University New HavenCT06520 United States Ewha Womans University Department of Electronic and Electrical Engineering Graduate Program in Smart Factory Seoul03760 Korea Republic of

Conventional unsupervised domain adaptation (UDA) requires access to source data and/or source model parameters, prohibiting its practical application in terms of privacy, security, and intellectual property. Recent black-box UDA (BDA) reduces such constraints by defining a pseudo label from a single encapsulated source application programming interface (API) prediction, which allows for self-training of the target model. Nonetheless, existing methods have limited consideration for multi-source settings, in which multiple source domain APIs are available to generate pseudo labels. In this work, we introduce a novel training framework for multi-source BDA (MSBDA), dubbed Label Space-Induced Pseudo Label Refinement (LPR). Specifically, LPR incorporates a Pseudo label Refinery Network (PRN) that learns the relationship among source domains conditioned by the target domain only utilizing source API’s prediction. The target model is adapted by our dual phases PRN. First, a warm-up phase targets to avoid failure due to noisy samples and provide an initial pseudo-label, which is followed by a label refinement phase with domain relationship exploration. We provide theoretical support for the mechanism of the LPR. Experimental results on four benchmark datasets demonstrate that MSBDA using LPR achieves competitive performance compared to state-of-the-art approaches with different DA settings. © 1992-2012 IEEE.

关键词： Adaptation models Labeling Data models Predictive models Training Closed box Application programming interfaces Upper bound Sports Smoothing methods

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CNN Performances for Stunting Face Image Classification 4

CNN Performances for Stunting Face Image Classification

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4th International Conference on electrical engineering and Computer Science, ICECOS 2024

作者： Yunidar Roslidar Yusni Nasaruddin Fitriarnia Faculty of Engineering Universitas Syiah Kuala Doctoral Program School of Engineering Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Faculty of Engineering Universitas Syiah Kuala Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Faculty of Medicine Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (数字)9798350368253

ISBN: (纸本)9798350368253

Detecting stunting in children is an important issue currently faced by healthcare providers to follow up efforts to reduce the number of stunted children in a region. Emerging research has applied deep learning to many aspects of stunting, including classifying stunting in children through facial images. This study aims to extend previous work on CNN benchmarking in classifying facial images of stunted children and normal children. Here, we trained, validated, and tested five more popular CNN architectures, namely ShuffleNetV2, InceptionV3, MobileNetV2, EfficientNetB0, and VGG19;the research was applied to the same dataset built in previous work. We presented the training and validation loss graphs, and the confusion matrices of all architectures. We measured CNN's accuracy, precision, recall, and F1-measures of the testing stage. Simulation results indicated that three CNN models, namely MobileNetV2, VGG19, and EfficientNetB0 performed comparably or better than previous CNNs in classifying children's facial images. VGG19 and EfficientNetB0 achieved testing accuracies of 100%. © 2024 IEEE.

关键词： Convolutional neural networks

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Algorithmic optimization of quantum optical storage in solids

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Physical Review Research 2024年第3期6卷 033153-033153页

作者： Yisheng Lei Haechan An Zongfeng Li Mahdi Hosseini Department of Electrical and Computer Engineering and Applied Physics Program Elmore Family School of Electrical and Computer Engineering

Quantum memory devices with high storage efficiency and bandwidth are essential elements for future quantum networks. Solid-state quantum memories can provide broadband storage, but they primarily suffer from low storage efficiency. We use passive optimization and algorithmic optimization techniques to demonstrate nearly a sixfold enhancement in quantum memory efficiency. In this regime, we demonstrate coherent and single-photon-level storage with a high signal-to-noise ratio. The optimization technique presented here can be applied to most solid-state quantum memories to significantly improve the storage efficiency without compromising the memory bandwidth.

关键词： Quantum communication

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A Multifunctional Hydrogel with Multimodal Self-Powered Sensing Capability and Stable Direct Current Output for Outdoor Plant Monitoring Systems

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Nano-Micro Letters 2025年第4期17卷 1-24页

作者： Xinge Guo Luwei Wang Zhenyang Jin Chengkuo Lee Department of Electrical&Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117576Singapore Center for Intelligent Sensors and MEMS(CISM) National University of Singapore5 Engineering Drive 1Singapore 117608Singapore NUS Graduate School-Integrative Sciences and Engineering Program(ISEP) National University of SingaporeSingapore 119077Singapore Research Center for Sustainable Urban Farming(SUrF) National University of SingaporeSingapore 117558Singapore

Smart farming with outdoor monitoring systems is critical to address food shortages and sustainability *** systems facilitate informed decisions that enhance efficiency in broader environmental *** outdoor systems equipped with energy harvesters and self-powered sensors often struggle with fluctuating energy sources,low durability under harsh conditions,non-transparent or non-biocompatible materials,and complex ***,a multifunctional hydrogel is developed,which can fulfill all the above requirements and build selfsustainable outdoor monitoring systems solely by *** can serve as a stable energy harvester that continuously generates direct current output with an average power density of 1.9 W m^(-3)for nearly 60 days of operation in normal environments(24℃,60%RH),with an energy density of around 1.36×10^(7)J m^(-3).It also shows good self-recoverability in severe environments(45℃,30%RH)in nearly 40 days of continuous ***,this hydrogel enables noninvasive and self-powered monitoring of leaf relative water content,providing critical data on evaluating plant health,previously obtainable only through invasive or high-power consumption *** potential extends to acting as other self-powered environmental *** multifunctional hydrogel enables self-sustainable outdoor systems with scalable and low-cost production,paving the way for future agriculture.

关键词： Self-powered sensor Hydrogel Energy harvester Outdoor farming Self-sustainable IoT

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