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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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Change of electrical and Transport Properties of Nickel Oxide by Carrier Concentration and Temperature through First‑Principle Calculations

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Nanomanufacturing and Metrology 2023年第4期6卷 109-118页

作者： Nayem Md.Reza Shah Chang‑Dong Yeo Minyeong Choi Yang‑Ki Hong Jeong H.You Department of Mechanical Engineering Texas Tech UniversityLubbockTX 79409USA Department of Electrical and Computer Engineering and Material Science Ph.D.Program The University of AlabamaTuscaloosaAL 35487USA Department of Mechanical Engineering School of EngineeringUniversity of St.ThomasSt.PaulMN 55105USA

Nickel is typically used as one of the main components in electrical contact devices or *** oxide(NiO)is usually formed on the surfaces of electrodes and can negatively impact system performance by introducing electrical contact *** thermal,electrical,and transport properties of NiO,as a Mott insulator or a p-type semiconductor,can be altered by operating and environmental conditions such as temperature and stress/strain by *** this study,we inves-tigate the fundamental material properties of NiO through the first-principle ***,we obtain and compare the lattice parameter,magnetic moment,and electronic structure for NiO via the WIEN2K simulations with four different poten-tials(i.e.,GGA,GGA+U,LSDA,and LSDA+U).Then,using the WIEN2K simulation results with LSDA+U potential that produces a highly accurate bandgap for NiO,we calculate the electrical conductivity and electrical part of the thermal conductivity of nickel and NiO as a function of temperature and carrier concentration through the BoltzTraP *** simulation results revealed that the electrical conductivity relative to the relaxation time for NiO increases with the carrier concentration,while it shows a slightly decreasing trend with temperature under a fixed carrier *** contrast,the electrical part of the thermal conductivity shows an increasing trend considering carrier concentration and temperature.

关键词： Nickel oxide Magnetic moments Density of states Seebeck coefcient electrical conductivity

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Environmental Disinfection Based on Mobile Intelligent Networking Highly Efficient Ultraviolet Light Machines

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Aerosol and Air Quality Research 2024年第2期24卷 1-10页

作者： Chung, Chun-Yen Sung, Wen-Tsai Wang, Lin-Chi Siami, Lailatus Santos, John Paul Computer and Game Development Program Kun Shan University Tainan710303 Taiwan Department of Electrical Engineering National Chin-Yi University of Technology Taichung411030 Taiwan Department of Marine Environmental Engineering National Kaohsiung University of Science and Technology Kaohsiung81157 Taiwan Department of Environmental Engineering Chung Yuan Christian University Taoyuan32023 Taiwan Department of Civil Engineering Chung Yuan Christian University Taoyuan32023 Taiwan

Since 2019, the world has suffered from the outbreak of the "Coronavirus Disease (COVID)-19 pandemic. This study used the principle of Ultraviolet (UV)-C disinfection to produce a mobile high-power UV-C sterilizer. It focused on combining intelligent control systems, human sensors, and Internet of Things (IoT) systems to carry out disinfection tasks in unmanned indoor spaces. Because of its mobile nature, the sterilizer could be moved at any time in various learning, research, and activity rooms for disinfection. In addition to manually controlling the sterilizer, a Wi-Fi or Narrowband (NB)-IoT-enabled remote control was provided to start the disinfection process. In order to prevent ultraviolet light from harming the human body, sensors were employed self-acting to detect the presence of people and turn off the ultraviolet light when people were detected in the area. At the same time, a camera on the sterilizer would capture an image and notify the relevant personnel through a LINE notification, to prevent the human body from being exposed to ultraviolet light, causing harm to the teachers and students on campus. During operation, voice broadcast reminders and a status indicator could provide information about the operation status. After disinfection, users could view the disinfection process through a mobile device application or a webpage, which would automatically notify relevant personnel of the disinfection status through a LINE notification. In this way, the disinfection progress could be updated at anytime, anywhere and the current disinfection status in the classroom could be displayed in real-time, allowing teachers and students on campus to carry out teaching and learning in a safe and secure environment. © The Author(s).

关键词： Internet of things

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Hotel Information System Management Audit Using COBIT 2019 - APO12 2

Hotel Information System Management Audit Using COBIT 2019 -...

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2nd International Conference for Innovation in Technology, INOCON 2023

作者： Utamajaya, Joy Nashar Supangat, Suhono Harso Gaol, Ford Lumban Ranti, Benny BINUS University BINUS Graduate Program School of Information System Jakarta Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatic Bandung Indonesia Bina Nusantara University Computer Science Department BINUS Graduate Program Computer Science Jakarta Indonesia University of Indonesia Computer Science Department Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9798350320923

In general, public or private organizations or companies have used information-based technology as a support to improve business performance to be more effective and efficient in order to achieve a company's business goals. Given the large contribution of information technology in the application of hotel applications as a supporting information system, it is a vital system that must avoid risks that can hinder and cause harm to the hotel management business processes. Therefore, it is necessary to carry out risk management using the COBIT 5 framework to manage possible risks that may occur based on the APO12 (Manage Risk) domain. From the evaluation results of the data obtained through observation and interviews as well as the calculation of the results of the questionnaire based on 6 APO12 subdomains, the results of the risk management level capability assessment in hotel applications are still at level 3 or have reached the level of established process with the target to be achieved at level 4 resulting in a gap of 1 level. Based on the results obtained, it is necessary to propose recommendations that can be used by hotels in improving the application of information technology risk management so that in the future it can achieve the expected target level so that the APO12 level of capability can increase and become more optimal. © 2023 IEEE.

关键词： Risk management

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Prediction of Internet Broadband Connectivity Classification in West Java Indonesia using Machine Learning

Prediction of Internet Broadband Connectivity Classification...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Kusuma Atmaja, Wahyu Haris Heryadi, Yaya Sonata, Ilvico Lukas Suparta, Wayan Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department West Jakarta Indonesia Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Engineering Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350327472

A location's Take-up Rate was significantly influenced by its Internet connectivity and availability. The purpose of this research is to answer concerns about internal Internet Service Provider issues that affect Internet connectivity in clusters of West Java, Indonesia. It also aims to evaluate the results of models that forecast how these internal factors will affect internet connectivity. According to the research findings, the FTTH Cluster has eight significant Internet predictor connectivity out of 18 elements. Activation delay and Administration Documentation of Activation with Connectivity were the two major parameters modeled out of the 18 components. Artificial Neural Network (ANN), Support Vector Machine (SVM), and Decision Tree (DT) models were used to predict Internet Broadband Connectivity Classification based on the two most important variables. According to the study, ANN is the best model, with 99% accuracy and 99.6% precision when compared to SVM and DT. Furthermore, it exceeds previous studies that used the ANN model and achieved 97.92% accuracy. © 2023 IEEE.

关键词： Prediction models

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Revolutionizing Neurological Diagnostics: Integrating 6G Technology with Deep Learning for Enhanced Detection of Multiple Sclerosis and Myelitis

Revolutionizing Neurological Diagnostics: Integrating 6G Tec...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Noureldeen, Rana M. Elshshtawy, Rahma A. Elgohary, Omnia A. Wahba, Fatma Alzahraa A. Attia, Marco K. Abo Elazm, Zeinab B. Khattap, Mohamed G. Galal Eldeen Mohamed Ali Hassan, Hend Mostafa, Mariam Ibrahim, Nayra Ramadan, Reem Refaat, Yomna Khaled, Mennatallah Maher, Ali Abd Elaziz, Mohamed Gamal Abdellatif Ibrahim, Ahmed Faculty of Applied Health Sciences Galala University Suez435611 Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Air Defense College Department of Electronics and Electrical Communications Engineering Alexandria Egypt

ISBN: (纸本)9798350351408

This paper proposes an alternative detection frame-work for multiple sclerosis (MS) and idiopathic acute transverse myelitis (ATM) within the 6G-enabled Internet of Medical Things (IoMT) environment. The developed framework relies on the implementation of a deep learning technique known as Dense Convolutional Networks (DenseNets) in the 6G-enabled IoMT to enhance prediction performance. To validate the performance of DenseNets, we compared it with other deep learning techniques, including Convolutional Neural Networks (CNN) and MobileNet, using real-world datasets. The experimental results show the high performance of DenseNets in predicting MS and ATM compared to other methods, achieving an accuracy of nearly 90 %. © 2024 IEEE.

关键词： Convolutional neural networks

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A CT-based Prediction System for Determining Respiratory Support Level in COVID-19 Patients

A CT-based Prediction System for Determining Respiratory Sup...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Sharafeldeen, Ahmed Balaha, Hossam Magdy Farahat, Ibrahim Shawky Ghazal, Mohammed Connelly, James van Bogaert, Eric El-Baz, Ayman Bioengineering Department J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Department of Computer Science Mansoura University Mansoura Egypt Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi United Arab Emirates Department of Radiology Faculty of Medicine University of Louisville LouisvilleKY United States

ISBN: (纸本)9798350368741

A novel diagnostic system is introduced for assessing the required level of respiratory support for COVID-19 patients. It bases its assessments on the correlation between detected COVID-19 lesions and the respiratory support levels administered to the patients. The correlation with computed tomography (CT) scans will focus on three respiratory support levels, including minimal support (Level 0), non-invasive support (Level 1, such as soft oxygen), and invasive support (Level 3, e.g., mechanical ventilation). The system initially outlines the pulmonary regions from CT scans, then identifies COVID-19 lesions within these segmented lung regions. Subsequently, three second-order texture features are extracted from the identified COVID-19 lesion regions to detect differences and abnormalities across varying severity levels in COVID-19 cases. To obtain the suitable level of respiratory support for each patient, a fusion mechanism based on backpropagation neural network is utilized to integrate the diagnosis of these three features, individually generated by the support vector machine (SVM) classifier. The system's performance is evaluated on 307 COVID-19 patients using a hold-out validation approach. This evaluation included various metrics, such as sensitivity, specificity, F1-score, Cohen's kappa, and accuracy, demonstrating impressive results. Specifically, it achieved a score of 97.25%, 98.56%, 97.26%, 95.79%, and 97.25%, respectively. The results demonstrate the effectiveness of the integrated system, which uses various second-order features, in predicting respiratory support needs for COVID-19 patients, outperforming both its individual components and other machine learning-based classification systems. © 2025 IEEE.

关键词： computer-Aided Diagnosis (CAD) GLCM GLRLM Machine Learning (ML) Neural Network (NN) SFM

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Optimal Resource Allocation for NOMA Wireless Networks

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computers, Materials & Continua 2023年第2期74卷 3249-3261页

作者： Fahad R.Albogamy M.A.Aiyashi Fazirul Hisyam Hashim Imran Khan Bong Jun Choi Turabah University College Computer Sciences ProgramTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Jazan UniversityJazanP.O.Box 218Saudi Arabia Department of Computer and Communication Systems Engineering Faculty of EngineeringUniversiti Putra Malaysia(UPM)43400SerdangMalaysia Department of Electrical Engineering University of Engineering and Technology PeshawarP.O.Box 814Pakistan School of Computer Science and Engineering Soongsil UniversitySeoulKorea

The non-orthogonal multiple access(NOMA)method is a novel multiple access technique that aims to increase spectral efficiency(SE)and accommodate enormous user ***-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information,and multi-user detection algorithms,such as successive interference cancellation(SIC),are performed at the receiving end to demodulate the necessary user *** its basic signal waveform,like LTE baseline,could be based on orthogonal frequency division multiple access(OFDMA)or discrete Fourier transform(DFT)-spread OFDM,NOMA superimposes numerous users in the power *** contrast to the orthogonal transmission method,the nonorthogonal method can achieve higher spectrum ***,it will increase the complexity of its *** power allocation techniques will have a direct impact on the system’s *** a result,in order to boost the system capacity,an efficient power allocation mechanism must be *** research developed an efficient technique based on conjugate gradient to solve the problem of downlink power *** major goal is to maximize the users’maximum weighted sum *** suggested algorithm’s most notable feature is that it converges to the global optimal *** compared to existing methods,simulation results reveal that the suggested technique has a better power allocation capability.

关键词： Non-orthogonalmultiple access wireless networks power domain 5G networks

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Effect of electrolyte anions on the cycle life of a polymer electrode in aqueous batteries

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escience 2022年第1期2卷 110-115页

作者： Ye Zhang Lihong Zhao Yanliang Liang Xiaojun Wang Yan Yao Department of Electrical and Computer Engineering Materials Science and Engineering ProgramUniversity of HoustonHoustonTX77204USA Texas Center for Superconductivity at the University of Houston HoustonTX77204USA

Redox polymers are a class of high-capacity, low-cost electrode materials for electrochemical energy storage, butthe mechanisms governing their cycling stability are not well understood. Here we investigate the effect of anionson the longevity of a p-dopable polymer through comparing two aqueous zinc-based electrolytes. Galvanostaticcycling studies reveal the polymer has better capacity retention in the presence of triflate anions than that withsulfate anions. Based on electrode microstructural analysis and evolution profiles of the cell stacking pressure, theorigin of capacity decay is ascribed to mechanical fractures induced by volume change of the polymer activematerials during repeated cycling. The volume change of the polymer with the triflate anion is 61% less than thatwith the sulfate anion, resulting in fewer cracks in the electrodes. The difference is related to the different anionsolvation structures—the triflate anion has fewer solvated water molecules compared with the sulfate anion,leading to smaller volume expansion. This work highlights that anions with low solvation degree are preferablefor long-term cycling.

关键词： Aqueous batteries Polymer electrode Anions Volume chang

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Performance Evaluation of Coffee Bean Binary Classification Through Deep Learning Techniques 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Islamy, Fajrul Muchtar, Kahlil Arnia, Fitri Dawood, Rahmad Febriana, Alifya Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Master of Computer Science Program BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Coffee beans are one of the high-value commodities in Indonesia, but the sorting method for the quality of coffee beans still uses visual methods and sieves with mechanical machines. This study aims to provide an alternative to classifying coffee beans using Deep Learning algorithms, such as the ResNet-18 and MobileNetV2. The dataset used is USK-COFFEE which consists of two classes, the normal coffee bean class, and the defect coffee bean class. The models were trained using the balanced and imbalanced datasets, meaning that two scenarios were performed. From the results of this study, the accuracy value for the ResNet-18 model was 91.50% when trained on the balanced dataset and 90.87% on the imbalanced dataset. It outperformed the MobileNetV2 architecture, which only achieved 0.8125 and 0.8112 accuracy scores when trained on the balanced and imbalanced datasets, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： computer vision

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