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1st International Conference on Emerging Smart Cities, ICESC 2022

作者： Hidayat, Dina P. A. Legowo, W. D. Sri Farid, Mohammad Doctoral Program of Civil Engineering Institut Teknologi Bandung Bandung Indonesia Water Resources Engineering Research Group Faculty of Civil and Environmental Engineering Institut Teknologi Bandung Bandung Indonesia Civil Engineering Department Universitas Trisakti Jakarta Indonesia

ISBN: (纸本)9789819911103

Rainfall-runoff model is one way to model the amount of rainfall that will become runoff and baseflow. Many models have been developed such as the Mock model which is quite simple but has a fairly good level of accuracy. This study aims to model rainfall runoff with the Mock formula and calibrate the model with measured discharge at one discharge station. This study uses the Cisadane watershed in West Java Indonesia for the location study and Serpong station as the discharge station. The modeling will be carried out with 2 scenarios, a monthly rainfall-runoff model in 1 year and the average monthly rainfall-runoff model for several years. The calibration parameter used is Nash Sutcliffe Efficiency (NSE) and coefficient of determination (R2). From the model results, it is obtained that the model calibration results with a fairly good measured discharge with a satisfactory NSE value and R2 is close to 1. © 2024, Institute of Technology PETRONAS Sdn Bhd.

关键词： Rain

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Leveraging Heart Rate Variability and Machine Learning for Daily Activity Classification in Healthy Adult Males 18

Leveraging Heart Rate Variability and Machine Learning for D...

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18th International Conference on Telecommunication Systems, Services, and Applications, TSSA 2024

作者： Ikawati, Vidya Yusuf, Rahadian Prihatmanto, Ary Setijadi Sudrajat, Tatang Saepudin, Didin Surjono, Welly Doctoral Program of School of Electrical and Information Engineering Institute of Technology Bandung Bandung Indonesia Institute of Technology Bandung Departement of Electrical and Information Engineering Bandung Indonesia Universitas Sangga Buana Bandung Indonesia

ISBN: (纸本)9798331527686

Advancements in machine learning techniques for analyzing heart rate variability (HRV) show great potential in accurately measuring various activities in the field of health monitoring. This work examines the feasibility of using non-invasive HRV data gathered during everyday activities to train machine learning models for accurate activity classification. In the field of personalization, the study focuses on the difficulty of adapting to individual differences in baseline HRV. It also acknowledges that HRV is influenced by stress, physical activity, and external influences, and that it is a dynamic measure. The experimental methodology consists of a pilot study using a retrospective observational cohort approach. It utilizes a waistband device to continuously record heart rate variability (HRV) during different daily activities. The study utilizes a thorough methodology that includes statistical data description, correlation analysis, variance analysis, outlier detection, and identification of cycle periods using power spectral density. The performance of various machine learning models, such as Random Forest, Naïve Bayes, Decision Tree, and Logistic Regression, was evaluated for activity classification. The Decision Tree model exhibited superior performance. The work provides useful insights into the potential of machine learning for HRV-based activity monitoring, highlighting the importance of collecting reliable data and developing tailored models to improve accuracy and practicality. © 2024 IEEE.

关键词： Heart

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Comparison of Feature Selection Methods on Medical Record Data

Comparison of Feature Selection Methods on Medical Record Da...

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2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Astuti, Lastri Widya Ermatita Rini, Dian Palupi Informatic Engineering Doctoral Program Sriwijaya University Palembang Indonesia Sriwijaya University Information System Palembang Indonesia Sriwijaya University Informatic Engineering Palembang Indonesia

ISBN: (纸本)9798350369359

Medical record data are an essential part of medicine, but the effective use of data has become difficult due to the rapid increase in data. Determining the selected features allows researchers to extract the information from medical data. Feature selection in classification provides the advantage of reduced time and storage. In addition, feature selection methods can improve performance by removing redundant or irrelevant data based on their weights. The benefits of feature selection are that it speeds up the training time, simplifies the model, makes interpretation easier, and improves generalization by reducing overfitting. Feature selection in this research uses two methods: Binary Whale Optimization (BWOA), Particle Swarm Optimization, and Hybrid with Gray Wolf Optimization (PSOGWO). Two feature selection methods were compared to determine the most optimal method for the classification process. A comparison of the two feature selection methods shows that the classification process with many attributes produces an optimal classification process, where the number of representative variables in the breast cancer dataset in the BWOA method has five features, and the PSOGWO method has 11 attributes. Meanwhile, the highest accuracy results in breast cancer classification using the BWOA average of 95.90% and PSOGWO Method average of 97.64% © 2023 IEEE.

关键词： Feature Selection

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A Proposal of the Improved Method for Measuring Insulation Resistance between a Photovoltaic Module String and the Earth using the Self-biased Method

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IEEJ Transactions on Power and Energy 2025年第1期145卷 13-20页

作者： Kazama, Takuro Kato, Kazuhiko Doctoral Program in Risk and Resilience Engineering University of Tsukuba 1-1-1 Tennodai Tsukuba305-8573 Japan Renewable Energy Research Center National Institute of Advanced Industrial Science and Technology 2-2-9 Machiikedai Koriyama963-0298 Japan

For safe operation of a photovoltaic generating system, an insulation monitoring function, which can immediately detect deterioration of insulation between photovoltaic module strings and the earth, is indispensable. This function requires accurate measurement, low leakage current and suppression of rising voltage against the earth. In this paper, a new insulation monitoring method of photovoltaic module strings is proposed by the improved existing self-biased method that has an inevitable voltage rise under measurement. In this new method, both terminals of photovoltaic module strings are always connected to the ground with appropriate resistors. Theoretical discussion and experiments for this new method showed precise measurement, and acceptable leakage current under various outdoor conditions. In addition, it was confirmed that this method could sufficiently decrease in the voltage rise compared with the existing self-biased method. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Photovoltaics

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Optimizing Indoor Vertical Farming with DDPG Reinforcement Learning: Enhancing Control of Nutrient Levels, Climate Conditions, and Plant Health 5

Optimizing Indoor Vertical Farming with DDPG Reinforcement L...

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5th IEEE International Conference on Technology, Informatics, Management, engineering and Environment, TIME-E 2024

作者： Albert Panggabean, Good Fried Romadhon, Rahmat Lubis, Ramot Joelianto, Endra Institut Teknologi Bandung Doctoral Program in Engineering Physics Bandung Indonesia Institut Teknologi Del Faculty of Informatics and Electrical Engineering Toba Indonesia Institut Teknologi Bandung Engineering Physics Study Program Bandung Indonesia Institut Teknologi Bandung Instrumentation Control and Automation Research Group Bandung Indonesia

ISBN: (纸本)9798350364743

In indoor vertical farming systems, managing dynamic and complex environments poses significant challenges. Traditional control strategies often fall short in adapting to rapid changes in critical parameters such as nutrient levels, climate conditions, and plant health indicators. To address these challenges, this paper presents a novel approach for optimizing indoor vertical farming using Deep Deterministic Policy Gradient (DDPG), a reinforcement learning algorithm well-suited for environments with continuous action spaces. Our study focused on the control and monitoring of key parameters, including nutrient levels (pH and EC), climate conditions (temperature, humidity, light, and CO2 levels), and plant health indicators (biomass accumulation and leaf size structure). By defining a comprehensive state and action space, we simulated an indoor farming environment and trained the DDPG model to maximize crop yield and health. © 2024 IEEE.

关键词： DDPG indoor vertical farming precision agriculture reinforcement learning smart farming

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Performance Evaluation of Coverage Prediction in 4G Networks Using Machine Learning Classification Algorithm 10

Performance Evaluation of Coverage Prediction in 4G Networks...

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10th International Conference on Wireless and Telematics, ICWT 2024

作者： Yuliana, Hajiar Iskandar Hendrawan Ridwan, Azwar Mudzakkir Charisma, Atik Somantri, Nivika Tiffany Universitas Jenderal Achmad Yani Faculty of Engineering Department of Electrical Engineering Indonesia Institut Teknologi Bandung Doctoral Program of School of Elecrtical Engineering and Informatics Indonesia Bandung Indonesia UIN Sunan Gunung Djati Bandung Department of Electrical Engineering Indonesia

ISBN: (纸本)9798350377057

Network coverage prediction is an important aspect of 4G network planning and optimization. In this study, we conducted a comprehensive analysis of the performance of various machine learning algorithms in predicting 4G network coverage. The data collected includes information on location, signal intensity, topography, and other important variables. Data pre-processing is performed to clean, normalize, and select relevant features. Next, we compared the performance of several machine learning algorithms, including Logistic Regression, KNN, Naïve Bayes, Decision Tree, SVM, XGBoost, AdaBoost, and Bayesian Network Classifier. The model training process is performed using subsets of training, validation, and testing data, with evaluation performed using various evaluation metrics such as accuracy, precision, recall, F1-score, RMSE, MAE, and R-squared. The experimental results show that Random Forest stands out with excellent performance, while Naïve Bayes tends to produce lower performance. The conclusion of this study provides valuable insights into the potential applications of machine learning algorithms in predicting 4G network coverage with accuracy and reliability. © 2024 IEEE.

关键词： Adversarial machine learning

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Parallel Computing Approach for Preprocessing AIS Data in Transshipment Feature Extraction

Parallel Computing Approach for Preprocessing AIS Data in Tr...

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2023 International Conference on Smart-Green Technology in Electrical and Information Systems, ICSGTEIS 2023

作者： Setiawan, Widyadi Linawati, Linawati Made Oka Widyantara, I. Made Wiharta, Dewa Mertasana, Putu Arya Andriati Asri, Sri Udayana University Doctoral Program of Engineering Science Denpasar Indonesia Udayana University Electrical Engineering Departement Denpasar Indonesia Politeknik Negeri Bali Electrical Engineering Departement Denpasar Indonesia

ISBN: (纸本)9798350382822

AIS (Automatic Identification System) data is an important data source in the shipping and logistics industry to track and monitor ships in real-time. Extraction of transshipment features from AIS data is essential in understanding the pattern and characteristics of transshipment activity at a particular port or place. This study discusses an efficient AIS data preprocessing framework to prepare the transshipment feature extraction. The preprocessing steps include data collection, data cleansing, trajectory extraction, trajectory cleaning, and overlapping trajectory extraction. One of the crucial approaches used in this study is the application of parallel computing. Parallel computing allows us to execute independent data processing tasks at the same time simultaneously. In AIS data preprocessing, the parallel approach allows us to clean, populate, and process data from many vessels in less time. To test the applicability, we created a final comparison for different types of ships based on its AIS data for Indonesia from November 2022 to April 2023. Using parallel computing to process AIS data makes data processing more efficient, especially for large and complex AIS data. This efficiency accelerates data preparation before transshipment feature extraction. The results show that the framework can accelerate runtime and improve trajectory quality. © 2023 IEEE.

关键词： Feature extraction

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Adaptive Strategies and Handover Optimization in 5G Networks to Address High Mobility Challenges

Adaptive Strategies and Handover Optimization in 5G Networks...

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2024 International Conference on Electrical engineering and Informatics, ICELTICs 2024

作者： Murhaban Syahrial Arif, Teuku Yuliar Arnia, Fitri Doctoral Program School of Engineering Universitas Syiah Kuala Universitas Teuku Umar Departement Information Technology Engineering Banda Aceh Aceh Barat Indonesia Universitas Syiah Kuala Department of Electrical and Computer Engineering Banda Aceh Indonesia

ISBN: (纸本)9798350366822

This study develops an adaptive handover strategy in 5G networks to tackle high mobility challenges and improve service continuity and quality. The research method uses a 5G network emulator with a topology of four base stations (gNBs) to test user equipment (UE) speeds of 20 km/h and 130 km/h. The adaptive algorithm developed utilizes data from the Received Signal Strength Indicator (RSSI) as well as the speed and direction of user mobility to make optimal handover decisions. The results show that at a speed of 130 km/h, the average handover latency reaches 12 ms, compared to 8 ms at 80 km/h. Additionally, the signal quality (RSSI) is lower at higher speeds, negatively affecting service quality. This adaptive algorithm successfully reduces handover latency and improves the stability and efficiency of the overall network. The testing also indicates that this adaptive algorithm can predict handover targets by considering UE speed and direction and uses hysteresis mechanisms to prevent overly frequent handovers. By employing technologies such as Multiple Input Multiple Output (MIMO) and beamforming, this strategy is expected to maintain high service quality even under high mobility conditions. © 2024 IEEE.

关键词： 5G mobile communication systems

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VidyaMedic: Smart Alert System For Continous Vital Sign Monitoring

VidyaMedic: Smart Alert System For Continous Vital Sign Moni...

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2023 International Conference on Electrical engineering and Informatics, ICEEI 2023

作者： Ikawati, Vidya Prihatmanto, Ary Setijadi School of Electrical Engineering and Informatics Bandung Institute of Technology Doctoral Program of Electrical Engineering and Informatics Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798350381290

The proposed smart system namely VidyaMedic utilizes continuous heart rate monitoring through sensors, with data transmission to the cloud via an IoT platform. Testing was conducted on a 40-year-old male participant who wore the Polar H10 heart rate monitor for 24 days. The heart rate profile captures the circadian rhythm, the natural internal biological clock governing cardiovascular and behavioral processes over a 24-hour cycle, influencing morning, daytime, and nighttime activities. Health conditions can be assessed using an algorithm which calculates the running sum of standard deviation for heart rate data over this period. Initially, standard deviation is computed for each data point, quantifying data dispersion from the mean. Subsequently, the running sum of standard deviation enables the detection of significant changes over time, abrupt fluctuations indicate outliers or extreme values, potentially impacting overall standard deviation and signaling significant data changes. Deviations from the expected or previous standard deviation values, marked by a regression coefficient less than or greater than 1, may suggest cardiac abnormalities or heart rhythm disturbances. © 2023 IEEE.

关键词： Heart

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A Real-time Affordance-based Object Pose Estimation Approach for Robotic Grasp Pose Estimation

A Real-time Affordance-based Object Pose Estimation Approach...

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2023 International Conference on System Science and engineering, ICSSE 2023

作者： Wong, Shang-Wen Chiu, Yu-Chen Tsai, Chi-Yi Tamkang University Doctoral Program in Robotics College of Engineering New Taipei City Taiwan Tamkang University Department of Electrical and Computer Engineering New Taipei City Taiwan

ISBN: (纸本)9798350322941

This paper proposes a pose estimation system for robot grasping based on a novel Object Affordance Detection and Segmentation (OADS) network. The proposed system consists of four modules: (1) OADS network;(2) point cloud extraction;(3) object pose estimation;(4) grasp pose estimation. Based on the OADS network, the proposed system achieves affordance-based object pose estimation results. The proposed grasp pose estimation system is evaluated on a laboratory-made dual-arm robot. Experimental results show that the proposed system achieves high detection rate and high accuracy in affordance detection and segmentation tasks, leading to a high success rate in object grasping tasks with lab-made dual-arm robot. © 2023 IEEE.

关键词： Object detection

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