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2nd International Conference on engineering and Information Technology for Sustainable Industry, ICONETSI 2022

作者： Norman, Ferly Hadiwijaya, Syarif Gunawan, Fergyanto E. Master Industrial Engineering Binus Graduate Program Bina Nusantara University Indonesia

ISBN: (纸本)9781450397186

The 2019 election is the most expensive, complicated, and the worst of the 12 elections in Indonesian history. There are 894 election officers' death due to overworking load and it cost the nation 25 trillion Rupiah or about 1.8 billion USD, involved 16 parties, 192.2 million voters, 7.6 million officers, and 810,329 polling stations spreading in 83,405 villages throughout Indonesia. The election was held simultaneously for selecting legislative members and the president, utilized five ballots, and was held one day, including for polling, and counting, making the 2019 elections the most complicated in the world. The time for processing five ballots for voting took about 5-7 minutes on average and another five minutes for counting. Moreover, the ballot tabulation process from polling stations to the national level was manual and took 36 calendar days, adding complexity to the post-election process. This work aims to propose an approach to improve the efficiency and accountability of the voting and tabulation process. Propose to use a single ballot like a computer answer sheet, marked with a regular marker, optical scanned, and finally processed by Digital Mark Reader (DMR) software. The proposal can save 280.7 million square meters of paper use nationally, shorten 80% of voting time to no more than one minute, increase pooling station capacity by 930%, and 8 working hours for officers in. And in the Kapuk-Jakarta Administration Area alone, it can save a budget of Rp 3.4 billion and 38,556 working hours. © 2022 ACM.

关键词： Budget control

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Prediction of Powerplant Performance by Machine Learning Technique 8

Prediction of Powerplant Performance by Machine Learning Tec...

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8th IEEE International Conference on Recent Advances and Innovations in engineering, ICRAIE 2023

作者： Gunawan, Fergyanto E. Wulandari, Rahmanda Wahyuningtyas, Dian Asrol, Mohammad Bina Nusantara University BINUS Graduate Program-Master of Industrial Engineering Industrial Engineering Department Jakarta11480 Indonesia Bina Nusantara University BINUS-ASO School of Engineering Business Engineering Program Industrial Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9798350315516

The IEEE Standard 762-1987 introduced a comprehensive set of 25 indices for reporting power plant reliability, availability, and productivity. The sheer number of indices can be impractical, and an univariate indicator is preferable for real-world applications. Developing a single indicator derived from the 25 indices is the work's main theme. This study addresses this issue using machine learning to consolidate these indices, ultimately classifying power plant performance into "performed"and "unperformed."Monthly performance data from 2020 were collected from 570 power plants in the Java-Sumatra interconnected electric system. These data were obtained from the Generation Availability Information System, a proprietary application owned by the Indonesia National Electric Power Company. Using the k-means clustering method, we classified the performance of these power plants. The clustering process was supplemented with the opinions of seven experts with extensive experience in the system. The comparison between machine learning classifications and expert opinions is presented. © 2023 IEEE.

关键词： IEEE Standards

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Identification of User's Interests by Deep-Learning Based Pose Estimation with Angle Relationship from Keypoints for Smart Advertising Displays

Identification of User's Interests by Deep-Learning Based Po...

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Lee, Yu-En Fan, Chih-Peng National Chung Hsing University Master Program in Semiconductor and Green Technology Taichung Taiwan National Chung Hsing University Department of Electrical Engineering Taichung Taiwan

ISBN: (纸本)9798331530839

In this paper, a deep-learning based pose estimation technology for recognizing user's interests on interactive advertising displays is developed by leveraging the YOLO-Pose architecture. The primary goal is to predict user's attention areas by analyzing angular relationships with human body poses. The methodology uses the YOLO-Pose model to identify 17 keypoints on the human body and the angles among the selected body keypoints are calculated. The proposed design incorporates the left and right hand identification and the height determination to address accuracy issues from variations in user's height or body size. By integrating the estimation data for angles, user's height, and handedness, the horizontal and vertical SVM classifiers will be pre-trained. After the SVM classifiers are well trained, the combination results from both SVM classifiers enables the prediction of the user's area of interests. Experimental results with four, six, and eight grid divisions on the display reveals that the average identifying accuracies for horizontal and vertical grid classification can be up to 94% and 83%, respectively. By height adjusted information, the horizontal and vertical classification accuracies of the SVM classifiers can improve by 11% and 20% respectively within corresponding height ranges. For the embedded device implementation, the YOLO model and SVM classifier are deployed on the Jetson AGX Orin platform to achieve an average processing speed of 15 frames per second. © 2024 IEEE.

关键词： Marketing

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Trajectory detection in furrows to automate the navigation of Agrobot with AI

Trajectory detection in furrows to automate the navigation o...

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2023 IEEE International Humanitarian Technology Conference, IHTC 2023

作者： Torres Castillo, Christian Camilo Alberto Chaparro, Javier College Program in Electronic Engineering Escuela Colombiana de Ingeniería Julio Garavito Bogotá Colombia Master's Program in Electronic Engineering Escuela Colombiana de Ingeniería Julio Garavito Bogotá Colombia

ISBN: (纸本)9798350314311

Robotics is a technology that is used in various fields;one of these in agriculture, where the robots are known as agrobots. Bearing in mind that in countries like Colombia, the cultivated lands are irregular, most of the time in mountains, and the furrows (which follow paths with very irregular shapes), it is very difficult to use automated machinery to support the cultivation work. Therefore, it is very difficult to use ground or aerial robots to support tasks such as planting, eliminating weeds, detecting diseases, the lack of fertilizers or the presence of water stress. In developed countries, where crops are grown using straight furrows and flat terrain, it is easy to have assistant robots for agricultural work, but in Colombia, where geography imposes irregular terrain, it is not easy and therefore growers cannot have technologies that support their daily work. In order to address this problem, and facilitate the use of agrobots in different crops, mainly potatoes and vegetables, research has been carried out at the Colombian School of engineering Julio Garavito to propose solutions that give robots autonomy on the ground and air for exploration of crops. One of these works is related to the real-time processing of images, captured by an RGB camera placed on the front of a rover, and processed with deep learning techniques, to establish the correctness of the route that a robot must follow when running furrows in potato crops. Initially, a previous image processing is done to deliver a binary image with the shape of the furrows to a neural network. Said network analyzes the image and determines the correction angle that the robot's direction must take. For this training, a dataset of 1,000 images per class was created, taken from simulated furrows with artificial plants. The classes used correspond to a discrete scale at intervals of 10 degrees. The results show a degree of accuracy greater than 90% in the automatic detection of the angle that the robot must c

关键词： Crops

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Enabling Smart Home Energy Management through Gesture-Based Control and IoT Technology

Enabling Smart Home Energy Management through Gesture-Based ...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Rambe, Khairul Hawani Ou, Chia-Ho International Master Program in Information Technology and Applications Sweden National Pingtung University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324174

The Internet of Things (IoTs) have become ubiquitous in all aspects of public needs today. The application of IoT technology is crucial for promoting energy-saving behavior. This paper presents a control system used for smart home devices based on hand gesture control and Android applications. The hardware design uses the ESP32 microcontroller, which has an in-chip Wi-Fi module, making it very suitable for creating IoT application systems. The software was designed using the Arduino IDE, and the application display design was developed using Kodular Creator, which is used to control the lamps and switches. The experiment results show that the electrical equipment can be activated using the Android application with gesture sensors by waving down, up, left, and right. © 2023 IEEE.

关键词： Internet of things

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RUL Estimation for Power Electronic Devices Using RNNs

RUL Estimation for Power Electronic Devices Using RNNs

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2024 Prognostics and System Health Management Conference, PHM 2024

作者： Cai, Congrui Lu, Zhonghai School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Embedded Systems Master Program Sweden School of Electrical Engineering and Computer Science Department of Electrical Engineering KTH Royal Institute of Technology Sweden

ISBN: (纸本)9798350360585

Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms are better suited to model time-series data. However, the impact of RNN complexity on estimation accuracy is rarely discussed in the literature. This issue is important because choosing a lower-complexity model that delivers the same or similar performance as a higher-complexity model can increase implementation efficiency. In the paper, we use three RNN models, namely, the vanilla version, LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) to conduct RUL estimation for power electronic devices. We use two accelerated aging datasets, one dataset targeting the package failure of MOSFETs, and the other dataset targeting package failure of power diodes. Our study shows that a lower-complexity RNN does not necessarily deliver a lower performance. Similarly, a higher-complexity model does not assure a higher performance. As such, our work highlights the importance of selecting a proper neural network for RUL estimation not biased towards complex models. This is especially useful and important for implementing such RUL estimation techniques in embedded resource-constrained and speed-limited computins platforms. © 2024 IEEE.

关键词： Long short-term memory

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Usability evaluation of high-fidelity simulation manikin for cardiopulmonary resuscitation training for medical students

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Research on Biomedical engineering 2024年第1期40卷 253-264页

作者： Silva, Lucas de Brito Gonçalves, Fabrícia Torres Arantes, Henrique Pierotti Garcia, Rodrigo Alves Gasparin, Maria Regina Franco Ribeiro dos Reis, Melissa Mariane Andrade, Adriano de Oliveira Pereira, Adriano Alves Milagre, Selma Terezinha Santos, Fernando Pasquini Faculty of Electrical Engineering Graduate Program in Biomedical Engineering Federal University of Uberlândia Campus Santa Mônica Bloco 1E MG Uberlândia38400-902 Brazil Instituto Master de Ensino Presidente Antônio Carlos MG Araguari Brazil

Purpose: With the increasing incidence of cardiac arrests in non-critical areas, it is crucial to provide comprehensive training to healthcare professionals. Simulation-based training using manikins offers a morally sound alternative to traditional methods and allows students to apply clinical skills in realistic scenarios. Usability evaluation, including assessment of efficiency, effectiveness, and user satisfaction, is essential for ensuring the effectiveness of the training. This research focuses on evaluating the usability of the simulation manikin for teaching cardiopulmonary resuscitation to medical students. Methods: Twenty-one volunteers participated in the experiments: 18 students and three teaching doctors (one instructor professor and two evaluator professors). The instructor professor was instructed to follow the same procedure used in the classroom setting. The evaluating professors were provided with appropriately labeled data collection instruments to evaluate the usability of manikins. The student performed the tasks as instructed, and after completion, they were directed to the debriefing room, where they filled out the feedback form. Results: The experimental data and the statistical analyses showed that regarding effectiveness, the study findings indicated that participants encountered difficulties in certain tasks, as evidenced by the need for significant intervention from the instructor professor. In relation to efficiency, the results demonstrated a clear correlation between the successful execution of tasks and the duration of instruction provided by the instructor, thereby indicating the presence of a usability problem associated with the simulation manikin. And users are not entirely satisfied with the manikin, considering the room for improvement indicated by the SUS score. Conclusion: The experiment and the results reported in this paper contribute to addressing the existing gap in the literature concerning the usability of high-fidelity s

关键词： Efficiency

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Style Transfer of Musical Performance Expression Using Note Classification Based on the Implication-Realization Model 23

Style Transfer of Musical Performance Expression Using Note ...

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7th International Conference on Computer Science and Application engineering, CSAE 2023

作者： Mizutani, Tetsuya Sasaki, Shigefumi Department of Information Engineering University of Tsukuba Tsukuba Japan Master's Program in Computer Science University of Tsukuba Tsukuba Japan

ISBN: (纸本)9798400700590

In recent years, the style transfer is being studied in the field of AI. It extracts a style from one data set and applies to another data set. This technique is actively studied mainly in computer vision. It is also being studied in music and speech processing with many deep learning methods. However, no research focused on changes in performance expression of music. Performance expression of music refers to the player's artistic interpretation of a piece of music, the changes in performance, and the techniques used to achieve these changes. Changes in performance are represented by slow and fast beats, changes in strength and weakness. In this study, as a part of the study of analysis and creation of performance expression models of music, the image style transfer method is applied to musical expression. In addition, in order to analyze the relationship between musical structure and musical expression, the implication-realization model of music theory is used to extract the structure of music to improve the performance of style transfer. The analysis result shows that the I-R model captures the feature of musical expression well, and moreover, the style transfer method is shown to be useful for analyzing performance expression of music. It will lead to the study of music performance expression style extraction and will contribute to the analysis and creation of performance expression models of music. © 2023 ACM.

关键词： Music

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Loss Balancing for Physics-Informed Neural Networks Considering Procedure for Solving Partial Differential Equations 13

Loss Balancing for Physics-Informed Neural Networks Consider...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Kojima, Manabu Otsuki, Kazuma Suzuki, Masato Abe, Masajiro Baba, Masaaki Yokota, Kazuya Master's Program in Mechanical Engineering Nagaoka University of Technology Nagaoka Japan Nagaoka University of Technology Department of System Safety Engineering Nagaoka Japan Nagaoka University of Technology Department of Mechanical Engineering Nagaoka Japan

ISBN: (纸本)9798350373332

With the development of machine learning, numerical analysis using neural networks has been devised. Remarkably, physics-informed neural networks (PINNs) enable physically consistent numerical analysis by introducing a loss function that conforms to relevant governing equations. Moreover, research is rapidly advancing for the application of PINNs to inverse analysis in areas such as physical property identification. For a PINN to learn governing equations, the weights of the loss functions should be properly determined because they have different roles in components such as boundary conditions, initial conditions, and governing equations. However, weight setting is generally challenging. We propose a loss balancing method considering the procedure for solving partial differential equations. The proposed method is validated through comparisons with conventional solvers. © 2024 IEEE.

关键词： Partial differential equations

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Development of Detection Lung Disease Stand Alone System Based on Convolutional Neural Network (CNN) 7

Development of Detection Lung Disease Stand Alone System Bas...

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7th International Conference of Computer and Informatics engineering, IC2IE 2024

作者： Liewarnata, Edgar Caesar Mariana, Yosica Tan, Sofyan Liawatimena, Suryadiputra Astuti, Winda Bina Nusantara University Binus Aso School of Engineering Automotive and Robotics Program Computer Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Aso School of Engineering Product Design Program Industrial Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Automotive and Robotics Program Computer Engineering Department Binus Aso School of Engineering Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331505530

Lung disease, especially Tuberculosis (TBC), placed the highest death rate in Indonesia. Tuberculosis (TB) in Indonesia is ranked second after India. Therefore, it is important to reduce or early detection of the lung disease, to prevent this disease and speed up handling. The system can recognize the disease lung identification, and the system applied as standalone system. In this work, the Convolutional Neural Network (CNN) approach for identifying diseases lung identification is proposed. The Mel Frequency Cepstral Coefficient (MFCC) applied to process the stethoscope sounds which will used as input to the CNN. The performance of the proposed system has been investigated and resulted. The accuracy of 99% and 98%, for training and testing accuracy respectively. Furthermore, the system accurately detects lung diseases identification. © 2024 IEEE.

关键词： Lung cancer

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