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6th International Conference on Technology and Vocational Teachers, ICTVT 2020

作者： Wati, M.S. Djatmiko, I.W. Master Education Program of Electrical Engineering Faculty of Engineering Universitas Negeri Yogyakarta Indonesia

The study aimed to find out the design of instructional media which is suitable for practical learning of electric motor installation. It used needs analysis and front-end analysis which was carried out at Vocational High Schools in Yogyakarta. The result showed that the developing two-or three-dimensional media in the form of virtual laboratories was strongly recommended. Virtual laboratories was able to visualize the components according to real conditions. The concepts and work principles of electric motor installations were showed in the form of a two-or three-dimensional simulation. Virtual laboratories was expected to help virtual learning and train students' independence and competence in practicing electric motor installation. © Published under licence by IOP Publishing Ltd.

关键词： Laboratories

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A Railway Inspection System Based On A Semantic Segmentation Network And Image Processing

A Railway Inspection System Based On A Semantic Segmentation...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Ming-An Chung Chen-You Gao Department of Electronic Engineering Master Program in Smart Railway National Taipei University of Technology Taipei Taiwan Department of Mechanical and Electrical Engineering Master Program in Smart Railway National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

Rail surface defects and missing fasteners are significant topics in railway detection. However, the detection of images may be subject to noise due to the diversity of environmental conditions, leading to a reduction in accuracy. Therefore, the paper proposes a method based on a semantic segmentation network and image processing. First, to address the problem of data preprocessing, this article introduces a bilateral filter into the U-Net algorithm to reduce noise and improve the efficiency of extracting edge features. Second, locations of rail surface defects and missing fasteners were detected from the U-Net. Finally, the article compares with other literature. The proposed method's effectiveness is demonstrated through experimental results, with accuracy rates of 99.9% for rail surface defects and 98.1% for missing fasteners, respectively.

关键词： Rails Accuracy Image synthesis Semantic segmentation Noise Fasteners Inspection

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The Design of Mini CNC Assembly Simulator Based on Virtual Reality Toolkit (VRTK) 4

The Design of Mini CNC Assembly Simulator Based on Virtual R...

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4th International Conference on electrical, Electronics, Informatics, and Vocational Education, ICE-ELINVO 2021

作者： Malik, C.A.J. Hadi, S. Master Education Program of Electrical Engineering Faculty of Engineering Universitas Negeri Yogyakarta Indonesia

Information and communication technology development today has provided the latest innovation in didactic world. One of such innovation is the usage of virtual reality (VR) as learning media. The application of virtual reality in learning environment started to be adopted because VR is considered more capable to explain learning material when compared with other learning material. Moreover, learning with VR has the capability for student to learn independently anytime and anywhere. One of VR utilization in learning environment is to use it in a Mini CNC machine assembly simulator. The learning media is meant to be used to improve student skill in assembling a Mini CNC machine independently. This research uses Research and Development method with waterfall model which has several stages which are communication, planning, modelling, construction, and deployment. However, this research only aims up to construction stage, because this research focuses on media functionality test. The media black box research has shown that the media is capable to function as expected. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Virtual reality

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RUL Estimation for Power Electronic Devices Using RNNs

RUL Estimation for Power Electronic Devices Using RNNs

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Prognostics and System Health Management Conference (PHM)

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

ISBN: (数字)9798350360585

ISBN: (纸本)9798350360592

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.

关键词： Performance evaluation Semiconductor device modeling Recurrent neural networks Computational modeling Estimation Power electronics Data models

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A Low-cost Raspberry Pi and Deep Learning System for Customer Analysis

A Low-cost Raspberry Pi and Deep Learning System for Custome...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Muhammad Jurej Al Hamdi Wildan Mumtaz Nizam Albar Teuku Abrar Mifta Jamalur Ridha Kahlil Muchtar Master of Electrical Engineering Study Program Universitas Syiah Kuala Aceh Indonesia Master of Electrical Engineering Study Program Universitas Syiah Kuala Telematics Research Center (TRC) Universitas Syiah Kuala Aceh Indonesia

Knowing what customer’s interest when they visit a store may help them increasing its efficiency and turnover. Using current technology advancements, this project intends to create a system that recognizes the presence of consumers visiting a store by constructing designated bounding boxes to compute how long the client stays in each retail area. Our system was developed utilizing the COCO dataset using SSD MobileNet V2 system based on Raspberry Pi. The Raspberry Pi is linked to a camera, which records customer activity in each indicated location and calculates the customer's time spent. A low-cost specification allows the system to be easily implemented in the real environment. From the simulations with two bounding boxes, in region 1 there are 7 people with an average time spent of 12 seconds and in region 2 there are 13 people with an average time spent reaching 20 seconds with an accuracy of 87.5 percent and 70 percent, respectively.

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DanceOnStage: positioning training of dancing stage performance in virtual reality 6

DanceOnStage: positioning training of dancing stage performa...

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6th IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2024

作者： Wu, Ssu-Hsuan Wang, Kai-Ting Chiang, Chia-Chen Chang, Hsin-Yu Yeh, Cheng-Yun Hu, Min-Chun National Tsing Hua University International Intercollegiate Ph.D.Program Hsinchu Taiwan National Tsing Hua University Master of Computer Science Program Hsinchu Taiwan National Tsing Hua University Interdisciplinary Program of Electrical Engineering and Computer Science Hsinchu Taiwan National Tsing Hua University Interdisciplinary Program of Technology and Art Hsinchu Taiwan National Tsing Hua University Hsinchu Taiwan

ISBN: (纸本)9798350372021

This research aims to create a virtual reality-based practice system that simulates real-stage performance environments, assisting amateur dancers in overcoming stage fright and practicing group choreography that is difficult to achieve during individual practice. In this study, users can engage in performance simulations within the created system using the HTC VIVE virtual reality device. Within the system, we have developed a virtual stage space system that only requires a 2m x 3.5m physical area to simulate a large virtual stage experience. Users can also simulate choreographic positioning with virtual dance partners within this system, providing amateur dancers with group dance rehearsal practice opportunities that are not feasible during solo practice. Additionally, by creating three different audience modes, we aim to help users simulate various audience scenarios they may encounter during a performance. The system includes a recording feature that captures users' simulated performances, allowing them to review their actions and receive feedback after practice. © 2024 IEEE.

关键词： Virtual reality

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Analyzing Political Figures in Real-Time: Leveraging YouTube Metadata for Sentiment Analysis

arXiv

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arXiv 2023年

作者： Putra, Danendra Athallariq Harya Muharram, Arief Purnama Master Program of Informatics School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

Sentiment analysis using big data from YouTube videos metadata can be conducted to analyze public opinions on various political figures who represent political parties. This is possible because YouTube has become one of the platforms for people to express themselves, including their opinions on various political figures. The resulting sentiment analysis can be useful for political executives to gain an understanding of public sentiment and develop appropriate and effective political strategies. This study aimed to build a sentiment analysis system leveraging YouTube videos metadata. The sentiment analysis system was built using Apache Kafka, Apache PySpark, and Hadoop for big data handling;TensorFlow for deep learning handling;and FastAPI for deployment on the server. The YouTube videos metadata used in this study is the video description. The sentiment analysis model was built using LSTM algorithm and produces two types of sentiments: positive and negative sentiments. The sentiment analysis results are then visualized in the form a simple web-based dashboard. © 2023, CC BY.

关键词： Sentiment analysis

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Analysis of Regenerative Braking Energy Utilization in KfW i9000 Electric Trains on the Palur-Yogyakarta Operation 7

Analysis of Regenerative Braking Energy Utilization in KfW i...

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7th Astechnova International Energy Conference, ASTECHNOVA 2023

作者： Darmawan, Arief Budiarto, Rachmawan Irnawan, Roni Master Program System Engineering Faculty of Engineering Universitas Gadjah Mada Jl. Teknika utara no 3 Yogyakarta55281 Indonesia Department of Nuclear Engineering and Engineering Physics Faculty of Engineering Universitas Gadjah Mada Jl. Grafika 2 Yogyakarta55281 Indonesia Department of Electrical Engineering and Information Technology Faculty of Engineering Universitas Gadjah Mada Jl. Grafika 2 Yogyakarta55281 Indonesia

This study explores the potential assessment. of regenerative braking energy in the operation of Electric Multiple Units (EMU) on the Yogyakarta-Palur railway line, a 65.03 km. Prompted by a noticeable increase in passenger numbers in the Yogyakarta metropolitan buffer zone, the study calls for future energy management strategies, involving regenerative braking to reduce energy consumption. Utilizing a mathematical model implemented via MATLAB Simulink, the research encompasses complex train movement scenarios, considering variables such as acceleration values of 0.558 m/s2 and 1 m/s2, and deceleration values of -0.555 m/s2 and 0.36 m/s2. Speed limitations, travel patterns, and energy profiles are rigorously examined, along with incline influences, such as tangent angles of 0.0032 degrees and 0.0017 degrees. In segments like Purwosari-Solo Jebres, electrical energy consumption is quantified at 23.02 kWh, with regenerative braking energy at 1.42 kWh;in Maguwo-Brambanan, consumption is 15.28 kWh, with regenerative energy at 2.42 kWh. The peak power requirements and regenerative braking potentials were also determined, with values such as 619.9 kW and 225.7 kW, and 1785 kW and 198.2 kW respectively. The total electrical consumption for the routes from Palur to Yogyakarta and vice versa was found to be 325.52 kWh and 276.73 kWh, with energy recovery of 31.43 kWh and 26.6 kWh, and average energy recovery ratios of 9.66% and 9.61%. © Published under licence by IOP Publishing Ltd.

关键词： Railroads

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Early Detection of Heart Rate Conditions in Autistic Children Using IoT-Based Systems

Early Detection of Heart Rate Conditions in Autistic Childre...

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International Conference on electrical engineering and Computer Science (ICECOS)

作者： Nizam Albar Melinda Melinda Yuwaldi Away dept. Electrical Engineering Master of Electrical Engineering Study Program Universitas Syiah Kuala Banda Aceh Indonesia dept. Electrical Engineering and Computer Engineering Faculty Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (数字)9798350368253

ISBN: (纸本)9798350368260

Early detection of heart rate conditions in autistic children is crucial for anticipating tantrum reactions and providing prompt medical intervention. Previous research designed a prototype for acquiring Electrocardiogram (ECG) signals using the AD8232 sensor, but it required a long time to identify heart rate conditions. This study aims to develop the previous prototype using Internet of Things (IoT) implementation to acquire and identify the heart rate conditions of autistic children in real time. The ECG signal data is analyzed using Butterworth Filter and Artificial Neural Network (ANN) integrated into Raspberry Pi and results are transmitted to the Blynk App. Testing over 20 minutes showed that in the first 10 minutes, the system identified a normal heart rate at 105.19 BPM with 67% accuracy, while in the second 10 minutes, the heart rate rose to 117.19 BPM with tantrum symptoms and 75% accuracy. This system helps medical personnel provide immediate intervention during tantrums.

关键词： Heart rate Pediatrics Accuracy Prototypes Artificial neural networks Electrocardiography Real-time systems Internet of Things Servers Testing

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Supervised Machine Learning Approach for Predicting Cardiovascular Complications Risk in Patients with Diabetes Mellitus

Supervised Machine Learning Approach for Predicting Cardiova...

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International Conference on electrical engineering and Informatics, ICEEI

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

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.

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