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EESRGAN: Efficient & Effective Super-Resolution Generative Adversarial Network

IETE Technical Review (Institution of Electronics and Telecommunication Engineers, India) 2024年第2期41卷 200-211页

作者： Tsai, An-Chao Tsou, Cheng-Han Wang, Jhing-Fa International Master Program of Information Technology and Application National Pingtung University Pingtung900 Taiwan Department of Electrical Engineering National Cheng Kung University Tainan Taiwan

In Taiwan, traditional production equipment for the mainframe panels is imported from overseas, and the parameters are adjusted through the operation panel for automated manufacturing. However, these parameters are slightly different from those used in Taiwan, requiring manual recording of warning parameters to ensure quality control. Currently, the only way to capture the operation records of these machines is by manually recording the system panel information, which is a time-consuming and laborious process that is expensive for production line personnel. In this work, we use image recognition to capture and analyze data externally without damaging the machine. This research improves the ESRGAN network to restore the image details and textures of the mainframe panels. It also captures relevant data with Google OCR to convert panel images into parameter values. The captured results are combined with data analysis to provide more accurate standard mainframe panel information. Even under different interference source conditions, data extraction and analysis can still be carried out to output the text files, effectively assisting the production line in recording the machine panel parameters and reducing personnel’s workload. © 2024 IETE.

关键词： Extraction

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Using Machine Learning to Predict the Language Proficiency of Thai Students Studying in Taiwan 10

Using Machine Learning to Predict the Language Proficiency o...

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10th international Conference on Applied System Innovation, ICASI 2024

作者： Moocharoen, Charoenwat Chang, Wen-Chih National Pingtung University International Master Program in Information Technology and Applications Taiwan

ISBN: (纸本)9798350394924

Currently, there are students from Thailand who wish to study abroad. The researchers have chosen Taiwan as the focus. Among the students who come for further studies, there are those proficient in the English language and those who are not. Factors such as language readiness, financial preparedness, scholarship availability, and the university's reputation are crucial in the decision-making process for choosing an educational institution. Simultaneously, some students had the opportunity to study in Taiwan but, due to language unpreparedness, had to withdraw from their studies and return to their home country. The researchers emphasize the importance of language readiness among students. Data has been collected from 316 data including undergraduate, master's, doctoral, and language students of Thai students throughout Taiwan, and a model has been developed to predict the English language proficiency of Thai students. The goal is to create a model with the highest level of accuracy to produce reliable results. This model can be utilized by educational institutions to tailor their courses to match the language abilities of Thai students, thereby enhancing the overall learning experience. © 2024 IEEE.

关键词： Forecasting

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Advanced Pigmented Facial Skin Analysis Using Conditional Generative Adversarial Networks

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IEEE Access 2024年 12卷 46646-46656页

作者： Tsai, An-Chao Huang, Patrick Po-Han Wu, Zhong-Chong Wang, Jhing-Fa National Pingtung University International Master Program of Information Technology and Application Pingtung91201 Taiwan Huang Ph Dermatology and Aesthetics Kaohsiung80458 Taiwan National Cheng Kung University Department of Electrical Engineering Tainan70101 Taiwan

In recent years, artificial intelligence (AI) approaches in computer vision and medical technology have been combined to create various convenient and accurate tools to assist medical treatments. In this work, we propose conditional generative adversarial networks (conditional GANs)-based pigmented facial skin analysis system for melasma diagnosis. In the past, melasma diagnosis was based on subjective diagnoses from doctors, and there were few automatic melasma analysis methods. The proposed system helps to determine the region according to the melasma's severity. Areas associated with melasma and hemoglobin are detected to determine whether they may require special treatments. Furthermore, the proposed work cooperates with HUANGDERM dermatology to collect a facial skin pigmented dataset. We divide the dataset into 3,000 groups for training datasets and 678 groups for testing. Each group contains four categories of images: standard white light, polarized light, melanin and hemoglobin distribution. As a result, the proposed system successfully generates melasma and hemoglobin images and performs well with respect to subjective and objective evaluations. © 2013 IEEE.

关键词： Hemoglobin

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Fall Detection with Smart Devices for the Elderly Using Machine Learning 14th

Fall Detection with Smart Devices for the Elderly Using Mach...

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14th international Conference on Frontier Computing, FC 2024

作者： Yang, Hsuan-Che Chang, Wen-Chih Huang, Wei-Han Hsu, Chia-Hao Lin, Yen-Chen Li, Jie-Syuan Lee, Yun-Cheng Luo, Zeng-Sheng Department of Information Management Chihlee University of Technology New Taipei City220 Taiwan International Master Program of Information Technology and Applications National Ping-Tung University Ping-Tung Taiwan

ISBN: (纸本)9789819623945

The worldwide aging population has been on the rise due to the advancement of medical science and decreasing birth rates. Unfortunately, this has led to an increase in falls among the elderly, which has emerged as one of the top ten causes of death for elderly individuals in Taiwan. To address this issue, our study aims to leverage the widespread use of smartphones and smartwatches to detect and prevent falls in older adults. Our machine learning model is trained using the UMAFall dataset, which collects data on daily activities and falling actions through five sensing devices. We used Azure Machine Learning Studio to test six different machine learning algorithms to evaluate suitable algorithms. In the end, the Two-Class Decision Tree algorithm achieved the best results. In our next step, this method will be programmed with the TensorFlow Lite and train the model. This model will be integrated into Android-based apps that can be installed on smartphones. By continuously connecting the smartwatch and smartphone Apps, the accelerometer, gyroscope, and magnetometer in both devices can be compared and analyzed in real time using the TensorFlow Lite model, accurately determining the wearer's actions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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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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Key Factors for Unsubscribing from YouTube Channels: A Study of YouTubers in Taiwan 1

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13th international Conference on Frontier Computing, FC 2023

作者： Yang, Hsuan-Che Chang, Wen-Chih Department of Information Management Chihlee University of Technology New Taipei City220 Taiwan International Master Program of Information Technology and Applications National Pingtung University Pingtung City Taiwan

ISBN: (数字)9789819993420

ISBN: (纸本)9789819993413

YouTube has become a major advertisement media for industries. People watch TV shows, movie trailers, news and episodes, on-line shows streaming as well. When people want to purchase something, they can find some unboxing reviews, experiences shares, using instructions and services introductions on YouTube before making orders. Traveling guides and educational tutorials are also can be found on YouTube. Thousands of YouTube channels need channel’s subscribers directly or indirectly affect the revenue of channel owners. In addition to YouTube’s own profit from watching, the subscribers are also an important reference when manufacturers want to choose key opinion leaders (KOL) or internet celebrities to promote their products or services. Since, maintaining a stable growth of channel subscribers and reducing the occurrence of unsubscribing are the key issue that every YouTuber needs to understand urgently. In this study, we used a survey to understand subscribers and YouTubers on unsubscribing, and analyzed some key factors for unsubscribing. The data analyses include descriptive analysis, factor analysis, reliability analysis, t-test, one-way ANOVA, and Pearson’s correlation analysis. Through this study, people can understand key factors for unsubscribing reasons on YouTube platform and the priority of these factors. Whether there is special unsubscribe factors for different channel types and attributes. The key contribution of this study is: a questionnaire on key factors for unsubscribing on the YouTube platform was designed, and it could be utilized for further use in related studies in the future. We also summarize the responses and suggestions of we-media channel owners on the factors for unsubscribing. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Reliability analysis

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The Efficient Traffic Sign Detection and Recognition for Taiwan Road Using YOLO Model with Hybrid Dataset 9

The Efficient Traffic Sign Detection and Recognition for Tai...

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9th international Conference on Applied System Innovation, ICASI 2023

作者： Suwattanapunkul, Taweelap Wang, Lung-Jen National Pingtung University International Master Program in Information Technology and Applications Taiwan National Pingtung University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350398380

According to the trend of worldwide car sales have grown up, this cause may increase accidents on the road due to human error. The self-driverless car has been developed to solve this problem. One task of the self-driverless car is traffic sign detection and recognition (TSDR), which will help drivers notify the traffic sign installed on the road in advance. Taiwan roads have specific traffic signs, and no Taiwan traffic sign public dataset is available. In this paper, our proposed object detection method was experimentally performed using YOLOv5s6 and YOLOv8s models on three different datasets, as Tsinghua-Tencent 100K (TT100k), the self-created Taiwan traffic sign (TWTS), and the hybrid dataset, which combine the traffic scenes between TT100k and TWTS dataset. The output results from each dataset and each model, which is trained on the same parameter, will be compared to validate the proposed method. The experiment results' comparison of the hybrid dataset between YOLOv5s6 and YOLOv8s models display the results of the mAP@.5 is about 65% and 76.2%, respectively, which means the performance of the YOLOv8s is higher than the YOLOv5s6 when using hybrid dataset. © 2023 IEEE.

关键词： Object detection

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A Friendly Interface Medical Service Robot with Fault-tolerant Voice Recognition and Mobile Phone Remote Control 10

A Friendly Interface Medical Service Robot with Fault-tolera...

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10th international Conference on Orange technology, ICOT 2022

作者： Ou, Yang-Yen Tsai, An-Chao Hsu, Pin-Yang Chen, Che-Wen Wang, Jhing-Fa National Cheng-Kung University Department of Electrical Engineering Tainan Taiwan National Pingtung University International Master Program of Information Technology and Application Pingtung Taiwan

ISBN: (纸本)9781665454193

In recent years, due to the impact of COVID-19 around the world, there has been a serious shortage of medical resources. In order to supplement the manpower and fear that medical staff's contact with patients will cause a breach in the epidemic, reduce the workload of nurses, and help nurses perform repetitive tasks so that nurses can concentrate more on the patient's condition. Therefore, this paper proposes M-Robot, which is a friendly interface service robot based on the Android system and can be controlled by voice, touch, and remote control in the medical care field. The system is mainly divided into two parts. One is the web server. The web server is divided into two parts: front-end and back-end. The front-end is responsible for friendly user interface management, and the back-end is for accessing the SQLite database, as well as processing speech recognition and semantic understanding in voice services. In the other part, we use TEMI robot to develop and complete the desired service. Its service content includes environment introduction, delivery service, questionnaire survey, broadcast car, scheduling reminder, follow-up record, and patient instruction video. In the voice control mode, the user can say the wake-up word to the robot and say the required service content, and the robot will execute after receiving the message;in the remote control mode, we provide a friendly web interface for remote control. As well as the information needed to manage various services. © 2022 IEEE.

关键词： Remote control

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Tuberculosis Detection Based on Multiple Model Ensemble in Chest X-ray Image 10

Tuberculosis Detection Based on Multiple Model Ensemble in C...

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10th international Conference on Orange technology, ICOT 2022

作者： Tsai, An-Chao Zhou, Zhe-Guang Ou, Yang-Yen Wang, I Jhing-Fa International Master Program of Information Technology and Application National Pingtung University Pingtung Taiwan National Cheng-Kung University Department of Electrical Engineering Tainan Taiwan

ISBN: (纸本)9781665454193

Tuberculosis is a legal infectious lung disease, a chronic infectious disease caused by tuberculosis. The diagnosis of the confirmed person is usually judged by chest X-ray images, and the sputum smear is used to detect the presence of tuberculosis in the sputum. Screening high-risk individuals can effectively prevent the spread of tuberculosis;however, chest X-ray image interpretation and sputum testing take a long time. When infectious tuberculosis patients are found, tuberculosis has already spread. We propose a tuberculosis diagnostic triage system and a tuberculosis detection system based on preprocessing of lung image segmentation. In the tuberculosis diagnosis and triage system, AI interpretation is performed immediately after the patient's chest X-ray is taken, and abnormal tuberculosis cases are immediately listed. Lung field cropping through U-Net for classification in the form of a multiple ensemble system, including the use of EfficientNet-BO, Densenet121, and Xception. After marking abnormal lung images, we use a tuberculosis detection system to find patients with tuberculosis and use GRAD-Cam to display the image areas that are of most interest to the model, similar to a heat map, allowing us to know the model Predicting focus areas for tuberculosis. We used specificity, sensitivity, accuracy, and area under the receiver operating characteristic curve (AUROC) to evaluate the classification model performance. © 2022 IEEE.

关键词： Image segmentation

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3D object detection combined with inverse kinematics to achieve robotic arm grasping 10

3D object detection combined with inverse kinematics to achi...

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10th international Conference on Orange technology, ICOT 2022

作者： Chen, Che-Wen Tsai, An-Chao Zhang, Yu-Hong Wang, Jhing-Fa National Cheng-Kung University Department of Electrical Engineering Tainan Taiwan International Master Program of Information Technology and Application National Pingtung University Pingtung Taiwan

ISBN: (纸本)9781665454193

In recent years, the research of autonomous robotic arms has received high attention from academia and industry, so this thesis aims to develop a visual detection system for robotic arms to grasp and place objects. Object detection technology The purpose of which is to locate objects of interest (target) in a given image, providing object location bounding boxes and categories. ROS is a set of open-source software libraries designed to simplify the creation of complex and robust on various robotic platforms. the task of the robot behavior. The proposed scheme uses a camera with depth information and combines target localization and edge detection algorithms to accurately measure the relative distance between the object and the robot arm. The process includes deep neural network construction and use, robotic arm positioning, object bounding box position information, and robotic arm movement control. Considering the visual scale, the relative distance between the target and the robotic arm is calculated so that the robotic arm can grasp the target and place the object in a specific position. This paper introduces how to combine the deep learning object detection model and ROS moveit! to complete the object identification and grasp and place the robotic arm. © 2022 IEEE.

关键词： Object detection

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