Pub Date : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226689
Yi-Zeng Hsieh, Chia-Hsuan Wu, Cheng-Hou Chou, Chia-Ching Teng, Chih-Hsiang Ho
This paper uses an underwater unmanned vehicle to detect the distance and swimming direction of tilapia. As the underwater vehicle is equipped with a single camera system that lacks depth information, the YOLO3 architecture of deep learning is used to determine the relative distance of fish and further analyze the swimming direction of the fish group, which is of great help in analyzing fish group.
{"title":"Detecting the Underwater Distance and Swimming Direction of Tilapia using YOLO","authors":"Yi-Zeng Hsieh, Chia-Hsuan Wu, Cheng-Hou Chou, Chia-Ching Teng, Chih-Hsiang Ho","doi":"10.1109/ICCE-Taiwan58799.2023.10226689","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226689","url":null,"abstract":"This paper uses an underwater unmanned vehicle to detect the distance and swimming direction of tilapia. As the underwater vehicle is equipped with a single camera system that lacks depth information, the YOLO3 architecture of deep learning is used to determine the relative distance of fish and further analyze the swimming direction of the fish group, which is of great help in analyzing fish group.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115750396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10227069
Tzu-Min Chang, Hao-Yuan Chen, Chia-Yu Lin
Mura is usually caused by inhomogeneity and material defects in the manufacturing process. According to the JND value, it can be divided into light Mura and serious Mura. In order to optimize the repair process, the factory hopes to distinguish between light Mura and serious Mura before sending them to the repair site. However, the traditional AI model only distinguishes between normal and Mura and is ineffective in distinguishing between light Mura and serious Mura. To address this issue, we propose a Mura Detection System using an image inpainting model with a self-supervised technique and an attention module to distinguish light Mura and serious Mura. The experiment results show that the proposed method’s Area Under Curve (AUC) can reach 0.854.
{"title":"Image Inpainting with Self-Supervised Learning for Mura Detection System","authors":"Tzu-Min Chang, Hao-Yuan Chen, Chia-Yu Lin","doi":"10.1109/ICCE-Taiwan58799.2023.10227069","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10227069","url":null,"abstract":"Mura is usually caused by inhomogeneity and material defects in the manufacturing process. According to the JND value, it can be divided into light Mura and serious Mura. In order to optimize the repair process, the factory hopes to distinguish between light Mura and serious Mura before sending them to the repair site. However, the traditional AI model only distinguishes between normal and Mura and is ineffective in distinguishing between light Mura and serious Mura. To address this issue, we propose a Mura Detection System using an image inpainting model with a self-supervised technique and an attention module to distinguish light Mura and serious Mura. The experiment results show that the proposed method’s Area Under Curve (AUC) can reach 0.854.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116682525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226626
Ying-Xuan Li, Nan-Ching Tai
Teaching at the right moment can be effective in communicating with children to correct wrong behavior. In some cases, such teachings could even be more effective when they occur later in the day when emotions are calm. This study presents an innovative web-based system that allows parents to generate a print bedtime storybook that addresses timely issues through children’s favorite characters, helping parents to use the right material to teach at the right moment.
{"title":"Teaching at the Right Moment: A Generative AI-Enabled Bedtime Storybook Generation System Communicating Timely Issues","authors":"Ying-Xuan Li, Nan-Ching Tai","doi":"10.1109/ICCE-Taiwan58799.2023.10226626","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226626","url":null,"abstract":"Teaching at the right moment can be effective in communicating with children to correct wrong behavior. In some cases, such teachings could even be more effective when they occur later in the day when emotions are calm. This study presents an innovative web-based system that allows parents to generate a print bedtime storybook that addresses timely issues through children’s favorite characters, helping parents to use the right material to teach at the right moment.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127458236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Intelligent manufacturing practice fields often have poor information security; common security flaws in industrial control networks include messages are sending in plaintext, lack of source authentication, and lack of message integrity verification. In this study, methods for secure NC program updating, two-factor user authentication, and secure messages transmission module were proposed to improve the security of industrial control systems. Attacks were performed on a CNC machine to demonstrate that attackers could control the unprotected machines. We also verify the performance of the proposed M2M authentication scheme, which ensure message freshness, in preventing man-in-the-middle, impersonation attack, and replay attack.
{"title":"Smart Manufacturing Security Challenges and Solutions","authors":"Yu-Shan Hsu, Ming-Hour Yang, I-An Lin, Yao-Yang Tsai","doi":"10.1109/ICCE-Taiwan58799.2023.10226888","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226888","url":null,"abstract":"Intelligent manufacturing practice fields often have poor information security; common security flaws in industrial control networks include messages are sending in plaintext, lack of source authentication, and lack of message integrity verification. In this study, methods for secure NC program updating, two-factor user authentication, and secure messages transmission module were proposed to improve the security of industrial control systems. Attacks were performed on a CNC machine to demonstrate that attackers could control the unprotected machines. We also verify the performance of the proposed M2M authentication scheme, which ensure message freshness, in preventing man-in-the-middle, impersonation attack, and replay attack.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"321 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126027596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10227010
MinYen Lu, Chenhao Chen, Billy Dawton, Yugo Nakamura, Yutaka Arakawa
Human activity recognition (HAR) using the deep learning method has caught the attention of researchers thanks to its automatic feature extraction and accurate prediction capabilities. However, for applications based on a wearable sensor, such as an inertial measurement unit (IMU), the process of collecting and hand-labeling large amounts of data is complicated and labor-intensive, meaning that there is a limited amount of data available for model training. Therefore, there is a need to propose and develop data augmentation approaches to generate high quality data for the growth of HAR research. We propose a head-mounted virtual gyroscope signal generator to alleviate the problems caused by the lack of data in head movement-related applications. Unlike previous work, our system only generates head-motion related gyroscope data, minimizing system complexity. We trained a deep-learning model in a head motion-based application with different generated sensor data ratios, and show the viability of our proposed data generation method.
{"title":"Generating Virtual Head-Mounted Gyroscope Signals From Video Data","authors":"MinYen Lu, Chenhao Chen, Billy Dawton, Yugo Nakamura, Yutaka Arakawa","doi":"10.1109/ICCE-Taiwan58799.2023.10227010","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10227010","url":null,"abstract":"Human activity recognition (HAR) using the deep learning method has caught the attention of researchers thanks to its automatic feature extraction and accurate prediction capabilities. However, for applications based on a wearable sensor, such as an inertial measurement unit (IMU), the process of collecting and hand-labeling large amounts of data is complicated and labor-intensive, meaning that there is a limited amount of data available for model training. Therefore, there is a need to propose and develop data augmentation approaches to generate high quality data for the growth of HAR research. We propose a head-mounted virtual gyroscope signal generator to alleviate the problems caused by the lack of data in head movement-related applications. Unlike previous work, our system only generates head-motion related gyroscope data, minimizing system complexity. We trained a deep-learning model in a head motion-based application with different generated sensor data ratios, and show the viability of our proposed data generation method.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126047916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226999
Ouyang Yu, Sheng-Ming Wang
This study developed a framework based on deep learning algorithms for processing 360° videos for detecting pedestrian spatial behavior in urban spaces. Information divergence is determined through the sampling and conversion of spatiotemporal behavior data for pedestrian flow analysis. Traditional videos, such as those captured by one-way security cameras, cannot be used to fully analyze the flow of pedestrians in cities. Therefore, a 360° camera is used to capture panoramic videos of city spaces over time. Subsequently, deep learning algorithms are used to process the videos and obtain pedestrian trajectory data for analyzing their spatial behavior and interactions. The results of real-world implementation indicate that the proposed method and analytical framework can be used to detect pedestrians and collect data related to pedestrians’ spatial behavior. However, the sampling rate and application of pedestrians’ trajectory data must be explored in future studies.
{"title":"Detecting Pedestrian Spatial Behavior in City Spaces by Processing 360° Videos","authors":"Ouyang Yu, Sheng-Ming Wang","doi":"10.1109/ICCE-Taiwan58799.2023.10226999","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226999","url":null,"abstract":"This study developed a framework based on deep learning algorithms for processing 360° videos for detecting pedestrian spatial behavior in urban spaces. Information divergence is determined through the sampling and conversion of spatiotemporal behavior data for pedestrian flow analysis. Traditional videos, such as those captured by one-way security cameras, cannot be used to fully analyze the flow of pedestrians in cities. Therefore, a 360° camera is used to capture panoramic videos of city spaces over time. Subsequently, deep learning algorithms are used to process the videos and obtain pedestrian trajectory data for analyzing their spatial behavior and interactions. The results of real-world implementation indicate that the proposed method and analytical framework can be used to detect pedestrians and collect data related to pedestrians’ spatial behavior. However, the sampling rate and application of pedestrians’ trajectory data must be explored in future studies.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123791239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recently, deep reinforcement learning (DRL) methods attract much attention for solving job-shop scheduling problem (JSP), a NP-hard optimization problem. One of DRL methods is based on priority dispatching rules (PDRs), which is easy to be implemented, to dispatch operations to machines. In this paper, we propose a graph neural network (GNN) to enhance Luo's method [1] to choose a PDR to dispatch. With GNN, our method, trained with small JSP problems, also performs well in large JSP problems. Our experiments show that our method outperforms PDR methods and most of other DRL methods, particularly for large JSP problems.
{"title":"Deep Reinforcement Learning Based on Graph Neural Networks for Job-shop Scheduling","authors":"Kuo-Hao Ho, Ji-Han Wu, Chiang Fan, Yuan-Yu Wu, Sheng-I Chen, Ted T. Kuo, Feng Wang, I-Chen Wu","doi":"10.1109/ICCE-Taiwan58799.2023.10226873","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226873","url":null,"abstract":"Recently, deep reinforcement learning (DRL) methods attract much attention for solving job-shop scheduling problem (JSP), a NP-hard optimization problem. One of DRL methods is based on priority dispatching rules (PDRs), which is easy to be implemented, to dispatch operations to machines. In this paper, we propose a graph neural network (GNN) to enhance Luo's method [1] to choose a PDR to dispatch. With GNN, our method, trained with small JSP problems, also performs well in large JSP problems. Our experiments show that our method outperforms PDR methods and most of other DRL methods, particularly for large JSP problems.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125301332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226874
Ryota Goka, Yuya Moroto, Keisuke Maeda, Takahiro Ogawa, M. Haseyama
This paper presents a method for shoot event prediction in soccer considering expected goals based on the players’ positions. To quantify players’ and teams’ performance, various ways based on the chance of shoot events have been proposed in recent years for soccer analytics. In soccer, since the players’ positions in soccer change little with respect to the soccer court, it can be difficult to directly introduce the tracking data of players, that is, players’ positions, into the shoot event prediction model. We tackle this problem with expected goals estimated from the field position as the player’s importance. At the end of this paper, we confirm the effectiveness of our method through experiments using actual soccer videos.
{"title":"Shoot Event Prediction in Soccer Considering Expected Goals Based on Players’ Positions","authors":"Ryota Goka, Yuya Moroto, Keisuke Maeda, Takahiro Ogawa, M. Haseyama","doi":"10.1109/ICCE-Taiwan58799.2023.10226874","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226874","url":null,"abstract":"This paper presents a method for shoot event prediction in soccer considering expected goals based on the players’ positions. To quantify players’ and teams’ performance, various ways based on the chance of shoot events have been proposed in recent years for soccer analytics. In soccer, since the players’ positions in soccer change little with respect to the soccer court, it can be difficult to directly introduce the tracking data of players, that is, players’ positions, into the shoot event prediction model. We tackle this problem with expected goals estimated from the field position as the player’s importance. At the end of this paper, we confirm the effectiveness of our method through experiments using actual soccer videos.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115174487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10227045
He Zhu, Ren Togo, Takahiro Ogawa, M. Haseyama
This paper proposes a medical visual question generation model for generating higher-quality questions from medical images. The visual question generation model can guide the diagnostic process and improve the utilization of medical resources by reducing the dependence on physician involvement. Our model uses cross-attention and the large language model to preserve inherent information and addresses the issue of inferior generation performance in the medical domain due to a lack of data. We also control the category of generated questions by setting guidance sentences that include interrogative words. The experimental results demonstrate that our method generates higher-quality questions than previous approaches.
{"title":"A Medical Domain Visual Question Generation Model via Large Language Model","authors":"He Zhu, Ren Togo, Takahiro Ogawa, M. Haseyama","doi":"10.1109/ICCE-Taiwan58799.2023.10227045","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10227045","url":null,"abstract":"This paper proposes a medical visual question generation model for generating higher-quality questions from medical images. The visual question generation model can guide the diagnostic process and improve the utilization of medical resources by reducing the dependence on physician involvement. Our model uses cross-attention and the large language model to preserve inherent information and addresses the issue of inferior generation performance in the medical domain due to a lack of data. We also control the category of generated questions by setting guidance sentences that include interrogative words. The experimental results demonstrate that our method generates higher-quality questions than previous approaches.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115538749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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