Pub Date : 2021-07-27DOI: 10.1109/IAICT52856.2021.9532535
Yudha Afriansyah, Ratna Astuti Nugrahaeni, Anggunmeka Luhur Prasasti
One of the important steps of testing out applications such as video game is getting the information regarding user experience. Emotion from the testers while playing can be used as a parameter of the user experience. Emotions such as anger, happiness, sadness, or surprise can be seen from changes in facial expressions. These emotional parameters can be used as feedback for satisfaction or deficiency in the video game so that developers can increase the improvement of the final product of the game. This project discusses the human facial expression classification system to test video games using the K-Nearest Neighbor (KNN) classification method and using the Indonesia Mixed Emotion Dataset (IMED) as training data and trial data. In this system, there are several processes, namely preprocessing, feature extraction, and classification. Finally, this system issues a classification of facial expressions detected in the form of chart that can be used in user experience testing. The result of this research is that the K-Nearest Neighbor (KNN) algorithm results in training model accuracy rate of 98.24% and real-time human facial expressions with up to 56% accuracy.
{"title":"Facial Expression Classification for User Experience Testing Using K-Nearest Neighbor","authors":"Yudha Afriansyah, Ratna Astuti Nugrahaeni, Anggunmeka Luhur Prasasti","doi":"10.1109/IAICT52856.2021.9532535","DOIUrl":"https://doi.org/10.1109/IAICT52856.2021.9532535","url":null,"abstract":"One of the important steps of testing out applications such as video game is getting the information regarding user experience. Emotion from the testers while playing can be used as a parameter of the user experience. Emotions such as anger, happiness, sadness, or surprise can be seen from changes in facial expressions. These emotional parameters can be used as feedback for satisfaction or deficiency in the video game so that developers can increase the improvement of the final product of the game. This project discusses the human facial expression classification system to test video games using the K-Nearest Neighbor (KNN) classification method and using the Indonesia Mixed Emotion Dataset (IMED) as training data and trial data. In this system, there are several processes, namely preprocessing, feature extraction, and classification. Finally, this system issues a classification of facial expressions detected in the form of chart that can be used in user experience testing. The result of this research is that the K-Nearest Neighbor (KNN) algorithm results in training model accuracy rate of 98.24% and real-time human facial expressions with up to 56% accuracy.","PeriodicalId":416542,"journal":{"name":"2021 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116484177","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 : 2021-07-27DOI: 10.1109/IAICT52856.2021.9532585
Andy Maulana Yusuf, S. Suyanto
A banknote is an economic tool used as a generally accepted medium of exchange. However, it is prone to counterfeiting, such as in Indonesia, in which the case of banknotes counterfeiting continues to increase. Hence, some computer-based applications have been developed to detect the authenticity of banknotes to reduce counterfeiting cases. Unfortunately, they focus on either nominal detection only or authenticity detection only. Besides, they use noiseless datasets and augmentation processes to be subject to overfitting or prediction errors. In this paper, the Indonesian banknote detection system is developed to identify both authenticity and nominal using the region of interest (ROI) and convolutional neural network (CNN). The evaluation shows that the authenticity model achieves a high accuracy of 95%, while the nominal classification model achieves an accuracy of 99%.
{"title":"Authenticity and Nominal Detection of Indonesian Banknotes Using ROI and CNN","authors":"Andy Maulana Yusuf, S. Suyanto","doi":"10.1109/IAICT52856.2021.9532585","DOIUrl":"https://doi.org/10.1109/IAICT52856.2021.9532585","url":null,"abstract":"A banknote is an economic tool used as a generally accepted medium of exchange. However, it is prone to counterfeiting, such as in Indonesia, in which the case of banknotes counterfeiting continues to increase. Hence, some computer-based applications have been developed to detect the authenticity of banknotes to reduce counterfeiting cases. Unfortunately, they focus on either nominal detection only or authenticity detection only. Besides, they use noiseless datasets and augmentation processes to be subject to overfitting or prediction errors. In this paper, the Indonesian banknote detection system is developed to identify both authenticity and nominal using the region of interest (ROI) and convolutional neural network (CNN). The evaluation shows that the authenticity model achieves a high accuracy of 95%, while the nominal classification model achieves an accuracy of 99%.","PeriodicalId":416542,"journal":{"name":"2021 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117198413","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 : 2021-07-27DOI: 10.1109/IAICT52856.2021.9532576
Fazza Fakhri Rabbany, Ahmad Qurthobi, A. Suhendi
In this paper, a self-balancing virtual reality robot was designed. It is a type of robot that can maintain a upright position and is equipped with a camera for survey and exploration purposes. The self-balancing robot control system uses the principle of an inverted pendulum with two wheels. The system was connected to Android application as a means of streaming data from the camera module and as a guide for servo movement. This system was created using the CODESYS application installed on the Raspberry Pi 3B+. The main objective of this research is to implement a virtual reality self-balancing robot design with wheel movement using the PID control method and a complementary filter. The MPU6050 sensor and Complementary filter are used as feedback controls to estimate the robot relative upright position which is then calculated using PID to control the motor so that the speed and acceleration of the motor can be varied to keep the robot's balanced in its upright position.
{"title":"Design of Self-Balancing Virtual Reality Robot Using PID Control Method and Complementary Filter","authors":"Fazza Fakhri Rabbany, Ahmad Qurthobi, A. Suhendi","doi":"10.1109/IAICT52856.2021.9532576","DOIUrl":"https://doi.org/10.1109/IAICT52856.2021.9532576","url":null,"abstract":"In this paper, a self-balancing virtual reality robot was designed. It is a type of robot that can maintain a upright position and is equipped with a camera for survey and exploration purposes. The self-balancing robot control system uses the principle of an inverted pendulum with two wheels. The system was connected to Android application as a means of streaming data from the camera module and as a guide for servo movement. This system was created using the CODESYS application installed on the Raspberry Pi 3B+. The main objective of this research is to implement a virtual reality self-balancing robot design with wheel movement using the PID control method and a complementary filter. The MPU6050 sensor and Complementary filter are used as feedback controls to estimate the robot relative upright position which is then calculated using PID to control the motor so that the speed and acceleration of the motor can be varied to keep the robot's balanced in its upright position.","PeriodicalId":416542,"journal":{"name":"2021 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134340995","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 : 2021-07-27DOI: 10.1109/IAICT52856.2021.9532546
Muhammad Kemal Hernandi, S. Wibowo, S. Suyanto
Sentiment analysis is the method of extracting opinions from texts written in human language. Sentiment analysis can be used to analyze and evaluate the customer experience of the services that have been provided. With easy access to social media, sentiment analysis can be applied from people's comments on social media. One of the social media that is suitable for sentiment analysis is Twitter. In this paper, we focus on negative sentiment detection using tweets on Twitter by Indihome consumers. The system is designed to apply sentiment analysis using the BiLSTM method. Using BiLSTM, the accuracy 88 % is achieved.
{"title":"Sentiment Analysis Implementation For Detecting Negative Sentiment Towards Indihome In Twitter Using Bidirectional Long Short Term Memory","authors":"Muhammad Kemal Hernandi, S. Wibowo, S. Suyanto","doi":"10.1109/IAICT52856.2021.9532546","DOIUrl":"https://doi.org/10.1109/IAICT52856.2021.9532546","url":null,"abstract":"Sentiment analysis is the method of extracting opinions from texts written in human language. Sentiment analysis can be used to analyze and evaluate the customer experience of the services that have been provided. With easy access to social media, sentiment analysis can be applied from people's comments on social media. One of the social media that is suitable for sentiment analysis is Twitter. In this paper, we focus on negative sentiment detection using tweets on Twitter by Indihome consumers. The system is designed to apply sentiment analysis using the BiLSTM method. Using BiLSTM, the accuracy 88 % is achieved.","PeriodicalId":416542,"journal":{"name":"2021 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121632514","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 : 2021-07-27DOI: 10.1109/iaict52856.2021.9532511
{"title":"[Front matter]","authors":"","doi":"10.1109/iaict52856.2021.9532511","DOIUrl":"https://doi.org/10.1109/iaict52856.2021.9532511","url":null,"abstract":"","PeriodicalId":416542,"journal":{"name":"2021 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116541756","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 : 2021-07-27DOI: 10.1109/IAICT52856.2021.9532540
Sinan Chen, Masahide Nakamura, S. Saiki
The burden on family caregivers for in-home care is increasing in a super-aging society. Realizing conversational agents adapted to individual situations for in-home care assistance is a hard technical issue. The purpose of this paper is to realize a method that can create a personalized conversational agent by the end-user. The challenging points of this paper include two aspects: (1) Editing conversation scenarios easier for end-users. (2) Sharing edited conversation scenarios with others. As the approach, we first present a platform of personalized conversation scenario (PopCS). Then, we create a logs-as-scenarios concept to improve the personalized contents and the edited efficiency. By selectively sharing the conversation logs, we build a connection between the local conversational agent and the remote that seems like telemedicine. Since different family caregivers and healthcare specialists join in it, continuing to improve the conversation scenarios for in-home care assistance is promising.
{"title":"Developing a Platform of Personalized Conversation Scenarios for In-home Care Assistance","authors":"Sinan Chen, Masahide Nakamura, S. Saiki","doi":"10.1109/IAICT52856.2021.9532540","DOIUrl":"https://doi.org/10.1109/IAICT52856.2021.9532540","url":null,"abstract":"The burden on family caregivers for in-home care is increasing in a super-aging society. Realizing conversational agents adapted to individual situations for in-home care assistance is a hard technical issue. The purpose of this paper is to realize a method that can create a personalized conversational agent by the end-user. The challenging points of this paper include two aspects: (1) Editing conversation scenarios easier for end-users. (2) Sharing edited conversation scenarios with others. As the approach, we first present a platform of personalized conversation scenario (PopCS). Then, we create a logs-as-scenarios concept to improve the personalized contents and the edited efficiency. By selectively sharing the conversation logs, we build a connection between the local conversational agent and the remote that seems like telemedicine. Since different family caregivers and healthcare specialists join in it, continuing to improve the conversation scenarios for in-home care assistance is promising.","PeriodicalId":416542,"journal":{"name":"2021 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131305169","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 : 2021-07-27DOI: 10.1109/IAICT52856.2021.9532520
Muhammad Fachri Mahyudin, L. Novamizanti, Sofia Sa’idah
The number of medical digital images continues to grow, thus requiring the protection of patient privacy. It is the reason watermarking on medical images has received special attention. This study proposes a watermarking scheme on the image using the Arnold cat map and hybrid transform (Fast Discrete Curvelet Transforms (FDCuT), Discrete Cosine Transform (DCT), Singular Value Decomposition (SVD)). The Arnold Cat Map technique is applied to pre-processing to increase security and reduce the likelihood of targeted attacks. Experiments were carried out on six modalities: MRI image, x-Ray, CT scan, ultrasound, eye image, and fundus scan. The experimental results show that the watermarked image produces excellent imperceptibility with a peak signal-to-noise ratio (PSNR) above 50 dB, and the structural similarity index (SSIM) is close to 1. The value of bit error rate (BER) 0 and normalized correlation (NC) 1 are obtained in conditions without attack. This technique is resistant to various attacks, namely JPEG compression, adding noise, and filtering. Therefore, the proposed watermarking scheme has good imperceptibility and robustness to be applied in e-health applications.
{"title":"Robust Watermarking using Arnold and Hybrid Transform in Medical Images","authors":"Muhammad Fachri Mahyudin, L. Novamizanti, Sofia Sa’idah","doi":"10.1109/IAICT52856.2021.9532520","DOIUrl":"https://doi.org/10.1109/IAICT52856.2021.9532520","url":null,"abstract":"The number of medical digital images continues to grow, thus requiring the protection of patient privacy. It is the reason watermarking on medical images has received special attention. This study proposes a watermarking scheme on the image using the Arnold cat map and hybrid transform (Fast Discrete Curvelet Transforms (FDCuT), Discrete Cosine Transform (DCT), Singular Value Decomposition (SVD)). The Arnold Cat Map technique is applied to pre-processing to increase security and reduce the likelihood of targeted attacks. Experiments were carried out on six modalities: MRI image, x-Ray, CT scan, ultrasound, eye image, and fundus scan. The experimental results show that the watermarked image produces excellent imperceptibility with a peak signal-to-noise ratio (PSNR) above 50 dB, and the structural similarity index (SSIM) is close to 1. The value of bit error rate (BER) 0 and normalized correlation (NC) 1 are obtained in conditions without attack. This technique is resistant to various attacks, namely JPEG compression, adding noise, and filtering. Therefore, the proposed watermarking scheme has good imperceptibility and robustness to be applied in e-health applications.","PeriodicalId":416542,"journal":{"name":"2021 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133273027","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 : 2021-07-27DOI: 10.1109/IAICT52856.2021.9532547
Julianne Dreyer, Marten Fischer, R. Tönjes
The Industry 4.0 is offering novel opportunities for large enterprises or even small and medium enterprises (SME) to digitize and also simplify their existing production workflows. With the uprise of the Distributed Ledger Technology (DLT), the Industry 4.0 also receives new possibilities to set up a more secure, redundant and decentralized data infrastructure. Existing database systems can be exchanged with Blockchain systems, thus making use of its inherent immutability. Popular open-source Blockchain frameworks such as Hyperledger Fabric offer large customizability, which makes them attractive for dynamic Industry 4.0 applications. Though, the configuration of the required networks is not a trivial task. Due to its high flexibility and numerous configuration parameters, an interested developer needs to have a deep understanding or even some experience with the Hyperledger Fabric framework. Since most SMEs do not have a dedicated IT department, this paper aims to provide a design guideline for interested developers to optimally set up a Hyperledger Fabric v2.0 business network, which is suited for the desired use-case. With the special demands of Industry 4.0 in mind, this paper also provides three example use-cases, which can benefit from the use of DLT to enhance data security. The results are generalizable and comparable to previous works but differ in some version-specific aspects of Fabric.
{"title":"Towards configuring Hyperledger Fabric 2.0 Blockchain Platform for Industry 4.0 applications","authors":"Julianne Dreyer, Marten Fischer, R. Tönjes","doi":"10.1109/IAICT52856.2021.9532547","DOIUrl":"https://doi.org/10.1109/IAICT52856.2021.9532547","url":null,"abstract":"The Industry 4.0 is offering novel opportunities for large enterprises or even small and medium enterprises (SME) to digitize and also simplify their existing production workflows. With the uprise of the Distributed Ledger Technology (DLT), the Industry 4.0 also receives new possibilities to set up a more secure, redundant and decentralized data infrastructure. Existing database systems can be exchanged with Blockchain systems, thus making use of its inherent immutability. Popular open-source Blockchain frameworks such as Hyperledger Fabric offer large customizability, which makes them attractive for dynamic Industry 4.0 applications. Though, the configuration of the required networks is not a trivial task. Due to its high flexibility and numerous configuration parameters, an interested developer needs to have a deep understanding or even some experience with the Hyperledger Fabric framework. Since most SMEs do not have a dedicated IT department, this paper aims to provide a design guideline for interested developers to optimally set up a Hyperledger Fabric v2.0 business network, which is suited for the desired use-case. With the special demands of Industry 4.0 in mind, this paper also provides three example use-cases, which can benefit from the use of DLT to enhance data security. The results are generalizable and comparable to previous works but differ in some version-specific aspects of Fabric.","PeriodicalId":416542,"journal":{"name":"2021 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123960842","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 : 2021-07-27DOI: 10.1109/IAICT52856.2021.9532543
Thrivikram Gl, Vidya Ganesh, T. Sethuraman, Satheesh K. Perepu
Deep learning models are proven to deliver satisfactory results on training a complex non-linear relationship between the set of input features and different task outputs. However, they are memory intensive and require good computational power for both training as well as inferencing. In literature one can find different model compression techniques which enables easy deployment on edge devices. Knowledge distillation is one such approach where the knowledge of complex teacher model is transferred to a lower parameter student model. However, the limitation is that the architecture of the student model should be comparable to the complex teacher model for better knowledge transfer. Due to this limitation, we cannot deploy this student model that learns from a complex and huge teacher on edge devices. In this work, we propose to use a combined student approach wherein different student models learn from a common teacher model. Further, we propose a unique loss function which will train multiple student models simultaneously. An advantage of this approach is that these student models can be as simple as possible when compared with traditional single student model and also the complex teacher model. Finally, we provide an extensive evaluation to prove that our approach can improve the overall accuracy significantly and allow a further compression by 10% when compared with generic model.
{"title":"Efficient knowledge distillation of teacher model to multiple student models","authors":"Thrivikram Gl, Vidya Ganesh, T. Sethuraman, Satheesh K. Perepu","doi":"10.1109/IAICT52856.2021.9532543","DOIUrl":"https://doi.org/10.1109/IAICT52856.2021.9532543","url":null,"abstract":"Deep learning models are proven to deliver satisfactory results on training a complex non-linear relationship between the set of input features and different task outputs. However, they are memory intensive and require good computational power for both training as well as inferencing. In literature one can find different model compression techniques which enables easy deployment on edge devices. Knowledge distillation is one such approach where the knowledge of complex teacher model is transferred to a lower parameter student model. However, the limitation is that the architecture of the student model should be comparable to the complex teacher model for better knowledge transfer. Due to this limitation, we cannot deploy this student model that learns from a complex and huge teacher on edge devices. In this work, we propose to use a combined student approach wherein different student models learn from a common teacher model. Further, we propose a unique loss function which will train multiple student models simultaneously. An advantage of this approach is that these student models can be as simple as possible when compared with traditional single student model and also the complex teacher model. Finally, we provide an extensive evaluation to prove that our approach can improve the overall accuracy significantly and allow a further compression by 10% when compared with generic model.","PeriodicalId":416542,"journal":{"name":"2021 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117042423","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 : 2021-07-27DOI: 10.1109/IAICT52856.2021.9532554
A. Jaya, S. Nojeng, H. M. Pakka, Agung Pramono
Protection of the transmission line has a very important role in the electric power system. The line protection relay is a component system that used to detect a fault condition on the transmission line. Overhead Lines transmission line disturbances often occur due to the touch of a tree. To find out the type of disturbance that occurs, permanent or non-permanent, and to normalize the disturbance can only be done by inspection road on the transmission line from the substation to the location of the disturbance. To ascertain the type of disturbance condition it is permanent or not permanent on the transmission line is very difficult. For this reason, a fault detector using capacitive voltage is made using the PMT Auxiliary Contact, CB Status. The results of the design show that if there is interference, the capacitive voltage is zero so that the LED lamp will off, while in a healthy phase the LED lamp will on.
{"title":"The Transmission Fault Detection Method Using Capacitive Properties on the Transmission Line: Case Study of South Sulawesi Power System","authors":"A. Jaya, S. Nojeng, H. M. Pakka, Agung Pramono","doi":"10.1109/IAICT52856.2021.9532554","DOIUrl":"https://doi.org/10.1109/IAICT52856.2021.9532554","url":null,"abstract":"Protection of the transmission line has a very important role in the electric power system. The line protection relay is a component system that used to detect a fault condition on the transmission line. Overhead Lines transmission line disturbances often occur due to the touch of a tree. To find out the type of disturbance that occurs, permanent or non-permanent, and to normalize the disturbance can only be done by inspection road on the transmission line from the substation to the location of the disturbance. To ascertain the type of disturbance condition it is permanent or not permanent on the transmission line is very difficult. For this reason, a fault detector using capacitive voltage is made using the PMT Auxiliary Contact, CB Status. The results of the design show that if there is interference, the capacitive voltage is zero so that the LED lamp will off, while in a healthy phase the LED lamp will on.","PeriodicalId":416542,"journal":{"name":"2021 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125772756","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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