The primary issue of garbage is its direct impact on the environment and quality of life. Several types of trash have piled up in Thailand. Many individuals don’t use the correct way of waste management because they have lacked knowledge and understanding of how to sort rubbish since childhood. To increase the possibility to fully utilize the knowledge of waste separation correctly, the researcher aims to promote computational thinking through the learning of a game called “Trashman”. This game simulates various objects for situations of sorting garbage into the appropriate sorting bin. Trashman has 3 levels in the game. Each level increases the number of bins and the amount of garbage, starting from level 1-3 which refers to easy, moderate, and difficult levels, respectively. The level indicates the error of separating the waste if putting it into a wrong bin type. The educating about different types of waste separation through the game utilizes Mixed Reality (MR) technology of the Magic Leap One device. The developer’s expectation for an alternative learning medium in the form of a game is to allow players to apply their knowledge by practicing computational skills by sorting garbage correctly. The game evaluates and summarizes scores of each level at the game’s end. This will evaluate player’s performance of CT through their ability separating trash. Moreover, the Sustainable Development Goals can be applied for lifelong learning to follow-up and assessment in furtherance of correct trash sorting.
{"title":"The Waste Separation Game to Promote Computational Thinking Through Mixed Reality Technology","authors":"Worapat Phatarametravorakul, Suphitsara Cheevanantaporn, Pornsuree Jamsri","doi":"10.1109/ICITEE56407.2022.9954123","DOIUrl":"https://doi.org/10.1109/ICITEE56407.2022.9954123","url":null,"abstract":"The primary issue of garbage is its direct impact on the environment and quality of life. Several types of trash have piled up in Thailand. Many individuals don’t use the correct way of waste management because they have lacked knowledge and understanding of how to sort rubbish since childhood. To increase the possibility to fully utilize the knowledge of waste separation correctly, the researcher aims to promote computational thinking through the learning of a game called “Trashman”. This game simulates various objects for situations of sorting garbage into the appropriate sorting bin. Trashman has 3 levels in the game. Each level increases the number of bins and the amount of garbage, starting from level 1-3 which refers to easy, moderate, and difficult levels, respectively. The level indicates the error of separating the waste if putting it into a wrong bin type. The educating about different types of waste separation through the game utilizes Mixed Reality (MR) technology of the Magic Leap One device. The developer’s expectation for an alternative learning medium in the form of a game is to allow players to apply their knowledge by practicing computational skills by sorting garbage correctly. The game evaluates and summarizes scores of each level at the game’s end. This will evaluate player’s performance of CT through their ability separating trash. Moreover, the Sustainable Development Goals can be applied for lifelong learning to follow-up and assessment in furtherance of correct trash sorting.","PeriodicalId":246279,"journal":{"name":"2022 14th International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128961037","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 : 2022-10-18DOI: 10.1109/ICITEE56407.2022.9954122
Ulya Ganeswara Alamy, Eka Marliana, A. Wahjudi, I. M. L. Batan, Latifah Nurahmi
An early detection control system for high-speed and objectivity welding defects is needed. Visual Inspection (VT) is an important method and the initial stage before a welded material will be tested at destructive testing. So far, VT has only used human vision, which takes a protracted process and is highly subjective. This paper will contribute to the VT method to control the Friction Stir Welding (FSW) process by detecting the flash defect using image processing and Convolutional Neural Network (CNN). Thus, flash defects in the FSW process can be minimised and detected as early as possible. Image processing and CNN serve as a substitute for human vision. The selection of CNN is considered suitable for detecting an image because the process is fast and detects key features without human supervision, which is carried out by a continuous learning process. 620 images from the FSW process were processed into two groups of datasets. It was processed with two types of CNN architecture, including AlexNet and VGG16. Based on the VT results by CNN, the AlexNet model showed a detection accuracy of 91.03%, while the VGG16 model showed a detection accuracy of 77.35%. From these results, CNN’s success in conducting VT on FSW process control is relatively high and can play a more significant role in checking the results of the FSW process. Therefore, the possibility of flash defects can be minimised and detected as early as possible.
{"title":"Flash Defect Detection System of Friction Stir Welding Process Based on Convolutional Neural Networks for AA 6061-T651","authors":"Ulya Ganeswara Alamy, Eka Marliana, A. Wahjudi, I. M. L. Batan, Latifah Nurahmi","doi":"10.1109/ICITEE56407.2022.9954122","DOIUrl":"https://doi.org/10.1109/ICITEE56407.2022.9954122","url":null,"abstract":"An early detection control system for high-speed and objectivity welding defects is needed. Visual Inspection (VT) is an important method and the initial stage before a welded material will be tested at destructive testing. So far, VT has only used human vision, which takes a protracted process and is highly subjective. This paper will contribute to the VT method to control the Friction Stir Welding (FSW) process by detecting the flash defect using image processing and Convolutional Neural Network (CNN). Thus, flash defects in the FSW process can be minimised and detected as early as possible. Image processing and CNN serve as a substitute for human vision. The selection of CNN is considered suitable for detecting an image because the process is fast and detects key features without human supervision, which is carried out by a continuous learning process. 620 images from the FSW process were processed into two groups of datasets. It was processed with two types of CNN architecture, including AlexNet and VGG16. Based on the VT results by CNN, the AlexNet model showed a detection accuracy of 91.03%, while the VGG16 model showed a detection accuracy of 77.35%. From these results, CNN’s success in conducting VT on FSW process control is relatively high and can play a more significant role in checking the results of the FSW process. Therefore, the possibility of flash defects can be minimised and detected as early as possible.","PeriodicalId":246279,"journal":{"name":"2022 14th International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122214774","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 : 2022-10-18DOI: 10.1109/ICITEE56407.2022.9954090
W. Hidayat, H. Elmunsyah, Luluk Iwanatul Bariroh, T. A. Sutikno
The decrease in learning motivation significantly impacts students’ understanding and learning outcomes. This study aims to develop gamification-based e-learning for vocational high school students and describe the feasibility level of e-learning. The implementation of gamification aims to increase student involvement, motivation, and learning experience. This development research adapts the SAM (Successive Approximation Model) 1 development model through three iterative stages: evaluate, design, and develop. Evaluation is carried out through the stages of alpha testing by experts and beta testing by users. Results of alpha testing are: (1) Material expert validation is very valid with a percentage of 89.06%. (2) Media experts’ validation is very valid, with a percentage of 92.26%. Beta testing attended by 45 VHS students resulted in 81.3% with very valid criteria. The learning motivation test resulted in 81.04% with very high motivation criteria, which showed the effect of gamification implementation in fostering student learning motivation. Media experts, material experts, and students have tested E-learning for feasibility. Therefore, e-learning products are feasible and can be used as a medium to support learning in database subjects at VHS.
{"title":"Gamification-Based E-learning Design for Vocational Software Engineering Subjects","authors":"W. Hidayat, H. Elmunsyah, Luluk Iwanatul Bariroh, T. A. Sutikno","doi":"10.1109/ICITEE56407.2022.9954090","DOIUrl":"https://doi.org/10.1109/ICITEE56407.2022.9954090","url":null,"abstract":"The decrease in learning motivation significantly impacts students’ understanding and learning outcomes. This study aims to develop gamification-based e-learning for vocational high school students and describe the feasibility level of e-learning. The implementation of gamification aims to increase student involvement, motivation, and learning experience. This development research adapts the SAM (Successive Approximation Model) 1 development model through three iterative stages: evaluate, design, and develop. Evaluation is carried out through the stages of alpha testing by experts and beta testing by users. Results of alpha testing are: (1) Material expert validation is very valid with a percentage of 89.06%. (2) Media experts’ validation is very valid, with a percentage of 92.26%. Beta testing attended by 45 VHS students resulted in 81.3% with very valid criteria. The learning motivation test resulted in 81.04% with very high motivation criteria, which showed the effect of gamification implementation in fostering student learning motivation. Media experts, material experts, and students have tested E-learning for feasibility. Therefore, e-learning products are feasible and can be used as a medium to support learning in database subjects at VHS.","PeriodicalId":246279,"journal":{"name":"2022 14th International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125586169","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 : 2022-10-18DOI: 10.1109/ICITEE56407.2022.9954105
C. W. D. Lumoindong, Rila Mandala
As a part of the video classification task, action recognition is also known as a task with heavy computational load, with models mostly trained on devices with multiple GPUs. The pre-trained models suffer from large size and take a long time to infer the test data, especially on devices with low specifications and mobile devices. The recent development of neural networks introduces the Binarized Neural Network (BNN), which offers a solution to these problems. BNNs are trained with binary activations and weights, which reduces the computation from 32-bits to 1-bit. Theoretically, this feature can perform using 32x less memory and hardware resource compared to the conventional, full-precision neural networks. Theoretically, the conversion from full-precision CNN to BNN should result in a smaller model size and faster inference time. In this research, a binarized 3D CNN model is built using the principles of BNN and tested against the full-precision CNN. The BNN model is able to reach 78.7% train accuracy, 76.3% validation accuracy, and 79.6% inference accuracy, which means that the model is working according to the standards defined in this research.
{"title":"Binarized and Full-Precision 3D-CNN in Action Recognition","authors":"C. W. D. Lumoindong, Rila Mandala","doi":"10.1109/ICITEE56407.2022.9954105","DOIUrl":"https://doi.org/10.1109/ICITEE56407.2022.9954105","url":null,"abstract":"As a part of the video classification task, action recognition is also known as a task with heavy computational load, with models mostly trained on devices with multiple GPUs. The pre-trained models suffer from large size and take a long time to infer the test data, especially on devices with low specifications and mobile devices. The recent development of neural networks introduces the Binarized Neural Network (BNN), which offers a solution to these problems. BNNs are trained with binary activations and weights, which reduces the computation from 32-bits to 1-bit. Theoretically, this feature can perform using 32x less memory and hardware resource compared to the conventional, full-precision neural networks. Theoretically, the conversion from full-precision CNN to BNN should result in a smaller model size and faster inference time. In this research, a binarized 3D CNN model is built using the principles of BNN and tested against the full-precision CNN. The BNN model is able to reach 78.7% train accuracy, 76.3% validation accuracy, and 79.6% inference accuracy, which means that the model is working according to the standards defined in this research.","PeriodicalId":246279,"journal":{"name":"2022 14th International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133487390","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}
Algorithm has been heavily used to provide a comfort life for us: from finding the best route in Google map to biometrics authentication on your phone. The significant of algorithm defines Computational thinking (CT) as one of the 21st Century skills. Although CT has been integrated into the education in the past years, many students still struggle with CT concept due to the complication of the topic and limited learning methods provided in school environment. To provide alternative learning approach for algorithmic thinking, we propose a game-base learning system called Protobot. Protobot requires students to apply their CT knowledge, especially the algorithmic thinking to solve problems in the gameplay. The experimental results demonstrate that Protobot fosters the algorithmic thinking skill of the players as well as provides the amusement during the gameplay.
{"title":"Protobot: An Educational Game for Algorithmic Thinking","authors":"Thanachote Lertlapnon, Naruebes Lueangrungudom, Sirion Vittayakorn","doi":"10.1109/ICITEE56407.2022.9954081","DOIUrl":"https://doi.org/10.1109/ICITEE56407.2022.9954081","url":null,"abstract":"Algorithm has been heavily used to provide a comfort life for us: from finding the best route in Google map to biometrics authentication on your phone. The significant of algorithm defines Computational thinking (CT) as one of the 21st Century skills. Although CT has been integrated into the education in the past years, many students still struggle with CT concept due to the complication of the topic and limited learning methods provided in school environment. To provide alternative learning approach for algorithmic thinking, we propose a game-base learning system called Protobot. Protobot requires students to apply their CT knowledge, especially the algorithmic thinking to solve problems in the gameplay. The experimental results demonstrate that Protobot fosters the algorithmic thinking skill of the players as well as provides the amusement during the gameplay.","PeriodicalId":246279,"journal":{"name":"2022 14th International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128609150","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 : 2022-10-18DOI: 10.1109/ICITEE56407.2022.9954118
Pankaj Kumar, K. Koley, R. Goswami, A. Maurya, Subindu Kumar
This work investigates the numerical analysis based on multiple noise figures of merit in a high-k gate-all-around MOSFET by considering two-port device analysis. The noise in the device is calculated by analyzing the statistical behavior of random voltage sources at the terminals of the MOSFET represented as a two-port system. Although, HfO2 based GAA MOSFET device shows better ON-state current, yet, the device shows degradation in minimum noise figure, autocorrelation, cross-correlation, optimum source impedance, and noise conductance when compared to SiO2 based devices.
{"title":"Electrical Noise Behaviour of High-k Gate-All-Around MOSFET Based on Two-Port Device Network Analysis","authors":"Pankaj Kumar, K. Koley, R. Goswami, A. Maurya, Subindu Kumar","doi":"10.1109/ICITEE56407.2022.9954118","DOIUrl":"https://doi.org/10.1109/ICITEE56407.2022.9954118","url":null,"abstract":"This work investigates the numerical analysis based on multiple noise figures of merit in a high-k gate-all-around MOSFET by considering two-port device analysis. The noise in the device is calculated by analyzing the statistical behavior of random voltage sources at the terminals of the MOSFET represented as a two-port system. Although, HfO2 based GAA MOSFET device shows better ON-state current, yet, the device shows degradation in minimum noise figure, autocorrelation, cross-correlation, optimum source impedance, and noise conductance when compared to SiO2 based devices.","PeriodicalId":246279,"journal":{"name":"2022 14th International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128954572","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 : 2022-10-18DOI: 10.1109/ICITEE56407.2022.9954101
Dwi Wahyudi, I. Soesanti, H. A. Nugroho
The field of computer vision, particularly object detection, has undergone significant changes. Most cutting-edge object detectors can accurately detect medium and large objects. Small object detection remains challenging for the majority of object detectors due to low resolution, lack of feature information, small objects appearing in unexpected areas or overlapping with other objects, and small object dataset limitations. Several solutions have been developed to address this issue. This paper provides a brief description and analysis of contemporary general object detectors, such as Faster R-CNN, SSD, and YOLO. In addition, we investigate several techniques to improve object detection performance, particularly for small object detection, from three perspectives: network improvement (multiscale feature, contextual information), input data optimization (super-resolution, image tiling), and dataset enhancement (data augmentation, creating own dataset). Implementing these techniques has been shown to improve the accuracy of contemporary object detectors, particularly for small objects.
{"title":"Toward Detection of Small Objects Using Deep Learning Methods: A Review","authors":"Dwi Wahyudi, I. Soesanti, H. A. Nugroho","doi":"10.1109/ICITEE56407.2022.9954101","DOIUrl":"https://doi.org/10.1109/ICITEE56407.2022.9954101","url":null,"abstract":"The field of computer vision, particularly object detection, has undergone significant changes. Most cutting-edge object detectors can accurately detect medium and large objects. Small object detection remains challenging for the majority of object detectors due to low resolution, lack of feature information, small objects appearing in unexpected areas or overlapping with other objects, and small object dataset limitations. Several solutions have been developed to address this issue. This paper provides a brief description and analysis of contemporary general object detectors, such as Faster R-CNN, SSD, and YOLO. In addition, we investigate several techniques to improve object detection performance, particularly for small object detection, from three perspectives: network improvement (multiscale feature, contextual information), input data optimization (super-resolution, image tiling), and dataset enhancement (data augmentation, creating own dataset). Implementing these techniques has been shown to improve the accuracy of contemporary object detectors, particularly for small objects.","PeriodicalId":246279,"journal":{"name":"2022 14th International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129379913","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}
Manual lymph node classification is a tedious and time-consuming task. It requires a histopathologist to discriminate a lymph node from other look-alike kinds of tissues. The lymph node is easily misunderstood with other tissues because its shape and color might be similar to the others tissue around it. To automate this task, we present an automatic lymph node classification with convolutional neural network (CNN). In addition, we compared eight existing CNNs to ensure that we discover the best architecture for discriminating lymph node. DenseNet architecture provided the highest performance among AlexNet, VGG, GoogLeNet, ResNet, SqueezeNet, MobileNet, and EfficientNet, the highest accuracy at 0.994 and an F1score of 0.996. DenseNet accomplished the highest performance from two advantages: (i) fewer parameters and (ii) Dense connectivity.
{"title":"Automatic Lymph Node Classification with Convolutional Neural Network","authors":"Ason Uthatham, Nutcha Yodrabum, Chanya Sinmaroeng, Taravichet Titijaroonroj","doi":"10.1109/ICITEE56407.2022.9954045","DOIUrl":"https://doi.org/10.1109/ICITEE56407.2022.9954045","url":null,"abstract":"Manual lymph node classification is a tedious and time-consuming task. It requires a histopathologist to discriminate a lymph node from other look-alike kinds of tissues. The lymph node is easily misunderstood with other tissues because its shape and color might be similar to the others tissue around it. To automate this task, we present an automatic lymph node classification with convolutional neural network (CNN). In addition, we compared eight existing CNNs to ensure that we discover the best architecture for discriminating lymph node. DenseNet architecture provided the highest performance among AlexNet, VGG, GoogLeNet, ResNet, SqueezeNet, MobileNet, and EfficientNet, the highest accuracy at 0.994 and an F1score of 0.996. DenseNet accomplished the highest performance from two advantages: (i) fewer parameters and (ii) Dense connectivity.","PeriodicalId":246279,"journal":{"name":"2022 14th International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127000140","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 : 2022-10-18DOI: 10.1109/ICITEE56407.2022.9954117
Frida Hasana, S. P. Hadi, M. I. B. Setyonegoro, Tumiran
It is widely recognized that using conventional power plants requires an expensive and massive fossil fuel supply. This can be minimized by doing a hybrid conventional power plants and renewable energy sources (RES), along with pumped storage. In this paper, the optimal scheduling of that hybrid system is simulated using MATPOWER Optimal Scheduling Tool (MOST) to obtained the most optimal and economic condition. Utilize the scheduling outcomes and the assumption that power wheeling is implemented, this paper calculates the network lease. This paper provides the leasing calculation using MW-km method with the reverse, absolute, and dominant approach by considering the direction of power flow. According to the simulation results, a scheduling method that includes pumped storage and RES can reduce conventional plant operations. Moreover, this paper shows that the reverse approach produced the lowest calculation results compared to the other approaches.
{"title":"Power Wheeling Hybrid System of PV-Pumped Storage Using MW-KM Method","authors":"Frida Hasana, S. P. Hadi, M. I. B. Setyonegoro, Tumiran","doi":"10.1109/ICITEE56407.2022.9954117","DOIUrl":"https://doi.org/10.1109/ICITEE56407.2022.9954117","url":null,"abstract":"It is widely recognized that using conventional power plants requires an expensive and massive fossil fuel supply. This can be minimized by doing a hybrid conventional power plants and renewable energy sources (RES), along with pumped storage. In this paper, the optimal scheduling of that hybrid system is simulated using MATPOWER Optimal Scheduling Tool (MOST) to obtained the most optimal and economic condition. Utilize the scheduling outcomes and the assumption that power wheeling is implemented, this paper calculates the network lease. This paper provides the leasing calculation using MW-km method with the reverse, absolute, and dominant approach by considering the direction of power flow. According to the simulation results, a scheduling method that includes pumped storage and RES can reduce conventional plant operations. Moreover, this paper shows that the reverse approach produced the lowest calculation results compared to the other approaches.","PeriodicalId":246279,"journal":{"name":"2022 14th International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126621677","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 : 2022-10-18DOI: 10.1109/ICITEE56407.2022.9954115
Agta Wijaya Kurniawan, E. Firmansyah, F. D. Wijaya
DC-DC converter battery charger application commonly applied a high-frequency switching method. The high-frequency technique aims for a smaller size transformer size and weight. Overall, the chosen strategy leads to more economical end-product. The SiC-MOSFET becomes an additional solution to achieve high power and high frequency application applications. This paper focused on the design of the gate driver for SiC-MOSFET that can be applied in many applications including battery charger and inverter applications. The result showed that the gate driver designed had successfully switched SiC-MOSFET up to 35 kHz.
{"title":"Experiment Result of High Frequency Switching SiC Mosfet Gate Driver","authors":"Agta Wijaya Kurniawan, E. Firmansyah, F. D. Wijaya","doi":"10.1109/ICITEE56407.2022.9954115","DOIUrl":"https://doi.org/10.1109/ICITEE56407.2022.9954115","url":null,"abstract":"DC-DC converter battery charger application commonly applied a high-frequency switching method. The high-frequency technique aims for a smaller size transformer size and weight. Overall, the chosen strategy leads to more economical end-product. The SiC-MOSFET becomes an additional solution to achieve high power and high frequency application applications. This paper focused on the design of the gate driver for SiC-MOSFET that can be applied in many applications including battery charger and inverter applications. The result showed that the gate driver designed had successfully switched SiC-MOSFET up to 35 kHz.","PeriodicalId":246279,"journal":{"name":"2022 14th International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130047462","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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