Ground station receivers or networks’ location uses different geodetic datums. The geodetic datum defines size and shape of the Earth model and origin of orientation of coordinate systems used to map the Earth. They are modified to converge to the International Terrestrial Reference Frame (ITRF). The Thailand geodetic reference frame has been maintained by the Royal Thai Survey Department (RTSD); currently, it is based on GNSS permanent networks. Precise orbit and clock solutions, earth orientation parameters and tropospheric gradients are applied as fixing parameters to estimate station positions using a network approach where all station coordinates are simultaneously adjusted; defined as a zero-order network. They are determined with respected to ITRF with centimetre precisions and accuracies. The shape of the Earth varies over time due to oceanic tides and plate movements. Changing are more gradually when earthquakes and volcanic eruptions happen. ITRF and other geodetic datums are changing continually; therefore, frame transformations are required. The latest adjusted coordinates are based on ITRF2005, 2008 and 2014 frame and velocities are set at the epoch 2008.83, 2013.81 and 2021.93 respectively. Constraints are cast in form of transformation parameters. The grid shift is then generated to transform from global coordinate systems to local geocentric reference frames. GNSS measurements obtained from another set of ground stations are used as check coordinates. Transformed coordinates are at 1 centimetre-level of accuracy both horizontally and vertically at 95 percent confidence level. Coordinate transformations can be applied to map Thailand based on one map on one datum policy.
{"title":"Precision coordinate transformations for Thai national geodetic infrastructure","authors":"Korakod Butwong, Thayathip Thongtan, Kriengkrai Boonterm","doi":"10.1109/ECTI-CON58255.2023.10153290","DOIUrl":"https://doi.org/10.1109/ECTI-CON58255.2023.10153290","url":null,"abstract":"Ground station receivers or networks’ location uses different geodetic datums. The geodetic datum defines size and shape of the Earth model and origin of orientation of coordinate systems used to map the Earth. They are modified to converge to the International Terrestrial Reference Frame (ITRF). The Thailand geodetic reference frame has been maintained by the Royal Thai Survey Department (RTSD); currently, it is based on GNSS permanent networks. Precise orbit and clock solutions, earth orientation parameters and tropospheric gradients are applied as fixing parameters to estimate station positions using a network approach where all station coordinates are simultaneously adjusted; defined as a zero-order network. They are determined with respected to ITRF with centimetre precisions and accuracies. The shape of the Earth varies over time due to oceanic tides and plate movements. Changing are more gradually when earthquakes and volcanic eruptions happen. ITRF and other geodetic datums are changing continually; therefore, frame transformations are required. The latest adjusted coordinates are based on ITRF2005, 2008 and 2014 frame and velocities are set at the epoch 2008.83, 2013.81 and 2021.93 respectively. Constraints are cast in form of transformation parameters. The grid shift is then generated to transform from global coordinate systems to local geocentric reference frames. GNSS measurements obtained from another set of ground stations are used as check coordinates. Transformed coordinates are at 1 centimetre-level of accuracy both horizontally and vertically at 95 percent confidence level. Coordinate transformations can be applied to map Thailand based on one map on one datum policy.","PeriodicalId":340768,"journal":{"name":"2023 20th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123330782","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 : 2023-05-09DOI: 10.1109/ECTI-CON58255.2023.10153328
S. Sitjongsataporn, Piyaporn Nurarak
This paper presents a combined diffusion policy on the orthogonal gradient-based algorithm using adaptive averaging leaky criterion. A mixed-node criterion is described by orthogonal projection matrix of its own tap-weight vector, while information of other connected nodes are undisturbed. An adaptive leaky algorithm is applied for fast convergence with low complexity. By minimising a mixed-node cost function, the distributed estimation is verified in terms of adapt-then-combine strategy over the distributed network. Statistical experimental results on system identification examine that a proposed algorithm can provide promising results in form of mean square error criterion.
{"title":"Combined Diffusion Adaptation on Adaptive Leaky Criterion and Orthogonal Gradient Algorithm","authors":"S. Sitjongsataporn, Piyaporn Nurarak","doi":"10.1109/ECTI-CON58255.2023.10153328","DOIUrl":"https://doi.org/10.1109/ECTI-CON58255.2023.10153328","url":null,"abstract":"This paper presents a combined diffusion policy on the orthogonal gradient-based algorithm using adaptive averaging leaky criterion. A mixed-node criterion is described by orthogonal projection matrix of its own tap-weight vector, while information of other connected nodes are undisturbed. An adaptive leaky algorithm is applied for fast convergence with low complexity. By minimising a mixed-node cost function, the distributed estimation is verified in terms of adapt-then-combine strategy over the distributed network. Statistical experimental results on system identification examine that a proposed algorithm can provide promising results in form of mean square error criterion.","PeriodicalId":340768,"journal":{"name":"2023 20th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123090750","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 : 2023-05-09DOI: 10.1109/ECTI-CON58255.2023.10153183
Rattikorn Sombutkaew, O. Chitsobhuk
A wide range of health-related innovation have been developed to serve as an alternative personal health monitoring and tracking on exercise and dietary planning. The goal is to enable individuals to keep themselves healthy and disease-free. Assessing food calories helps to assist consumers in determining their calories intake each meal, leading to a strategy that regulates the amount of food they consume, and contributing to improve a control on nutrition consumption and weight loss. In this paper, we proposed a calorie estimation system on an android mobile application. Calorie estimation is performed using a food image captured from a mobile camera and the depth image from AR core library. The food area is segmented using our finetuned Mask R-CNN with Thai food image dataset. Finally, machine learning methods including Linear Regression, Support Vector Regression, K-Nearest Neighbor, and Deep Neural Network are used to estimate the amount of food calories included in a meal of each image. As a result, Deep Neural Network offers best prediction results with the most accurate prediction, the lowest error rate and the highest R-Square score.
{"title":"Image-based Thai Food Recognition and Calorie Estimation using Machine Learning Techniques","authors":"Rattikorn Sombutkaew, O. Chitsobhuk","doi":"10.1109/ECTI-CON58255.2023.10153183","DOIUrl":"https://doi.org/10.1109/ECTI-CON58255.2023.10153183","url":null,"abstract":"A wide range of health-related innovation have been developed to serve as an alternative personal health monitoring and tracking on exercise and dietary planning. The goal is to enable individuals to keep themselves healthy and disease-free. Assessing food calories helps to assist consumers in determining their calories intake each meal, leading to a strategy that regulates the amount of food they consume, and contributing to improve a control on nutrition consumption and weight loss. In this paper, we proposed a calorie estimation system on an android mobile application. Calorie estimation is performed using a food image captured from a mobile camera and the depth image from AR core library. The food area is segmented using our finetuned Mask R-CNN with Thai food image dataset. Finally, machine learning methods including Linear Regression, Support Vector Regression, K-Nearest Neighbor, and Deep Neural Network are used to estimate the amount of food calories included in a meal of each image. As a result, Deep Neural Network offers best prediction results with the most accurate prediction, the lowest error rate and the highest R-Square score.","PeriodicalId":340768,"journal":{"name":"2023 20th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134003960","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}
Self-learning enables students to learn at their own pace. The process itself has changed significantly from reading textbooks to viewing ebooks on electronic devices, asking questions on online discussion boards or watching video clips from other parts of the world. The change has come with a massive increase in the amount of information available. It is time-consuming for a student to obtain the exact item of class material wanted. We developed a search system that can store relevant class materials in its own database and give suggestions when a student needs them. The system is built as a chatbot in the LINE application, using the NLP algorithm, to interact between users and the system via Artificial Intelligence technology. The system was developed for two subjects in Electrical Engineering: Signals and Systems and Control Systems. It was able to correctly interpret the users’ requests with up to 80% accuracy. It can give suggestions of class materials based on rankings given by users’ scores. This search system makes self-learning less time consuming and may provide more encouragement to students.
自学使学生能够按照自己的节奏学习。从阅读教科书到在电子设备上观看电子书、在在线讨论板上提问或观看来自世界其他地方的视频剪辑，这一过程本身已经发生了重大变化。这种变化伴随着信息量的大量增加。学生要想准确获得所需的课堂资料，需要耗费大量时间。我们开发了一个搜索系统，它可以在自己的数据库中存储相关的课堂资料，并在学生需要时给出建议。该系统以聊天机器人的形式建立在 LINE 应用程序中，使用 NLP 算法，通过人工智能技术实现用户与系统之间的互动。该系统是为电气工程的两个科目开发的：信号与系统和控制系统。它能够正确解释用户的请求，准确率高达 80%。它可以根据用户的评分排名给出课件建议。该搜索系统可减少自学时间，并可为学生提供更多鼓励。
{"title":"Development of a Class Materials Search System using LINE Chatbot","authors":"Budsakorn Thaiprasert, Pannawat Chimprasert, Warunya Saeninyod, Phattanard Phattanasri, Aranee Pangarad","doi":"10.1109/ECTI-CON58255.2023.10153145","DOIUrl":"https://doi.org/10.1109/ECTI-CON58255.2023.10153145","url":null,"abstract":"Self-learning enables students to learn at their own pace. The process itself has changed significantly from reading textbooks to viewing ebooks on electronic devices, asking questions on online discussion boards or watching video clips from other parts of the world. The change has come with a massive increase in the amount of information available. It is time-consuming for a student to obtain the exact item of class material wanted. We developed a search system that can store relevant class materials in its own database and give suggestions when a student needs them. The system is built as a chatbot in the LINE application, using the NLP algorithm, to interact between users and the system via Artificial Intelligence technology. The system was developed for two subjects in Electrical Engineering: Signals and Systems and Control Systems. It was able to correctly interpret the users’ requests with up to 80% accuracy. It can give suggestions of class materials based on rankings given by users’ scores. This search system makes self-learning less time consuming and may provide more encouragement to students.","PeriodicalId":340768,"journal":{"name":"2023 20th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128024864","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 : 2023-05-09DOI: 10.1109/ECTI-CON58255.2023.10153143
Joshua Chun-Ken Dardchuntuk, D. Banjerdpongchai
An accurate estimation of the state of charge (SoC) of lithium titanate (LTO) batteries is required for their effective operation and management. In this study, we propose an unscented Kalman filter (UKF) approach for estimating the SoC of LTO batteries, which are challenging to assess due to the nonlinear voltage-SoC relationship and aging impact. Our approach uses a state and measurement model based on LTO’s electrochemical characteristics and employs sigma points and weights to address nonlinearities. According to the findings of our research, the UKF-based methodology has high accuracy, rapid convergence, and resilience to discharge rate, outperforming or matching the capabilities of existing state-of-the-art approaches. This work provides a novel and effective solution for LTO battery SoC estimation, useful for applications in electric vehicles, energy storage, and smart grid energy systems.
{"title":"Unscented Kalman Filter for State of Charge Estimation of Lithium Titanate Battery","authors":"Joshua Chun-Ken Dardchuntuk, D. Banjerdpongchai","doi":"10.1109/ECTI-CON58255.2023.10153143","DOIUrl":"https://doi.org/10.1109/ECTI-CON58255.2023.10153143","url":null,"abstract":"An accurate estimation of the state of charge (SoC) of lithium titanate (LTO) batteries is required for their effective operation and management. In this study, we propose an unscented Kalman filter (UKF) approach for estimating the SoC of LTO batteries, which are challenging to assess due to the nonlinear voltage-SoC relationship and aging impact. Our approach uses a state and measurement model based on LTO’s electrochemical characteristics and employs sigma points and weights to address nonlinearities. According to the findings of our research, the UKF-based methodology has high accuracy, rapid convergence, and resilience to discharge rate, outperforming or matching the capabilities of existing state-of-the-art approaches. This work provides a novel and effective solution for LTO battery SoC estimation, useful for applications in electric vehicles, energy storage, and smart grid energy systems.","PeriodicalId":340768,"journal":{"name":"2023 20th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129067379","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 : 2023-05-09DOI: 10.1109/ECTI-CON58255.2023.10153360
N. Rojrattanatrai, T. Kasetkasem, T. Phatrapornant, C. Theerawitaya, D. Chungloo, S. Cha-um, Masahiro Yamaguchi
Hyperspectral reflectance data in the VNIR-SWIR range (400-2500nm) are commonly used to non-destructively and contactless measure the chemical composition of the plants. Most traditional methods are based on non-spatial analysis, that method required the average spectral data to represent the entire image, resulting in the loss of spatial information. To address this issue, we utilize a U-Net network to preserve spatial information while also allowing for the identification and quantification of lactone content in the image. The resulting distribution map provides a clear visualization of lactone content throughout the field or crop, making it easy to identify areas with high or low lactone levels. The pre-processing method includes image registration, outlier removal, spectral smoothing, and normalization. These steps are designed to correct errors and improve the quality of the image and masking. According to the experimental results, the U-Net model achieved R2, RMSE, and DICE of 0.718, 11.66, and 89.92%, respectively. The results show that using hyperspectral images combined with the U-Net network can perform a reliable and accurate prediction model for determining lactone content in A. paniculata.
{"title":"On the lactone content distribution estimation in Andrographis paniculata (burm.f.) wall.ex nees using Hyperspectral Images and U-Net Network","authors":"N. Rojrattanatrai, T. Kasetkasem, T. Phatrapornant, C. Theerawitaya, D. Chungloo, S. Cha-um, Masahiro Yamaguchi","doi":"10.1109/ECTI-CON58255.2023.10153360","DOIUrl":"https://doi.org/10.1109/ECTI-CON58255.2023.10153360","url":null,"abstract":"Hyperspectral reflectance data in the VNIR-SWIR range (400-2500nm) are commonly used to non-destructively and contactless measure the chemical composition of the plants. Most traditional methods are based on non-spatial analysis, that method required the average spectral data to represent the entire image, resulting in the loss of spatial information. To address this issue, we utilize a U-Net network to preserve spatial information while also allowing for the identification and quantification of lactone content in the image. The resulting distribution map provides a clear visualization of lactone content throughout the field or crop, making it easy to identify areas with high or low lactone levels. The pre-processing method includes image registration, outlier removal, spectral smoothing, and normalization. These steps are designed to correct errors and improve the quality of the image and masking. According to the experimental results, the U-Net model achieved R2, RMSE, and DICE of 0.718, 11.66, and 89.92%, respectively. The results show that using hyperspectral images combined with the U-Net network can perform a reliable and accurate prediction model for determining lactone content in A. paniculata.","PeriodicalId":340768,"journal":{"name":"2023 20th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123868063","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 : 2023-05-09DOI: 10.1109/ECTI-CON58255.2023.10153161
Rithea Sum, Chanon Khongprasongsiri, W. Suwansantisuk, P. Kumhom
The output from a digital Micro-Electrical- Mechanical System (MEMS) microphone in the form of PDM often needs to be converted to PCM before further processing, as PCM signals are easier to analyze. The current hardware-based PDM-to-PCM converters uses cascaded integrator-comb (CIC) based and finite impulse response (FIR) filters, which result in high hardware utilization to achieve a high signal-to-noise ratio (SNR). To strike a balance between performance and power consumption, a one-dimensional convolutional neural network (1D-CNN) has been applied in a PDM-to-PCM converter. Although this method resolves the aforementioned issues, an improvement to the system latency and throughput is possible. This paper proposes a fast method for a hardware-based PDM-to-PCM converter by cascading a digital low-pass filter and an existing ID-CNN-based low-pass filter. The approximation results show that the output PCM signal has the mean absolute error (MAE) of only 0.0026 compared to the original PCM signal. The proposed method has been implemented on the Xilinx PYNQ-ZI field programmable gate array (FPGA). While there is a slight increase in hardware utilization due to an additional required hardware, the latency has improved by 61% compared to the existing ID-CNN-based PDM-to-PCM converter. This research reduces the time taken to process each PCM data from PDM in a hardware-based system.
{"title":"Low Latency PDM-to-PCM Decoder","authors":"Rithea Sum, Chanon Khongprasongsiri, W. Suwansantisuk, P. Kumhom","doi":"10.1109/ECTI-CON58255.2023.10153161","DOIUrl":"https://doi.org/10.1109/ECTI-CON58255.2023.10153161","url":null,"abstract":"The output from a digital Micro-Electrical- Mechanical System (MEMS) microphone in the form of PDM often needs to be converted to PCM before further processing, as PCM signals are easier to analyze. The current hardware-based PDM-to-PCM converters uses cascaded integrator-comb (CIC) based and finite impulse response (FIR) filters, which result in high hardware utilization to achieve a high signal-to-noise ratio (SNR). To strike a balance between performance and power consumption, a one-dimensional convolutional neural network (1D-CNN) has been applied in a PDM-to-PCM converter. Although this method resolves the aforementioned issues, an improvement to the system latency and throughput is possible. This paper proposes a fast method for a hardware-based PDM-to-PCM converter by cascading a digital low-pass filter and an existing ID-CNN-based low-pass filter. The approximation results show that the output PCM signal has the mean absolute error (MAE) of only 0.0026 compared to the original PCM signal. The proposed method has been implemented on the Xilinx PYNQ-ZI field programmable gate array (FPGA). While there is a slight increase in hardware utilization due to an additional required hardware, the latency has improved by 61% compared to the existing ID-CNN-based PDM-to-PCM converter. This research reduces the time taken to process each PCM data from PDM in a hardware-based system.","PeriodicalId":340768,"journal":{"name":"2023 20th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125071965","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}
The objective of this study is to develop an exams item bank management system on the internet. The exam item analysis system is used to determine the reliability of the exams using K-R 20 and K-R 21 formulas. The difficulty and power of discrimination values are determined using a choice analysis and percentages of high and lower groups technique. The create exams set system can be performed randomly or identified through the difficulty level. The examination can be performed online and the exam results can be immediately announced. The grading system is used to support the decision-making to evaluate learning achievement. The system’s accuracy was tested and found to be accurate compared with the hand calculation results. The system was tested with a sample group and satisfaction was assessed through questionnaires. The satisfaction of teachers and students was highest, while that of administrators was high. The developed exams item bank management system operates as a web application with a security system for supporting in learning activity.
{"title":"Exams Item Analysis, Online Examination, and Education Evaluation System Via Internet","authors":"Kamonlak Chaidee, Kanchana Boontasri, E. Chaidee, Kittichai Punthura, Narong Kumtip, Yotsanan Pankham","doi":"10.1109/ECTI-CON58255.2023.10153296","DOIUrl":"https://doi.org/10.1109/ECTI-CON58255.2023.10153296","url":null,"abstract":"The objective of this study is to develop an exams item bank management system on the internet. The exam item analysis system is used to determine the reliability of the exams using K-R 20 and K-R 21 formulas. The difficulty and power of discrimination values are determined using a choice analysis and percentages of high and lower groups technique. The create exams set system can be performed randomly or identified through the difficulty level. The examination can be performed online and the exam results can be immediately announced. The grading system is used to support the decision-making to evaluate learning achievement. The system’s accuracy was tested and found to be accurate compared with the hand calculation results. The system was tested with a sample group and satisfaction was assessed through questionnaires. The satisfaction of teachers and students was highest, while that of administrators was high. The developed exams item bank management system operates as a web application with a security system for supporting in learning activity.","PeriodicalId":340768,"journal":{"name":"2023 20th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117311072","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 : 2023-05-09DOI: 10.1109/ECTI-CON58255.2023.10153314
S. Yukhalang, T. Sopapirm, V. Kinnares
This paper proposes a performance analysis of using a static volt-amp-reactive (VAR) compensator for a three-phase self-excited induction generator (SEIG) operating as a single-phase induction generator. The study proposes appropriate capacitor for building up voltage supplying power to load with static VAR for regulating terminal voltage. The simulation model is analyzed by using MATLAB/Simulink under various conditions such as reactive power supplied, dynamic response, steady state, building up voltage for startup, linear load, non-linear load and harmonics in system. From the study (based on the study, the findings revealed it is found) that using the static VAR compensator connected in parallel with loads for supplying reactive power to the load systems is needed for maintaining terminal voltage.
{"title":"Three-phase Self-Excited Induction Generator Operating as Single-phase Induction Generator using Static VAR Compensator","authors":"S. Yukhalang, T. Sopapirm, V. Kinnares","doi":"10.1109/ECTI-CON58255.2023.10153314","DOIUrl":"https://doi.org/10.1109/ECTI-CON58255.2023.10153314","url":null,"abstract":"This paper proposes a performance analysis of using a static volt-amp-reactive (VAR) compensator for a three-phase self-excited induction generator (SEIG) operating as a single-phase induction generator. The study proposes appropriate capacitor for building up voltage supplying power to load with static VAR for regulating terminal voltage. The simulation model is analyzed by using MATLAB/Simulink under various conditions such as reactive power supplied, dynamic response, steady state, building up voltage for startup, linear load, non-linear load and harmonics in system. From the study (based on the study, the findings revealed it is found) that using the static VAR compensator connected in parallel with loads for supplying reactive power to the load systems is needed for maintaining terminal voltage.","PeriodicalId":340768,"journal":{"name":"2023 20th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117241584","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 : 2023-05-09DOI: 10.1109/ECTI-CON58255.2023.10153207
K. Angkeaw, Surachai Chanchay, P. Thongdit
This paper presents a demodulator circuit for LVDT based on the field programmable analog array implemented. The proposed circuit uses the ratio matrix method and employs a single CMOS Field Programmable Analog Array (FPAA) device. It consists of a summing amplifier, divider circuit, and lowpass filter. The demodulator circuit’s functional elements are realized by employing the available Configurable Analogue Modules (CAMs) of the FPAA AN231E04 from AnadigmDesigner®2. Thus, direct current (DC) voltage between -2.43 and +2.3 at 2.5 kHz is proportional to displacement -3 mm until +2.8 mm is outputted. Results show that displacement sensor nonlinear error is γ = 0.27%.
{"title":"A Development of LVDT Demodulator Circuit Based on FPAA Chip","authors":"K. Angkeaw, Surachai Chanchay, P. Thongdit","doi":"10.1109/ECTI-CON58255.2023.10153207","DOIUrl":"https://doi.org/10.1109/ECTI-CON58255.2023.10153207","url":null,"abstract":"This paper presents a demodulator circuit for LVDT based on the field programmable analog array implemented. The proposed circuit uses the ratio matrix method and employs a single CMOS Field Programmable Analog Array (FPAA) device. It consists of a summing amplifier, divider circuit, and lowpass filter. The demodulator circuit’s functional elements are realized by employing the available Configurable Analogue Modules (CAMs) of the FPAA AN231E04 from AnadigmDesigner®2. Thus, direct current (DC) voltage between -2.43 and +2.3 at 2.5 kHz is proportional to displacement -3 mm until +2.8 mm is outputted. Results show that displacement sensor nonlinear error is γ = 0.27%.","PeriodicalId":340768,"journal":{"name":"2023 20th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129516448","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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