Pub Date : 2020-06-01DOI: 10.1109/ecti-con49241.2020.9158305
S. Yamaguchi
A cybersecurity system called Botnet Defense System (BDS) uses white-hat worms to defend IoT systems against malware like Mirai. This paper analyzes the influence that whitehat worms come under from network structure. The analysis is focused on network density. Using agent-oriented Petri nets, we expressed IoT systems having different network density. Through the simulation evaluation of the model, we revealed that if the network density is low, the white-hat worms become inefficient. In addition, based on the result we proposed a basic strategy for BDS: If a given IoT system has low network density, the BDS should launch as many worms as possible.
{"title":"Influence Analysis of Network Density on White-Hat Worm and Basic Strategy for Botnet Defense System","authors":"S. Yamaguchi","doi":"10.1109/ecti-con49241.2020.9158305","DOIUrl":"https://doi.org/10.1109/ecti-con49241.2020.9158305","url":null,"abstract":"A cybersecurity system called Botnet Defense System (BDS) uses white-hat worms to defend IoT systems against malware like Mirai. This paper analyzes the influence that whitehat worms come under from network structure. The analysis is focused on network density. Using agent-oriented Petri nets, we expressed IoT systems having different network density. Through the simulation evaluation of the model, we revealed that if the network density is low, the white-hat worms become inefficient. In addition, based on the result we proposed a basic strategy for BDS: If a given IoT system has low network density, the BDS should launch as many worms as possible.","PeriodicalId":371552,"journal":{"name":"2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125947545","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 : 2020-06-01DOI: 10.1109/ecti-con49241.2020.9158089
A. Puttarach, T. Kasirawat, S. Panta, A. Phayomhom, C. Pongsriwat, Wirat Nakkrongdee
This paper presents a technique for grounding system design in a small area distribution substation. Compression ratio adjustment in order to decrease touch voltage is hard to design with this situation due to the limitation of area. Furthermore, this condition can’t use the ground rod length increasing method since the length doesn’t reach bottom layer of the ground. So this paper will show a method for solve this problem by using apparent resistivity from field, then analyze and estimate the resistivity with steepest descent method, after that simulate grounding system with two layers of soil by using CDEGS software base on IEEE 80-2000. The results show that touch voltage decrease 47.48 percent when increase ground grid and ground rod in square type at the edge of substation although bottom layer has more soil resistivity than top layer.
{"title":"Optimal Design in Distribution Substation’s Grounding System for Decreasing Touch Voltage","authors":"A. Puttarach, T. Kasirawat, S. Panta, A. Phayomhom, C. Pongsriwat, Wirat Nakkrongdee","doi":"10.1109/ecti-con49241.2020.9158089","DOIUrl":"https://doi.org/10.1109/ecti-con49241.2020.9158089","url":null,"abstract":"This paper presents a technique for grounding system design in a small area distribution substation. Compression ratio adjustment in order to decrease touch voltage is hard to design with this situation due to the limitation of area. Furthermore, this condition can’t use the ground rod length increasing method since the length doesn’t reach bottom layer of the ground. So this paper will show a method for solve this problem by using apparent resistivity from field, then analyze and estimate the resistivity with steepest descent method, after that simulate grounding system with two layers of soil by using CDEGS software base on IEEE 80-2000. The results show that touch voltage decrease 47.48 percent when increase ground grid and ground rod in square type at the edge of substation although bottom layer has more soil resistivity than top layer.","PeriodicalId":371552,"journal":{"name":"2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126427311","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}
Extracting keywords from text on social media facilitates people to update news and trends. It reduces time spent for identifying main content from huge amount of data, and it can be used to identify situations or events that most of people mention in each period of time. This paper proposes a method for extracting keywords from Thai text on social media. A N-gram-based word-combination technique is presented to segment words that are not in dictionaries and increase the precision of word segmentation. Posts on Twitter concerning universities in Thailand are used as a case study for extracting keywords and analyzing trends. The experimental results show that the proposed method yield the highest precision of 70%.
{"title":"Extraction of Trend Keywords from Thai Twitters using N-Gram Word Combination","authors":"Tanatorn Tanantong, Sasitorn Kreangkriwanich, Nasith Laosen","doi":"10.1109/ecti-con49241.2020.9158061","DOIUrl":"https://doi.org/10.1109/ecti-con49241.2020.9158061","url":null,"abstract":"Extracting keywords from text on social media facilitates people to update news and trends. It reduces time spent for identifying main content from huge amount of data, and it can be used to identify situations or events that most of people mention in each period of time. This paper proposes a method for extracting keywords from Thai text on social media. A N-gram-based word-combination technique is presented to segment words that are not in dictionaries and increase the precision of word segmentation. Posts on Twitter concerning universities in Thailand are used as a case study for extracting keywords and analyzing trends. The experimental results show that the proposed method yield the highest precision of 70%.","PeriodicalId":371552,"journal":{"name":"2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124863498","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 : 2020-06-01DOI: 10.1109/ECTI-CON49241.2020.9158115
A. Asaduzzaman, D. Gupta
Cyberinfrastructure (CI) has potential to assist economic activities that involve real-time data analytics. Important challenges include the integration of essential geospatial principles (such as spatial constraints in assessing events) with CI to offer a promising pathway for solving complex problems and improving just-in-time decision-making practices for economic success. As a new contribution to extend the effectiveness of CI, we propose a novel geospatial CI (GCI) that provides support for making immediate business decisions by conducting vehicular traffic data acquisition, analysis, and distribution. Important features of the proposed GCI include heuristic traffic data portals (DPs), real-time analytic engine (AE), Cloud-Fog-Mist computing, distribution mechanism (DM), and business model (BM). According to the preliminary results through MATLAB and Python simulation using synthetic workload, the proposed GCI assists increase profit up to 90% and 70% for a fast food restaurant and a gas station, respectively. The proposed GCI can be extended for sustaining regional economic growth through the adoption of emerging technologies such as Internet-of-Things (IoT).
{"title":"Geospatial Cyberinfrastructure for Regional Economic Growth","authors":"A. Asaduzzaman, D. Gupta","doi":"10.1109/ECTI-CON49241.2020.9158115","DOIUrl":"https://doi.org/10.1109/ECTI-CON49241.2020.9158115","url":null,"abstract":"Cyberinfrastructure (CI) has potential to assist economic activities that involve real-time data analytics. Important challenges include the integration of essential geospatial principles (such as spatial constraints in assessing events) with CI to offer a promising pathway for solving complex problems and improving just-in-time decision-making practices for economic success. As a new contribution to extend the effectiveness of CI, we propose a novel geospatial CI (GCI) that provides support for making immediate business decisions by conducting vehicular traffic data acquisition, analysis, and distribution. Important features of the proposed GCI include heuristic traffic data portals (DPs), real-time analytic engine (AE), Cloud-Fog-Mist computing, distribution mechanism (DM), and business model (BM). According to the preliminary results through MATLAB and Python simulation using synthetic workload, the proposed GCI assists increase profit up to 90% and 70% for a fast food restaurant and a gas station, respectively. The proposed GCI can be extended for sustaining regional economic growth through the adoption of emerging technologies such as Internet-of-Things (IoT).","PeriodicalId":371552,"journal":{"name":"2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"343 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122475958","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 : 2020-06-01DOI: 10.1109/ecti-con49241.2020.9158126
Natin Janjamraj, Wichian Ooppakaew, V. Pirajnanchai
This paper presents the control systems of hybrid VSC-HVDC transmission systems is applied for offshore wind farm applications. The conventional voltage source converter (VSC) is applied for rectifier-side converter and modular multilevel converter (MMC) is applied for inverter-side converter. This topology is maintained the flexible control of active and reactive power and it can improve the voltage waveforms and harmonics of output power qualities. The simulation systems are simulated by MATLAB/Simulink program. The simulation results are shown performances of the control systems are proposed and output voltage waveforms and total harmonic distortion (THD) of the proposed system are improved the power quality without output filter required.
{"title":"Control and Simulation of Hybrid VSC-HVDC Transmission Systems for Offshore Wind Farm Applications","authors":"Natin Janjamraj, Wichian Ooppakaew, V. Pirajnanchai","doi":"10.1109/ecti-con49241.2020.9158126","DOIUrl":"https://doi.org/10.1109/ecti-con49241.2020.9158126","url":null,"abstract":"This paper presents the control systems of hybrid VSC-HVDC transmission systems is applied for offshore wind farm applications. The conventional voltage source converter (VSC) is applied for rectifier-side converter and modular multilevel converter (MMC) is applied for inverter-side converter. This topology is maintained the flexible control of active and reactive power and it can improve the voltage waveforms and harmonics of output power qualities. The simulation systems are simulated by MATLAB/Simulink program. The simulation results are shown performances of the control systems are proposed and output voltage waveforms and total harmonic distortion (THD) of the proposed system are improved the power quality without output filter required.","PeriodicalId":371552,"journal":{"name":"2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"552 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123309709","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 : 2020-06-01DOI: 10.1109/ecti-con49241.2020.9158137
N. Li, Worapan Kusakunniran, S. Hotta
There are many attempts on detecting animal using Convolutional Neural Network. However, many of them failed to detect animals behind cage bars as the mesh patterns of such bars usually affected a detectability of a detection model. A main hypothesis is that most of existing models trained for detecting animals does not have enough pictures of animals behind cage bars as in a training set. In this paper, panda and deer are used as case examples. The training data is gathered specifically for this research work. The M2Det is used as the main network together with the transfer learning approach and its pretrained weights. In our experiments, it is found that a number of training images of animals behind cage bars greatly affects the detection performance. Also, adding more training images of animals without cages could also improve the performance of the detection model on the same task.
{"title":"Detection of Animal Behind Cages Using Convolutional Neural Network","authors":"N. Li, Worapan Kusakunniran, S. Hotta","doi":"10.1109/ecti-con49241.2020.9158137","DOIUrl":"https://doi.org/10.1109/ecti-con49241.2020.9158137","url":null,"abstract":"There are many attempts on detecting animal using Convolutional Neural Network. However, many of them failed to detect animals behind cage bars as the mesh patterns of such bars usually affected a detectability of a detection model. A main hypothesis is that most of existing models trained for detecting animals does not have enough pictures of animals behind cage bars as in a training set. In this paper, panda and deer are used as case examples. The training data is gathered specifically for this research work. The M2Det is used as the main network together with the transfer learning approach and its pretrained weights. In our experiments, it is found that a number of training images of animals behind cage bars greatly affects the detection performance. Also, adding more training images of animals without cages could also improve the performance of the detection model on the same task.","PeriodicalId":371552,"journal":{"name":"2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124723909","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 : 2020-06-01DOI: 10.1109/ecti-con49241.2020.9158214
Nongnuch Ketui, Nattapong Tongtep, T. Theeramunkong
In the context of information extraction, a person’s name is one of the important named entities to be extracted which are applied to the question-answering and summarizing tasks. However, the boundary of a person’s name is still ambiguous since there are several writing patterns of a person’s name from online public data sources such as news, events, and researcher corpora. To extract, identify, and unify the person’s name, discovering the name prefix can be applied as clue words or phrases to such processes. In this paper, the name prefix discovering framework is proposed for collecting the integrated researcher corpus from various data sources and extracting name prefix patterns. Four main functions of the proposed framework are collecting data from data sources, tagging entities, preprocessing the researcher’s names, and finding the pattern of the personal name prefix. In this work, six data sources are gathered and ten entities related to the research domain are focused. The preprocessing data uses three sub-processes to provide the researcher’s name. The result shows that the 408 personal name prefixes are extracted. Moreover, the API development for extracting a person or researcher’s name is implemented using a Flask Python framework. The output of this work can be used to support the researcher’s name identification from the integrated researcher corpus.
{"title":"Discovering of Personal Name Prefix Patterns in Thai Researcher Corpus and Its Application","authors":"Nongnuch Ketui, Nattapong Tongtep, T. Theeramunkong","doi":"10.1109/ecti-con49241.2020.9158214","DOIUrl":"https://doi.org/10.1109/ecti-con49241.2020.9158214","url":null,"abstract":"In the context of information extraction, a person’s name is one of the important named entities to be extracted which are applied to the question-answering and summarizing tasks. However, the boundary of a person’s name is still ambiguous since there are several writing patterns of a person’s name from online public data sources such as news, events, and researcher corpora. To extract, identify, and unify the person’s name, discovering the name prefix can be applied as clue words or phrases to such processes. In this paper, the name prefix discovering framework is proposed for collecting the integrated researcher corpus from various data sources and extracting name prefix patterns. Four main functions of the proposed framework are collecting data from data sources, tagging entities, preprocessing the researcher’s names, and finding the pattern of the personal name prefix. In this work, six data sources are gathered and ten entities related to the research domain are focused. The preprocessing data uses three sub-processes to provide the researcher’s name. The result shows that the 408 personal name prefixes are extracted. Moreover, the API development for extracting a person or researcher’s name is implemented using a Flask Python framework. The output of this work can be used to support the researcher’s name identification from the integrated researcher corpus.","PeriodicalId":371552,"journal":{"name":"2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"2008 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129659510","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 : 2020-06-01DOI: 10.1109/ecti-con49241.2020.9158228
Ronnarong Dusitakorn, Sasiporn Usanavasin, W. Kongprawechnon
Nowadays, solar photovoltaic (PV) systems are rapidly growing worldwide. The utility needs to grasp the changing trends for power system planning, and penalize an illegal installation solar system in order to prevent impacts on the grid. Therefore, this paper aims to detect a solar customer from weekly consumption patterns by three classification algorithms: Logistic regression, cosine similarity and K-nearest neighbors. Furthermore, the clustering methods, K-means and Density-based spatial clustering of applications with noise (DBSCAN), are utilized for similarity grouping, and computational cost reduction with the two stage clustering technique. The study has been conducted with the non-resident customers in Thailand, the classification results are discussed.
{"title":"Solar Customer Detection based on Power Consumption Patterns","authors":"Ronnarong Dusitakorn, Sasiporn Usanavasin, W. Kongprawechnon","doi":"10.1109/ecti-con49241.2020.9158228","DOIUrl":"https://doi.org/10.1109/ecti-con49241.2020.9158228","url":null,"abstract":"Nowadays, solar photovoltaic (PV) systems are rapidly growing worldwide. The utility needs to grasp the changing trends for power system planning, and penalize an illegal installation solar system in order to prevent impacts on the grid. Therefore, this paper aims to detect a solar customer from weekly consumption patterns by three classification algorithms: Logistic regression, cosine similarity and K-nearest neighbors. Furthermore, the clustering methods, K-means and Density-based spatial clustering of applications with noise (DBSCAN), are utilized for similarity grouping, and computational cost reduction with the two stage clustering technique. The study has been conducted with the non-resident customers in Thailand, the classification results are discussed.","PeriodicalId":371552,"journal":{"name":"2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129884233","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 : 2020-06-01DOI: 10.1109/ecti-con49241.2020.9158257
Katawut Kaewbanjong, Sarun Intakosum
We analyzed a volume of software project data and found significant user satisfaction in several software project factors. statistical significance A analysis (logistic regression) a collinearity analysis and determined the significance factors from a group of 71 pre-defined factors from 191 software projects in ISBSG Release 12. Eight prediction models were used to test the prediction potential of these factors: Neural network, k-NN, Naïve Bayes, Random forest, Decision tree, Gradient boosted tree, linear regression and logistic regression prediction model. Fifteen pre-defined factors were significant in predicting user satisfaction: client-server, personnel changes, total defects delivered, project inactive time, industry sector, application type, development type, how methodology was acquired, development techniques, decision making process, intended market, size estimate approach, size estimate method, cost recording method, and effort estimate method. They provided 82.71% prediction accuracy when used with a neural network prediction model. These findings may directly benefit software development managers.
{"title":"Statistical Analysis with Prediction Models of User Satisfaction in Software Project Factors","authors":"Katawut Kaewbanjong, Sarun Intakosum","doi":"10.1109/ecti-con49241.2020.9158257","DOIUrl":"https://doi.org/10.1109/ecti-con49241.2020.9158257","url":null,"abstract":"We analyzed a volume of software project data and found significant user satisfaction in several software project factors. statistical significance A analysis (logistic regression) a collinearity analysis and determined the significance factors from a group of 71 pre-defined factors from 191 software projects in ISBSG Release 12. Eight prediction models were used to test the prediction potential of these factors: Neural network, k-NN, Naïve Bayes, Random forest, Decision tree, Gradient boosted tree, linear regression and logistic regression prediction model. Fifteen pre-defined factors were significant in predicting user satisfaction: client-server, personnel changes, total defects delivered, project inactive time, industry sector, application type, development type, how methodology was acquired, development techniques, decision making process, intended market, size estimate approach, size estimate method, cost recording method, and effort estimate method. They provided 82.71% prediction accuracy when used with a neural network prediction model. These findings may directly benefit software development managers.","PeriodicalId":371552,"journal":{"name":"2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128861763","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 : 2020-06-01DOI: 10.1109/ecti-con49241.2020.9158072
Pattawut Manapongpun, Dahmmaet Bunnjaweht
A measurement approach of resistive sensor arrays has posed more obstacles than in single point resistive sensors. The effect of a crosstalk current in the array is the main factor that results in an inaccurate resistance scanning. In the application of pressure sensors, the presence of crosstalk current will cause ghost images on the area where no actual force is exerted onto the sensor array. In this paper, the enhanced measurement circuit has been specifically designed based on the zero potential circuit method to overcome the crosstalk error for improved accuracy. PSPICE simulation results on the 5×5 resistive array showed improved accuracy compared to the previous voltage dividing measurement approach, with the error reducing from 12.987% to 1.191%.
{"title":"An Enhanced Measurement Circuit for Piezoresistive Pressure Sensor Array","authors":"Pattawut Manapongpun, Dahmmaet Bunnjaweht","doi":"10.1109/ecti-con49241.2020.9158072","DOIUrl":"https://doi.org/10.1109/ecti-con49241.2020.9158072","url":null,"abstract":"A measurement approach of resistive sensor arrays has posed more obstacles than in single point resistive sensors. The effect of a crosstalk current in the array is the main factor that results in an inaccurate resistance scanning. In the application of pressure sensors, the presence of crosstalk current will cause ghost images on the area where no actual force is exerted onto the sensor array. In this paper, the enhanced measurement circuit has been specifically designed based on the zero potential circuit method to overcome the crosstalk error for improved accuracy. PSPICE simulation results on the 5×5 resistive array showed improved accuracy compared to the previous voltage dividing measurement approach, with the error reducing from 12.987% to 1.191%.","PeriodicalId":371552,"journal":{"name":"2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131189254","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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