Pub Date : 2022-04-01DOI: 10.24191/jeesr.v20i1.017
K. Burhanudin, M. H. Jusoh, Z. I. A Latiff, A. Zainuddin, F. Kamaruddin, M. Hashim, Magdas CPMN Group
120 Abstract— Magnetic Data Acquisition System (MAGDAS) is a system of the real-time magnetometer deploy by Kyushu University of Fukuoka, Japan as a part of the contribution to the International Heliophysical Year of United Nations[1]. The magnetometer used for real-time data acquisition is YU-8T SN-59, which is a part of the first model of the MAGDAS magnetometer. This paper discussed the characteristic of the magnetic data from the MAGDAS BTG and the possibility of the data to be used for further research study in the field of space and weather. There is 3 main magnetic field component from YU-8T magnetometer that describes the magnetic field which is Horizontal Intensity (Hcomponent), Declination (D-component) and Vertical Component (Z-component). The installation of the MAGDAS system is located at Malaysian Space Agency (MYSA), Banting (BTG), Malaysia (geographic latitude and longitude: 2.78O, 101.51O, and geomagnetic latitude and longitude: 6.86O, 174.10O). The YU-8T magnetometer measures the earth’s magnetic field in horizontal intensity(H), declination(D), and field down(Z). The magnetic field data were collected, analyze, and compared with the MAGDAS LKW, USM, and the World Magnetic Model (WMM) to verify the performance of the MAGDAS BTG. The importance of the study is to identify the reliability and characteristic of the magnetic data from the MAGDAS BTG in response to the solar event and GIC formation.
120摘要-磁数据采集系统(MAGDAS)是日本福冈九州大学部署的实时磁强计系统，作为对联合国国际太阳物理年的一部分贡献。用于实时数据采集的磁力计是YU-8T SN-59，这是MAGDAS磁力计第一模型的一部分。本文讨论了MAGDAS BTG磁数据的特点，以及该数据在空间和气象领域进一步研究的可能性。MAGDAS系统的安装位置位于马来西亚班亭(BTG)的马来西亚航天局(MYSA)(地理经纬度:2.78O, 101.51O，地磁经纬度:6.86O, 174.10O)。采集、分析磁场数据，并与MAGDAS LKW、USM和World magnetic Model (WMM)进行比较，验证MAGDAS BTG的性能。该研究的重要性在于确定MAGDAS BTG磁数据响应太阳事件和GIC形成的可靠性和特征。
{"title":"The Analysis of the Geomagnetic Data during the installation of the First MAGDAS System at the Malaysian Space Agency","authors":"K. Burhanudin, M. H. Jusoh, Z. I. A Latiff, A. Zainuddin, F. Kamaruddin, M. Hashim, Magdas CPMN Group","doi":"10.24191/jeesr.v20i1.017","DOIUrl":"https://doi.org/10.24191/jeesr.v20i1.017","url":null,"abstract":"120 Abstract— Magnetic Data Acquisition System (MAGDAS) is a system of the real-time magnetometer deploy by Kyushu University of Fukuoka, Japan as a part of the contribution to the International Heliophysical Year of United Nations[1]. The magnetometer used for real-time data acquisition is YU-8T SN-59, which is a part of the first model of the MAGDAS magnetometer. This paper discussed the characteristic of the magnetic data from the MAGDAS BTG and the possibility of the data to be used for further research study in the field of space and weather. There is 3 main magnetic field component from YU-8T magnetometer that describes the magnetic field which is Horizontal Intensity (Hcomponent), Declination (D-component) and Vertical Component (Z-component). The installation of the MAGDAS system is located at Malaysian Space Agency (MYSA), Banting (BTG), Malaysia (geographic latitude and longitude: 2.78O, 101.51O, and geomagnetic latitude and longitude: 6.86O, 174.10O). The YU-8T magnetometer measures the earth’s magnetic field in horizontal intensity(H), declination(D), and field down(Z). The magnetic field data were collected, analyze, and compared with the MAGDAS LKW, USM, and the World Magnetic Model (WMM) to verify the performance of the MAGDAS BTG. The importance of the study is to identify the reliability and characteristic of the magnetic data from the MAGDAS BTG in response to the solar event and GIC formation.","PeriodicalId":313365,"journal":{"name":"Journal of Electrical & Electronic Systems Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132188924","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 : 2022-04-01DOI: 10.24191/jeesr.v20i1.004
Nur Ayuni Nor Sobri, Mohamad Aqib Haqmi Abas, A. I. Mohd Yassin, M. S. A. Megat Ali, N. Md. Tahir, A. Zabidi
29 Abstract—The increase and growing number of users in the internet gives a higher requirement to backend application systems nowadays to be designed to handle thousands of users traffic concurrently. Microservice architecture is also in a rising trend which they allow for each service to scale horizontally by their throughput and load helps to scale the system efficiently without waste of resources like in the traditional monolithic application system. Database connection pool helps for backend systems to access databases efficiently. The present issue is determining the optimal number of database connections to use in a microservice based backend system. This paper aims to find the most suitable amount of database connections in a microservice setting, where multiple instances of the service are used for scalability and high availability purposes of the system. The experiment was conducted by varying the number of database connections from one to five to ten in both single instance and three instance services, where the service being examined is the backend system's roles and permissions service. The results of this experiment indicate that five database connections provide the best performance latency result in a microservice architecture with three service instances. With 2000 requests per second, the maximum latency was 53ms, while the 99th percentile latency was 19ms. The study contributes by determining the optimal size of a database connection pool for use in a microservice architecture with several instances of the service are operating concurrently.
{"title":"Database Connection Pool in Microservice Architecture","authors":"Nur Ayuni Nor Sobri, Mohamad Aqib Haqmi Abas, A. I. Mohd Yassin, M. S. A. Megat Ali, N. Md. Tahir, A. Zabidi","doi":"10.24191/jeesr.v20i1.004","DOIUrl":"https://doi.org/10.24191/jeesr.v20i1.004","url":null,"abstract":"29 Abstract—The increase and growing number of users in the internet gives a higher requirement to backend application systems nowadays to be designed to handle thousands of users traffic concurrently. Microservice architecture is also in a rising trend which they allow for each service to scale horizontally by their throughput and load helps to scale the system efficiently without waste of resources like in the traditional monolithic application system. Database connection pool helps for backend systems to access databases efficiently. The present issue is determining the optimal number of database connections to use in a microservice based backend system. This paper aims to find the most suitable amount of database connections in a microservice setting, where multiple instances of the service are used for scalability and high availability purposes of the system. The experiment was conducted by varying the number of database connections from one to five to ten in both single instance and three instance services, where the service being examined is the backend system's roles and permissions service. The results of this experiment indicate that five database connections provide the best performance latency result in a microservice architecture with three service instances. With 2000 requests per second, the maximum latency was 53ms, while the 99th percentile latency was 19ms. The study contributes by determining the optimal size of a database connection pool for use in a microservice architecture with several instances of the service are operating concurrently.","PeriodicalId":313365,"journal":{"name":"Journal of Electrical & Electronic Systems Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123369883","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 : 2022-04-01DOI: 10.24191/jeesr.v20i1.013
A’zraaAfhzan Ab Rahim, N. Buniyamin
92 Abstract— Current literature review indicates a void of an accurate predictive tool to assist educators and administrators in analyzing and monitoring student performance in Malaysia. Wellknown data mining classifiers such as Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), and K-nearest neighbor (KNN) have been traditionally used but often suffer from the high variance and overfitting issues indicated by good performance on training data but relatively poor on unseen data. To address these problems, alternative ensemble classifiers such as Extreme Gradient Boosting (XGB), Random Forest (RF), and Heterogeneous Ensemble Method (HEM) are evaluated/proposed. This paper aims to compare the performance of single versus ensemble classifiers. In addition, another overarching research objective is to predict students' CGPA during their final semester grades by augmenting the more widely used cognitive with non-cognitive features to obtain a holistic solution. Not only will the accuracy among classifiers be compared, but another priority measure is their recall value to ensure each sample is classified correctly. It is found that ensemble classifiers outperform their single classifiers in terms of both accuracy and recall. Preliminary results indicate that augmenting cognitive features with non-cognitive features results in better accuracy in classifiers and can classify samples according to their respective classes with less variability.
{"title":"Predicting Engineering Students' Academic Performance using Ensemble Classifiers- A Preliminary Finding","authors":"A’zraaAfhzan Ab Rahim, N. Buniyamin","doi":"10.24191/jeesr.v20i1.013","DOIUrl":"https://doi.org/10.24191/jeesr.v20i1.013","url":null,"abstract":"92 Abstract— Current literature review indicates a void of an accurate predictive tool to assist educators and administrators in analyzing and monitoring student performance in Malaysia. Wellknown data mining classifiers such as Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), and K-nearest neighbor (KNN) have been traditionally used but often suffer from the high variance and overfitting issues indicated by good performance on training data but relatively poor on unseen data. To address these problems, alternative ensemble classifiers such as Extreme Gradient Boosting (XGB), Random Forest (RF), and Heterogeneous Ensemble Method (HEM) are evaluated/proposed. This paper aims to compare the performance of single versus ensemble classifiers. In addition, another overarching research objective is to predict students' CGPA during their final semester grades by augmenting the more widely used cognitive with non-cognitive features to obtain a holistic solution. Not only will the accuracy among classifiers be compared, but another priority measure is their recall value to ensure each sample is classified correctly. It is found that ensemble classifiers outperform their single classifiers in terms of both accuracy and recall. Preliminary results indicate that augmenting cognitive features with non-cognitive features results in better accuracy in classifiers and can classify samples according to their respective classes with less variability.","PeriodicalId":313365,"journal":{"name":"Journal of Electrical & Electronic Systems Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124084835","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 : 2022-04-01DOI: 10.24191/jeesr.v20i1.010
Atiqah Liyana Md Said, N. Ishak, M. Tajjudin
70 Abstract—This paper focuses on the modeling, development, and implementation of a Fuzzy PID controller in controlling the heating system. This study will look into the effectiveness of a fuzzy Proportional Integral Derivative (PID) control scheme for this application. Instead of using a trial-and-error method for the controller tuning, this study proposes a fuzzy PID control to tune the controller parameters and to improve the conventional PID controller transient response. Modeling of the PT326 heating system is required before designing the controller. Through the MATLAB System Identification Toolbox, a discrete-time model is obtained and represented by an ARX model structure. A simulation study had been implemented on a unit step input. The results demonstrated that the system shows positive improvement in terms of rise time and settling time when fuzzy PID controller was applied.
{"title":"Implementation of Fuzzy PID Controller on Hot Air Blower System","authors":"Atiqah Liyana Md Said, N. Ishak, M. Tajjudin","doi":"10.24191/jeesr.v20i1.010","DOIUrl":"https://doi.org/10.24191/jeesr.v20i1.010","url":null,"abstract":"70 Abstract—This paper focuses on the modeling, development, and implementation of a Fuzzy PID controller in controlling the heating system. This study will look into the effectiveness of a fuzzy Proportional Integral Derivative (PID) control scheme for this application. Instead of using a trial-and-error method for the controller tuning, this study proposes a fuzzy PID control to tune the controller parameters and to improve the conventional PID controller transient response. Modeling of the PT326 heating system is required before designing the controller. Through the MATLAB System Identification Toolbox, a discrete-time model is obtained and represented by an ARX model structure. A simulation study had been implemented on a unit step input. The results demonstrated that the system shows positive improvement in terms of rise time and settling time when fuzzy PID controller was applied.","PeriodicalId":313365,"journal":{"name":"Journal of Electrical & Electronic Systems Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134128307","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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