Pub Date : 2021-09-16DOI: 10.1109/scse53661.2021.9568324
E. Ekanayake, Ruwan Dharshana Nawarathna
To encourage sustainable progress, it is suggested that in a world connected by virtual platforms, modern society should merge big data, artificial intelligence, machine learning, information and communication technology (ICT), as well as the “Internet of Things” (IoT). When real-life problems are considered, the above technology processes are essential in solving the issues. Food is an essential need of human beings. Food supply has become crucial, and it is very important to increase the adequate cultivation of plants for large populations due to huge population growth. At the same time, farmers are struggling with a variety of food plant diseases that significantly affect the harvesting and production in agricultural fields. Nevertheless, the agricultural productivity of rural areas is directly involved with the increase in the economic growth of developing countries such as Sri Lanka, India, Myanmar and Indonesia. Early identification of crop disease, using a well-established modern technique, is vital. It necessitates a number of processes observing large-scale agricultural fields as a disease can infect different parts of the plant such as leaf, roots, stem and fruit. Most diseases appear in plant leaves and have the potential to spread them all over the field within a very short time. This paper reviews several state-of-the-art methods that can be used for plant leaf disease recognition with a special reference to deep learning based methods.
{"title":"Novel deep learning approaches for crop leaf disease classification: A review","authors":"E. Ekanayake, Ruwan Dharshana Nawarathna","doi":"10.1109/scse53661.2021.9568324","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568324","url":null,"abstract":"To encourage sustainable progress, it is suggested that in a world connected by virtual platforms, modern society should merge big data, artificial intelligence, machine learning, information and communication technology (ICT), as well as the “Internet of Things” (IoT). When real-life problems are considered, the above technology processes are essential in solving the issues. Food is an essential need of human beings. Food supply has become crucial, and it is very important to increase the adequate cultivation of plants for large populations due to huge population growth. At the same time, farmers are struggling with a variety of food plant diseases that significantly affect the harvesting and production in agricultural fields. Nevertheless, the agricultural productivity of rural areas is directly involved with the increase in the economic growth of developing countries such as Sri Lanka, India, Myanmar and Indonesia. Early identification of crop disease, using a well-established modern technique, is vital. It necessitates a number of processes observing large-scale agricultural fields as a disease can infect different parts of the plant such as leaf, roots, stem and fruit. Most diseases appear in plant leaves and have the potential to spread them all over the field within a very short time. This paper reviews several state-of-the-art methods that can be used for plant leaf disease recognition with a special reference to deep learning based methods.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125923998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-16DOI: 10.1109/scse53661.2021.9568315
S. Thalagala, C. Walgampaya
Automated inspection of surface defects is beneficial for casting product manufacturers in terms of inspection cost and time, which ultimately affect overall business performance. Intelligent systems that are capable of image classification are widely applied in visual inspection as a major component of modern smart manufacturing. Image classification tasks performed by Convolutional Neural Networks (CNNs) have recently shown significant performance over the conventional machine learning techniques. Particularly, AlexNet CNN architecture, which was proposed at the early stages of the development of CNN architectures, shows outstanding performance. In this paper, we investigate the application of AlexN et CNN architecture-based transfer learning for the classification of casting surface defects. We used a dataset containing casting surface defect images of a pump impeller for testing the performance. We examined four experimental schemes where the degree of the knowledge obtained from the pre-trained model is varied in each experiment. Furthermore, using a simple grid search method we explored the best overall setting for two crucial hyperparameters. Our results show that despite the simple architecture, AlexN et with transfer learning can be successfully applied for the recognition of casting surface defects of the pump impeller.
表面缺陷的自动化检测在检测成本和时间方面有利于铸造产品制造商，最终影响整体经营绩效。具有图像分类能力的智能系统作为现代智能制造的重要组成部分，在视觉检测领域得到了广泛的应用。卷积神经网络(cnn)执行的图像分类任务最近显示出比传统机器学习技术显著的性能。特别是在CNN架构发展的早期阶段提出的AlexNet CNN架构，表现出了出色的性能。在本文中，我们研究了基于AlexN et CNN架构的迁移学习在铸件表面缺陷分类中的应用。我们使用了包含泵叶轮铸造表面缺陷图像的数据集来测试性能。我们检查了四个实验方案，其中从预训练模型中获得的知识程度在每个实验中都是不同的。此外，使用简单的网格搜索方法，我们探索了两个关键超参数的最佳总体设置。结果表明，尽管结构简单，但结合迁移学习的AlexN网络可以成功地用于泵叶轮铸件表面缺陷的识别。
{"title":"Application of AlexNet convolutional neural network architecture-based transfer learning for automated recognition of casting surface defects","authors":"S. Thalagala, C. Walgampaya","doi":"10.1109/scse53661.2021.9568315","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568315","url":null,"abstract":"Automated inspection of surface defects is beneficial for casting product manufacturers in terms of inspection cost and time, which ultimately affect overall business performance. Intelligent systems that are capable of image classification are widely applied in visual inspection as a major component of modern smart manufacturing. Image classification tasks performed by Convolutional Neural Networks (CNNs) have recently shown significant performance over the conventional machine learning techniques. Particularly, AlexNet CNN architecture, which was proposed at the early stages of the development of CNN architectures, shows outstanding performance. In this paper, we investigate the application of AlexN et CNN architecture-based transfer learning for the classification of casting surface defects. We used a dataset containing casting surface defect images of a pump impeller for testing the performance. We examined four experimental schemes where the degree of the knowledge obtained from the pre-trained model is varied in each experiment. Furthermore, using a simple grid search method we explored the best overall setting for two crucial hyperparameters. Our results show that despite the simple architecture, AlexN et with transfer learning can be successfully applied for the recognition of casting surface defects of the pump impeller.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"109 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130071158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-16DOI: 10.1109/scse53661.2021.9568287
N. T. H. Thalagahage, A. Wijayanayake, D. Niwunhella
The apparel industry is considered as one of the most labor-intensive industries where Production Planning and Control (PPC) is considered as an important function, because of its involvement from scheduling each task in the process to the delivery of customer demand. Line planning is a sub-process within PPC, through which the production orders are allocated to production lines according to their setting and due dates of production completion. The decisions that address line planning functions still heavily rely on the expertise of the production planner. When production planners are required to select production lines for the production of a particular type of product, little emphasis has been placed on ways to apportion certain production orders to the most appropriate production system. In this research, a framework is developed using Analytical Network Process (ANP) which is a Multi-Criteria Decision Making (MCDM) method, enabling the incorporation of all the planning criteria in the selection of a production line. The weighted scores obtained by the best alternative production lines are used in a Linear Programming model to optimize the resource allocation in an apparel firm.
{"title":"A MILP model to optimize the proportion of production quantities considering the ANP composite performance index","authors":"N. T. H. Thalagahage, A. Wijayanayake, D. Niwunhella","doi":"10.1109/scse53661.2021.9568287","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568287","url":null,"abstract":"The apparel industry is considered as one of the most labor-intensive industries where Production Planning and Control (PPC) is considered as an important function, because of its involvement from scheduling each task in the process to the delivery of customer demand. Line planning is a sub-process within PPC, through which the production orders are allocated to production lines according to their setting and due dates of production completion. The decisions that address line planning functions still heavily rely on the expertise of the production planner. When production planners are required to select production lines for the production of a particular type of product, little emphasis has been placed on ways to apportion certain production orders to the most appropriate production system. In this research, a framework is developed using Analytical Network Process (ANP) which is a Multi-Criteria Decision Making (MCDM) method, enabling the incorporation of all the planning criteria in the selection of a production line. The weighted scores obtained by the best alternative production lines are used in a Linear Programming model to optimize the resource allocation in an apparel firm.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129284440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-16DOI: 10.1109/scse53661.2021.9568361
M. S. H. Peiris, S. Sotheeswaran
Detection, and classification of medical images have become a trending field of study during the last few decades. There is a considerable amount of vital challenges to be overcome. Ample work has been carried out to provide proper solutions for those key challenges. This study was carried out to extend one such medical image classification process to classify the stages of Diabetic Retinopathy (DR) images from colour fundus images. The study proposes a novel Convolutional Neural Network (CNN) architecture which is considered to be one of the most trending and efficient forms of classification of DR stages. Initially, the preprocessing techniques were employed to the DR fundus images with Green channel extraction and Contrast Limited Adaptive Histogram Equalization (CLAHE). The data augmentation strategy was utilised to increase training images from the DR images. Finally, Feature extraction and classification were carried out by using the proposed CNN architecture. It consists of a 14 layered CNN model, which continues three main classifications. In this proposed classification, the images were classified into a tree structure based binary classification as No_DR and DR at the beginning, and then the DR images were again classified into two classes, namely Pre_Intermediate and Post_Intermediate. Moreover, those two classes were again separately classified into Mild, Moderate, and Proliferate_DR, Severe, respectively. The Kaggle is one of the benchmark dataset repositories which was used in this study. The proposed model was able to achieve accuracies of 81 %, 96%, 84%, and 97% for the above-mentioned classifications, respectively.
{"title":"A tree structure-based classification of diabetic retinopathy stages using convolutional neural network","authors":"M. S. H. Peiris, S. Sotheeswaran","doi":"10.1109/scse53661.2021.9568361","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568361","url":null,"abstract":"Detection, and classification of medical images have become a trending field of study during the last few decades. There is a considerable amount of vital challenges to be overcome. Ample work has been carried out to provide proper solutions for those key challenges. This study was carried out to extend one such medical image classification process to classify the stages of Diabetic Retinopathy (DR) images from colour fundus images. The study proposes a novel Convolutional Neural Network (CNN) architecture which is considered to be one of the most trending and efficient forms of classification of DR stages. Initially, the preprocessing techniques were employed to the DR fundus images with Green channel extraction and Contrast Limited Adaptive Histogram Equalization (CLAHE). The data augmentation strategy was utilised to increase training images from the DR images. Finally, Feature extraction and classification were carried out by using the proposed CNN architecture. It consists of a 14 layered CNN model, which continues three main classifications. In this proposed classification, the images were classified into a tree structure based binary classification as No_DR and DR at the beginning, and then the DR images were again classified into two classes, namely Pre_Intermediate and Post_Intermediate. Moreover, those two classes were again separately classified into Mild, Moderate, and Proliferate_DR, Severe, respectively. The Kaggle is one of the benchmark dataset repositories which was used in this study. The proposed model was able to achieve accuracies of 81 %, 96%, 84%, and 97% for the above-mentioned classifications, respectively.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128066682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-16DOI: 10.1109/scse53661.2021.9568322
{"title":"[Copyright notice]","authors":"","doi":"10.1109/scse53661.2021.9568322","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568322","url":null,"abstract":"","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"11 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120899510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-16DOI: 10.1109/scse53661.2021.9568369
M. G. M. Milani, M. Ramashini, Krishani Murugiah, Lanka Geeganage Shamaan Chamal
The world is facing an extreme crisis due to the COVID-19 pandemic. The COVID-19 virus interrupts the world's economy and social factors; thus, many countries fall into poverty. Also, they lack expertise in this field and could not make an effort to perform the necessary polymerase chain reaction (PCR) or other expensive laboratory tests. Therefore, it is important to find an alternative solution to the early prediction of COVID-19 infected persons with a low-cost method. The objective of this study is to detect COVID-19 infected individuals through their breathing sounds. To perform this task, twenty-two (22) acoustic features are extracted. The optimum features in each COVID-19 infected breathing sound is identified among these features through a feature engineering method. This proposed feature engineering method is a hybrid model that includes; statistical feature evaluation, PCA, and k-mean clustering techniques. The final results of this proposed Optimum Acoustic Feature Engineering (OAFE) model show that breathing sound signals' Kurtosis feature is more effective in distinguishing COVID-19 infected individuals from healthy individuals.
{"title":"Exploiting optimum acoustic features in COVID-19 individual's breathing sounds","authors":"M. G. M. Milani, M. Ramashini, Krishani Murugiah, Lanka Geeganage Shamaan Chamal","doi":"10.1109/scse53661.2021.9568369","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568369","url":null,"abstract":"The world is facing an extreme crisis due to the COVID-19 pandemic. The COVID-19 virus interrupts the world's economy and social factors; thus, many countries fall into poverty. Also, they lack expertise in this field and could not make an effort to perform the necessary polymerase chain reaction (PCR) or other expensive laboratory tests. Therefore, it is important to find an alternative solution to the early prediction of COVID-19 infected persons with a low-cost method. The objective of this study is to detect COVID-19 infected individuals through their breathing sounds. To perform this task, twenty-two (22) acoustic features are extracted. The optimum features in each COVID-19 infected breathing sound is identified among these features through a feature engineering method. This proposed feature engineering method is a hybrid model that includes; statistical feature evaluation, PCA, and k-mean clustering techniques. The final results of this proposed Optimum Acoustic Feature Engineering (OAFE) model show that breathing sound signals' Kurtosis feature is more effective in distinguishing COVID-19 infected individuals from healthy individuals.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126563365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-16DOI: 10.1109/scse53661.2021.9568334
T. Sarathchandra, Damith Jayawikrama
Billions of people use social networks, and they playa significant role in people's lifestyles in the current world. At the same time, due to globalization and other factors, the use of these social platforms is expanding daily, and a variety of activities take place inside these platforms. These networks are centralized, allowing social network-owned companies to track and observe the activities of their users. Therefore, this has been challenged to the privacy of the data of users. Also, these companies tend to sell them to third parties keeping huge profits without users' permission. Since data is the most valuable asset in today's and tomorrow's world, many have pointed out this issue. Even though decentralized, community-driven applications have come to playas a solution to this problem, there is still no successful application that competes with centralized social network platforms. Therefore, this study attempted to develop a decentralized social network architecture with the basic functionalities of a social media platform to assure the privacy of the users' data.
{"title":"A decentralized social network architecture","authors":"T. Sarathchandra, Damith Jayawikrama","doi":"10.1109/scse53661.2021.9568334","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568334","url":null,"abstract":"Billions of people use social networks, and they playa significant role in people's lifestyles in the current world. At the same time, due to globalization and other factors, the use of these social platforms is expanding daily, and a variety of activities take place inside these platforms. These networks are centralized, allowing social network-owned companies to track and observe the activities of their users. Therefore, this has been challenged to the privacy of the data of users. Also, these companies tend to sell them to third parties keeping huge profits without users' permission. Since data is the most valuable asset in today's and tomorrow's world, many have pointed out this issue. Even though decentralized, community-driven applications have come to playas a solution to this problem, there is still no successful application that competes with centralized social network platforms. Therefore, this study attempted to develop a decentralized social network architecture with the basic functionalities of a social media platform to assure the privacy of the users' data.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115319321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-16DOI: 10.1109/scse53661.2021.9568282
P. Rathnayake, J. M. D. Senanayake, D. Wickramaarachchi
Coronavirus disease was first discovered in December 2019. As of July 2021, within nineteen months since this infectious disease started, more than one hundred and eighty million cases have been reported. The incubation period of the virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), can be defined as the period between exposure to the virus and symptom onset. Most of the affected cases are asymptomatic during this period, but they can transmit the virus to others. The incubation period is an important factor in deciding quarantine or isolation periods. According to current studies, the incubation period of SARS-CoV-2 ranges from2 to 14 days. Since there is a range, it is difficult to identify a specific incubation period for suspected cases. Therefore, all suspected cases should undergo an isolation period of 14 days, and it may lead to unnecessarily allocation of resources. The main objective of this research is to develop a classification model to classify the incubation period using machine learning techniques after identifying the factors affecting the incubation period. Patient records within the age group 5–80 years were used in this study. The dataset consists of 500 patient records from various countries such as China, Japan, South Korea and the USA. This study identified that the patients' age, immunocompetent state, gender, direct/indirect contact with the affected patients and the residing location affect the incubation period. Several supervised learning classification algorithms were compared in this study to find the best performing algorithm to classify the incubation classes. The weighted average of each incubation class was used to evaluate the overall model performance. The random forest algorithm outperformed other algorithms achieving 0.78 precision, 0.84 recall, and 0.80 F1-score in classifying the incubation classes. To fine-tune the model AdaBoost algorithm was used.
{"title":"Estimation of the incubation period of COVID-19 using boosted random forest algorithm","authors":"P. Rathnayake, J. M. D. Senanayake, D. Wickramaarachchi","doi":"10.1109/scse53661.2021.9568282","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568282","url":null,"abstract":"Coronavirus disease was first discovered in December 2019. As of July 2021, within nineteen months since this infectious disease started, more than one hundred and eighty million cases have been reported. The incubation period of the virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), can be defined as the period between exposure to the virus and symptom onset. Most of the affected cases are asymptomatic during this period, but they can transmit the virus to others. The incubation period is an important factor in deciding quarantine or isolation periods. According to current studies, the incubation period of SARS-CoV-2 ranges from2 to 14 days. Since there is a range, it is difficult to identify a specific incubation period for suspected cases. Therefore, all suspected cases should undergo an isolation period of 14 days, and it may lead to unnecessarily allocation of resources. The main objective of this research is to develop a classification model to classify the incubation period using machine learning techniques after identifying the factors affecting the incubation period. Patient records within the age group 5–80 years were used in this study. The dataset consists of 500 patient records from various countries such as China, Japan, South Korea and the USA. This study identified that the patients' age, immunocompetent state, gender, direct/indirect contact with the affected patients and the residing location affect the incubation period. Several supervised learning classification algorithms were compared in this study to find the best performing algorithm to classify the incubation classes. The weighted average of each incubation class was used to evaluate the overall model performance. The random forest algorithm outperformed other algorithms achieving 0.78 precision, 0.84 recall, and 0.80 F1-score in classifying the incubation classes. To fine-tune the model AdaBoost algorithm was used.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115369442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-16DOI: 10.1109/scse53661.2021.9568289
L. Weerasinghe, I. Perera
With the continuous progress in technology during the past few decades, cloud computing has become a fast-growing technology in the world, making computerized systems widespread. The emergence of Cloud Computing has evolved towards microservice concepts, which are highly demanded by corporates for enterprise application level. Most enterprise applications have moved away from traditional unified models of software programs like monolithic architecture and traditional SOA architecture to microservice architecture to ensure better scalability, lesser investment in hardware, and high performance. The monolithic architecture is designed in a manner that all the components and the modules are packed together and deployed on a single binary. However, in the microservice architecture, components are developed as small services so that horizontally and vertically scaling is made easier in comparison to monolith or SOA architecture. SOA and monolithic architecture are at a disadvantage compared to Microservice architecture, as they require colossal hardware specifications to scale the software. In general terms, the system performance of these architectures can be measured considering different aspects such as system capacity, throughput, and latency. This research focuses on how scalability and performance software quality attributes behave when converting the SOA system to microservice architecture. Experimental results have shown that microservice architecture can bring more scalability with a minimum cost generation. Nevertheless, specific gaps in performance are identified in the perspective of the final user experiences due to the interservice communication in the microservice architecture in a distributed environment.
{"title":"An exploratory evaluation of replacing ESB with microservices in service-oriented architecture","authors":"L. Weerasinghe, I. Perera","doi":"10.1109/scse53661.2021.9568289","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568289","url":null,"abstract":"With the continuous progress in technology during the past few decades, cloud computing has become a fast-growing technology in the world, making computerized systems widespread. The emergence of Cloud Computing has evolved towards microservice concepts, which are highly demanded by corporates for enterprise application level. Most enterprise applications have moved away from traditional unified models of software programs like monolithic architecture and traditional SOA architecture to microservice architecture to ensure better scalability, lesser investment in hardware, and high performance. The monolithic architecture is designed in a manner that all the components and the modules are packed together and deployed on a single binary. However, in the microservice architecture, components are developed as small services so that horizontally and vertically scaling is made easier in comparison to monolith or SOA architecture. SOA and monolithic architecture are at a disadvantage compared to Microservice architecture, as they require colossal hardware specifications to scale the software. In general terms, the system performance of these architectures can be measured considering different aspects such as system capacity, throughput, and latency. This research focuses on how scalability and performance software quality attributes behave when converting the SOA system to microservice architecture. Experimental results have shown that microservice architecture can bring more scalability with a minimum cost generation. Nevertheless, specific gaps in performance are identified in the perspective of the final user experiences due to the interservice communication in the microservice architecture in a distributed environment.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127976531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-16DOI: 10.1109/scse53661.2021.9568358
Yashodha Karunarathna, J. Wijayakulasooriya, J. Ekanayake, Pasindu Perera
Over half of all electrical fires in installations are caused by arcing due to poorly connected equipment or wiring system failures. Therefore, it is essential to detect arcs and interrupt them using a suitable protective device. This paper provides a modelling simulation and experimental approach to obtain arc voltage and current. The parameters for the theoretical model were turned based on the experimental results. A realistic case study was done to obtain the arc current under parallel and series arcs. As seen from the results, a parallel arc creates a current much higher than the load current, whereas a series arc current is often lower than the load current. Even though a parallel arc current may be detected by an overcurrent device, as it is often intermittent, it may not sustain to be captured by existing protection devices. Therefore, both parallel and series arc detection and interruption demand a reliable protection device.
{"title":"Modelling and validation of arc-fault currents under resistive and inductive loads","authors":"Yashodha Karunarathna, J. Wijayakulasooriya, J. Ekanayake, Pasindu Perera","doi":"10.1109/scse53661.2021.9568358","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568358","url":null,"abstract":"Over half of all electrical fires in installations are caused by arcing due to poorly connected equipment or wiring system failures. Therefore, it is essential to detect arcs and interrupt them using a suitable protective device. This paper provides a modelling simulation and experimental approach to obtain arc voltage and current. The parameters for the theoretical model were turned based on the experimental results. A realistic case study was done to obtain the arc current under parallel and series arcs. As seen from the results, a parallel arc creates a current much higher than the load current, whereas a series arc current is often lower than the load current. Even though a parallel arc current may be detected by an overcurrent device, as it is often intermittent, it may not sustain to be captured by existing protection devices. Therefore, both parallel and series arc detection and interruption demand a reliable protection device.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"17 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124388448","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：