Pub Date : 2023-10-01DOI: 10.4186/ej.2023.27.10.93
Chanatip Thongdonnoi, P. Chutima, A. Jiamsanguanwong
{"title":"Application of Collaborative Robots for Increasing Productivity in an Eyeglasses Lenses Manufacturer","authors":"Chanatip Thongdonnoi, P. Chutima, A. Jiamsanguanwong","doi":"10.4186/ej.2023.27.10.93","DOIUrl":"https://doi.org/10.4186/ej.2023.27.10.93","url":null,"abstract":"","PeriodicalId":11618,"journal":{"name":"Engineering Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139325279","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}
Pimpichcha Teawpanich, S. Saisinchai, A. Numprasanthai, Raphael Bissen, O. Juntarasakul, C. Tabelin, Theerayut Phengsaart
{"title":"Quality Improvement of Low-Grade Calcium Carbonate Using Induced Roll Magnetic Separator","authors":"Pimpichcha Teawpanich, S. Saisinchai, A. Numprasanthai, Raphael Bissen, O. Juntarasakul, C. Tabelin, Theerayut Phengsaart","doi":"10.4186/ej.2023.27.10.1","DOIUrl":"https://doi.org/10.4186/ej.2023.27.10.1","url":null,"abstract":"","PeriodicalId":11618,"journal":{"name":"Engineering Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139328461","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 : 2023-10-01DOI: 10.4186/ej.2023.27.10.21
Jia Heng Ong, K. Chia
{"title":"Developing an Optimal Brain Computer Interface Model using Functional Near Infrared Spectroscopy: A Review","authors":"Jia Heng Ong, K. Chia","doi":"10.4186/ej.2023.27.10.21","DOIUrl":"https://doi.org/10.4186/ej.2023.27.10.21","url":null,"abstract":"","PeriodicalId":11618,"journal":{"name":"Engineering Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139331475","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 : 2023-10-01DOI: 10.4186/ej.2023.27.10.81
{"title":"Evaluation of The Road Vulnerability Network During the Evacuation Process (A Case Study in A Coastal Area of Bengkulu City, Indonesia)","authors":"","doi":"10.4186/ej.2023.27.10.81","DOIUrl":"https://doi.org/10.4186/ej.2023.27.10.81","url":null,"abstract":"","PeriodicalId":11618,"journal":{"name":"Engineering Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139326587","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}
Sivasamy Ramasamy, David Banjerdpongchai, PooGyeon Park
. The existence of chaos in ecological models is quite obvious due to the presence of nonlinear terms. Controlling chaotic phenomena in ecological systems remains a difficult task due to their unpredictability, and thus chaos control is one of the main objectives for constructing mathematical models in ecology today. Our aim in this paper is to review chaos control strategies for the tri-trophic food chain models by using various ecological factors. The factors include additional food, prey refuge, the Allee effect, the fear effect, and harvesting. We establish the essential conditions for the existence of ecologically feasible equilibrium points in the food chain ecological systems and their local stability. This paper provides a unified overview of recent research on the chaos control of ecological systems. The theoretical results suggest a way to control populations of species in ecological systems for fishing and pest management in farming. Numerical examples are performed to justify and compare the theoretical findings through phase portraits and bifurcation diagrams.
{"title":"A Review of Chaos Control Strategies for Tri-trophic Food Chain Ecological Systems","authors":"Sivasamy Ramasamy, David Banjerdpongchai, PooGyeon Park","doi":"10.4186/ej.2023.27.9.39","DOIUrl":"https://doi.org/10.4186/ej.2023.27.9.39","url":null,"abstract":". The existence of chaos in ecological models is quite obvious due to the presence of nonlinear terms. Controlling chaotic phenomena in ecological systems remains a difficult task due to their unpredictability, and thus chaos control is one of the main objectives for constructing mathematical models in ecology today. Our aim in this paper is to review chaos control strategies for the tri-trophic food chain models by using various ecological factors. The factors include additional food, prey refuge, the Allee effect, the fear effect, and harvesting. We establish the essential conditions for the existence of ecologically feasible equilibrium points in the food chain ecological systems and their local stability. This paper provides a unified overview of recent research on the chaos control of ecological systems. The theoretical results suggest a way to control populations of species in ecological systems for fishing and pest management in farming. Numerical examples are performed to justify and compare the theoretical findings through phase portraits and bifurcation diagrams.","PeriodicalId":11618,"journal":{"name":"Engineering Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135641230","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}
. This paper proposes a hierarchical analytic process framework to facilitate practitioners to determine improvement directions for a manufacturing system efficiently and systematically. The proposed framework analyzes current state performances of a manufacturing system by applying the knowledge of science of manufacturing systems which describe relationship control factors, performance measures, and improvement objectives. Then, concrete directions for improvements are suggested. The analysis process embeds the concept of diagnostic tree which makes it an easy-to-handle framework. Under the diagnostic tree concept, it decomposes the high-level business goal into successively low-level activities to give more comprehensive areas of improvement. The proposed framework comprises of three key elements: Operation Performance Measures (OPMs), Diagnostic tree (D-Tree), and Action guidelines. The OPMs are used in the D-Tree to determine improvement objectives. Then the Action guidelines suggest how to adjust control factors in a manufacturing system according to each improvement objective. The proposed diagnostic framework is demonstrated by Promodel simulation of a case study. The simulation model includes physical resources, flow lines, WIP, and replenishment signals of the case. By following the analytic process in the framework, the performance measures have shown improvements according to action guidelines and the result of improvements meets the requirement of a factory in the case study.
{"title":"A Hierarchical Analytic Process Framework for Manufacturing System Improvement","authors":"Jongkon Sukjamnong, Paveena Chaovalitwongse, Siravit Swangnop, Poom Luangjarmekorn","doi":"10.4186/ej.2023.27.9.55","DOIUrl":"https://doi.org/10.4186/ej.2023.27.9.55","url":null,"abstract":". This paper proposes a hierarchical analytic process framework to facilitate practitioners to determine improvement directions for a manufacturing system efficiently and systematically. The proposed framework analyzes current state performances of a manufacturing system by applying the knowledge of science of manufacturing systems which describe relationship control factors, performance measures, and improvement objectives. Then, concrete directions for improvements are suggested. The analysis process embeds the concept of diagnostic tree which makes it an easy-to-handle framework. Under the diagnostic tree concept, it decomposes the high-level business goal into successively low-level activities to give more comprehensive areas of improvement. The proposed framework comprises of three key elements: Operation Performance Measures (OPMs), Diagnostic tree (D-Tree), and Action guidelines. The OPMs are used in the D-Tree to determine improvement objectives. Then the Action guidelines suggest how to adjust control factors in a manufacturing system according to each improvement objective. The proposed diagnostic framework is demonstrated by Promodel simulation of a case study. The simulation model includes physical resources, flow lines, WIP, and replenishment signals of the case. By following the analytic process in the framework, the performance measures have shown improvements according to action guidelines and the result of improvements meets the requirement of a factory in the case study.","PeriodicalId":11618,"journal":{"name":"Engineering Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135641228","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}
Jesmin Akther, Al-Akhir Nayan, Muhammad Harun-Or-Roshid
. Potato cultivation is vital in numerous countries, contributing to food security and economic value. However, crop diseases, particularly early and late blight, pose significant challenges to potato production. The accurate diagnosis of these diseases remains unclear to many individuals. This study leverages the increasing penetration of smartphones and recent advancements in deep learning to develop a Convolutional Neural Network (CNN) model for real-time detection of early and late blight in potatoes. The dataset was pre-processed by normalizing, dividing, and extracting images using the Python data processing library. The approach incorporates slight variations in the network layers to optimize the model's performance. The method was evaluated using classification optimizers, metrics, and loss functions and further refined using layer-by-layer TensorBoard analysis. Hyperparameters such as features, labels, validation split, batch size, and training epochs were carefully selected. The final model demonstrated promising results, achieving an accuracy of 96.09% on the survey dataset. Experimental findings highlight the approach's potential for automatically detecting both early, late blight and healthy, thereby significantly improving the accuracy of disease diagnosis .
{"title":"Potato Leaves Blight Disease Recognition and Categorization Using Deep Learning","authors":"Jesmin Akther, Al-Akhir Nayan, Muhammad Harun-Or-Roshid","doi":"10.4186/ej.2023.27.9.27","DOIUrl":"https://doi.org/10.4186/ej.2023.27.9.27","url":null,"abstract":". Potato cultivation is vital in numerous countries, contributing to food security and economic value. However, crop diseases, particularly early and late blight, pose significant challenges to potato production. The accurate diagnosis of these diseases remains unclear to many individuals. This study leverages the increasing penetration of smartphones and recent advancements in deep learning to develop a Convolutional Neural Network (CNN) model for real-time detection of early and late blight in potatoes. The dataset was pre-processed by normalizing, dividing, and extracting images using the Python data processing library. The approach incorporates slight variations in the network layers to optimize the model's performance. The method was evaluated using classification optimizers, metrics, and loss functions and further refined using layer-by-layer TensorBoard analysis. Hyperparameters such as features, labels, validation split, batch size, and training epochs were carefully selected. The final model demonstrated promising results, achieving an accuracy of 96.09% on the survey dataset. Experimental findings highlight the approach's potential for automatically detecting both early, late blight and healthy, thereby significantly improving the accuracy of disease diagnosis .","PeriodicalId":11618,"journal":{"name":"Engineering Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135641225","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 durability of cementitious materials can be improved with the widespread utilization of fly ash (FA). Although FA has been available for use in cement and concrete industries for decades, there is still a practical barrier associated with its application. The difficulty stems from its wide variety and heterogeneity. The purpose of this research is to conduct both experimental and numerical investigations to achieve a better understanding of managing the variation of FA, which reflects its durability. The chemical properties and particle size distribution of FA from five distinct sources in ASEAN region were analyzed. In addition, the degree of reactivity, flow, toughened porosity, and apparent chloride diffusivity coefficients of blended FA-cement systems were studied (D a ). The Life365 service life model was executed. Using analysis of variance (ANOVA) and sensitivity analysis using linear regression, the experimental outcomes were statistically examined. Having a 15% FA replacement level resulted in a roughly 70% decrease of the D a value, extending its serviceability by around 13%. The chemo-physical processes in multi-scale structures were shown to be the most important element by statistical analysis, and the degree of response in blended FA-cement systems and its toughened porosity were found to be among the most beneficial aspects affecting its durability.
{"title":"Exploring ASEAN Fly Ash for Enhancing Cement Hydration and Service Life Prediction of Portland Cement Mortar","authors":"Thwe Thwe Win, Rungrawee Wattanapornprom, Lapyote Prasittisopin, Withit Pansuk, Phoonsak Pheinsusom","doi":"10.4186/ej.2023.27.9.1","DOIUrl":"https://doi.org/10.4186/ej.2023.27.9.1","url":null,"abstract":". The durability of cementitious materials can be improved with the widespread utilization of fly ash (FA). Although FA has been available for use in cement and concrete industries for decades, there is still a practical barrier associated with its application. The difficulty stems from its wide variety and heterogeneity. The purpose of this research is to conduct both experimental and numerical investigations to achieve a better understanding of managing the variation of FA, which reflects its durability. The chemical properties and particle size distribution of FA from five distinct sources in ASEAN region were analyzed. In addition, the degree of reactivity, flow, toughened porosity, and apparent chloride diffusivity coefficients of blended FA-cement systems were studied (D a ). The Life365 service life model was executed. Using analysis of variance (ANOVA) and sensitivity analysis using linear regression, the experimental outcomes were statistically examined. Having a 15% FA replacement level resulted in a roughly 70% decrease of the D a value, extending its serviceability by around 13%. The chemo-physical processes in multi-scale structures were shown to be the most important element by statistical analysis, and the degree of response in blended FA-cement systems and its toughened porosity were found to be among the most beneficial aspects affecting its durability.","PeriodicalId":11618,"journal":{"name":"Engineering Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135640099","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}
. This paper applies the Omega method to creep life prediction for Hastelloy XR at temperatures ranging from 850 to 950 o C in an air environment. The creep data were obtained from literature. Three life prediction scenarios were studied including constant stress, constant load, and continuous monitoring where creep data is simulated for sequential acquisition. The constant stress creep data at each temperature were used to determine the Omega model parameters, and empirical equations for each parameter were developed. The predicted creep lives under constant stress were within a factor of 2 in almost all cases. For a life prediction under constant load, the actual applied stress was estimated and used in the creep constitutive equation as well as for calculating the model parameters. The predicted creep lives were also to be within a factor of 2 in almost all cases. The Omega model was found to be applicable to a continuous creep data acquisition scenario as well. An appropriate scheme for continuous monitoring scenario was suggested, and statistical analysis by the Monte Carlo simulation was demonstrated.
{"title":"Creep Life Prediction for Hastelloy XR Using the Omega Method","authors":"Surat Kwanmuang, Jirapong Kasivitamnuay","doi":"10.4186/ej.2023.27.9.15","DOIUrl":"https://doi.org/10.4186/ej.2023.27.9.15","url":null,"abstract":". This paper applies the Omega method to creep life prediction for Hastelloy XR at temperatures ranging from 850 to 950 o C in an air environment. The creep data were obtained from literature. Three life prediction scenarios were studied including constant stress, constant load, and continuous monitoring where creep data is simulated for sequential acquisition. The constant stress creep data at each temperature were used to determine the Omega model parameters, and empirical equations for each parameter were developed. The predicted creep lives under constant stress were within a factor of 2 in almost all cases. For a life prediction under constant load, the actual applied stress was estimated and used in the creep constitutive equation as well as for calculating the model parameters. The predicted creep lives were also to be within a factor of 2 in almost all cases. The Omega model was found to be applicable to a continuous creep data acquisition scenario as well. An appropriate scheme for continuous monitoring scenario was suggested, and statistical analysis by the Monte Carlo simulation was demonstrated.","PeriodicalId":11618,"journal":{"name":"Engineering Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135641227","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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