As a plan, Industry 4.0 encourages manufacturing companies to switch from conventional Product-Service Systems to Digital Product-Service Systems. Systems of goods, services, and digital technologies known as “Digital Product-Service Systems” are provided to improve consumer satisfaction and business success in the marketplace. Previous studies have looked into various elements of this area for industrial companies and academic institutions. Digital Product-Service Systems’ overall worth and expected course of growth are still ignored. The authors use bibliometric analysis to organize the body of prior knowledge in this discipline and, more significantly, to identify areas for further study in order to cover the literature deficit. The results of the most esteemed authors, nations, and sources in the subject were given by this study. The findings also show that terms like digitization, sustainability, and business have grown in popularity over the previous year. This study also offered insight into how Industry 5.0, a new manufacturing strategy, would include Digital Product-Service Systems. Finally, the findings of this research demonstrate three new service orientations, namely resilient, sustainable, and human-centric, in manufacturing firms.
{"title":"Development and Future Trends of Digital Product-Service Systems: A Bibliometric Analysis Approach","authors":"Slavko Rakic, Nenad Medic, Janika Leoste, Teodora Vuckovic, Ugljesa Marjanovic","doi":"10.3390/asi6050089","DOIUrl":"https://doi.org/10.3390/asi6050089","url":null,"abstract":"As a plan, Industry 4.0 encourages manufacturing companies to switch from conventional Product-Service Systems to Digital Product-Service Systems. Systems of goods, services, and digital technologies known as “Digital Product-Service Systems” are provided to improve consumer satisfaction and business success in the marketplace. Previous studies have looked into various elements of this area for industrial companies and academic institutions. Digital Product-Service Systems’ overall worth and expected course of growth are still ignored. The authors use bibliometric analysis to organize the body of prior knowledge in this discipline and, more significantly, to identify areas for further study in order to cover the literature deficit. The results of the most esteemed authors, nations, and sources in the subject were given by this study. The findings also show that terms like digitization, sustainability, and business have grown in popularity over the previous year. This study also offered insight into how Industry 5.0, a new manufacturing strategy, would include Digital Product-Service Systems. Finally, the findings of this research demonstrate three new service orientations, namely resilient, sustainable, and human-centric, in manufacturing firms.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136342953","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}
Muhammad Sabih, Muhammad Shahid Farid, Mahnoor Ejaz, Muhammad Husam, Muhammad Hassan Khan, Umar Farooq
Industries are rapidly moving toward mitigating errors and manual interventions by automating their process. The same motivation is carried out in this research which targets to study a conveyor system installed in soda ash manufacturing plants. Our aim is to automate the determination of optimal parameters, which are chosen by identifying the flow rate of the materials available on the conveyor belt for maintaining the ratio between raw materials being carried. The ratio is essential to produce 40% pure carbon dioxide gas needed for soda ash production. A visual sensor mounted on the conveyor belt is used to estimate the flow rate of the raw materials. After selecting the region of interest, a segmentation algorithm is defined based on a voting-based technique to segment the most confident region. Moments and contour features are extracted and passed to machine learning algorithms to estimate the flow rate of different experiments. An in-depth analysis is completed on various techniques and convincing results are achieved on the final data split with the best parameters using the Bagging regressor. Each step of the process is made resilient enough to work in a challenging environment even if the belt is placed in an outdoor environment. The proposed solution caters to the current challenges and serves as a practical solution for estimating material flow without manual intervention.
{"title":"Raw Material Flow Rate Measurement on Belt Conveyor System Using Visual Data","authors":"Muhammad Sabih, Muhammad Shahid Farid, Mahnoor Ejaz, Muhammad Husam, Muhammad Hassan Khan, Umar Farooq","doi":"10.3390/asi6050088","DOIUrl":"https://doi.org/10.3390/asi6050088","url":null,"abstract":"Industries are rapidly moving toward mitigating errors and manual interventions by automating their process. The same motivation is carried out in this research which targets to study a conveyor system installed in soda ash manufacturing plants. Our aim is to automate the determination of optimal parameters, which are chosen by identifying the flow rate of the materials available on the conveyor belt for maintaining the ratio between raw materials being carried. The ratio is essential to produce 40% pure carbon dioxide gas needed for soda ash production. A visual sensor mounted on the conveyor belt is used to estimate the flow rate of the raw materials. After selecting the region of interest, a segmentation algorithm is defined based on a voting-based technique to segment the most confident region. Moments and contour features are extracted and passed to machine learning algorithms to estimate the flow rate of different experiments. An in-depth analysis is completed on various techniques and convincing results are achieved on the final data split with the best parameters using the Bagging regressor. Each step of the process is made resilient enough to work in a challenging environment even if the belt is placed in an outdoor environment. The proposed solution caters to the current challenges and serves as a practical solution for estimating material flow without manual intervention.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136344023","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}
In this study, we delve into the realm of image segmentation, a field characterized by a multitude of approaches; one frequently used technique is thresholding-based image segmentation. This process divides intensity levels into different regions based on a specified threshold value. Minimum Cross-Entropy Thresholding (MCET) stands out as an independent objective function that can be applied with any distribution and is regarded as a mean-based thresholding method. In certain cases, images exhibit diverse structures that result in different histogram distributions. Some images possess symmetric histograms, while others feature asymmetric ones. Traditional mean-based thresholding methods are well-suited for symmetric image histograms, relying on Gaussian distribution definitions for mean estimations. However, in situations involving asymmetric distributions, such as left and right-skewed histograms, a different approach is required. In this paper, we propose the utilization of a Maximum Likelihood Estimation (MLE) of Gumbel’s distribution or Extreme Value Type I (EVI) distribution for the objective function of an MCET. Our goal is to introduce a dedicated image-thresholding model designed to enhance the accuracy and efficiency of image-segmentation tasks. This model determines optimal thresholds for image segmentation, facilitating precise data analysis for specific image types and yielding improved segmentation results by considering the impact of mean values on thresholding objective functions. We compare our proposed model with original methods and related studies in the literature. Our model demonstrates better performance in terms of segmentation accuracy, as assessed through both unsupervised and supervised evaluations for image segmentation.
{"title":"Gumbel (EVI)-Based Minimum Cross-Entropy Thresholding for the Segmentation of Images with Skewed Histograms","authors":"Walaa Ali H. Jumiawi, Ali El-Zaart","doi":"10.3390/asi6050087","DOIUrl":"https://doi.org/10.3390/asi6050087","url":null,"abstract":"In this study, we delve into the realm of image segmentation, a field characterized by a multitude of approaches; one frequently used technique is thresholding-based image segmentation. This process divides intensity levels into different regions based on a specified threshold value. Minimum Cross-Entropy Thresholding (MCET) stands out as an independent objective function that can be applied with any distribution and is regarded as a mean-based thresholding method. In certain cases, images exhibit diverse structures that result in different histogram distributions. Some images possess symmetric histograms, while others feature asymmetric ones. Traditional mean-based thresholding methods are well-suited for symmetric image histograms, relying on Gaussian distribution definitions for mean estimations. However, in situations involving asymmetric distributions, such as left and right-skewed histograms, a different approach is required. In this paper, we propose the utilization of a Maximum Likelihood Estimation (MLE) of Gumbel’s distribution or Extreme Value Type I (EVI) distribution for the objective function of an MCET. Our goal is to introduce a dedicated image-thresholding model designed to enhance the accuracy and efficiency of image-segmentation tasks. This model determines optimal thresholds for image segmentation, facilitating precise data analysis for specific image types and yielding improved segmentation results by considering the impact of mean values on thresholding objective functions. We compare our proposed model with original methods and related studies in the literature. Our model demonstrates better performance in terms of segmentation accuracy, as assessed through both unsupervised and supervised evaluations for image segmentation.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135246872","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 utilization of data mining techniques for the prompt prediction of academic success has gained significant importance in the current era. There is an increasing interest in utilizing these methodologies to forecast the academic performance of students, thereby facilitating educators to intervene and furnish suitable assistance when required. The purpose of this study was to determine the optimal methods for feature engineering and selection in the context of regression and classification tasks. This study compared the Boruta algorithm and Lasso regression for regression, and Recursive Feature Elimination (RFE) and Random Forest Importance (RFI) for classification. According to the findings, Gradient Boost for the regression part of this study had the least Mean Absolute Error (MAE) and Root-Mean-Square Error (RMSE) of 12.93 and 18.28, respectively, in the case of the Boruta selection method. In contrast, RFI was found to be the superior classification method, yielding an accuracy rate of 78% in the classification part. This research emphasized the significance of employing appropriate feature engineering and selection methodologies to enhance the efficacy of machine learning algorithms. Using a diverse set of machine learning techniques, this study analyzed the OULA dataset, focusing on both feature engineering and selection. Our approach was to systematically compare the performance of different models, leading to insights about the most effective strategies for predicting student success.
{"title":"Predictive Analysis of Students’ Learning Performance Using Data Mining Techniques: A Comparative Study of Feature Selection Methods","authors":"S. M. F. D. Syed Mustapha","doi":"10.3390/asi6050086","DOIUrl":"https://doi.org/10.3390/asi6050086","url":null,"abstract":"The utilization of data mining techniques for the prompt prediction of academic success has gained significant importance in the current era. There is an increasing interest in utilizing these methodologies to forecast the academic performance of students, thereby facilitating educators to intervene and furnish suitable assistance when required. The purpose of this study was to determine the optimal methods for feature engineering and selection in the context of regression and classification tasks. This study compared the Boruta algorithm and Lasso regression for regression, and Recursive Feature Elimination (RFE) and Random Forest Importance (RFI) for classification. According to the findings, Gradient Boost for the regression part of this study had the least Mean Absolute Error (MAE) and Root-Mean-Square Error (RMSE) of 12.93 and 18.28, respectively, in the case of the Boruta selection method. In contrast, RFI was found to be the superior classification method, yielding an accuracy rate of 78% in the classification part. This research emphasized the significance of employing appropriate feature engineering and selection methodologies to enhance the efficacy of machine learning algorithms. Using a diverse set of machine learning techniques, this study analyzed the OULA dataset, focusing on both feature engineering and selection. Our approach was to systematically compare the performance of different models, leading to insights about the most effective strategies for predicting student success.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135245880","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}
Retailers must have accurate sales forecasts to efficiently and effectively operate their businesses and remain competitive in the marketplace. Global forecasting models like RNNs can be a powerful tool for forecasting in retail settings, where multiple time series are often interrelated and influenced by a variety of external factors. By including covariates in a forecasting model, we can often better capture the various factors that can influence sales in a retail setting. This can help improve the accuracy of our forecasts and enable better decision making for inventory management, purchasing, and other operational decisions. In this study, we investigate how the accuracy of global forecasting models is affected by the inclusion of different potential demand covariates. To ensure the significance of the study’s findings, we used the M5 forecasting competition’s openly accessible and well-established dataset. The results obtained from DeepAR models trained on different combinations of features indicate that the inclusion of time-, event-, and ID-related features consistently enhances the forecast accuracy. The optimal performance is attained when all these covariates are employed together, leading to a 1.8% improvement in RMSSE and a 6.5% improvement in MASE compared to the baseline model without features. It is noteworthy that all DeepAR models, both with and without covariates, exhibit a significantly superior forecasting performance in comparison to the seasonal naïve benchmark.
{"title":"Robust Sales forecasting Using Deep Learning with Static and Dynamic Covariates","authors":"Patrícia Ramos, José Manuel Oliveira","doi":"10.3390/asi6050085","DOIUrl":"https://doi.org/10.3390/asi6050085","url":null,"abstract":"Retailers must have accurate sales forecasts to efficiently and effectively operate their businesses and remain competitive in the marketplace. Global forecasting models like RNNs can be a powerful tool for forecasting in retail settings, where multiple time series are often interrelated and influenced by a variety of external factors. By including covariates in a forecasting model, we can often better capture the various factors that can influence sales in a retail setting. This can help improve the accuracy of our forecasts and enable better decision making for inventory management, purchasing, and other operational decisions. In this study, we investigate how the accuracy of global forecasting models is affected by the inclusion of different potential demand covariates. To ensure the significance of the study’s findings, we used the M5 forecasting competition’s openly accessible and well-established dataset. The results obtained from DeepAR models trained on different combinations of features indicate that the inclusion of time-, event-, and ID-related features consistently enhances the forecast accuracy. The optimal performance is attained when all these covariates are employed together, leading to a 1.8% improvement in RMSSE and a 6.5% improvement in MASE compared to the baseline model without features. It is noteworthy that all DeepAR models, both with and without covariates, exhibit a significantly superior forecasting performance in comparison to the seasonal naïve benchmark.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135387378","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}
Marek Kciuk, Zygmunt Kowalik, Grazia Lo Sciuto, Sebastian Sławski, Stefano Mastrostefano
The promising research on flexible and tactile sensors requires conducting polymer materials and an accurate system for the transduction of pressure into electrical signals. In this paper, the intelligent sensitive mat, based on Velostat, which is a polymeric material impregnated with carbon black, is investigated. Various designs and geometries for home-made sensor mats have been proposed, and their electrical and mechanical properties, including reproducibility, have been studied through the tests performed. The mat pressure sensors have been interfaced with an Arduino microcontroller in order to monitor, read with high precision, and control the variation of the resistance under applied pressure. An approximation method was then developed based on a neural network algorithm to explore the relationship between different mat shapes, the pressure and stresses applied on the mat, the resistance of the conductive Velostat material, and the number of active sensing cells in order to control system input signal management.
{"title":"Intelligent Medical Velostat Pressure Sensor Mat Based on Artificial Neural Network and Arduino Embedded System","authors":"Marek Kciuk, Zygmunt Kowalik, Grazia Lo Sciuto, Sebastian Sławski, Stefano Mastrostefano","doi":"10.3390/asi6050084","DOIUrl":"https://doi.org/10.3390/asi6050084","url":null,"abstract":"The promising research on flexible and tactile sensors requires conducting polymer materials and an accurate system for the transduction of pressure into electrical signals. In this paper, the intelligent sensitive mat, based on Velostat, which is a polymeric material impregnated with carbon black, is investigated. Various designs and geometries for home-made sensor mats have been proposed, and their electrical and mechanical properties, including reproducibility, have been studied through the tests performed. The mat pressure sensors have been interfaced with an Arduino microcontroller in order to monitor, read with high precision, and control the variation of the resistance under applied pressure. An approximation method was then developed based on a neural network algorithm to explore the relationship between different mat shapes, the pressure and stresses applied on the mat, the resistance of the conductive Velostat material, and the number of active sensing cells in order to control system input signal management.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134961141","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}
Laura Plaza, Lourdes Araujo, Fernando López-Ostenero, Juan Martínez-Romo
Online learning is quickly becoming a popular choice instead of traditional education. One of its key advantages lies in the flexibility it offers, allowing individuals to tailor their learning experiences to their unique schedules and commitments. Moreover, online learning enhances accessibility to education, breaking down geographical and economical boundaries. In this study, we propose the use of advanced natural language processing techniques to design and implement a recommender that supports e-learning students by tailoring materials and reinforcement activities to students’ needs. When a student posts a query in the course forum, our recommender system provides links to other discussion threads where related questions have been raised and additional activities to reinforce the study of topics that have been challenging. We have developed a content-based recommender that utilizes an algorithm capable of extracting key phrases, terms, and embeddings that describe the concepts in the student query and those present in other conversations and reinforcement activities with high precision. The recommender considers the similarity of the concepts extracted from the query and those covered in the course discussion forum and the exercise database to recommend the most relevant content for the student. Our results indicate that we can recommend both posts and activities with high precision (above 80%) using key phrases to represent the textual content. The primary contributions of this research are three. Firstly, it centers on a remarkably specialized and novel domain; secondly, it introduces an effective recommendation approach exclusively guided by the student’s query. Thirdly, the recommendations not only provide answers to immediate questions, but also encourage further learning through the recommendation of supplementary activities.
{"title":"Automatic Recommendation of Forum Threads and Reinforcement Activities in a Data Structure and Programming Course","authors":"Laura Plaza, Lourdes Araujo, Fernando López-Ostenero, Juan Martínez-Romo","doi":"10.3390/asi6050083","DOIUrl":"https://doi.org/10.3390/asi6050083","url":null,"abstract":"Online learning is quickly becoming a popular choice instead of traditional education. One of its key advantages lies in the flexibility it offers, allowing individuals to tailor their learning experiences to their unique schedules and commitments. Moreover, online learning enhances accessibility to education, breaking down geographical and economical boundaries. In this study, we propose the use of advanced natural language processing techniques to design and implement a recommender that supports e-learning students by tailoring materials and reinforcement activities to students’ needs. When a student posts a query in the course forum, our recommender system provides links to other discussion threads where related questions have been raised and additional activities to reinforce the study of topics that have been challenging. We have developed a content-based recommender that utilizes an algorithm capable of extracting key phrases, terms, and embeddings that describe the concepts in the student query and those present in other conversations and reinforcement activities with high precision. The recommender considers the similarity of the concepts extracted from the query and those covered in the course discussion forum and the exercise database to recommend the most relevant content for the student. Our results indicate that we can recommend both posts and activities with high precision (above 80%) using key phrases to represent the textual content. The primary contributions of this research are three. Firstly, it centers on a remarkably specialized and novel domain; secondly, it introduces an effective recommendation approach exclusively guided by the student’s query. Thirdly, the recommendations not only provide answers to immediate questions, but also encourage further learning through the recommendation of supplementary activities.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136236383","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 issue of Automated Meter Management (AMM), an integral part of modern energy smart grid systems, has become a hot topic in recent years. With the current energy crisis, and given the new approaches to smart energy and its regulation, implemented at the level of the European Union, the gradual introduction of AMM as a standard for the regulation and management of the distribution system is an absolute necessity. Modern smart grids incorporate elements of smart regulation that rely heavily on the availability and quality of the data generated or used during AMM as part of the smart grid. In this paper, based on an analytical view of AMM as a whole and guided interviews with the sponsors of each service and owners of each dataset, criteria are proposed and a Business Impact Analysis (BIA) is implemented, the results of which are used to determine security measures for the safe and reliable running of the AMM system. This paper offers a unique view of the AMM system as an integral part of modern smart grid networks from a data-driven perspective that enables the subsequent implementation and fulfillment of security requirements by ISO/IEC 27001 and national security standards, as the AMM system is also a critical information system under the EU directive regarding the cybersecurity of network and information systems, which are subject to newly defined security requirements in the field of cybersecurity.
{"title":"Business Impact Analysis of AMM Data: A Case Study","authors":"Josef Horalek","doi":"10.3390/asi6050082","DOIUrl":"https://doi.org/10.3390/asi6050082","url":null,"abstract":"The issue of Automated Meter Management (AMM), an integral part of modern energy smart grid systems, has become a hot topic in recent years. With the current energy crisis, and given the new approaches to smart energy and its regulation, implemented at the level of the European Union, the gradual introduction of AMM as a standard for the regulation and management of the distribution system is an absolute necessity. Modern smart grids incorporate elements of smart regulation that rely heavily on the availability and quality of the data generated or used during AMM as part of the smart grid. In this paper, based on an analytical view of AMM as a whole and guided interviews with the sponsors of each service and owners of each dataset, criteria are proposed and a Business Impact Analysis (BIA) is implemented, the results of which are used to determine security measures for the safe and reliable running of the AMM system. This paper offers a unique view of the AMM system as an integral part of modern smart grid networks from a data-driven perspective that enables the subsequent implementation and fulfillment of security requirements by ISO/IEC 27001 and national security standards, as the AMM system is also a critical information system under the EU directive regarding the cybersecurity of network and information systems, which are subject to newly defined security requirements in the field of cybersecurity.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135437928","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}
Mohamed Magdy Mohamed Abdo, Haitham El-Hussieny, Tomoyuki Miyashita, Sabah M. Ahmed
Recent developments in electromagnetic launchers have created potential applications in transportation, space, and defense systems. However, the total efficiency of these launchers has yet to be fully realized and optimized. Therefore, this paper introduces a new design idea based on increasing the magnetic flux lines that facilitate high output velocity without adding any excess energy. This design facilitates obtaining a mathematical equation for the launcher inductance which is difficult to analytically represent. This modification raises the launcher efficiency to 36% higher than that of the ordinary launcher at low operating voltage. The proposed design has proven its superiority to traditional launchers, which are limited in their ability to accelerate microsatellites from the ground to low Earth orbit due to altitude and velocity constraints. Therefore, an aircraft is used as a flying launchpad to carry the launcher and bring it to the required height to launch. Meanwhile, it is demonstrated experimentally that magnetic dipoles in the projectile material allow the launcher coil’s magnetic field to accelerate the projectile. This system consists of the launcher coil that must be triggered with a high amplitude current from the high DC voltage capacitor bank. In addition, a microcontroller unit controls all processes, including the capacitor bank charging, triggering, and velocity measurement.
{"title":"Design of A New Electromagnetic Launcher Based on the Magnetic Reluctance Control for the Propulsion of Aircraft-Mounted Microsatellites","authors":"Mohamed Magdy Mohamed Abdo, Haitham El-Hussieny, Tomoyuki Miyashita, Sabah M. Ahmed","doi":"10.3390/asi6050081","DOIUrl":"https://doi.org/10.3390/asi6050081","url":null,"abstract":"Recent developments in electromagnetic launchers have created potential applications in transportation, space, and defense systems. However, the total efficiency of these launchers has yet to be fully realized and optimized. Therefore, this paper introduces a new design idea based on increasing the magnetic flux lines that facilitate high output velocity without adding any excess energy. This design facilitates obtaining a mathematical equation for the launcher inductance which is difficult to analytically represent. This modification raises the launcher efficiency to 36% higher than that of the ordinary launcher at low operating voltage. The proposed design has proven its superiority to traditional launchers, which are limited in their ability to accelerate microsatellites from the ground to low Earth orbit due to altitude and velocity constraints. Therefore, an aircraft is used as a flying launchpad to carry the launcher and bring it to the required height to launch. Meanwhile, it is demonstrated experimentally that magnetic dipoles in the projectile material allow the launcher coil’s magnetic field to accelerate the projectile. This system consists of the launcher coil that must be triggered with a high amplitude current from the high DC voltage capacitor bank. In addition, a microcontroller unit controls all processes, including the capacitor bank charging, triggering, and velocity measurement.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135979273","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}
In this paper, we propose a novel methodology that combines the opposition Nelder–Mead algorithm and the selection phase of the genetic algorithm. This integration aims to enhance the performance of the overall algorithm. To evaluate the effectiveness of our methodology, we conducted a comprehensive comparative study involving 11 state-of-the-art algorithms renowned for their exceptional performance in the 2022 IEEE Congress on Evolutionary Computation (CEC 2022). Following rigorous analysis, which included a Friedman test and subsequent Dunn’s post hoc test, our algorithm demonstrated outstanding performance. In fact, our methodology exhibited equal or superior performance compared to the other algorithms in the majority of cases examined. These results highlight the effectiveness and competitiveness of our proposed approach, showcasing its potential to achieve state-of-the-art performance in solving optimization problems.
{"title":"Integrating the Opposition Nelder–Mead Algorithm into the Selection Phase of the Genetic Algorithm for Enhanced Optimization","authors":"Farouq Zitouni, Saad Harous","doi":"10.3390/asi6050080","DOIUrl":"https://doi.org/10.3390/asi6050080","url":null,"abstract":"In this paper, we propose a novel methodology that combines the opposition Nelder–Mead algorithm and the selection phase of the genetic algorithm. This integration aims to enhance the performance of the overall algorithm. To evaluate the effectiveness of our methodology, we conducted a comprehensive comparative study involving 11 state-of-the-art algorithms renowned for their exceptional performance in the 2022 IEEE Congress on Evolutionary Computation (CEC 2022). Following rigorous analysis, which included a Friedman test and subsequent Dunn’s post hoc test, our algorithm demonstrated outstanding performance. In fact, our methodology exhibited equal or superior performance compared to the other algorithms in the majority of cases examined. These results highlight the effectiveness and competitiveness of our proposed approach, showcasing its potential to achieve state-of-the-art performance in solving optimization problems.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41635796","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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