Analytics最新文献_第2页

Interoperable Information Flow as Enabler for Efficient Predictive Maintenance 可互操作的信息流是高效预测性维护的推动力

Analytics Pub Date : 2024-02-01 DOI: 10.3390/analytics3010006

Marco Franke, Quan Deng, Zisis Kyroudis, Maria Psarodimou, Jovana Milenkovic, Ioannis Meintanis, Dimitris Lokas, Stefano Borgia, K. Thoben

{"title":"Interoperable Information Flow as Enabler for Efficient Predictive Maintenance","authors":"Marco Franke, Quan Deng, Zisis Kyroudis, Maria Psarodimou, Jovana Milenkovic, Ioannis Meintanis, Dimitris Lokas, Stefano Borgia, K. Thoben","doi":"10.3390/analytics3010006","DOIUrl":"https://doi.org/10.3390/analytics3010006","url":null,"abstract":"Industry 4.0 enables the modernisation of machines and opens up the digitalisation of processes in the manufacturing industry. As a result, these machines are ready for predictive maintenance as part of Industry 4.0 services. The benefit of predictive maintenance is that it can significantly extend the life of machines. The integration of predictive maintenance into existing production environments faces challenges in terms of data understanding and data preparation for machines and legacy systems. Current AI frameworks lack adequate support for the ongoing task of data integration. In this context, adequate support means that the data analyst does not need to know the technical background of the pilot’s data sources in terms of data formats and schemas. It should be possible to perform data analyses without knowing the characteristics of the pilot’s specific data sources. The aim is to achieve a seamless integration of data as information for predictive maintenance. For this purpose, the developed data-sharing infrastructure enables automatic data acquisition and data integration for AI frameworks using interoperability methods. The evaluation, based on two pilot projects, shows that the step of data understanding and data preparation for predictive maintenance is simplified and that the solution is applicable for new pilot projects.","PeriodicalId":512104,"journal":{"name":"Analytics","volume":"70 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139829172","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}

引用次数: 0

Interoperable Information Flow as Enabler for Efficient Predictive Maintenance 可互操作的信息流是高效预测性维护的推动力

Analytics Pub Date : 2024-02-01 DOI: 10.3390/analytics3010006

Marco Franke, Quan Deng, Zisis Kyroudis, Maria Psarodimou, Jovana Milenkovic, Ioannis Meintanis, Dimitris Lokas, Stefano Borgia, K. Thoben

{"title":"Interoperable Information Flow as Enabler for Efficient Predictive Maintenance","authors":"Marco Franke, Quan Deng, Zisis Kyroudis, Maria Psarodimou, Jovana Milenkovic, Ioannis Meintanis, Dimitris Lokas, Stefano Borgia, K. Thoben","doi":"10.3390/analytics3010006","DOIUrl":"https://doi.org/10.3390/analytics3010006","url":null,"abstract":"Industry 4.0 enables the modernisation of machines and opens up the digitalisation of processes in the manufacturing industry. As a result, these machines are ready for predictive maintenance as part of Industry 4.0 services. The benefit of predictive maintenance is that it can significantly extend the life of machines. The integration of predictive maintenance into existing production environments faces challenges in terms of data understanding and data preparation for machines and legacy systems. Current AI frameworks lack adequate support for the ongoing task of data integration. In this context, adequate support means that the data analyst does not need to know the technical background of the pilot’s data sources in terms of data formats and schemas. It should be possible to perform data analyses without knowing the characteristics of the pilot’s specific data sources. The aim is to achieve a seamless integration of data as information for predictive maintenance. For this purpose, the developed data-sharing infrastructure enables automatic data acquisition and data integration for AI frameworks using interoperability methods. The evaluation, based on two pilot projects, shows that the step of data understanding and data preparation for predictive maintenance is simplified and that the solution is applicable for new pilot projects.","PeriodicalId":512104,"journal":{"name":"Analytics","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139889217","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}

引用次数: 0

Code Plagiarism Checking Function and Its Application for Code Writing Problem in Java Programming Learning Assistant System 代码抄袭检查功能及其在 Java 编程学习辅助系统中代码编写问题中的应用

Analytics Pub Date : 2024-01-17 DOI: 10.3390/analytics3010004

Ei Ei Htet, Khaing Hsu Wai, Soe Thandar Aung, Nobuo Funabiki, Xiqin Lu, Htoo Htoo Sandi Kyaw, W. Kao

{"title":"Code Plagiarism Checking Function and Its Application for Code Writing Problem in Java Programming Learning Assistant System","authors":"Ei Ei Htet, Khaing Hsu Wai, Soe Thandar Aung, Nobuo Funabiki, Xiqin Lu, Htoo Htoo Sandi Kyaw, W. Kao","doi":"10.3390/analytics3010004","DOIUrl":"https://doi.org/10.3390/analytics3010004","url":null,"abstract":"A web-based Java programming learning assistant system (JPLAS) has been developed for novice students to study Java programming by themselves while enhancing code reading and code writing skills. One type of the implemented exercise problem is code writing problem (CWP), which asks students to create a source code that can pass the given test code. The correctness of this answer code is validated by running them on JUnit. In previous works, a Python-based answer code validation program was implemented to assist teachers. It automatically verifies the source codes from all the students for one test code, and reports the number of passed test cases by each code in the CSV file. While this program plays a crucial role in checking the correctness of code behaviors, it cannot detect code plagiarism that can often happen in programming courses. In this paper, we implement a code plagiarism checking function in the answer code validation program, and present its application results to a Java programming course at Okayama University, Japan. This function first removes the whitespace characters and the comments using the regular expressions. Next, it calculates the Levenshtein distance and similarity score for each pair of source codes from different students in the class. If the score is larger than a given threshold, they are regarded as plagiarism. Finally, it outputs the scores as a CSV file with the student IDs. For evaluations, we applied the proposed function to a total of 877 source codes for 45 CWP assignments submitted from 9 to 39 students and analyzed the results. It was found that (1) CWP assignments asking for shorter source codes generate higher scores than those for longer codes due to the use of test codes, (2) proper thresholds are different by assignments, and (3) some students often copied source codes from certain students.","PeriodicalId":512104,"journal":{"name":"Analytics","volume":" 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139616669","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}

引用次数: 0

An Optimal House Price Prediction Algorithm: XGBoost 最佳房价预测算法：XGBoost

Analytics Pub Date : 2024-01-02 DOI: 10.3390/analytics3010003

Hemlata Sharma, Hitesh Harsora, Bayode Ogunleye

{"title":"An Optimal House Price Prediction Algorithm: XGBoost","authors":"Hemlata Sharma, Hitesh Harsora, Bayode Ogunleye","doi":"10.3390/analytics3010003","DOIUrl":"https://doi.org/10.3390/analytics3010003","url":null,"abstract":"An accurate prediction of house prices is a fundamental requirement for various sectors, including real estate and mortgage lending. It is widely recognized that a property’s value is not solely determined by its physical attributes but is significantly influenced by its surrounding neighborhood. Meeting the diverse housing needs of individuals while balancing budget constraints is a primary concern for real estate developers. To this end, we addressed the house price prediction problem as a regression task and thus employed various machine learning (ML) techniques capable of expressing the significance of independent variables. We made use of the housing dataset of Ames City in Iowa, USA to compare XGBoost, support vector regressor, random forest regressor, multilayer perceptron, and multiple linear regression algorithms for house price prediction. Afterwards, we identified the key factors that influence housing costs. Our results show that XGBoost is the best performing model for house price prediction. Our findings present valuable insights and tools for stakeholders, facilitating more accurate property price estimates and, in turn, enabling more informed decision making to meet the housing needs of diverse populations while considering budget constraints.","PeriodicalId":512104,"journal":{"name":"Analytics","volume":"131 43","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139453381","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}

引用次数: 0