A. El-shenawy, Mohamed El-Gamel, Muhammad E. Anany
{"title":"A Novel Scheme Based on Bessel Operational Matrices for Solving a Class of Nonlinear Systems of Differential Equations","authors":"A. El-shenawy, Mohamed El-Gamel, Muhammad E. Anany","doi":"10.58491/2735-4202.3192","DOIUrl":"https://doi.org/10.58491/2735-4202.3192","url":null,"abstract":"","PeriodicalId":510600,"journal":{"name":"Mansoura Engineering Journal","volume":"32 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140519065","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 Elmadawy, Abdelhady Ghanem, Sayed Abulanwar, Ahmed Shahin
Fault distance estimation in DC microgrids is critical due to the growing adoption of DC-based distribution systems. Current methods face limitations like sensitivity to system parameters and high-resistance fault detection, necessitating improved accuracy. This study proposes a neural network approach to locate fault distances in multiterminal DC microgrids. Three different structures based on back propagation algorithms are developed and trained to accrately estimate fault distances with high precision. These structures can handle various fault scenarios, including different fault resistances and the presence of noise. Two structures can predict fault distances from one side locally, achieving low error rates of 0.3 % for the source side and 0.6 % for the load side. The third structure incorporates input variables from both sides, resulting in even more accurate predictions with an error rate of less than 0.15 % for both terminals. A comparative analysis was performed to evaluate the proposed fault distance estimation structures regarding error percentage, cost, fault resistance, and reliance on communication systems. The results demonstrated the superiority of the proposed structures in all aspects, emphasizing their effectiveness in improving the performance of the protection system.
{"title":"Neural Network-Based Fault Distance Estimation for Multi-Terminal DC Microgrids","authors":"Mohamed Elmadawy, Abdelhady Ghanem, Sayed Abulanwar, Ahmed Shahin","doi":"10.58491/2735-4202.3187","DOIUrl":"https://doi.org/10.58491/2735-4202.3187","url":null,"abstract":"Fault distance estimation in DC microgrids is critical due to the growing adoption of DC-based distribution systems. Current methods face limitations like sensitivity to system parameters and high-resistance fault detection, necessitating improved accuracy. This study proposes a neural network approach to locate fault distances in multiterminal DC microgrids. Three different structures based on back propagation algorithms are developed and trained to accrately estimate fault distances with high precision. These structures can handle various fault scenarios, including different fault resistances and the presence of noise. Two structures can predict fault distances from one side locally, achieving low error rates of 0.3 % for the source side and 0.6 % for the load side. The third structure incorporates input variables from both sides, resulting in even more accurate predictions with an error rate of less than 0.15 % for both terminals. A comparative analysis was performed to evaluate the proposed fault distance estimation structures regarding error percentage, cost, fault resistance, and reliance on communication systems. The results demonstrated the superiority of the proposed structures in all aspects, emphasizing their effectiveness in improving the performance of the protection system.","PeriodicalId":510600,"journal":{"name":"Mansoura Engineering Journal","volume":"326 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140521303","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}
{"title":"A 10-Gb/s Single-Loop Half-Rate DLL-Based Clock and Data Recovery Circuit for Forwarded-Clock Wireline Transceivers","authors":"Abdallah Mohamed, Sameh A. Ibrahim, M. Abo-Elsoud","doi":"10.58491/2735-4202.3190","DOIUrl":"https://doi.org/10.58491/2735-4202.3190","url":null,"abstract":"","PeriodicalId":510600,"journal":{"name":"Mansoura Engineering Journal","volume":"55 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140526121","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}
A. M. Salama, Mohamed Abdelazim Mohamed, Eman AbdElhalim
{"title":"Enhancing Network Security in IoT Applications through DDoS Attack Detection Using ML","authors":"A. M. Salama, Mohamed Abdelazim Mohamed, Eman AbdElhalim","doi":"10.58491/2735-4202.3181","DOIUrl":"https://doi.org/10.58491/2735-4202.3181","url":null,"abstract":"","PeriodicalId":510600,"journal":{"name":"Mansoura Engineering Journal","volume":"51 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140519431","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}
Ahmed AbdelMonteleb M. Ali, Essam S. Almahmoud, Azzam I. Aljutayli, Basem O. Elgendy, Meshal A. Alhrabi, Khattab I. Alyahya
{"title":"Retrofitting the building materials and techniques of a Residential Villa: Finding Alternative Solutions","authors":"Ahmed AbdelMonteleb M. Ali, Essam S. Almahmoud, Azzam I. Aljutayli, Basem O. Elgendy, Meshal A. Alhrabi, Khattab I. Alyahya","doi":"10.58491/2735-4202.3200","DOIUrl":"https://doi.org/10.58491/2735-4202.3200","url":null,"abstract":"","PeriodicalId":510600,"journal":{"name":"Mansoura Engineering Journal","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140523746","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}
{"title":"The Dynamic Stability of the Sewing Needle’s Mechanical Vibrations under the Impact of a Time-dependent Penetration Force in the Multilayered Sewn Fabrics","authors":"W. Hashima, I. Elhawary","doi":"10.58491/2735-4202.3194","DOIUrl":"https://doi.org/10.58491/2735-4202.3194","url":null,"abstract":"","PeriodicalId":510600,"journal":{"name":"Mansoura Engineering Journal","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140522033","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 International Roughness Index (IRI) serves as a crucial indicator for ride quality and user comfort. As road roughness escalates, road serviceability diminishes, resulting in reduced vehicle speed and increased travel time, and consequently higher carbon dioxide emissions. Predicting the IRI is of utmost importance for pavement management systems and sustainable development overall. While numerous studies have forecasted the IRI of fl exible pavements, there is a notable scarcity of research focusing on rigid pavement performance prediction. This study addresses the gap in predicting IRI for Continuous Reinforced Concrete Pavements (CRCP), an understudied aspect of pavement engineering. Leveraging the Long-Term Pavement Performance database, different machine learning techniques were applied to different input parameter representations. There are 90 measurements for the data points of the IRI. The input variables include the initial IRI, counts of medium-severity and high-severity transverse cracks, counts of medium-severity and high-severity punchouts, the percentage of pavement surface with patching (ranging from medium to high severity in both fl exible and rigid pavements), pavement age, freezing index, and the percentage of subgrade material passing through the No. 200 US sieve. Through data analysis and machine learning algorithms, an accurate IRI prediction model for CRCP is developed. The results of this study show that the adaptive boosting algorithm model for CRCP yielded very good prediction accuracy ( R 2 ¼ 0.90 and 0.83 for training and testing datasets, respectively) with low bias. The study fi ndings offer valuable insights into CRCP IRI prediction, bene fi ting pavement management and maintenance strategies.
国际路面粗糙度指数（IRI）是衡量行驶质量和用户舒适度的重要指标。随着路面粗糙度的增加，路面的适用性也会降低，从而导致车速降低、行车时间增加，二氧化碳排放量也会随之增加。预测 IRI 对路面管理系统和整体可持续发展至关重要。虽然许多研究都对可挠性路面的 IRI 进行了预测，但针对刚性路面性能预测的研究却明显不足。本研究填补了连续加筋混凝土路面 (CRCP) IRI 预测方面的空白，这也是路面工程研究不足的一个方面。利用长期路面性能数据库，不同的机器学习技术被应用于不同的输入参数表示。IRI 数据点共有 90 个测量值。输入变量包括初始 IRI、中度和高度横向裂缝计数、中度和高度冲孔计数、路面表面修补百分比（可挠和刚性路面的严重程度从中度到高度不等）、路面龄期、冰冻指数以及通过美国 200 号筛网的基层材料百分比。通过数据分析和机器学习算法，开发出了 CRCP 的精确 IRI 预测模型。研究结果表明，针对 CRCP 的自适应提升算法模型具有很高的预测精度（训练数据集和测试数据集的 R 2 ¼ 分别为 0.90 和 0.83），偏差较小。研究结果为 CRCP IRI 预测提供了有价值的见解，有利于路面管理和维护策略。
{"title":"Machine Learning-Based Prediction of International Roughness Index for Continuous Reinforced Concrete Pavements","authors":"R. A. El-Hakim, Ahmed N. Awaad, S. El-Badawy","doi":"10.58491/2735-4202.3195","DOIUrl":"https://doi.org/10.58491/2735-4202.3195","url":null,"abstract":"The International Roughness Index (IRI) serves as a crucial indicator for ride quality and user comfort. As road roughness escalates, road serviceability diminishes, resulting in reduced vehicle speed and increased travel time, and consequently higher carbon dioxide emissions. Predicting the IRI is of utmost importance for pavement management systems and sustainable development overall. While numerous studies have forecasted the IRI of fl exible pavements, there is a notable scarcity of research focusing on rigid pavement performance prediction. This study addresses the gap in predicting IRI for Continuous Reinforced Concrete Pavements (CRCP), an understudied aspect of pavement engineering. Leveraging the Long-Term Pavement Performance database, different machine learning techniques were applied to different input parameter representations. There are 90 measurements for the data points of the IRI. The input variables include the initial IRI, counts of medium-severity and high-severity transverse cracks, counts of medium-severity and high-severity punchouts, the percentage of pavement surface with patching (ranging from medium to high severity in both fl exible and rigid pavements), pavement age, freezing index, and the percentage of subgrade material passing through the No. 200 US sieve. Through data analysis and machine learning algorithms, an accurate IRI prediction model for CRCP is developed. The results of this study show that the adaptive boosting algorithm model for CRCP yielded very good prediction accuracy ( R 2 ¼ 0.90 and 0.83 for training and testing datasets, respectively) with low bias. The study fi ndings offer valuable insights into CRCP IRI prediction, bene fi ting pavement management and maintenance strategies.","PeriodicalId":510600,"journal":{"name":"Mansoura Engineering Journal","volume":"39 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140522660","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}
{"title":"Predicting the role of connectivity in the development of border regions in Egypt","authors":"Shimaa Mohammed Hamdy Derbala","doi":"10.58491/2735-4202.3184","DOIUrl":"https://doi.org/10.58491/2735-4202.3184","url":null,"abstract":"","PeriodicalId":510600,"journal":{"name":"Mansoura Engineering Journal","volume":"2020 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140516262","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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