Mansoura Engineering Journal最新文献_第2页

A Novel Scheme Based on Bessel Operational Matrices for Solving a Class of Nonlinear Systems of Differential Equations 基于贝塞尔运算矩阵的新方案，用于求解一类非线性微分方程系统

Mansoura Engineering Journal Pub Date : 2024-01-01 DOI: 10.58491/2735-4202.3192

A. El-shenawy, Mohamed El-Gamel, Muhammad E. Anany

引用次数: 0

Neural Network-Based Fault Distance Estimation for Multi-Terminal DC Microgrids 基于神经网络的多终端直流微电网故障距离估计

Mansoura Engineering Journal Pub Date : 2024-01-01 DOI: 10.58491/2735-4202.3187

Mohamed Elmadawy, Abdelhady Ghanem, Sayed Abulanwar, Ahmed Shahin

{"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}

引用次数: 0

A 10-Gb/s Single-Loop Half-Rate DLL-Based Clock and Data Recovery Circuit for Forwarded-Clock Wireline Transceivers 用于前向时钟有线收发器的基于 DLL 的 10-Gb/s 单环半速率时钟和数据恢复电路

Mansoura Engineering Journal Pub Date : 2024-01-01 DOI: 10.58491/2735-4202.3190

Abdallah Mohamed, Sameh A. Ibrahim, M. Abo-Elsoud

引用次数: 0

Enhancing Network Security in IoT Applications through DDoS Attack Detection Using ML 利用 ML 检测 DDoS 攻击，增强物联网应用中的网络安全

Mansoura Engineering Journal Pub Date : 2024-01-01 DOI: 10.58491/2735-4202.3181

A. M. Salama, Mohamed Abdelazim Mohamed, Eman AbdElhalim

引用次数: 0

Retrofitting the building materials and techniques of a Residential Villa: Finding Alternative Solutions 改造住宅别墅的建筑材料和技术：寻找替代解决方案

Mansoura Engineering Journal Pub Date : 2024-01-01 DOI: 10.58491/2735-4202.3200

Ahmed AbdelMonteleb M. Ali, Essam S. Almahmoud, Azzam I. Aljutayli, Basem O. Elgendy, Meshal A. Alhrabi, Khattab I. Alyahya

引用次数: 0

The Dynamic Stability of the Sewing Needle’s Mechanical Vibrations under the Impact of a Time-dependent Penetration Force in the Multilayered Sewn Fabrics 多层缝纫织物在随时间变化的穿透力影响下缝针机械振动的动态稳定性

Mansoura Engineering Journal Pub Date : 2024-01-01 DOI: 10.58491/2735-4202.3194

W. Hashima, I. Elhawary

引用次数: 0

Machine Learning-Based Prediction of International Roughness Index for Continuous Reinforced Concrete Pavements 基于机器学习的连续钢筋混凝土路面国际粗糙度指数预测

Mansoura Engineering Journal Pub Date : 2024-01-01 DOI: 10.58491/2735-4202.3195

R. A. El-Hakim, Ahmed N. Awaad, S. El-Badawy

{"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}

引用次数: 0

Predicting the role of connectivity in the development of border regions in Egypt 预测连通性在埃及边境地区发展中的作用

Mansoura Engineering Journal Pub Date : 2024-01-01 DOI: 10.58491/2735-4202.3184

Shimaa Mohammed Hamdy Derbala

引用次数: 0