Enhancement of Mechanical Properties in Glass-Fiber Woven Reinforced Hybrid Composites for Aerospace Applications: An Empirical Investigation

作者： Abeer   Farouk   Al   Attar     Hussein   Alaa   Jaber     Ammar   Mousa   Hasan   来源： Revue des Composites et des Matériaux Avancés-Journal of Composite and Advanced Materials​ 年份： 2023 文献类型 ： 期刊 关键词： glass   fiber   E   woven   mechanical   Al2O3   red   (ANOVA)   hybrid   variance   Analysis   composite   of   kaolin   properties  

描述： This experimental study aims to create innovative hybrid composites (HCs) for aerospace applications using the hand lay-up technique. Different volume fractions (Vf%) of distinctive natural (white clay (W) and red clay (R)) and synthetic (alumina (A) and woven glass fibers (E-GFW) reinforcing unsaturated polyester (UP). The mechanical and physical characterizations of these innovative HCs were estimated according to the American Society for Testing and Materials (ASTM) standards. The findings show that adding more white clay, red clay, and alumina to the HCs makes a big difference in improving their mechanical properties, such as Hardness Shore D (HSD), True Tensile Strength (Ttσ), Impact Strength (iσ), and Fracture Toughness (Kc). Analysis of variance (ANOVA) revealed noteworthy variations in the assessed parameters, emphasizing the potential suitability of these materials for aeronautical applications. This research contributes to the ongoing progress in material engineering, specifically in enhancing the mechanical resilience of composites utilized in the aerospace industry.

Enhancement of Mechanical Properties in Glass-Fiber Woven Reinforced Hybrid Composites for Aerospace Applications: An Empirical Investigation

作者： Abeer   Farouk   Al   Attar     Hussein   Alaa   Jaber     Ammar   Mousa   Hasan   来源： Revue des Composites et des Matériaux Avancés-Journal of Composite and Advanced Materials​ 年份： 2023 文献类型 ： 期刊 关键词： glass   fiber   E   woven   mechanical   Al2O3   red   (ANOVA)   hybrid   variance   Analysis   composite   of   kaolin   properties  

描述： This experimental study aims to create innovative hybrid composites (HCs) for aerospace applications using the hand lay-up technique. Different volume fractions (Vf%) of distinctive natural (white clay (W) and red clay (R)) and synthetic (alumina (A) and woven glass fibers (E-GFW) reinforcing unsaturated polyester (UP). The mechanical and physical characterizations of these innovative HCs were estimated according to the American Society for Testing and Materials (ASTM) standards. The findings show that adding more white clay, red clay, and alumina to the HCs makes a big difference in improving their mechanical properties, such as Hardness Shore D (HSD), True Tensile Strength (Ttσ), Impact Strength (iσ), and Fracture Toughness (Kc). Analysis of variance (ANOVA) revealed noteworthy variations in the assessed parameters, emphasizing the potential suitability of these materials for aeronautical applications. This research contributes to the ongoing progress in material engineering, specifically in enhancing the mechanical resilience of composites utilized in the aerospace industry.

CNN with New Spatial Pyramid Pooling and Advanced Filter-Based Techniques: Revolutionizing Traffic Monitoring via Aerial Images

作者： Irfan   Javid     Rozaida   Ghazali     Waddah   Saeed     Tuba   Batool     Ebrahim   Al   Wajih   来源： Sustainability 年份： 2023 文献类型 ： 期刊 关键词： semantic   Kalman   categorization   recognition   extended   vehicle   filter   pooling   U   and   pyramid   segmentation   spatial   Net  

描述： The escalation in vehicular traffic, in conjunction with global population growth, has precipitated heightened road congestion, augmented air pollution, and a rise in vehicular accidents. Over the past decade, the global vehicular count has witnessed a substantial surge. In this context, traffic monitoring emerges as a paramount challenge, especially within developing nations. This research introduces an innovative system for vehicle detection and categorization aimed at intelligent traffic monitoring. The system utilizes a convolutional neural network-based U-Net model for the segmentation of aerial images. After segmentation, the outputs are further analyzed for vehicle identification. This vehicle detection utilizes an advanced spatial pyramid pooling (ASPP) mechanism which refines the spatial partitions of the image and captures intricate details, enhancing the accuracy and precision of the detection process. Detected vehicles are then categorized into distinct subcategories. For the effective management and control of high-density traffic flow, the extended Kalman filter (EKF) technique is employed, thereby reducing the reliance on human oversight. In experimental evaluations, our proposed model exhibits exemplary vehicle detection capabilities across the German Aerospace Center (DLR3K) and the Vehicle Detection in Aerial Imagery (VEDAI) datasets. Potential applications of the system encompass vehicle identification in traffic streams, traffic congestion assessment, intersection traffic density analysis, differentiation of vehicle types, and pedestrian pathway determination.

CNN with New Spatial Pyramid Pooling and Advanced Filter-Based Techniques: Revolutionizing Traffic Monitoring via Aerial Images

作者： Irfan   Javid     Rozaida   Ghazali     Waddah   Saeed     Tuba   Batool     Ebrahim   Al   Wajih   来源： Sustainability 年份： 2023 文献类型 ： 期刊 关键词： semantic   Kalman   categorization   recognition   extended   vehicle   filter   pooling   U   and   pyramid   segmentation   spatial   Net  

描述： The escalation in vehicular traffic, in conjunction with global population growth, has precipitated heightened road congestion, augmented air pollution, and a rise in vehicular accidents. Over the past decade, the global vehicular count has witnessed a substantial surge. In this context, traffic monitoring emerges as a paramount challenge, especially within developing nations. This research introduces an innovative system for vehicle detection and categorization aimed at intelligent traffic monitoring. The system utilizes a convolutional neural network-based U-Net model for the segmentation of aerial images. After segmentation, the outputs are further analyzed for vehicle identification. This vehicle detection utilizes an advanced spatial pyramid pooling (ASPP) mechanism which refines the spatial partitions of the image and captures intricate details, enhancing the accuracy and precision of the detection process. Detected vehicles are then categorized into distinct subcategories. For the effective management and control of high-density traffic flow, the extended Kalman filter (EKF) technique is employed, thereby reducing the reliance on human oversight. In experimental evaluations, our proposed model exhibits exemplary vehicle detection capabilities across the German Aerospace Center (DLR3K) and the Vehicle Detection in Aerial Imagery (VEDAI) datasets. Potential applications of the system encompass vehicle identification in traffic streams, traffic congestion assessment, intersection traffic density analysis, differentiation of vehicle types, and pedestrian pathway determination.

Transforming Customer Experience in the Airline Industry: A Comprehensive Analysis of Twitter Sentiments Using Machine Learning and Association Rule Mining

作者： Maliha   Tayaba     Eftekhar   Hossain   Ayon     Md   Tuhin   Mia     Malay   Sarkar     Rejon   Kumar   Ray     Md   Salim   Chowdhury     Md   Al   Imran     Nur   Nobe     Bishnu   Padh   Ghosh     MD   Tanvir   Islam     Aisharyja   Roy   Puja   来源： Journal of Computer Science and Technology Studies 年份： 2023 文献类型 ： 期刊

描述： The airline industry places significant emphasis on improving customer experience, and Twitter has emerged as a key platform for passengers to share their opinions. This research introduces a machine learning approach to analyze tweets and enhance customer experience. Features are extracted from tweets using both the Glove dictionary and n-gram methods for word embedding. The study explores various artificial neural network (ANN) architectures and Support Vector Machines (SVM) to create a classification model for categorizing tweets into positive and negative sentiments. Additionally, a Convolutional Neural Network (CNN) is developed for tweet classification, and its performance is compared with the most accurate model identified among SVM and multiple ANN architectures. The results indicate that the CNN model surpasses the SVM and ANN models. To provide further insights, association rule mining is applied to different tweet categories, revealing connections with sentiment categories. These findings offer valuable information to help airline industries refine and enhance their customer experience strategies.

Transforming Customer Experience in the Airline Industry: A Comprehensive Analysis of Twitter Sentiments Using Machine Learning and Association Rule Mining

作者： Maliha   Tayaba     Eftekhar   Hossain   Ayon     Md   Tuhin   Mia     Malay   Sarkar     Rejon   Kumar   Ray     Md   Salim   Chowdhury     Md   Al   Imran     Nur   Nobe     Bishnu   Padh   Ghosh     MD   Tanvir   Islam     Aisharyja   Roy   Puja   来源： Journal of Computer Science and Technology Studies 年份： 2023 文献类型 ： 期刊

描述： The airline industry places significant emphasis on improving customer experience, and Twitter has emerged as a key platform for passengers to share their opinions. This research introduces a machine learning approach to analyze tweets and enhance customer experience. Features are extracted from tweets using both the Glove dictionary and n-gram methods for word embedding. The study explores various artificial neural network (ANN) architectures and Support Vector Machines (SVM) to create a classification model for categorizing tweets into positive and negative sentiments. Additionally, a Convolutional Neural Network (CNN) is developed for tweet classification, and its performance is compared with the most accurate model identified among SVM and multiple ANN architectures. The results indicate that the CNN model surpasses the SVM and ANN models. To provide further insights, association rule mining is applied to different tweet categories, revealing connections with sentiment categories. These findings offer valuable information to help airline industries refine and enhance their customer experience strategies.