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Full-Text Articles in Navigation, Guidance, Control, and Dynamics
Valve Train Design And Material Testing, Luis Luna
Valve Train Design And Material Testing, Luis Luna
Publications and Research
In automotive engineering, CAD software like SolidWorks enables engineers to design and simulate mechanical components efficiently, reducing the need for extensive prototyping. This project examines the rocker arm's performance and durability within a valve train system. Components like pushrods, camshafts, and rocker arms control air intake and exhaust timing. Due to its high-impact motion and exposure to elevated temperatures, the rocker arm endures considerable wear. This study uses SolidWorks and Finite Element Analysis (FEA) to analyze the rocker arm's stress, deformation, and wear potential during camshaft movement. Material testing and motion analysis results provide insights into improving the durability of …
Self-Driving Toy Car Using Deep Learning, Fahim Ahmed, Suleyman Turac, Mubtasem Ali
Self-Driving Toy Car Using Deep Learning, Fahim Ahmed, Suleyman Turac, Mubtasem Ali
Publications and Research
Our research focuses on building a student affordable platform for scale model self-driving cars. The goal of this project is to explore current developments of Open Source hardware and software to build a low-cost platform consisting of the car chassis/framework, sensors, and software for the autopilot. Our research will allow other students with low budget to enter into the world of Deep Learning, self-driving cars, and autonomous cars racing competitions.
Unsupervised Feature Learning For Point Cloud By Contrasting And Clustering With Graph Convolutional Neural Network, Ling Zhang
Dissertations and Theses
Recently, deep graph neural networks (GNNs) have attracted significant attention for point cloud understanding tasks, including classification, segmentation, and detection. However, the training of such deep networks still requires a large amount of annotated data, which is both expensive and time-consuming. To alleviate the cost of collecting and annotating large-scale point cloud datasets, we propose an unsupervised learning approach to learn features from unlabeled point cloud ”3D object” dataset by using part contrasting and object clustering with GNNs. In the contrast learning step, all the samples in the 3D object dataset are cut into two parts and put into a …