Automotive Engineering Commons^™

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 …

The Design, Implementation, Evaluation And Results Of A Race Car For The Collegiate Formula Sae Electric Competition, Quinn Jasha Bryan Sullivan

Dissertations and Theses

The Formula SAE Electric competition is a collegiate autocross event in which teams design, build, and race an open-wheeled electric race car. The main motivation is the efficiency advantage of electric motors over internal combustion motors. This thesis presents the design and evaluation of two generations of Portland State University electric race cars.

The constraints are the competition rules, finances, human resources, and time required to complete a race car in one year. The design includes the implementation of existing components: battery cells, controllers, electric motors, drivetrains, and tire data for an optimized race car. Also, several circuits were designed …