Microstructual and Thermal Analysis of Aluminum-Silicon and Magnesium-Aluminum Alloys Subjected to High Cooling Rates

This thesis is an examination of the effects of varying cooling rate on the solidification properties of AlSi and MgAl alloys. Rapid cooling accessories for the Universal Metallurgical Simulator and Analyzer Technology Platform (UMSA) were developed that enabled quenching up to peak rates of 520°C/s. Samples from four automotive production alloys (aluminum A356 and a Sr-modified Al-20wt.%-Si alloy as well as magnesium AM60B and AE44) were resolidified under a range of cooling rates. Resultant micrographs revealed improvements to the microstructure, especially for the modified hypereutectic alloy. The AM60B microstructure also indicated invariance to cooling rate. Thermal data from the magnesium alloys was used in the development of baseline and fraction solid calculations that are extensible to higher cooling rates. Though these techniques cannot yet be applied to the highest cooling rates, excellent phase composition data for the AM60B alloy was generated for cooling rates of approximately 20°C/s (peak)

An Evaluation of Deep CNN Baselines for Scene-Independent Person Re-Identification

In recent years, a variety of proposed methods based on deep convolutional neural networks (CNNs) have improved the state of the art for large-scale person re-identification (ReID). While a large number of optimizations and network improvements have been proposed, there has been relatively little evaluation of the influence of training data and baseline network architecture. In particular, it is usually assumed either that networks are trained on labeled data from the deployment location (scene-dependent), or else adapted with unlabeled data, both of which complicate system deployment. In this paper, we investigate the feasibility of achieving scene-independent person ReID by forming a large composite dataset for training. We present an in-depth comparison of several CNN baseline architectures for both scene-dependent and scene-independent ReID, across a range of training dataset sizes. We show that scene-independent ReID can produce leading-edge results, competitive with unsupervised domain adaption techniques. Finally, we introduce a new dataset for comparing within-camera and across-camera person ReID.Comment: To be published in 2018 15th Conference on Computer and Robot Vision (CRV