AN EXAMINATION OF 19TH CENTURY AUSTROGERMAN VIOLIN SONATAS

For my dissertation recital project, I traced the course of the violin-piano sonata in Austro- German in the 19th century, after Beethoven. My project presented works in three general categories. First, I presented works that are frequently-played standards of the violin sonata repertoire, works by Johannes Brahms, Franz Schubert, and Robert Schumann. The Second category is works by composers better known for their other compositions: Felix Mendelssohn and Richard Strauss. Finally, I choose the works seldom played these days, but worth of consideration, by Carl Maria von Weber and Max Reger. For my first recital, I performed Schubert's Violin Sonata, No. 1, Op. 137 in D major, Schumann's Violin Sonata, No. 1, Op. 105 in a minor, and Brahms' Violin Sonata, No.3, Op. 108 in d minor, with Naoko Takao as pianist. My second recital included works of Weber's Sonata, No. 1, Op. lob, in F major, Mendelssohn's Sonata, in F major (1838), and Schumann's Sonata, No.Z,Op.121 in d minor with Grace Cho. I concluded my final recital with the works of Reger's Violin Sonata, No. 1, Op. 1 in d minor and Strauss' Violin Sonata, Op. 18 in E flat major, Soo-Young Jung at the piano. All three programs are documented in a digital audio format available on compact disc, with accompanying programs also available in digital format.

Generating Discriminative Objective Proposals via Submodular Ranking

Object recognition has long been a core problem in computer vision. To improve object spatial support and speed up object localization for object recognition, generating high-quality category-independent object proposals as the input for object recognition system has drawn attention recently. Given an image, we generate a limited number of high-quality and category-independent object proposals in advance and used as inputs for many computer vision tasks. We present an efficient dictionary-based model for image classification task. We further extend the work to a discriminative dictionary learning method for tensor sparse coding. In the first part, a multi-scale greedy-based object proposal generation approach is presented. Based on the multi-scale nature of objects in images, our approach is built on top of a hierarchical segmentation. We first identify the representative and diverse exemplar clusters within each scale. Object proposals are obtained by selecting a subset from the multi-scale segment pool via maximizing a submodular objective function, which consists of a weighted coverage term, a single-scale diversity term and a multi-scale reward term. The weighted coverage term forces the selected set of object proposals to be representative and compact; the single-scale diversity term encourages choosing segments from different exemplar clusters so that they will cover as many object patterns as possible; the multi-scale reward term encourages the selected proposals to be discriminative and selected from multiple layers generated by the hierarchical image segmentation. The experimental results on the Berkeley Segmentation Dataset and PASCAL VOC2012 segmentation dataset demonstrate the accuracy and efficiency of our object proposal model. Additionally, we validate our object proposals in simultaneous segmentation and detection and outperform the state-of-art performance. To classify the object in the image, we design a discriminative, structural low-rank framework for image classification. We use a supervised learning method to construct a discriminative and reconstructive dictionary. By introducing an ideal regularization term, we perform low-rank matrix recovery for contaminated training data from all categories simultaneously without losing structural information. A discriminative low-rank representation for images with respect to the constructed dictionary is obtained. With semantic structure information and strong identification capability, this representation is good for classification tasks even using a simple linear multi-classifier.

A Thermal Management Solution for Compact Power Converters

Regulated Transformer Rectifier Units contain several power electronic boards to facilitate AC to DC power conversion. As these units become smaller, the number of devices on each board increases while their distance from each other decreases, making active cooling essential to maintaining reliable operation. Although it is widely accepted that liquid is a far superior heat transfer medium to air, the latter is still capable of yielding low device operating temperatures with proper heat sink and airflow design. The purpose of this study is to describe the models and methods used to design and build the thermal management system for one of the power electronic boards in a compact, high power regulated transformer rectifier unit. Maximum device temperature, available pressure drop and manufacturability were assessed when selecting the final design for testing. Once constructed, the thermal management system’s performance was experimentally verified at three different power levels.