Compact Bilinear Pooling

Bilinear models has been shown to achieve impressive performance on a wide range of visual tasks, such as semantic segmentation, fine grained recognition and face recognition. However, bilinear features are high dimensional, typically on the order of hundreds of thousands to a few million, which makes them impractical for subsequent analysis. We propose two compact bilinear representations with the same discriminative power as the full bilinear representation but with only a few thousand dimensions. Our compact representations allow back-propagation of classification errors enabling an end-to-end optimization of the visual recognition system. The compact bilinear representations are derived through a novel kernelized analysis of bilinear pooling which provide insights into the discriminative power of bilinear pooling, and a platform for further research in compact pooling methods. Experimentation illustrate the utility of the proposed representations for image classification and few-shot learning across several datasets.Comment: Camera ready version for CVP

Parameter Identification of Micro-Grid Control System

Micro-grid provides an effective means of integrating distributed energy resource (DER) units into the power systems. A micro-grid is defined as an independent low- or medium-voltage distribution network comprising various DER units, power-electronic interfaces, controllable loads, and monitoring and protection devices. Following the development of the renewable energy, micro-grid has attracted much attention. This thesis emphasizes on the parameter identification of the control system of the micro-grid. The control system plays an important role in the stable operation of the micro-grid. The micro-grid has two operation modes, which are grid-connected operation mode and islanded operation mode. The transition between two operation modes of the micro-grid often occurs according to the condition of the entire grid. In order to make this process smooth, the control system is crucial, and the parameters of the control system is critical to the disturbance suppression during the process of transition. In the thesis, a method combining least square method with Newton-Raphson algorithm is proposed. In order to prove the utility of the method, the parameter identification of a typical control system and its several separated elements are simulated in MATLAB. This method can identify multiple parameters at the same time and have fast convergence

The Short and Long-Run Financial Impact of Corporate Outsourcing Transactions

This dissertation investigates the financial impact of a large sample of outsourcing contracts signed by corporations listed on the US markets from 1990 through 2003. We construct a data set that identifies the outsourcing client and vendor firms and use this data set to examine (a) the announcement effects of outsourcing contracts on firm value, (b) the impact of outsourcing contracts on long-run stock and accounting performance and (c) the impact of outsourcing contracts on the relation between client and vendor firms