16979811
Neural adaptive control for a class of nonlinear systems with unknown deadzone
This paper focuses on the adaptive control of a class of nonlinear systems with unknown deadzone using neural networks. By constructing a deadzone pre-compensator, a neural adaptive control scheme is developed using backstepping design techniques. Transient performance is guaranteed and semi-globally uniformly ultimately bounded stability is obtained. Another feature of this scheme is that the neural networks reconstruction error bound is assumed to be unknown and can be estimated online. Simulation results are given to demonstrate the effectiveness of the proposed controller.
44946141
The Design of an Observer-Based Neural Adaptive Controller for a Class of Nonlinear Systems with Input-Constrained
9299231
Language Models for Expert Finding--UIUC TREC 2006 Enterprise Track Experiments
In this paper, we report our experiments in the TREC 2006 Enterprise Track. Our focus is to study a language model for expert finding. We extend an existing language model for expert retrieval in three aspects. First, we model the document-expert association using a mixure model instead of name matching heuristics as in the existing work; such a mixture model allows us to put different weights on email matching and name matching. Second, we propose to model the prior of an expert based on the counts of email matches in the supporting documents instead of using uniform prior as in the previous work. Finally, we perform topic expansion and generalize the model from computing the likelihood to computing the cross entropy. Our experiments show that the first two extensions are more effective than the third extension, though when optimized, they all seem to be effective.
7037192
Interactive material replacement in photographs
Material replacement has wide application throughout the entertainment industry, particularly for post-production make-up application or wardrobe adjustment. More generally, any low-cost mock-up object can be processed to have the appearance of expensive, high-quality materials. We demonstrate a new system that allows fast, intuitive material replacement in photographs. We extend recent work in object selection and fast texture synthesis, as well as develop a novel approach to shape-from-shading capable of handling objects with albedo changes. Each component of our system runs with interactive speed, allowing for easy experimentation and refinement of results.
6933858
Region Growing with Automatic Seeding for Semantic Video Object Segmentation
As content-based multimedia applications become increasingly important, demand for technologies on semantic video object segmentation is growing, where the segmented objects are expected to be in line with human visual perception. Existing research is limited to semi-automatic approach, in which human intervene is often required. These include manual selection of seeds for region growing or manual classification of background edges etc. In this paper, we propose an automatic region growing algorithm for video object segmentation, which features in automatic selection of seeds and thus the entire segmentation does not require any action from human users. Experimental results show that the proposed algorithm performs well in terms of the effectiveness in video object segmentation.
18215845
A hybrid scheme for temporal video segmentation
Video temporal segmentation is normally the first and important step for content-based video applications. Although existing research on shot cut detection is active and extensive, it still remains a challenge to achieve accurate detection of all types of shot boundaries. In this paper, we propose a hybrid scheme to combine some features and techniques for detecting all sorts of shot cuts inside general videos. The hybrid scheme contains two processing modes, which are unified by a mode-selector to decide which mode the scheme should work on in order to achieve accurate temporal video segmentation. Via using the publicly test data set from Carleton University, we report extensive experimental results to evaluate the proposed algorithm. In comparison with existing algorithms, the experimental results support that the proposed algorithm outperforms the benchmark in terms of the precision-recall rates in detecting all types of shot cuts.
16418680
Term feedback for information retrieval with language models
In this paper we study term-based feedback for information retrieval in the language modeling approach. With term feedback a user directly judges the relevance of individual terms without interaction with feedback documents, taking full control of the query expansion process. We propose a cluster-based method for selecting terms to present to the user for judgment, as well as effective algorithms for constructing refined query language models from user term feedback. Our algorithms are shown to bring significant improvement in retrieval accuracy over a non-feedback baseline, and achieve comparable performance to relevance feedback. They are helpful even when there are no relevant documents in the top.