Showing papers by "Yang Gao published in 2004"

The refined theory of magnetoelastic rectangular beams

Abstract: The problem of deducing a one-dimensional theory from a three-dimensional theory for a soft ferromagnetic elastic isotropic body is investigated. Based on the linear magnetoelasticity, the refined theory of magnetoelastic beams is presented by using the general solution for the soft ferromagnetic elastic solids and the Lur’e method. Based on the refined theory of magnetoelastic beams, the exact equations and solutions for the homogeneous beams are derived and the equations can be decomposed into three governing differential equations: the fourth-order equation, the transcendental equation and the magnetic equation. Moreover, the approximate equations and solutions for the beam under transverse loadings and magnetic field perturbations are derived directly from the refined beam theory. By omitting higher order terms and coupling effects, the refined beam theory can be degenerated into other well-known elastic and magnetoelastic theoretical models.

Object-Based Techniques for Image Retrieval

Abstract: To overcome the drawback of using only low-level features for the description of image content and to fill the gap between the perceptual property and semantic meaning, this chapter presents an object-based scheme and some object level techniques for image retrieval. According to a multi-layer description model, images are analyzed in different levels for progressive understanding, and this procedure helps to gain comprehensive representations of the objects in images. The main propulsion of the chapter includes a multi-layer description model that describes the image content with a hierarchical structure; an efficient region-based scheme for meaningful information extraction; a combined feature set to represent the image at a visual perception level; an iterative training-and-testing procedure for object region recognition; a decision function for reflecting common contents in object description and a combined feature and object matching process, as well as a self-adaptive relevance feedback that could work with or without memory. With the proposed techniques, a prototype retrieval This chapter appears in the book, Multimedia Systems and Content-Based Image Retrieval, edited by Sagarmay Deb. Copyright © 2004, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.idea-group.com IDEA GROUP PUBLISHING Object-Based Techniques for Image Retrieval 157 Copyright © 2004, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. system has been implemented. Real retrieval experiments have been conducted; results show that the object-based scheme is quite efficient and the performance of object level techniques have been confirmed. INTRODUCTION Fast technique advancement and the rapid information increments mark the new century. Along with the progress of imaging modality and the wide utility of digital image in various fields, many potential content producers have emerged, and many image databases have been built. How to quickly access and manage these large, both in the sense of information contents and data volume, databases has become an urgent problem to solve. In the past 10 years, image retrieval techniques have drawn much interest, and content-based image retrieval (CBIR) techniques are proposed in this context to search information from image databases quickly and efficiently (Kato, 1992). With the advantage of comprehensive descriptions of image contents and consistence to human visual perception, research in this direction is considered as one of the hottest research points in the new century (Zhang, 2003). Though many efforts have been put on CBIR, many techniques have been proposed and many prototype systems have been developed, the problems in retrieving images according to image content are far from being solved. Most of current techniques and systems for image retrieval just take into consideration low-level visual features, such as color and texture of image, or shape of objects and spatial relationships among different regions in images, to describe image contents. However, there is a considerable difference between the users’ interest in reality, and the image contents described by only using the above low-level image features. In other words, there is a large gap between such image content description based on low-level features and that of human beings’ understanding. As a result, these low-level feature-based approaches often lead to unsatisfying querying results in many cases. In this chapter, a general scheme and some object-based techniques are proposed to efficiently fill the gap between the low-level feature and high-level semantic description of images. This is in the hope of making content-based image retrieval more like its real meaning, instead of just considering the visual perception. Throughout this chapter, several techniques are proposed, and all these techniques are gathered together into an object-based framework for image retrieval. The proposed structure of this chapter is as follows: Background, (1) Extraction of Interesting Regions, (2) Object-level Processing, (3) Self-Adaptive Relevance Feedback; Main Thrust of Chapter, (1) Multi-Layer Description Model, (2) Meaningful Region Extraction, (3) Perceptual Feature Extraction, (4) Object Recognition, (5) Object Description and Matching, (6) Experiments and Discussions; Direction of Future Research; and Conclusion. BACKGROUND In content-based image retrieval, how to represent and describe the content of an image is a central issue. Many methods have been used, three broad categories are: synthetic, semantic and semiotic (Bimbo, 1999; Vailaya, 2000; Djeraba, 2002). The 24 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the publisher's webpage: www.igi-global.com/chapter/object-based-techniques-imageretrieval/27058

Distinguishing technicolor models via tt production at polarized photon colliders

Abstract: We study top quark pair productions at a polarized photon collider from an e{sup +}e{sup -} linear collider (LC) in various improved technicolor models, namely, the one-family walking technicolor model, the top-color-assisted technicolor model, and the top-color-assisted multiscale technicolor model. Recent constraint on the top-pion mass from the precision data of R{sub b} is considered. It is shown that, considering only the statistical errors, a polarized photon collider from a 500 GeV LC with an integrated luminosity of 500 fb{sup -1} is sufficient for distinguishing the three improved technicolor models experimentally.

Adaptive Modeling and Control of Drug Delivery Systems Using Generalized Fuzzy Neural Networks

Abstract: This chapter presents an adaptive modeling and control scheme for drug delivery systems based on a Generalized Fuzzy Neural Network (G-FNN). The proposed G-FNN is a novel intelligent modeling tool, which can model the unknown nonlinearities of complex drug delivery systems and adapt to changes and uncertainties in these systems on line. It offers salient features, such as dynamic fuzzy neural structure, fast online learning ability and adaptability, etc. System approximation formulated by the G-FNN is thus employed in the adaptive controller design for drug infusion. In particular, this chapter investigates automated regulation of Mean Arterial Pressure (MAP) through the intravenous infusion of Sodium NitroPrusside (SNP), which is one of the attractive applications in automation of drug delivery. Simulation study demonstrates superior performance of the proposed approach for estimating the drug’s effect and regulating blood pressure at a prescribed level.