1,721,064 research outputs found
Discriminative models and dimensionality reduction for regression
No full textMany prediction problems that arise in computer vision and robotics can be formulated within a regression framework. Unlike traditional regression problems, vision and robotics tasks are often characterized by a varying number of output variables with complex dependency structures. The problems are further aggravated by the high dimensionality of the input. In this thesis, I address two challenging tasks related to learning of regressors in such settings: (1) developing discriminative approaches that can handle structured output variables, and (2) reducing the dimensionality of the input while preserving the statistical correlation with the output.
A complex dependency structure in the output variables can be effectively captured by probabilistic graphical models. In contrast to traditional joint data modeling for probabilistic models, I propose conditional models and a discriminative learning approach that are directly related to the ultimate prediction objective. While discriminative learning of structured models such as Conditional Random Fields (CRFs) has attracted significant interest in the past, learning structured models in the regression setting has been rarely explored. In this work I first extend the CRF and the discriminatively trained HMM methods to the structured output regression problem. I propose two different approaches based on directed and undirected models. In the second approach the parameter learning is cast as a convex optimization problem, accompanied by a new approach that effective handles the density integrability constraint. Experiments in several problem domains, including human motion and robot-arm state estimation, indicate that the new models yield high prediction accuracy comparable to or better than state-of-the-art approaches.
In the second part of the thesis, I consider the task of finding a low-dimensional representation of the input covariates while preserving the statistical correlation in regressing the output. This task, known as the dimensionality reduction for regression (DRR), is particularly useful when visualizing high-dimensional data, efficiently designing regressors with a reduced input dimension, and eliminating noise in the input data by uncovering essential information for predicting the output. While the dimensionality reduction methods are common in many machine learning tasks, their use in the regression settings has not been widespread. A number of recent methods for DRR have been proposed in the statistics community but suffer from several limitations, including non-convexity and the need for slicing of potentially high-dimensional output space. I address these issues by proposing a novel approach based on covariance operators in reproducing kernel Hilbert spaces (RKHSes) that provide a closed-form DRR solution without the need for explicit slicing. The benefits of this approach are demonstrated in a comprehensive set of evaluations on several important regression problems in computer vision and pattern recognition.Ph.D.Includes bibliographical references (p. 94-97)
Silica nanoparticle-based transparent superhydrophobic surfaces prepared by a simple thermally activated equilibration reaction
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Efficiency enhancement of spin based thermoelectric devices via separation of phponon and magnon currents
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Validity of the equations for the contact angle on real surfaces
No full textThe wetting property between liquid and solid is very important in many industries besides natural systems. The simplest method to determine the wetting property is just dropping a liquid drop on a solid surface and measuring a contact angle from the shape of the drop. Since the Young's equation has been used as a basic equation to relate a contact angle and interfacial tensions over 200 years, it is important to understand a derivation and limits of the Young's equation. We derived the Young's equation following energy minimization with simple mathematics. By expanding the derivation, the modified forms of the Cassie-Baxter equation and the Wenzel equation were also derived. From analyses of the derivations, it was deduced that a contact angle on an ideal surface is only related to the infinitesimal region in the vicinity of contact line, not internal area surrounded by the contact line. Although the Cassie-Baxter model and the Wenzel model were not rigorously built, they have been widely used for a superhydrophobic surface, because the apparent forms are similar to those of rigorously derived models when the contact line can easily move on the surface
Transparent superhydrophobic surface by silicone oil combustion
No full textA transparent superhydrophobic coating can be easily created through the use of commercial silicone oil and controlled combustion. In this simple fabrication, silicone oil is sprayed onto a hot glass heated to about 550 degrees C, resulting in the transparent superhydrophobic coating on the glass. The coating is stable at temperature up to about 450 degrees C, and against a saline solution and acidic or basic solutions with pH from 4 to 10. This silicone oil-based process does not require any additional solvent, further surface treatment, drying process or post-treatment process. Several applications of this process are exemplified through the proof-of-concept demonstrations
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