Month: July 2015

Real-Time Shape Estimation for Wire-Driven Flexible Robots With Multiple Bending Sections Based on Quadratic Bรฉzier Curves

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Abstract

The wire-driven flexible robot with multiple bending sections is an efficient approach for the minimally invasive surgery and diagnosis. It can function properly in the complicated and restrained environment. One drawback of this technology is that the real time positional and shape information cannot be well estimated. In order to settle this limitation, we proposed a novel shape estimation method for a wire-driven flexible robot with multiple bending sections in this paper. Each bending section can be controlled independently to deform as an arc with different curvature. This method is based on the positional and directional information of limited specific joints on the robot, which can be estimated with an effective positioning method, such as electromagnetic tracking method. The number and position of these specific joints are only determined by the number of sections. Based on the positional and directional information, as well as the curve length information, the shape reconstruction algorithm can be carried out by fitting multiple quadratic Bยดezier curves. Real time shape sensing platform is built to verify the proposed method. Experimental results show that the method works well and the mean position error is 1.7mm.

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Variable Selection Based on Information Tree for Spectroscopy Quantitative Analysis

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Abstract

Spectroscopy is a fast and efficient component analysis method, and full spectrum prediction model may be redundant and inaccurate. This paper proposes a variable selection method based on information tree for spectroscopy quantitative analysis. Firstly, a feature training set that indicates the information of the selected variables is generated. Then, the partial least squares (PLS) is performed on the spectral calibration set, and root-mean-square error of cross-validation is used to evaluate the feature training set. According to the corresponding evaluation results, the information gain of each wavelength is calculated. The wavelength with maximum information gain is de๏ฌned as the root node, and an information tree is built based on the information gain where each leaf node represents a wavelength. The ๏ฌnal selection result is a conjunction path of the leaf nodes that has bigger information gain. The full spectrum PLS, the uninformative variable elimination with PLS method, the genetic algorithm with PLS method and the proposed method are conducted on the real spectral dataset of ๏ฌ‚ue gas, and the effectiveness of the methods are compared and discussed. The experimental results verify that the prediction precision and the compression ability of the proposed method is higher.