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Your search keyword '"Jin, Chao"' showing total 45 results
Start Over Author "Jin, Chao" Publication Type Books
45 results on '"Jin, Chao"'

43. Short Term Load Forecasting Model Based on Support Vector Machine.

Author

Yeung, Daniel S., Zhi-Qiang Liu, Xi-Zhao Wang, Hong Yan, Dong-Xiao Niu, Qiang Wang, and Jin-Chao Li

Abstract

An artificial neural network was used for sample preprocessing in this paper. Firstly, the data points were classified into three types as follows: the high load type, the medium load type and the low load type. Then, the artificial neural network was adopted to forecast the load type of the predict point. Finally, a support vector machine forecasting model was created on the basis of data points whose load type is the same as the predict point. Comparisons were made between different methods. The results show that the model established in this paper is better than other methods in forecasting accuracy and computing speed. [ABSTRACT FROM AUTHOR]

Published
2006
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44. Fast and Robust Clinical Triple-Region Image Segmentation Using One Level Set Function.

Author

Larsen, Rasmus, Nielsen, Mads, Sporring, Jon, Li, Shuo, Fevens, Thomas, Krzyżak, Adam, Jin, Chao, and Li, Song

Abstract

This paper proposes a novel method for clinical triple-region image segmentation using a single level set function. Triple-region image segmentation finds wide application in the computer aided X-ray, CT, MRI and ultrasound image analysis and diagnosis. Usually multiple level set functions are used consecutively or simultaneously to segment triple-region medical images. These approaches are either time consuming or suffer from the convergence problems. With the new proposed triple-regions level set energy modelling, the triple-region segmentation is handled within the two region level set framework where only one single level set function needed. Since only a single level set function is used, the segmentation is much faster and more robust than using multiple level set functions. Adapted to the clinical setting, individual principal component analysis and a support vector machine classifier based clinical acceleration scheme are used to accelerate the segmentation. The clinical acceleration scheme takes the strengths of both machine learning and the level set method while limiting their weaknesses to achieve automatic and fast clinical segmentation. Both synthesized and practical images are used to test the proposed method. These results show that the proposed method is able to successfully segment the triple-region using a single level set function. Also this segmentation is very robust to the placement of initial contour. While still quickly converging to the final image, with the clinical acceleration scheme, our proposed method can be used during pre-processing for automatic computer aided diagnosis and surgery. [ABSTRACT FROM AUTHOR]

Published
2006
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45. Toward Automatic Computer Aided Dental X-ray Analysis Using Level Set Method.

Author

Duncan, James S., Gerig, Guido, Li, Shuo, Fevens, Thomas, Krzyżak, Adam, Jin, Chao, and Li, Song

Abstract

A Computer Aided Dental X-rays Analysis (CADXA) framework is proposed to semi-automatically detect areas of bone loss and root decay in digital dental X-rays. In this framework, first, a new proposed competitive coupled level set method is proposed to segment the image into three pathologically meaningful regions using two coupled level set functions. Tailored for the dental clinical environment, the segmentation stage uses a trained support vector machine (SVM) classifier to provide initial contours. Then, based on the segmentation results, an analysis scheme is applied. First, the scheme builds an uncertainty map from which those areas with bone loss will be automatically detected. Secondly, the scheme employs a method based on the SVM and the average intensity profile to isolate the teeth and detect root decay. Experimental results show that our proposed framework is able to automatically detect the areas of bone loss and, when given the orientation of the teeth, it is able to automatically detect the root decay with a seriousness level marked for diagnosis. [ABSTRACT FROM AUTHOR]

Published
2005
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