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Copyright of Journal of Liaoning Technical University (Natural Science Edition) / Liaoning Gongcheng Jishu Daxue Xuebao (Ziran Kexue Ban) is the property of Journal of Liaoning Technical University (Natural Science) Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The current functional data analysis focuses on the amplitude variability, while ignoring the phase variability. In many cases, phase variability is a kind of useful information for statistical analysis. Based on partial least square method, this paper proposed a feature extraction procedure for functional data by combining phase and amplitude information. Firstly, the proposed procedure used a function alignment technique to obtain the time warping function containing phase variation. Secondly, it combined the aligned function and the warping function in the form of piecewise function. Finally, it used the partial least square approach to extract component features from the combined function and utilized those features on the regression and classification model. Experimental results show that the proposed method can obtain better prediction performance than the principal component analysis method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Since the representation ability of statistical global feature for functional data classification algorithm is limited, and the salient point feature is susceptible to noise disturbance, this paper proposed a segmental feature extraction algorithm based on statistical depth notion. Firstly, it used the smoothing technique to pre-smooth the discrete observed data, and introduced the first and second derivatives of the functional data . Then, it calculated depths of Mahalanobis integral of the functions and its low-order derivatives in segments, and thus constructed feature vectors of function curves based on the depth measures. Finally, it selected the optimal number of segments for classification by data-driven, and studied the binary classification of function data. Compared with the other curve feature extraction algorithms, experiments on UCR datasets show that the proposed algorithm performs well in extracting the feature of curve, and improves the classification accuracy effectively. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Veterinary Science / Zhongguo Shouyi Xuebao is the property of Chinese Society of Veterinary Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)