Principal Component Analysis as a Novel Method for the Assessment of the Enclosure Use Patterns of Captive Livingstone’s Fruit Bats (Pteropus livingstonii)

Author(s):  
Morgan J. Edwards ◽  
Charlotte A. Hosie ◽  
Tessa E. Smith ◽  
Dominic Wormell ◽  
Eluned Price ◽  
...  
1995 ◽  
Vol 50 (11-12) ◽  
pp. 757-765 ◽  
Author(s):  
Yasunobu Sakoda ◽  
Kenji Matsui ◽  
Tadahiko Kajiwara ◽  
Akikazu Hatanaka

In order to elucidate chemical structure-odor correlation in the all isomers of n-nonen-1- ols, an entire series of these alcohols were synthesized stereo-selectively in high purity. For unequivocal syntheses of them, geometrically selective hydrogenation of the respective acetylenic compound was adopted. The synthesized alcohols were converted to their 3,5-dinitrobenzoate derivatives with 3,5-dinitrobenzoyl chloride, and then purified by repeated recrystallization. Chemical structure-odor correlations in all the isomers of n-nonen-1-ols were elucidated by introducing a novel method to evaluate odor characteristics and by treating the obtained data statistically with the principal component analysis method (Cramer et al., 1988). The odor profiles of the tested compounds were attributable largely to the positions of the carbon- double bond. The geometries of compounds had only a little effect. With the principal component analysis, the odor profiles of the series of compounds were successfully integrated into the first and the second principal components. The first component (PC-1) consisted of combined characteristics of fruity, fresh, sweet, herbal and oily-fatty, in which herbal and oily-fatty were conversely correlated each other to the position of double-bond of the tested compounds. Of these, only (6Z)-nonen-1-ol deviated markedly from the correlation, indicative of some special interaction between the spatial structure of this compound and the sensory machinery of human.


2019 ◽  
Vol 109 ◽  
pp. 1-11 ◽  
Author(s):  
M. Savić ◽  
A. Dragić ◽  
D. Maletić ◽  
N. Veselinović ◽  
R. Banjanac ◽  
...  

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jialiang Zhang ◽  
Jie Wu ◽  
Xiaoqian Zhang

For fault diagnosis of the two-input two-output mass-spring-damper system, a novel method based on the nonlinear output frequency response function (NOFRF) and multiblock principal component analysis (MBPCA) is proposed. The NOFRF is the extension of the frequency response function of the linear system to the nonlinear system, which can reflect the inherent characteristics of the nonlinear system. Therefore, the NOFRF is used to obtain the original fault feature data. In order to reduce the amount of feature data, a multiblock principal component analysis method is used for fault feature extraction. The least squares support vector machine (LSSVM) is used to construct a multifault classifier. A simplified LSSVM model is adopted to improve the training speed, and the conjugate gradient algorithm is used to reduce the required storage of LSSVM training. A fault diagnosis simulation experiment of a two-input two-output mass-spring-damper system is carried out. The results show that the proposed method has good diagnosis performance, and the training speed of the simplified LSSVM model is significantly higher than the traditional LSSVM.


2021 ◽  
Vol 3 (1) ◽  
Author(s):  
O.Y. Adebayo

Multimodal biometrics has engrossed a lot of interest in recent years as it offers a more dependable scheme for verification and authentication. Multimodal biometrics includes a combination of information from different biometric modalities. This research work simulates a novel method with a view of attendance management using ear and tongue images. Principal Component Analysis was employed for feature extraction of the biometric modalities and Self Organizing Feature Map was used for training and testing of the system. The method was evaluated using six thousand ear images and four thousand tongue images. The acquired images were preprocessed and features extracted were fused at this stage. The fusion results of ear and tongue images demonstrated better performance for attendance management.


2011 ◽  
Vol 181-182 ◽  
pp. 902-907
Author(s):  
Xian Ye Ben ◽  
Shi An ◽  
Jian Wang ◽  
Hai Yang Liu

We propose a novel method for data reduction in gait recognition, called Subblock Complete Two Dimensional Principal Component Analysis (SbC2DPCA). GEIs were divided into smaller sub-images and redundant subblocks were adaptively removed. Complete Two Dimensional Principal Component Analysis (C2DPCA) was then applied to every sub-image directly, to acquire a set of projection sub-vectors for both row and column directions and these were synthesized into whole features for subsequent classification using nearest neighbor classifier. We evaluate the proposed gait recognition method on the CASIA gait database. The experimental results and analysis show the recognition accuracy of SbC2DPCA to be superior to C2DPCA, with C2DPCA being a special case of SbC2DPCA. The novelty of the proposed method lies in the adaptive removal of redundant data while extracting local features. This translates to data reduction with very minimal loss of information, as demonstrated by the remarkable recognition accuracy when subjects change clothing or have a backpack.


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