Papers by Keyword: Principal Component Analysis (PCA)

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Authors: Agnese Brangule, Ingus Skadins, Aigars Reinis, Kārlis Agris Gross, Juta Kroica
Abstract: The activity of antibacterial material is conventionally estimated by using an indirect method – a bacteria suspension is inoculated onto a surface, and then the bacteria are collected from the surface and examined as to whether they can form colonies on the agar plate. In the present study, the presence of bacteria was examined by direct detection. Our study is based on FTIR-PAS with an interferometer cantilever detector. Our work discusses the possibility of identifying and distinguishing the presence of different bacteria (Staphylococcus epidermidis and Pseudomonas aeruginosa) and the possibility to evaluate the crystallization processes on the pressed calcium phosphate surface.
Authors: Larry L. Hench, Ioan Notingher
Abstract: We present a new bio-photonic method based on Raman spectroscopy able to characterize living cells in in-vitro cultures. The main advantages of this technology are: no labels or other contrast enhancers are required; provides real-time analysis; cells can be maintained in physiological conditions during the measurements; no cell-damage is induced during the measurements; it is rich in information about the biochemical composition of the cell. The results show that this spectroscopic method can be used to study the most important cellular functions involved in cell-biomaterial interactions, such as cell death, differentiation, de-differentiation and mineralization. The method offers the potential for studying cell-bioceramics interaction and reduce the need of animal testing until the final steps of proving efficacy prior to clinical trials.
Authors: Virginia Martina, M. Federica de Riccardis, Daniela Carbone
Abstract: Poly(etherether-ketone) (PEEK) suspensions in ethanol and isopropanol containing also α-alumina and hydroxyapatite powders were studied. An innovative method was used in the study of the quality of suspensions. We studied suspensions by means of zeta potential and grain-size measurements. The comparison amongst the different suspensions was carried out by using statistical and chemometric tools, especially by the use of an explorative test based on the Principal Component Analysis (PCA). The chemometric analysis was performed by involving different combinations of each type of powder and each type of solvent.
Authors: Hai Yang Kong, Lan Xiang Sun, Jing Tao Hu, Yong Xin, Zhi Bo Cong
Abstract: Spectra of 27 steel samples were acquired by Laser-Induced Breakdown Spectroscopy (LIBS) for steel classification. Two methods were used to reduce dimensions: the first is to select characteristic lines of elements contained in the samples manually and the second is to do principal component analysis (PCA) of original spectra. Then the data after reducing dimensions was used as the input of artificial neural networks (ANN) to classify steel samples. The results show that, the better result can be achieved by selecting peak lines manually, but this solution needs much priori knowledge and wastes much time. The principal components (PCs) of original spectra were utilized as the input of artificial neural networks can also attain a good result nevertheless and this method can be developed into an automatic solution without any priori knowledge.
Authors: Yan Shuang She, Meng Gang Li, Jing Hua Sha
Abstract: The sustainable development of resource-based cities is a hot issue in China. This paper will focus on the industrial structure of sustainable development. There is close relationship between regional economic development and industrial structure transformation Capacity. Comparison research of cities’ regional industrial structure capacity will help to understand variation status and potential of regional industrial structures. With principal components analysis, this paper based on the regional industrial structure comparison theory, analyzed industrial structure transformation Capacity of 20 resource-based cities in China west. It will provide references for promoting optimization and upgrading of regional industrial structure, making regional industrial development strategy, improving environmental conditions, and enhancing local economic development.
Authors: Cheng Bo Yu, Jun Tan, Lei Yu, Yin Li Tian
Abstract: This paper puts forward a finger vein classification algorithm which combines Principal Component Analysis (PCA) with Radial Basis Function (RBF) neural network algorithm, named the PCA-RBF algorithm. Use the training sample to reduce PCA dimensions, and abstract the main component of the image. Because of the advantages of RBF neural network classifying, put finger vein images into different classes, and then use the shortest distance to recognize. Through the experiment result comparing with Back Propagation (BP) neural network, PCA-RBF neural network is better in finger vein recognition. The result shows that PCA-RBF has faster training speed, simpler algorithm and higher recognition rate.
Authors: Hong Yi Li, Jun Jie Chen, Xin Li, Di Zhao
Abstract: Gesture recognition has many applications in fields such as the intelligent robot, human computer interaction and so on. The classical BP neural network has its advantages in modeling the highly nonlinear mapping from features to gesture meanings, and could avoid hard-coded feature extraction. However, it usually takes a rather long training and testing time, especially in dealing with redundant high dimensional data. To address this drawback, we combine the BP neural network and PCA, and propose an improved algorithm. Experiments demonstrate the feasibility and efficiency of the proposed algorithm by comparing with the classical one.
Authors: Shi Jun He, Shi Ting Zhao, Fan Bai, Jia Wei
Abstract: The spatial data which acquired by 3D laser scanning is huge, aiming at the iteration time is long with classic ICP algorithm, a improved registration algorithm of spatial data ICP algorithm which based on principal component analysis (PCA) is proposed in this paper (PCA-ICP), the basic principle and steps of PCA-ICP algorithm are given. The experiment results show that this method is feasible and the iterative time of PCA-ICP algorithm is shorter than classical ICP algorithm.
Authors: Guan Guan, Mei Shen, Yan Lin, Zhuo Shang Ji
Abstract: The deviation analysis of hull construction is a common problem in shipbuilding. In this paper, we present an automatic registration method of hull blocks points measured by Total Station based on principal component analysis (PCA) and the iterative closest point (ICP) algorithm. The method is divided into two steps. The first step rough registration based on PCA can narrow the dislocation between measurement points and CAD model points giving closed initialization. The second step refined registration based on ICP can obtain the optimal solution. The algorithm can automatically match measurement data with CAD model without prior information on transformation. We have applied this method to the registration of the Hull Block Point Clouds in a bulk carrier. Our result shows that the algorithm works efficiently.
Authors: Xiang Shun Chen, Hu Biao Zeng, Zhi Xiong Li
Abstract: Rolling bearings are widely used in various areas including aircraft, mining, manufacturing, and agriculture, etc. The breakdowns of the rotational machinery resulted from the rolling bearing failures account for 30%. It is therefore imperative to monitor the rolling bearing conditions in time in order to prevent the malfunctions of the plants. In the present paper is described a fault detection and diagnosis technique for rolling bearing multi-faults based on wavelet-principle component analysis (PCA) and fuzzy k-nearest neighbor (FKNN). In the diagnosis process, the wavelet analysis was firstly employed to decompose the vibration data of the rolling bearings under eight different operating conditions, and for each sample its energy of each sub-band was calculated to obtain the original feature space. Then, the PCA was used to reduce the dimensionality of the original feature vector and hence the most important features could be gotten. Lastly, the FKNN algorithm was employed in the pattern recognition to identify the conditions of the bearings of interest. The experimental results suggest that the sensitive fault features can be extracted efficiently after the wavelet-PCA processing, and the proposed diagnostic system is effective for the rolling bearing multi-fault diagnosis. In addition, the proposed method can achieve higher performance than that without PCA with respect to the classification rate.
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