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Journal articles on the topic 'Three Component Analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

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1

Kivits, Robbert A. "Three component stakeholder analysis." International Journal of Multiple Research Approaches 5, no. 3 (2011): 318–33. http://dx.doi.org/10.5172/mra.2011.5.3.318.

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2

Everitt, B. S., and P. M. Kroonenberg. "Three-Mode Principal Component Analysis." Biometrics 42, no. 1 (1986): 224. http://dx.doi.org/10.2307/2531268.

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3

Miles, D. R., G. Gassaway, L. Bennett, and R. Brown. "Three-component amplitude versus offset analysis." Exploration Geophysics 20, no. 2 (1989): 257. http://dx.doi.org/10.1071/eg989257.

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Three-component (3-C) amplitude versus offset (AVO) inversion is the AVO analysis of the three major energies in the seismic data, P-waves, S-waves and converted waves. For each type of energy the reflection coefficients at the boundary are a function of the contrast across the boundary in velocity, density and Poisson's ratio, and of the angle of incidence of the incoming wave. 3-C AVO analysis exploits these relationships to analyse the AVO changes in the P, S, and converted waves. 3-C AVO analysis is generally done on P, S, and converted wave data collected from a single source on 3-C geoph

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4

Jepsen, D. C., and B. L. N. Kennett. "Three-component analysis of regional seismograms." Bulletin of the Seismological Society of America 80, no. 6B (1990): 2032–52. http://dx.doi.org/10.1785/bssa08006b2032.

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Abstract Both phased array techniques for single-component sensors and vectorial analysis of three-component recordings can provide estimates of the azimuth and slowness of seismic phases. However, a combination of these approaches provides a more powerful tool to estimate the propagation characteristics of different seismic phases at regional distances. Conventional approaches to the analysis of three-component seismic records endeavor to exploit the apparent angles of propagation in horizontal and vertical planes as well as the polarization of the waves. The basic assumption is that for a gi

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5

Millsap, Roger E., and William Meredith. "Some Mathematical Relationships Between Three-Mode Component Analysis and Stationary Component Analysis." Multivariate Behavioral Research 26, no. 3 (1991): 413–19. http://dx.doi.org/10.1207/s15327906mbr2603_2.

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6

Ogoshi, M., B. S. Kim, S. Kurokawa, et al. "Three-dimensional MCG Analysis with Independent Component Analysis." Journal of the Magnetics Society of Japan 29, no. 5 (2005): 598–602. http://dx.doi.org/10.3379/jmsjmag.29.598.

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7

Timmerman, Marieke E., and Henk A. L. Kiers. "Three-way component analysis with smoothness constraints." Computational Statistics & Data Analysis 40, no. 3 (2002): 447–70. http://dx.doi.org/10.1016/s0167-9473(02)00059-2.

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8

Simbrunner, J., and G. Zieger. "Analysis of Three-Component Composite 180° Pulses." Journal of Magnetic Resonance, Series B 106, no. 2 (1995): 142–46. http://dx.doi.org/10.1006/jmrb.1995.1024.

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9

Harshman, Richard A. "Book Review : Three-Mode Principal Component Analysis." Applied Psychological Measurement 9, no. 3 (1985): 327–32. http://dx.doi.org/10.1177/014662168500900313.

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10

Doelman, Arjen, Peter van Heijster, and Tasso J. Kaper. "Pulse Dynamics in a Three-Component System: Existence Analysis." Journal of Dynamics and Differential Equations 21, no. 1 (2008): 73–115. http://dx.doi.org/10.1007/s10884-008-9125-2.

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11

Wang, Cheng, Wei Guan, Jin Gou, Feng Hou, Junqing Bai, and Guirong Yan. "Principal component analysis based three-dimensional operational modal analysis." International Journal of Applied Electromagnetics and Mechanics 45, no. 1-4 (2014): 137–44. http://dx.doi.org/10.3233/jae-141823.

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12

van Heijster, P., A. Doelman, T. J. Kaper, and K. Promislow. "Front Interactions in a Three-Component System." SIAM Journal on Applied Dynamical Systems 9, no. 2 (2010): 292–332. http://dx.doi.org/10.1137/080744785.

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13

Guo, Hao, Kurt J. Marfurt, and Jianlei Liu. "Principal component spectral analysis." GEOPHYSICS 74, no. 4 (2009): P35—P43. http://dx.doi.org/10.1190/1.3119264.

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Spectral decomposition methods help illuminate lateral changes in porosity and thin-bed thickness. For broadband data, an interpreter might generate 80 or more somewhat redundant amplitude and phase spectral components spanning the usable seismic bandwidth at [Formula: see text] intervals. Large numbers of components can overload not only the interpreter but also the display hardware. We have used principal component analysis to reduce the multiplicity of spectral data and enhance the most energetic trends inside the data. Each principal component spectrum is mathematically orthogonal to other

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14

van Heijster, Peter, Chao-Nien Chen, Yasumasa Nishiura, and Takashi Teramoto. "Pinned Solutions in a Heterogeneous Three-Component FitzHugh–Nagumo Model." Journal of Dynamics and Differential Equations 31, no. 1 (2018): 153–203. http://dx.doi.org/10.1007/s10884-018-9694-7.

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15

Ushio, Kenjiro, Toshiaki Yagi, and Tetsushi Morizono. "Three-Component Analysis of Benign Paroxysmal Positional Nystagmus." Equilibrium Research 53, Suppl-10 (1994): 80–83. http://dx.doi.org/10.3757/jser.53.suppl-10_80.

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16

Luo, Shui Liang, Lin Xiang Li, Ming Li, Qing Bo Ma, and Bao Qing He. "Theory Analysis of Three-Component Induction Resistivity Log." Advanced Materials Research 499 (April 2012): 491–94. http://dx.doi.org/10.4028/www.scientific.net/amr.499.491.

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This paper shows enumerated electrical field is divided into primary field and secondary field according to two Maxwell circuitation equations combined with fact of induction resistivity log, and the primary field can be solved by analytic geometry method, the secondary field can be solved by finite difference Crisscross grid method. Conductivity selection of primary field and secondary field computation formula of three-component induction resistivity log is developed based on finite difference crisscross grid method in this paper.

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17

Yagi, T., S. Kurosaki, S. Yamanobe, and T. Morizono. "Three-Component Analysis of Caloric Nystagmus in Humans." Archives of Otolaryngology - Head and Neck Surgery 118, no. 10 (1992): 1077–80. http://dx.doi.org/10.1001/archotol.1992.01880100069015.

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18

Giordani, Paolo, and Henk A. L. Kiers. "Three-way component analysis of interval-valued data." Journal of Chemometrics 18, no. 5 (2004): 253–64. http://dx.doi.org/10.1002/cem.868.

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19

Kiers, Henk A. L., and Iven Van Mechelen. "Three-way component analysis: Principles and illustrative application." Psychological Methods 6, no. 1 (2001): 84–110. http://dx.doi.org/10.1037/1082-989x.6.1.84.

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20

Fäh, Donat, Gabriela Stamm, and Hans-Balder Havenith. "Analysis of three-component ambient vibration array measurements." Geophysical Journal International 172, no. 1 (2008): 199–213. http://dx.doi.org/10.1111/j.1365-246x.2007.03625.x.

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21

Ushio, Kenjiro, Tetsushi Morizono, and Toshiaki Yagi. "Three-Component Analysis of Benign Paroxysmal Positional Nystagmus." Acta Oto-Laryngologica 115, sup519 (1995): 107–9. http://dx.doi.org/10.3109/00016489509121881.

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22

van der Kloot, Willem A., and Pieter M. Kroonenberg. "External analysis with three-mode principal component models." Psychometrika 50, no. 4 (1985): 479–94. http://dx.doi.org/10.1007/bf02296265.

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23

Isezaki, Nobuhiro. "A new shipboard three‐component magnetometer." GEOPHYSICS 51, no. 10 (1986): 1992–98. http://dx.doi.org/10.1190/1.1442054.

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The three components of the geomagnetic field were measured at sea by STCM (Shipboard Three Component Magnetometer) with a relative accuracy of 50 ± 25 nT. The STCM was controlled by a microcomputer, and the three geomagnetic components were calculated by reducing the magnetic field due to the ship’s remanent and induced magnetic moments, the only assumption being that the magnetic field is linearly related to the magnetic moments. Although STCM has no particular magnetic compensation device, it can be set at any place on board of any kind of ship. In the KH82-5 cruise, the three component mea

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24

Török, Evelin, István Komlósi, Béla Béri, Imre Füller, Barnabás Vágó, and János Posta. "Principal component analysis of conformation traits in Hungarian Simmental cows." Czech Journal of Animal Science 66, No. 2 (2021): 39–45. http://dx.doi.org/10.17221/155/2020-cjas.

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The aim of the current research was to analyze the linear type traits of Hungarian Simmental dual-purpose cows scored in the first lactation using principal component analysis and cluster analysis. Data collected by the Association of Hungarian Simmental Breeders were studied during the work. The filtered database contained the results of 8 868 cows, born after 1997. From the evaluation of main conformation traits, the highest correlations (r = 0.35, P &lt; 0.05) were found between mammary system and feet and legs traits. Within linear type traits, the highest correlation was observed betw

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25

Pravdova, V., C. Boucon, S. de Jong, B. Walczak, and D. L. Massart. "Three-way principal component analysis applied to food analysis: an example." Analytica Chimica Acta 462, no. 2 (2002): 133–48. http://dx.doi.org/10.1016/s0003-2670(02)00318-5.

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26

Cordella, Christophe B. Y., Riccardo Leardi, and Douglas N. Rutledge. "Three-way principal component analysis applied to noodles sensory data analysis." Chemometrics and Intelligent Laboratory Systems 106, no. 1 (2011): 125–30. http://dx.doi.org/10.1016/j.chemolab.2010.05.017.

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27

Adar, Mustapha, Youssef Najih, Mohamed Gouskir, Ahmed Chebak, Mustapha Mabrouki, and Amin Bennouna. "Three PV plants performance analysis using the principal component analysis method." Energy 207 (September 2020): 118315. http://dx.doi.org/10.1016/j.energy.2020.118315.

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28

Kozak, M., S. Samborski, M. S. Kang, and J. Rozbicki. "Applying statistics for nonsequential yield component analysis – Information." Plant, Soil and Environment 53, No. 10 (2008): 456–63. http://dx.doi.org/10.17221/2199-pse.

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In the paper, an application of the methodology for analyzing yield as affected by its components that develop at the same ontogenetic level is discussed; it may also be applied to any model in which several traits developing non-sequentially affect their product. The methodology is called “nonsequential yield component analysis”. Two applications are presented; the proposed approach is compared with path analysis, commonly applied for yield component analysis, and Piepho’s approach. In one example, grain yield of two cultivars of winter triticale (×<i>Triticosecale</i> Wittmack),

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29

Nakamura, Yuko. "Nonmetric three-mode principal component analysis for qualitative data." Kodo Keiryogaku (The Japanese Journal of Behaviormetrics) 42, no. 2 (2015): 105–15. http://dx.doi.org/10.2333/jbhmk.42.105.

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30

Njemanze, Philip C., and Añuri Chidi-Ebere. "Three-Dimensional Vector Component Analysis of Neurological Stroke Scales." Stroke 30, no. 8 (1999): 1731–33. http://dx.doi.org/10.1161/01.str.30.8.1731.

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31

Wagner, G. S., and T. J. Owens. "Broadband eigen-analysis for three-component seismic array data." IEEE Transactions on Signal Processing 43, no. 7 (1995): 1738–41. http://dx.doi.org/10.1109/78.398741.

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32

Balandin, Dmitry V., Nikolai N. Bolotnik, Walter D. Pilkey, and Sergey V. Purtsezov. "Impact Isolation Limiting Performance Analysis for Three-Component Models." Journal of Dynamic Systems, Measurement, and Control 127, no. 3 (2004): 463–71. http://dx.doi.org/10.1115/1.1978914.

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For the crashworthiness analysis of transport vehicles a three-component system that consists of a base, a container, and an object to be protected, connected by shock isolators, can be utilized as a model. An approach for a limiting performance analysis of shock isolation for such a model is proposed. This approach involves the reduction of the optimal control problem for the three-component system to an auxiliary optimal control problem for a two-component system. A detailed description of the technique for the determination of the absolute minimum of the performance index and construction o

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33

Moropoulou, Antonia, and Kyriaki Polikreti. "Principal Component Analysis in monument conservation: Three application examples." Journal of Cultural Heritage 10, no. 1 (2009): 73–81. http://dx.doi.org/10.1016/j.culher.2008.03.007.

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34

Dijksterhuis, Garmt, Margo Flipsen, and Pieter Punter. "Principal component analysis of TI-curves: Three methods compared." Food Quality and Preference 5, no. 1-2 (1994): 121–27. http://dx.doi.org/10.1016/0950-3293(94)90017-5.

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35

Kracke, D. "A three-component event detector based on waveform analysis." Computers & Geosciences 19, no. 2 (1993): 117–22. http://dx.doi.org/10.1016/0098-3004(93)90108-h.

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36

Nakamura, Yuko. "Three-way principal component analysis by using quantification method." Proceedings of the Annual Convention of the Japanese Psychological Association 78 (September 10, 2014): 2EV—2–004–2EV—2–004. http://dx.doi.org/10.4992/pacjpa.78.0_2ev-2-004.

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37

Bataille, K., and J. M. Chiu. "Polarization analysis of high-frequency, three-component seismic data." Bulletin of the Seismological Society of America 81, no. 2 (1991): 622–42. http://dx.doi.org/10.1785/bssa0810020622.

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Abstract We present a method to determine the polarization of body waves from three-component, high-frequency data and examples of its application. The method is based on the principal component approach. One advantage of this approach is that the polarization state can be determined for small time windows compared with the predominant period of the wave. This is particularly useful for identifying converted waves within the crust. The stability of the result is analyzed with synthetic cases by adding simultaneous arrivals from waves and random noise. The method works well with both synthetic

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38

Bu, Dongsheng, and Chris W. Brown. "Self-Modeling Mixture Analysis by Interactive Principal Component Analysis." Applied Spectroscopy 54, no. 8 (2000): 1214–21. http://dx.doi.org/10.1366/0003702001950797.

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A key procedure for mixture analysis in self-modeling methods is to identify a pure wavelength (or pure variable) for each component in the mixture. A pure wavelength has intensity contributions from only one of the components in a mixture. In this paper, an interactive approach based on principal component analysis (IPCA) is presented for the pure wavelength selection. The approach is developed from a combination of key set factor analysis (KSFA) and SIMPLISMA (simple-to-use interactive self-modeling mixture analysis). Since all significant principal components are included and user interacti

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39

van Heijster, Peter, Chao-Nien Chen, Yasumasa Nishiura, and Takashi Teramoto. "Correction to: Pinned Solutions in a Heterogeneous Three-Component FitzHugh–Nagumo Model." Journal of Dynamics and Differential Equations 31, no. 1 (2018): 205. http://dx.doi.org/10.1007/s10884-018-9700-0.

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40

Hyvärinen, Aapo. "Independent component analysis: recent advances." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371, no. 1984 (2013): 20110534. http://dx.doi.org/10.1098/rsta.2011.0534.

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Independent component analysis is a probabilistic method for learning a linear transform of a random vector. The goal is to find components that are maximally independent and non-Gaussian (non-normal). Its fundamental difference to classical multi-variate statistical methods is in the assumption of non-Gaussianity, which enables the identification of original, underlying components, in contrast to classical methods. The basic theory of independent component analysis was mainly developed in the 1990s and summarized, for example, in our monograph in 2001. Here, we provide an overview of some rec

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41

Mi, Yongsheng, and Chunlai Mu. "On a three-Component Camassa-Holm equation with peakons." Kinetic & Related Models 7, no. 2 (2014): 305–39. http://dx.doi.org/10.3934/krm.2014.7.305.

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42

Sanchez, F. "Three-dimensional synchronous fluorescence spectrometry for the analysis of three-component alkaloid mixtures." Talanta 37, no. 6 (1990): 579–84. http://dx.doi.org/10.1016/0039-9140(90)80199-p.

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43

Do, Cuong Manh, and Bahram Javidi. "Three-dimensional computational holographic imaging and recognition using independent component analysis." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 464, no. 2090 (2007): 409–22. http://dx.doi.org/10.1098/rspa.2007.0167.

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We present computational holographic three-dimensional imaging and automated object recognition based on independent component analysis (ICA). Three-dimensional sensing of the scene is performed by computational holographic imaging of the objects using phase-shifting digital holography. We used principal components analysis to reduce data dimension and ICA to recognize the three-dimensional objects. In this paper, kurtosis maximization-based algorithm is used. To the best of our knowledge, this paper is the first to report using ICA in three-dimensional imaging technology.

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44

Teramoto, Takashi, and Peter van Heijster. "Traveling Pulse Solutions in a Three-Component FitzHugh--Nagumo Model." SIAM Journal on Applied Dynamical Systems 20, no. 1 (2021): 371–402. http://dx.doi.org/10.1137/20m1334942.

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45

Chen, Chow-Son, Wei-Hsuan Chiu, and Ching-Ren Lin. "Three Component Time-domain Electromagnetic Surveying: Modeling and Data Analysis." PIERS Online 4, no. 4 (2008): 475–80. http://dx.doi.org/10.2529/piers070927214722.

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46

KROONENBERG, PIETER M., CORNELIS J. LAMMERS, and INEKE STOOP. "Three-Mode Principal Component Analysis of Multivariate Longitudinal Organizational Data." Sociological Methods & Research 14, no. 2 (1985): 99–136. http://dx.doi.org/10.1177/0049124185014002001.

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47

Allen, Heidelinde. "A three-component analysis of Hoffman's model of verbal hallucinations." Behavioral and Brain Sciences 9, no. 3 (1986): 518. http://dx.doi.org/10.1017/s0140525x0004680x.

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48

Gasanaliev, A. M., B. Yu Gamataeva, T. I. Seferaliev, and M. T. Tagzirov. "Thermal analysis of the LiF-SrFCl-SrCO3 three-component system." Russian Journal of Inorganic Chemistry 54, no. 10 (2009): 1669–73. http://dx.doi.org/10.1134/s0036023609100271.

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49

Liu Ming, 刘鸣, 舒勤 Shu Qin, 杨赟秀 Yang Yunxiu, and 袁菲 Yuan Fei. "Three-Dimensional Point Cloud Registration Based on Independent Component Analysis." Laser & Optoelectronics Progress 56, no. 1 (2019): 011203. http://dx.doi.org/10.3788/lop56.011203.

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50

Yagi, Toshiaki, and Kenjiro Ushio. "Nystagmus in Benign Paroxysmal Positional Vertigo: A Three-Component Analysis." Acta Oto-Laryngologica 115, sup520 (1995): 238–40. http://dx.doi.org/10.3109/00016489509125237.

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