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DETERMINATION OF FACTORS CONTROLLING THE FATTY-ACID COMPOSITION OF SETTLING PARTICLES IN THE WATER COLUMN BY principal-component analysis AND THEIR QUANTITATIVE ASSESSMENT BY MULTIPLE-REGRESSION

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ORGANIC GEOCHEMISTRY 1992年第1期18卷 121-129页

作者： REEMTSMA, T ITTEKKOT, V UNIV HAMBURG INST BIOGEOCHEM & MEERESCHEMW-2000 HAMBURG 13GERMANY

A principal-component analysis, based on the correlation matrix of fatty acid data obtained from the analysis of sediment trap samples taken in the Bay of Bengal, helps to determine the origins of the settling organic material. After EQUAMAX-rotation, six vectors were identified which could best be interpreted as impact factors ascribed to four sources, namely, phytoplankton, zooplankton, bacteria and land derived material. A multiple regression procedure, originally developed for receptor modeling, is adapted for this work and allows these impact factors to be quantitatively assessed. The complete method is illustrated by its application to samples of fluvial and deep-sea surface sediments and settling particles from the water column.

关键词： BAY OF BENGAL MULTIPLE REGRESSION PARTICULATE FATTY ACIDS principal-component analysis SEDIMENT TRAP

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Detection of hidden structures in a photoexcited semiconductor via principal-component analysis

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SOLID-STATE ELECTRONICS 2006年第2期50卷 191-198页

作者： Shiau, YH Natl Dong Hwa Univ Dept Phys Shoufeng 974 Hualien Taiwan

We study the transport properties of a GaAs-based semiconductor under local optical excitation via direct numerical simulation. The simulation results propose a hypothesis which describes the possibility to control the high-field domain in terms of tunable modulations of the doping profile and the length of the notch region. This hypothesis can be verified, both quantitative and qualitative agreement, via principal-component analysis. Besides, higher harmonic modes embedded in the high-field domain also can be automatically extracted from principal-component analysis. This study might be useful to identify the "effective" length and shape of the cathode notch and to restore the doping concentration in semiconductor devices. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： principal-component analysis semiconductors transport

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Sensor-fault detection, diagnosis and estimation for centrifugal chiller systems using principal-component analysis method

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APPLIED ENERGY 2005年第3期82卷 197-213页

作者： Wang, SW Cui, JT Hong Kong Polytech Univ Dept Bldg Serv Engn Kowloon Hong Kong Peoples R China

An online strategy is developed to detect, diagnose and validate sensor faults in centrifugal chillers. Considering thermophysical characteristics of the water-cooled centrifugal chillers, a dozen sensors of great concern in the chiller-system monitoring and controls were assigned into two models based on principal-component analysis. Each of the two models can group a set of correlated variables and capture the systematic trends of the chillers. The Q-statistic and Q-contribution plot were used to detect and diagnose the sensor faults, respectively. In addition, an approach based on the minimization of squared prediction error of reconstructed vector of variables was used to reconstruct the identified faulty-sensors, i.e., estimate their bias magnitudes. The sensor-fault detection, diagnosis and estimation strategy was validated using an existing building chiller plant while various sensor faults were introduced. (c) 2004 Elsevier Ltd. All rights reserved.

关键词： centrifugal chiller sensor fault sensor bias fault detection fault diagnosis sensor estimation principal-component analysis

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Detection and quantification of peanut traces in wheat flour by near infrared hyperspectral imaging spectroscopy using principal-component analysis

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JOURNAL OF NEAR INFRARED SPECTROSCOPY 2015年第1期23卷 15-22页

作者： Mishra, Puneet Herrero-Langreo, Ana Barreiro, Pilar Roger, Jean Michel Diezma, Belen Gorretta, Nathatie Lleo, Lourdes Univ Politecn Madrid CEI Moncloa ETSI Agron Dept Ingn RuralLPF Tagralia E-28040 Madrid Spain Univ Politecn Madrid CEI Moncloa ETSI Agron Dept Ingn RuralLPF Tagralia E-28040 Madrid Spain Irstea UMR ITAP F-34196 Montpellier 5 France

The use of a common environment for processing different powder foods in the industry has increased the risk of finding peanut traces in powder foods. The analytical methods commonly used for detection of peanut such as enzyme-linked immunosorbent assay (ELISA) and real-time polymerase chain reaction (RT-PCR) represent high specificity and sensitivity but are destructive and time-consuming, and require highly skilled experimenters. The feasibility of NIR hyperspectral imaging WISH is studied for the detection of peanut traces down to 0.01% by weight. A principal-component analysis IPCA) was carried out on a dataset of peanut and flour spectra. The obtained Loadings were applied to the HSI images of adulterated wheat flour samples with peanut traces. As a result, HSI images were reduced to score images with enhanced contrast between peanut and flour particles. Finally, a threshold was fixed in score images to obtain a binary classification image, and the percentage of peanut adulteration was compared with the percentage of pixels identified as peanut particles. This study allowed the detection of traces of peanut down to 0.01% and quantification of peanut adulteration from 10% to 0.1% with a coefficient of determination (r(2)) of 0.946. These results show the feasibility of using HSI systems for the detection of peanut traces in conjunction with chemical procedures, such as RT-PCR and ELISA to facilitate enhanced quality-control surveillance on food-product processing lines.

关键词： hyperspectral NIR principal-component analysis food peanut adulteration classification

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APPLICATION OF principal-component analysis ON NEAR-INFRARED SPECTROSCOPIC DATA OF VEGETABLE-OILS FOR THEIR CLASSIFICATION

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JOURNAL OF THE AMERICAN OIL CHEMISTS SOCIETY 1994年第3期71卷 293-298页

作者： SATO, T Department of Crop Breeding Kyushu National Agricultural Experiment Station Ministry of Agriculture Forestry and Fisheries (MAFF) Nishigoshi Kumamoto-ken Japan

In the near-infrared (NIR) spectra of oil, information about fatty acid composition is concentrated in the range of 1600-2200 mm. principal-component analysis (PCA) was applied on the standardized full NIR spectral data of this region for vegetable oils to totally capture the NIR spectral pattern. Nine varieties of vegetable oils (soybean, corn, cottonseed, olive, rice bran, peanut, rapeseed, sesame and coconut oil) could be successfully classified from their PCA scores. Examining the contribution of wavelengths to PCA scores showed that wavelengths with a high loading weight were assigned to characteristic absorption regions that correspond to specific fatty acid moieties. This classification is related to the fatty acid composition of an oil, and it can be carried out rapidly and easily after eigenvectors were obtained.

关键词： CLASSIFICATION FATTY ACID COMPOSITION NEAR-INFRARED SPECTRA principal-component analysis SPECTROSCOPY VEGETABLE OIL

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FUNCTIONAL CONNECTIVITY - THE principal-component analysis OF LARGE (PET) DATA SETS

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JOURNAL OF CEREBRAL BLOOD FLOW AND METABOLISM 1993年第1期13卷 5-14页

作者： FRISTON, KJ FRITH, CD LIDDLE, PF FRACKOWIAK, RSJ HAMMERSMITH HOSP MRCCYCLOTRON UNITLONDON W12 0HSENGLAND

The distributed brain systems associated with performance of a verbal fluency task were identified in a nondirected correlational analysis of neurophysiological data obtained with positron tomography. This analysis used a recursive principal-component analysis developed specifically for large data sets. This analysis is interpreted in terms of functional connectivity, defined as the temporal correlation of a neurophysiological index measured in different brain areas. The results suggest that the variance in neurophysiological measurements, introduced experimentally, was accounted for by two independent principal components. The first, and considerably larger, highlighted an intentional brain system seen in previous studies of verbal fluency. The second identified a distributed brain system including the anterior cingulate and Wernicke's area that reflected monotonic time effects. We propose that this system has an attentional bias.

关键词： PET principal-component analysis FUNCTIONAL CONNECTIVITY EFFECTIVE CONNECTIVITY VERBAL FLUENCY NEURAL NETWORKS

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APPLICATION OF principal-component analysis TO THE INTERPRETATION OF BROWN-COAL PROPERTIES

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FUEL 1995年第7期74卷 978-982页

作者： TESCH, S OTTO, M Institute for Analytical Chemistry TU Bergakademie Freiberg Leipziger Str. 29 D-09599 Freiberg Federal Republic of Germany

The characterization of coal properties using principal-component analysis is described. The aim is to obtain correlations between a large number of chemical and technological parameters as well as FT-i.r. spectroscopic data. A database on 44 brown coals from different deposits was interpreted. After computation of the principal components, scatterplots and component-weight plots are presented for the first two or three principal components. The overlap of the component-weights plot and the scatterplot (biplot) shows how it is possible to classify brown coals by means of selected characteristics.

关键词： BROWN COALS CLASSIFICATION principal-component analysis

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L1-Norm principal-component analysis of Complex Data

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2018年第12期66卷 3256-3267页

作者： Tsagkarakis, Nicholas Markopoulos, Panos P. Sklivanitis, George Pados, Dimitris A. SUNY Buffalo Dept Elect Engn Buffalo NY 14260 USA Ericsson AB S-41756 Gothenburg Sweden Rochester Inst Technol Dept Elect & Microelect Engn Rochester NY 14623 USA Florida Atlantic Univ Dept Comp & Elect Engn & Comp Sci Boca Raton FL 33431 USA

L1-norm principal-component analysis (L1-PCA) of real-valued data has attracted significant research interest over the past decade. L1-PCA of complex-valued data remains to date unexplored despite the many possible applications (in communication systems, for example). In this paper, we establish theoretical and algorithmic foundations of L1-PCA of complex-valued data matrices. Specifically, we first show that, in contrast to the realvalued case for which an optimal polynomial-cost algorithm was recently reported by Markopoulos, Karystinos, and Pados, complex L1-PCA is formally NP-hard. Then, casting complex L1-PCA as a unimodular optimization problem, we present the first two suboptimal algorithms in the literature for its solution. Extensive experimental studies included in this paper illustrate the sturdy resistance of complex L1-PCA against faulty measurements/outliers in the processed data.

关键词： Data analytics dimensionality reduction erroneous data faulty measurements L1-norm machine learning principal-component analysis outlier resistance

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L1-norm principal-component analysis via Bit Flipping 15

L1-norm Principal-Component Analysis via Bit Flipping

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15th IEEE International Conference on Machine Learning and Applications (ICMLA)

作者： Markopoulos, Panos P. Kundu, Sandipan Chamadia, Shubham Pados, Dimitris A. Rochester Inst Technol Dept Elect & Microelect Engn Rochester NY 14623 USA Qualcomm Technol Inc San Jose CA 95110 USA SUNY Buffalo Dept Elect Engn Buffalo NY 14260 USA

ISBN: (纸本)9781509061679

The K L1-norm principal components (L1-PCs) of a data matrix X is an element of R-DxN can be found optimally with cost O(2(NK)), in the general case, and O(N-rank(X) K (K+1)), when rank(X) is a constant with respect to N [1], [2]. Certainly, in real-world applications where N is large, even the latter polynomial cost is prohibitive. In this work, we present L1BF: a novel, near-optimal algorithm that calculates the K L1PCs of X with cost O(N Dmin{N, D} + N-2 (K-4 + DK2) + DNK3), comparable to that of standard (L2-norm) principal-component analysis. Our numerical studies illustrate that the proposed algorithm attains optimality with very high frequency while, at the same time, it outperforms on the L1-PCA metric any counterpart of comparable computational cost. The outlier-resistance of the L1-PCs calculated by L1-BF is documented with experiments on dimensionality reduction and genomic data classification for disease diagnosis.

关键词： Anomaly detection data contamination genomic data L1 norm L2 norm principal-component analysis outliers singular-value decomposition subspace learning

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Direction Finding by Complex L₁-principal-component analysis 16

Direction Finding by Complex <i>L</i><sub>1</sub>-Principal-...

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16th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Tsagkarakis, Nicholas Markopoulos, Panos P. Pados, Dimitris A. SUNY Buffalo Univ Buffalo Dept Elect Engn Buffalo NY 14260 USA

ISBN: (纸本)9781479919314

In the light of recent developments in optimal real L-1-norm principal-component analysis (PCA), we provide the first algorithm in the literature to carry out L-1-PCA of complex-valued data. Then, we use this algorithm to develop a novel subspace-based direction-of-arrival (DoA) estimation method that is resistant to faulty measurements or jamming. As demonstrated by numerical experiments, the proposed algorithm is as effective as state-of-the-art L-2-norm methods in clean-data environments and significantly superior when operating on corrupted data.

关键词： Antenna arrays data contamination direction of arrival estimation L-1-norm principal-component analysis

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