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Le, Hanh T. Banking & Finance Australian School of Business UNSW. "Discrete PCA: an application to corporate governance research." Awarded by:University of New South Wales. Banking & Finance, 2007. http://handle.unsw.edu.au/1959.4/40753.
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This thesis introduces the application of discrete Principal Component Analysis (PCA) to corporate governance research. Given the presence of many discrete variables in typical governance studies, I argue that this method is superior to standard PCA that has been employed by others working in the area. Using a dataset of 244 companies listed on the London Stock Exchange in the year 2002-2003, I find that Pearson's correlations underestimate the strength of association between two variables, when at least one of them is discrete. Accordingly, standard PCA performed on the Pearson correlation matrix results in biased estimates. Applying discrete PCA on the polychoric correlation matrix, I extract from 28 corporate governance variables 10 significant factors. These factors represent 8 main aspects of the governance system, namely auditor reputation, large shareholder influence, size of board committees, social responsibility, risk optimisation, director independence level, female representation and institutional ownership. Finally, I investigate the relationship between corporate governance and a firm's long-run share market performance, with the former being the factors extracted. Consistent with Demsetz' (1983) argument, I document limited explanatory power for these governance factors.
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Allemang, Matthew R. "Comparison of Automotive Structures Using Transmissibility Functions and Principal Component Analysis." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1367944783.
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Massaro, James. "A PCA based method for image and video pose sequencing /." Online version of thesis, 2010. http://hdl.handle.net/1850/11991.
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Solat, Karo. "Generalized Principal Component Analysis." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/83469.
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The primary objective of this dissertation is to extend the classical Principal Components Analysis (PCA), aiming to reduce the dimensionality of a large number of Normal interrelated variables, in two directions. The first is to go beyond the static (contemporaneous or synchronous) covariance matrix among these interrelated variables to include certain forms of temporal (over time) dependence. The second direction takes the form of extending the PCA model beyond the Normal multivariate distribution to the Elliptically Symmetric family of distributions, which includes the Normal, the Student's t, the Laplace and the Pearson type II distributions as special cases. The result of these extensions is called the Generalized principal component analysis (GPCA).
The GPCA is illustrated using both Monte Carlo simulations as well as an empirical study, in an attempt to demonstrate the enhanced reliability of these more general factor models in the context of out-of-sample forecasting. The empirical study examines the predictive capacity of the GPCA method in the context of Exchange Rate Forecasting, showing how the GPCA method dominates forecasts based on existing standard methods, including the random walk models, with or without including macroeconomic fundamentals.Ph. D.
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Ragozzine, Brett A. "Modeling the Point Spread Function Using Principal Component Analysis." Ohio University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1224684806.
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Renkjumnong, Wasuta. "SVD and PCA in Image Processing." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/math_theses/31.
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The Singular Value Decomposition is one of the most useful matrix factorizations in applied linear algebra, the Principal Component Analysis has been called one of the most valuable results of applied linear algebra. How and why principal component analysis is intimately related to the technique of singular value decomposition is shown. Their properties and applications are described. Assumptions behind this techniques as well as possible extensions to overcome these limitations are considered. This understanding leads to the real world applications, in particular, image processing of neurons. Noise reduction, and edge detection of neuron images are investigated.
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Li, Liubo Li. "Trend-Filtered Projection for Principal Component Analysis." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1503277234178696.
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Nelson, Philip R. C. MacGregor John F. Taylor Paul A. "The treatment of missing measurements in PCA and PLS models /." *McMaster only, 2002.
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Bianchi, Marcelo Franceschi de. "Extração de características de imagens de faces humanas através de wavelets, PCA e IMPCA." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-10072006-002119/.
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Reconhecimento de padrões em imagens é uma área de grande interesse no mundo científico. Os chamados métodos de extração de características, possuem as habilidades de extrair características das imagens e também de reduzir a dimensionalidade dos dados gerando assim o chamado vetor de características. Considerando uma imagem de consulta, o foco de um sistema de reconhecimento de imagens de faces humanas é pesquisar em um banco de imagens, a imagem mais similar à imagem de consulta, de acordo com um critério dado. Este trabalho de pesquisa foi direcionado para a geração de vetores de características para um sistema de reconhecimento de imagens, considerando bancos de imagens de faces humanas, para propiciar tal tipo de consulta. Um vetor de características é uma representação numérica de uma imagem ou parte dela, descrevendo seus detalhes mais representativos. O vetor de características é um vetor n-dimensional contendo esses valores. Essa nova representação da imagem propicia vantagens ao processo de reconhecimento de imagens, pela redução da dimensionalidade dos dados. Uma abordagem alternativa para caracterizar imagens para um sistema de reconhecimento de imagens de faces humanas é a transformação do domínio. A principal vantagem de uma transformação é a sua efetiva caracterização das propriedades locais da imagem. As wavelets diferenciam-se das tradicionais técnicas de Fourier pela forma de localizar a informação no plano tempo-freqüência; basicamente, têm a capacidade de mudar de uma resolução para outra, o que as fazem especialmente adequadas para análise, representando o sinal em diferentes bandas de freqüências, cada uma com resoluções distintas correspondentes a cada escala. As wavelets foram aplicadas com sucesso na compressão, melhoria, análise, classificação, caracterização e recuperação de imagens. Uma das áreas beneficiadas onde essas propriedades tem encontrado grande relevância é a área de visão computacional, através da representação e descrição de imagens. Este trabalho descreve uma abordagem para o reconhecimento de imagens de faces humanas com a extração de características baseado na decomposição multiresolução de wavelets utilizando os filtros de Haar, Daubechies, Biorthogonal, Reverse Biorthogonal, Symlet, e Coiflet. Foram testadas em conjunto as técnicas PCA (Principal Component Analysis) e IMPCA (Image Principal Component Analysis), sendo que os melhores resultados foram obtidos utilizando a wavelet Biorthogonal com a técnica IMPCAImage pattern recognition is an interesting area in the scientific world. The features extraction method refers to the ability to extract features from images, reduce the dimensionality and generates the features vector. Given a query image, the goal of a features extraction system is to search the database and return the most similar to the query image according to a given criteria. Our research addresses the generation of features vectors of a recognition image system for human faces databases. A feature vector is a numeric representation of an image or part of it over its representative aspects. The feature vector is a n-dimensional vector organizing such values. This new image representation can be stored into a database and allow a fast image retrieval. An alternative for image characterization for a human face recognition system is the domain transform. The principal advantage of a transform is its effective characterization for their local image properties. In the past few years researches in applied mathematics and signal processing have developed practical wavelet methods for the multi scale representation and analysis of signals. These new tools differ from the traditional Fourier techniques by the way in which they localize the information in the time-frequency plane; in particular, they are capable of trading on type of resolution for the other, which makes them especially suitable for the analysis of non-stationary signals. The wavelet transform is a set basis function that represents signals in different frequency bands, each one with a resolution matching its scale. They have been successfully applied to image compression, enhancement, analysis, classification, characterization and retrieval. One privileged area of application where these properties have been found to be relevant is computer vision, especially human faces imaging. In this work we describe an approach to image recognition for human face databases focused on feature extraction based on multiresolution wavelets decomposition, taking advantage of Biorthogonal, Reverse Biorthogonal, Symlet, Coiflet, Daubechies and Haar. They were tried in joint the techniques together the PCA (Principal Component Analysis) and IMPCA (Image Principal Component Analysis)
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Anjasmara, Ira Mutiara. "Spatio-temporal analysis of GRACE gravity field variations using the principal component analysis." Thesis, Curtin University, 2008. http://hdl.handle.net/20.500.11937/957.
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Gravity Recovery and Climate Experiment (GRACE) mission has amplified the knowledge of both static and time-variable part of the Earth’s gravity field. Currently, GRACE maps the Earth’s gravity field with a near-global coverage and over a five year period, which makes it possible to apply statistical analysis techniques to the data. The objective of this study is to analyse the most dominant spatial and temporal variability of the Earth’s gravity field observed by GRACE using a combination of analytical and statistical methods such as Harmonic Analysis (HA) and Principal Component Analysis (PCA). The HA is used to gain general information of the variability whereas the PCA is used to find the most dominant spatial and temporal variability components without having to introduce any presetting. The latter is an important property that allows for the detection of anomalous or a-periodic behaviour that will be useful for the study of various geophysical processes such as the effect from earthquakes. The analyses are performed for the whole globe as well as for the regional areas of: Sumatra- Andaman, Australia, Africa, Antarctica, South America, Arctic, Greenland, South Asia, North America and Central Europe. On a global scale the most dominant temporal variation is an annual signal followed by a linear trend. Similar results mostly associated to changing land hydrology and/or snow cover are obtained for most regional areas except over the Arctic and Antarctic where the secular trend is the prevailing temporal variability.Apart from these well-known signals, this contribution also demonstrates that the PCA is able to reveal longer periodic and a-periodic signal. A prominent example for the latter is the gravity signal of the Sumatra-Andaman earthquake in late 2004. In an attempt to isolate these signals, linear trend and annual signal are removed from the original data and the PCA is once again applied to the reduced data. For a complete overview of these results the most dominant PCA modes for the global and regional gravity field solutions are presented and discussed.
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Jot, Sapan. "pcaL1: An R Package of Principal Component Analysis using the L1 Norm." VCU Scholars Compass, 2011. http://scholarscompass.vcu.edu/etd/2488.
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Principal component analysis (PCA) is a dimensionality reduction tool which captures the features of data set in low dimensional subspace. Traditional PCA uses L2-PCA and has much desired orthogonality properties, but is sensitive to outliers. PCA using L1 norm has been proposed as an alternative to counter the effect of outliers. The R environment for statistical computing already provides L2-PCA function prcomp(), but there are not many options for L1 norm PCA methods. The goal of the research was to create one R package with different options of PCA methods using L1 norm. So, we choose three different L1-PCA algorithms: PCA-L1 proposed by Kwak [10], L1-PCA* by Brooks et. al. [1], and L1-PCA by Ke and Kanade [9]; to create a package pcaL1 in R, interfacing with C implementation of these algorithms. An open source software for solving linear problems, CLP, is used to solve the optimization problems for L1-PCA* and L1-PCA. We use this package on human microbiome data to investigate the relationship between people based on colonizing bacteria.
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Anjasmara, Ira Mutiara. "Spatio-temporal analysis of GRACE gravity field variations using the principal component analysis." Curtin University of Technology, Department of Spatial Sciences, 2008. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=18720.
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Gravity Recovery and Climate Experiment (GRACE) mission has amplified the knowledge of both static and time-variable part of the Earth’s gravity field. Currently, GRACE maps the Earth’s gravity field with a near-global coverage and over a five year period, which makes it possible to apply statistical analysis techniques to the data. The objective of this study is to analyse the most dominant spatial and temporal variability of the Earth’s gravity field observed by GRACE using a combination of analytical and statistical methods such as Harmonic Analysis (HA) and Principal Component Analysis (PCA). The HA is used to gain general information of the variability whereas the PCA is used to find the most dominant spatial and temporal variability components without having to introduce any presetting. The latter is an important property that allows for the detection of anomalous or a-periodic behaviour that will be useful for the study of various geophysical processes such as the effect from earthquakes. The analyses are performed for the whole globe as well as for the regional areas of: Sumatra- Andaman, Australia, Africa, Antarctica, South America, Arctic, Greenland, South Asia, North America and Central Europe. On a global scale the most dominant temporal variation is an annual signal followed by a linear trend. Similar results mostly associated to changing land hydrology and/or snow cover are obtained for most regional areas except over the Arctic and Antarctic where the secular trend is the prevailing temporal variability.Apart from these well-known signals, this contribution also demonstrates that the PCA is able to reveal longer periodic and a-periodic signal. A prominent example for the latter is the gravity signal of the Sumatra-Andaman earthquake in late 2004. In an attempt to isolate these signals, linear trend and annual signal are removed from the original data and the PCA is once again applied to the reduced data. For a complete overview of these results the most dominant PCA modes for the global and regional gravity field solutions are presented and discussed.
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Yang, Libin. "An Application of Principal Component Analysis to Stock Portfolio Management." Thesis, University of Canterbury. Department of economics and finance, 2015. http://hdl.handle.net/10092/10293.
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This thesis investigates the application of principal component analysis to the Australian stock market using ASX200 index and its constituents from April 2000 to February 2014. The first ten principal components were retained to present the major risk sources in the stock market. We constructed portfolio based on each of the ten principal components and named these “principal portfolios
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Balasubramanian, Vijay. "Variance reduction and outlier identification for IDDQ testing of integrated chips using principal component analysis." Texas A&M University, 2006. http://hdl.handle.net/1969.1/4766.
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Integrated circuits manufactured in current technology consist of millions of
transistors with dimensions shrinking into the nanometer range. These small transistors
have quiescent (leakage) currents that are increasingly sensitive to process variations,
which have increased the variation in good-chip quiescent current and consequently
reduced the effectiveness of IDDQ testing. This research proposes the use of a multivariate
statistical technique known as principal component analysis for the purpose of variance
reduction. Outlier analysis is applied to the reduced leakage current values as well as the
good chip leakage current estimate, to identify defective chips. The proposed idea is
evaluated using IDDQ values from multiple wafers of an industrial chip fabricated in 130
nm technology. It is shown that the proposed method achieves significant variance
reduction and identifies many outliers that escape identification by other established
techniques. For example, it identifies many of the absolute outliers in bad
neighborhoods, which are not detected by Nearest Neighbor Residual and Nearest
Current Ratio. It also identifies many of the spatial outliers that pass when using Current
Ratio. The proposed method also identifies both active and passive defects.
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Gonzalez, Nicolas Alejandro. "Principal Components Analysis, Factor Analysis and Trend Correlations of Twenty-Eight Years of Water Quality Data of Deer Creek Reservoir, Utah." BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3309.
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I evaluated twenty-eight years (1980-2007) of spatial-temporal water quality data from Deer Creek Reservoir in Utah. The data came from three sampling points representing the lotic, transitional and lentic zones. The data included measurements of climatological, hydrological and water quality conditions at four depths; Surface, Above Thermocline, Below Thermocline and Bottom. The time frame spanned dates before and after the completion of the Jordanelle Reservoir (1987-1992), approximately fourteen miles upstream of Deer Creek. I compared temporal groupings and found that a traditional month distribution following standard seasons was not effective in characterizing the measured conditions; I developed a more representative seasonal grouping by performing a Tukey-Kramer multiple comparisons adjustment and a Bonferronian correction of the Student's t comparison. Based on these analyses, I determined the best groupings were Cold (December - April), Semi-Cold (May and November), Semi-Warm (June and October), Warm (July and September) and Transition (August). I performed principal component analysis (PCA) and factor analysis (FA) to determine principal parameters associated with the variability of the water quality of the reservoir. These parameters confirmed our seasonal groups showing the Cold, Transition and Warm seasons as distinct groups. The PCA and FA showed that the variables that drive most of the variability in the reservoir are specific conductivity and variables related with temperature. The PCA and FA showed that the reservoir is highly variable. The first 3 principal components and rotated factors explained a cumulative 59% and 47%, respectively of the variability in Deer Creek. Both parametric and nonparametric approaches provided similar correlations but the evaluations that included censored data (nutrients) were considerably different with the nonparametric approach being preferred.
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Aguirre, Jurado Ricardo. "Resilient Average and Distortion Detection in Sensor Networks." ScholarWorks@UNO, 2009. http://scholarworks.uno.edu/td/962.
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In this paper a resilient sensor network is built in order to lessen the effects of a small portion of corrupted sensors when an aggregated result such as the average needs to be obtained. By examining the variance in sensor readings, a change in the pattern can be spotted and minimized in order to maintain a stable aggregated reading. Offset in sensors readings are also analyzed and compensated to help reduce a bias change in average. These two analytical techniques are later combined in Kalman filter to produce a smooth and resilient average given by the readings of individual sensors. In addition, principal components analysis is used to detect variations in the sensor network. Experiments are held using real sensors called MICAz, which are use to gather light measurements in a small area and display the light average generated in that area.
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Marques, Miguel Alexandre Castanheira. "On-line system for faults detection in induction motors based on PCA." Master's thesis, Faculdade de Ciências e Tecnologia, 2012. http://hdl.handle.net/10362/8578.
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Dissertation to obtain the degree of Master in Electrical and Computer EngineeringNowadays in the industry there many processes where human intervention is replaced by electrical machines, especially induction machines due to his robustness, performance and low cost. Although, induction machines are a high reliable device, they are also susceptible to faults. Therefore, the study of induction machine state is essential to reduce human and financial costs. The faults in induction machines can be divided mainly into two types: electrical faults and mechanical faults. Electrical faults represent between 40% and 50% of the reported faults and can be divided essentially in 2 types: stator unbalances and broken rotor bars. Taking into account the high dependency of induction machines and the massive use of automatic processes the industrial level, it is necessary to have diagnostic and monitoring systems these machines. It is presented in this work an on-line system for detection and diagnosis of electrical faults in induction motors based on computer-aided monitoring of the supply currents. The main objective is to detect and identify the presence of broken rotor bars and stator short-circuits in the induction motor. The presence of faults in the machine causes different disturbances in the supply currents. Through a stationary reference frame, such as αβ transform it is possible to extract and manipulate the results obtained from the supply currents using Eigen decomposition.
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Alberi, Matteo. "La PCA per la riduzione dei dati di SPHERE IFS." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/6563/.
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Nel primo capitolo di questa tesi viene presentata una panoramica dei principali metodi di rivelazione degli esopianeti: il metodo della Velocità Radiale, il metodo Astrometrico, il metodo del Pulsar Timing, il metodo del Transito, il metodo del Microlensing ed infine il metodo del Direct Imaging che verrà approfondito nei capitoli successivi. Nel secondo capitolo vengono presentati i principi della diffrazione, viene mostrato come attenuare la luce stellare con l'uso del coronografo; vengono descritti i fenomeni di aberrazione della luce provocati dalla strumentazione e dagli effetti distorsivi dell'atmosfera che originano le cosiddette speckle; vengono poi presentate le moderne soluzioni tecniche come l'ottica attiva e adattiva, che hanno permesso un considerevole miglioramento della qualità delle osservazioni. Nel terzo capitolo sono illustrate le tecniche di Differential Imaging che permettono di rimuovere efficacemente le speckle e di migliorare il contrasto delle immagini. Nel quarto viene presentata una descrizione matematica della Principal Component Analysis (Analisi delle Componenti Principali), il metodo statistico utilizzato per la riduzione dei dati astronomici. Il quinto capitolo è dedicato a SPHERE, lo strumento progettato per il Very Large Telescope (VLT), in particolare viene descritto il suo spettrografo IFS con il quale sono stati ottenuti, nella fase di test, i dati analizzati nel lavoro di tesi. Nel sesto capitolo vengono mostrate le procedure di riduzione dati e l'applicazione dell'algoritmo di IDL LA_SVD che applica la Principal Component Analysis e ha permesso, analogamente ai metodi di Differenzial Imaging visti in precedenza, di rimuovere le speckle e migliorare il contrasto delle immagini. Nella parte conclusiva, vengono discussi i risultati.
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Landgraf, Andrew J. "Generalized Principal Component Analysis: Dimensionality Reduction through the Projection of Natural Parameters." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1437610558.
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Sanderson, Conrad, and conradsand@ieee org. "Automatic Person Verification Using Speech and Face Information." Griffith University. School of Microelectronic Engineering, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20030422.105519.
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Identity verification systems are an important part of our every day life. A typical example is the Automatic Teller Machine (ATM) which employs a simple identity verification scheme: the user is asked to enter their secret password after inserting their ATM card; if the password matches the one prescribed to the card, the user is allowed access to their bank account. This scheme suffers from a major drawback: only the validity of the combination of a certain possession (the ATM card) and certain knowledge (the password) is verified. The ATM card can be lost or stolen, and the password can be compromised. Thus new verification methods have emerged, where the password has either been replaced by, or used in addition to, biometrics such as the persons speech, face image or fingerprints. Apart from the ATM example described above, biometrics can be applied to other areas, such as telephone & internet based banking, airline reservations & check-in, as well as forensic work and law enforcement applications. Biometric systems based on face images and/or speech signals have been shown to be quite effective. However, their performance easily degrades in the presence of a mismatch between training and testing conditions. For speech based systems this is usually in the form of channel distortion and/or ambient noise; for face based systems it can be in the form of a change in the illumination direction. A system which uses more than one biometric at the same time is known as a multi-modal verification system; it is often comprised of several modality experts and a decision stage. Since a multi-modal system uses complimentary discriminative information, lower error rates can be achieved; moreover, such a system can also be more robust, since the contribution of the modality affected by environmental conditions can be decreased. This thesis makes several contributions aimed at increasing the robustness of single- and multi-modal verification systems. Some of the major contributions are listed below. The robustness of a speech based system to ambient noise is increased by using Maximum Auto-Correlation Value (MACV) features, which utilize information from the source part of the speech signal. A new facial feature extraction technique is proposed (termed DCT-mod2), which utilizes polynomial coefficients derived from 2D Discrete Cosine Transform (DCT) coefficients of spatially neighbouring blocks. The DCT-mod2 features are shown to be robust to an illumination direction change as well as being over 80 times quicker to compute than 2D Gabor wavelet derived features. The fragility of Principal Component Analysis (PCA) derived features to an illumination direction change is solved by introducing a pre-processing step utilizing the DCT-mod2 feature extraction. We show that the enhanced PCA technique retains all the positive aspects of traditional PCA (that is, robustness to compression artefacts and white Gaussian noise) while also being robust to the illumination direction change. Several new methods, for use in fusion of speech and face information under noisy conditions, are proposed; these include a weight adjustment procedure, which explicitly measures the quality of the speech signal, and a decision stage comprised of a structurally noise resistant piece-wise linear classifier, which attempts to minimize the effects of noisy conditions via structural constraints on the decision boundary.
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FANEGAN, JULIUS BOLUDE. "A FUZZY MODEL FOR ESTIMATING REMAINING LIFETIME OF A DIESEL ENGINE." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1188951646.
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Yu, Xiaoqian. "The Impact of Latency Jitter on the Interpretation of P300 in the Assessment of Cognitive Function." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6443.
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When stimuli processing time varies in an oddball paradigm, the latency of the P300 will vary across trials. In an oddball task requiring difficult response selections, as the variation of stimuli processing time increases, so does the variation of the P300 latency, causing latency jitters in the measurement. Averaging the P300 across different trials without adjusting this latency jitter will lead to diminished P300 amplitude, resulting in inaccurate conclusions from the data. Verleger et al. (2014) reported a diminished P300 amplitude in a difficult oddball task that required subjects to make response selections among stimuli that are difficult to distinguish, but his work did not correct for any latency jitter observed within his sample. The current study replicated the easy and hard oddball tasks conducted in Verleger et al.. Raw ERPs obtained from 16 subjects indicated a successful replication of the study. An examination of the behavioral data showed that there was substantial variation in the P300 during the hard oddball tasks, and a latency jitter correction was applied in the analysis. Results indicated that there was a significant increase in the amplitude of P300 after latency jitter correction, and that this P300 amplitude did not differ significantly between easy and hard oddball tasks. These results suggest that difficult decision requirement does not reduce the amplitude of the P300, and that latency jitter should be accounted for when analyzing data from tasks involving a difficult decision requirement.
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Strindlund, Olle. "Evaluation of Homogeneity in Drug Seizures Using Near-Infrared (NIR) Hyperspectral Imaging and Principal Component Analysis (PCA)." Thesis, Linköpings universitet, Kemi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166747.
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The selection of a representative sample is a delicate problem when drug seizures comprised of large number of units arrive at the Swedish National Forensic Centre (NFC). If deviating objects in the selected sample size are found, additional analyzes are required to investigate how representative the results are for the entire population. This generates further pressure on operational analysis flow. With the goal to provide a tool which forensic scientists at NFC can base their assessment of the representative nature of the selected sampling of large drug seizures on, this project investigated the possibilities of evaluating the level of homogeneity in drug seizures using near-infrared (NIR) hyperspectral imaging along with principal component analysis (PCA). A total of 27 sample groups (homogeneous, heterogeneous and seized sample groups) were analyzed and different predictive models were developed. The models were either based on quantifying the variation in NIR spectra or in PCA scores plots. It was shown that in the spectral range of 1300-2000 nm, using a pre-processing combination of area normalization, quadratic (second polynomial) detrending and mean centering, promising predictive abilities of the models in their evaluation of the level of homogeneity in drug seizures were achieved. A model where the approximated signal-dependent variation was related to the quotient of significant and noise explained variance given by PCA indicated most promising predictive abilities when quantifying the variation in NIR spectra. Similarly, a model where a rectangular area, defined by the maximum distances along PC1 and PC2, was related to the cumulative explained variance of the two PCs showed most promising predictive abilities when quantifying the variation in PCA scores plots. Different zones for which within sample groups are expected to appear based upon their degree of homogeneity could be established for both models. The two models differed in sensitivity. However, more comprehensive studies are required to evaluate the models applicability from an operational point-of-view.
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You, Xiaozhen. "Principal Component Analysis and Assessment of Language Network Activation Patterns in Pediatric Epilepsy." FIU Digital Commons, 2010. http://digitalcommons.fiu.edu/etd/176.
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This dissertation establishes a novel data-driven method to identify language network activation patterns in pediatric epilepsy through the use of the Principal Component Analysis (PCA) on functional magnetic resonance imaging (fMRI). A total of 122 subjects’ data sets from five different hospitals were included in the study through a web-based repository site designed here at FIU. Research was conducted to evaluate different classification and clustering techniques in identifying hidden activation patterns and their associations with meaningful clinical variables. The results were assessed through agreement analysis with the conventional methods of lateralization index (LI) and visual rating. What is unique in this approach is the new mechanism designed for projecting language network patterns in the PCA-based decisional space. Synthetic activation maps were randomly generated from real data sets to uniquely establish nonlinear decision functions (NDF) which are then used to classify any new fMRI activation map into typical or atypical. The best nonlinear classifier was obtained on a 4D space with a complexity (nonlinearity) degree of 7. Based on the significant association of language dominance and intensities with the top eigenvectors of the PCA decisional space, a new algorithm was deployed to delineate primary cluster members without intensity normalization. In this case, three distinct activations patterns (groups) were identified (averaged kappa with rating 0.65, with LI 0.76) and were characterized by the regions of: 1) the left inferior frontal Gyrus (IFG) and left superior temporal gyrus (STG), considered typical for the language task; 2) the IFG, left mesial frontal lobe, right cerebellum regions, representing a variant left dominant pattern by higher activation; and 3) the right homologues of the first pattern in Broca's and Wernicke's language areas. Interestingly, group 2 was found to reflect a different language compensation mechanism than reorganization. Its high intensity activation suggests a possible remote effect on the right hemisphere focus on traditionally left-lateralized functions. In retrospect, this data-driven method provides new insights into mechanisms for brain compensation/reorganization and neural plasticity in pediatric epilepsy.
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Surtees, Alexander Peter Harrison. "Development of geochemical identification and discrimination by Raman spectroscopy : the development of Raman spectroscopic methods for application to whole soil analysis and the separation of volcanic ashes for tephrachronology." Thesis, University of Bradford, 2015. http://hdl.handle.net/10454/14409.
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Geochemistry plays a vital role in our understanding mechanisms behind major geological systems such as the Earth's crust and its oceans (Albarède, F. 2003). More recently, geo-chemistry has played a vital role in the field of forensic investigation and in period dating. Forensic soil samples have been traditionally analysed via examinations of colour, texture and mineral content by physical or chemical methods. However, these methods leave any organic or water-soluble fractions unexamined. Tephrochronology (the dating of sedimentary sequences using volcanic ash layers) is an important tool for the dating and correlation of sedimentary sequences containing archives and proxies of past environmental change. Its importance in this area has increased since the increased free carbon in out atmosphere has made radio-carbon dating unreliable. Tephrochronology requires successful geo-chemical identification of the tephras, a method reliant on electron probe micro-analysis (EPMA) to analyse major element composition. However, it is often impossible to differentiate key tephra layers using EPMA alone. Raman spectroscopy is commonly used in chemistry, since vibrational information is specific to the chemical bonds and symmetry of molecules, and can provide a fingerprint by which these can be identified. Here, we demonstrate how Raman spectroscopy can be used for the successful discrimination of mineral species in tephra through the analysis of individual glass shards. We further demonstrate how, with the use of oxidative preparation methods, Raman spectroscopy can be used to successfully discriminate between soil types using mineralogy as well as the organic and water-soluble fractions of soils.
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Surtees, Alexander P. H. "Development of geochemical identification and discrimination by Raman spectroscopy. The development of Raman spectroscopic methods for application to whole soil analysis and the separation of volcanic ashes for tephrachronology." Thesis, University of Bradford, 2015. http://hdl.handle.net/10454/14409.
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Geochemistry plays a vital role in our understanding mechanisms behind major geological systems such as the Earth's crust and its oceans (Albarède, F. 2003). More recently, geo-chemistry has played a vital role in the field of forensic investigation and in period dating. Forensic soil samples have been traditionally analysed via examinations of colour, texture and mineral content by physical or chemical methods. However, these methods leave any organic or water-soluble fractions unexamined.
Tephrochronology (the dating of sedimentary sequences using volcanic ash layers) is an important tool for the dating and correlation of sedimentary sequences containing archives and proxies of past environmental change. Its importance in this area has increased since the increased free carbon in out atmosphere has made radio-carbon dating unreliable. Tephrochronology requires successful geo-chemical identification of the tephras, a method reliant on electron probe micro-analysis (EPMA) to analyse major element composition. However, it is often impossible to differentiate key tephra layers using EPMA alone.
Raman spectroscopy is commonly used in chemistry, since vibrational information is specific to the chemical bonds and symmetry of molecules, and can provide a fingerprint by which these can be identified. Here, we demonstrate how Raman spectroscopy can be used for the successful discrimination of mineral species in tephra through the analysis of individual glass shards. We further demonstrate how, with the use of oxidative preparation methods, Raman spectroscopy can be used to successfully discriminate between soil types using mineralogy as well as the organic and water-soluble fractions of soils.
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Radjabi, Ryan F. "WILDFIRE DETECTION SYSTEM BASED ON PRINCIPAL COMPONENT ANALYSIS AND IMAGE PROCESSING OF REMOTE-SENSED VIDEO." DigitalCommons@CalPoly, 2016. https://digitalcommons.calpoly.edu/theses/1621.
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Early detection and mitigation of wildfires can reduce devastating property damage, firefighting costs, pollution, and loss of life. This thesis proposes the method of Principal Component Analysis (PCA) of images in the temporal domain to identify a smoke plume in wildfires. Temporal PCA is an effective motion detector, and spatial filtering of the output Principal Component images can segment the smoke plume region. The effective use of other image processing techniques to identify smoke plumes and heat plumes are compared. The best attributes of smoke plume detectors and heat plume detectors are evaluated for combination in an improved wildfire detection system. PCA of visible blue images at an image sampling rate of 2 seconds per image effectively exploits a smoke plume signal. PCA of infrared images is the fundamental technique for exploiting a heat plume signal. A system architecture is proposed for the implementation of image processing techniques. The real-world deployment and usability are described for this system.
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Rodeia, José Pedro dos Santos. "Analysis and recognition of similar environmental sounds." Master's thesis, FCT - UNL, 2009. http://hdl.handle.net/10362/2305.
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Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia InformáticaHumans have the ability to identify sound sources just by hearing a sound. Adapting the same problem to computers is called (automatic) sound recognition. Several sound recognizers have been developed throughout the years. The accuracy provided by these recognizers is influenced by the features they use and the classification method implemented. While there are many approaches in sound feature extraction and in sound classification, most have been used to classify sounds with very different characteristics. Here, we implemented a similar sound recognizer. This recognizer uses sounds with very similar properties making the recognition process harder. Therefore, we will use both temporal and spectral properties of the sound. These properties will be extracted using the Intrinsic Structures Analysis (ISA) method, which uses Independent Component Analysis and Principal Component Analysis. We will implement the classification method based on k-Nearest Neighbor algorithm. Here we prove that the features extracted in this way are powerful in sound recognition. We tested our recognizer with several sets of features the ISA method retrieves, and achieved great results. We, finally, did a user study to compare human performance distinguishing similar sounds against our recognizer. The study allowed us to conclude the sounds are in fact really similar and difficult to distinguish and that our recognizer has much more ability than humans to identify them.
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Gomes, Róbson Koszeniewski. "Uma Abordagem para Detecção Automática de Planos em Modelos Digitais de Afloramentos Baseada em PCA." Universidade do Vale do Rio dos Sinos, 2014. http://www.repositorio.jesuita.org.br/handle/UNISINOS/4465.
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PROCERGS - Companhia de Processamento Dados do Estado Rio Grande Sul
A coleta de dados espaciais tem sido intensamente empregada na área geológica, através da técnica de LIDAR (Light Detection and Ranging). Este tipo de sensoriamento digital remoto de alta resolução e precisão, resulta em modelos digitais 3D que permitem uma análise mais detalhada e quantitativa de estruturas heterôgeneas, como afloramentos. Um dos estudos realizados pelos geólogos são análises sobre a geometria da formação de rochas, onde a informação de orientação de um plano inclinado é um indicativo para a compreensão global da estrutura. Este trabalho propõe a utilização da técnica de Análise de Componentes Principais (PCA) para calcular e detectar automaticamente todos os planos em uma nuvem de pontos. Uma ferramenta foi construída para implementar a visualização do modelo digital e apurar os melhores planos. Um estudo foi realizado a fim de validar as informações encontradas pelo método proposto e dados medidos em campo.
The use of LIDAR (Light Detection and Ranging) systems for gathering spatial data has been extensively used in geological studies. This type of digital remote sensing delivers high resolution and accuracy, resulting in 3D digital models which allow a more detailed and quantitative analysis of heterogeneous structures, as outcrops. One of the studies is based on analysis of the forming rocks geometry. The orientation of a slope plane is an indication for the overall undestanding of the structure. This work proposes a new method to automatically compute and detect all possible planes in a point cloud, based on Principal Component Analysis (PCA) technique. A software tool was constructed to implement the digital model visualization and compute the best planes. A study was conducted to compare and validate the results of the method and the field data collected.
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Britto, Rodrigo da Silva. "Detecção de falhas com PCA e PLS aplicados a uma planta didática." Pós-Graduação em Engenharia Elétrica, 2014. https://ri.ufs.br/handle/riufs/5012.
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A fault monitoring system in general, which, beside the detection, isolation, diagnosis and fault recuperation steps is a research area of great interest, since the fault occurrence may lead to negative consequences on different levels on social, economical and environmental bases. With the increasing complexity of the industrial process, it is often necessary a quick detection leading to an optimized fault management system and therefore avoiding the loss of material and human resources. This work develops a study on fault detection statistical techniques applied to a didactic plant. The didactic plant deployed in this study comprises a controlled simple industrial process. For the fault detection in this process it were applied the main statistical methods: Principal Component Analysis (PCA) and Partial Least Squares (PLS). Those methods were implemented and applied on the process aiming a comparative analysis concerning themselves. As a result, the methods were able to detect every kind of emulated fault, with little or none detection delay and with similar performances.Um sistema de monitoramento de falhas em geral, que além da detecção inclui etapas de isolamento, diagnóstico e recuperação das falhas, é uma área de pesquisa de grande interesse, uma vez que a ocorrência de falhas pode ter consequências negativas em diversos níveis, com impactos socioeconômicos e ambientais. Em processos industriais cada vez mais complexos, é necessária uma rápida detecção de falhas, exigindo um sistema de gerenciamento de falhas otimizado, de modo a evitar perdas de recursos materiais e humanos. Este trabalho desenvolve um estudo sobre técnicas estatísticas de detecção de falhas aplicadas numa planta didática. A planta didática empregada no estudo compreende um processo industrial simples controlado. Para a detecção das falhas nesse processo, foram aplicados os principais métodos estatísticos: Análise de Componentes Principais (PCA) e Mínimos Quadrados Parciais (PLS). Estes métodos foram implementados e aplicados ao processo objetivando uma análise comparativa entre os mesmos. Como resultado, os métodos foram capazes de detectar todos os diferentes tipos de falhas emuladas, com pouco ou nenhum atraso na detecção e com desempenhos similares.
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Sousa, Patrícia Ferreira Cunha [UNESP]. "Avaliação de laranjeiras doces quanto à qualidade de frutos, períodos de maturação e resistência a Guignardia citricarpa." Universidade Estadual Paulista (UNESP), 2009. http://hdl.handle.net/11449/102823.
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sousa_pfc_dr_jabo.pdf: 387633 bytes, checksum: 521ab7a95343ec6b201a18d943d41027 (MD5)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Apesar de sua importância comercial, o número de variedades de laranjas é muito restrito no Brasil. Os Bancos de Germoplasmas de citros possuem grande número de genótipos de laranjas doces para serem explorados e avaliados quanto aos aspectos botânicos, genéticos e agronômicos, visando elevar a variabilidade genética e as qualidades agronômicas das cultivares. Como parte desse trabalho, avaliou-se 58 genótipos de laranjeiras doces em relação aos caracteres físicos, visando mercado in natura por meio de 9 caracteres físicos (diâmetro, perímetro, altura e peso dos frutos, espessuras da casca, albedo e polpa e número de sementes) e 7 caracteres visando qualidade industrial (acidez total titulável, sólidos solúveis totais, “ratio”, peso dos frutos, rendimento de suco, ácido ascórbico e índice tecnológico= kg sólidos solúveis/40,8kg). A análise multivariada indicou a existência de variabilidade entre os genótipos em relação aos caracteres físicos visando mercado in natura e qualidade industrial. Dois componentes principais, com autovalores > 1, representaram 66,03% da variância total para os caracteres físicos. As variáveis com maior poder discriminatório na primeira componente principal foram: diâmetro, perímetro, peso e altura dos frutos. Os escores desse componente foram designados MI-CP1 (mercado in natura), e os genótipos com os maiores valores foram os mais indicados para o mercado de fruta fresca. Na segunda componente principal, as variáveis mais discriminantes foram espessura do endocarpo e rendimento de suco, cujos escores foram nomeados (S-CP2), caracteres físicos esses ideais para a qualidade industrial. Nos escores dos dois componentes principais (MI-CP1 e S-CP2), o genótipo 22- ‘Lanelate’ foi destaque, seguido por 43-Telde, 39-Rotuna, 44-Torregrossa, 46-Tua Mamede e 17-Grada. Quanto às avaliações visando qualidade industrial...
Although its commercial importance, the number of you cultivate of oranges it is very restricted in Brazil. The Banks of Germoplasmas of citros possess innumerable accesses of oranges candies to be explored and evaluated how much to the botanical, genetic and agronomics aspects, aiming at to raise the genetic variability and the agronomics qualities cultivating of them. As part of that work, was sought to evaluate 58 genotypes of sweet orange trees in relation to the physical characters, seeking market in nature and industry quality, through 9 physical characters (diameter, perimeter, height and weight of the fruits, thickness of the peel, albedo and pulp and number of seeds) and 7 characters seeking industrial quality (acidity total titillate, total soluble solids, ratio , weight of the fruits, juice revenue, ascorbic acid and technological index = kg solid solutes/40,8kg). The analysis multivariate indicated the variability existence among the genotypes in relation to the physical characters and industrial quality. Two main components, with autovalues> 1, they represented 66,03% of the total variance for the physical characters. The variables with larger power discriminate in the first main component were: diameter, perimeter, weight and height of the fruits; we named the scores of that component of MI-CP1 (market in nature), genotypes with the largest values were the most suitable to the market of fresh fruit; in the second main component the variables more discriminate were thickness of the endocarp and juice revenue, it was named (S-CP2), characters physical ideas for the industrial quality. In the scores of the two main components (MI-CP1 and S-CP2), the genotype 22-Lanelate was prominence, followed for 43-Telde, 39-Rotuna, 44- Torregrossa, 46-Tua Mamede and it 17-Grada. How much to the evaluations aiming at industrial quality (INDUST-CP1), had been distinguished: ...(Complete abstract click electronic access below)
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Veverka, Vojtěch. "Automatické rozměření vícesvodových EKG signálů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-316834.
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This semester thesis is focused on automated measurement of ECG signal. The theoretical part describes the rise and options ECG signal. Furthermore, the issue is staged principal components analysis, whose output is used as input signal for seasons. They describe the basic methods used in measurement to ECG signal. The practical part is designed in measurement algorithm for ECG signal that has been tested on basic CSE database. The results are discussed in the conclusion.
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Hassling, Andreas, and Simon Flink. "SYSTEM IDENTIFICATION OF A WASTE-FIRED CFB BOILER : Using Principal Component Analysis (PCA) and Partial Least Squares Regression modeling (PLS-R)." Thesis, Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-34979.
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Heat and electricity production along with waste management are two modern day challenges for society. One of the possible solution to both of them is the incineration of household waste to produce heat and electricity. Incineration is a waste-to-energy treatment process, which can reduce the need for landfills and save the use of more valuable fuels, thereby conserving natural resources. This report/paper investigates the performance and emissions of a municipal solid waste (MSW) fueled industrial boiler by performing a system identification analysis using Principle Component Analysis (PCA) and Partial Least Squares Regression (PLS-R) modeling. The boiler is located in Västerås, Sweden and has a maximum capacity of 167MW. It produces heat and electricity for the city of Västerås and is operated by Mälarenergi AB. A dataset containing 148 different boilers variables, measured with a one hour interval over 2 years, was used for the system identification analysis. The dataset was visually inspected to remove obvious outliers before beginning the analysis using a multivariate data analysis software called The Unscrambler X (Version 10.3, CAMO Software, Norway). Correlations found using PCA was taken in account during the PLSR modelling where models were created for one response each. Some variables had an unexpected impact on the models while others were fully logical regarding combustion theory. Results found during the system analysis process are regarded as reliable. Any errors may be due to outlier data points and model inadequacies.
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Edberg, Alexandra. "Monitoring Kraft Recovery Boiler Fouling by Multivariate Data Analysis." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230906.
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This work deals with fouling in the recovery boiler at Montes del Plata, Uruguay. Multivariate data analysis has been used to analyze the large amount of data that was available in order to investigate how different parameters affect the fouling problems. Principal Component Analysis (PCA) and Partial Least Square Projection (PLS) have in this work been used. PCA has been used to compare average values between time periods with high and low fouling problems while PLS has been used to study the correlation structures between the variables and consequently give an indication of which parameters that might be changed to improve the availability of the boiler. The results show that this recovery boiler tends to have problems with fouling that might depend on the distribution of air, the black liquor pressure or the dry solid content of the black liquor. The results also show that multivariate data analysis is a powerful tool for analyzing these types of fouling problems.Detta arbete handlar om inkruster i sodapannan pa Montes del Plata, Uruguay. Multivariat dataanalys har anvands for att analysera den stora datamangd som fanns tillganglig for att undersoka hur olika parametrar paverkar inkrusterproblemen. Principal·· Component Analysis (PCA) och Partial Least Square Projection (PLS) har i detta jobb anvants. PCA har anvants for att jamfora medelvarden mellan tidsperioder med hoga och laga inkrusterproblem medan PLS har anvants for att studera korrelationen mellan variablema och darmed ge en indikation pa vilka parametrar som kan tankas att andras for att forbattra tillgangligheten pa sodapannan. Resultaten visar att sodapannan tenderar att ha problem med inkruster som kan hero pa fdrdelningen av luft, pa svartlutens tryck eller pa torrhalten i svartluten. Resultaten visar ocksa att multivariat dataanalys ar ett anvandbart verktyg for att analysera dessa typer av inkrusterproblem.
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Xian, Qing. "Statistical Assessment of Hydrochemical Characteristics of Streams and Rivers in Eastern New England." Thesis, Boston College, 2009. http://hdl.handle.net/2345/1364.
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Thesis advisor: Rudolph HonThis study characterizes the current state of water quality of surface streams and rivers in the eastern New England region. A set of water quality data for nine rivers, part of the USGS National Water-Quality Assessment (NAWQA) Program was statistically evaluated to identify natural and anthropogenic persistent influential factors on water quality in surface waters. Binary analysis and multivariate analysis, mainly Principal Component Analysis (PCA) and Factor Analysis (FA) were applied to determine the least number of independent relationships among multiple chemical components in the data set. Statistical results show that in eight of the nine rivers included in this study, four principal components can explain about 80% of the total variance of the original data. The most significant contributing factors can be identified with: (1) chemical weathering; (2) road salt applications; (3) nutrient cycling; and (4) agricultural/waste water
Thesis (MS) — Boston College, 2009
Submitted to: Boston College. Graduate School of Arts and Sciences
Discipline: Geology and Geophysics
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Razifar, Pasha. "Novel Approaches for Application of Principal Component Analysis on Dynamic PET Images for Improvement of Image Quality and Clinical Diagnosis." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-6053.
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Iriarte, Martel Jorge Hugo [UNESP]. "Caracterização de germoplasma de pupunha (Bactris gasipaes Kunth) por descritores morfológicos." Universidade Estadual Paulista (UNESP), 2002. http://hdl.handle.net/11449/102849.
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martel_jhi_dr_jabo.pdf: 1142341 bytes, checksum: d5876bdaae6caa03d42f9ab8eff903df (MD5)A pupunheira tem um potencial econômico e social muito grande, sendo a palmeira mais importante na América pré-colombiana, constituindo junto com o milho e a mandioca, a base da alimentação dos povos primitivos. Os principais produtos extraídos são o palmito e os frutos para o consumo humano direto, alimento animal, farinhas para consumo humano e óleo vegetal. Os objetivos do presente trabalho foram de utilizar uma lista de descritores morfológicos recomendada, para discriminar primeiramente as raças Pará e Putumayo e após sua validação estatística, verificar também a existência da raça Solimões, que até hoje tem sido negada. Foram aplicadas técnicas estatísticas univariadas e multivariadas na tentativa de discriminar as raças. Dos 42 descritores iniciais, 25 apresentaram diferenças significativas entre as raças e 15 tiveram aproximação normal. A análise discriminante mostrou que a raça Pará possuía 15% das plantas mal classificadas e Putumayo 14%, já com a seleção de desenvolmer para componentes principais, as percentagens foram 9 e 19%, respectivamente, para as duas raças. A população de Manacapuru, não formou grupo nas duas primeiras análises de agrupamento e nem com componentes principais. As três análises em conjunto, conseguiram discriminar as raças Pará, Putumayo e Solimões, sendo os descritores mais importantes nesta discriminação e classificação das raças: número de espigas por cacho, comprimento da ráquis, peso dos frutos, espessura das cascas, facilidade para descascar os frutos, peso das cascas, sabor dos frutos, espessura da polpa, distância morfológica dos frutos e peso das sementes.
The peach palm has a economic and social potential very great being the palm most important in the América pre-Colombian, contribuiting together with the maize and the cassava in the indenous feeds. The target of the present work was: to use a morphological descriptor list recommended, to discriminate between two landraces and descriptors validation , to verify the existence of solimoes landraces. Univariated and multivariated statistical techniques were used to attemp discriminate the landraces. Form fort yone initial descriptors, twenty five had presented significant difference between the landraces and fifteen had presented normal approach. The discriminant analysis have showed that Pará landrace possessed fifteen percent of the plant badly c1assified and Putumayo about fourteen percent to it. In the analysis of principal component, the percentages were nine and nineteen percent, respectively, for the two landraces. Manacapuru population did not form c1usterin in the two first one analysis of and nor with principal components. Three joint analysis in the set had obtained to discriminate the Pará, Putumayo and Solimoes landraces and the discrimnant analysis with three landraces, c1assified Manacapuru of the Putumayo landrace inside. The most important descriptors in the discrimination between landraces were: numbers of ears per raceme, raquis length, fruit weight, thickness of fruits bark, facility to peel fruits, weight of fruit bark, fruit flavor, pulp thickness, morphological distance between fruits and seed weight.
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Stark, Love. "Outlier detection with ensembled LSTM auto-encoders on PCA transformed financial data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-296161.
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Financial institutions today generate a large amount of data, data that can contain interesting information to investigate to further the economic growth of said institution. There exists an interest in analyzing these points of information, especially if they are anomalous from the normal day-to-day work. However, to find these outliers is not an easy task and not possible to do manually due to the massive amounts of data being generated daily. Previous work to solve this has explored the usage of machine learning to find outliers in these financial datasets. Previous studies have shown that the pre-processing of data usually stands for a big part in information loss. This work aims to study if there is a proper balance in how the pre-processing is carried out to retain the highest amount of information while simultaneously not letting the data remain too complex for the machine learning models. The dataset used consisted of Foreign exchange transactions supplied by the host company and was pre-processed through the use of Principal Component Analysis (PCA). The main purpose of this work is to test if an ensemble of Long Short-Term Memory Recurrent Neural Networks (LSTM), configured as autoencoders, can be used to detect outliers in the data and if the ensemble is more accurate than a single LSTM autoencoder. Previous studies have shown that Ensemble autoencoders can prove more accurate than a single autoencoder, especially when SkipCells have been implemented (a configuration that skips over LSTM cells to make the model perform with more variation). A datapoint will be considered an outlier if the LSTM model has trouble properly recreating it, i.e. a pattern that is hard to classify, making it available for further investigations done manually. The results show that the ensembled LSTM model proved to be more accurate than that of a single LSTM model in regards to reconstructing the dataset, and by our definition of an outlier, more accurate in outlier detection. The results from the pre-processing experiments reveal different methods of obtaining an optimal number of components for your data. One of those is by studying retained variance and accuracy of PCA transformation compared to model performance for a certain number of components. One of the conclusions from the work is that ensembled LSTM networks can prove very powerful, but that alternatives to pre-processing should be explored such as categorical embedding instead of PCA.Finansinstitut genererar idag en stor mängd data, data som kan innehålla intressant information värd att undersöka för att främja den ekonomiska tillväxten för nämnda institution. Det finns ett intresse för att analysera dessa informationspunkter, särskilt om de är avvikande från det normala dagliga arbetet. Att upptäcka dessa avvikelser är dock inte en lätt uppgift och ej möjligt att göra manuellt på grund av de stora mängderna data som genereras dagligen. Tidigare arbete för att lösa detta har undersökt användningen av maskininlärning för att upptäcka avvikelser i finansiell data. Tidigare studier har visat på att förbehandlingen av datan vanligtvis står för en stor del i förlust av emphinformation från datan. Detta arbete syftar till att studera om det finns en korrekt balans i hur förbehandlingen utförs för att behålla den högsta mängden information samtidigt som datan inte förblir för komplex för maskininlärnings-modellerna. Det emphdataset som användes bestod av valutatransaktioner som tillhandahölls av värdföretaget och förbehandlades genom användning av Principal Component Analysis (PCA). Huvudsyftet med detta arbete är att undersöka om en ensemble av Long Short-Term Memory Recurrent Neural Networks (LSTM), konfigurerad som autoenkodare, kan användas för att upptäcka avvikelser i data och om ensemblen är mer precis i sina predikteringar än en ensam LSTM-autoenkodare. Tidigare studier har visat att en ensembel avautoenkodare kan visa sig vara mer precisa än en singel autokodare, särskilt när SkipCells har implementerats (en konfiguration som hoppar över vissa av LSTM-cellerna för att göra modellerna mer varierade). En datapunkt kommer att betraktas som en avvikelse om LSTM-modellen har problem med att återskapa den väl, dvs ett mönster som nätverket har svårt att återskapa, vilket gör datapunkten tillgänglig för vidare undersökningar. Resultaten visar att en ensemble av LSTM-modeller predikterade mer precist än en singel LSTM-modell när det gäller att återskapa datasetet, och då enligt vår definition av avvikelser, mer precis avvikelse detektering. Resultaten från förbehandlingen visar olika metoder för att uppnå ett optimalt antal komponenter för dina data genom att studera bibehållen varians och precision för PCA-transformation jämfört med modellprestanda. En av slutsatserna från arbetet är att en ensembel av LSTM-nätverk kan visa sig vara mycket kraftfulla, men att alternativ till förbehandling bör undersökas, såsom categorical embedding istället för PCA.
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Santos, Anderson Rodrigo dos. "Identificação de faces humanas através de PCA-LDA e redes neurais SOM." Universidade de São Paulo, 2005. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-21042006-222231/.
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O uso de dados biométricos da face para verificação automática de identidade é um dos maiores desafios em sistemas de controle de acesso seguro. O processo é extremamente complexo e influenciado por muitos fatores relacionados à forma, posição, iluminação, rotação, translação, disfarce e oclusão de características faciais. Hoje existem muitas técnicas para se reconhecer uma face. Esse trabalho apresenta uma investigação buscando identificar uma face no banco de dados ORL com diferentes grupos de treinamento. É proposto um algoritmo para o reconhecimento de faces baseado na técnica de subespaço LDA (PCA + LDA) utilizando uma rede neural SOM para representar cada classe (face) na etapa de classificação/identificação. Aplicando o método do subespaço LDA busca-se extrair as características mais importantes na identificação das faces previamente conhecidas e presentes no banco de dados, criando um espaço dimensional menor e discriminante com relação ao espaço original. As redes SOM são responsáveis pela memorização das características de cada classe. O algoritmo oferece maior desempenho (taxas de reconhecimento entre 97% e 98%) com relação às adversidades e fontes de erros que prejudicam os métodos de reconhecimento de faces tradicionais.The use of biometric technique for automatic personal identification is one of the biggest challenges in the security field. The process is complex because it is influenced by many factors related to the form, position, illumination, rotation, translation, disguise and occlusion of face characteristics. Now a days, there are many face recognition techniques. This work presents a methodology for searching a face in the ORL database with some different training sets. The algorithm for face recognition was based on sub-space LDA (PCA + LDA) technique using a SOM neural net to represent each class (face) in the stage of classification/identification. By applying the sub-space LDA method, we extract the most important characteristics in the identification of previously known faces that belong to the database, creating a reduced and more discriminated dimensional space than the original space. The SOM nets are responsible for the memorization of each class characteristic. The algorithm offers great performance (recognition rates between 97% and 98%) considering the adversities and sources of errors inherent to the traditional methods of face recognition.
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Fontes, Nayanne Maria Garcia Rego. "Monitoramento e avaliação de desempenho de sistemas MPC utilizando métodos estatísticos multivariados." Universidade Federal de Sergipe, 2017. http://ri.ufs.br:8080/xmlui/handle/123456789/5037.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESMonitoring of process control systems is extremely important for industries to ensure the quality of the product and the safety of the process. Predictive controllers, also known by MPC (Model Predictive Control), usually has a well performance initially. However, after a period, many factors contribute to the deterioration of its performance. This highlights the importance of monitoring the MPC control systems. In this work, tools based on multivariate statistical methods are discussed and applied to the problem of monitoring and Performance Assessment of predictive controllers. The methods presented here are: PCA (Principal Component Analysis) and ICA (Independent Component Analysis). Both are techniques that use data collected directly from the process. The first is widely used in Performance Assessment of predictive controllers. The second is a more recent technique that has arisen, mainly in order to be used in fault detection systems. The analyzes are made when applied in simulated processes characteristic of the petrochemical industry operating under MPC control.
O monitoramento de sistemas de controle de processos é extremamente importante no que diz respeito às indústrias, para garantir a qualidade do que é produzido e a segurança do processo. Os controladores preditivos, também conhecidos pela sigla em inglês MPC (Model Predictive Control), costumam ter um bom desempenho inicialmente. Entretanto, após um certo período, muitos fatores contribuem para a deterioração de seu desempenho. Isto evidencia a importância do monitoramento dos sistemas de controle MPC. Neste trabalho aborda-se ferramentas, baseada em métodos estatísticos multivariados, aplicados ao problema de monitoramento e avaliação de desempenho de controladores preditivos. Os métodos aqui apresentados são: o PCA (Análise por componentes principais) e o ICA (Análise por componentes independentes). Ambas são técnicas que utilizam dados coletados diretamente do processo. O primeiro é largamente utilizado na avaliação de desempenho de controladores preditivos. Já o segundo, é uma técnica mais recente que surgiu, principalmente, com o intuito de ser utilizado em sistemas de detecção de falhas. As análises são feitas quando aplicadas em processos simulados característicos da indústria petroquímica operando sob controle MPC.
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Ergin, Emre. "Investigation Of Music Algorithm Based And Wd-pca Method Based Electromagnetic Target Classification Techniques For Their Noise Performances." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12611218/index.pdf.
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Multiple Signal Classification (MUSIC) Algorithm based and Wigner Distribution-Principal Component Analysis (WD-PCA) based classification techniques are very recently suggested resonance region approaches for electromagnetic target classification. In this thesis, performances of these two techniques will be compared concerning their robustness for noise and their capacity to handle large number of candidate targets. In this context, classifier design simulations will be demonstrated for target libraries containing conducting and dielectric spheres and for dielectric coated conducting spheres. Small scale aircraft targets modeled by thin conducting wires will also be used in classifier design demonstrations.
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Saleh, Mohamed Ibrahim. "Using Ears for Human Identification." Thesis, Virginia Tech, 2007. http://hdl.handle.net/10919/33158.
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Biometrics includes the study of automatic methods for distinguishing human beings based on physical or behavioral traits. The problem of finding good biometric features and recognition methods has been researched extensively in recent years. Our research considers the use of ears as a biometric for human recognition. Researchers have not considered this biometric as much as others, which include fingerprints, irises, and faces. This thesis presents a novel approach to recognize individuals based on their outer ear images through spatial segmentation. This approach to recognizing is also good for dealing with occlusions. The study will present several feature extraction techniques based on spatial segmentation of the ear image. The study will also present a method for classifier fusion. Principal components analysis (PCA) is used in this research for feature extraction and dimensionality reduction. For classification, nearest neighbor classifiers are used. The research also investigates the use of ear images as a supplement to face images in a multimodal biometric system. Our base eigen-ear experiment results in an 84% rank one recognition rate, and the segmentation method yielded improvements up to 94%. Face recognition by itself, using the same approach, gave a 63% rank one recognition rate, but when complimented with ear images in a multimodal system improved to 94% rank one recognition rate.Master of Science
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Owen, Jade Denise. "Investigation of the elemental profiles of Hypericum perforatum as used in herbal remedies." Thesis, University of Hertfordshire, 2014. http://hdl.handle.net/2299/13233.
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The work presented in this thesis has demonstrated that the use of elemental profiles for the quality control of herbal medicines can be applied to multiple stages of processing. A single method was developed for the elemental analysis of a variety of St John’s Wort (Hypericum perforatum) preparations using Inductively Coupled Plasma – Optical Emission Spectroscopy (ICP-OES). The optimised method consisted of using 5 ml of nitric acid and microwave digestion reaching temperatures of 185⁰C. Using NIST Polish tea (NIST INCT-TL- 1) the method was found to be accurate and the matrix effect from selected St John’s Wort (SJW) preparations was found to be ≤22%. The optimised method was then used to determine the elemental profiles for a larger number of SJW preparations (raw herbs=22, tablets=20 and capsules=12). Specifically, the method was used to determine the typical concentrations of 25 elements (Al, As, B, Ba, Be, Ca, Cd, Co, Cr, Cu, Fe, Hg, In, Mg, Mn, Mo, Ni, Pb, Pt, Sb, Se, Sr, V, Y and Zn) for each form of SJW which ranged from not detected to 200 mg/g. To further interpret the element profiles, Principal Component Analysis (PCA) was carried out. This showed that different forms of SJW could be differentiated based on their elemental profile and the SJW ingredient used (i.e. extract or raw herb) identified. The differences in the profiles were likely due to two factors: (1) the addition of bulking agents and (2) solvent extraction. In order to further understand how the elemental profile changes when producing the extract from the raw plant, eight SJW herb samples were extracted with four solvents (100% water, 60% ethanol, 80% ethanol and 100% ethanol) and analysed for their element content. The results showed that the transfer of elements from the raw herb to an extract was solvent and metal dependent. Generally the highest concentrations of an element were extracted with 100% water, which decreased as the concentration of ethanol increased. However, the transfer efficiency for the element Cu was highest with 60% ethanol. The solvents utilised in industry (60% and 80% ethanol) were found to preconcentrate some elements; Cu (+119%), Mg (+93%), Ni (+183%) and Zn (+12%) were found to preconcentrate in 60 %v/v ethanol extracts and Cu (+5%) and Ni (+30%). PCA of the elemental profiles of the four types of extract showed that differentiation was observed between the different solvents and as the ethanol concentration increased, the extracts became more standardised. Analysis of the bioactive compounds rutin, hyperoside, quercetin, hyperforin and adhyperforin followed by subsequent Correlation Analysis (CA) displayed relationships between the elemental profiles and the molecular profiles. For example strong correlations were seen between hyperoside and Cr as well as Quercetin and Fe. This shows potential for tuning elemental extractions for metal-bioactive compounds for increased bioactivity and bioavailability; however further work in needed in this area.
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Geschwinder, Lukáš. "Možnosti využití metod vícerozměrné statistické analýzy dat při hodnocení spolehlivosti distribučních sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-217824.
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The aim of this study is evaluation of using multi-dimensional statistical analyses methods as a tool for simulations of reliability of distribution network. Prefered methods are a cluster analysis (CLU) and a principal component analysis (PCA). CLU is used for a division of objects on the basis of their signs and a calculation of the distance between objects into groups whose characteristics should be similar. The readout can reveal a secret structure in data. PCA is used for a location of a structure in signs of multi-dimensional matrix data. Signs present separate quantities describing the given object. PCA uses a dissolution of a primary matrix data to structural and noise matrix data. It concerns the transformation of primary matrix data into new grid system of principal components. New conversion data are called a score. Principal components generating orthogonal system of new position. Distribution network from the aspect of reliability can be characterized by a number of new statistical quantities. Reliability indicators might be: interruption numbers, interruption time. Integral reliability indicators might be: system average interruption frequency index (SAIFI) and system average interruption duration index (SAIDI). In conclusion, there is a comparison of performed SAIFI simulation according to negatively binomial division and provided values from a distribution company. It is performed a test at description of sign dependences and outlet divisions.
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SINGH, BHUPINDER. "A HYBRID MSVM COVID-19 IMAGE CLASSIFICATION ENHANCED USING PARTICLE SWARM OPTIMIZATION." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18864.
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COVID-19 (novel coronavirus disease) is a serious illness that has killed millions of
civilians and affected millions around the world. Mostly as result, numerous
technologies that enable both the rapid and accurate identification of COVID-19
illnesses will provide much assistance to healthcare practitioners. A machine learning-
based approach is used for the detection of COVID-19. In general, artificial intelligence
(AI) approaches have yielded positive outcomes in healthcare visual processing and
analysis. CXR is the digital image processing method that plays a vital role in the
analysis of Covid-19 disease. Due to the maximum accessibility of huge scale annotated
image databases, excessive success has been done using multiclass support vector
machines for image classification. Image classification is the main challenge to detect
medical diagnosis. The existing work used CNN with a transfer learning mechanism
that can give a solution by transferring information from GENETIC object recognition
tasks. The DeTrac method has been used to detect the disease in CXR images. DeTrac
method accuracy achieved 93.1~ 97 percent. In this proposed work, the hybridization
PSO+MSVM method has worked with irregularities in the CXR images database by
studying its group distances using a group or class mechanism. At the initial phase of
the process, a median filter is used for the noise reduction from the image. Edge
detection is an essential step in the process of COVID-19 detection. The canny edge
detector is implemented for the detection of edges in the chest x-ray images. The PCA
(Principal Component Analysis) method is implemented for the feature extraction
phase. There are multiple features extracted through PCA and the essential features are
optimized by an optimization technique known as swarm optimization is used for
feature optimization. For the detection of COVID-19 through CXR images, a hybrid
multi-class support vector machine technique is implemented. The PSO (particle swarm
optimization) technique is used for feature optimization. The comparative analysis of
various existing techniques is also depicted in this work. The proposed system has
achieved an accuracy of 97.51 percent, SP of 97.49 percent, and 98.0 percent of SN.
The proposed system is compared with existing systems and achieved better
performance and the compared systems are DeTrac, GoogleNet, and SqueezeNet.
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Šrenk, David. "Vizualizace spektroskopických dat pomocí metody analýzy hlavních komponent." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-401532.
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This diploma thesis deals with using laser-induced breakdown plasma spectroscopy for determining the elemental structure of unknown samples. It was necessary to design an appropriate method to qualify material by laser-induced emission spectrum. Pretreatment of data and using a variety of chemometrics methods had to be done in order to qualify the structure of elements. We achieved a required solution by projecting the data to a new PCA space, creating clusters and computing the Euclidean distance between each cluster. The experiment in the practical part was set to detect an interface of two elements. We created a data file simulating the ablation on the interface. This data set was gradually processed applying a mathematical-chemical-physical view. Several data procedures have been compiled: approximation by Lorenz, Gauss and Voigt function and also a pretreatment method such as the detection of outliers, standardization by several procedures and subsequent use of principal components analysis. A summarization of processes for input data is fully described in the thesis.
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Ivan, Jean-Paul. "Principal Component Modelling of Fuel Consumption ofSeagoing Vessels and Optimising Fuel Consumption as a Mixed-Integer Problem." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-51847.
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The fuel consumption of a seagoing vessel is, through a combination of Box-Cox transforms and principal component analysis, reduced to a univariatefunction of the primary principle component with mean model error −3.2%and error standard deviation 10.3%. In the process, a Latin-hypercube-inspired space partitioning sampling technique is developed and successfully used to produce a representative sampleused in determining the regression coefficients. Finally, a formal optimisation problem for minimising the fuel use is described. The problem is derived from a parametrised expression for the fuel consumption, and has only 3, or 2 if simplified, free variables at each timestep. Some information has been redacted in order to comply with NDA restrictions. Most redactions are either names (of vessels or otherwise), units, andin some cases (especially on figures) quantities.Presentation was performed remotely using Zoom.
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Königsson, Sofia. "PCA för detektering av avvikande händelser i en kraftvärmeprocess." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-347516.
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Panna 6 på Högdalenverket i södra Stockholm (P6) med tillhörande ångturbin producerar kraftvärme genom förbränning av utsorterat returbränsle från industri och samhälle. För att minimera underhållskostnader och öka anläggningens tillgänglighet är det viktigt att fel och oönskat processbeteende kan upptäckas i ett tidigt skede. I detta syfte testas här en metod för detektering av avvikande händelser med hjälp av principalkomponentanalys (PCA) på produktionsprocessen för kraftvärme. En PCA-modell med reducerad dimension skapas utifrån processdata från en problemfri driftperiod och används som mall för inkommande data att jämföras med i ett kontrolldigram. Avvikelser ifrån modellen bör vara en indikation på att ett onormalt drifttillstånd har uppkommit och orsaker till avvikelsen analyseras. Som avvikande händelse testas två fall av tubläckage som uppstod i ett av tubpaketen för kylning av rökgaserna under 2014 och 2015. Resultatet visar att processavvikelser ifrån normallägesmodellerna tydligt syns i kontrolldiagrammen vid båda tubläckagen och avvikelserna kan härledas till variabler som är kopplade till tubläckage. Det finns potential för att tillämpa metoden för övervakning av processen, en svårighet ligger i att skapa en modell som representerar processen när den är stabil på grund av att det finns många varierande driftfall som anses stabila, detta kräver vidare arbete. Metoden kan redan användas som analysverktyg exempelvis vid misstanke om tubläckage.Boiler 6 at the Högdalen facility in southern Stockholm (P6) combined with a a steam turbine produces Combined Heat and Power (CHP) through combustion of treated industry waste. In order to minimise maintenance costs and increase plant availability it is of importance to detect process faults and deviations at an early state. In this study a method for outlier detection using Principal Component Analysis (PCA) is applied on the CHP production process. A PCA model with reduced dimension is created using process data from a problem free period and is used as a template for new operating data to be compared with in a control chart. Deviations from the model should be an indication of the presence of abnormal conditions and the reasons for the deviations are analysed. Two cases of tube failure in 2014 and 2015 are used to study the deviations. The result shows that process deviations from the models can be detected in the control chart in both cases of tube failure and the variables known to be associated with tube failure contributes highly to the deviating behaviour. There is potential for applying this method for process control, a difficulty lies in creating a model that represents the stable process when there are big variances within what is considererd a stable process state. The method can be used for data analysis when suspecting a tube failure.
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Gavioli, Alan. "Módulos computacionais para seleção de variáveis e Análise de agrupamento para definição de zonas de manejo." Universidade Estadual do Oeste do Paraná, 2017. http://tede.unioeste.br/handle/tede/3063.
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Two basic activities for the definition of quality management zones (MZs) are the variable selection task and the cluster analysis task. There are several methods proposed to execute them, but due to their complexity, they need to be made available by computer systems. In this study, 5 methods based on spatial correlation analysis, principal component analysis (PCA) and multivariate spatial analysis based on Moran’s index and PCA (MULTISPATI-PCA) were evaluated. A new variable selection algorithm, named MPCA-SC, based on the combined use of spatial correlation analysis and MULTISPATI-PCA, was proposed. The potential use of 20 clustering algorithms for the generation of MZs was evaluated: average linkage, bagged clustering, centroid linkage, clustering large applications, complete linkage, divisive analysis, fuzzy analysis clustering (fanny), fuzzy c-means, fuzzy c-shells, hard competitive learning, hybrid hierarchical clustering, k-means, McQuitty’s method (mcquitty), median linkage, neural gas, partitioning around medoids, single linkage, spherical k-means, unsupervised fuzzy competitive learning, and Ward’s method. Two computational modules developed to provide the variable selection and data clustering methods for definition of MZs were also presented. The evaluations were conducted with data obtained between 2010 and 2015 in three commercial agricultural areas, cultivated with soybean and corn, in the state of Paraná, Brazil. The experiments performed to evaluate the 5 variable selection algorithms showed that the new method MPCA-SC can improve the quality of MZs in several aspects, even obtaining satisfactory results with the other 4 algorithms. The evaluation experiments of the 20 clustering methods showed that 17 of them were suitable for the delineation of MZs, especially fanny and mcquitty. Finally, it was concluded that the two computational modules developed made it possible to obtain quality MZs. Furthermore, these modules constitute a more complete computer system than other free-to-use software such as FuzME, MZA, and SDUM, in terms of the diversity of variable selection and data clustering algorithms.
A seleção de variáveis e a análise de agrupamento de dados são atividades fundamentais para a definição de zonas de manejo (ZMs) de qualidade. Para executar essas duas atividades, existem diversos métodos propostos, que devido à sua complexidade precisam ser executados por meio da utilização de sistemas computacionais. Neste trabalho, avaliaramse 5 métodos de seleção de variáveis baseados em análise de correlação espacial, análise de componentes principais (ACP) e análise espacial multivariada baseada no índice de Moran e em ACP (MULTISPATI-PCA). Propôs-se um novo algoritmo de seleção de variáveis, denominado MPCA-SC, desenvolvido a partir da aplicação conjunta da análise de correlação espacial e de MULTISPATI-PCA. Avaliou-se a viabilidade de aplicação de 20 algoritmos de agrupamento de dados para a geração de ZMs: average linkage, bagged clustering, centroid linkage, clustering large applications, complete linkage, divisive analysis, fuzzy analysis clustering (fanny), fuzzy c-means, fuzzy c-shells, hard competitive learning, hybrid hierarchical clustering, k-means, median linkage, método de McQuitty (mcquitty), método de Ward, neural gas, partitioning around medoids, single linkage, spherical k-means e unsupervised fuzzy competitive learning. Apresentaram-se ainda dois módulos computacionais desenvolvidos para disponibilizar os métodos de seleção de variáveis e de agrupamento de dados para a definição de ZMs. As avaliações foram realizadas com dados obtidos entre os anos de 2010 e 2015 de três áreas agrícolas comerciais, localizadas no estado do Paraná, nas quais cultivaram-se milho e soja. Os experimentos efetuados para avaliar os 5 algoritmos de seleção de variáveis mostraram que o novo método MPCA-SC pode melhorar a qualidade de ZMs em diversos aspectos, mesmo obtendo-se resultados satisfatórios com os outros 4 algoritmos. Os experimentos de avaliação dos 20 métodos de agrupamento citados mostraram que 17 deles foram adequados para o delineamento de ZMs, com destaque para fanny e mcquitty. Por fim, concluiu-se que os dois módulos computacionais desenvolvidos possibilitaram a obtenção de ZMs de qualidade. Além disso, esses módulos constituem uma ferramenta computacional mais abrangente que outros softwares de uso gratuito, como FuzME, MZA e SDUM, em relação à diversidade de algoritmos disponibilizados para selecionar variáveis e agrupar dados.
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Nordahl, Åke. "Metoder för informationsoptimering vid organisk syntes." Doctoral thesis, Umeå universitet, Kemiska institutionen, 1990. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-102557.
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