Relevant bibliographies by topics / Analysis (PCA)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Analysis (PCA)'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "Analysis (PCA)"

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Lee, Myeounggon, Changhong Youm, Byungjoo Noh, and Hwayoung Park. "Gait Characteristics Based on Shoe-Type Inertial Measurement Units in Healthy Young Adults during Treadmill Walking." Sensors 20, no. 7 (2020): 2095. http://dx.doi.org/10.3390/s20072095.

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This study investigated the gait characteristics of healthy young adults using shoe-type inertial measurement units (IMU) during treadmill walking. A total of 1478 participants were tested. Principal component analyses (PCA) were conducted to determine which principal components (PCs) best defined the characteristics of healthy young adults. A non-hierarchical cluster analysis was conducted to evaluate the essential gait ability, according to the results of the PC1 score. One-way repeated analysis of variance with the Bonferroni correction was used to compare gait performances in the cluster groups. PCA outcomes indicated 76.9% variance for PC1–PC6, where PC1 (gait variability (GV): 18.5%), PC2 (pace: 17.8%), PC3 (rhythm and phase: 13.9%), and PC4 (bilateral coordination: 11.2%) were the gait-related factors. All of the pace, rhythm, GV, and variables for bilateral coordination classified the gait ability in the cluster groups. We suggest that the treadmill walking task may be reliable to evaluate the gait performances, which may provide insight into understanding the decline of gait ability. The presented results are considered meaningful for understanding the gait patterns of healthy adults and may prove useful as reference outcomes for future gait analyses.
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Wulandari, Sari Ayu, Rudy Tjahyono, and Dian Retno Sawitri. "PERBANDINGAN TINGKAT PENGENALAN CITRA DIABETIC RETINOPATHY PADA KOMBINASI PRINCIPLE COMPONENT DARI 4 CIRI BERBASIS METODE SVM (SUPPORT VECTOR MACHINE)." Majalah Ilmiah Teknologi Elektro 15, no. 1 (2016): 95. http://dx.doi.org/10.24843/mite.2016.v15i01p17.

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Perbedaan pigmentasi mempengaruhi me­­­­tode pengenalan pola citra retinopati di­a­betik beserta set­ting poinnya. Di­butuhkan sebuah pe­rangkat lunak, yang mampu menjadi alat bantu pengenalan citra retinopati diabetik. Telah dilakukan penelitian tentang pe­nge­nalan po­la citra retinopati dia­be­tik, dengan meng­gunakan citra kanal ku­ning (Yello­w), dengan menggunakan filter gabor dan ciri yang diambil dari tiap citra ada­lah ciri rerata (Means), variasi Varians), skewness dan entropy, yang dilanjutkan de­ngan ekstraksi ciri PCA (Principle Com­­ponent Analysis). Pada ekstraksi ci­ri PCA, Matriks hasil PCA meru­pakan ma­triks bujur sangkar, yang jumlah ko­lom­nya, sama dengan jumlah ciri. Pe­ne­li­tian menggunakan 4 ciri, dengan de­mi­­kian, terdapat 4 buah PC (Principle Com­ponent), PC1, PC2, PC3 dan PC4. Pada artikel ini akan dibahas mengenai tingkat akurasi tertinggi dari peng­gunaan pasangan PC. Tingkat aku­ra­si, dihitung dengan meng­gu­­nakan mo­del linear dari SVM. Model de­ngan akurasi tertinggi dan tercepat ada­lah model pasangan PC1 dan PC2, yang mempunyai akurasi citra pem­be­lajaran tertinggi yaitu 100% dan waktu terce­pat, yang secara eksplisit diperli­hat­kan pada jumlah support vektor ter­kecil, yaitu 2. Pasa­ngan yang mempu­nyai ting­kat akurasi terburuk adalah PC3 dan PC4. Pengenalan turun pada citra pengu­jian, yaitu hanya 93,75%, hal ini disebabkan oleh pelebaran daerah ca­ku­pan. Pelebaran daerah cakupan ke­mungkinan disebabkan oleh pemi­lihan nilai rerata pada PCA, sebelum matriks reduksi. Pada penelitian berikutnya, bi­sa dilakukan dengan menggunakan pencarian nilai standart deviasi atau varians, dengan begitu, akan diketahui matriks reduksi yang mewakili sebaran angka pada matriks. DOI: 10.24843/MITE.1501.17
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Sekandari, Milad, and Amin Beiranvand Pour. "Fuzzy Logic Modeling for Integrating the Thematic Layers Derived from Remote Sensing Imagery: A Mineral Exploration Technique." Environmental Sciences Proceedings 6, no. 1 (2021): 8. http://dx.doi.org/10.3390/iecms2021-09349.

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In this study, fuzzy logic modeling was implemented to fuse the thematic layers derived from principal components analysis (PCA) in order to generate mineral prospectivity maps. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and WorldView-3 (WV-3) satellite remote sensing data were used. A spatial subset zone of the Central Iranian Terrane (CIT), Iran was selected in this study. The PCA technique was implemented for the processing of the datasets and for the production of alteration thematic layers. PCA4, PCA5, and PCA8 were selected as the most rational alteration thematic layers of ASTER for the generation of a prospectivity map. The fuzzy gamma operator was used to fuse the selected alteration thematic layers. The PCA3, PCA4, and PCA6 thematic layers (most rational alteration thematic layers) of WV-3 were fused using the fuzzy AND operator. Field reconnaissance, X-ray diffraction (XRD) analysis, and Analytical Spectral Devices (ASD) spectroscopy were carried out to verify the image processing results. Subsequently, mineral prospectivity maps were produced showing high-potential zones of Pb-Zn mineralization in the study area.
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Gibson, Sigrid, and Margaret Ashwell. "Dietary patterns among British adults: compatibility with dietary guidelines for salt/sodium, fat, saturated fat and sugars." Public Health Nutrition 14, no. 8 (2011): 1323–36. http://dx.doi.org/10.1017/s1368980011000875.

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AbstractObjectiveTo examine dietary patterns among British adults, associations with Na and macronutrient intakes, and implications for dietary advice.DesignPrincipal component analysis of 7 d weighed dietary records.SubjectsAdults aged 19–64 years (n 1724).SettingNational Diet and Nutrition Survey (2000/2001).ResultsHigh Na intake was associated with more energy-dense diets, higher in fat and SFA (percentage of energy) but lower in non-milk extrinsic sugars (NMES). Eight patterns (PC1 to PC8) explained 40 % of the total variance in food intakes. Three patterns – PC3 (high loadings on bread, fats and cheese), PC2 (meat products, eggs and chips) and PC7 (red meat, sauces and alcohol) – were associated with high Na intake. Of these, PC3 correlated with high Na density and Na:K ratio, while PC2 correlated with fat. By contrast, three patterns – ‘health-conscious’ (PC1; vegetables, fruit, fruit juice, fish), ‘breakfast cereals and milk’ (PC6) and ‘chicken and rice’ (PC8) – were associated with modest Na intake, lower Na density and lower fat and SFA. PC2 was positively correlated, and PC1 was negatively correlated, with adding salt to food. Other patterns were ‘tea/coffee and cakes’ (PC4; associated with high SFA and NMES) and ‘soft drinks and snacks’ (PC5; associated with high NMES but not fat or SFA). The dietary patterns of males and females differed slightly.ConclusionsDietary patterns PC1, PC6, PC8 (vegetables, fruit, fish, milk, breakfast cereals, poultry) were broadly compatible with guidelines for salt, fat, SFA and NMES. However, other patterns tended to be high in either salt or NMES.
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Byrne, A., and N. Hall. "Against the PCA-analysis." Analysis 58, no. 1 (1998): 38–44. http://dx.doi.org/10.1093/analys/58.1.38.

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Benson, Lauren C., and Kristian M. O’Connor. "The Effect of Exertion on Joint Kinematics and Kinetics During Running Using a Waveform Analysis Approach." Journal of Applied Biomechanics 31, no. 4 (2015): 250–57. http://dx.doi.org/10.1123/jab.2014-0138.

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About half of all runners sustain a running-related injury every year. Exertion may contribute to risk of injury by altering joint mechanics. The purpose of this study was to examine the effects of exertion on runners’ joint mechanics using principal component analysis (PCA). Three-dimensional motion analysis of the lower extremity was performed on 16 healthy female runners before and after their typical training run. PCA was used to determine exertion-related changes in joint mechanics at the ankle, knee, and hip. Statistical significance for repeated-measures MANOVA of the retained principal components at each joint and plane of motion was at P < .05. Exercise effects were identified at the ankle (greater rate of eversion [PC2: P = .027], and decreased plantar flexion moment [overall: P = .044] and external rotation moment [PC3: P = .003]), knee (increased adduction [overall: P = .044] and internal rotation [PC3: P = .034], and decreased abduction moment [overall: P = .045]), and hip (increased internal rotation [PC1: P = .013] and range of mid- to late-stance rotation [PC2: P = .009], and decreased internal rotation moment [PC1: P = .001]). The observed changes in running mechanics reflect a gait profile that is often linked to running injury. The effects of more strenuous activity may result in mechanics that present an even greater risk for injury.
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Parker, James, and Lina Lundgren. "Surfing the Waves of the CMJ; Are There between-Sport Differences in the Waveform Data?" Sports 6, no. 4 (2018): 168. http://dx.doi.org/10.3390/sports6040168.

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The ability to analyse countermovement jump (CMJ) waveform data using statistical methods, like principal component analysis, can provide additional information regarding the different phases of the CMJ, compared to jump height or peak power alone. The aim of this study was to investigate the between-sport force-time curve differences in the CMJ. Eighteen high level golfers (male = 10, female = 8) and eighteen high level surfers (male = 10, female = 8) performed three separate countermovement jumps on a force platform. Time series of data from the force platform was normalized to body weight and each repetition was then normalized to 0–100 percent. Principal component analyses (PCA) were performed on force waveforms and the first six PCs explained 35% of the variance in force parameters. The main features of the movement cycles were characterized by magnitude (PC1 and PC5), waveform (PC2 and PC4), and phase shift features (PC3). Surf athletes differ in their CMJ technique and show a greater negative centre of mass displacement when compared to golfers (PC1), although these differences are not necessarily associated with greater jump height. Principal component 5 demonstrated the largest correlation with jump height (R2 = 0.52). Further studies are recommended in this area, to reveal which features of the CMJ that relate to jumping performance, and sport specific adaptations.
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Harp, Jordan P., Lisa M. Koehl, Kathryn L. Van Pelt, et al. "Cognitive and Behavioral Domains That Reliably Differentiate Normal Aging and Dementia in Down Syndrome." Brain Sciences 11, no. 9 (2021): 1128. http://dx.doi.org/10.3390/brainsci11091128.

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Primary care integration of Down syndrome (DS)-specific dementia screening is strongly advised. The current study employed principal components analysis (PCA) and classification and regression tree (CART) analyses to identify an abbreviated battery for dementia classification. Scale- and subscale-level scores from 141 participants (no dementia n = 68; probable Alzheimer’s disease n = 73), for the Severe Impairment Battery (SIB), Dementia Scale for People with Learning Disabilities (DLD), and Vineland Adaptive Behavior Scales—Second Edition (Vineland-II) were analyzed. Two principle components (PC1, PC2) were identified with the odds of a probable dementia diagnosis increasing 2.54 times per PC1 unit increase and by 3.73 times per PC2 unit increase. CART analysis identified that the DLD sum of cognitive scores (SCS < 35 raw) and Vineland-II community subdomain (<36 raw) scores best classified dementia. No significant difference in the PCA versus CART area under the curve (AUC) was noted (D(65.196) = −0.57683; p = 0.57; PCA AUC = 0.87; CART AUC = 0.91). The PCA sensitivity was 80% and specificity was 70%; CART was 100% and specificity was 81%. These results support an abbreviated dementia screening battery to identify at-risk individuals with DS in primary care settings to guide specialized diagnostic referral.
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Maćkiewicz, Andrzej, and Waldemar Ratajczak. "Principal components analysis (PCA)." Computers & Geosciences 19, no. 3 (1993): 303–42. http://dx.doi.org/10.1016/0098-3004(93)90090-r.

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Tóthová, J., J. Sádecká, and P. Májek. "Total luminescence spectroscopy for differentiating between brandies and wine distillates." Czech Journal of Food Sciences 27, No. 6 (2009): 425–32. http://dx.doi.org/10.17221/125/2008-cjfs.

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In this study, the differentiation was investigated between brandy and wine distillate samples by fluorescence spectroscopy in combination with multivariate analysis. The samples corresponding to eight brandies from three producers and sixteen wine distillates from five producers were acquired in the local supermarkets. Total luminescence spectra of diluted and undiluted samples were recorded. In order to extract reliable information from the data sets, two multivariate analysis methods, Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA), were applied separately on the excitation and emission spectra. The best differentiation was achieved using the emission spectra (400–470 nm) recorded at the excitation wavelength of 340 nm, or the excitation spectra (240–380 nm) recorded at the emission wavelength of 450 nm. The similarity map defined by the PC1 and PC2 of the PCA performed on the excitation spectra accounted for 94.9% of the total variance (PC1 90.3%, PC2 4.6%) and allowed a good discrimination between the beverages. Although the PCA similarity map defined by the PC1 (84.2%) and PC2 (13.0%) performed on the emission spectra did not lead to a clear discrimination between the beverages, a general trend pointing out the brandies and wine distillates was observed on the map. HCA performed on the excitation spectra provided a better differentiation between the two classes, without any classification error, while HCA performed on the emission spectra allowed 95.8% correct classification.
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Dissertations / Theses on the topic "Analysis (PCA)"

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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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Le, Hanh T. Banking &amp 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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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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Moravčíková, Simona. "Stanovenie hodnoty firmy PCA Slovakia, s.r.o." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-206699.

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The aim of this thesis is to estimate the value of the company PCA Slovakia, s.r.o. to the 31st December 2015. The thesis is divided into two parts, in the concrete the theoretical and practical parts. The theoretical part describes the basic concepts necessary for the valuation of the company and it is kind of the point for the practical part. On the other hand, the practical part is focused on the introduction of the company and the application of strategic analysis and financial analysis, the prognosis of revenue and other value drivers, financial plan and finally the actual valuation of the company. There was used the DCF method of valuation in the term of FCFF and EVA for the valuation of the company.
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Vogetseder, Georg. "Functional Analysis of Real World Truck Fuel Consumption Data." Thesis, Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-1148.

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<p>This thesis covers the analysis of sparse and irregular fuel consumption data of long</p><p>distance haulage articulate trucks. It is shown that this kind of data is hard to analyse with multivariate as well as with functional methods. To be able to analyse the data, Principal Components Analysis through Conditional Expectation (PACE) is used, which enables the use of observations from many trucks to compensate for the sparsity of observations in order to get continuous results. The principal component scores generated by PACE, can then be used to get rough estimates of the trajectories for single trucks as well as to detect outliers. The data centric approach of PACE is very useful to enable functional analysis of sparse and irregular data. Functional analysis is desirable for this data to sidestep feature extraction and enabling a more natural view on the data.</p>
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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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Moussavinasab, Nouraddin. "Analysis of point process data arising in self administered analgesia." Thesis, University of Newcastle Upon Tyne, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308325.

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Eklund, Linnea. "Analysis of Instagram Stories: A quantitative study of Instagram Stories using PCA." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-418824.

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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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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.<br>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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Books on the topic "Analysis (PCA)"

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), Peace Corps (U S. PACA: Using Participatory Analysis for Community Action. Peace Corps, 2005.

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PACA: Using Participatory Analysis for Community Action. Peace Corps, 2005.

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Services, Kingston Polytechnic Computing. SAS userguide: Statistical analysis package for PCs. Kingston Polytechnic, 1989.

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Newton, C. R. PCR. BIOS Scientific in association with the Biochemical Society, 1994.

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A, Graham, ed. PCR. 2nd ed. BIOS Scientific Publishers, 1997.

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Greenbaum, John R., Les Besser, Brian Biehl, Bruce D. Pollard, and Robert Osann. Analysis and Design of Electronic Circuits Using PCs. Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-011-7018-5.

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Sentā, Nihon Kankyō Eisei. Heisei 23-nendo biryō PCB no kan'i sokutei ni kakaru ankēto chōsa gyōmu. Nihon Kankyō Eisei Sentā, 2012.

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Dennis, Philip Christopher. Community analysis of an anaerobic tetrachloroethene (PCE) degrading bacterial consortium. National Library of Canada, 2000.

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PAC-Bayesian supervised classification: The thermodynamics of statistical learning. Institute of Mathematical Statistics, 2007.

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Massey, David E. Pulse code modulation (PCM) data storage and analysis using a microcomputer. Goddard Space Flight Center, 1986.

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Book chapters on the topic "Analysis (PCA)"

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Storer, Markus, Peter M. Roth, Martin Urschler, and Horst Bischof. "Fast-Robust PCA." In Image Analysis. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02230-2_44.

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Jöreskog, Karl G., Ulf H. Olsson, and Fan Y. Wallentin. "Principal Components (PCA)." In Multivariate Analysis with LISREL. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33153-9_5.

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Oh, Jiyong, and Nojun Kwak. "Robust PCAs and PCA Using Generalized Mean." In Advances in Principal Component Analysis. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6704-4_4.

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Kurita, Takio. "Principal Component Analysis (PCA)." In Computer Vision. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-03243-2_649-1.

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Kurita, Takio. "Principal Component Analysis (PCA)." In Computer Vision. Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_649.

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Ruby-Figueroa, René. "Principal Component Analysis (PCA)." In Encyclopedia of Membranes. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-40872-4_1999-1.

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Bisong, Ekaba. "Principal Component Analysis (PCA)." In Building Machine Learning and Deep Learning Models on Google Cloud Platform. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4470-8_26.

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Guebel, Daniel V., and Néstor V. Torres. "Principal Component Analysis (PCA)." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1276.

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Trendafilov, Nickolay, and Michele Gallo. "Principal component analysis (PCA)." In Multivariate Data Analysis on Matrix Manifolds. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76974-1_4.

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Tripathy, B. K., S. Anveshrithaa, and Shrusti Ghela. "Principal Component Analysis (PCA)." In Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization. CRC Press, 2021. http://dx.doi.org/10.1201/9781003190554-2.

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Conference papers on the topic "Analysis (PCA)"

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Rehman, Atiqur, Aurangzeb Khan, Muhammad Akhtar Ali, Muhammad Umair Khan, Shafqat Ullah Khan, and Liaqat Ali. "Performance Analysis of PCA, Sparse PCA, Kernel PCA and Incremental PCA Algorithms for Heart Failure Prediction." In 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE). IEEE, 2020. http://dx.doi.org/10.1109/icecce49384.2020.9179199.

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Salgado, Paulo, Lio Gonçalves, Getúlio Igrejas, et al. "Sliding PCA Fuzzy Clustering Algorithm." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3637005.

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Chang-Zhi Wen and Jia-Shu Zhang. "Palmprint recognition based on Gabor wavelets and 2-dimensional PCA&PCA." In 2007 International Conference on Wavelet Analysis and Pattern Recognition. IEEE, 2007. http://dx.doi.org/10.1109/icwapr.2007.4421691.

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Wang, Qianqian, Quanxue Gao, Xinbo Gao, and Feiping Nie. "Angle Principal Component Analysis." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/409.

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Recently, many ℓ1-norm based PCA methods have been developed for dimensionality reduction, but they do not explicitly consider the reconstruction error. Moreover, they do not take into account the relationship between reconstruction error and variance of projected data. This reduces the robustness of algorithms. To handle this problem, a novel formulation for PCA, namely angle PCA, is proposed. Angle PCA employs ℓ2-norm to measure reconstruction error and variance of projected da-ta and maximizes the summation of ratio between variance and reconstruction error of each data. Angle PCA not only is robust to outliers but also retains PCA’s desirable property such as rotational invariance. To solve Angle PCA, we propose an iterative algorithm, which has closed-form solution in each iteration. Extensive experiments on several face image databases illustrate that our method is overall superior to the other robust PCA algorithms, such as PCA, PCA-L1 greedy, PCA-L1 nongreedy and HQ-PCA.
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Sushma Niket Borade and Ramesh P. Adgaonkar. "Comparative analysis of PCA and LDA." In 2011 International Conference on Business, Engineering and Industrial Applications (ICBEIA). IEEE, 2011. http://dx.doi.org/10.1109/icbeia.2011.5994243.

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Limpiti, T., A. Intarapanich, A. Assawamakin, P. Wangkumhang, and S. Tongsima. "Iterative PCA for population structure analysis." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5946474.

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"HUMAN BREAST SHAPE ANALYSIS USING PCA." In International Conference on Bio-inspired Systems and Signal Processing. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0002691501630167.

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Rathi, Yogesh, Samuel Dambreville, and Allen Tannenbaum. "Statistical shape analysis using kernel PCA." In Electronic Imaging 2006, edited by Edward R. Dougherty, Jaakko T. Astola, Karen O. Egiazarian, Nasser M. Nasrabadi, and Syed A. Rizvi. SPIE, 2006. http://dx.doi.org/10.1117/12.641417.

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Gonçalves, Lio, Paulo Salgado, Theodore E. Simos, George Psihoyios, Ch Tsitouras, and Zacharias Anastassi. "Forecasting Portugal’s Wind Power Production by a Fuzzy-PCA Approach." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3637004.

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Fukuda, Kensuke, Toshio Hirotsu, Osamu Akashi, and Toshiharu Sugawara. "A PCA Analysis of Daily Unwanted Traffic." In 2010 24th IEEE International Conference on Advanced Information Networking and Applications. IEEE, 2010. http://dx.doi.org/10.1109/aina.2010.79.

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Reports on the topic "Analysis (PCA)"

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NGUYEN, D. M. PCB Analysis Plan for Tank Archive Samples. Office of Scientific and Technical Information (OSTI), 2001. http://dx.doi.org/10.2172/806789.

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Ott, K. A fast reactor transient analysis methodology for PCs. Office of Scientific and Technical Information (OSTI), 1991. http://dx.doi.org/10.2172/5856130.

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Ott, K. O. A faster reactor transient analysis methodology for PCs. Office of Scientific and Technical Information (OSTI), 1991. http://dx.doi.org/10.2172/5778630.

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Plum, M., and R. Geimer. PCB/transformer techno-economic analysis model: User manual. Office of Scientific and Technical Information (OSTI), 1989. http://dx.doi.org/10.2172/7013022.

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BANNING, D. L. Interim Basis for PCB Sampling and Analyses. Office of Scientific and Technical Information (OSTI), 2001. http://dx.doi.org/10.2172/805951.

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BANNING, D. L. Interim Basis for PCB Sampling and Analyses. Office of Scientific and Technical Information (OSTI), 2001. http://dx.doi.org/10.2172/806801.

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Prescott, Steven, Ramprasad Sampath, and John Biersdorf. Risk-Informed Systems Analysis (RISA) Dynamic Fire PRA Roadmap. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1467474.

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McGregor, Lisa, Sarah Frazer, and Derick Brinkerhoff. Thinking and Working Politically: Lessons from Diverse and Inclusive Applied Political Economy Analysis. RTI Press, 2020. http://dx.doi.org/10.3768/rtipress.2020.rr.0038.2004.

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Political economy analysis (PEA) has emerged as a valuable approach for assessing context and the local systems where international development actors seek to intervene. PEA approaches and tools have grown and adapted over the last 40 years through innovations by donor agencies and practitioners. Our analysis of nine PEAs reveals the following findings: PEAs can make positive contributions to technical interventions; engaging project staff in PEAs increases the likelihood that they will be open to a thinking and working politically mindset and approach; inclusion of gender equity and social inclusion (GESI) in PEAs helps to uncover and address hidden power dynamics; and explicitly connecting PEA findings to project implementation facilitates adaptive management. Implementation lessons learned include careful consideration of logistics, timing, and team members. Our experience and research suggest applied PEAs provide valuable evidence for strengthening evidence-based, adaptive, international development programming. The findings highlight the promise of PEA as well as the need for ongoing learning and research to address continued challenges.
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Munguti, Nzoya, Moses Mokua, Rick Homan, and Harriet Birungi. Cost analysis of reproductive health services in PCEA Chogoria Hospital, Kenya. Population Council, 2006. http://dx.doi.org/10.31899/rh4.1178.

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Melvin, Matt G. China's Military Modernization; An Analysis of the PLA Improved Logistic Capability. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ad1018814.

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