Relevant bibliographies by topics / Independent Component Analysis (ICA)

Academic literature on the topic 'Independent Component Analysis (ICA)'

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Published: 4 June 2021
Last updated: 26 January 2023

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Journal articles on the topic "Independent Component Analysis (ICA)"

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Unnisa, Yaseen, Danh Tran, and Fu Chun Huang. "Statistical Independence and Independent Component Analysis." Applied Mechanics and Materials 553 (May 2014): 564–69. http://dx.doi.org/10.4028/www.scientific.net/amm.553.564.

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Independent Component Analysis (ICA) is a recent method of blind source separation, it has been employed in medical image processing and structural damge detection. It can extract source signals and the unmixing matrix of the system using mixture signals only. This novel method relies on the assumption that source signals are statistically independent. This paper looks at various measures of statistical independence (SI) employed in ICA, the measures proposed by Bakirov and his associates, and the effects of levels of SI of source signals on the output of ICA. Firstly, two statistical independent signals in the form of uniform random signals and a mixing matrix were used to simulate mixture signals to be anlysed byfastICApackage, secondly noise was added onto the signals to investigate effects of levels of SI on the output of ICA in the form of soure signals, the mixing and unmixing matrix. It was found that for p-value given by Bakirov’s SI statistical testing of the null hypothesis H0is a good indication of the SI between two variables and that for p-value larger than 0.05, fastICA performs satisfactorily.
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Mahmoudishadi, S., A. Malian, and F. Hosseinali. "COMPARING INDEPENDENT COMPONENT ANALYSIS WITH PRINCIPLE COMPONENT ANALYSIS IN DETECTING ALTERATIONS OF PORPHYRY COPPER DEPOSIT (CASE STUDY: ARDESTAN AREA, CENTRAL IRAN)." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4 (September 26, 2017): 161–66. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-161-2017.

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The image processing techniques in transform domain are employed as analysis tools for enhancing the detection of mineral deposits. The process of decomposing the image into important components increases the probability of mineral extraction. In this study, the performance of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) has been evaluated for the visible and near-infrared (VNIR) and Shortwave infrared (SWIR) subsystems of ASTER data. Ardestan is located in part of Central Iranian Volcanic Belt that hosts many well-known porphyry copper deposits. This research investigated the propylitic and argillic alteration zones and outer mineralogy zone in part of Ardestan region. The two mentioned approaches were applied to discriminate alteration zones from igneous bedrock using the major absorption of indicator minerals from alteration and mineralogy zones in spectral rang of ASTER bands. Specialized PC components (PC2, PC3 and PC6) were used to identify pyrite and argillic and propylitic zones that distinguish from igneous bedrock in RGB color composite image. Due to the eigenvalues, the components 2, 3 and 6 account for 4.26% ,0.9% and 0.09% of the total variance of the data for Ardestan scene, respectively. For the purpose of discriminating the alteration and mineralogy zones of porphyry copper deposit from bedrocks, those mentioned percentages of data in ICA independent components of IC2, IC3 and IC6 are more accurately separated than noisy bands of PCA. The results of ICA method conform to location of lithological units of Ardestan region, as well.
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Suzuki, Taiji, and Masashi Sugiyama. "Least-Squares Independent Component Analysis." Neural Computation 23, no. 1 (January 2011): 284–301. http://dx.doi.org/10.1162/neco_a_00062.

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Accurately evaluating statistical independence among random variables is a key element of independent component analysis (ICA). In this letter, we employ a squared-loss variant of mutual information as an independence measure and give its estimation method. Our basic idea is to estimate the ratio of probability densities directly without going through density estimation, thereby avoiding the difficult task of density estimation. In this density ratio approach, a natural cross-validation procedure is available for hyperparameter selection. Thus, all tuning parameters such as the kernel width or the regularization parameter can be objectively optimized. This is an advantage over recently developed kernel-based independence measures and is a highly useful property in unsupervised learning problems such as ICA. Based on this novel independence measure, we develop an ICA algorithm, named least-squares independent component analysis.
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Zhan, Xin Wu, and Wu Jiao Dai. "Dam Deformation Analysis Based on Independent Component Analysis." Applied Mechanics and Materials 212-213 (October 2012): 859–62. http://dx.doi.org/10.4028/www.scientific.net/amm.212-213.859.

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Independent component analysis (ICA) is a recent and well-known technique used to separate mixtures of signals. It can separate independent components from mixed signals and has many advantages in blind signal separation, redundancy removal and processing of frequency aliasing problems. Deformation monitoring data can be regarded as the digital signals series which is composed of different frequency. After making test on ICA in processing dam observation data we can draw a conclusion that it is practical and applicative for ICA to evaluate the stability of the dam and reflect the working condition of dam.
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Bellini, Fabio, and Ernesto Salinelli. "Independent Component Analysis and Immunization: An Exploratory Study." International Journal of Theoretical and Applied Finance 06, no. 07 (November 2003): 721–38. http://dx.doi.org/10.1142/s0219024903002201.

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In this work we apply Independent Component Analysis (ICA) to the identification of the factors driving spot rates curve movements. A comparison between the standard Principal Components Analysis (PCA) approach and ICA is carried out both from a theoretical point of view, critically analyzing the negentropy based approach to ICA, and from an empirical point of view, where the performance of immunization strategies based on PCA and ICA are tested.
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Honório, Bruno César Zanardo, Alexandre Cruz Sanchetta, Emilson Pereira Leite, and Alexandre Campane Vidal. "Independent component spectral analysis." Interpretation 2, no. 1 (February 1, 2014): SA21—SA29. http://dx.doi.org/10.1190/int-2013-0074.1.

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Spectral decomposition techniques can break down the broadband seismic records into a series of frequency components that are useful for seismic interpretation and reservoir characterization. However, it is laborious and time-consuming to analyze and to interpret each seismic frequency volume taking all the usable seismic bandwidth. In this context, we propose a multivariate technique based on independent component analysis (ICA) with the goal of choosing the spectral components that best represent the whole seismic spectrum while keeping the main geological information. The ICA-based method goes beyond the Gaussian assumption and takes advantage of higher order statistics to find a new set of variables that are independent of each other. The independence between two components is a more general statistical concept than the noncorrelation and, in principle, allows the extraction of more significant information from the data. We have tested four different contrast functions to estimate the independent components (ICs), which we could verify a better channel system identification depending on the contrast function used. By stacking the ICs in the red-green-blue color space, we could represent the main information in a single, good quality image. To illustrate the proposed method, we have applied it to a seismic volume which was acquired over the F3 block in the Dutch sector of the North Sea. We also compared the results with those obtained by principal component analysis. In this case, the ICA-based method could generate a better image and faithfully delineate a channel system presented in the studied seismic volume.
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Ke, Qiao, Jiangshe Zhang, H. M. Srivastava, Wei Wei, and Guang-Sheng Chen. "Independent Component Analysis Based on Information Bottleneck." Abstract and Applied Analysis 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/386201.

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The paper is mainly used to provide the equivalence of two algorithms of independent component analysis (ICA) based on the information bottleneck (IB). In the viewpoint of information theory, we attempt to explain the two classical algorithms of ICA by information bottleneck. Furthermore, via the numerical experiments with the synthetic data, sonic data, and image, ICA is proved to be an edificatory way to solve BSS successfully relying on the information theory. Finally, two realistic numerical experiments are conducted via FastICA in order to illustrate the efficiency and practicality of the algorithm as well as the drawbacks in the process of the recovery images the mixing images.
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KARHUNEN, JUHA, SIMONA MĂlĂROIU, and MIKA ILMONIEMI. "LOCAL LINEAR INDEPENDENT COMPONENT ANALYSIS BASED ON CLUSTERING." International Journal of Neural Systems 10, no. 06 (December 2000): 439–51. http://dx.doi.org/10.1142/s0129065700000429.

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In standard Independent Component Analysis (ICA), a linear data model is used for a global description of the data. Even though linear ICA yields meaningful results in many cases, it can provide a crude approximation only for general nonlinear data distributions. In this paper a new structure is proposed, where local ICA models are used in connection with a suitable grouping algorithm clustering the data. The clustering part is responsible for an overall coarse nonlinear representation of the data, while linear ICA models of each cluster are used for describing local features of the data. The goal is to represent the data better than in linear ICA while avoiding computational difficulties related with nonlinear ICA. Several data grouping methods are considered, including standard K-means clustering, self-organizing maps, and neural gas. Connections to existing methods are discussed, and experimental results are given for artificial data and natural images. Furthermore, a general theoretical framework encompassing a large number of methods for representing data is introduced. These range from global, dense representation methods to local, very sparse coding methods. The proposed local ICA methods lie between these two extremes.
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MEYER-BÄSE, ANKE, OLIVER LANGE, AXEL WISMÜLLER, and HELGE RITTER. "MODEL-FREE FUNCTIONAL MRI ANALYSIS USING TOPOGRAPHIC INDEPENDENT COMPONENT ANALYSIS." International Journal of Neural Systems 14, no. 04 (August 2004): 217–28. http://dx.doi.org/10.1142/s0129065704002017.

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Data-driven fMRI analysis techniques include independent component analysis (ICA) and different types of clustering in the temporal domain. Since each of these methods has its particular strengths, it is natural to look for an approach that unifies Kohonen's self-organizing map and ICA. This is given by the topographic independent component analysis. While achieved by a slight modification of the ICA model, it can be at the same time used to define a topographic order (clusters) between the components, and thus has the usual computational advantages associated with topographic maps. In this contribution, we can show that when applied to fMRI analysis it outperforms FastICA.
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Erdogmus, Deniz, Kenneth E. Hild, Yadunandana N. Rao, and José C. Príncipe. "Minimax Mutual Information Approach for Independent Component Analysis." Neural Computation 16, no. 6 (June 1, 2004): 1235–52. http://dx.doi.org/10.1162/089976604773717595.

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Minimum output mutual information is regarded as a natural criterion for independent component analysis (ICA) and is used as the performance measure in many ICA algorithms. Two common approaches in information-theoretic ICA algorithms are minimum mutual information and maximum output entropy approaches. In the former approach, we substitute some form of probability density function (pdf) estimate into the mutual information expression, and in the latter we incorporate the source pdf assumption in the algorithm through the use of nonlinearities matched to the corresponding cumulative density functions (cdf). Alternative solutions to ICA use higher-order cumulant-based optimization criteria, which are related to either one of these approaches through truncated series approximations for densities. In this article, we propose a new ICA algorithm motivated by the maximum entropy principle (for estimating signal distributions). The optimality criterion is the minimum output mutual information, where the estimated pdfs are from the exponential family and are approximate solutions to a constrained entropy maximization problem. This approach yields an upper bound for the actual mutual information of the output signals—hence, the name minimax mutual information ICA algorithm. In addition, we demonstrate that for a specific selection of the constraint functions in the maximum entropy density estimation procedure, the algorithm relates strongly to ICA methods using higher-order cumulants.
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Dissertations / Theses on the topic "Independent Component Analysis (ICA)"

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Harmeling, Stefan. "Independent component analysis and beyond." Phd thesis, [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=973631805.

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Lai, Di. "Independent component analysis (ICA) applied to ultrasound image processing and tissue characterization /." Online version of thesis, 2009. http://hdl.handle.net/1850/11367.

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Abou, Elseoud A. (Ahmed). "Exploring functional brain networks using independent component analysis:functional brain networks connectivity." Doctoral thesis, Oulun yliopisto, 2013. http://urn.fi/urn:isbn:9789526201597.

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Abstract Functional communication between brain regions is likely to play a key role in complex cognitive processes that require continuous integration of information across different regions of the brain. This makes the studying of functional connectivity in the human brain of high importance. It also provides new insights into the hierarchical organization of the human brain regions. Resting-state networks (RSNs) can be reliably and reproducibly detected using independent component analysis (ICA) at both individual subject and group levels. A growing number of ICA studies have reported altered functional connectivity in clinical populations. In the current work, it was hypothesized that ICA model order selection influences characteristics of RSNs as well as their functional connectivity. In addition, it was suggested that high ICA model order could be a useful tool to provide more detailed functional connectivity results. RSNs’ characteristics, i.e. spatial features, volume and repeatability of RSNs, were evaluated, and also differences in functional connectivity were investigated across different ICA model orders. ICA model order estimation had a significant impact on the spatial characteristics of the RSNs as well as their parcellation into sub-networks. Notably, at low model orders neuroanatomically and functionally different units tend to aggregate into large singular RSN components, while at higher model orders these units become separate RSN components. Disease-related differences in functional connectivity also seem to alter as a function of ICA model order. The volume of between-group differences reached maximum at high model orders. These findings demonstrate that fine-grained RSNs can provide detailed, disease-specific functional connectivity alterations. Finally, in order to overcome the multiple comparisons problem encountered at high ICA model orders, a new framework for group-ICA analysis was introduced. The framework involved concatenation of IC maps prior to permutation tests, which enables statistical inferences from all selected RSNs. In SAD patients, this new correction enabled the detection of significantly increased functional connectivity in eleven RSNs
Tiivistelmä Toiminnallisten aivoalueiden välinen viestintä on todennäköisesti avainasemassa kognitiivisissa prosesseissa, jotka edellyttävät jatkuvaa tiedon integraatiota aivojen eri alueiden välillä. Tämä tekee ihmisaivojen toiminnallisen kytkennällisyyden tutkimuksesta erittäin tärkeätä. Kytkennälllisyyden tutkiminen antaa myös uutta tietoa ihmisaivojen osa-alueiden välisestä hierarkiasta. Aivojen hermoverkot voidaan luotettavasti ja toistettavasti havaita lepotilan toiminnasta yksilö- ja ryhmätasolla käyttämällä itsenäisten komponenttien analyysia (engl. Independent component analysis, ICA). Yhä useammat ICA-tutkimukset ovat raportoineet poikkeuksellisia toiminnallisen konnektiviteetin muutoksia kliinisissä populaatioissa. Tässä tutkimuksessa hypotetisoitiin, että ICA:lla laskettaujen komponenttien lukumäärä (l. asteluku) vaikuttaa tuloksena saatujen hermoverkkojen ominaisuuksiin kuten tilavuuteen ja kytkennällisyyteen. Lisäksi oletettiin, että korkea ICA-asteluku voisi olla herkempit tuottamaan yksityiskohtaisia toiminnallisen jaottelun tuloksia. Aivojen lepotilan hermoverkkojen ominaisuudet, kuten anatominen jakautuminen, volyymi ja lepohermoverkkojen havainnoinnin toistettavuus evaluoitin. Myös toiminnallisen kytkennällisyyden erot tutkitaan eri ICA-asteluvuilla. Havaittiin että asteluvulla on huomattava vaikutus aivojen lepotilan hermoverkkojen tilaominaisuuksiin sekä niiden jakautumiseen alaverkoiksi. Pienillä asteluvuilla hermoverkojen neuroanatomisesti erilliset yksiköt pyrkivät keräytymään laajoiksi yksittäisiksi komponenteiksi, kun taas korkeammilla asteluvuilla ne havaitaan erillisinä. Sairauksien aiheuttamat muutokset toiminnallisessa kytkennällisyydessä näyttävät muuttuvan myös ICA asteluvun mukaan saavuttaen maksiminsa korkeilla asteluvuilla. Korkeilla asteluvuilla voidaan havaita yksityiskohtaisia, sairaudelle ominaisia toiminnallisen konnektiviteetin muutoksia. Korkeisiin ICA asteluvun liittyvän tilastollisen monivertailuongelman ratkaisemiseksi kehitimme uuden menetelmän, jossa permutaatiotestejä edeltävien itsenäisten IC-karttoja yhdistämällä voidaan tehdä luotettava tilastollinen arvio yhtä aikaa lukuisista hermoverkoista. Kaamosmasennuspotilailla esimerkiksi kehittämämme korjaus paljastaa merkittävästi lisääntynyttä toiminnallista kytkennällisyyttä yhdessätoista hermoverkossa
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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 Informática
Humans 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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Nichele, Cristina. "Independent Component Analysis of GPS time series in the Altotiberina fault region in the Northern Apennines (Italy)." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/12437/.

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L'analisi delle componenti indipendenti (Independent Component Analysis, ICA) è una tecnica di statistica multivariata che consente di scomporre un segnale complesso in un certo numero di componenti, tra loro statisticamente indipendenti, che rappresentano le principali sorgenti di quel segnale. La tecnica ICA è stata applicata a serie temporali di spostamento GPS relative a 30 stazioni localizzate nell'Alta Valle del Tevere, nell'Appennino settentrionale. In quest'area, una faglia normale a basso angolo di immersione (circa 15°), faglia Altotiberina (Altotiberina fault, ATF), risulta attiva e responsabile di microsismicità (ML< 3), nonostante la teoria di Anderson sulla fagliazione affermi che non dovrebbe esserci scorrimento su faglie normali di questo tipo. L'interesse per l'ATF è inoltre dovuto al suo potenziale sismogenetico: un evento che dovesse attivare l'intera faglia (lunga circa 70 km) avrebbe infatti magnitudo intorno a 7. Per questo motivo la zona è monitorata da reti multiparametriche (sismiche, geodetiche, geochimiche) che registrano dati in maniera continuativa, rendendo possibile l’individuazione anche di piccoli segnali di deformazione transiente. Applicando la ICA alle serie temporali GPS si ottiene una scomposizione del segnale in 4 componenti indipendenti. L’analisi di queste componenti ha portato all’individuazione di correlazioni con serie temporali di piovosità, temperatura e sismicità. Una delle componenti, che presenta un segnale transiente di origine tettonica, è stata poi invertita per determinare la distribuzione dello scorrimento su piani di faglia noti. Il momento sismico associato allo scorrimento sulle faglie risulta maggiore del momento sismico associato ai terremoti registrati nell'area, suggerendo quindi che una parte dello scorrimento sia dovuto a movimenti asismici.
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Wiedemeyer, Christian [Verfasser], and Carsten [Akademischer Betreuer] Konrad. "Anwendungsmöglichkeiten und Praktikabilität der Independent Component Analysis (ICA) in der funktionellen Magnetresonanztomographie (fMRT) / Christian Wiedemeyer. Betreuer: Carsten Konrad." Marburg : Philipps-Universität Marburg, 2011. http://d-nb.info/1013288475/34.

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Naik, Ganesh Ramachandra, and ganesh naik@rmit edu au. "Iterative issues of ICA, quality of separation and number of sources: a study for biosignal applications." RMIT University. Electrical and Computer Engineering, 2009. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20090320.115103.

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This thesis has evaluated the use of Independent Component Analysis (ICA) on Surface Electromyography (sEMG), focusing on the biosignal applications. This research has identified and addressed the following four issues related to the use of ICA for biosignals: • The iterative nature of ICA • The order and magnitude ambiguity problems of ICA • Estimation of number of sources based on dependency and independency nature of the signals • Source separation for non-quadratic ICA (undercomplete and overcomplete) This research first establishes the applicability of ICA for sEMG and also identifies the shortcomings related to order and magnitude ambiguity. It has then developed, a mitigation strategy for these issues by using a single unmixing matrix and neural network weight matrix corresponding to the specific user. The research reports experimental verification of the technique and also the investigation of the impact of inter-subject and inter-experimental variations. The results demonstrate that while using sEMG without separation gives only 60% accuracy, and sEMG separated using traditional ICA gives an accuracy of 65%, this approach gives an accuracy of 99% for the same experimental data. Besides the marked improvement in accuracy, the other advantages of such a system are that it is suitable for real time operations and is easy to train by a lay user. The second part of this thesis reports research conducted to evaluate the use of ICA for the separation of bioelectric signals when the number of active sources may not be known. The work proposes the use of value of the determinant of the Global matrix generated using sparse sub band ICA for identifying the number of active sources. The results indicate that the technique is successful in identifying the number of active muscles for complex hand gestures. The results support the applications such as human computer interface. This thesis has also developed a method of determining the number of independent sources in a given mixture and has also demonstrated that using this information, it is possible to separate the signals in an undercomplete situation and reduce the redundancy in the data using standard ICA methods. The experimental verification has demonstrated that the quality of separation using this method is better than other techniques such as Principal Component Analysis (PCA) and selective PCA. This has number of applications such as audio separation and sensor networks.
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Whinnett, Mark. "Analysis of face specific visual processing in humans by applying independent components analysis(ICA) to magnetoencephalographic (MEG) data." Thesis, Open University, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.607160.

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Face recognition is a key human brain function as faces convey a wealth of information about a person's mood, intentions, interest, health, direction of gaze, intelligence and trustworthiness, among many factors. Previous studies gained from behavioural, functional magnetic resonance imaging (fMRI), electroencephalographic (EEG) and MEG studies have shown that face processing involves activity in many specialised areas of the brain, which are known collectively as the face processing system. The aim of this thesis has been to develop, apply and assess a novel technique of analysis in order to gain information about the face processing system. The new technique involves using Independent Components Analysis (ICA) to identify significant features in the data for each subject and then using k-means clustering to aggregate results across subjects. A key feature of this new technique is that it does not impose a priori assumptions on the localisation of the face processing system in either time or space, and in particular does not assume that the latency of evoked responses is the same between subjects. The new technique is evaluated for robustness, stability and validity by comparing it quantitatively to the well established technique of weighted Minimum Norm Estimation (wMNE). This thesis describes a visually evoked response experiment involving 23 healthy adult subjects in which MEG data was recorded as subjects viewed a sequence of images from three categories: human faces, monkey faces or motorbikes. This MEG data was co-registered with a standard head model (the MNI30S brain) so that inter-subject comparisons could be made. We identify six clusters of brain activity with peak responses in the latency range from lOOms to 3S0ms and give the relative weighting for each cluster for each the three image categories. We use a bootstrap technique to assess the significance of these weightings and find that the only cluster where the human face response was significantly stronger than the motorbike image response was a cluster with peak latency of l72ms, which confirms earlier studies. For this cluster the response to monkey face images was not significantly different to the human face image response at the 99% confidence level. Other significant differences between brain response to the image categories are reported. For each cluster of brain activity we estimate the activity within each labelled region of the MNI30S brain and again use a bootstrap technique to determine brain areas where activity is significantly above the median level of activity. In a similar way we investigate whether activity shows hemispherical bias by reporting the probability that we reject the null hypothesis that the left and right hemispheres have the same level of activation. For the clu~ter with peak latency at 172ms mentioned above we find that the response is right lateralised, which again confirms earlier studies. In addition to this information about the location of brain activity, the techniques used give detailed information about time evolution (and sequencing) that other techniques such as fMRI are unable to provide. This time evolution of the clusters shows some evidence for priming activity that may give advance notice of the importance of a new visual stimulus, and also some support for a theory of anterior temporal lobe involvement in face identification (Kriegeskorte2007). We also describe activity that could be attributed to executive systems and memory access,'
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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 - CAPES
Monitoring 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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Rafique, Muhammad T. "Monitoring, diagnostics and improvement of process performance." Thesis, Curtin University, 2009. http://hdl.handle.net/20.500.11937/1333.

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The data generated in a chemical industry is a reflection of the process. With the modern computer control systems and data logging facilities, there is an increasing ability to collect large amounts of data. As there are many underlying aspects of the process in that data, with its proper utilization, it is possible to obtain useful information for process monitoring and fault diagnosis in addition to many other decision making activities. The purpose of this research is to utilize the data driven multivariate techniques of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) for the estimation of process parameters. This research also includes analysis and comparison of these techniques for fault detection and diagnosis along with introduction, explanation and results from a new methodology developed in this research work namely Hybrid Independent Component Analysis (HICA).The first part of this research is the utilization of models of PCA and ICA for estimation of process parameters. The individual techniques of PCA and ICA are applied separately to the original data set of a waste water treatment plant (WWTP) and the process parameters for the unknown conditions of the process are calculated. For each of the techniques (PCA and ICA), the validation of the calculated parameters is carried out by construction of Decision Trees on WWTP dataset using inductive data mining and See 5.0. Both individual techniques were able to estimate all parameters successfully. The minor limitation in the validation of all results may be due to the strict application of these techniques to Gaussian and non-Gaussian data sets respectively. Using statistical analysis it was shown that the data set used in this work exhibits Gaussian and non-Gaussian behaviour.In the second part of this work multivariate techniques of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) have been used for fault detection and diagnosis of a process along with introduction of the new technique, Hybrid Independent Component Analysis (HICA). The techniques are applied to two case studies, the waste water treatment plant (WWTP) and an Air pollution data set. As reported in literature, PCA and ICA proved to be useful tools for process monitoring on both data set, but a comparison of PCA and ICA along with the newly developed technique (HICA) illustrated the superiority of HICA over PCA and ICA. It is evident from the fact that PCA detected 74% and 67% of the faults in the WWTP data and Air pollution data set respectively. ICA successfully detected 61.3% and 62% of the faults from these datasets. Finally HICA showed improved results by the detection of 90% and 81% of the faults in both case studies. This showed that the new developed algorithm is more effective than the individual techniques of PCA and ICA. For fault diagnosis using PCA, ICA and HICA, contribution plots are constructed leading to the identification of responsible variable/s for a particular fault. This part also includes the work done for the estimation of process parameters using HICA technique as was done with PCA and ICA in the first part of the research. As expected HICA technique was more successful in estimation of parameters than PCA and ICA in line with its working for process monitoring.
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Books on the topic "Independent Component Analysis (ICA)"

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Tülay, Adali, ed. Independent component analysis and signal separation: 8th International Conference, ICA 2009, Paraty, Brazil, March 15 - 18, 2009 proceedings. Berlin: Springer, 2009.

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E, Davies Mike, ed. Independent component analysis and signal separation: 7th international conference, ICA 2007, London, UK, September 9-12, 2007 : proceedings. Berlin: Springer, 2007.

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ICA 2004 (2004 Granada, Spain). Independent component analysis and blind signal separation: Fifth international conference, ICA 2004, Granada, Spain, September 22-24, 2004 : proceedings. Berlin: Springer, 2004.

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ICA 2004 (2004 Granada, Spain). Database and expert systems applications: 5th international conference, ICA 2004, Granada, Spain, September 22-24, 2004 : proceedings. Berlin: Springer, 2004.

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Hyvarinen, Aapo. Independent component analysis. New York: J. Wiley, 2001.

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Juha, Karhunen, and Oja Erkki, eds. Independent component analysis. New York: J. Wiley, 2001.

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Lee, Te-Won. Independent Component Analysis. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2851-4.

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Girolami, Mark, ed. Advances in Independent Component Analysis. London: Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0443-8.

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Girolami, Mark. Advances in Independent Component Analysis. London: Springer London, 2000.

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Lee, Te-Won. Independent Component Analysis: Theory and Applications. Boston, MA: Springer US, 1998.

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Book chapters on the topic "Independent Component Analysis (ICA)"

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Lee, Te-Won. "ICA Using Overcomplete Representations." In Independent Component Analysis, 111–21. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2851-4_5.

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Lee, Te-Won. "Biomedical Applications of ICA." In Independent Component Analysis, 145–66. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2851-4_7.

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Lee, Te-Won. "ICA for Feature Extraction." In Independent Component Analysis, 167–75. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2851-4_8.

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Lee, Te-Won. "First Steps Towards Nonlinear ICA." In Independent Component Analysis, 123–37. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2851-4_6.

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Lee, Te-Won. "Unsupervised Classification with ICA Mixture Models." In Independent Component Analysis, 177–83. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2851-4_9.

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Lee, Te-Won. "A Unifying Information-Theoretic Framework for ICA." In Independent Component Analysis, 67–80. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2851-4_3.

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Everson, Richard M., and Stephen J. Roberts. "Particle Filters for Non-Stationary ICA." In Advances in Independent Component Analysis, 23–41. London: Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0443-8_2.

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Wang, Baijie, Ercan E. Kuruoglu, and Junying Zhang. "ICA by Maximizing Non-stability." In Independent Component Analysis and Signal Separation, 179–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00599-2_23.

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Zhang, Kun, Heng Peng, Laiwan Chan, and Aapo Hyvärinen. "ICA with Sparse Connections: Revisited." In Independent Component Analysis and Signal Separation, 195–202. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00599-2_25.

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Yeredor, Arie. "ICA in Boolean XOR Mixtures." In Independent Component Analysis and Signal Separation, 827–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74494-8_103.

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Conference papers on the topic "Independent Component Analysis (ICA)"

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Al Nadi, Dia Abu, and Ayman M. Mansour. "Independent Component Analysis (ICA) for texture classification." In 2008 5th International Multi-Conference on Systems, Signals and Devices (SSD). IEEE, 2008. http://dx.doi.org/10.1109/ssd.2008.4632793.

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Szu, Harold H., Charles C. Hsu, and Takeshi Yamakawa. "Independent component analysis (ICA) using wavelet subband orthogonality." In Aerospace/Defense Sensing and Controls, edited by Harold H. Szu. SPIE, 1998. http://dx.doi.org/10.1117/12.304868.

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Turnip, Arjon, Mery Siahaan, Suprijanto, and Affan Kaysa Waafi. "P300 detection using nonlinear independent component analysis." In 2013 3rd International Conference on Instrumentation Control and Automation (ICA). IEEE, 2013. http://dx.doi.org/10.1109/ica.2013.6734054.

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Tan, Chin An, Arvind Gupta, and Shaungqing Li. "Application of Independent Component Analysis for Sound Source Separation." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35834.

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In this paper, experiments on the application of the independent component analysis (ICA) technique to separate unknown source signals are reported. ICA is one of the fastest growing fields in signal processing with applications to speech recognition systems, telecommunications, and biomedical signal processing. It is a data-transformation technique that finds independent sources of activity from linear mixtures of unknown independent sources. The statistical method to measure independence is to find a linear representation of the non-Gaussian data so that the components are as independent as possible and the mutual information between them is minimum. Although extensive simulations have been performed to demonstrate the power of the learning algorithm for the problems of instantaneous mixing and un-mixing of sources, its application to the noise diagnosis and separation in an industrial setting has not been considered. Noise separation in machinery has a strong basis in the “cocktail problem” in which it is difficult to separate/isolate the voice of a person in a room filled with competing voices and noises. The experiments conducted consist of separating several artificially generated sources of noise. Our results demonstrate that ICA can be effectively employed for such kinds of applications. The underdetermined problem in which there are fewer sensors than sources in the ICA formulation is also examined by applying a time-invariant linear transformation of the acquired signals to identify a single source.
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Guney, Irfan, Erdal Kilic, Okan Ozgonenel, Mustafa Ulutas, and Erol Karadeniz. "Fault detection in induction motors with independent component analysis (ICA)." In 2009 IEEE Bucharest PowerTech (POWERTECH). IEEE, 2009. http://dx.doi.org/10.1109/ptc.2009.5282251.

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Kinage, K. S., and S. G. Bhirud. "Face Recognition using independent component analysis of GaborJet (GaborJet-ICA)." In its Applications (CSPA). IEEE, 2010. http://dx.doi.org/10.1109/cspa.2010.5545318.

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Tuta, Leontin, Georgiana Rosu, Cristina Popovici, and Ioan Nicolaescu. "Real- Time EEG Data Processing Using Independent Component Analysis (ICA)." In 2022 14th International Conference on Communications (COMM). IEEE, 2022. http://dx.doi.org/10.1109/comm54429.2022.9817209.

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Song, Zhu Mei, Di Li, and Feng Ye. "An Application of Independent Component Analysis to Gas Metal Arc Welding." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-60204.

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A novel method, independent component analysis (ICA), is introduced to gas metal arc welding (GMAW) process monitoring. ICA was applied to arc signals, i.e. welding current and arc voltage, to remove the correlation between them and extract an independent component (IC). Two series of robotic GMAW experiments were carried out to study the feasibility of ICA for online monitoring. It was found that IC put up an abnormity when there was a step disturbance in the welding process. Experimental results showed that the IC could be used as a state variable representing the process variation. By applying statistical process control (SPC) for the obtained IC, a burning-through defect was isolated from the normal operation. The comparison between ICA and PCA was also made for the processes, which led an interesting result and was in need for further study.
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Tuta, Leontin, Mircea Nicolaescu, Georgiana Rosu, Alexandru Grivei, and Bogdan Barbulescu. "A Robust Adaptive Filtering Method based on Independent Component Analysis (ICA)." In 2020 13th International Conference on Communications (COMM). IEEE, 2020. http://dx.doi.org/10.1109/comm48946.2020.9141995.

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He, Chen, and Jane Wang. "An Independent Component Analysis (ICA) Based Approach for EEG Person Authentication." In 2009 3rd International Conference on Bioinformatics and Biomedical Engineering (iCBBE). IEEE, 2009. http://dx.doi.org/10.1109/icbbe.2009.5162328.

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Reports on the topic "Independent Component Analysis (ICA)"

1

Kolski, Jeffrey S., Robert J. Macek, and Rodney C. McCrady. Application of Independent Component Analysis (ICA) to Long Bunch Beams in the Los Alamos Storage Ring. Office of Scientific and Technical Information (OSTI), January 2011. http://dx.doi.org/10.2172/1008001.

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Nieto-Castanon, Alfonso. CONN functional connectivity toolbox (RRID:SCR_009550), Version 18. Hilbert Press, 2018. http://dx.doi.org/10.56441/hilbertpress.1818.9585.

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CONN is a Matlab-based cross-platform software for the computation, display, and analysis of functional connectivity in fMRI (fcMRI). Connectivity measures include seed-to-voxel connectivity maps, ROI-to- ROI connectivity matrices, graph properties of connectivity networks, generalized psychophysiological interaction models (gPPI), intrinsic connectivity, local correlation and other voxel-to-voxel measures, independent component analyses (ICA), and dynamic component analyses (dyn-ICA). CONN is available for resting state data (rsfMRI) as well as task-related designs. It covers the entire pipeline from raw fMRI data to hypothesis testing, including spatial coregistration, ART-based scrubbing, aCompCor strategy for control of physiological and movement confounds, first-level connectivity estimation, and second-level random-effect analyses and hypothesis testing.
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Nieto-Castanon, Alfonso. CONN functional connectivity toolbox (RRID:SCR_009550), Version 20. Hilbert Press, 2020. http://dx.doi.org/10.56441/hilbertpress.2048.3738.

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CONN is a Matlab-based cross-platform software for the computation, display, and analysis of functional connectivity in fMRI (fcMRI). Connectivity measures include seed-to-voxel connectivity maps, ROI-to- ROI connectivity matrices, graph properties of connectivity networks, generalized psychophysiological interaction models (gPPI), intrinsic connectivity, local correlation and other voxel-to-voxel measures, independent component analyses (ICA), and dynamic component analyses (dyn-ICA). CONN is available for resting state data (rsfMRI) as well as task-related designs. It covers the entire pipeline from raw fMRI data to hypothesis testing, including spatial coregistration, ART-based scrubbing, aCompCor strategy for control of physiological and movement confounds, first-level connectivity estimation, and second-level random-effect analyses and hypothesis testing.
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Nieto-Castanon, Alfonso. CONN functional connectivity toolbox (RRID:SCR_009550), Version 19. Hilbert Press, 2019. http://dx.doi.org/10.56441/hilbertpress.1927.9364.

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CONN is a Matlab-based cross-platform software for the computation, display, and analysis of functional connectivity in fMRI (fcMRI). Connectivity measures include seed-to-voxel connectivity maps, ROI-to- ROI connectivity matrices, graph properties of connectivity networks, generalized psychophysiological interaction models (gPPI), intrinsic connectivity, local correlation and other voxel-to-voxel measures, independent component analyses (ICA), and dynamic component analyses (dyn-ICA). CONN is available for resting state data (rsfMRI) as well as task-related designs. It covers the entire pipeline from raw fMRI data to hypothesis testing, including spatial coregistration, ART-based scrubbing, aCompCor strategy for control of physiological and movement confounds, first-level connectivity estimation, and second-level random-effect analyses and hypothesis testing.
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Schennach, Susanne M., and Florian Gunsilius. Independent nonlinear component analysis. The IFS, September 2019. http://dx.doi.org/10.1920/wp.cem.2019.4619.

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Robin, Jean-Marc, and Stéphane Bonhomme. Consistent noisy independent component analysis. Institute for Fiscal Studies, February 2008. http://dx.doi.org/10.1920/wp.cem.2008.0408.

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Salerno, Marc L. An Independent Component Analysis Blind Beamformer. Fort Belvoir, VA: Defense Technical Information Center, December 2000. http://dx.doi.org/10.21236/ada384795.

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Qi, Yuan. Learning Algorithms for Audio and Video Processing: Independent Component Analysis and Support Vector Machine Based Approaches. Fort Belvoir, VA: Defense Technical Information Center, August 2000. http://dx.doi.org/10.21236/ada458739.

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