Longitudinal functional principal component modelling via Stochastic Approximation Monte Carlo

KAUST Repository

Martinez, Josue G.

2010-06-01

The authors consider the analysis of hierarchical longitudinal functional data based upon a functional principal components approach. In contrast to standard frequentist approaches to selecting the number of principal components, the authors do model averaging using a Bayesian formulation. A relatively straightforward reversible jump Markov Chain Monte Carlo formulation has poor mixing properties and in simulated data often becomes trapped at the wrong number of principal components. In order to overcome this, the authors show how to apply Stochastic Approximation Monte Carlo (SAMC) to this problem, a method that has the potential to explore the entire space and does not become trapped in local extrema. The combination of reversible jump methods and SAMC in hierarchical longitudinal functional data is simplified by a polar coordinate representation of the principal components. The approach is easy to implement and does well in simulated data in determining the distribution of the number of principal components, and in terms of its frequentist estimation properties. Empirical applications are also presented.

PCA: Principal Component Analysis for spectra modeling

Science.gov (United States)

Hurley, Peter D.; Oliver, Seb; Farrah, Duncan; Wang, Lingyu; Efstathiou, Andreas

2012-07-01

The mid-infrared spectra of ultraluminous infrared galaxies (ULIRGs) contain a variety of spectral features that can be used as diagnostics to characterize the spectra. However, such diagnostics are biased by our prior prejudices on the origin of the features. Moreover, by using only part of the spectrum they do not utilize the full information content of the spectra. Blind statistical techniques such as principal component analysis (PCA) consider the whole spectrum, find correlated features and separate them out into distinct components. This code, written in IDL, classifies principal components of IRS spectra to define a new classification scheme using 5D Gaussian mixtures modelling. The five PCs and average spectra for the four classifications to classify objects are made available with the code.

MUSIC-CONTENT-ADAPTIVE ROBUST PRINCIPAL COMPONENT ANALYSIS FOR A SEMANTICALLY CONSISTENT SEPARATION OF FOREGROUND AND BACKGROUND IN MUSIC AUDIO SIGNALS

OpenAIRE

Papadopoulos , Hélène; Ellis , Daniel P.W.

2014-01-01

International audience; Robust Principal Component Analysis (RPCA) is a technique to decompose signals into sparse and low rank components, and has recently drawn the attention of the MIR field for the problem of separating leading vocals from accompaniment, with appealing re-sults obtained on small excerpts of music. However, the perfor-mance of the method drops when processing entire music tracks. We present an adaptive formulation of RPCA that incorporates music content information to guid...

ANOVA-principal component analysis and ANOVA-simultaneous component analysis: a comparison.

NARCIS (Netherlands)

Zwanenburg, G.; Hoefsloot, H.C.J.; Westerhuis, J.A.; Jansen, J.J.; Smilde, A.K.

2011-01-01

ANOVA-simultaneous component analysis (ASCA) is a recently developed tool to analyze multivariate data. In this paper, we enhance the explorative capability of ASCA by introducing a projection of the observations on the principal component subspace to visualize the variation among the measurements.

An Introductory Application of Principal Components to Cricket Data

Science.gov (United States)

Manage, Ananda B. W.; Scariano, Stephen M.

2013-01-01

Principal Component Analysis is widely used in applied multivariate data analysis, and this article shows how to motivate student interest in this topic using cricket sports data. Here, principal component analysis is successfully used to rank the cricket batsmen and bowlers who played in the 2012 Indian Premier League (IPL) competition. In…

Wavelet decomposition based principal component analysis for face recognition using MATLAB

Science.gov (United States)

Sharma, Mahesh Kumar; Sharma, Shashikant; Leeprechanon, Nopbhorn; Ranjan, Aashish

2016-03-01

For the realization of face recognition systems in the static as well as in the real time frame, algorithms such as principal component analysis, independent component analysis, linear discriminate analysis, neural networks and genetic algorithms are used for decades. This paper discusses an approach which is a wavelet decomposition based principal component analysis for face recognition. Principal component analysis is chosen over other algorithms due to its relative simplicity, efficiency, and robustness features. The term face recognition stands for identifying a person from his facial gestures and having resemblance with factor analysis in some sense, i.e. extraction of the principal component of an image. Principal component analysis is subjected to some drawbacks, mainly the poor discriminatory power and the large computational load in finding eigenvectors, in particular. These drawbacks can be greatly reduced by combining both wavelet transform decomposition for feature extraction and principal component analysis for pattern representation and classification together, by analyzing the facial gestures into space and time domain, where, frequency and time are used interchangeably. From the experimental results, it is envisaged that this face recognition method has made a significant percentage improvement in recognition rate as well as having a better computational efficiency.

Interpretable functional principal component analysis.

Science.gov (United States)

Lin, Zhenhua; Wang, Liangliang; Cao, Jiguo

2016-09-01

Functional principal component analysis (FPCA) is a popular approach to explore major sources of variation in a sample of random curves. These major sources of variation are represented by functional principal components (FPCs). The intervals where the values of FPCs are significant are interpreted as where sample curves have major variations. However, these intervals are often hard for naïve users to identify, because of the vague definition of "significant values". In this article, we develop a novel penalty-based method to derive FPCs that are only nonzero precisely in the intervals where the values of FPCs are significant, whence the derived FPCs possess better interpretability than the FPCs derived from existing methods. To compute the proposed FPCs, we devise an efficient algorithm based on projection deflation techniques. We show that the proposed interpretable FPCs are strongly consistent and asymptotically normal under mild conditions. Simulation studies confirm that with a competitive performance in explaining variations of sample curves, the proposed FPCs are more interpretable than the traditional counterparts. This advantage is demonstrated by analyzing two real datasets, namely, electroencephalography data and Canadian weather data. © 2015, The International Biometric Society.

Sparse Principal Component Analysis in Medical Shape Modeling

DEFF Research Database (Denmark)

Sjöstrand, Karl; Stegmann, Mikkel Bille; Larsen, Rasmus

2006-01-01

Principal component analysis (PCA) is a widely used tool in medical image analysis for data reduction, model building, and data understanding and exploration. While PCA is a holistic approach where each new variable is a linear combination of all original variables, sparse PCA (SPCA) aims...... analysis in medicine. Results for three different data sets are given in relation to standard PCA and sparse PCA by simple thresholding of sufficiently small loadings. Focus is on a recent algorithm for computing sparse principal components, but a review of other approaches is supplied as well. The SPCA...

An Operational Semantics for Trust Policies

DEFF Research Database (Denmark)

Krukow, Karl

2006-01-01

In the trust-structure framework for trust management, principals specify their trusting relationships in terms of trust policies. In their paper on trust structures, Carbone et al. present a language for such policies, and provide a suitable denotational semantics. The semantics ensures that for......In the trust-structure framework for trust management, principals specify their trusting relationships in terms of trust policies. In their paper on trust structures, Carbone et al. present a language for such policies, and provide a suitable denotational semantics. The semantics ensures...... that for any collection of policies, there is always a unique global trust-state, compatible with all the policies, specifying everyone's degree of trust in everyone else. However, as the authors themselves point out, the language lacks an operational model: the global trust-state is a well......-defined mathematical object, but it is not clear how principals can actually compute it. This becomes even more apparent when one considers the intended application environment: vast numbers of autonomous principals, distributed and possibly mobile. We provide a compositional operational semantics for a language...

Identifying the Component Structure of Satisfaction Scales by Nonlinear Principal Components Analysis

NARCIS (Netherlands)

Manisera, M.; Kooij, A.J. van der; Dusseldorp, E.

2010-01-01

The component structure of 14 Likert-type items measuring different aspects of job satisfaction was investigated using nonlinear Principal Components Analysis (NLPCA). NLPCA allows for analyzing these items at an ordinal or interval level. The participants were 2066 workers from five types of social

Principal Components as a Data Reduction and Noise Reduction Technique

Science.gov (United States)

Imhoff, M. L.; Campbell, W. J.

1982-01-01

The potential of principal components as a pipeline data reduction technique for thematic mapper data was assessed and principal components analysis and its transformation as a noise reduction technique was examined. Two primary factors were considered: (1) how might data reduction and noise reduction using the principal components transformation affect the extraction of accurate spectral classifications; and (2) what are the real savings in terms of computer processing and storage costs of using reduced data over the full 7-band TM complement. An area in central Pennsylvania was chosen for a study area. The image data for the project were collected using the Earth Resources Laboratory's thematic mapper simulator (TMS) instrument.

Determining the number of components in principal components analysis: A comparison of statistical, crossvalidation and approximated methods

NARCIS (Netherlands)

Saccenti, E.; Camacho, J.

2015-01-01

Principal component analysis is one of the most commonly used multivariate tools to describe and summarize data. Determining the optimal number of components in a principal component model is a fundamental problem in many fields of application. In this paper we compare the performance of several

Integrating Data Transformation in Principal Components Analysis

KAUST Repository

Maadooliat, Mehdi; Huang, Jianhua Z.; Hu, Jianhua

2015-01-01

Principal component analysis (PCA) is a popular dimension reduction method to reduce the complexity and obtain the informative aspects of high-dimensional datasets. When the data distribution is skewed, data transformation is commonly used prior

Functional principal component analysis of glomerular filtration rate curves after kidney transplant.

Science.gov (United States)

Dong, Jianghu J; Wang, Liangliang; Gill, Jagbir; Cao, Jiguo

2017-01-01

This article is motivated by some longitudinal clinical data of kidney transplant recipients, where kidney function progression is recorded as the estimated glomerular filtration rates at multiple time points post kidney transplantation. We propose to use the functional principal component analysis method to explore the major source of variations of glomerular filtration rate curves. We find that the estimated functional principal component scores can be used to cluster glomerular filtration rate curves. Ordering functional principal component scores can detect abnormal glomerular filtration rate curves. Finally, functional principal component analysis can effectively estimate missing glomerular filtration rate values and predict future glomerular filtration rate values.

Principal Component Analysis of Body Measurements In Three ...

African Journals Online (AJOL)

This study was conducted to explore the relationship among body measurements in 3 strains of broilers chicken (Arbor Acre, Marshal and Ross) using principal component analysis with the view of identifying those components that define body conformation in broilers. A total of 180 birds were used, 60 per strain.

Detecting Market Transitions and Energy Futures Risk Management Using Principal Components

NARCIS (Netherlands)

Borovkova, S.A.

2006-01-01

An empirical approach to analysing the forward curve dynamics of energy futures is presented. For non-seasonal commodities-such as crude oil-the forward curve is well described by the first three principal components: the level, slope and curvature. A principal component indicator is described that

Social Semantics for an Effective Enterprise

Science.gov (United States)

Berndt, Sarah; Doane, Mike

2012-01-01

An evolution of the Semantic Web, the Social Semantic Web (s2w), facilitates knowledge sharing with "useful information based on human contributions, which gets better as more people participate." The s2w reaches beyond the search box to move us from a collection of hyperlinked facts, to meaningful, real time context. When focused through the lens of Enterprise Search, the Social Semantic Web facilitates the fluid transition of meaningful business information from the source to the user. It is the confluence of human thought and computer processing structured with the iterative application of taxonomies, folksonomies, ontologies, and metadata schemas. The importance and nuances of human interaction are often deemphasized when focusing on automatic generation of semantic markup, which results in dissatisfied users and unrealized return on investment. Users consistently qualify the value of information sets through the act of selection, making them the de facto stakeholders of the Social Semantic Web. Employers are the ultimate beneficiaries of s2w utilization with a better informed, more decisive workforce; one not achieved with an IT miracle technology, but by improved human-computer interactions. Johnson Space Center Taxonomist Sarah Berndt and Mike Doane, principal owner of Term Management, LLC discuss the planning, development, and maintenance stages for components of a semantic system while emphasizing the necessity of a Social Semantic Web for the Enterprise. Identification of risks and variables associated with layering the successful implementation of a semantic system are also modeled.

An Operational Semantics for Trust Policies

DEFF Research Database (Denmark)

Krukow, Karl Kristian

2005-01-01

In the trust-structure model of trust management, principals specify their trusting relationships with other principals in terms of trust policies. In their paper on trust structures, Carbone et al. present a language for trust policies, and provide a suitable denotational semantics. The semantics...... ensures that for any collection of trust policies, there is always a unique global trust-state, compatible with all the policies, specifying everyone's degree of trust in everyone else. However, as the authors themselves point out, the language lacks an operational model: the global trust-state is a well......-defined mathematical object, but it is not clear how principals can actually compute it. This becomes even more apparent when one considers the intended application environment: vast numbers of autonomous principals, distributed and possibly mobile. We provide a compositional operational semantics for a language...

PEMBUATAN PERANGKAT LUNAK PENGENALAN WAJAH MENGGUNAKAN PRINCIPAL COMPONENTS ANALYSIS

Directory of Open Access Journals (Sweden)

Kartika Gunadi

2001-01-01

Full Text Available Face recognition is one of many important researches, and today, many applications have implemented it. Through development of techniques like Principal Components Analysis (PCA, computers can now outperform human in many face recognition tasks, particularly those in which large database of faces must be searched. Principal Components Analysis was used to reduce facial image dimension into fewer variables, which are easier to observe and handle. Those variables then fed into artificial neural networks using backpropagation method to recognise the given facial image. The test results show that PCA can provide high face recognition accuracy. For the training faces, a correct identification of 100% could be obtained. From some of network combinations that have been tested, a best average correct identification of 91,11% could be obtained for the test faces while the worst average result is 46,67 % correct identification Abstract in Bahasa Indonesia : Pengenalan wajah manusia merupakan salah satu bidang penelitian yang penting, dan dewasa ini banyak aplikasi yang dapat menerapkannya. Melalui pengembangan suatu teknik seperti Principal Components Analysis (PCA, komputer sekarang dapat melebihi kemampuan otak manusia dalam berbagai tugas pengenalan wajah, terutama tugas-tugas yang membutuhkan pencarian pada database wajah yang besar. Principal Components Analysis digunakan untuk mereduksi dimensi gambar wajah sehingga menghasilkan variabel yang lebih sedikit yang lebih mudah untuk diobsevasi dan ditangani. Hasil yang diperoleh kemudian akan dimasukkan ke suatu jaringan saraf tiruan dengan metode Backpropagation untuk mengenali gambar wajah yang telah diinputkan ke dalam sistem. Hasil pengujian sistem menunjukkan bahwa penggunaan PCA untuk pengenalan wajah dapat memberikan tingkat akurasi yang cukup tinggi. Untuk gambar wajah yang diikutsertakankan dalam latihan, dapat diperoleh 100% identifikasi yang benar. Dari beberapa kombinasi jaringan yang

Extraction of Independent Structural Images for Principal Component Thermography

Directory of Open Access Journals (Sweden)

Dmitry Gavrilov

2018-03-01

Full Text Available Thermography is a powerful tool for non-destructive testing of a wide range of materials. Thermography has a number of approaches differing in both experiment setup and the way the collected data are processed. Among such approaches is the Principal Component Thermography (PCT method, which is based on the statistical processing of raw thermal images collected by thermal camera. The processed images (principal components or empirical orthogonal functions form an orthonormal basis, and often look like a superposition of all possible structural features found in the object under inspection—i.e., surface heating non-uniformity, internal defects and material structure. At the same time, from practical point of view it is desirable to have images representing independent structural features. The work presented in this paper proposes an approach for separation of independent image patterns (archetypes from a set of principal component images. The approach is demonstrated in the application of inspection of composite materials as well as the non-invasive analysis of works of art.

Russian nominal semantics and morphology

DEFF Research Database (Denmark)

Nørgård-Sørensen, Jens

The principal idea behind this book is that lexis and grammar make up a single coherent structure. It is shown that the grammatical patterns of the different classes of Russian nominals are closely interconnected. They can be described as reflecting a limited set of semantic distinctions which ar...... or weaker, of Russian. Students will see a pattern in what is traditionally described as disparate subsystems, and linguists may be inspired to consider the theoretical points concerning language as a coherent system, determining usage.......The principal idea behind this book is that lexis and grammar make up a single coherent structure. It is shown that the grammatical patterns of the different classes of Russian nominals are closely interconnected. They can be described as reflecting a limited set of semantic distinctions which...... are also rooted in the lexical-semantic classification of Russian nouns. The presentation focuses on semantics, both lexical and grammatical, and not least the connection between these two levels of content. The principal theoretical impact is the insight that grammar and lexis should not be seen...

Age-related changes in ERP components of semantic and syntactic processing in a verb final language

Directory of Open Access Journals (Sweden)

Jee Eun Sung

2014-04-01

Both syntactic and semantic violations elicited negativity effects at 300-500ms time window, and the negativity effects were slightly attenuated in the elderly group. The results suggested that Korean speakers may process a syntactic component of a case marker under the semantic frame integration, eliciting the negativity effects associated with semantic violations. Elderly adults showed attenuated effects compared to the young group, indicating age-related changes emerged during real-time sentence processing.

Fast principal component analysis for stacking seismic data

Science.gov (United States)

Wu, Juan; Bai, Min

2018-04-01

Stacking seismic data plays an indispensable role in many steps of the seismic data processing and imaging workflow. Optimal stacking of seismic data can help mitigate seismic noise and enhance the principal components to a great extent. Traditional average-based seismic stacking methods cannot obtain optimal performance when the ambient noise is extremely strong. We propose a principal component analysis (PCA) algorithm for stacking seismic data without being sensitive to noise level. Considering the computational bottleneck of the classic PCA algorithm in processing massive seismic data, we propose an efficient PCA algorithm to make the proposed method readily applicable for industrial applications. Two numerically designed examples and one real seismic data are used to demonstrate the performance of the presented method.

Experimental and principal component analysis of waste ...

African Journals Online (AJOL)

The present study is aimed at determining through principal component analysis the most important variables affecting bacterial degradation in ponds. Data were collected from literature. In addition, samples were also collected from the waste stabilization ponds at the University of Nigeria, Nsukka and analyzed to ...

A Genealogical Interpretation of Principal Components Analysis

Science.gov (United States)

McVean, Gil

2009-01-01

Principal components analysis, PCA, is a statistical method commonly used in population genetics to identify structure in the distribution of genetic variation across geographical location and ethnic background. However, while the method is often used to inform about historical demographic processes, little is known about the relationship between fundamental demographic parameters and the projection of samples onto the primary axes. Here I show that for SNP data the projection of samples onto the principal components can be obtained directly from considering the average coalescent times between pairs of haploid genomes. The result provides a framework for interpreting PCA projections in terms of underlying processes, including migration, geographical isolation, and admixture. I also demonstrate a link between PCA and Wright's fst and show that SNP ascertainment has a largely simple and predictable effect on the projection of samples. Using examples from human genetics, I discuss the application of these results to empirical data and the implications for inference. PMID:19834557

Physicochemical properties of different corn varieties by principal components analysis and cluster analysis

International Nuclear Information System (INIS)

Zeng, J.; Li, G.; Sun, J.

2013-01-01

Principal components analysis and cluster analysis were used to investigate the properties of different corn varieties. The chemical compositions and some properties of corn flour which processed by drying milling were determined. The results showed that the chemical compositions and physicochemical properties were significantly different among twenty six corn varieties. The quality of corn flour was concerned with five principal components from principal component analysis and the contribution rate of starch pasting properties was important, which could account for 48.90%. Twenty six corn varieties could be classified into four groups by cluster analysis. The consistency between principal components analysis and cluster analysis indicated that multivariate analyses were feasible in the study of corn variety properties. (author)

Assessment of drinking water quality using principal component ...

African Journals Online (AJOL)

Assessment of drinking water quality using principal component analysis and partial least square discriminant analysis: a case study at water treatment plants, ... water and to detect the source of pollution for the most revealing parameters.

Fault Localization for Synchrophasor Data using Kernel Principal Component Analysis

Directory of Open Access Journals (Sweden)

CHEN, R.

2017-11-01

Full Text Available In this paper, based on Kernel Principal Component Analysis (KPCA of Phasor Measurement Units (PMU data, a nonlinear method is proposed for fault location in complex power systems. Resorting to the scaling factor, the derivative for a polynomial kernel is obtained. Then, the contribution of each variable to the T2 statistic is derived to determine whether a bus is the fault component. Compared to the previous Principal Component Analysis (PCA based methods, the novel version can combat the characteristic of strong nonlinearity, and provide the precise identification of fault location. Computer simulations are conducted to demonstrate the improved performance in recognizing the fault component and evaluating its propagation across the system based on the proposed method.

Principal component analysis of psoriasis lesions images

DEFF Research Database (Denmark)

Maletti, Gabriela Mariel; Ersbøll, Bjarne Kjær

2003-01-01

A set of RGB images of psoriasis lesions is used. By visual examination of these images, there seem to be no common pattern that could be used to find and align the lesions within and between sessions. It is expected that the principal components of the original images could be useful during future...

Principal Component Analysis as an Efficient Performance ...

African Journals Online (AJOL)

This paper uses the principal component analysis (PCA) to examine the possibility of using few explanatory variables (X's) to explain the variation in Y. It applied PCA to assess the performance of students in Abia State Polytechnic, Aba, Nigeria. This was done by estimating the coefficients of eight explanatory variables in a ...

Aeromagnetic Compensation Algorithm Based on Principal Component Analysis

Directory of Open Access Journals (Sweden)

Peilin Wu

2018-01-01

Full Text Available Aeromagnetic exploration is an important exploration method in geophysics. The data is typically measured by optically pumped magnetometer mounted on an aircraft. But any aircraft produces significant levels of magnetic interference. Therefore, aeromagnetic compensation is important in aeromagnetic exploration. However, multicollinearity of the aeromagnetic compensation model degrades the performance of the compensation. To address this issue, a novel aeromagnetic compensation method based on principal component analysis is proposed. Using the algorithm, the correlation in the feature matrix is eliminated and the principal components are using to construct the hyperplane to compensate the platform-generated magnetic fields. The algorithm was tested using a helicopter, and the obtained improvement ratio is 9.86. The compensated quality is almost the same or slightly better than the ridge regression. The validity of the proposed method was experimentally demonstrated.

Identifying apple surface defects using principal components analysis and artifical neural networks

Science.gov (United States)

Artificial neural networks and principal components were used to detect surface defects on apples in near-infrared images. Neural networks were trained and tested on sets of principal components derived from columns of pixels from images of apples acquired at two wavelengths (740 nm and 950 nm). I...

Identifying Meaning Components in the Translation of Medical Terms from English into Indonesian: A Semantic Approach

Directory of Open Access Journals (Sweden)

I Gusti Agung Sri Rwa Jayantini

2017-10-01

Full Text Available This paper focuses on identifying meaning components in the translation of English medical terms into Indonesian. The data used in this study are the English medical term disorder and its Indonesian equivalent penyakit (disease. The two terms are purposively chosen as the data of the present study, which is a comparative research on the lexical meaning investigation in two different languages. The investigation involving a particular term in one language and its equivalent in the other language is worth doing since the lexicons in every language have their own specific concepts that may be synonymous, yet they are not always interchangeable in all contexts. The analysis into meaning components is called decomposition by means of several semantic theories to analyse the meaning of a lexical item (Löbner 2013. Here, the meaning components of the two compared terms are demonstrated through a semantic approach, particularly Natural Semantic Metalanguage (NSM supported by the investigation on their synonyms and how the terms are used in different contexts. The results show that the meaning components of a particular term in one language like the English term disorder are not always found in the Indonesian term penyakit, or, conversely, some of the meaning components of the Indonesian term do not always exist in the English term.

The Logical-Semantic Basis for Formation of Main Components of Enterprise Strategy

Directory of Open Access Journals (Sweden)

Polyakova Yana O.

2016-11-01

Full Text Available The modern interpretation of the essence of enterprise strategy implies transformation of internal characteristics of the enterprise into key success factors in accordance with conditions of the functioning of external environment in order to ensure its sustainable leadership position in the long term and is one of the main elements of the system of enterprise strategic management in today’s business environment. The result of the conducted research is the improvement of the logical-semantic structure of main components of enterprise strategy through the implementation of research by the inductive method, which allowed to determine the logical sequence of stages of forming key success factors, reveal the conditions for transformation of main elements of each stage and conduct a semantic evaluation of the structure of main components of enterprise strategy at each individual stage to implement the processes of goal setting and evaluate results of the implementation of the chosen business strategy.

Principal Component Analysis: Most Favourite Tool in Chemometrics

Indian Academy of Sciences (India)

Abstract. Principal component analysis (PCA) is the most commonlyused chemometric technique. It is an unsupervised patternrecognition technique. PCA has found applications in chemistry,biology, medicine and economics. The present work attemptsto understand how PCA work and how can we interpretits results.

EXAFS and principal component analysis : a new shell game

International Nuclear Information System (INIS)

Wasserman, S.

1998-01-01

The use of principal component (factor) analysis in the analysis EXAFS spectra is described. The components derived from EXAFS spectra share mathematical properties with the original spectra. As a result, the abstract components can be analyzed using standard EXAFS methodology to yield the bond distances and other coordination parameters. The number of components that must be analyzed is usually less than the number of original spectra. The method is demonstrated using a series of spectra from aqueous solutions of uranyl ions

Combined principal component preprocessing and n-tuple neural networks for improved classification

DEFF Research Database (Denmark)

Høskuldsson, Agnar; Linneberg, Christian

2000-01-01

We present a combined principal component analysis/neural network scheme for classification. The data used to illustrate the method consist of spectral fluorescence recordings from seven different production facilities, and the task is to relate an unknown sample to one of these seven factories....... The data are first preprocessed by performing an individual principal component analysis on each of the seven groups of data. The components found are then used for classifying the data, but instead of making a single multiclass classifier, we follow the ideas of turning a multiclass problem into a number...... of two-class problems. For each possible pair of classes we further apply a transformation to the calculated principal components in order to increase the separation between the classes. Finally we apply the so-called n-tuple neural network to the transformed data in order to give the classification...

Scalable Robust Principal Component Analysis Using Grassmann Averages

DEFF Research Database (Denmark)

Hauberg, Søren; Feragen, Aasa; Enficiaud, Raffi

2016-01-01

In large datasets, manual data verification is impossible, and we must expect the number of outliers to increase with data size. While principal component analysis (PCA) can reduce data size, and scalable solutions exist, it is well-known that outliers can arbitrarily corrupt the results. Unfortu...

Efficacy of the Principal Components Analysis Techniques Using ...

African Journals Online (AJOL)

Second, the paper reports results of principal components analysis after the artificial data were submitted to three commonly used procedures; scree plot, Kaiser rule, and modified Horn's parallel analysis, and demonstrate the pedagogical utility of using artificial data in teaching advanced quantitative concepts. The results ...

Multistage principal component analysis based method for abdominal ECG decomposition

International Nuclear Information System (INIS)

Petrolis, Robertas; Krisciukaitis, Algimantas; Gintautas, Vladas

2015-01-01

Reflection of fetal heart electrical activity is present in registered abdominal ECG signals. However this signal component has noticeably less energy than concurrent signals, especially maternal ECG. Therefore traditionally recommended independent component analysis, fails to separate these two ECG signals. Multistage principal component analysis (PCA) is proposed for step-by-step extraction of abdominal ECG signal components. Truncated representation and subsequent subtraction of cardio cycles of maternal ECG are the first steps. The energy of fetal ECG component then becomes comparable or even exceeds energy of other components in the remaining signal. Second stage PCA concentrates energy of the sought signal in one principal component assuring its maximal amplitude regardless to the orientation of the fetus in multilead recordings. Third stage PCA is performed on signal excerpts representing detected fetal heart beats in aim to perform their truncated representation reconstructing their shape for further analysis. The algorithm was tested with PhysioNet Challenge 2013 signals and signals recorded in the Department of Obstetrics and Gynecology, Lithuanian University of Health Sciences. Results of our method in PhysioNet Challenge 2013 on open data set were: average score: 341.503 bpm 2 and 32.81 ms. (paper)

Selecting the Number of Principal Components in Functional Data

KAUST Repository

Li, Yehua

2013-12-01

Functional principal component analysis (FPCA) has become the most widely used dimension reduction tool for functional data analysis. We consider functional data measured at random, subject-specific time points, contaminated with measurement error, allowing for both sparse and dense functional data, and propose novel information criteria to select the number of principal component in such data. We propose a Bayesian information criterion based on marginal modeling that can consistently select the number of principal components for both sparse and dense functional data. For dense functional data, we also develop an Akaike information criterion based on the expected Kullback-Leibler information under a Gaussian assumption. In connecting with the time series literature, we also consider a class of information criteria proposed for factor analysis of multivariate time series and show that they are still consistent for dense functional data, if a prescribed undersmoothing scheme is undertaken in the FPCA algorithm. We perform intensive simulation studies and show that the proposed information criteria vastly outperform existing methods for this type of data. Surprisingly, our empirical evidence shows that our information criteria proposed for dense functional data also perform well for sparse functional data. An empirical example using colon carcinogenesis data is also provided to illustrate the results. Supplementary materials for this article are available online. © 2013 American Statistical Association.

[Content of mineral elements of Gastrodia elata by principal components analysis].

Science.gov (United States)

Li, Jin-ling; Zhao, Zhi; Liu, Hong-chang; Luo, Chun-li; Huang, Ming-jin; Luo, Fu-lai; Wang, Hua-lei

2015-03-01

To study the content of mineral elements and the principal components in Gastrodia elata. Mineral elements were determined by ICP and the data was analyzed by SPSS. K element has the highest content-and the average content was 15.31 g x kg(-1). The average content of N element was 8.99 g x kg(-1), followed by K element. The coefficient of variation of K and N was small, but the Mn was the biggest with 51.39%. The highly significant positive correlation was found among N, P and K . Three principal components were selected by principal components analysis to evaluate the quality of G. elata. P, B, N, K, Cu, Mn, Fe and Mg were the characteristic elements of G. elata. The content of K and N elements was higher and relatively stable. The variation of Mn content was biggest. The quality of G. elata in Guizhou and Yunnan was better from the perspective of mineral elements.

The influence of iliotibial band syndrome history on running biomechanics examined via principal components analysis.

Science.gov (United States)

Foch, Eric; Milner, Clare E

2014-01-03

Iliotibial band syndrome (ITBS) is a common knee overuse injury among female runners. Atypical discrete trunk and lower extremity biomechanics during running may be associated with the etiology of ITBS. Examining discrete data points limits the interpretation of a waveform to a single value. Characterizing entire kinematic and kinetic waveforms may provide additional insight into biomechanical factors associated with ITBS. Therefore, the purpose of this cross-sectional investigation was to determine whether female runners with previous ITBS exhibited differences in kinematics and kinetics compared to controls using a principal components analysis (PCA) approach. Forty participants comprised two groups: previous ITBS and controls. Principal component scores were retained for the first three principal components and were analyzed using independent t-tests. The retained principal components accounted for 93-99% of the total variance within each waveform. Runners with previous ITBS exhibited low principal component one scores for frontal plane hip angle. Principal component one accounted for the overall magnitude in hip adduction which indicated that runners with previous ITBS assumed less hip adduction throughout stance. No differences in the remaining retained principal component scores for the waveforms were detected among groups. A smaller hip adduction angle throughout the stance phase of running may be a compensatory strategy to limit iliotibial band strain. This running strategy may have persisted after ITBS symptoms subsided. © 2013 Published by Elsevier Ltd.

Incremental principal component pursuit for video background modeling

Science.gov (United States)

Rodriquez-Valderrama, Paul A.; Wohlberg, Brendt

2017-03-14

An incremental Principal Component Pursuit (PCP) algorithm for video background modeling that is able to process one frame at a time while adapting to changes in background, with a computational complexity that allows for real-time processing, having a low memory footprint and is robust to translational and rotational jitter.

Principal component analysis study of visual and verbal metaphoric comprehension in children with autism and learning disabilities.

Science.gov (United States)

Mashal, Nira; Kasirer, Anat

2012-01-01

This research extends previous studies regarding the metaphoric competence of autistic and learning disable children on different measures of visual and verbal non-literal language comprehension, as well as cognitive abilities that include semantic knowledge, executive functions, similarities, and reading fluency. Thirty seven children with autism (ASD), 20 children with learning disabilities (LD), and 21 typically developed (TD) children participated in the study. Principal components analysis was used to examine the interrelationship among the various tests in each group. Results showed different patterns in the data according to group. In particular, the results revealed that there is no dichotomy between visual and verbal metaphors in TD children but rather metaphor are classified according to their familiarity level. In the LD group visual metaphors were classified independently of the verbal metaphors. Verbal metaphoric understanding in the ASD group resembled the LD group. In addition, our results revealed the relative weakness of the ASD and LD children in suppressing irrelevant information. Copyright © 2011 Elsevier Ltd. All rights reserved.

How doctors apply semantic components to specify search in work-related information retrieval

DEFF Research Database (Denmark)

Lykke, Marianne; Price, Susan L.; Delcambre, Lois L. M.

2012-01-01

Workplace searching is often context-specific and targets a “right answer” within some domain-specific aspect of the search topic. We have developed the semantic component (SC) model that allows searchers to specify a search within context-specific aspects of the main topic of documents. The goal...

Quality Aware Compression of Electrocardiogram Using Principal Component Analysis.

Science.gov (United States)

Gupta, Rajarshi

2016-05-01

Electrocardiogram (ECG) compression finds wide application in various patient monitoring purposes. Quality control in ECG compression ensures reconstruction quality and its clinical acceptance for diagnostic decision making. In this paper, a quality aware compression method of single lead ECG is described using principal component analysis (PCA). After pre-processing, beat extraction and PCA decomposition, two independent quality criteria, namely, bit rate control (BRC) or error control (EC) criteria were set to select optimal principal components, eigenvectors and their quantization level to achieve desired bit rate or error measure. The selected principal components and eigenvectors were finally compressed using a modified delta and Huffman encoder. The algorithms were validated with 32 sets of MIT Arrhythmia data and 60 normal and 30 sets of diagnostic ECG data from PTB Diagnostic ECG data ptbdb, all at 1 kHz sampling. For BRC with a CR threshold of 40, an average Compression Ratio (CR), percentage root mean squared difference normalized (PRDN) and maximum absolute error (MAE) of 50.74, 16.22 and 0.243 mV respectively were obtained. For EC with an upper limit of 5 % PRDN and 0.1 mV MAE, the average CR, PRDN and MAE of 9.48, 4.13 and 0.049 mV respectively were obtained. For mitdb data 117, the reconstruction quality could be preserved up to CR of 68.96 by extending the BRC threshold. The proposed method yields better results than recently published works on quality controlled ECG compression.

On Bayesian Principal Component Analysis

Czech Academy of Sciences Publication Activity Database

Šmídl, Václav; Quinn, A.

2007-01-01

Roč. 51, č. 9 (2007), s. 4101-4123 ISSN 0167-9473 R&D Projects: GA MŠk(CZ) 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : Principal component analysis ( PCA ) * Variational bayes (VB) * von-Mises–Fisher distribution Subject RIV: BC - Control Systems Theory Impact factor: 1.029, year: 2007 http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V8V-4MYD60N-6&_user=10&_coverDate=05%2F15%2F2007&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=b8ea629d48df926fe18f9e5724c9003a

Principal Component Analysis - A Powerful Tool in Computing Marketing Information

Directory of Open Access Journals (Sweden)

Constantin C.

2014-12-01

Full Text Available This paper is about an instrumental research regarding a powerful multivariate data analysis method which can be used by the researchers in order to obtain valuable information for decision makers that need to solve the marketing problem a company face with. The literature stresses the need to avoid the multicollinearity phenomenon in multivariate analysis and the features of Principal Component Analysis (PCA in reducing the number of variables that could be correlated with each other to a small number of principal components that are uncorrelated. In this respect, the paper presents step-by-step the process of applying the PCA in marketing research when we use a large number of variables that naturally are collinear.

A Note on McDonald's Generalization of Principal Components Analysis

Science.gov (United States)

Shine, Lester C., II

1972-01-01

It is shown that McDonald's generalization of Classical Principal Components Analysis to groups of variables maximally channels the totalvariance of the original variables through the groups of variables acting as groups. An equation is obtained for determining the vectors of correlations of the L2 components with the original variables.…

Principal Component Surface (2011) for Fish Bay, St. John

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This image represents a 0.3x0.3 meter principal component analysis (PCA) surface for areas inside Fish Bay, St. John in the U.S. Virgin Islands (USVI). It was...

Principal Component Surface (2011) for Coral Bay, St. John

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This image represents a 0.3x0.3 meter principal component analysis (PCA) surface for areas inside Coral Bay, St. John in the U.S. Virgin Islands (USVI). It was...

Functional Principal Components Analysis of Shanghai Stock Exchange 50 Index

Directory of Open Access Journals (Sweden)

Zhiliang Wang

2014-01-01

Full Text Available The main purpose of this paper is to explore the principle components of Shanghai stock exchange 50 index by means of functional principal component analysis (FPCA. Functional data analysis (FDA deals with random variables (or process with realizations in the smooth functional space. One of the most popular FDA techniques is functional principal component analysis, which was introduced for the statistical analysis of a set of financial time series from an explorative point of view. FPCA is the functional analogue of the well-known dimension reduction technique in the multivariate statistical analysis, searching for linear transformations of the random vector with the maximal variance. In this paper, we studied the monthly return volatility of Shanghai stock exchange 50 index (SSE50. Using FPCA to reduce dimension to a finite level, we extracted the most significant components of the data and some relevant statistical features of such related datasets. The calculated results show that regarding the samples as random functions is rational. Compared with the ordinary principle component analysis, FPCA can solve the problem of different dimensions in the samples. And FPCA is a convenient approach to extract the main variance factors.

Exploring functional data analysis and wavelet principal component analysis on ecstasy (MDMA wastewater data

Directory of Open Access Journals (Sweden)

Stefania Salvatore

2016-07-01

Full Text Available Abstract Background Wastewater-based epidemiology (WBE is a novel approach in drug use epidemiology which aims to monitor the extent of use of various drugs in a community. In this study, we investigate functional principal component analysis (FPCA as a tool for analysing WBE data and compare it to traditional principal component analysis (PCA and to wavelet principal component analysis (WPCA which is more flexible temporally. Methods We analysed temporal wastewater data from 42 European cities collected daily over one week in March 2013. The main temporal features of ecstasy (MDMA were extracted using FPCA using both Fourier and B-spline basis functions with three different smoothing parameters, along with PCA and WPCA with different mother wavelets and shrinkage rules. The stability of FPCA was explored through bootstrapping and analysis of sensitivity to missing data. Results The first three principal components (PCs, functional principal components (FPCs and wavelet principal components (WPCs explained 87.5-99.6 % of the temporal variation between cities, depending on the choice of basis and smoothing. The extracted temporal features from PCA, FPCA and WPCA were consistent. FPCA using Fourier basis and common-optimal smoothing was the most stable and least sensitive to missing data. Conclusion FPCA is a flexible and analytically tractable method for analysing temporal changes in wastewater data, and is robust to missing data. WPCA did not reveal any rapid temporal changes in the data not captured by FPCA. Overall the results suggest FPCA with Fourier basis functions and common-optimal smoothing parameter as the most accurate approach when analysing WBE data.

Optimal pattern synthesis for speech recognition based on principal component analysis

Science.gov (United States)

Korsun, O. N.; Poliyev, A. V.

2018-02-01

The algorithm for building an optimal pattern for the purpose of automatic speech recognition, which increases the probability of correct recognition, is developed and presented in this work. The optimal pattern forming is based on the decomposition of an initial pattern to principal components, which enables to reduce the dimension of multi-parameter optimization problem. At the next step the training samples are introduced and the optimal estimates for principal components decomposition coefficients are obtained by a numeric parameter optimization algorithm. Finally, we consider the experiment results that show the improvement in speech recognition introduced by the proposed optimization algorithm.

Group-wise Principal Component Analysis for Exploratory Data Analysis

NARCIS (Netherlands)

Camacho, J.; Rodriquez-Gomez, Rafael A.; Saccenti, E.

2017-01-01

In this paper, we propose a new framework for matrix factorization based on Principal Component Analysis (PCA) where sparsity is imposed. The structure to impose sparsity is defined in terms of groups of correlated variables found in correlation matrices or maps. The framework is based on three new

Principal Component Clustering Approach to Teaching Quality Discriminant Analysis

Science.gov (United States)

Xian, Sidong; Xia, Haibo; Yin, Yubo; Zhai, Zhansheng; Shang, Yan

2016-01-01

Teaching quality is the lifeline of the higher education. Many universities have made some effective achievement about evaluating the teaching quality. In this paper, we establish the Students' evaluation of teaching (SET) discriminant analysis model and algorithm based on principal component clustering analysis. Additionally, we classify the SET…

Basic semantics of product sounds

NARCIS (Netherlands)

Özcan Vieira, E.; Van Egmond, R.

2012-01-01

Product experience is a result of sensory and semantic experiences with product properties. In this paper, we focus on the semantic attributes of product sounds and explore the basic components for product sound related semantics using a semantic differential paradigmand factor analysis. With two

Principal component analysis of tomato genotypes based on some morphological and biochemical quality indicators

Directory of Open Access Journals (Sweden)

Glogovac Svetlana

2012-01-01

Full Text Available This study investigates variability of tomato genotypes based on morphological and biochemical fruit traits. Experimental material is a part of tomato genetic collection from Institute of Filed and Vegetable Crops in Novi Sad, Serbia. Genotypes were analyzed for fruit mass, locule number, index of fruit shape, fruit colour, dry matter content, total sugars, total acidity, lycopene and vitamin C. Minimum, maximum and average values and main indicators of variability (CV and σ were calculated. Principal component analysis was performed to determinate variability source structure. Four principal components, which contribute 93.75% of the total variability, were selected for analysis. The first principal component is defined by vitamin C, locule number and index of fruit shape. The second component is determined by dry matter content, and total acidity, the third by lycopene, fruit mass and fruit colour. Total sugars had the greatest part in the fourth component.

Longitudinal functional principal component modelling via Stochastic Approximation Monte Carlo

KAUST Repository

Martinez, Josue G.; Liang, Faming; Zhou, Lan; Carroll, Raymond J.

2010-01-01

model averaging using a Bayesian formulation. A relatively straightforward reversible jump Markov Chain Monte Carlo formulation has poor mixing properties and in simulated data often becomes trapped at the wrong number of principal components. In order

High Performance Descriptive Semantic Analysis of Semantic Graph Databases

Energy Technology Data Exchange (ETDEWEB)

Joslyn, Cliff A.; Adolf, Robert D.; al-Saffar, Sinan; Feo, John T.; Haglin, David J.; Mackey, Greg E.; Mizell, David W.

2011-06-02

As semantic graph database technology grows to address components ranging from extant large triple stores to SPARQL endpoints over SQL-structured relational databases, it will become increasingly important to be able to understand their inherent semantic structure, whether codified in explicit ontologies or not. Our group is researching novel methods for what we call descriptive semantic analysis of RDF triplestores, to serve purposes of analysis, interpretation, visualization, and optimization. But data size and computational complexity makes it increasingly necessary to bring high performance computational resources to bear on this task. Our research group built a novel high performance hybrid system comprising computational capability for semantic graph database processing utilizing the large multi-threaded architecture of the Cray XMT platform, conventional servers, and large data stores. In this paper we describe that architecture and our methods, and present the results of our analyses of basic properties, connected components, namespace interaction, and typed paths such for the Billion Triple Challenge 2010 dataset.

Principal component structure and sport-specific differences in the running one-leg vertical jump.

Science.gov (United States)

Laffaye, G; Bardy, B G; Durey, A

2007-05-01

The aim of this study is to identify the kinetic principal components involved in one-leg running vertical jumps, as well as the potential differences between specialists from different sports. The sample was composed of 25 regional skilled athletes who play different jumping sports (volleyball players, handball players, basketball players, high jumpers and novices), who performed a running one-leg jump. A principal component analysis was performed on the data obtained from the 200 tested jumps in order to identify the principal components summarizing the six variables extracted from the force-time curve. Two principal components including six variables accounted for 78 % of the variance in jump height. Running one-leg vertical jump performance was predicted by a temporal component (that brings together impulse time, eccentric time and vertical displacement of the center of mass) and a force component (who brings together relative peak of force and power, and rate of force development). A comparison made among athletes revealed a temporal-prevailing profile for volleyball players, and a force-dominant profile for Fosbury high jumpers. Novices showed an ineffective utilization of the force component, while handball and basketball players showed heterogeneous and neutral component profiles. Participants will use a jumping strategy in which variables related to either the magnitude or timing of force production will be closely coupled; athletes from different sporting backgrounds will use a jumping strategy that reflects the inherent demands of their chosen sport.

Water quality of the Chhoti Gandak River using principal component ...

Indian Academy of Sciences (India)

; therefore water samples were collected to analyse its quality along the entire length of Chhoti Gandak. River. The principal components of water quality are controlled by lithology, gentle slope gradient, poor drainage, long residence of water, ...

On the structure of dynamic principal component analysis used in statistical process monitoring

DEFF Research Database (Denmark)

Vanhatalo, Erik; Kulahci, Murat; Bergquist, Bjarne

2017-01-01

When principal component analysis (PCA) is used for statistical process monitoring it relies on the assumption that data are time independent. However, industrial data will often exhibit serial correlation. Dynamic PCA (DPCA) has been suggested as a remedy for high-dimensional and time...... for determining the number of principal components to retain. The number of retained principal components is determined by visual inspection of the serial correlation in the squared prediction error statistic, Q (SPE), together with the cumulative explained variance of the model. The methods are illustrated using...... driven method to determine the maximum number of lags in DPCA with a foundation in multivariate time series analysis. The method is based on the behavior of the eigenvalues of the lagged autocorrelation and partial autocorrelation matrices. Given a specific lag structure we also propose a method...

Selective principal component regression analysis of fluorescence hyperspectral image to assess aflatoxin contamination in corn

Science.gov (United States)

Selective principal component regression analysis (SPCR) uses a subset of the original image bands for principal component transformation and regression. For optimal band selection before the transformation, this paper used genetic algorithms (GA). In this case, the GA process used the regression co...

Principal component analysis for neural electron/jet discrimination in highly segmented calorimeters

International Nuclear Information System (INIS)

Vassali, M.R.; Seixas, J.M.

2001-01-01

A neural electron/jet discriminator based on calorimetry is developed for the second-level trigger system of the ATLAS detector. As preprocessing of the calorimeter information, a principal component analysis is performed on each segment of the two sections (electromagnetic and hadronic) of the calorimeter system, in order to reduce significantly the dimension of the input data space and fully explore the detailed energy deposition profile, which is provided by the highly-segmented calorimeter system. It is shown that projecting calorimeter data onto 33 segmented principal components, the discrimination efficiency of the neural classifier reaches 98.9% for electrons (with only 1% of false alarm probability). Furthermore, restricting data projection onto only 9 components, an electron efficiency of 99.1% is achieved (with 3% of false alarm), which confirms that a fast triggering system may be designed using few components

The analysis of multivariate group differences using common principal components

NARCIS (Netherlands)

Bechger, T.M.; Blanca, M.J.; Maris, G.

2014-01-01

Although it is simple to determine whether multivariate group differences are statistically significant or not, such differences are often difficult to interpret. This article is about common principal components analysis as a tool for the exploratory investigation of multivariate group differences

Nonparametric inference in nonlinear principal components analysis : exploration and beyond

NARCIS (Netherlands)

Linting, Mariëlle

2007-01-01

In the social and behavioral sciences, data sets often do not meet the assumptions of traditional analysis methods. Therefore, nonlinear alternatives to traditional methods have been developed. This thesis starts with a didactic discussion of nonlinear principal components analysis (NLPCA),

Predicting Insolvency : A comparison between discriminant analysis and logistic regression using principal components

OpenAIRE

Geroukis, Asterios; Brorson, Erik

2014-01-01

In this study, we compare the two statistical techniques logistic regression and discriminant analysis to see how well they classify companies based on clusters – made from the solvency ratio ­– using principal components as independent variables. The principal components are made with different financial ratios. We use cluster analysis to find groups with low, medium and high solvency ratio of 1200 different companies found on the NASDAQ stock market and use this as an apriori definition of ...

Analysis and Classification of Acoustic Emission Signals During Wood Drying Using the Principal Component Analysis

Energy Technology Data Exchange (ETDEWEB)

Kang, Ho Yang [Korea Research Institute of Standards and Science, Daejeon (Korea, Republic of); Kim, Ki Bok [Chungnam National University, Daejeon (Korea, Republic of)

2003-06-15

In this study, acoustic emission (AE) signals due to surface cracking and moisture movement in the flat-sawn boards of oak (Quercus Variablilis) during drying under the ambient conditions were analyzed and classified using the principal component analysis. The AE signals corresponding to surface cracking showed higher in peak amplitude and peak frequency, and shorter in rise time than those corresponding to moisture movement. To reduce the multicollinearity among AE features and to extract the significant AE parameters, correlation analysis was performed. Over 99% of the variance of AE parameters could be accounted for by the first to the fourth principal components. The classification feasibility and success rate were investigated in terms of two statistical classifiers having six independent variables (AE parameters) and six principal components. As a result, the statistical classifier having AE parameters showed the success rate of 70.0%. The statistical classifier having principal components showed the success rate of 87.5% which was considerably than that of the statistical classifier having AE parameters

Analysis and Classification of Acoustic Emission Signals During Wood Drying Using the Principal Component Analysis

International Nuclear Information System (INIS)

Kang, Ho Yang; Kim, Ki Bok

2003-01-01

In this study, acoustic emission (AE) signals due to surface cracking and moisture movement in the flat-sawn boards of oak (Quercus Variablilis) during drying under the ambient conditions were analyzed and classified using the principal component analysis. The AE signals corresponding to surface cracking showed higher in peak amplitude and peak frequency, and shorter in rise time than those corresponding to moisture movement. To reduce the multicollinearity among AE features and to extract the significant AE parameters, correlation analysis was performed. Over 99% of the variance of AE parameters could be accounted for by the first to the fourth principal components. The classification feasibility and success rate were investigated in terms of two statistical classifiers having six independent variables (AE parameters) and six principal components. As a result, the statistical classifier having AE parameters showed the success rate of 70.0%. The statistical classifier having principal components showed the success rate of 87.5% which was considerably than that of the statistical classifier having AE parameters

The effect of combined sensory and semantic components on audio-visual speech perception in older adults

Directory of Open Access Journals (Sweden)

Corrina eMaguinness

2011-12-01

Full Text Available Previous studies have found that perception in older people benefits from multisensory over uni-sensory information. As normal speech recognition is affected by both the auditory input and the visual lip-movements of the speaker, we investigated the efficiency of audio and visual integration in an older population by manipulating the relative reliability of the auditory and visual information in speech. We also investigated the role of the semantic context of the sentence to assess whether audio-visual integration is affected by top-down semantic processing. We presented participants with audio-visual sentences in which the visual component was either blurred or not blurred. We found that there was a greater cost in recall performance for semantically meaningless speech in the audio-visual blur compared to audio-visual no blur condition and this effect was specific to the older group. Our findings have implications for understanding how aging affects efficient multisensory integration for the perception of speech and suggests that multisensory inputs may benefit speech perception in older adults when the semantic content of the speech is unpredictable.

Principal Component Analysis of Some Quantitative and Qualitative Traits in Iranian Spinach Landraces

Directory of Open Access Journals (Sweden)

Mohebodini Mehdi

2017-08-01

Full Text Available Landraces of spinach in Iran have not been sufficiently characterised for their morpho-agronomic traits. Such characterisation would be helpful in the development of new genetically improved cultivars. In this study 54 spinach accessions collected from the major spinach growing areas of Iran were evaluated to determine their phenotypic diversity profile of spinach genotypes on the basis of 10 quantitative and 9 qualitative morpho-agronomic traits. High coefficients of variation were recorded in some quantitative traits (dry yield and leaf area and all of the qualitative traits. Using principal component analysis, the first four principal components with eigen-values more than 1 contributed 87% of the variability among accessions for quantitative traits, whereas the first four principal components with eigen-values more than 0.8 contributed 79% of the variability among accessions for qualitative traits. The most important relations observed on the first two principal components were a strong positive association between leaf width and petiole length; between leaf length and leaf numbers in flowering; and among fresh yield, dry yield and petiole diameter; a near zero correlation between days to flowering with leaf width and petiole length. Prickly seeds, high percentage of female plants, smooth leaf texture, high numbers of leaves at flowering, greygreen leaves, erect petiole attitude and long petiole length are important characters for spinach breeding programmes.

Revealing the equivalence of two clonal survival models by principal component analysis

International Nuclear Information System (INIS)

Lachet, Bernard; Dufour, Jacques

1976-01-01

The principal component analysis of 21 chlorella cell survival curves, adjusted by one-hit and two-hit target models, lead to quite similar projections on the principal plan: the homologous parameters of these models are linearly correlated; the reason for the statistical equivalence of these two models, in the present state of experimental inaccuracy, is revealed [fr

Radar fall detection using principal component analysis

Science.gov (United States)

Jokanovic, Branka; Amin, Moeness; Ahmad, Fauzia; Boashash, Boualem

2016-05-01

Falls are a major cause of fatal and nonfatal injuries in people aged 65 years and older. Radar has the potential to become one of the leading technologies for fall detection, thereby enabling the elderly to live independently. Existing techniques for fall detection using radar are based on manual feature extraction and require significant parameter tuning in order to provide successful detections. In this paper, we employ principal component analysis for fall detection, wherein eigen images of observed motions are employed for classification. Using real data, we demonstrate that the PCA based technique provides performance improvement over the conventional feature extraction methods.

Airborne electromagnetic data levelling using principal component analysis based on flight line difference

Science.gov (United States)

Zhang, Qiong; Peng, Cong; Lu, Yiming; Wang, Hao; Zhu, Kaiguang

2018-04-01

A novel technique is developed to level airborne geophysical data using principal component analysis based on flight line difference. In the paper, flight line difference is introduced to enhance the features of levelling error for airborne electromagnetic (AEM) data and improve the correlation between pseudo tie lines. Thus we conduct levelling to the flight line difference data instead of to the original AEM data directly. Pseudo tie lines are selected distributively cross profile direction, avoiding the anomalous regions. Since the levelling errors of selective pseudo tie lines show high correlations, principal component analysis is applied to extract the local levelling errors by low-order principal components reconstruction. Furthermore, we can obtain the levelling errors of original AEM data through inverse difference after spatial interpolation. This levelling method does not need to fly tie lines and design the levelling fitting function. The effectiveness of this method is demonstrated by the levelling results of survey data, comparing with the results from tie-line levelling and flight-line correlation levelling.

Principal component analysis of image gradient orientations for face recognition

NARCIS (Netherlands)

Tzimiropoulos, Georgios; Zafeiriou, Stefanos; Pantic, Maja

We introduce the notion of Principal Component Analysis (PCA) of image gradient orientations. As image data is typically noisy, but noise is substantially different from Gaussian, traditional PCA of pixel intensities very often fails to estimate reliably the low-dimensional subspace of a given data

Multi-Trait analysis of growth traits: fitting reduced rank models using principal components for Simmental beef cattle

Directory of Open Access Journals (Sweden)

Rodrigo Reis Mota

2016-09-01

Full Text Available ABSTRACT: The aim of this research was to evaluate the dimensional reduction of additive direct genetic covariance matrices in genetic evaluations of growth traits (range 100-730 days in Simmental cattle using principal components, as well as to estimate (covariance components and genetic parameters. Principal component analyses were conducted for five different models-one full and four reduced-rank models. Models were compared using Akaike information (AIC and Bayesian information (BIC criteria. Variance components and genetic parameters were estimated by restricted maximum likelihood (REML. The AIC and BIC values were similar among models. This indicated that parsimonious models could be used in genetic evaluations in Simmental cattle. The first principal component explained more than 96% of total variance in both models. Heritability estimates were higher for advanced ages and varied from 0.05 (100 days to 0.30 (730 days. Genetic correlation estimates were similar in both models regardless of magnitude and number of principal components. The first principal component was sufficient to explain almost all genetic variance. Furthermore, genetic parameter similarities and lower computational requirements allowed for parsimonious models in genetic evaluations of growth traits in Simmental cattle.

A Cure for Variance Inflation in High Dimensional Kernel Principal Component Analysis

DEFF Research Database (Denmark)

Abrahamsen, Trine Julie; Hansen, Lars Kai

2011-01-01

Small sample high-dimensional principal component analysis (PCA) suffers from variance inflation and lack of generalizability. It has earlier been pointed out that a simple leave-one-out variance renormalization scheme can cure the problem. In this paper we generalize the cure in two directions......: First, we propose a computationally less intensive approximate leave-one-out estimator, secondly, we show that variance inflation is also present in kernel principal component analysis (kPCA) and we provide a non-parametric renormalization scheme which can quite efficiently restore generalizability in kPCA....... As for PCA our analysis also suggests a simplified approximate expression. © 2011 Trine J. Abrahamsen and Lars K. Hansen....

Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis

Directory of Open Access Journals (Sweden)

Xiaoming Xu

2017-01-01

Full Text Available In traditional principle component analysis (PCA, because of the neglect of the dimensions influence between different variables in the system, the selected principal components (PCs often fail to be representative. While the relative transformation PCA is able to solve the above problem, it is not easy to calculate the weight for each characteristic variable. In order to solve it, this paper proposes a kind of fault diagnosis method based on information entropy and Relative Principle Component Analysis. Firstly, the algorithm calculates the information entropy for each characteristic variable in the original dataset based on the information gain algorithm. Secondly, it standardizes every variable’s dimension in the dataset. And, then, according to the information entropy, it allocates the weight for each standardized characteristic variable. Finally, it utilizes the relative-principal-components model established for fault diagnosis. Furthermore, the simulation experiments based on Tennessee Eastman process and Wine datasets demonstrate the feasibility and effectiveness of the new method.

Sparse principal component analysis in medical shape modeling

Science.gov (United States)

Sjöstrand, Karl; Stegmann, Mikkel B.; Larsen, Rasmus

2006-03-01

Principal component analysis (PCA) is a widely used tool in medical image analysis for data reduction, model building, and data understanding and exploration. While PCA is a holistic approach where each new variable is a linear combination of all original variables, sparse PCA (SPCA) aims at producing easily interpreted models through sparse loadings, i.e. each new variable is a linear combination of a subset of the original variables. One of the aims of using SPCA is the possible separation of the results into isolated and easily identifiable effects. This article introduces SPCA for shape analysis in medicine. Results for three different data sets are given in relation to standard PCA and sparse PCA by simple thresholding of small loadings. Focus is on a recent algorithm for computing sparse principal components, but a review of other approaches is supplied as well. The SPCA algorithm has been implemented using Matlab and is available for download. The general behavior of the algorithm is investigated, and strengths and weaknesses are discussed. The original report on the SPCA algorithm argues that the ordering of modes is not an issue. We disagree on this point and propose several approaches to establish sensible orderings. A method that orders modes by decreasing variance and maximizes the sum of variances for all modes is presented and investigated in detail.

Characteristic gene selection via weighting principal components by singular values.

Directory of Open Access Journals (Sweden)

Jin-Xing Liu

Full Text Available Conventional gene selection methods based on principal component analysis (PCA use only the first principal component (PC of PCA or sparse PCA to select characteristic genes. These methods indeed assume that the first PC plays a dominant role in gene selection. However, in a number of cases this assumption is not satisfied, so the conventional PCA-based methods usually provide poor selection results. In order to improve the performance of the PCA-based gene selection method, we put forward the gene selection method via weighting PCs by singular values (WPCS. Because different PCs have different importance, the singular values are exploited as the weights to represent the influence on gene selection of different PCs. The ROC curves and AUC statistics on artificial data show that our method outperforms the state-of-the-art methods. Moreover, experimental results on real gene expression data sets show that our method can extract more characteristic genes in response to abiotic stresses than conventional gene selection methods.

Source Signals Separation and Reconstruction Following Principal Component Analysis

Directory of Open Access Journals (Sweden)

WANG Cheng

2014-02-01

Full Text Available For separation and reconstruction of source signals from observed signals problem, the physical significance of blind source separation modal and independent component analysis is not very clear, and its solution is not unique. Aiming at these disadvantages, a new linear and instantaneous mixing model and a novel source signals separation reconstruction solving method from observed signals based on principal component analysis (PCA are put forward. Assumption of this new model is statistically unrelated rather than independent of source signals, which is different from the traditional blind source separation model. A one-to-one relationship between linear and instantaneous mixing matrix of new model and linear compound matrix of PCA, and a one-to-one relationship between unrelated source signals and principal components are demonstrated using the concept of linear separation matrix and unrelated of source signals. Based on this theoretical link, source signals separation and reconstruction problem is changed into PCA of observed signals then. The theoretical derivation and numerical simulation results show that, in despite of Gauss measurement noise, wave form and amplitude information of unrelated source signal can be separated and reconstructed by PCA when linear mixing matrix is column orthogonal and normalized; only wave form information of unrelated source signal can be separated and reconstructed by PCA when linear mixing matrix is column orthogonal but not normalized, unrelated source signal cannot be separated and reconstructed by PCA when mixing matrix is not column orthogonal or linear.

Dynamic of consumer groups and response of commodity markets by principal component analysis

Science.gov (United States)

Nobi, Ashadun; Alam, Shafiqul; Lee, Jae Woo

2017-09-01

This study investigates financial states and group dynamics by applying principal component analysis to the cross-correlation coefficients of the daily returns of commodity futures. The eigenvalues of the cross-correlation matrix in the 6-month timeframe displays similar values during 2010-2011, but decline following 2012. A sharp drop in eigenvalue implies the significant change of the market state. Three commodity sectors, energy, metals and agriculture, are projected into two dimensional spaces consisting of two principal components (PC). We observe that they form three distinct clusters in relation to various sectors. However, commodities with distinct features have intermingled with one another and scattered during severe crises, such as the European sovereign debt crises. We observe the notable change of the position of two dimensional spaces of groups during financial crises. By considering the first principal component (PC1) within the 6-month moving timeframe, we observe that commodities of the same group change states in a similar pattern, and the change of states of one group can be used as a warning for other group.

Cloud Masking for Remotely Sensed Data Using Spectral and Principal Components Analysis

Directory of Open Access Journals (Sweden)

A. Ahmad

2012-06-01

Full Text Available Two methods of cloud masking tuned to tropical conditions have been developed, based on spectral analysis and Principal Components Analysis (PCA of Moderate Resolution Imaging Spectroradiometer (MODIS data. In the spectral approach, thresholds were applied to four reflective bands (1, 2, 3, and 4, three thermal bands (29, 31 and 32, the band 2/band 1 ratio, and the difference between band 29 and 31 in order to detect clouds. The PCA approach applied a threshold to the first principal component derived from the seven quantities used for spectral analysis. Cloud detections were compared with the standard MODIS cloud mask, and their accuracy was assessed using reference images and geographical information on the study area.

A novel principal component analysis for spatially misaligned multivariate air pollution data.

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Jandarov, Roman A; Sheppard, Lianne A; Sampson, Paul D; Szpiro, Adam A

2017-01-01

We propose novel methods for predictive (sparse) PCA with spatially misaligned data. These methods identify principal component loading vectors that explain as much variability in the observed data as possible, while also ensuring the corresponding principal component scores can be predicted accurately by means of spatial statistics at locations where air pollution measurements are not available. This will make it possible to identify important mixtures of air pollutants and to quantify their health effects in cohort studies, where currently available methods cannot be used. We demonstrate the utility of predictive (sparse) PCA in simulated data and apply the approach to annual averages of particulate matter speciation data from national Environmental Protection Agency (EPA) regulatory monitors.

Evaluation of functional scintigraphy of gastric emptying by the principal component method

Energy Technology Data Exchange (ETDEWEB)

Haeussler, M.; Eilles, C.; Reiners, C.; Moll, E.; Boerner, W.

1980-10-01

Gastric emptying of a standard semifluid test-meal, labeled with /sup 99/sup(m)Tc-DTPA, was studied by functional scintigraphy in 88 subjects (normals, patients with duodenal and gastric ulcer before and after selective proximal vagotomy with and without pyloroplasty). Gastric emptying curves were analysed by the method of principal components. Patients after selective proximal vagotomy with pyloroplasty showed an rapid initial emptying, whereas this was a rare finding in patients after selective proximal vagotomy without pyloroplasty. The method of principal components is well suited for mathematical analysis of gastric emptying; nevertheless the results are difficult to interpret. The method has advantages when looking at larger collectives and allows a separation into groups with different gastric emptying.

Obesity, metabolic syndrome, impaired fasting glucose, and microvascular dysfunction: a principal component analysis approach.

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Panazzolo, Diogo G; Sicuro, Fernando L; Clapauch, Ruth; Maranhão, Priscila A; Bouskela, Eliete; Kraemer-Aguiar, Luiz G

2012-11-13

We aimed to evaluate the multivariate association between functional microvascular variables and clinical-laboratorial-anthropometrical measurements. Data from 189 female subjects (34.0 ± 15.5 years, 30.5 ± 7.1 kg/m2), who were non-smokers, non-regular drug users, without a history of diabetes and/or hypertension, were analyzed by principal component analysis (PCA). PCA is a classical multivariate exploratory tool because it highlights common variation between variables allowing inferences about possible biological meaning of associations between them, without pre-establishing cause-effect relationships. In total, 15 variables were used for PCA: body mass index (BMI), waist circumference, systolic and diastolic blood pressure (BP), fasting plasma glucose, levels of total cholesterol, high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), triglycerides (TG), insulin, C-reactive protein (CRP), and functional microvascular variables measured by nailfold videocapillaroscopy. Nailfold videocapillaroscopy was used for direct visualization of nutritive capillaries, assessing functional capillary density, red blood cell velocity (RBCV) at rest and peak after 1 min of arterial occlusion (RBCV(max)), and the time taken to reach RBCV(max) (TRBCV(max)). A total of 35% of subjects had metabolic syndrome, 77% were overweight/obese, and 9.5% had impaired fasting glucose. PCA was able to recognize that functional microvascular variables and clinical-laboratorial-anthropometrical measurements had a similar variation. The first five principal components explained most of the intrinsic variation of the data. For example, principal component 1 was associated with BMI, waist circumference, systolic BP, diastolic BP, insulin, TG, CRP, and TRBCV(max) varying in the same way. Principal component 1 also showed a strong association among HDL-c, RBCV, and RBCV(max), but in the opposite way. Principal component 3 was associated only with microvascular

Obesity, metabolic syndrome, impaired fasting glucose, and microvascular dysfunction: a principal component analysis approach

Directory of Open Access Journals (Sweden)

Panazzolo Diogo G

2012-11-01

Full Text Available Abstract Background We aimed to evaluate the multivariate association between functional microvascular variables and clinical-laboratorial-anthropometrical measurements. Methods Data from 189 female subjects (34.0±15.5 years, 30.5±7.1 kg/m2, who were non-smokers, non-regular drug users, without a history of diabetes and/or hypertension, were analyzed by principal component analysis (PCA. PCA is a classical multivariate exploratory tool because it highlights common variation between variables allowing inferences about possible biological meaning of associations between them, without pre-establishing cause-effect relationships. In total, 15 variables were used for PCA: body mass index (BMI, waist circumference, systolic and diastolic blood pressure (BP, fasting plasma glucose, levels of total cholesterol, high-density lipoprotein cholesterol (HDL-c, low-density lipoprotein cholesterol (LDL-c, triglycerides (TG, insulin, C-reactive protein (CRP, and functional microvascular variables measured by nailfold videocapillaroscopy. Nailfold videocapillaroscopy was used for direct visualization of nutritive capillaries, assessing functional capillary density, red blood cell velocity (RBCV at rest and peak after 1 min of arterial occlusion (RBCVmax, and the time taken to reach RBCVmax (TRBCVmax. Results A total of 35% of subjects had metabolic syndrome, 77% were overweight/obese, and 9.5% had impaired fasting glucose. PCA was able to recognize that functional microvascular variables and clinical-laboratorial-anthropometrical measurements had a similar variation. The first five principal components explained most of the intrinsic variation of the data. For example, principal component 1 was associated with BMI, waist circumference, systolic BP, diastolic BP, insulin, TG, CRP, and TRBCVmax varying in the same way. Principal component 1 also showed a strong association among HDL-c, RBCV, and RBCVmax, but in the opposite way. Principal component 3 was

A multi-dimensional functional principal components analysis of EEG data.

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Hasenstab, Kyle; Scheffler, Aaron; Telesca, Donatello; Sugar, Catherine A; Jeste, Shafali; DiStefano, Charlotte; Şentürk, Damla

2017-09-01

The electroencephalography (EEG) data created in event-related potential (ERP) experiments have a complex high-dimensional structure. Each stimulus presentation, or trial, generates an ERP waveform which is an instance of functional data. The experiments are made up of sequences of multiple trials, resulting in longitudinal functional data and moreover, responses are recorded at multiple electrodes on the scalp, adding an electrode dimension. Traditional EEG analyses involve multiple simplifications of this structure to increase the signal-to-noise ratio, effectively collapsing the functional and longitudinal components by identifying key features of the ERPs and averaging them across trials. Motivated by an implicit learning paradigm used in autism research in which the functional, longitudinal, and electrode components all have critical interpretations, we propose a multidimensional functional principal components analysis (MD-FPCA) technique which does not collapse any of the dimensions of the ERP data. The proposed decomposition is based on separation of the total variation into subject and subunit level variation which are further decomposed in a two-stage functional principal components analysis. The proposed methodology is shown to be useful for modeling longitudinal trends in the ERP functions, leading to novel insights into the learning patterns of children with Autism Spectrum Disorder (ASD) and their typically developing peers as well as comparisons between the two groups. Finite sample properties of MD-FPCA are further studied via extensive simulations. © 2017, The International Biometric Society.

Process parameter optimization based on principal components analysis during machining of hardened steel

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Suryakant B. Chandgude

2015-09-01

Full Text Available The optimum selection of process parameters has played an important role for improving the surface finish, minimizing tool wear, increasing material removal rate and reducing machining time of any machining process. In this paper, optimum parameters while machining AISI D2 hardened steel using solid carbide TiAlN coated end mill has been investigated. For optimization of process parameters along with multiple quality characteristics, principal components analysis method has been adopted in this work. The confirmation experiments have revealed that to improve performance of cutting; principal components analysis method would be a useful tool.

On combining principal components with Fisher's linear discriminants for supervised learning

NARCIS (Netherlands)

Pechenizkiy, M.; Tsymbal, A.; Puuronen, S.

2006-01-01

"The curse of dimensionality" is pertinent to many learning algorithms, and it denotes the drastic increase of computational complexity and classification error in high dimensions. In this paper, principal component analysis (PCA), parametric feature extraction (FE) based on Fisher’s linear

Integrating Data Transformation in Principal Components Analysis

KAUST Repository

Maadooliat, Mehdi

2015-01-02

Principal component analysis (PCA) is a popular dimension reduction method to reduce the complexity and obtain the informative aspects of high-dimensional datasets. When the data distribution is skewed, data transformation is commonly used prior to applying PCA. Such transformation is usually obtained from previous studies, prior knowledge, or trial-and-error. In this work, we develop a model-based method that integrates data transformation in PCA and finds an appropriate data transformation using the maximum profile likelihood. Extensions of the method to handle functional data and missing values are also developed. Several numerical algorithms are provided for efficient computation. The proposed method is illustrated using simulated and real-world data examples.

Priority of VHS Development Based in Potential Area using Principal Component Analysis

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Meirawan, D.; Ana, A.; Saripudin, S.

2018-02-01

The current condition of VHS is still inadequate in quality, quantity and relevance. The purpose of this research is to analyse the development of VHS based on the development of regional potential by using principal component analysis (PCA) in Bandung, Indonesia. This study used descriptive qualitative data analysis using the principle of secondary data reduction component. The method used is Principal Component Analysis (PCA) analysis with Minitab Statistics Software tool. The results of this study indicate the value of the lowest requirement is a priority of the construction of development VHS with a program of majors in accordance with the development of regional potential. Based on the PCA score found that the main priority in the development of VHS in Bandung is in Saguling, which has the lowest PCA value of 416.92 in area 1, Cihampelas with the lowest PCA value in region 2 and Padalarang with the lowest PCA value.

Principal component reconstruction (PCR) for cine CBCT with motion learning from 2D fluoroscopy.

Science.gov (United States)

Gao, Hao; Zhang, Yawei; Ren, Lei; Yin, Fang-Fang

2018-01-01

This work aims to generate cine CT images (i.e., 4D images with high-temporal resolution) based on a novel principal component reconstruction (PCR) technique with motion learning from 2D fluoroscopic training images. In the proposed PCR method, the matrix factorization is utilized as an explicit low-rank regularization of 4D images that are represented as a product of spatial principal components and temporal motion coefficients. The key hypothesis of PCR is that temporal coefficients from 4D images can be reasonably approximated by temporal coefficients learned from 2D fluoroscopic training projections. For this purpose, we can acquire fluoroscopic training projections for a few breathing periods at fixed gantry angles that are free from geometric distortion due to gantry rotation, that is, fluoroscopy-based motion learning. Such training projections can provide an effective characterization of the breathing motion. The temporal coefficients can be extracted from these training projections and used as priors for PCR, even though principal components from training projections are certainly not the same for these 4D images to be reconstructed. For this purpose, training data are synchronized with reconstruction data using identical real-time breathing position intervals for projection binning. In terms of image reconstruction, with a priori temporal coefficients, the data fidelity for PCR changes from nonlinear to linear, and consequently, the PCR method is robust and can be solved efficiently. PCR is formulated as a convex optimization problem with the sum of linear data fidelity with respect to spatial principal components and spatiotemporal total variation regularization imposed on 4D image phases. The solution algorithm of PCR is developed based on alternating direction method of multipliers. The implementation is fully parallelized on GPU with NVIDIA CUDA toolbox and each reconstruction takes about a few minutes. The proposed PCR method is validated and

Machine learning of frustrated classical spin models. I. Principal component analysis

Science.gov (United States)

Wang, Ce; Zhai, Hui

2017-10-01

This work aims at determining whether artificial intelligence can recognize a phase transition without prior human knowledge. If this were successful, it could be applied to, for instance, analyzing data from the quantum simulation of unsolved physical models. Toward this goal, we first need to apply the machine learning algorithm to well-understood models and see whether the outputs are consistent with our prior knowledge, which serves as the benchmark for this approach. In this work, we feed the computer data generated by the classical Monte Carlo simulation for the X Y model in frustrated triangular and union jack lattices, which has two order parameters and exhibits two phase transitions. We show that the outputs of the principal component analysis agree very well with our understanding of different orders in different phases, and the temperature dependences of the major components detect the nature and the locations of the phase transitions. Our work offers promise for using machine learning techniques to study sophisticated statistical models, and our results can be further improved by using principal component analysis with kernel tricks and the neural network method.

Facilitating in vivo tumor localization by principal component analysis based on dynamic fluorescence molecular imaging

Science.gov (United States)

Gao, Yang; Chen, Maomao; Wu, Junyu; Zhou, Yuan; Cai, Chuangjian; Wang, Daliang; Luo, Jianwen

2017-09-01

Fluorescence molecular imaging has been used to target tumors in mice with xenograft tumors. However, tumor imaging is largely distorted by the aggregation of fluorescent probes in the liver. A principal component analysis (PCA)-based strategy was applied on the in vivo dynamic fluorescence imaging results of three mice with xenograft tumors to facilitate tumor imaging, with the help of a tumor-specific fluorescent probe. Tumor-relevant features were extracted from the original images by PCA and represented by the principal component (PC) maps. The second principal component (PC2) map represented the tumor-related features, and the first principal component (PC1) map retained the original pharmacokinetic profiles, especially of the liver. The distribution patterns of the PC2 map of the tumor-bearing mice were in good agreement with the actual tumor location. The tumor-to-liver ratio and contrast-to-noise ratio were significantly higher on the PC2 map than on the original images, thus distinguishing the tumor from its nearby fluorescence noise of liver. The results suggest that the PC2 map could serve as a bioimaging marker to facilitate in vivo tumor localization, and dynamic fluorescence molecular imaging with PCA could be a valuable tool for future studies of in vivo tumor metabolism and progression.

Episodic memory, semantic memory, and amnesia.

Science.gov (United States)

Squire, L R; Zola, S M

1998-01-01

Episodic memory and semantic memory are two types of declarative memory. There have been two principal views about how this distinction might be reflected in the organization of memory functions in the brain. One view, that episodic memory and semantic memory are both dependent on the integrity of medial temporal lobe and midline diencephalic structures, predicts that amnesic patients with medial temporal lobe/diencephalic damage should be proportionately impaired in both episodic and semantic memory. An alternative view is that the capacity for semantic memory is spared, or partially spared, in amnesia relative to episodic memory ability. This article reviews two kinds of relevant data: 1) case studies where amnesia has occurred early in childhood, before much of an individual's semantic knowledge has been acquired, and 2) experimental studies with amnesic patients of fact and event learning, remembering and knowing, and remote memory. The data provide no compelling support for the view that episodic and semantic memory are affected differently in medial temporal lobe/diencephalic amnesia. However, episodic and semantic memory may be dissociable in those amnesic patients who additionally have severe frontal lobe damage.

Fluoride in the Serra Geral Aquifer System: Source Evaluation Using Stable Isotopes and Principal Component Analysis

OpenAIRE

Nanni, Arthur Schmidt; Roisenberg, Ari; de Hollanda, Maria Helena Bezerra Maia; Marimon, Maria Paula Casagrande; Viero, Antonio Pedro; Scheibe, Luiz Fernando

2013-01-01

Groundwater with anomalous fluoride content and water mixture patterns were studied in the fractured Serra Geral Aquifer System, a basaltic to rhyolitic geological unit, using a principal component analysis interpretation of groundwater chemical data from 309 deep wells distributed in the Rio Grande do Sul State, Southern Brazil. A four-component model that explains 81% of the total variance in the Principal Component Analysis is suggested. Six hydrochemical groups were identified. δ18O and δ...

Resource Loss and Depressive Symptoms Following Hurricane Katrina: A Principal Component Regression Study

OpenAIRE

Liang L; Hayashi K; Bennett P; Johnson T. J; Aten J. D

2015-01-01

To understand the relationship between the structure of resource loss and depression after disaster exposure, the components of resource loss and the impact of these resource loss components on depression was examined among college students (N=654) at two universities who were affected by Hurricane Katrina. The component of resource loss was analyzed by principal component analysis first. Gender, social relationship loss, and financial loss were then examined with the regression model on depr...

Efficient real time OD matrix estimation based on principal component analysis

NARCIS (Netherlands)

Djukic, T.; Flötteröd, G.; Van Lint, H.; Hoogendoorn, S.P.

2012-01-01

In this paper we explore the idea of dimensionality reduction and approximation of OD demand based on principal component analysis (PCA). First, we show how we can apply PCA to linearly transform the high dimensional OD matrices into the lower dimensional space without significant loss of accuracy.

Principal component analysis of dynamic fluorescence images for diagnosis of diabetic vasculopathy

Science.gov (United States)

Seo, Jihye; An, Yuri; Lee, Jungsul; Ku, Taeyun; Kang, Yujung; Ahn, Chulwoo; Choi, Chulhee

2016-04-01

Indocyanine green (ICG) fluorescence imaging has been clinically used for noninvasive visualizations of vascular structures. We have previously developed a diagnostic system based on dynamic ICG fluorescence imaging for sensitive detection of vascular disorders. However, because high-dimensional raw data were used, the analysis of the ICG dynamics proved difficult. We used principal component analysis (PCA) in this study to extract important elements without significant loss of information. We examined ICG spatiotemporal profiles and identified critical features related to vascular disorders. PCA time courses of the first three components showed a distinct pattern in diabetic patients. Among the major components, the second principal component (PC2) represented arterial-like features. The explained variance of PC2 in diabetic patients was significantly lower than in normal controls. To visualize the spatial pattern of PCs, pixels were mapped with red, green, and blue channels. The PC2 score showed an inverse pattern between normal controls and diabetic patients. We propose that PC2 can be used as a representative bioimaging marker for the screening of vascular diseases. It may also be useful in simple extractions of arterial-like features.

Principal Component Analysis Based Measure of Structural Holes

Science.gov (United States)

Deng, Shiguo; Zhang, Wenqing; Yang, Huijie

2013-02-01

Based upon principal component analysis, a new measure called compressibility coefficient is proposed to evaluate structural holes in networks. This measure incorporates a new effect from identical patterns in networks. It is found that compressibility coefficient for Watts-Strogatz small-world networks increases monotonically with the rewiring probability and saturates to that for the corresponding shuffled networks. While compressibility coefficient for extended Barabasi-Albert scale-free networks decreases monotonically with the preferential effect and is significantly large compared with that for corresponding shuffled networks. This measure is helpful in diverse research fields to evaluate global efficiency of networks.

Nonlinear Principal Component Analysis Using Strong Tracking Filter

Institute of Scientific and Technical Information of China (English)

无

2007-01-01

The paper analyzes the problem of blind source separation (BSS) based on the nonlinear principal component analysis (NPCA) criterion. An adaptive strong tracking filter (STF) based algorithm was developed, which is immune to system model mismatches. Simulations demonstrate that the algorithm converges quickly and has satisfactory steady-state accuracy. The Kalman filtering algorithm and the recursive leastsquares type algorithm are shown to be special cases of the STF algorithm. Since the forgetting factor is adaptively updated by adjustment of the Kalman gain, the STF scheme provides more powerful tracking capability than the Kalman filtering algorithm and recursive least-squares algorithm.

Fast grasping of unknown objects using principal component analysis

Science.gov (United States)

Lei, Qujiang; Chen, Guangming; Wisse, Martijn

2017-09-01

Fast grasping of unknown objects has crucial impact on the efficiency of robot manipulation especially subjected to unfamiliar environments. In order to accelerate grasping speed of unknown objects, principal component analysis is utilized to direct the grasping process. In particular, a single-view partial point cloud is constructed and grasp candidates are allocated along the principal axis. Force balance optimization is employed to analyze possible graspable areas. The obtained graspable area with the minimal resultant force is the best zone for the final grasping execution. It is shown that an unknown object can be more quickly grasped provided that the component analysis principle axis is determined using single-view partial point cloud. To cope with the grasp uncertainty, robot motion is assisted to obtain a new viewpoint. Virtual exploration and experimental tests are carried out to verify this fast gasping algorithm. Both simulation and experimental tests demonstrated excellent performances based on the results of grasping a series of unknown objects. To minimize the grasping uncertainty, the merits of the robot hardware with two 3D cameras can be utilized to suffice the partial point cloud. As a result of utilizing the robot hardware, the grasping reliance is highly enhanced. Therefore, this research demonstrates practical significance for increasing grasping speed and thus increasing robot efficiency under unpredictable environments.

EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation

Directory of Open Access Journals (Sweden)

Suwicha Jirayucharoensak

2014-01-01

Full Text Available Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN to discover unknown feature correlation between input signals that is crucial for the learning task. The DLN is implemented with a stacked autoencoder (SAE using hierarchical feature learning approach. Input features of the network are power spectral densities of 32-channel EEG signals from 32 subjects. To alleviate overfitting problem, principal component analysis (PCA is applied to extract the most important components of initial input features. Furthermore, covariate shift adaptation of the principal components is implemented to minimize the nonstationary effect of EEG signals. Experimental results show that the DLN is capable of classifying three different levels of valence and arousal with accuracy of 49.52% and 46.03%, respectively. Principal component based covariate shift adaptation enhances the respective classification accuracy by 5.55% and 6.53%. Moreover, DLN provides better performance compared to SVM and naive Bayes classifiers.

Getting connected: Both associative and semantic links structure semantic memory for newly learned persons.

Science.gov (United States)

Wiese, Holger; Schweinberger, Stefan R

2015-01-01

The present study examined whether semantic memory for newly learned people is structured by visual co-occurrence, shared semantics, or both. Participants were trained with pairs of simultaneously presented (i.e., co-occurring) preexperimentally unfamiliar faces, which either did or did not share additionally provided semantic information (occupation, place of living, etc.). Semantic information could also be shared between faces that did not co-occur. A subsequent priming experiment revealed faster responses for both co-occurrence/no shared semantics and no co-occurrence/shared semantics conditions, than for an unrelated condition. Strikingly, priming was strongest in the co-occurrence/shared semantics condition, suggesting additive effects of these factors. Additional analysis of event-related brain potentials yielded priming in the N400 component only for combined effects of visual co-occurrence and shared semantics, with more positive amplitudes in this than in the unrelated condition. Overall, these findings suggest that both semantic relatedness and visual co-occurrence are important when novel information is integrated into person-related semantic memory.

Fall detection in walking robots by multi-way principal component analysis

NARCIS (Netherlands)

Karssen, J.G.; Wisse, M.

2008-01-01

Large disturbances can cause a biped to fall. If an upcoming fall can be detected, damage can be minimized or the fall can be prevented. We introduce the multi-way principal component analysis (MPCA) method for the detection of upcoming falls. We study the detection capability of the MPCA method in

Assessing prescription drug abuse using functional principal component analysis (FPCA) of wastewater data.

Science.gov (United States)

Salvatore, Stefania; Røislien, Jo; Baz-Lomba, Jose A; Bramness, Jørgen G

2017-03-01

Wastewater-based epidemiology is an alternative method for estimating the collective drug use in a community. We applied functional data analysis, a statistical framework developed for analysing curve data, to investigate weekly temporal patterns in wastewater measurements of three prescription drugs with known abuse potential: methadone, oxazepam and methylphenidate, comparing them to positive and negative control drugs. Sewage samples were collected in February 2014 from a wastewater treatment plant in Oslo, Norway. The weekly pattern of each drug was extracted by fitting of generalized additive models, using trigonometric functions to model the cyclic behaviour. From the weekly component, the main temporal features were then extracted using functional principal component analysis. Results are presented through the functional principal components (FPCs) and corresponding FPC scores. Clinically, the most important weekly feature of the wastewater-based epidemiology data was the second FPC, representing the difference between average midweek level and a peak during the weekend, representing possible recreational use of a drug in the weekend. Estimated scores on this FPC indicated recreational use of methylphenidate, with a high weekend peak, but not for methadone and oxazepam. The functional principal component analysis uncovered clinically important temporal features of the weekly patterns of the use of prescription drugs detected from wastewater analysis. This may be used as a post-marketing surveillance method to monitor prescription drugs with abuse potential. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Demixed principal component analysis of neural population data.

Science.gov (United States)

Kobak, Dmitry; Brendel, Wieland; Constantinidis, Christos; Feierstein, Claudia E; Kepecs, Adam; Mainen, Zachary F; Qi, Xue-Lian; Romo, Ranulfo; Uchida, Naoshige; Machens, Christian K

2016-04-12

Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure.

Modeling the variability of solar radiation data among weather stations by means of principal components analysis

International Nuclear Information System (INIS)

Zarzo, Manuel; Marti, Pau

2011-01-01

Research highlights: →Principal components analysis was applied to R s data recorded at 30 stations. → Four principal components explain 97% of the data variability. → The latent variables can be fitted according to latitude, longitude and altitude. → The PCA approach is more effective for gap infilling than conventional approaches. → The proposed method allows daily R s estimations at locations in the area of study. - Abstract: Measurements of global terrestrial solar radiation (R s ) are commonly recorded in meteorological stations. Daily variability of R s has to be taken into account for the design of photovoltaic systems and energy efficient buildings. Principal components analysis (PCA) was applied to R s data recorded at 30 stations in the Mediterranean coast of Spain. Due to equipment failures and site operation problems, time series of R s often present data gaps or discontinuities. The PCA approach copes with this problem and allows estimation of present and past values by taking advantage of R s records from nearby stations. The gap infilling performance of this methodology is compared with neural networks and alternative conventional approaches. Four principal components explain 66% of the data variability with respect to the average trajectory (97% if non-centered values are considered). A new method based on principal components regression was also developed for R s estimation if previous measurements are not available. By means of multiple linear regression, it was found that the latent variables associated to the four relevant principal components can be fitted according to the latitude, longitude and altitude of the station where data were recorded from. Additional geographical or climatic variables did not increase the predictive goodness-of-fit. The resulting models allow the estimation of daily R s values at any location in the area under study and present higher accuracy than artificial neural networks and some conventional approaches

Principal and secondary luminescence lifetime components in annealed natural quartz

International Nuclear Information System (INIS)

Chithambo, M.L.; Ogundare, F.O.; Feathers, J.

2008-01-01

Time-resolved luminescence spectra from quartz can be separated into components with distinct principal and secondary lifetimes depending on certain combinations of annealing and measurement temperature. The influence of annealing on properties of the lifetimes related to irradiation dose and temperature of measurement has been investigated in sedimentary quartz annealed at various temperatures up to 900 deg. C. Time-resolved luminescence for use in the analysis was pulse stimulated from samples at 470 nm between 20 and 200 deg. C. Luminescence lifetimes decrease with measurement temperature due to increasing thermal effect on the associated luminescence with an activation energy of thermal quenching equal to 0.68±0.01eV for the secondary lifetime but only qualitatively so for the principal lifetime component. Concerning the influence of annealing temperature, luminescence lifetimes measured at 20 deg. C are constant at about 33μs for annealing temperatures up to 600 0 C but decrease to about 29μs when the annealing temperature is increased to 900 deg. C. In addition, it was found that lifetime components in samples annealed at 800 deg. C are independent of radiation dose in the range 85-1340 Gy investigated. The dependence of lifetimes on both the annealing temperature and magnitude of radiation dose is described as being due to the increasing importance of a particular recombination centre in the luminescence emission process as a result of dynamic hole transfer between non-radiative and radiative luminescence centres

Principal Component and Cluster Analysis as a Tool in the Assessment of Tomato Hybrids and Cultivars

Directory of Open Access Journals (Sweden)

G. Evgenidis

2011-01-01

Full Text Available Determination of germplasm diversity and genetic relationships among breeding materials is an invaluable aid in crop improvement strategies. This study assessed the breeding value of tomato source material. Two commercial hybrids along with an experimental hybrid and four cultivars were assessed with cluster and principal component analyses based on morphophysiological data, yield and quality, stability of performance, heterosis, and combining abilities. The assessment of commercial hybrids revealed a related origin and subsequently does not support the identification of promising offspring in their crossing. The assessment of the cultivars discriminated them according to origin and evolutionary and selection effects. On the Principal Component 1, the largest group with positive loading included, yield components, heterosis, general and specific combining ability, whereas the largest negative loading was obtained by qualitative and descriptive traits. The Principal Component 2 revealed two smaller groups, a positive one with phenotypic traits and a negative one with tolerance to inbreeding. Stability of performance was loaded positively and/or negatively. In conclusion, combing ability, yield components, and heterosis provided a mechanism for ensuring continued improvement in plant selection programs.

Principal Component Analysis: Resources for an Essential Application of Linear Algebra

Science.gov (United States)

Pankavich, Stephen; Swanson, Rebecca

2015-01-01

Principal Component Analysis (PCA) is a highly useful topic within an introductory Linear Algebra course, especially since it can be used to incorporate a number of applied projects. This method represents an essential application and extension of the Spectral Theorem and is commonly used within a variety of fields, including statistics,…

Application of principal component analysis (PCA) as a sensory assessment tool for fermented food products.

Science.gov (United States)

Ghosh, Debasree; Chattopadhyay, Parimal

2012-06-01

The objective of the work was to use the method of quantitative descriptive analysis (QDA) to describe the sensory attributes of the fermented food products prepared with the incorporation of lactic cultures. Panellists were selected and trained to evaluate various attributes specially color and appearance, body texture, flavor, overall acceptability and acidity of the fermented food products like cow milk curd and soymilk curd, idli, sauerkraut and probiotic ice cream. Principal component analysis (PCA) identified the six significant principal components that accounted for more than 90% of the variance in the sensory attribute data. Overall product quality was modelled as a function of principal components using multiple least squares regression (R (2) = 0.8). The result from PCA was statistically analyzed by analysis of variance (ANOVA). These findings demonstrate the utility of quantitative descriptive analysis for identifying and measuring the fermented food product attributes that are important for consumer acceptability.

Principal Component Surface (2011) for St. Thomas East End Reserve, St. Thomas

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This image represents a 0.3x0.3 meter principal component analysis (PCA) surface for areas the St. Thomas East End Reserve (STEER) in the U.S. Virgin Islands (USVI)....

APPLICATION OF PRINCIPAL COMPONENT ANALYSIS TO RELAXOGRAPHIC IMAGES

International Nuclear Information System (INIS)

STOYANOVA, R.S.; OCHS, M.F.; BROWN, T.R.; ROONEY, W.D.; LI, X.; LEE, J.H.; SPRINGER, C.S.

1999-01-01

Standard analysis methods for processing inversion recovery MR images traditionally have used single pixel techniques. In these techniques each pixel is independently fit to an exponential recovery, and spatial correlations in the data set are ignored. By analyzing the image as a complete dataset, improved error analysis and automatic segmentation can be achieved. Here, the authors apply principal component analysis (PCA) to a series of relaxographic images. This procedure decomposes the 3-dimensional data set into three separate images and corresponding recovery times. They attribute the 3 images to be spatial representations of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) content

PRINCIPAL COMPONENT ANALYSIS (PCA DAN APLIKASINYA DENGAN SPSS

Directory of Open Access Journals (Sweden)

Hermita Bus Umar

2009-03-01

Full Text Available PCA (Principal Component Analysis are statistical techniques applied to a single set of variables when the researcher is interested in discovering which variables in the setform coherent subset that are relativity independent of one another.Variables that are correlated with one another but largely independent of other subset of variables are combined into factors. The Coals of PCA to which each variables is explained by each dimension. Step in PCA include selecting and mean measuring a set of variables, preparing the correlation matrix, extracting a set offactors from the correlation matrixs. Rotating the factor to increase interpretabilitv and interpreting the result.

Power Transformer Differential Protection Based on Neural Network Principal Component Analysis, Harmonic Restraint and Park's Plots

OpenAIRE

Tripathy, Manoj

2012-01-01

This paper describes a new approach for power transformer differential protection which is based on the wave-shape recognition technique. An algorithm based on neural network principal component analysis (NNPCA) with back-propagation learning is proposed for digital differential protection of power transformer. The principal component analysis is used to preprocess the data from power system in order to eliminate redundant information and enhance hidden pattern of differential current to disc...

The contribution of executive control to semantic cognition: Convergent evidence from semantic aphasia and executive dysfunction

OpenAIRE

Thompson, Hannah E; Almaghyuli, Azizah; Noonan, Krist A.; Barak, Ohr; Lambon Ralph, Matthew; Jefferies, Elizabeth

2018-01-01

Semantic cognition, as described by the Controlled Semantic Cognition (CSC) framework (Rogers, Patterson, Jefferies, & Lambon Ralph, 2015), involves two key components: activation of coherent, generalizable concepts within a heteromodal ‘hub’ in combination with modality-specific features (spokes), and a constraining mechanism that manipulates and gates this knowledge to generate time- and task- appropriate behaviour. Executive-semantic goal representations, largely supported by executive...

The use of principal components and univariate charts to control multivariate processes

Directory of Open Access Journals (Sweden)

Marcela A. G. Machado

2008-04-01

Full Text Available In this article, we evaluate the performance of the T² chart based on the principal components (PC X chart and the simultaneous univariate control charts based on the original variables (SU charts or based on the principal components (SUPC charts. The main reason to consider the PC chart lies on the dimensionality reduction. However, depending on the disturbance and on the way the original variables are related, the chart is very slow in signaling, except when all variables are negatively correlated and the principal component is wisely selected. Comparing the SU , the SUPC and the T² charts we conclude that the SU X charts (SUPC charts have a better overall performance when the variables are positively (negatively correlated. We also develop the expression to obtain the power of two S² charts designed for monitoring the covariance matrix. These joint S² charts are, in the majority of the cases, more efficient than the generalized variance chart.Neste artigo, avaliamos o desempenho do gráfico de T² baseado em componentes principais (gráfico PC e dos gráficos de controle simultâneos univariados baseados nas variáveis originais (gráfico SU X ou baseados em componentes principais (gráfico SUPC. A principal razão para o uso do gráfico PC é a redução de dimensionalidade. Entretanto, dependendo da perturbação e da correlação entre as variáveis originais, o gráfico é lento em sinalizar, exceto quando todas as variáveis são negativamente correlacionadas e a componente principal é adequadamente escolhida. Comparando os gráficos SU X, SUPC e T² concluímos que o gráfico SU X (gráfico SUPC tem um melhor desempenho global quando as variáveis são positivamente (negativamente correlacionadas. Desenvolvemos também uma expressão para obter o poder de detecção de dois gráficos de S² projetados para controlar a matriz de covariâncias. Os gráficos conjuntos de S² são, na maioria dos casos, mais eficientes que o gr

Principal component analysis of FDG PET in amnestic MCI

International Nuclear Information System (INIS)

Nobili, Flavio; Girtler, Nicola; Brugnolo, Andrea; Dessi, Barbara; Rodriguez, Guido; Salmaso, Dario; Morbelli, Silvia; Piccardo, Arnoldo; Larsson, Stig A.; Pagani, Marco

2008-01-01

The purpose of the study is to evaluate the combined accuracy of episodic memory performance and 18 F-FDG PET in identifying patients with amnestic mild cognitive impairment (aMCI) converting to Alzheimer's disease (AD), aMCI non-converters, and controls. Thirty-three patients with aMCI and 15 controls (CTR) were followed up for a mean of 21 months. Eleven patients developed AD (MCI/AD) and 22 remained with aMCI (MCI/MCI). 18 F-FDG PET volumetric regions of interest underwent principal component analysis (PCA) that identified 12 principal components (PC), expressed by coarse component scores (CCS). Discriminant analysis was performed using the significant PCs and episodic memory scores. PCA highlighted relative hypometabolism in PC5, including bilateral posterior cingulate and left temporal pole, and in PC7, including the bilateral orbitofrontal cortex, both in MCI/MCI and MCI/AD vs CTR. PC5 itself plus PC12, including the left lateral frontal cortex (LFC: BAs 44, 45, 46, 47), were significantly different between MCI/AD and MCI/MCI. By a three-group discriminant analysis, CTR were more accurately identified by PET-CCS + delayed recall score (100%), MCI/MCI by PET-CCS + either immediate or delayed recall scores (91%), while MCI/AD was identified by PET-CCS alone (82%). PET increased by 25% the correct allocations achieved by memory scores, while memory scores increased by 15% the correct allocations achieved by PET. Combining memory performance and 18 F-FDG PET yielded a higher accuracy than each single tool in identifying CTR and MCI/MCI. The PC containing bilateral posterior cingulate and left temporal pole was the hallmark of MCI/MCI patients, while the PC including the left LFC was the hallmark of conversion to AD. (orig.)

Principal component analysis of FDG PET in amnestic MCI

Energy Technology Data Exchange (ETDEWEB)

Nobili, Flavio; Girtler, Nicola; Brugnolo, Andrea; Dessi, Barbara; Rodriguez, Guido [University of Genoa, Clinical Neurophysiology, Department of Endocrinological and Medical Sciences, Genoa (Italy); S. Martino Hospital, Alzheimer Evaluation Unit, Genoa (Italy); S. Martino Hospital, Head-Neck Department, Genoa (Italy); Salmaso, Dario [CNR, Institute of Cognitive Sciences and Technologies, Rome (Italy); CNR, Institute of Cognitive Sciences and Technologies, Padua (Italy); Morbelli, Silvia [University of Genoa, Nuclear Medicine Unit, Department of Internal Medicine, Genoa (Italy); Piccardo, Arnoldo [Galliera Hospital, Nuclear Medicine Unit, Department of Imaging Diagnostics, Genoa (Italy); Larsson, Stig A. [Karolinska Hospital, Department of Nuclear Medicine, Stockholm (Sweden); Pagani, Marco [CNR, Institute of Cognitive Sciences and Technologies, Rome (Italy); CNR, Institute of Cognitive Sciences and Technologies, Padua (Italy); Karolinska Hospital, Department of Nuclear Medicine, Stockholm (Sweden)

2008-12-15

The purpose of the study is to evaluate the combined accuracy of episodic memory performance and {sup 18}F-FDG PET in identifying patients with amnestic mild cognitive impairment (aMCI) converting to Alzheimer's disease (AD), aMCI non-converters, and controls. Thirty-three patients with aMCI and 15 controls (CTR) were followed up for a mean of 21 months. Eleven patients developed AD (MCI/AD) and 22 remained with aMCI (MCI/MCI). {sup 18}F-FDG PET volumetric regions of interest underwent principal component analysis (PCA) that identified 12 principal components (PC), expressed by coarse component scores (CCS). Discriminant analysis was performed using the significant PCs and episodic memory scores. PCA highlighted relative hypometabolism in PC5, including bilateral posterior cingulate and left temporal pole, and in PC7, including the bilateral orbitofrontal cortex, both in MCI/MCI and MCI/AD vs CTR. PC5 itself plus PC12, including the left lateral frontal cortex (LFC: BAs 44, 45, 46, 47), were significantly different between MCI/AD and MCI/MCI. By a three-group discriminant analysis, CTR were more accurately identified by PET-CCS + delayed recall score (100%), MCI/MCI by PET-CCS + either immediate or delayed recall scores (91%), while MCI/AD was identified by PET-CCS alone (82%). PET increased by 25% the correct allocations achieved by memory scores, while memory scores increased by 15% the correct allocations achieved by PET. Combining memory performance and {sup 18}F-FDG PET yielded a higher accuracy than each single tool in identifying CTR and MCI/MCI. The PC containing bilateral posterior cingulate and left temporal pole was the hallmark of MCI/MCI patients, while the PC including the left LFC was the hallmark of conversion to AD. (orig.)

Teaching Principal Components Using Correlations.

Science.gov (United States)

Westfall, Peter H; Arias, Andrea L; Fulton, Lawrence V

2017-01-01

Introducing principal components (PCs) to students is difficult. First, the matrix algebra and mathematical maximization lemmas are daunting, especially for students in the social and behavioral sciences. Second, the standard motivation involving variance maximization subject to unit length constraint does not directly connect to the "variance explained" interpretation. Third, the unit length and uncorrelatedness constraints of the standard motivation do not allow re-scaling or oblique rotations, which are common in practice. Instead, we propose to motivate the subject in terms of optimizing (weighted) average proportions of variance explained in the original variables; this approach may be more intuitive, and hence easier to understand because it links directly to the familiar "R-squared" statistic. It also removes the need for unit length and uncorrelatedness constraints, provides a direct interpretation of "variance explained," and provides a direct answer to the question of whether to use covariance-based or correlation-based PCs. Furthermore, the presentation can be made without matrix algebra or optimization proofs. Modern tools from data science, including heat maps and text mining, provide further help in the interpretation and application of PCs; examples are given. Together, these techniques may be used to revise currently used methods for teaching and learning PCs in the behavioral sciences.

The contribution of executive control to semantic cognition: Convergent evidence from semantic aphasia and executive dysfunction.

Science.gov (United States)

Thompson, Hannah E; Almaghyuli, Azizah; Noonan, Krist A; Barak, Ohr; Lambon Ralph, Matthew A; Jefferies, Elizabeth

2018-01-03

Semantic cognition, as described by the controlled semantic cognition (CSC) framework (Rogers et al., , Neuropsychologia, 76, 220), involves two key components: activation of coherent, generalizable concepts within a heteromodal 'hub' in combination with modality-specific features (spokes), and a constraining mechanism that manipulates and gates this knowledge to generate time- and task-appropriate behaviour. Executive-semantic goal representations, largely supported by executive regions such as frontal and parietal cortex, are thought to allow the generation of non-dominant aspects of knowledge when these are appropriate for the task or context. Semantic aphasia (SA) patients have executive-semantic deficits, and these are correlated with general executive impairment. If the CSC proposal is correct, patients with executive impairment should not only exhibit impaired semantic cognition, but should also show characteristics that align with those observed in SA. This possibility remains largely untested, as patients selected on the basis that they show executive impairment (i.e., with 'dysexecutive syndrome') have not been extensively tested on tasks tapping semantic control and have not been previously compared with SA cases. We explored conceptual processing in 12 patients showing symptoms consistent with dysexecutive syndrome (DYS) and 24 SA patients, using a range of multimodal semantic assessments which manipulated control demands. Patients with executive impairments, despite not being selected to show semantic impairments, nevertheless showed parallel patterns to SA cases. They showed strong effects of distractor strength, cues and miscues, and probe-target distance, plus minimal effects of word frequency on comprehension (unlike semantic dementia patients with degradation of conceptual knowledge). This supports a component process account of semantic cognition in which retrieval is shaped by control processes, and confirms that deficits in SA patients reflect

Finger crease pattern recognition using Legendre moments and principal component analysis

Science.gov (United States)

Luo, Rongfang; Lin, Tusheng

2007-03-01

The finger joint lines defined as finger creases and its distribution can identify a person. In this paper, we propose a new finger crease pattern recognition method based on Legendre moments and principal component analysis (PCA). After obtaining the region of interest (ROI) for each finger image in the pre-processing stage, Legendre moments under Radon transform are applied to construct a moment feature matrix from the ROI, which greatly decreases the dimensionality of ROI and can represent principal components of the finger creases quite well. Then, an approach to finger crease pattern recognition is designed based on Karhunen-Loeve (K-L) transform. The method applies PCA to a moment feature matrix rather than the original image matrix to achieve the feature vector. The proposed method has been tested on a database of 824 images from 103 individuals using the nearest neighbor classifier. The accuracy up to 98.584% has been obtained when using 4 samples per class for training. The experimental results demonstrate that our proposed approach is feasible and effective in biometrics.

The Use of Principal Components in Long-Range Forecasting

Science.gov (United States)

Chern, Jonq-Gong

Large-scale modes of the global sea surface temperatures and the Northern Hemisphere tropospheric circulation are described by principal component analysis. The first and the second SST components well describe the El Nino episodes, and the El Nino index (ENI), suggested in this study, is consistent with the winter Southern Oscillation index (SOI), where this ENI is a composite component of the weighted first and second SST components. The large-scale interactive modes of the coupling ocean-atmosphere system are identified by cross-correlation analysis The result shows that the first SST component is strongly correlated with the first component of geopotential height in lead time of 6 months. In the El Nino-Southern Oscillation (ENSO) evolution, the El Nino mode strongly influences the winter tropospheric circulation in the mid -latitudes for up to three leading seasons. The regional long-range variation of climate is investigated with these major components of the SST and the tropospheric circulation. In the mid-latitude, the climate of the central United States shows a weak linkage with these large-scale circulations, and the climate of the western United States appears to be consistently associated with the ENSO modes. These El Nino modes also show a dominant influence on Eastern Asia as evidenced in Taiwan Mei-Yu patterns. Possible regional long-range forecasting schemes, utilizing the complementary characteristics of the winter El Nino mode and SST anomalies, are examined with the Taiwan Mei-Yu.

Signal-to-noise contribution of principal component loads in reconstructed near-infrared Raman tissue spectra.

Science.gov (United States)

Grimbergen, M C M; van Swol, C F P; Kendall, C; Verdaasdonk, R M; Stone, N; Bosch, J L H R

2010-01-01

The overall quality of Raman spectra in the near-infrared region, where biological samples are often studied, has benefited from various improvements to optical instrumentation over the past decade. However, obtaining ample spectral quality for analysis is still challenging due to device requirements and short integration times required for (in vivo) clinical applications of Raman spectroscopy. Multivariate analytical methods, such as principal component analysis (PCA) and linear discriminant analysis (LDA), are routinely applied to Raman spectral datasets to develop classification models. Data compression is necessary prior to discriminant analysis to prevent or decrease the degree of over-fitting. The logical threshold for the selection of principal components (PCs) to be used in discriminant analysis is likely to be at a point before the PCs begin to introduce equivalent signal and noise and, hence, include no additional value. Assessment of the signal-to-noise ratio (SNR) at a certain peak or over a specific spectral region will depend on the sample measured. Therefore, the mean SNR over the whole spectral region (SNR(msr)) is determined in the original spectrum as well as for spectra reconstructed from an increasing number of principal components. This paper introduces a method of assessing the influence of signal and noise from individual PC loads and indicates a method of selection of PCs for LDA. To evaluate this method, two data sets with different SNRs were used. The sets were obtained with the same Raman system and the same measurement parameters on bladder tissue collected during white light cystoscopy (set A) and fluorescence-guided cystoscopy (set B). This method shows that the mean SNR over the spectral range in the original Raman spectra of these two data sets is related to the signal and noise contribution of principal component loads. The difference in mean SNR over the spectral range can also be appreciated since fewer principal components can

APPLYING PRINCIPAL COMPONENT ANALYSIS, MULTILAYER PERCEPTRON AND SELF-ORGANIZING MAPS FOR OPTICAL CHARACTER RECOGNITION

Directory of Open Access Journals (Sweden)

Khuat Thanh Tung

2016-11-01

Full Text Available Optical Character Recognition plays an important role in data storage and data mining when the number of documents stored as images is increasing. It is expected to find the ways to convert images of typewritten or printed text into machine-encoded text effectively in order to support for the process of information handling effectively. In this paper, therefore, the techniques which are being used to convert image into editable text in the computer such as principal component analysis, multilayer perceptron network, self-organizing maps, and improved multilayer neural network using principal component analysis are experimented. The obtained results indicated the effectiveness and feasibility of the proposed methods.

Semantic Keys and Reading

Directory of Open Access Journals (Sweden)

Zev bar-Lev

2016-12-01

Full Text Available Semantic Keys are elements (word-parts of written language that give an iconic, general representation of the whole word’s meaning. In written Sino-Japanese the “radical” or semantic components play this role. For example, the character meaning ‘woman, female’ is the Semantic Key of the character for Ma ‘Mama’ (alongside the phonetic component Ma, which means ‘horse’ as a separate character. The theory of semantic Keys in both graphic and phonemic aspects is called qTheory or nanosemantics. The most innovative aspect of the present article is the hypothesis that, in languages using alphabetic writing systems, the role of Semantic Key is played by consonants, more specifically the first consonant. Thus, L meaning ‘LIFT’ is the Semantic Key of English Lift, Ladle, Lofty, aLps, eLevator, oLympus; Spanish Leva, Lecantarse, aLto, Lengua; Arabic aLLah, and Hebrew① ªeL-ºaL ‘upto-above’ (the Israeli airline, Polish Lot ‘flight’ (the Polish airline; Hebrew ªeL, ªeLohim ‘God’, and haLLeluyah ‘praise-ye God’ (using Parallels, ‘Lift up God’. Evidence for the universality of the theory is shown by many examples drawn from various languages, including Indo-European Semitic, Chinese and Japanese. The theory reveals hundreds of relationships within and between languages, related and unrelated, that have been “Hiding in Plain Sight”, to mention just one example: the Parallel between Spanish Pan ‘bread’ and Mandarin Fan ‘rice’.

Vibrational spectroscopy and principal component analysis for conformational study of virus nucleic acids

Science.gov (United States)

Dovbeshko, G. I.; Repnytska, O. P.; Pererva, T.; Miruta, A.; Kosenkov, D.

2004-07-01

Conformation analysis of mutated DNA-bacteriophages (PLys-23, P23-2, P47- the numbers have been assigned by T. Pererva) induced by MS2 virus incorporated in Ecoli AB 259 Hfr 3000 has been done. Surface enhanced infrared absorption (SEIRA) spectroscopy and principal component analysis has been applied for solving this problem. The nucleic acids isolated from the mutated phages had a form of double stranded DNA with different modifications. The nucleic acid from phage P47 was undergone the structural rearrangement in the most degree. The shape and position ofthe fine structure of the Phosphate asymmetrical band at 1071cm-1 as well as the stretching OH vibration at 3370-3390 cm-1 has indicated to the appearance ofadditional OH-groups. The Z-form feature has been found in the base vibration region (1694 cm-1) and the sugar region (932 cm-1). A supposition about modification of structure of DNA by Z-fragments for P47 phage has been proposed. The P23-2 and PLys-23 phages have showed the numerous minor structural changes also. On the basis of SEIRA spectra we have determined the characteristic parameters of the marker bands of nucleic acid used for construction of principal components. Contribution of different spectral parameters of nucleic acids to principal components has been estimated.

Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection

DEFF Research Database (Denmark)

Sharifzadeh, Sara; Ghodsi, Ali; Clemmensen, Line H.

2017-01-01

Principal component analysis (PCA) is one of the main unsupervised pre-processing methods for dimension reduction. When the training labels are available, it is worth using a supervised PCA strategy. In cases that both dimension reduction and variable selection are required, sparse PCA (SPCA...

Principal modes of rupture encountered in expertise of advanced components

International Nuclear Information System (INIS)

Tavassoli, A.A.; Bougault, A.

1986-10-01

Failure of many metallic components investigated can be classified into two categories: intergranular or transgranular according to their principal mode of rupture. Intergranular ruptures are often provoked by segregation of impurities at the grain boundaries. Three examples are cited where this phenomenon occured, one of them is a steel (A 508 cl 3) used for PWR vessel. Intergranular failures are in general induced by fatigue in the advanced components operating under thermal or load transients. One example concerning a sodium mixer which was subjected to thermal loadings is presented. Examples of stress corrosion and intergranular sensitization failures are cited. These examples show the importance of fractography for the determination of rupture causes [fr

The contribution of executive control to semantic cognition: Convergent evidence from semantic aphasia and executive dysfunction

OpenAIRE

Thompson, Hannah; Almaghyuli, Azizah; Noonan, Krist A.; barak, Ohr; Lambon Ralph, Matthew A.; Jefferies, Elizabeth

2018-01-01

Semantic cognition, as described by the controlled semantic cognition (CSC) framework (Rogers et al., 2015, Neuropsychologia, 76, 220), involves two key components: activation of coherent, generalizable concepts within a heteromodal ‘hub’ in combination with modality-specific features (spokes), and a constraining mechanism that manipulates and gates this knowledge to generate time- and task-appropriate behaviour. Executive–semantic goal representations, largely supported by executive regions ...

Research on Air Quality Evaluation based on Principal Component Analysis

Science.gov (United States)

Wang, Xing; Wang, Zilin; Guo, Min; Chen, Wei; Zhang, Huan

2018-01-01

Economic growth has led to environmental capacity decline and the deterioration of air quality. Air quality evaluation as a fundamental of environmental monitoring and air pollution control has become increasingly important. Based on the principal component analysis (PCA), this paper evaluates the air quality of a large city in Beijing-Tianjin-Hebei Area in recent 10 years and identifies influencing factors, in order to provide reference to air quality management and air pollution control.

Semantic memory in object use.

Science.gov (United States)

Silveri, Maria Caterina; Ciccarelli, Nicoletta

2009-10-01

We studied five patients with semantic memory disorders, four with semantic dementia and one with herpes simplex virus encephalitis, to investigate the involvement of semantic conceptual knowledge in object use. Comparisons between patients who had semantic deficits of different severity, as well as the follow-up, showed that the ability to use objects was largely preserved when the deficit was mild but progressively decayed as the deficit became more severe. Naming was generally more impaired than object use. Production tasks (pantomime execution and actual object use) and comprehension tasks (pantomime recognition and action recognition) as well as functional knowledge about objects were impaired when the semantic deficit was severe. Semantic and unrelated errors were produced during object use, but actions were always fluent and patients performed normally on a novel tools task in which the semantic demand was minimal. Patients with severe semantic deficits scored borderline on ideational apraxia tasks. Our data indicate that functional semantic knowledge is crucial for using objects in a conventional way and suggest that non-semantic factors, mainly non-declarative components of memory, might compensate to some extent for semantic disorders and guarantee some residual ability to use very common objects independently of semantic knowledge.

Brain network of semantic integration in sentence reading: insights from independent component analysis and graph theoretical analysis.

Science.gov (United States)

Ye, Zheng; Doñamayor, Nuria; Münte, Thomas F

2014-02-01

A set of cortical and sub-cortical brain structures has been linked with sentence-level semantic processes. However, it remains unclear how these brain regions are organized to support the semantic integration of a word into sentential context. To look into this issue, we conducted a functional magnetic resonance imaging (fMRI) study that required participants to silently read sentences with semantically congruent or incongruent endings and analyzed the network properties of the brain with two approaches, independent component analysis (ICA) and graph theoretical analysis (GTA). The GTA suggested that the whole-brain network is topologically stable across conditions. The ICA revealed a network comprising the supplementary motor area (SMA), left inferior frontal gyrus, left middle temporal gyrus, left caudate nucleus, and left angular gyrus, which was modulated by the incongruity of sentence ending. Furthermore, the GTA specified that the connections between the left SMA and left caudate nucleus as well as that between the left caudate nucleus and right thalamus were stronger in response to incongruent vs. congruent endings. Copyright © 2012 Wiley Periodicals, Inc.

The application of principal component analysis to quantify technique in sports.

Science.gov (United States)

Federolf, P; Reid, R; Gilgien, M; Haugen, P; Smith, G

2014-06-01

Analyzing an athlete's "technique," sport scientists often focus on preselected variables that quantify important aspects of movement. In contrast, coaches and practitioners typically describe movements in terms of basic postures and movement components using subjective and qualitative features. A challenge for sport scientists is finding an appropriate quantitative methodology that incorporates the holistic perspective of human observers. Using alpine ski racing as an example, this study explores principal component analysis (PCA) as a mathematical method to decompose a complex movement pattern into its main movement components. Ski racing movements were recorded by determining the three-dimensional coordinates of 26 points on each skier which were subsequently interpreted as a 78-dimensional posture vector at each time point. PCA was then used to determine the mean posture and principal movements (PMk ) carried out by the athletes. The first four PMk contained 95.5 ± 0.5% of the variance in the posture vectors which quantified changes in body inclination, vertical or fore-aft movement of the trunk, and distance between skis. In summary, calculating PMk offered a data-driven, quantitative, and objective method of analyzing human movement that is similar to how human observers such as coaches or ski instructors would describe the movement. © 2012 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Iris recognition based on robust principal component analysis

Science.gov (United States)

Karn, Pradeep; He, Xiao Hai; Yang, Shuai; Wu, Xiao Hong

2014-11-01

Iris images acquired under different conditions often suffer from blur, occlusion due to eyelids and eyelashes, specular reflection, and other artifacts. Existing iris recognition systems do not perform well on these types of images. To overcome these problems, we propose an iris recognition method based on robust principal component analysis. The proposed method decomposes all training images into a low-rank matrix and a sparse error matrix, where the low-rank matrix is used for feature extraction. The sparsity concentration index approach is then applied to validate the recognition result. Experimental results using CASIA V4 and IIT Delhi V1iris image databases showed that the proposed method achieved competitive performances in both recognition accuracy and computational efficiency.

The structure of semantic person memory: evidence from semantic priming in person recognition.

Science.gov (United States)

Wiese, Holger

2011-11-01

This paper reviews research on the structure of semantic person memory as examined with semantic priming. In this experimental paradigm, a familiarity decision on a target face or written name is usually faster when it is preceded by a related as compared to an unrelated prime. This effect has been shown to be relatively short lived and susceptible to interfering items. Moreover, semantic priming can cross stimulus domains, such that a written name can prime a target face and vice versa. However, it remains controversial whether representations of people are stored in associative networks based on co-occurrence, or in more abstract semantic categories. In line with prominent cognitive models of face recognition, which explain semantic priming by shared semantic information between prime and target, recent research demonstrated that priming could be obtained from purely categorically related, non-associated prime/target pairs. Although strategic processes, such as expectancy and retrospective matching likely contribute, there is also evidence for a non-strategic contribution to priming, presumably related to spreading activation. Finally, a semantic priming effect has been demonstrated in the N400 event-related potential (ERP) component, which may reflect facilitated access to semantic information. It is concluded that categorical relatedness is one organizing principle of semantic person memory. ©2011 The British Psychological Society.

Application of principal component regression and partial least squares regression in ultraviolet spectrum water quality detection

Science.gov (United States)

Li, Jiangtong; Luo, Yongdao; Dai, Honglin

2018-01-01

Water is the source of life and the essential foundation of all life. With the development of industrialization, the phenomenon of water pollution is becoming more and more frequent, which directly affects the survival and development of human. Water quality detection is one of the necessary measures to protect water resources. Ultraviolet (UV) spectral analysis is an important research method in the field of water quality detection, which partial least squares regression (PLSR) analysis method is becoming predominant technology, however, in some special cases, PLSR's analysis produce considerable errors. In order to solve this problem, the traditional principal component regression (PCR) analysis method was improved by using the principle of PLSR in this paper. The experimental results show that for some special experimental data set, improved PCR analysis method performance is better than PLSR. The PCR and PLSR is the focus of this paper. Firstly, the principal component analysis (PCA) is performed by MATLAB to reduce the dimensionality of the spectral data; on the basis of a large number of experiments, the optimized principal component is extracted by using the principle of PLSR, which carries most of the original data information. Secondly, the linear regression analysis of the principal component is carried out with statistic package for social science (SPSS), which the coefficients and relations of principal components can be obtained. Finally, calculating a same water spectral data set by PLSR and improved PCR, analyzing and comparing two results, improved PCR and PLSR is similar for most data, but improved PCR is better than PLSR for data near the detection limit. Both PLSR and improved PCR can be used in Ultraviolet spectral analysis of water, but for data near the detection limit, improved PCR's result better than PLSR.

Principal component analysis and the locus of the Fréchet mean in the space of phylogenetic trees.

Science.gov (United States)

Nye, Tom M W; Tang, Xiaoxian; Weyenberg, Grady; Yoshida, Ruriko

2017-12-01

Evolutionary relationships are represented by phylogenetic trees, and a phylogenetic analysis of gene sequences typically produces a collection of these trees, one for each gene in the analysis. Analysis of samples of trees is difficult due to the multi-dimensionality of the space of possible trees. In Euclidean spaces, principal component analysis is a popular method of reducing high-dimensional data to a low-dimensional representation that preserves much of the sample's structure. However, the space of all phylogenetic trees on a fixed set of species does not form a Euclidean vector space, and methods adapted to tree space are needed. Previous work introduced the notion of a principal geodesic in this space, analogous to the first principal component. Here we propose a geometric object for tree space similar to the [Formula: see text]th principal component in Euclidean space: the locus of the weighted Fréchet mean of [Formula: see text] vertex trees when the weights vary over the [Formula: see text]-simplex. We establish some basic properties of these objects, in particular showing that they have dimension [Formula: see text], and propose algorithms for projection onto these surfaces and for finding the principal locus associated with a sample of trees. Simulation studies demonstrate that these algorithms perform well, and analyses of two datasets, containing Apicomplexa and African coelacanth genomes respectively, reveal important structure from the second principal components.

Identifying sources of emerging organic contaminants in a mixed use watershed using principal components analysis.

Science.gov (United States)

Karpuzcu, M Ekrem; Fairbairn, David; Arnold, William A; Barber, Brian L; Kaufenberg, Elizabeth; Koskinen, William C; Novak, Paige J; Rice, Pamela J; Swackhamer, Deborah L

2014-01-01

Principal components analysis (PCA) was used to identify sources of emerging organic contaminants in the Zumbro River watershed in Southeastern Minnesota. Two main principal components (PCs) were identified, which together explained more than 50% of the variance in the data. Principal Component 1 (PC1) was attributed to urban wastewater-derived sources, including municipal wastewater and residential septic tank effluents, while Principal Component 2 (PC2) was attributed to agricultural sources. The variances of the concentrations of cotinine, DEET and the prescription drugs carbamazepine, erythromycin and sulfamethoxazole were best explained by PC1, while the variances of the concentrations of the agricultural pesticides atrazine, metolachlor and acetochlor were best explained by PC2. Mixed use compounds carbaryl, iprodione and daidzein did not specifically group with either PC1 or PC2. Furthermore, despite the fact that caffeine and acetaminophen have been historically associated with human use, they could not be attributed to a single dominant land use category (e.g., urban/residential or agricultural). Contributions from septic systems did not clarify the source for these two compounds, suggesting that additional sources, such as runoff from biosolid-amended soils, may exist. Based on these results, PCA may be a useful way to broadly categorize the sources of new and previously uncharacterized emerging contaminants or may help to clarify transport pathways in a given area. Acetaminophen and caffeine were not ideal markers for urban/residential contamination sources in the study area and may need to be reconsidered as such in other areas as well.

CMB constraints on principal components of the inflaton potential

International Nuclear Information System (INIS)

Dvorkin, Cora; Hu, Wayne

2010-01-01

We place functional constraints on the shape of the inflaton potential from the cosmic microwave background through a variant of the generalized slow-roll approximation that allows large amplitude, rapidly changing deviations from scale-free conditions. Employing a principal component decomposition of the source function G ' ≅3(V ' /V) 2 -2V '' /V and keeping only those measured to better than 10% results in 5 nearly independent Gaussian constraints that may be used to test any single-field inflationary model where such deviations are expected. The first component implies <3% variations at the 100 Mpc scale. One component shows a 95% CL preference for deviations around the 300 Mpc scale at the ∼10% level but the global significance is reduced considering the 5 components examined. This deviation also requires a change in the cold dark matter density which in a flat ΛCDM model is disfavored by current supernova and Hubble constant data and can be tested with future polarization or high multipole temperature data. Its impact resembles a local running of the tilt from multipoles 30-800 but is only marginally consistent with a constant running beyond this range. For this analysis, we have implemented a ∼40x faster WMAP7 likelihood method which we have made publicly available.

Principal Component Analysis of Working Memory Variables during Child and Adolescent Development.

Science.gov (United States)

Barriga-Paulino, Catarina I; Rodríguez-Martínez, Elena I; Rojas-Benjumea, María Ángeles; Gómez, Carlos M

2016-10-03

Correlation and Principal Component Analysis (PCA) of behavioral measures from two experimental tasks (Delayed Match-to-Sample and Oddball), and standard scores from a neuropsychological test battery (Working Memory Test Battery for Children) was performed on data from participants between 6-18 years old. The correlation analysis (p 1), the scores of the first extracted component were significantly correlated (p < .05) to most behavioral measures, suggesting some commonalities of the processes of age-related changes in the measured variables. The results suggest that this first component would be related to age but also to individual differences during the cognitive maturation process across childhood and adolescence stages. The fourth component would represent the speed-accuracy trade-off phenomenon as it presents loading components with different signs for reaction times and errors.

Efficient training of multilayer perceptrons using principal component analysis

International Nuclear Information System (INIS)

Bunzmann, Christoph; Urbanczik, Robert; Biehl, Michael

2005-01-01

A training algorithm for multilayer perceptrons is discussed and studied in detail, which relates to the technique of principal component analysis. The latter is performed with respect to a correlation matrix computed from the example inputs and their target outputs. Typical properties of the training procedure are investigated by means of a statistical physics analysis in models of learning regression and classification tasks. We demonstrate that the procedure requires by far fewer examples for good generalization than traditional online training. For networks with a large number of hidden units we derive the training prescription which achieves, within our model, the optimal generalization behavior

QUANTITATIVE ELECTRONIC STRUCTURE - ACTIVITY RELATIONSHIP OF ANTIMALARIAL COMPOUND OF ARTEMISININ DERIVATIVES USING PRINCIPAL COMPONENT REGRESSION APPROACH

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Paul Robert Martin Werfette

2010-06-01

Full Text Available Analysis of quantitative structure - activity relationship (QSAR for a series of antimalarial compound artemisinin derivatives has been done using principal component regression. The descriptors for QSAR study were representation of electronic structure i.e. atomic net charges of the artemisinin skeleton calculated by AM1 semi-empirical method. The antimalarial activity of the compound was expressed in log 1/IC50 which is an experimental data. The main purpose of the principal component analysis approach is to transform a large data set of atomic net charges to simplify into a data set which known as latent variables. The best QSAR equation to analyze of log 1/IC50 can be obtained from the regression method as a linear function of several latent variables i.e. x1, x2, x3, x4 and x5. The best QSAR model is expressed in the following equation,  (;;   Keywords: QSAR, antimalarial, artemisinin, principal component regression

Characterization of reflectance variability in the industrial paint application of automotive metallic coatings by using principal component analysis

Science.gov (United States)

Medina, José M.; Díaz, José A.

2013-05-01

We have applied principal component analysis to examine trial-to-trial variability of reflectances of automotive coatings that contain effect pigments. Reflectance databases were measured from different color batch productions using a multi-angle spectrophotometer. A method to classify the principal components was used based on the eigenvalue spectra. It was found that the eigenvalue spectra follow distinct power laws and depend on the detection angle. The scaling exponent provided an estimation of the correlation between reflectances and it was higher near specular reflection, suggesting a contribution from the deposition of effect pigments. Our findings indicate that principal component analysis can be a useful tool to classify different sources of spectral variability in color engineering.

Local Prediction Models on Mid-Atlantic Ridge MORB by Principal Component Regression

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Ling, X.; Snow, J. E.; Chin, W.

2017-12-01

The isotopic compositions of the daughter isotopes of long-lived radioactive systems (Sr, Nd, Hf and Pb ) can be used to map the scale and history of mantle heterogeneities beneath mid-ocean ridges. Our goal is to relate the multidimensional structure in the existing isotopic dataset with an underlying physical reality of mantle sources. The numerical technique of Principal Component Analysis is useful to reduce the linear dependence of the data to a minimum set of orthogonal eigenvectors encapsulating the information contained (cf Agranier et al 2005). The dataset used for this study covers almost all the MORBs along mid-Atlantic Ridge (MAR), from 54oS to 77oN and 8.8oW to -46.7oW, including replicating the dataset of Agranier et al., 2005 published plus 53 basalt samples dredged and analyzed since then (data from PetDB). The principal components PC1 and PC2 account for 61.56% and 29.21%, respectively, of the total isotope ratios variability. The samples with similar compositions to HIMU and EM and DM are identified to better understand the PCs. PC1 and PC2 are accountable for HIMU and EM whereas PC2 has limited control over the DM source. PC3 is more strongly controlled by the depleted mantle source than PC2. What this means is that all three principal components have a high degree of significance relevant to the established mantle sources. We also tested the relationship between mantle heterogeneity and sample locality. K-means clustering algorithm is a type of unsupervised learning to find groups in the data based on feature similarity. The PC factor scores of each sample are clustered into three groups. Cluster one and three are alternating on the north and south MAR. Cluster two exhibits on 45.18oN to 0.79oN and -27.9oW to -30.40oW alternating with cluster one. The ridge has been preliminarily divided into 16 sections considering both the clusters and ridge segments. The principal component regression models the section based on 6 isotope ratios and PCs. The

Semantic 3D Modeling Based on CityGML for Ancient Chinese-Style Architectural Roofs of Digital Heritage

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Lin Li

2017-04-01

Full Text Available Ancient Chinese-style architecture has received increased attention during the last century as a segment of cultural heritage and is of great significance, specifically in regard to the process of digitizing and modeling these buildings to preserve and protect this heritage. Because the roof form reflects the age of the structure, the structural character and the historical culture of the ancient building, constructing a refined model for the roof is a primary aspect of the 3D modeling procedure. To avoid cumbersome traditional modeling approaches that use geometry units, such as points, lines and triangles, a flexible semantic method is proposed in this study to improve modeling efficiency and reduce the professional requirements. In this method, a two-level semantic decomposition of the roof is presented according to the characteristics of ancient Chinese-style architecture. The structural level reveals the basic components that determine its structural shape, and the decorative level refers to the attached components that influence the exterior appearance. The assembly validity of the decomposed elements and the combined diversity of the integrated entities are ensured by topological constraints and derived transformations of the semantic components. This proposed method was implemented by utilizing CityGML (City Geography Markup Language via the ADE (Application Domain Extension mechanism and was tested by modeling the principal buildings included in the Palace Museum.

Water reuse systems: A review of the principal components

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Lucchetti, G.; Gray, G.A.

1988-01-01

Principal components of water reuse systems include ammonia removal, disease control, temperature control, aeration, and particulate filtration. Effective ammonia removal techniques include air stripping, ion exchange, and biofiltration. Selection of a particular technique largely depends on site-specific requirements (e.g., space, existing water quality, and fish densities). Disease control, although often overlooked, is a major problem in reuse systems. Pathogens can be controlled most effectively with ultraviolet radiation, ozone, or chlorine. Simple and inexpensive methods are available to increase oxygen concentration and eliminate gas supersaturation, these include commercial aerators, air injectors, and packed columns. Temperature control is a major advantage of reuse systems, but the equipment required can be expensive, particularly if water temperature must be rigidly controlled and ambient air temperature fluctuates. Filtration can be readily accomplished with a hydrocyclone or sand filter that increases overall system efficiency. Based on criteria of adaptability, efficiency, and reasonable cost, we recommend components for a small water reuse system.

Impact of Autocorrelation on Principal Components and Their Use in Statistical Process Control

DEFF Research Database (Denmark)

Vanhatalo, Erik; Kulahci, Murat

2015-01-01

A basic assumption when using principal component analysis (PCA) for inferential purposes, such as in statistical process control (SPC), is that the data are independent in time. In many industrial processes, frequent sampling and process dynamics make this assumption unrealistic rendering sampled...

The Influence of Semantic Neighbours on Visual Word Recognition

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Yates, Mark

2012-01-01

Although it is assumed that semantics is a critical component of visual word recognition, there is still much that we do not understand. One recent way of studying semantic processing has been in terms of semantic neighbourhood (SN) density, and this research has shown that semantic neighbours facilitate lexical decisions. However, it is not clear…

Nonlinear principal component analysis and its applications

CERN Document Server

Mori, Yuichi; Makino, Naomichi

2016-01-01

This book expounds the principle and related applications of nonlinear principal component analysis (PCA), which is useful method to analyze mixed measurement levels data. In the part dealing with the principle, after a brief introduction of ordinary PCA, a PCA for categorical data (nominal and ordinal) is introduced as nonlinear PCA, in which an optimal scaling technique is used to quantify the categorical variables. The alternating least squares (ALS) is the main algorithm in the method. Multiple correspondence analysis (MCA), a special case of nonlinear PCA, is also introduced. All formulations in these methods are integrated in the same manner as matrix operations. Because any measurement levels data can be treated consistently as numerical data and ALS is a very powerful tool for estimations, the methods can be utilized in a variety of fields such as biometrics, econometrics, psychometrics, and sociology. In the applications part of the book, four applications are introduced: variable selection for mixed...

Principal Component Analysis Based Two-Dimensional (PCA-2D) Correlation Spectroscopy: PCA Denoising for 2D Correlation Spectroscopy

International Nuclear Information System (INIS)

Jung, Young Mee

2003-01-01

Principal component analysis based two-dimensional (PCA-2D) correlation analysis is applied to FTIR spectra of polystyrene/methyl ethyl ketone/toluene solution mixture during the solvent evaporation. Substantial amount of artificial noise were added to the experimental data to demonstrate the practical noise-suppressing benefit of PCA-2D technique. 2D correlation analysis of the reconstructed data matrix from PCA loading vectors and scores successfully extracted only the most important features of synchronicity and asynchronicity without interference from noise or insignificant minor components. 2D correlation spectra constructed with only one principal component yield strictly synchronous response with no discernible a asynchronous features, while those involving at least two or more principal components generated meaningful asynchronous 2D correlation spectra. Deliberate manipulation of the rank of the reconstructed data matrix, by choosing the appropriate number and type of PCs, yields potentially more refined 2D correlation spectra

An application of principal component analysis to the clavicle and clavicle fixation devices.

Science.gov (United States)

Daruwalla, Zubin J; Courtis, Patrick; Fitzpatrick, Clare; Fitzpatrick, David; Mullett, Hannan

2010-03-26

Principal component analysis (PCA) enables the building of statistical shape models of bones and joints. This has been used in conjunction with computer assisted surgery in the past. However, PCA of the clavicle has not been performed. Using PCA, we present a novel method that examines the major modes of size and three-dimensional shape variation in male and female clavicles and suggests a method of grouping the clavicle into size and shape categories. Twenty-one high-resolution computerized tomography scans of the clavicle were reconstructed and analyzed using a specifically developed statistical software package. After performing statistical shape analysis, PCA was applied to study the factors that account for anatomical variation. The first principal component representing size accounted for 70.5 percent of anatomical variation. The addition of a further three principal components accounted for almost 87 percent. Using statistical shape analysis, clavicles in males have a greater lateral depth and are longer, wider and thicker than in females. However, the sternal angle in females is larger than in males. PCA confirmed these differences between genders but also noted that men exhibit greater variance and classified clavicles into five morphological groups. This unique approach is the first that standardizes a clavicular orientation. It provides information that is useful to both, the biomedical engineer and clinician. Other applications include implant design with regard to modifying current or designing future clavicle fixation devices. Our findings support the need for further development of clavicle fixation devices and the questioning of whether gender-specific devices are necessary.

THE STUDY OF THE CHARACTERIZATION INDICES OF FABRICS BY PRINCIPAL COMPONENT ANALYSIS METHOD

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HRISTIAN Liliana

2017-05-01

Full Text Available The paper was pursued to prioritize the worsted fabrics type, for the manufacture of outerwear products by characterization indeces of fabrics, using the mathematical model of Principal Component Analysis (PCA. There are a number of variables with a certain influence on the quality of fabrics, but some of these variables are more important than others, so it is useful to identify those variables to a better understanding the factors which can lead the improving of the fabrics quality. A solution to this problem can be the application of a method of factorial analysis, the so-called Principal Component Analysis, with the final goal of establishing and analyzing those variables which influence in a significant manner the internal structure of combed wool fabrics according to armire type. By applying PCA it is obtained a small number of the linear combinations (principal components from a set of variables, describing the internal structure of the fabrics, which can hold as much information as possible from the original variables. Data analysis is an important initial step in decision making, allowing identification of the causes that lead to a decision- making situations. Thus it is the action of transforming the initial data in order to extract useful information and to facilitate reaching the conclusions. The process of data analysis can be defined as a sequence of steps aimed at formulating hypotheses, collecting primary information and validation, the construction of the mathematical model describing this phenomenon and reaching these conclusions about the behavior of this model.

Analyzing Hidden Semantics in Social Bookmarking of Open Educational Resources

Science.gov (United States)

Minguillón, Julià

Web 2.0 services such as social bookmarking allow users to manage and share the links they find interesting, adding their own tags for describing them. This is especially interesting in the field of open educational resources, as delicious is a simple way to bridge the institutional point of view (i.e. learning object repositories) with the individual one (i.e. personal collections), thus promoting the discovering and sharing of such resources by other users. In this paper we propose a methodology for analyzing such tags in order to discover hidden semantics (i.e. taxonomies and vocabularies) that can be used to improve descriptions of learning objects and make learning object repositories more visible and discoverable. We propose the use of a simple statistical analysis tool such as principal component analysis to discover which tags create clusters that can be semantically interpreted. We will compare the obtained results with a collection of resources related to open educational resources, in order to better understand the real needs of people searching for open educational resources.

Visualizing solvent mediated phase transformation behavior of carbamazepine polymorphs by principal component analysis

DEFF Research Database (Denmark)

Tian, Fang; Rades, Thomas; Sandler, Niklas

2008-01-01

The purpose of this research is to gain a greater insight into the hydrate formation processes of different carbamazepine (CBZ) anhydrate forms in aqueous suspension, where principal component analysis (PCA) was applied for data analysis. The capability of PCA to visualize and to reveal simplified...

INDIA’S ELECTRICITY DEMAND FORECAST USING REGRESSION ANALYSIS AND ARTIFICIAL NEURAL NETWORKS BASED ON PRINCIPAL COMPONENTS

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S. Saravanan

2012-07-01

Full Text Available Power System planning starts with Electric load (demand forecasting. Accurate electricity load forecasting is one of the most important challenges in managing supply and demand of the electricity, since the electricity demand is volatile in nature; it cannot be stored and has to be consumed instantly. The aim of this study deals with electricity consumption in India, to forecast future projection of demand for a period of 19 years from 2012 to 2030. The eleven input variables used are Amount of CO2 emission, Population, Per capita GDP, Per capita gross national income, Gross Domestic savings, Industry, Consumer price index, Wholesale price index, Imports, Exports and Per capita power consumption. A new methodology based on Artificial Neural Networks (ANNs using principal components is also used. Data of 29 years used for training and data of 10 years used for testing the ANNs. Comparison made with multiple linear regression (based on original data and the principal components and ANNs with original data as input variables. The results show that the use of ANNs with principal components (PC is more effective.

Nonlinear Process Fault Diagnosis Based on Serial Principal Component Analysis.

Science.gov (United States)

Deng, Xiaogang; Tian, Xuemin; Chen, Sheng; Harris, Chris J

2018-03-01

Many industrial processes contain both linear and nonlinear parts, and kernel principal component analysis (KPCA), widely used in nonlinear process monitoring, may not offer the most effective means for dealing with these nonlinear processes. This paper proposes a new hybrid linear-nonlinear statistical modeling approach for nonlinear process monitoring by closely integrating linear principal component analysis (PCA) and nonlinear KPCA using a serial model structure, which we refer to as serial PCA (SPCA). Specifically, PCA is first applied to extract PCs as linear features, and to decompose the data into the PC subspace and residual subspace (RS). Then, KPCA is performed in the RS to extract the nonlinear PCs as nonlinear features. Two monitoring statistics are constructed for fault detection, based on both the linear and nonlinear features extracted by the proposed SPCA. To effectively perform fault identification after a fault is detected, an SPCA similarity factor method is built for fault recognition, which fuses both the linear and nonlinear features. Unlike PCA and KPCA, the proposed method takes into account both linear and nonlinear PCs simultaneously, and therefore, it can better exploit the underlying process's structure to enhance fault diagnosis performance. Two case studies involving a simulated nonlinear process and the benchmark Tennessee Eastman process demonstrate that the proposed SPCA approach is more effective than the existing state-of-the-art approach based on KPCA alone, in terms of nonlinear process fault detection and identification.

Usage of semantic representations in recognition memory.

Science.gov (United States)

Nishiyama, Ryoji; Hirano, Tetsuji; Ukita, Jun

2017-11-01

Meanings of words facilitate false acceptance as well as correct rejection of lures in recognition memory tests, depending on the experimental context. This suggests that semantic representations are both directly and indirectly (i.e., mediated by perceptual representations) used in remembering. Studies using memory conjunction errors (MCEs) paradigms, in which the lures consist of component parts of studied words, have reported semantic facilitation of rejection of the lures. However, attending to components of the lures could potentially cause this. Therefore, we investigated whether semantic overlap of lures facilitates MCEs using Japanese Kanji words in which a whole-word image is more concerned in reading. Experiments demonstrated semantic facilitation of MCEs in a delayed recognition test (Experiment 1), and in immediate recognition tests in which participants were prevented from using phonological or orthographic representations (Experiment 2), and the salient effect on individuals with high semantic memory capacities (Experiment 3). Additionally, analysis of the receiver operating characteristic suggested that this effect is attributed to familiarity-based memory judgement and phantom recollection. These findings indicate that semantic representations can be directly used in remembering, even when perceptual representations of studied words are available.

Principal component analysis of the nonlinear coupling of harmonic modes in heavy-ion collisions

Science.gov (United States)

BoŻek, Piotr

2018-03-01

The principal component analysis of flow correlations in heavy-ion collisions is studied. The correlation matrix of harmonic flow is generalized to correlations involving several different flow vectors. The method can be applied to study the nonlinear coupling between different harmonic modes in a double differential way in transverse momentum or pseudorapidity. The procedure is illustrated with results from the hydrodynamic model applied to Pb + Pb collisions at √{sN N}=2760 GeV. Three examples of generalized correlations matrices in transverse momentum are constructed corresponding to the coupling of v22 and v4, of v2v3 and v5, or of v23,v33 , and v6. The principal component decomposition is applied to the correlation matrices and the dominant modes are calculated.

Fault feature extraction method based on local mean decomposition Shannon entropy and improved kernel principal component analysis model

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Jinlu Sheng

2016-07-01

Full Text Available To effectively extract the typical features of the bearing, a new method that related the local mean decomposition Shannon entropy and improved kernel principal component analysis model was proposed. First, the features are extracted by time–frequency domain method, local mean decomposition, and using the Shannon entropy to process the original separated product functions, so as to get the original features. However, the features been extracted still contain superfluous information; the nonlinear multi-features process technique, kernel principal component analysis, is introduced to fuse the characters. The kernel principal component analysis is improved by the weight factor. The extracted characteristic features were inputted in the Morlet wavelet kernel support vector machine to get the bearing running state classification model, bearing running state was thereby identified. Cases of test and actual were analyzed.

Principal component analysis reveals gender-specific predictors of cardiometabolic risk in 6th graders

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Peterson Mark D

2012-11-01

Full Text Available Abstract Background The purpose of this study was to determine the sex-specific pattern of pediatric cardiometabolic risk with principal component analysis, using several biological, behavioral and parental variables in a large cohort (n = 2866 of 6th grade students. Methods Cardiometabolic risk components included waist circumference, fasting glucose, blood pressure, plasma triglycerides levels and HDL-cholesterol. Principal components analysis was used to determine the pattern of risk clustering and to derive a continuous aggregate score (MetScore. Stratified risk components and MetScore were analyzed for association with age, body mass index (BMI, cardiorespiratory fitness (CRF, physical activity (PA, and parental factors. Results In both boys and girls, BMI and CRF were associated with multiple risk components, and overall MetScore. Maternal smoking was associated with multiple risk components in girls and boys, as well as MetScore in boys, even after controlling for children’s BMI. Paternal family history of early cardiovascular disease (CVD and parental age were associated with increased blood pressure and MetScore for girls. Children’s PA levels, maternal history of early CVD, and paternal BMI were also indicative for various risk components, but not MetScore. Conclusions Several biological and behavioral factors were independently associated with children’s cardiometabolic disease risk, and thus represent a unique gender-specific risk profile. These data serve to bolster the independent contribution of CRF, PA, and family-oriented healthy lifestyles for improving children’s health.

2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications.

Science.gov (United States)

Du, Qi-Shi; Wang, Shu-Qing; Xie, Neng-Zhong; Wang, Qing-Yan; Huang, Ri-Bo; Chou, Kuo-Chen

2017-09-19

A two-level principal component predictor (2L-PCA) was proposed based on the principal component analysis (PCA) approach. It can be used to quantitatively analyze various compounds and peptides about their functions or potentials to become useful drugs. One level is for dealing with the physicochemical properties of drug molecules, while the other level is for dealing with their structural fragments. The predictor has the self-learning and feedback features to automatically improve its accuracy. It is anticipated that 2L-PCA will become a very useful tool for timely providing various useful clues during the process of drug development.

Oil classification using X-ray scattering and principal component analysis

Energy Technology Data Exchange (ETDEWEB)

Almeida, Danielle S.; Souza, Amanda S.; Lopes, Ricardo T., E-mail: dani.almeida84@gmail.com, E-mail: ricardo@lin.ufrj.br, E-mail: amandass@bioqmed.ufrj.br [Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ (Brazil); Oliveira, Davi F.; Anjos, Marcelino J., E-mail: davi.oliveira@uerj.br, E-mail: marcelin@uerj.br [Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro, RJ (Brazil). Inst. de Fisica Armando Dias Tavares

2015-07-01

X-ray scattering techniques have been considered promising for the classification and characterization of many types of samples. This study employed this technique combined with chemical analysis and multivariate analysis to characterize 54 vegetable oil samples (being 25 olive oils)with different properties obtained in commercial establishments in Rio de Janeiro city. The samples were chemically analyzed using the following indexes: iodine, acidity, saponification and peroxide. In order to obtain the X-ray scattering spectrum, an X-ray tube with a silver anode operating at 40kV and 50 μA was used. The results showed that oils cab ne divided in tow large groups: olive oils and non-olive oils. Additionally, in a multivariate analysis (Principal Component Analysis - PCA), two components were obtained and accounted for more than 80% of the variance. One component was associated with chemical parameters and the other with scattering profiles of each sample. Results showed that use of X-ray scattering spectra combined with chemical analysis and PCA can be a fast, cheap and efficient method for vegetable oil characterization. (author)

Oil classification using X-ray scattering and principal component analysis

International Nuclear Information System (INIS)

Almeida, Danielle S.; Souza, Amanda S.; Lopes, Ricardo T.; Oliveira, Davi F.; Anjos, Marcelino J.

2015-01-01

X-ray scattering techniques have been considered promising for the classification and characterization of many types of samples. This study employed this technique combined with chemical analysis and multivariate analysis to characterize 54 vegetable oil samples (being 25 olive oils)with different properties obtained in commercial establishments in Rio de Janeiro city. The samples were chemically analyzed using the following indexes: iodine, acidity, saponification and peroxide. In order to obtain the X-ray scattering spectrum, an X-ray tube with a silver anode operating at 40kV and 50 μA was used. The results showed that oils cab ne divided in tow large groups: olive oils and non-olive oils. Additionally, in a multivariate analysis (Principal Component Analysis - PCA), two components were obtained and accounted for more than 80% of the variance. One component was associated with chemical parameters and the other with scattering profiles of each sample. Results showed that use of X-ray scattering spectra combined with chemical analysis and PCA can be a fast, cheap and efficient method for vegetable oil characterization. (author)

Identification of Counterfeit Alcoholic Beverages Using Cluster Analysis in Principal-Component Space

Science.gov (United States)

Khodasevich, M. A.; Sinitsyn, G. V.; Gres'ko, M. A.; Dolya, V. M.; Rogovaya, M. V.; Kazberuk, A. V.

2017-07-01

A study of 153 brands of commercial vodka products showed that counterfeit samples could be identified by introducing a unified additive at the minimum concentration acceptable for instrumental detection and multivariate analysis of UV-Vis transmission spectra. Counterfeit products were detected with 100% probability by using hierarchical cluster analysis or the C-means method in two-dimensional principal-component space.

Geochemical differentiation processes for arc magma of the Sengan volcanic cluster, Northeastern Japan, constrained from principal component analysis

Science.gov (United States)

Ueki, Kenta; Iwamori, Hikaru

2017-10-01

In this study, with a view of understanding the structure of high-dimensional geochemical data and discussing the chemical processes at work in the evolution of arc magmas, we employed principal component analysis (PCA) to evaluate the compositional variations of volcanic rocks from the Sengan volcanic cluster of the Northeastern Japan Arc. We analyzed the trace element compositions of various arc volcanic rocks, sampled from 17 different volcanoes in a volcanic cluster. The PCA results demonstrated that the first three principal components accounted for 86% of the geochemical variation in the magma of the Sengan region. Based on the relationships between the principal components and the major elements, the mass-balance relationships with respect to the contributions of minerals, the composition of plagioclase phenocrysts, geothermal gradient, and seismic velocity structure in the crust, the first, the second, and the third principal components appear to represent magma mixing, crystallizations of olivine/pyroxene, and crystallizations of plagioclase, respectively. These represented 59%, 20%, and 6%, respectively, of the variance in the entire compositional range, indicating that magma mixing accounted for the largest variance in the geochemical variation of the arc magma. Our result indicated that crustal processes dominate the geochemical variation of magma in the Sengan volcanic cluster.

An application of principal component analysis to the clavicle and clavicle fixation devices

Directory of Open Access Journals (Sweden)

Fitzpatrick David

2010-03-01

Full Text Available Abstract Background Principal component analysis (PCA enables the building of statistical shape models of bones and joints. This has been used in conjunction with computer assisted surgery in the past. However, PCA of the clavicle has not been performed. Using PCA, we present a novel method that examines the major modes of size and three-dimensional shape variation in male and female clavicles and suggests a method of grouping the clavicle into size and shape categories. Materials and methods Twenty-one high-resolution computerized tomography scans of the clavicle were reconstructed and analyzed using a specifically developed statistical software package. After performing statistical shape analysis, PCA was applied to study the factors that account for anatomical variation. Results The first principal component representing size accounted for 70.5 percent of anatomical variation. The addition of a further three principal components accounted for almost 87 percent. Using statistical shape analysis, clavicles in males have a greater lateral depth and are longer, wider and thicker than in females. However, the sternal angle in females is larger than in males. PCA confirmed these differences between genders but also noted that men exhibit greater variance and classified clavicles into five morphological groups. Discussion And Conclusions This unique approach is the first that standardizes a clavicular orientation. It provides information that is useful to both, the biomedical engineer and clinician. Other applications include implant design with regard to modifying current or designing future clavicle fixation devices. Our findings support the need for further development of clavicle fixation devices and the questioning of whether gender-specific devices are necessary.

Scientific Datasets: Discovery and Aggregation for Semantic Interpretation.

Science.gov (United States)

Lopez, L. A.; Scott, S.; Khalsa, S. J. S.; Duerr, R.

2015-12-01

One of the biggest challenges that interdisciplinary researchers face is finding suitable datasets in order to advance their science; this problem remains consistent across multiple disciplines. A surprising number of scientists, when asked what tool they use for data discovery, reply "Google", which is an acceptable solution in some cases but not even Google can find -or cares to compile- all the data that's relevant for science and particularly geo sciences. If a dataset is not discoverable through a well known search provider it will remain dark data to the scientific world.For the past year, BCube, an EarthCube Building Block project, has been developing, testing and deploying a technology stack capable of data discovery at web-scale using the ultimate dataset: The Internet. This stack has 2 principal components, a web-scale crawling infrastructure and a semantic aggregator. The web-crawler is a modified version of Apache Nutch (the originator of Hadoop and other big data technologies) that has been improved and tailored for data and data service discovery. The second component is semantic aggregation, carried out by a python-based workflow that extracts valuable metadata and stores it in the form of triples through the use semantic technologies.While implementing the BCube stack we have run into several challenges such as a) scaling the project to cover big portions of the Internet at a reasonable cost, b) making sense of very diverse and non-homogeneous data, and lastly, c) extracting facts about these datasets using semantic technologies in order to make them usable for the geosciences community. Despite all these challenges we have proven that we can discover and characterize data that otherwise would have remained in the dark corners of the Internet. Having all this data indexed and 'triplelized' will enable scientists to access a trove of information relevant to their work in a more natural way. An important characteristic of the BCube stack is that all

Recommendations based on semantically enriched museum collections

NARCIS (Netherlands)

Wang, Y.; Stash, N.; Aroyo, L.M.; Gorgels, P.; Rutledge, L.W.; Schreiber, G.

2008-01-01

This article presents the CHIP demonstrator1 for providing personalized access to digital museum collections. It consists of three main components: Art Recommender, Tour Wizard, and Mobile Tour Guide. Based on the semantically enriched Rijksmuseum Amsterdam2 collection, we show how Semantic Web

Modelling the Load Curve of Aggregate Electricity Consumption Using Principal Components

OpenAIRE

Matteo Manera; Angelo Marzullo

2003-01-01

Since oil is a non-renewable resource with a high environmental impact, and its most common use is to produce combustibles for electricity, reliable methods for modelling electricity consumption can contribute to a more rational employment of this hydrocarbon fuel. In this paper we apply the Principal Components (PC) method to modelling the load curves of Italy, France and Greece on hourly data of aggregate electricity consumption. The empirical results obtained with the PC approach are compa...

Morphological evaluation of common bean diversity in Bosnia and Herzegovina using the discriminant analysis of principal components (DAPC multivariate method

Directory of Open Access Journals (Sweden)

Grahić Jasmin

2013-01-01

Full Text Available In order to analyze morphological characteristics of locally cultivated common bean landraces from Bosnia and Herzegovina (B&H, thirteen quantitative and qualitative traits of 40 P. vulgaris accessions, collected from four geographical regions (Northwest B&H, Northeast B&H, Central B&H and Sarajevo and maintained at the Gene bank of the Faculty of Agriculture and Food Sciences in Sarajevo, were examined. Principal component analysis (PCA showed that the proportion of variance retained in the first two principal components was 54.35%. The first principal component had high contributing factor loadings from seed width, seed height and seed weight, whilst the second principal component had high contributing factor loadings from the analyzed traits seed per pod and pod length. PCA plot, based on the first two principal components, displayed a high level of variability among the analyzed material. The discriminant analysis of principal components (DAPC created 3 discriminant functions (DF, whereby the first two discriminant functions accounted for 90.4% of the variance retained. Based on the retained DFs, DAPC provided group membership probabilities which showed that 70% of the accessions examined were correctly classified between the geographically defined groups. Based on the taxonomic distance, 40 common bean accessions analyzed in this study formed two major clusters, whereas two accessions Acc304 and Acc307 didn’t group in any of those. Acc360 and Acc362, as well as Acc324 and Acc371 displayed a high level of similarity and are probably the same landrace. The present diversity of Bosnia and Herzegovina’s common been landraces could be useful in future breeding programs.

Fault detection of flywheel system based on clustering and principal component analysis

Directory of Open Access Journals (Sweden)

Wang Rixin

2015-12-01

Full Text Available Considering the nonlinear, multifunctional properties of double-flywheel with closed-loop control, a two-step method including clustering and principal component analysis is proposed to detect the two faults in the multifunctional flywheels. At the first step of the proposed algorithm, clustering is taken as feature recognition to check the instructions of “integrated power and attitude control” system, such as attitude control, energy storage or energy discharge. These commands will ask the flywheel system to work in different operation modes. Therefore, the relationship of parameters in different operations can define the cluster structure of training data. Ordering points to identify the clustering structure (OPTICS can automatically identify these clusters by the reachability-plot. K-means algorithm can divide the training data into the corresponding operations according to the reachability-plot. Finally, the last step of proposed model is used to define the relationship of parameters in each operation through the principal component analysis (PCA method. Compared with the PCA model, the proposed approach is capable of identifying the new clusters and learning the new behavior of incoming data. The simulation results show that it can effectively detect the faults in the multifunctional flywheels system.

Principal component analysis of solar flares in the soft X-ray flux

International Nuclear Information System (INIS)

Teuber, D.L.; Reichmann, E.J.; Wilson, R.M.; National Aeronautics and Space Administration, Huntsville, AL

1979-01-01

Principal component analysis is a technique for extracting the salient features from a mass of data. It applies, in particular, to the analysis of nonstationary ensembles. Computational schemes for this task require the evaluation of eigenvalues of matrices. We have used EISPACK Matrix Eigen System Routines on an IBM 360-75 to analyze full-disk proportional-counter data from the X-ray event analyzer (X-REA) which was part of the Skylab ATM/S-056 experiment. Empirical orthogonal functions have been derived for events in the soft X-ray spectrum between 2.5 and 20 A during different time frames between June 1973 and January 1974. Results indicate that approximately 90% of the cumulative power of each analyzed flare is contained in the largest eigenvector. The first two largest eigenvectors are sufficient for an empirical curve-fit through the raw data and a characterization of solar flares in the soft X-ray flux. Power spectra of the two largest eigenvectors reveal a previously reported periodicity of approximately 5 min. Similar signatures were also obtained from flares that are synchronized on maximum pulse-height when subjected to a principal component analysis. (orig.)

Quantitative descriptive analysis and principal component analysis for sensory characterization of Indian milk product cham-cham.

Science.gov (United States)

Puri, Ritika; Khamrui, Kaushik; Khetra, Yogesh; Malhotra, Ravinder; Devraja, H C

2016-02-01

Promising development and expansion in the market of cham-cham, a traditional Indian dairy product is expected in the coming future with the organized production of this milk product by some large dairies. The objective of this study was to document the extent of variation in sensory properties of market samples of cham-cham collected from four different locations known for their excellence in cham-cham production and to find out the attributes that govern much of variation in sensory scores of this product using quantitative descriptive analysis (QDA) and principal component analysis (PCA). QDA revealed significant (p sensory attributes of cham-cham among the market samples. PCA identified four significant principal components that accounted for 72.4 % of the variation in the sensory data. Factor scores of each of the four principal components which primarily correspond to sweetness/shape/dryness of interior, surface appearance/surface dryness, rancid and firmness attributes specify the location of each market sample along each of the axes in 3-D graphs. These findings demonstrate the utility of quantitative descriptive analysis for identifying and measuring attributes of cham-cham that contribute most to its sensory acceptability.

Using principal component analysis to understand the variability of PDS 456

Science.gov (United States)

Parker, M. L.; Reeves, J. N.; Matzeu, G. A.; Buisson, D. J. K.; Fabian, A. C.

2018-02-01

We present a spectral-variability analysis of the low-redshift quasar PDS 456 using principal component analysis. In the XMM-Newton data, we find a strong peak in the first principal component at the energy of the Fe absorption line from the highly blueshifted outflow. This indicates that the absorption feature is more variable than the continuum, and that it is responding to the continuum. We find qualitatively different behaviour in the Suzaku data, which is dominated by changes in the column density of neutral absorption. In this case, we find no evidence of the absorption produced by the highly ionized gas being correlated with this variability. Additionally, we perform simulations of the source variability, and demonstrate that PCA can trivially distinguish between outflow variability correlated, anticorrelated and un-correlated with the continuum flux. Here, the observed anticorrelation between the absorption line equivalent width and the continuum flux may be due to the ionization of the wind responding to the continuum. Finally, we compare our results with those found in the narrow-line Seyfert 1 IRAS 13224-3809. We find that the Fe K UFO feature is sharper and more prominent in PDS 456, but that it lacks the lower energy features from lighter elements found in IRAS 13224-3809, presumably due to differences in ionization.

Principal component analysis of NEXAFS spectra for molybdenum speciation in hydrotreating catalysts

International Nuclear Information System (INIS)

Faro Junior, Arnaldo da C.; Rodrigues, Victor de O.; Eon, Jean-G.; Rocha, Angela S.

2010-01-01

Bulk and supported molybdenum based catalysts, modified by nickel, phosphorous or tungsten were studied by NEXAFS spectroscopy at the Mo L III and L II edges. The techniques of principal component analysis (PCA) together with a linear combination analysis (LCA) allowed the detection and quantification of molybdenum atoms in two different coordination states in the oxide form of the catalysts, namely tetrahedral and octahedral coordination. (author)

An Educational Tool for Browsing the Semantic Web

Science.gov (United States)

Yoo, Sujin; Kim, Younghwan; Park, Seongbin

2013-01-01

The Semantic Web is an extension of the current Web where information is represented in a machine processable way. It is not separate from the current Web and one of the confusions that novice users might have is where the Semantic Web is. In fact, users can easily encounter RDF documents that are components of the Semantic Web while they navigate…

TRECVid Semantic Indexing of Video: A 6-year Retrospective

NARCIS (Netherlands)

Awad, G.; Snoek, C.G.M.; Smeaton, A.F.; Quénot, G.

2016-01-01

Semantic indexing, or assigning semantic tags to video samples, is a key component for content-based access to video documents and collections. The Semantic Indexing task has been run at TRECVid from 2010 to 2015 with the support of NIST and the Quaero project. As with the previous High-Level

Application of principal component analysis to time series of daily air pollution and mortality

NARCIS (Netherlands)

Quant C; Fischer P; Buringh E; Ameling C; Houthuijs D; Cassee F; MGO

2004-01-01

We investigated whether cause-specific daily mortality can be attributed to specific sources of air pollution. To construct indicators of source-specific air pollution, we applied a principal component analysis (PCA) on routinely collected air pollution data in the Netherlands during the period

Assessing the effect of oil price on world food prices: Application of principal component analysis

International Nuclear Information System (INIS)

Esmaeili, Abdoulkarim; Shokoohi, Zainab

2011-01-01

The objective of this paper is to investigate the co-movement of food prices and the macroeconomic index, especially the oil price, by principal component analysis to further understand the influence of the macroeconomic index on food prices. We examined the food prices of seven major products: eggs, meat, milk, oilseeds, rice, sugar and wheat. The macroeconomic variables studied were crude oil prices, consumer price indexes, food production indexes and GDP around the world between 1961 and 2005. We use the Scree test and the proportion of variance method for determining the optimal number of common factors. The correlation coefficient between the extracted principal component and the macroeconomic index varies between 0.87 for the world GDP and 0.36 for the consumer price index. We find the food production index has the greatest influence on the macroeconomic index and that the oil price index has an influence on the food production index. Consequently, crude oil prices have an indirect effect on food prices. - Research Highlights: →We investigate the co-movement of food prices and the macroeconomic index. →The crude oil price has indirect effect on the world GDP via its impacts on food production index. →The food production index is the source of causation for CPI and GDP is affected by CPI. →The results confirm an indirect effect among oil price, food price principal component.

Variability search in M 31 using principal component analysis and the Hubble Source Catalogue

Science.gov (United States)

Moretti, M. I.; Hatzidimitriou, D.; Karampelas, A.; Sokolovsky, K. V.; Bonanos, A. Z.; Gavras, P.; Yang, M.

2018-06-01

Principal component analysis (PCA) is being extensively used in Astronomy but not yet exhaustively exploited for variability search. The aim of this work is to investigate the effectiveness of using the PCA as a method to search for variable stars in large photometric data sets. We apply PCA to variability indices computed for light curves of 18 152 stars in three fields in M 31 extracted from the Hubble Source Catalogue. The projection of the data into the principal components is used as a stellar variability detection and classification tool, capable of distinguishing between RR Lyrae stars, long-period variables (LPVs) and non-variables. This projection recovered more than 90 per cent of the known variables and revealed 38 previously unknown variable stars (about 30 per cent more), all LPVs except for one object of uncertain variability type. We conclude that this methodology can indeed successfully identify candidate variable stars.

k-t PCA: temporally constrained k-t BLAST reconstruction using principal component analysis

DEFF Research Database (Denmark)

Pedersen, Henrik; Kozerke, Sebastian; Ringgaard, Steffen

2009-01-01

in applications exhibiting a broad range of temporal frequencies such as free-breathing myocardial perfusion imaging. We show that temporal basis functions calculated by subjecting the training data to principal component analysis (PCA) can be used to constrain the reconstruction such that the temporal resolution...... is improved. The presented method is called k-t PCA....

Statistical intercomparison of global climate models: A common principal component approach with application to GCM data

International Nuclear Information System (INIS)

Sengupta, S.K.; Boyle, J.S.

1993-05-01

Variables describing atmospheric circulation and other climate parameters derived from various GCMs and obtained from observations can be represented on a spatio-temporal grid (lattice) structure. The primary objective of this paper is to explore existing as well as some new statistical methods to analyze such data structures for the purpose of model diagnostics and intercomparison from a statistical perspective. Among the several statistical methods considered here, a new method based on common principal components appears most promising for the purpose of intercomparison of spatio-temporal data structures arising in the task of model/model and model/data intercomparison. A complete strategy for such an intercomparison is outlined. The strategy includes two steps. First, the commonality of spatial structures in two (or more) fields is captured in the common principal vectors. Second, the corresponding principal components obtained as time series are then compared on the basis of similarities in their temporal evolution

Effects of physiotherapy treatment on knee osteoarthritis gait data using principal component analysis.

Science.gov (United States)

Gaudreault, Nathaly; Mezghani, Neila; Turcot, Katia; Hagemeister, Nicola; Boivin, Karine; de Guise, Jacques A

2011-03-01

Interpreting gait data is challenging due to intersubject variability observed in the gait pattern of both normal and pathological populations. The objective of this study was to investigate the impact of using principal component analysis for grouping knee osteoarthritis (OA) patients' gait data in more homogeneous groups when studying the effect of a physiotherapy treatment. Three-dimensional (3D) knee kinematic and kinetic data were recorded during the gait of 29 participants diagnosed with knee OA before and after they received 12 weeks of physiotherapy treatment. Principal component analysis was applied to extract groups of knee flexion/extension, adduction/abduction and internal/external rotation angle and moment data. The treatment's effect on parameters of interest was assessed using paired t-tests performed before and after grouping the knee kinematic data. Increased quadriceps and hamstring strength was observed following treatment (Pphysiotherapy on gait mechanics of knee osteoarthritis patients may be masked or underestimated if kinematic data are not separated into more homogeneous groups when performing pre- and post-treatment comparisons. Copyright © 2010 Elsevier Ltd. All rights reserved.

Detecting Genomic Signatures of Natural Selection with Principal Component Analysis: Application to the 1000 Genomes Data.

Science.gov (United States)

Duforet-Frebourg, Nicolas; Luu, Keurcien; Laval, Guillaume; Bazin, Eric; Blum, Michael G B

2016-04-01

To characterize natural selection, various analytical methods for detecting candidate genomic regions have been developed. We propose to perform genome-wide scans of natural selection using principal component analysis (PCA). We show that the common FST index of genetic differentiation between populations can be viewed as the proportion of variance explained by the principal components. Considering the correlations between genetic variants and each principal component provides a conceptual framework to detect genetic variants involved in local adaptation without any prior definition of populations. To validate the PCA-based approach, we consider the 1000 Genomes data (phase 1) considering 850 individuals coming from Africa, Asia, and Europe. The number of genetic variants is of the order of 36 millions obtained with a low-coverage sequencing depth (3×). The correlations between genetic variation and each principal component provide well-known targets for positive selection (EDAR, SLC24A5, SLC45A2, DARC), and also new candidate genes (APPBPP2, TP1A1, RTTN, KCNMA, MYO5C) and noncoding RNAs. In addition to identifying genes involved in biological adaptation, we identify two biological pathways involved in polygenic adaptation that are related to the innate immune system (beta defensins) and to lipid metabolism (fatty acid omega oxidation). An additional analysis of European data shows that a genome scan based on PCA retrieves classical examples of local adaptation even when there are no well-defined populations. PCA-based statistics, implemented in the PCAdapt R package and the PCAdapt fast open-source software, retrieve well-known signals of human adaptation, which is encouraging for future whole-genome sequencing project, especially when defining populations is difficult. © The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Discovery and Selection of Semantic Web Services

CERN Document Server

Wang, Xia

2013-01-01

For advanced web search engines to be able not only to search for semantically related information dispersed over different web pages, but also for semantic services providing certain functionalities, discovering semantic services is the key issue. Addressing four problems of current solution, this book presents the following contributions. A novel service model independent of semantic service description models is proposed, which clearly defines all elements necessary for service discovery and selection. It takes service selection as its gist and improves efficiency. Corresponding selection algorithms and their implementation as components of the extended Semantically Enabled Service-oriented Architecture in the Web Service Modeling Environment are detailed. Many applications of semantic web services, e.g. discovery, composition and mediation, can benefit from a general approach for building application ontologies. With application ontologies thus built, services are discovered in the same way as with single...

Varieties of semantic cognition revealed through simultaneous decomposition of intrinsic brain connectivity and behaviour.

Science.gov (United States)

Vatansever, Deniz; Bzdok, Danilo; Wang, Hao-Ting; Mollo, Giovanna; Sormaz, Mladen; Murphy, Charlotte; Karapanagiotidis, Theodoros; Smallwood, Jonathan; Jefferies, Elizabeth

2017-09-01

Contemporary theories assume that semantic cognition emerges from a neural architecture in which different component processes are combined to produce aspects of conceptual thought and behaviour. In addition to the state-level, momentary variation in brain connectivity, individuals may also differ in their propensity to generate particular configurations of such components, and these trait-level differences may relate to individual differences in semantic cognition. We tested this view by exploring how variation in intrinsic brain functional connectivity between semantic nodes in fMRI was related to performance on a battery of semantic tasks in 154 healthy participants. Through simultaneous decomposition of brain functional connectivity and semantic task performance, we identified distinct components of semantic cognition at rest. In a subsequent validation step, these data-driven components demonstrated explanatory power for neural responses in an fMRI-based semantic localiser task and variation in self-generated thoughts during the resting-state scan. Our findings showed that good performance on harder semantic tasks was associated with relative segregation at rest between frontal brain regions implicated in controlled semantic retrieval and the default mode network. Poor performance on easier tasks was linked to greater coupling between the same frontal regions and the anterior temporal lobe; a pattern associated with deliberate, verbal thematic thoughts at rest. We also identified components that related to qualities of semantic cognition: relatively good performance on pictorial semantic tasks was associated with greater separation of angular gyrus from frontal control sites and greater integration with posterior cingulate and anterior temporal cortex. In contrast, good speech production was linked to the separation of angular gyrus, posterior cingulate and temporal lobe regions. Together these data show that quantitative and qualitative variation in semantic

Semantic evaluations of noise with tonal components in Japan, France, and Germany: a cross-cultural comparison.

Science.gov (United States)

Hansen, Hans; Weber, Reinhard

2009-02-01

An evaluation of tonal components in noise using a semantic differential approach yields several perceptual and connotative factors. This study investigates the effect of culture on these factors with the aid of equivalent listening tests carried out in Japan (n=20), France (n=23), and Germany (n=20). The data's equivalence level is determined by a bias analysis. This analysis gives insight in the cross-cultural validity of the scales used for sound character determination. Three factors were extracted by factor analysis in all cultural subsamples: pleasant, metallic, and power. By employing appropriate target rotations of the factor spaces, the rotated factors were compared and they yield high similarities between the different cultural subsamples. To check cross-cultural differences in means, an item bias analysis was conducted. The a priori assumption of unbiased scales is rejected; the differences obtained are partially linked to bias effects. Acoustical sound descriptors were additionally tested for the semantic dimensions. The high agreement in judgments between the different cultural subsamples contrast the moderate success of the signal parameters to describe the dimensions.

A Novel Double Cluster and Principal Component Analysis-Based Optimization Method for the Orbit Design of Earth Observation Satellites

Directory of Open Access Journals (Sweden)

Yunfeng Dong

2017-01-01

Full Text Available The weighted sum and genetic algorithm-based hybrid method (WSGA-based HM, which has been applied to multiobjective orbit optimizations, is negatively influenced by human factors through the artificial choice of the weight coefficients in weighted sum method and the slow convergence of GA. To address these two problems, a cluster and principal component analysis-based optimization method (CPC-based OM is proposed, in which many candidate orbits are gradually randomly generated until the optimal orbit is obtained using a data mining method, that is, cluster analysis based on principal components. Then, the second cluster analysis of the orbital elements is introduced into CPC-based OM to improve the convergence, developing a novel double cluster and principal component analysis-based optimization method (DCPC-based OM. In DCPC-based OM, the cluster analysis based on principal components has the advantage of reducing the human influences, and the cluster analysis based on six orbital elements can reduce the search space to effectively accelerate convergence. The test results from a multiobjective numerical benchmark function and the orbit design results of an Earth observation satellite show that DCPC-based OM converges more efficiently than WSGA-based HM. And DCPC-based OM, to some degree, reduces the influence of human factors presented in WSGA-based HM.

Evaluation of skin melanoma in spectral range 450-950 nm using principal component analysis

Science.gov (United States)

Jakovels, D.; Lihacova, I.; Kuzmina, I.; Spigulis, J.

2013-06-01

Diagnostic potential of principal component analysis (PCA) of multi-spectral imaging data in the wavelength range 450- 950 nm for distant skin melanoma recognition is discussed. Processing of the measured clinical data by means of PCA resulted in clear separation between malignant melanomas and pigmented nevi.

Registration of dynamic dopamine D2receptor images using principal component analysis

International Nuclear Information System (INIS)

Acton, P.D.; Ell, P.J.; Pilowsky, L.S.; Brammer, M.J.; Suckling, J.

1997-01-01

This paper describes a novel technique for registering a dynamic sequence of single-photon emission tomography (SPET) dopamine D 2 receptor images, using principal component analysis (PCA). Conventional methods for registering images, such as count difference and correlation coefficient algorithms, fail to take into account the dynamic nature of the data, resulting in large systematic errors when registering time-varying images. However, by using principal component analysis to extract the temporal structure of the image sequence, misregistration can be quantified by examining the distribution of eigenvalues. The registration procedures were tested using a computer-generated dynamic phantom derived from a high-resolution magnetic resonance image of a realistic brain phantom. Each method was also applied to clinical SPET images of dopamine D 2 receptors, using the ligands iodine-123 iodobenzamide and iodine-123 epidepride, to investigate the influence of misregistration on kinetic modelling parameters and the binding potential. The PCA technique gave highly significant (P 123 I-epidepride scans. The PCA method produced data of much greater quality for subsequent kinetic modelling, with an improvement of nearly 50% in the χ 2 of the fit to the compartmental model, and provided superior quality registration of particularly difficult dynamic sequences. (orig.)

Portable XRF and principal component analysis for bill characterization in forensic science

International Nuclear Information System (INIS)

Appoloni, C.R.; Melquiades, F.L.

2014-01-01

Several modern techniques have been applied to prevent counterfeiting of money bills. The objective of this study was to demonstrate the potential of Portable X-ray Fluorescence (PXRF) technique and the multivariate analysis method of Principal Component Analysis (PCA) for classification of bills in order to use it in forensic science. Bills of Dollar, Euro and Real (Brazilian currency) were measured directly at different colored regions, without any previous preparation. Spectra interpretation allowed the identification of Ca, Ti, Fe, Cu, Sr, Y, Zr and Pb. PCA analysis separated the bills in three groups and subgroups among Brazilian currency. In conclusion, the samples were classified according to its origin identifying the elements responsible for differentiation and basic pigment composition. PXRF allied to multivariate discriminate methods is a promising technique for rapid and no destructive identification of false bills in forensic science. - Highlights: • The paper is about a direct method for bills discrimination by EDXRF and principal component analysis. • The bills are analyzed directly, without sample preparation and non destructively. • The results demonstrates that the methodology is feasible and could be applied in forensic science for identification of origin and false banknotes. • The novelty is that portable EDXRF is very fast and efficient for bills characterization

Boundary layer noise subtraction in hydrodynamic tunnel using robust principal component analysis.

Science.gov (United States)

Amailland, Sylvain; Thomas, Jean-Hugh; Pézerat, Charles; Boucheron, Romuald

2018-04-01

The acoustic study of propellers in a hydrodynamic tunnel is of paramount importance during the design process, but can involve significant difficulties due to the boundary layer noise (BLN). Indeed, advanced denoising methods are needed to recover the acoustic signal in case of poor signal-to-noise ratio. The technique proposed in this paper is based on the decomposition of the wall-pressure cross-spectral matrix (CSM) by taking advantage of both the low-rank property of the acoustic CSM and the sparse property of the BLN CSM. Thus, the algorithm belongs to the class of robust principal component analysis (RPCA), which derives from the widely used principal component analysis. If the BLN is spatially decorrelated, the proposed RPCA algorithm can blindly recover the acoustical signals even for negative signal-to-noise ratio. Unfortunately, in a realistic case, acoustic signals recorded in a hydrodynamic tunnel show that the noise may be partially correlated. A prewhitening strategy is then considered in order to take into account the spatially coherent background noise. Numerical simulations and experimental results show an improvement in terms of BLN reduction in the large hydrodynamic tunnel. The effectiveness of the denoising method is also investigated in the context of acoustic source localization.

Principal component and spatial correlation analysis of spectroscopic-imaging data in scanning probe microscopy

International Nuclear Information System (INIS)

Jesse, Stephen; Kalinin, Sergei V

2009-01-01

An approach for the analysis of multi-dimensional, spectroscopic-imaging data based on principal component analysis (PCA) is explored. PCA selects and ranks relevant response components based on variance within the data. It is shown that for examples with small relative variations between spectra, the first few PCA components closely coincide with results obtained using model fitting, and this is achieved at rates approximately four orders of magnitude faster. For cases with strong response variations, PCA allows an effective approach to rapidly process, de-noise, and compress data. The prospects for PCA combined with correlation function analysis of component maps as a universal tool for data analysis and representation in microscopy are discussed.

Tribological Performance Optimization of Electroless Ni-P-W Coating Using Weighted Principal Component Analysis

Directory of Open Access Journals (Sweden)

S. Roy

2013-12-01

Full Text Available The present investigation is an experimental approach to deposit electroless Ni-P-W coating on mild steel substrate and find out the optimum combination of various tribological performances on the basis of minimum friction and wear, using weighted principal component analysis (WPCA. In this study three main tribological parameters are chosen viz. load (A, speed (B and time(C. The responses are coefficient of friction and wear depth. Here Weighted Principal Component Analysis (WPCA method is adopted to convert the multi-responses into single performance index called multiple performance index (MPI and Taguchi L27 orthogonal array is used to design the experiment and to find the optimum combination of tribological parameters for minimum coefficient of friction and wear depth. ANOVA is performed to find the significance of the each tribological process parameters and their interactions. The EDX analysis, SEM and XRD are performed to study the composition and structural aspects.

The semantic structure of gratitude

Directory of Open Access Journals (Sweden)

Smirnov, Alexander V.

2016-06-01

Full Text Available In the modern social and economic environment of Russia, gratitude might be considered an ambiguous phenomenon. It can have different meaning for a person in different contexts and can manifest itself differently as well (that is, as an expression of sincere feelings or as an element of corruption. In this respect it is topical to investigate the system of meanings and relationships that define the semantic space of gratitude. The goal of the study was the investigation and description of the content and structure of the semantic space of the gratitude phenomenon as well as the determination of male, female, age, and ethnic peculiarities of the expression of gratitude. The objective was achieved by using the semantic differential designed by the authors to investigate attitudes toward gratitude. This investigation was carried out with the participation of 184 respondents (Russians, Tatars, Ukrainians, Jews living in the Russian Federation, Belarus, Kazakhstan, Tajikistan, Israel, Australia, Canada, and the United Kingdom and identifying themselves as representatives of one of these nationalities. The structural components of gratitude were singled out by means of exploratory factor analysis of the empirical data from the designed semantic differential. Gender, age, and ethnic differences were differentiated by means of Student’s t-test. Gratitude can be represented by material and nonmaterial forms as well as by actions in response to help given. The empirical data allowed us to design the ethnically nonspecified semantic structure of gratitude. During the elaboration of the differential, semantic universals of gratitude, which constitute its psychosemantic content, were distinguished. Peculiarities of attitudes toward gratitude by those in different age and gender groups were revealed. Differences in the degree of manifestation of components of the psychosemantic structure of gratitude related to ethnic characteristics were not discovered

Application of principal component and factor analyses in electron spectroscopy

International Nuclear Information System (INIS)

Siuda, R.; Balcerowska, G.

1998-01-01

Fundamentals of two methods, taken from multivariate analysis and known as principal component analysis (PCA) and factor analysis (FA), are presented. Both methods are well known in chemometrics. Since 1979, when application of the methods to electron spectroscopy was reported for the first time, they became to be more and more popular in different branches of electron spectroscopy. The paper presents examples of standard applications of the method of Auger electron spectroscopy (AES), X-ray photoelectron spectroscopy (XPS), and electron energy loss spectroscopy (EELS). Advantages one can take from application of the methods, their potentialities as well as their limitations are pointed out. (author)

IMPROVED SEARCH OF PRINCIPAL COMPONENT ANALYSIS DATABASES FOR SPECTRO-POLARIMETRIC INVERSION

International Nuclear Information System (INIS)

Casini, R.; Lites, B. W.; Ramos, A. Asensio; Ariste, A. López

2013-01-01

We describe a simple technique for the acceleration of spectro-polarimetric inversions based on principal component analysis (PCA) of Stokes profiles. This technique involves the indexing of the database models based on the sign of the projections (PCA coefficients) of the first few relevant orders of principal components of the four Stokes parameters. In this way, each model in the database can be attributed a distinctive binary number of 2 4n bits, where n is the number of PCA orders used for the indexing. Each of these binary numbers (indices) identifies a group of ''compatible'' models for the inversion of a given set of observed Stokes profiles sharing the same index. The complete set of the binary numbers so constructed evidently determines a partition of the database. The search of the database for the PCA inversion of spectro-polarimetric data can profit greatly from this indexing. In practical cases it becomes possible to approach the ideal acceleration factor of 2 4n as compared to the systematic search of a non-indexed database for a traditional PCA inversion. This indexing method relies on the existence of a physical meaning in the sign of the PCA coefficients of a model. For this reason, the presence of model ambiguities and of spectro-polarimetric noise in the observations limits in practice the number n of relevant PCA orders that can be used for the indexing

Recursive Principal Components Analysis Using Eigenvector Matrix Perturbation

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Deniz Erdogmus

2004-10-01

Full Text Available Principal components analysis is an important and well-studied subject in statistics and signal processing. The literature has an abundance of algorithms for solving this problem, where most of these algorithms could be grouped into one of the following three approaches: adaptation based on Hebbian updates and deflation, optimization of a second-order statistical criterion (like reconstruction error or output variance, and fixed point update rules with deflation. In this paper, we take a completely different approach that avoids deflation and the optimization of a cost function using gradients. The proposed method updates the eigenvector and eigenvalue matrices simultaneously with every new sample such that the estimates approximately track their true values as would be calculated from the current sample estimate of the data covariance matrix. The performance of this algorithm is compared with that of traditional methods like Sanger's rule and APEX, as well as a structurally similar matrix perturbation-based method.

Application of principal component analysis to ecodiversity assessment of postglacial landscape (on the example of Debnica Kaszubska commune, Middle Pomerania)

Science.gov (United States)

Wojciechowski, Adam

2017-04-01

In order to assess ecodiversity understood as a comprehensive natural landscape factor (Jedicke 2001), it is necessary to apply research methods which recognize the environment in a holistic way. Principal component analysis may be considered as one of such methods as it allows to distinguish the main factors determining landscape diversity on the one hand, and enables to discover regularities shaping the relationships between various elements of the environment under study on the other hand. The procedure adopted to assess ecodiversity with the use of principal component analysis involves: a) determining and selecting appropriate factors of the assessed environment qualities (hypsometric, geological, hydrographic, plant, and others); b) calculating the absolute value of individual qualities for the basic areas under analysis (e.g. river length, forest area, altitude differences, etc.); c) principal components analysis and obtaining factor maps (maps of selected components); d) generating a resultant, detailed map and isolating several classes of ecodiversity. An assessment of ecodiversity with the use of principal component analysis was conducted in the test area of 299,67 km2 in Debnica Kaszubska commune. The whole commune is situated in the Weichselian glaciation area of high hypsometric and morphological diversity as well as high geo- and biodiversity. The analysis was based on topographical maps of the commune area in scale 1:25000 and maps of forest habitats. Consequently, nine factors reflecting basic environment elements were calculated: maximum height (m), minimum height (m), average height (m), the length of watercourses (km), the area of water reservoirs (m2), total forest area (ha), coniferous forests habitats area (ha), deciduous forest habitats area (ha), alder habitats area (ha). The values for individual factors were analysed for 358 grid cells of 1 km2. Based on the principal components analysis, four major factors affecting commune ecodiversity

Measuring farm sustainability using data envelope analysis with principal components: the case of Wisconsin cranberry.

Science.gov (United States)

Dong, Fengxia; Mitchell, Paul D; Colquhoun, Jed

2015-01-01

Measuring farm sustainability performance is a crucial component for improving agricultural sustainability. While extensive assessments and indicators exist that reflect the different facets of agricultural sustainability, because of the relatively large number of measures and interactions among them, a composite indicator that integrates and aggregates over all variables is particularly useful. This paper describes and empirically evaluates a method for constructing a composite sustainability indicator that individually scores and ranks farm sustainability performance. The method first uses non-negative polychoric principal component analysis to reduce the number of variables, to remove correlation among variables and to transform categorical variables to continuous variables. Next the method applies common-weight data envelope analysis to these principal components to individually score each farm. The method solves weights endogenously and allows identifying important practices in sustainability evaluation. An empirical application to Wisconsin cranberry farms finds heterogeneity in sustainability practice adoption, implying that some farms could adopt relevant practices to improve the overall sustainability performance of the industry. Copyright © 2014 Elsevier Ltd. All rights reserved.

Evaluation of in-core measurements by means of principal components method

International Nuclear Information System (INIS)

Makai, M.; Temesvari, E.

1996-01-01

Surveillance of a nuclear reactor core comprehends determination of assemblies' three-dimensional (3D) power distribution. Derived from other assemblies' measured values, power of non-measured assembly is calculated for every assembly with the help of principal components method (PCM) which is also presented. The measured values are interpolated for different geometrical coverings of the WWER-440 core. Different procedures have been elaborated and investigated, among them the most successful methods are discussed. Each method offers self consistent means to determine numerical errors of the interpolated values. (author). 13 refs, 7 figs, 2 tabs

Assessment of oil weathering by gas chromatography-mass spectrometry, time warping and principal component analysis

DEFF Research Database (Denmark)

Malmquist, Linus M.V.; Olsen, Rasmus R.; Hansen, Asger B.

2007-01-01

weathering state and to distinguish between various weathering processes is investigated and discussed. The method is based on comprehensive and objective chromatographic data processing followed by principal component analysis (PCA) of concatenated sections of gas chromatography–mass spectrometry...

Fluvial facies reservoir productivity prediction method based on principal component analysis and artificial neural network

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Pengyu Gao

2016-03-01

Full Text Available It is difficult to forecast the well productivity because of the complexity of vertical and horizontal developments in fluvial facies reservoir. This paper proposes a method based on Principal Component Analysis and Artificial Neural Network to predict well productivity of fluvial facies reservoir. The method summarizes the statistical reservoir factors and engineering factors that affect the well productivity, extracts information by applying the principal component analysis method and approximates arbitrary functions of the neural network to realize an accurate and efficient prediction on the fluvial facies reservoir well productivity. This method provides an effective way for forecasting the productivity of fluvial facies reservoir which is affected by multi-factors and complex mechanism. The study result shows that this method is a practical, effective, accurate and indirect productivity forecast method and is suitable for field application.

Edge Principal Components and Squash Clustering: Using the Special Structure of Phylogenetic Placement Data for Sample Comparison

Science.gov (United States)

Matsen IV, Frederick A.; Evans, Steven N.

2013-01-01

Principal components analysis (PCA) and hierarchical clustering are two of the most heavily used techniques for analyzing the differences between nucleic acid sequence samples taken from a given environment. They have led to many insights regarding the structure of microbial communities. We have developed two new complementary methods that leverage how this microbial community data sits on a phylogenetic tree. Edge principal components analysis enables the detection of important differences between samples that contain closely related taxa. Each principal component axis is a collection of signed weights on the edges of the phylogenetic tree, and these weights are easily visualized by a suitable thickening and coloring of the edges. Squash clustering outputs a (rooted) clustering tree in which each internal node corresponds to an appropriate “average” of the original samples at the leaves below the node. Moreover, the length of an edge is a suitably defined distance between the averaged samples associated with the two incident nodes, rather than the less interpretable average of distances produced by UPGMA, the most widely used hierarchical clustering method in this context. We present these methods and illustrate their use with data from the human microbiome. PMID:23505415

A Filtering of Incomplete GNSS Position Time Series with Probabilistic Principal Component Analysis

Science.gov (United States)

Gruszczynski, Maciej; Klos, Anna; Bogusz, Janusz

2018-04-01

For the first time, we introduced the probabilistic principal component analysis (pPCA) regarding the spatio-temporal filtering of Global Navigation Satellite System (GNSS) position time series to estimate and remove Common Mode Error (CME) without the interpolation of missing values. We used data from the International GNSS Service (IGS) stations which contributed to the latest International Terrestrial Reference Frame (ITRF2014). The efficiency of the proposed algorithm was tested on the simulated incomplete time series, then CME was estimated for a set of 25 stations located in Central Europe. The newly applied pPCA was compared with previously used algorithms, which showed that this method is capable of resolving the problem of proper spatio-temporal filtering of GNSS time series characterized by different observation time span. We showed, that filtering can be carried out with pPCA method when there exist two time series in the dataset having less than 100 common epoch of observations. The 1st Principal Component (PC) explained more than 36% of the total variance represented by time series residuals' (series with deterministic model removed), what compared to the other PCs variances (less than 8%) means that common signals are significant in GNSS residuals. A clear improvement in the spectral indices of the power-law noise was noticed for the Up component, which is reflected by an average shift towards white noise from - 0.98 to - 0.67 (30%). We observed a significant average reduction in the accuracy of stations' velocity estimated for filtered residuals by 35, 28 and 69% for the North, East, and Up components, respectively. CME series were also subjected to analysis in the context of environmental mass loading influences of the filtering results. Subtraction of the environmental loading models from GNSS residuals provides to reduction of the estimated CME variance by 20 and 65% for horizontal and vertical components, respectively.

Quality analysis of commercial samples of Ziziphi spinosae semen (suanzaoren by means of chromatographic fingerprinting assisted by principal component analysis

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Shuai Sun

2014-06-01

Full Text Available Due to the scarcity of resources of Ziziphi spinosae semen (ZSS, many inferior goods and even adulterants are generally found in medicine markets. To strengthen the quality control, HPLC fingerprint common pattern established in this paper showed three main bioactive compounds in one chromatogram simultaneously. Principal component analysis based on DAD signals could discriminate adulterants and inferiorities. Principal component analysis indicated that all samples could be mainly regrouped into two main clusters according to the first principal component (PC1, redefined as Vicenin II and the second principal component (PC2, redefined as zizyphusine. PC1 and PC2 could explain 91.42% of the variance. Content of zizyphusine fluctuated more greatly than that of spinosin, and this result was also confirmed by the HPTLC result. Samples with low content of jujubosides and two common adulterants could not be used equivalently with authenticated ones in clinic, while one reference standard extract could substitute the crude drug in pharmaceutical production. Giving special consideration to the well-known bioactive saponins but with low response by end absorption, a fast and cheap HPTLC method for quality control of ZSS was developed and the result obtained was commensurate well with that of HPLC analysis. Samples having similar fingerprints to HPTLC common pattern targeting at saponins could be regarded as authenticated ones. This work provided a faster and cheaper way for quality control of ZSS and laid foundation for establishing a more effective quality control method for ZSS. Keywords: Adulterant, Common pattern, Principal component analysis, Quality control, Ziziphi spinosae semen

A principal components approach to parent-to-newborn body composition associations in South India

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Hill Jacqueline C

2009-02-01

Full Text Available Abstract Background Size at birth is influenced by environmental factors, like maternal nutrition and parity, and by genes. Birth weight is a composite measure, encompassing bone, fat and lean mass. These may have different determinants. The main purpose of this paper was to use anthropometry and principal components analysis (PCA to describe maternal and newborn body composition, and associations between them, in an Indian population. We also compared maternal and paternal measurements (body mass index (BMI and height as predictors of newborn body composition. Methods Weight, height, head and mid-arm circumferences, skinfold thicknesses and external pelvic diameters were measured at 30 ± 2 weeks gestation in 571 pregnant women attending the antenatal clinic of the Holdsworth Memorial Hospital, Mysore, India. Paternal height and weight were also measured. At birth, detailed neonatal anthropometry was performed. Unrotated and varimax rotated PCA was applied to the maternal and neonatal measurements. Results Rotated PCA reduced maternal measurements to 4 independent components (fat, pelvis, height and muscle and neonatal measurements to 3 components (trunk+head, fat, and leg length. An SD increase in maternal fat was associated with a 0.16 SD increase (β in neonatal fat (p Conclusion Principal components analysis is a useful method to describe neonatal body composition and its determinants. Newborn adiposity is related to maternal nutritional status and parity, while newborn length is genetically determined. Further research is needed to understand mechanisms linking maternal pelvic size to fetal growth and the determinants and implications of the components (trunk v leg length of fetal skeletal growth.

Retrieving the correlation matrix from a truncated PCA solution : The inverse principal component problem

NARCIS (Netherlands)

ten Berge, Jos M.F.; Kiers, Henk A.L.

When r Principal Components are available for k variables, the correlation matrix is approximated in the least squares sense by the loading matrix times its transpose. The approximation is generally not perfect unless r = k. In the present paper it is shown that, when r is at or above the Ledermann

Semantic Dysfunction in Women With Schizotypal Personality Disorder

OpenAIRE

Niznikiewicz, Margaret A.; Shenton, Martha E.; Voglmaier, Martina; Nestor, Paul G.; Dickey, Chandlee C.; Frumin, Melissa; Seidman, Larry J.; Allen, Christopher G.; McCarley, Robert W.

2002-01-01

Objective: This study examined whether early or late processes in semantic networks were abnormal in women with a diagnosis of schizotypal personality disorder. The N400 component of the EEG event-related potentials was used as a probe of semantic processes. Method: Word pairs were presented with short and long stimulus-onset asynchronies to investigate, respectively, early and late semantic processes in 16 women with schizotypal personality disorder and 15 normal female comparison subjects. ...

[Electrophysiological bases of semantic processing of objects].

Science.gov (United States)

Kahlaoui, Karima; Baccino, Thierry; Joanette, Yves; Magnié, Marie-Noële

2007-02-01

How pictures and words are stored and processed in the human brain constitute a long-standing question in cognitive psychology. Behavioral studies have yielded a large amount of data addressing this issue. Generally speaking, these data show that there are some interactions between the semantic processing of pictures and words. However, behavioral methods can provide only limited insight into certain findings. Fortunately, Event-Related Potential (ERP) provides on-line cues about the temporal nature of cognitive processes and contributes to the exploration of their neural substrates. ERPs have been used in order to better understand semantic processing of words and pictures. The main objective of this article is to offer an overview of the electrophysiologic bases of semantic processing of words and pictures. Studies presented in this article showed that the processing of words is associated with an N 400 component, whereas pictures elicited both N 300 and N 400 components. Topographical analysis of the N 400 distribution over the scalp is compatible with the idea that both image-mediated concrete words and pictures access an amodal semantic system. However, given the distinctive N 300 patterns, observed only during picture processing, it appears that picture and word processing rely upon distinct neuronal networks, even if they end up activating more or less similar semantic representations.

Chemical fingerprinting of terpanes and steranes by chromatographic alignment and principal component analysis

International Nuclear Information System (INIS)

Christensen, J.H.; Hansen, A.B.; Andersen, O.

2005-01-01

Biomarkers such as steranes and terpanes are abundant in crude oils, particularly in heavy distillate petroleum products. They are useful for matching highly weathered oil samples when other groups of petroleum hydrocarbons fail to distinguish oil samples. In this study, time warping and principal component analysis (PCA) were applied for oil hydrocarbon fingerprinting based on relative amounts of terpane and sterane isomers analyzed by gas chromatography and mass spectrometry. The 4 principal components were boiling point range, clay content, marine or organic terrestrial matter, and maturity based on differences in the terpane and sterane isomer patterns. This study is an extension of a previous fingerprinting study for identifying the sources of oil spill samples based only on the profiles of sterane isomers. Spill samples from the Baltic Carrier oil spill were correctly identified by inspection of score plots. The interpretation of the loading and score plots offered further chemical information about correlations between changes in the amounts of sterane and terpane isomers. It was concluded that this method is an objective procedure for analyzing chromatograms with more comprehensive data usage compared to other fingerprinting methods. 20 refs., 4 figs

Chemical fingerprinting of terpanes and steranes by chromatographic alignment and principal component analysis

Energy Technology Data Exchange (ETDEWEB)

Christensen, J.H. [Royal Veterinary and Agricultural Univ., Thorvaldsensvej (Denmark). Dept. of Natural Sciences; Hansen, A.B. [National Environmental Research Inst., Roskilde (Denmark). Dept. of Environmental Chemistry and Microbiology; Andersen, O. [Roskilde Univ., Roskilde (Denmark). Dept. of Life Sciences and Chemistry

2005-07-01

Biomarkers such as steranes and terpanes are abundant in crude oils, particularly in heavy distillate petroleum products. They are useful for matching highly weathered oil samples when other groups of petroleum hydrocarbons fail to distinguish oil samples. In this study, time warping and principal component analysis (PCA) were applied for oil hydrocarbon fingerprinting based on relative amounts of terpane and sterane isomers analyzed by gas chromatography and mass spectrometry. The 4 principal components were boiling point range, clay content, marine or organic terrestrial matter, and maturity based on differences in the terpane and sterane isomer patterns. This study is an extension of a previous fingerprinting study for identifying the sources of oil spill samples based only on the profiles of sterane isomers. Spill samples from the Baltic Carrier oil spill were correctly identified by inspection of score plots. The interpretation of the loading and score plots offered further chemical information about correlations between changes in the amounts of sterane and terpane isomers. It was concluded that this method is an objective procedure for analyzing chromatograms with more comprehensive data usage compared to other fingerprinting methods. 20 refs., 4 figs.

Measuring yield performance of upland cotton varieties using adaptability, stability and principal component analyses

International Nuclear Information System (INIS)

Baloch, M.J.

2003-01-01

Nine upland cotton varieties/strains were tested over 36 environments in Pakistan so as to determine their stability in yield performance. The regression coefficient (b) was used as a measure of adaptability, whereas parameters such as coefficient of determination (r2) and sum of squared deviations from regression (s/sup 2/d) were used as measure of stability. Although the regression coefficients (b) of all varieties did not deviate significantly from the unit slope, the varieties CRIS-5A. BII-89, DNH-40 and Rehmani gave b value closer to unity implying their better adaptation. Lower s/sub 2/d and higher r/sub 2/ of CRIS- 121 and DNH-40 suggest that both of these are fairly stable. The results indicate that, generally, adaptability and stability parameters are independent of each in as much as not all of the parameters simultaneously favoured one variety over the other excepting the variety DNH-40, which was stable based on majority of the parameters. Principal component analysis revealed that the first two components (latent roots) account for about 91.4% of the total variation. The latent vectors of first principal component (PCA1) were smaller and positive which also suggest that most of the varieties were quite adaptive to all of the test environments. (author)

Concealed semantic and episodic autobiographical memory electrified

Directory of Open Access Journals (Sweden)

Giorgio eGanis

2013-01-01

Full Text Available Electrophysiology-based concealed information tests (CIT try to determine whether somebody possesses concealed information about a probe item by comparing event-related potentials (ERPs between this item and comparison items (irrelevants. Although the broader field is sometimes referred to as memory detection, little attention has been paid to the precise type of underlying memory involved. This study begins addressing this issue by examining the key distinction between semantic and episodic memory in the autobiographical domain within a CIT paradigm. This study also addressed the issue of whether multiple repetitions of the items over the course of the session habituate the brain responses. Participants were tested in a 3-stimulus CIT with semantic autobiographical probes (their own date of birth and episodic autobiographical probes (a secret date learned just before the study. Results dissociated these two memory conditions on several ERP components. Semantic probes elicited a smaller frontal N2 than episodic probes, consistent with the idea that the frontal N2 decreases with greater pre-existing semantic knowledge about the item. Likewise, semantic probes elicited a smaller central N400 than episodic probes. Semantic probes also elicited a larger P3b than episodic probes because of their richer meaning. In contrast, episodic probes elicited a larger late positive component (LPC than semantic probes, because of the recent episodic memory associated with them. All these ERPs showed a difference between probes and irrelevants in both memory conditions, except for the N400, which showed a difference only in the semantic condition. Finally, although repetition affected the ERPs, it did not reduce the difference between probes and irrelevants. Thus, the type of memory associated with a probe has both theoretical and practical importance for CIT research.

Analysis of Moisture Content in Beetroot using Fourier Transform Infrared Spectroscopy and by Principal Component Analysis.

Science.gov (United States)

Nesakumar, Noel; Baskar, Chanthini; Kesavan, Srinivasan; Rayappan, John Bosco Balaguru; Alwarappan, Subbiah

2018-05-22

The moisture content of beetroot varies during long-term cold storage. In this work, we propose a strategy to identify the moisture content and age of beetroot using principal component analysis coupled Fourier transform infrared spectroscopy (FTIR). Frequent FTIR measurements were recorded directly from the beetroot sample surface over a period of 34 days for analysing its moisture content employing attenuated total reflectance in the spectral ranges of 2614-4000 and 1465-1853 cm -1 with a spectral resolution of 8 cm -1 . In order to estimate the transmittance peak height (T p ) and area under the transmittance curve [Formula: see text] over the spectral ranges of 2614-4000 and 1465-1853 cm -1 , Gaussian curve fitting algorithm was performed on FTIR data. Principal component and nonlinear regression analyses were utilized for FTIR data analysis. Score plot over the ranges of 2614-4000 and 1465-1853 cm -1 allowed beetroot quality discrimination. Beetroot quality predictive models were developed by employing biphasic dose response function. Validation experiment results confirmed that the accuracy of the beetroot quality predictive model reached 97.5%. This research work proves that FTIR spectroscopy in combination with principal component analysis and beetroot quality predictive models could serve as an effective tool for discriminating moisture content in fresh, half and completely spoiled stages of beetroot samples and for providing status alerts.

Principal Component Analysis for Normal-Distribution-Valued Symbolic Data.

Science.gov (United States)

Wang, Huiwen; Chen, Meiling; Shi, Xiaojun; Li, Nan

2016-02-01

This paper puts forward a new approach to principal component analysis (PCA) for normal-distribution-valued symbolic data, which has a vast potential of applications in the economic and management field. We derive a full set of numerical characteristics and variance-covariance structure for such data, which forms the foundation for our analytical PCA approach. Our approach is able to use all of the variance information in the original data than the prevailing representative-type approach in the literature which only uses centers, vertices, etc. The paper also provides an accurate approach to constructing the observations in a PC space based on the linear additivity property of normal distribution. The effectiveness of the proposed method is illustrated by simulated numerical experiments. At last, our method is applied to explain the puzzle of risk-return tradeoff in China's stock market.

A new methodology based on functional principal component analysis to study postural stability post-stroke.

Science.gov (United States)

Sánchez-Sánchez, M Luz; Belda-Lois, Juan-Manuel; Mena-Del Horno, Silvia; Viosca-Herrero, Enrique; Igual-Camacho, Celedonia; Gisbert-Morant, Beatriz

2018-05-05

A major goal in stroke rehabilitation is the establishment of more effective physical therapy techniques to recover postural stability. Functional Principal Component Analysis provides greater insight into recovery trends. However, when missing values exist, obtaining functional data presents some difficulties. The purpose of this study was to reveal an alternative technique for obtaining the Functional Principal Components without requiring the conversion to functional data beforehand and to investigate this methodology to determine the effect of specific physical therapy techniques in balance recovery trends in elderly subjects with hemiplegia post-stroke. A randomized controlled pilot trial was developed. Thirty inpatients post-stroke were included. Control and target groups were treated with the same conventional physical therapy protocol based on functional criteria, but specific techniques were added to the target group depending on the subjects' functional level. Postural stability during standing was quantified by posturography. The assessments were performed once a month from the moment the participants were able to stand up to six months post-stroke. The target group showed a significant improvement in postural control recovery trend six months after stroke that was not present in the control group. Some of the assessed parameters revealed significant differences between treatment groups (P Functional Principal Component Analysis to be performed when data is scarce. Moreover, it allowed the dynamics of recovery of two different treatment groups to be determined, showing that the techniques added in the target group increased postural stability compared to the base protocol. Copyright © 2018 Elsevier Ltd. All rights reserved.

Characterization of Type Ia Supernova Light Curves Using Principal Component Analysis of Sparse Functional Data

Science.gov (United States)

He, Shiyuan; Wang, Lifan; Huang, Jianhua Z.

2018-04-01

With growing data from ongoing and future supernova surveys, it is possible to empirically quantify the shapes of SNIa light curves in more detail, and to quantitatively relate the shape parameters with the intrinsic properties of SNIa. Building such relationships is critical in controlling systematic errors associated with supernova cosmology. Based on a collection of well-observed SNIa samples accumulated in the past years, we construct an empirical SNIa light curve model using a statistical method called the functional principal component analysis (FPCA) for sparse and irregularly sampled functional data. Using this method, the entire light curve of an SNIa is represented by a linear combination of principal component functions, and the SNIa is represented by a few numbers called “principal component scores.” These scores are used to establish relations between light curve shapes and physical quantities such as intrinsic color, interstellar dust reddening, spectral line strength, and spectral classes. These relations allow for descriptions of some critical physical quantities based purely on light curve shape parameters. Our study shows that some important spectral feature information is being encoded in the broad band light curves; for instance, we find that the light curve shapes are correlated with the velocity and velocity gradient of the Si II λ6355 line. This is important for supernova surveys (e.g., LSST and WFIRST). Moreover, the FPCA light curve model is used to construct the entire light curve shape, which in turn is used in a functional linear form to adjust intrinsic luminosity when fitting distance models.

Competition analysis on the operating system market using principal component analysis

Directory of Open Access Journals (Sweden)

Brătucu, G.

2011-01-01

Full Text Available Operating system market has evolved greatly. The largest software producer in the world, Microsoft, dominates the operating systems segment. With three operating systems: Windows XP, Windows Vista and Windows 7 the company held a market share of 87.54% in January 2011. Over time, open source operating systems have begun to penetrate the market very strongly affecting other manufacturers. Companies such as Apple Inc. and Google Inc. penetrated the operating system market. This paper aims to compare the best-selling operating systems on the market in terms of defining characteristics. To this purpose the principal components analysis method was used.

Classification of calcium supplements through application of principal component analysis: a study by inaa and aas

International Nuclear Information System (INIS)

Waheed, S.; Rahman, S.; Siddique, N.

2013-01-01

Different types of Ca supplements are available in the local markets of Pakistan. It is sometimes difficult to classify these with respect to their composition. In the present work principal component analysis (PCA) technique was applied to classify different Ca supplements on the basis of their elemental data obtained using instrumental neutron activation analysis (INAA) and atomic absorption spectrometry (AAS) techniques. The graphical representation of principal component analysis (PCA) scores utilizing intricate analytical data successfully generated four different types of Ca supplements with compatible samples grouped together. These included Ca supplements with CaCO/sub 3/as Ca source along with vitamin C, the supplements with CaCO/sub 3/ as Ca source along with vitamin D, Supplements with Ca from bone meal and supplements with chelated calcium. (author)

Principal components and iterative regression analysis of geophysical series: Application to Sunspot number (1750 2004)

Science.gov (United States)

Nordemann, D. J. R.; Rigozo, N. R.; de Souza Echer, M. P.; Echer, E.

2008-11-01

We present here an implementation of a least squares iterative regression method applied to the sine functions embedded in the principal components extracted from geophysical time series. This method seems to represent a useful improvement for the non-stationary time series periodicity quantitative analysis. The principal components determination followed by the least squares iterative regression method was implemented in an algorithm written in the Scilab (2006) language. The main result of the method is to obtain the set of sine functions embedded in the series analyzed in decreasing order of significance, from the most important ones, likely to represent the physical processes involved in the generation of the series, to the less important ones that represent noise components. Taking into account the need of a deeper knowledge of the Sun's past history and its implication to global climate change, the method was applied to the Sunspot Number series (1750-2004). With the threshold and parameter values used here, the application of the method leads to a total of 441 explicit sine functions, among which 65 were considered as being significant and were used for a reconstruction that gave a normalized mean squared error of 0.146.

Zero drift and solid Earth tide extracted from relative gravimetric data with principal component analysis

OpenAIRE

Hongjuan Yu; Jinyun Guo; Jiulong Li; Dapeng Mu; Qiaoli Kong

2015-01-01

Zero drift and solid Earth tide corrections to static relative gravimetric data cannot be ignored. In this paper, a new principal component analysis (PCA) algorithm is presented to extract the zero drift and the solid Earth tide, as signals, from static relative gravimetric data assuming that the components contained in the relative gravimetric data are uncorrelated. Static relative gravity observations from Aug. 15 to Aug. 23, 2014 are used as statistical variables to separate the signal and...

Using Graph Components Derived from an Associative Concept Dictionary to Predict fMRI Neural Activation Patterns that Represent the Meaning of Nouns.

Directory of Open Access Journals (Sweden)

Hiroyuki Akama

Full Text Available In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF. This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk and co-occurrence adjustment (degree balance and distribution. We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.

Dissociating the effects of semantic grouping and rehearsal strategies on event-related brain potentials.

Science.gov (United States)

Schleepen, T M J; Markus, C R; Jonkman, L M

2014-12-01

The application of elaborative encoding strategies during learning, such as grouping items on similar semantic categories, increases the likelihood of later recall. Previous studies have suggested that stimuli that encourage semantic grouping strategies had modulating effects on specific ERP components. However, these studies did not differentiate between ERP activation patterns evoked by elaborative working memory strategies like semantic grouping and more simple strategies like rote rehearsal. Identification of neurocognitive correlates underlying successful use of elaborative strategies is important to understand better why certain populations, like children or elderly people, have problems applying such strategies. To compare ERP activation during the application of elaborative versus more simple strategies subjects had to encode either four semantically related or unrelated pictures by respectively applying a semantic category grouping or a simple rehearsal strategy. Another goal was to investigate if maintenance of semantically grouped vs. ungrouped pictures modulated ERP-slow waves differently. At the behavioral level there was only a semantic grouping benefit in terms of faster responding on correct rejections (i.e. when the memory probe stimulus was not part of the memory set). At the neural level, during encoding semantic grouping only had a modest specific modulatory effect on a fronto-central Late Positive Component (LPC), emerging around 650 ms. Other ERP components (i.e. P200, N400 and a second Late Positive Component) that had been earlier related to semantic grouping encoding processes now showed stronger modulation by rehearsal than by semantic grouping. During maintenance semantic grouping had specific modulatory effects on left and right frontal slow wave activity. These results stress the importance of careful control of strategy use when investigating the neural correlates of elaborative encoding. Copyright © 2014 Elsevier B.V. All rights

Semantic category interference in overt picture naming: sharpening current density localization by PCA.

Science.gov (United States)

Maess, Burkhard; Friederici, Angela D; Damian, Markus; Meyer, Antje S; Levelt, Willem J M

2002-04-01

The study investigated the neuronal basis of the retrieval of words from the mental lexicon. The semantic category interference effect was used to locate lexical retrieval processes in time and space. This effect reflects the finding that, for overt naming, volunteers are slower when naming pictures out of a sequence of items from the same semantic category than from different categories. Participants named pictures blockwise either in the context of same- or mixed-category items while the brain response was registered using magnetoencephalography (MEG). Fifteen out of 20 participants showed longer response latencies in the same-category compared to the mixed-category condition. Event-related MEG signals for the participants demonstrating the interference effect were submitted to a current source density (CSD) analysis. As a new approach, a principal component analysis was applied to decompose the grand average CSD distribution into spatial subcomponents (factors). The spatial factor indicating left temporal activity revealed significantly different activation for the same-category compared to the mixed-category condition in the time window between 150 and 225 msec post picture onset. These findings indicate a major involvement of the left temporal cortex in the semantic interference effect. As this effect has been shown to take place at the level of lexical selection, the data suggest that the left temporal cortex supports processes of lexical retrieval during production.

Assessment of genetic divergence in tomato through agglomerative hierarchical clustering and principal component analysis

International Nuclear Information System (INIS)

Iqbal, Q.; Saleem, M.Y.; Hameed, A.; Asghar, M.

2014-01-01

For the improvement of qualitative and quantitative traits, existence of variability has prime importance in plant breeding. Data on different morphological and reproductive traits of 47 tomato genotypes were analyzed for correlation,agglomerative hierarchical clustering and principal component analysis (PCA) to select genotypes and traits for future breeding program. Correlation analysis revealed significant positive association between yield and yield components like fruit diameter, single fruit weight and number of fruits plant-1. Principal component (PC) analysis depicted first three PCs with Eigen-value higher than 1 contributing 81.72% of total variability for different traits. The PC-I showed positive factor loadings for all the traits except number of fruits plant-1. The contribution of single fruit weight and fruit diameter was highest in PC-1. Cluster analysis grouped all genotypes into five divergent clusters. The genotypes in cluster-II and cluster-V exhibited uniform maturity and higher yield. The D2 statistics confirmed highest distance between cluster- III and cluster-V while maximum similarity was observed in cluster-II and cluster-III. It is therefore suggested that crosses between genotypes of cluster-II and cluster-V with those of cluster-I and cluster-III may exhibit heterosis in F1 for hybrid breeding and for selection of superior genotypes in succeeding generations for cross breeding programme. (author)

Artificial neural network combined with principal component analysis for resolution of complex pharmaceutical formulations.

Science.gov (United States)

Ioele, Giuseppina; De Luca, Michele; Dinç, Erdal; Oliverio, Filomena; Ragno, Gaetano

2011-01-01

A chemometric approach based on the combined use of the principal component analysis (PCA) and artificial neural network (ANN) was developed for the multicomponent determination of caffeine (CAF), mepyramine (MEP), phenylpropanolamine (PPA) and pheniramine (PNA) in their pharmaceutical preparations without any chemical separation. The predictive ability of the ANN method was compared with the classical linear regression method Partial Least Squares 2 (PLS2). The UV spectral data between 220 and 300 nm of a training set of sixteen quaternary mixtures were processed by PCA to reduce the dimensions of input data and eliminate the noise coming from instrumentation. Several spectral ranges and different numbers of principal components (PCs) were tested to find the PCA-ANN and PLS2 models reaching the best determination results. A two layer ANN, using the first four PCs, was used with log-sigmoid transfer function in first hidden layer and linear transfer function in output layer. Standard error of prediction (SEP) was adopted to assess the predictive accuracy of the models when subjected to external validation. PCA-ANN showed better prediction ability in the determination of PPA and PNA in synthetic samples with added excipients and pharmaceutical formulations. Since both components are characterized by low absorptivity, the better performance of PCA-ANN was ascribed to the ability in considering all non-linear information from noise or interfering excipients.

Impact of different conditions on accuracy of five rules for principal components retention

Directory of Open Access Journals (Sweden)

Zorić Aleksandar

2013-01-01

Full Text Available Polemics about criteria for nontrivial principal components are still present in the literature. Finding of a lot of papers, is that the most frequently used Guttman Kaiser’s criterion has very poor performance. In the last three years some new criteria were proposed. In this Monte Carlo experiment we aimed to investigate the impact that sample size, number of analyzed variables, number of supposed factors and proportion of error variance have on the accuracy of analyzed criteria for principal components retention. We compared the following criteria: Bartlett’s χ2 test, Horn’s Parallel Analysis, Guttman-Kaiser’s eigenvalue over one, Velicer’s MAP and CHull originally proposed by Ceulemans & Kiers. Factors were systematically combined resulting in 690 different combinations. A total of 138,000 simulations were performed. Novelty in this research is systematic variation of the error variance. Performed simulations showed that, in favorable research conditions, all analyzed criteria work properly. Bartlett’s and Horns criterion expressed the robustness in most of analyzed situations. Velicer’s MAP had the best accuracy in situations with small number of subjects and high number of variables. Results confirm earlier findings of Guttman-Kaiser’s criterion having the worse performance.

Understanding deformation mechanisms during powder compaction using principal component analysis of compression data.

Science.gov (United States)

Roopwani, Rahul; Buckner, Ira S

2011-10-14

Principal component analysis (PCA) was applied to pharmaceutical powder compaction. A solid fraction parameter (SF(c/d)) and a mechanical work parameter (W(c/d)) representing irreversible compression behavior were determined as functions of applied load. Multivariate analysis of the compression data was carried out using PCA. The first principal component (PC1) showed loadings for the solid fraction and work values that agreed with changes in the relative significance of plastic deformation to consolidation at different pressures. The PC1 scores showed the same rank order as the relative plasticity ranking derived from the literature for common pharmaceutical materials. The utility of PC1 in understanding deformation was extended to binary mixtures using a subset of the original materials. Combinations of brittle and plastic materials were characterized using the PCA method. The relationships between PC1 scores and the weight fractions of the mixtures were typically linear showing ideal mixing in their deformation behaviors. The mixture consisting of two plastic materials was the only combination to show a consistent positive deviation from ideality. The application of PCA to solid fraction and mechanical work data appears to be an effective means of predicting deformation behavior during compaction of simple powder mixtures. Copyright © 2011 Elsevier B.V. All rights reserved.

A Principal Component Analysis of Project Management Construction Industry Competencies for the Ghanaian

Directory of Open Access Journals (Sweden)

Rockson Dobgegah

2011-03-01

Full Text Available The study adopts a data reduction technique to examine the presence of any complex structure among a set of project management competency variables. A structured survey questionnaire was administered to 100 project managers to elicit relevant data, and this achieved a relatively high response rate of 54%. After satisfying all the necessary tests of reliability of the survey instrument, sample size adequacy and population matrix, the data was subjected to principal component analysis, resulting in the identification of six new thematic project management competency areas ; and were explained in terms of human resource management and project control; construction innovation and communication; project financial resources management; project risk and quality management; business ethics and; physical resources and procurement management. These knowledge areas now form the basis for lateral project management training requirements in the context of the Ghanaian construction industry. Key contribution of the paper is manifested in the use of the principal component analysis, which has rigorously provided understanding into the complex structure and the relationship between the various knowledge areas. The originality and value of the paper is embedded in the use of contextual-task conceptual knowledge to expound the six uncorrelated empirical utility of the project management competencies.

Relationships between Association of Research Libraries (ARL) Statistics and Bibliometric Indicators: A Principal Components Analysis

Science.gov (United States)

Hendrix, Dean

2010-01-01

This study analyzed 2005-2006 Web of Science bibliometric data from institutions belonging to the Association of Research Libraries (ARL) and corresponding ARL statistics to find any associations between indicators from the two data sets. Principal components analysis on 36 variables from 103 universities revealed obvious associations between…

A developer's guide to the semantic web

CERN Document Server

Yu, Liyang

2014-01-01

The Semantic Web represents a vision for how to make the huge amount of information on the Web automatically processable by machines on a large scale. For this purpose, a whole suite of standards, technologies and related tools have been specified and developed over the last couple of years and they have now become the foundation for numerous new applications. A Developer's Guide to the Semantic Web helps the reader to learn the core standards, key components and underlying concepts. It provides in-depth coverage of both the what-is and how-to aspects of the Semantic Web. From Yu's presentat

A Developer's Guide to the Semantic Web

CERN Document Server

Yu, Liyang

2011-01-01

The Semantic Web represents a vision for how to make the huge amount of information on the Web automatically processable by machines on a large scale. For this purpose, a whole suite of standards, technologies and related tools have been specified and developed over the last couple of years, and they have now become the foundation for numerous new applications. A Developer's Guide to the Semantic Web helps the reader to learn the core standards, key components, and underlying concepts. It provides in-depth coverage of both the what-is and how-to aspects of the Semantic Web. From Yu's presentat

Influencing Factors of Catering and Food Service Industry Based on Principal Component Analysis

OpenAIRE

Zi Tang

2014-01-01

Scientific analysis of influencing factors is of great importance for the healthy development of catering and food service industry. This study attempts to present a set of critical indicators for evaluating the contribution of influencing factors to catering and food service industry in the particular context of Harbin City, Northeast China. Ten indicators that correlate closely with catering and food service industry were identified and performed by the principal component analysis method u...

An application of principal component analysis to the clavicle and clavicle fixation devices.

LENUS (Irish Health Repository)

Daruwalla, Zubin J

2010-01-01

Principal component analysis (PCA) enables the building of statistical shape models of bones and joints. This has been used in conjunction with computer assisted surgery in the past. However, PCA of the clavicle has not been performed. Using PCA, we present a novel method that examines the major modes of size and three-dimensional shape variation in male and female clavicles and suggests a method of grouping the clavicle into size and shape categories.

Feature extraction through parallel Probabilistic Principal Component Analysis for heart disease diagnosis

Science.gov (United States)

Shah, Syed Muhammad Saqlain; Batool, Safeera; Khan, Imran; Ashraf, Muhammad Usman; Abbas, Syed Hussnain; Hussain, Syed Adnan

2017-09-01

Automatic diagnosis of human diseases are mostly achieved through decision support systems. The performance of these systems is mainly dependent on the selection of the most relevant features. This becomes harder when the dataset contains missing values for the different features. Probabilistic Principal Component Analysis (PPCA) has reputation to deal with the problem of missing values of attributes. This research presents a methodology which uses the results of medical tests as input, extracts a reduced dimensional feature subset and provides diagnosis of heart disease. The proposed methodology extracts high impact features in new projection by using Probabilistic Principal Component Analysis (PPCA). PPCA extracts projection vectors which contribute in highest covariance and these projection vectors are used to reduce feature dimension. The selection of projection vectors is done through Parallel Analysis (PA). The feature subset with the reduced dimension is provided to radial basis function (RBF) kernel based Support Vector Machines (SVM). The RBF based SVM serves the purpose of classification into two categories i.e., Heart Patient (HP) and Normal Subject (NS). The proposed methodology is evaluated through accuracy, specificity and sensitivity over the three datasets of UCI i.e., Cleveland, Switzerland and Hungarian. The statistical results achieved through the proposed technique are presented in comparison to the existing research showing its impact. The proposed technique achieved an accuracy of 82.18%, 85.82% and 91.30% for Cleveland, Hungarian and Switzerland dataset respectively.

Pixel-level multisensor image fusion based on matrix completion and robust principal component analysis

Science.gov (United States)

Wang, Zhuozheng; Deller, J. R.; Fleet, Blair D.

2016-01-01

Acquired digital images are often corrupted by a lack of camera focus, faulty illumination, or missing data. An algorithm is presented for fusion of multiple corrupted images of a scene using the lifting wavelet transform. The method employs adaptive fusion arithmetic based on matrix completion and self-adaptive regional variance estimation. Characteristics of the wavelet coefficients are used to adaptively select fusion rules. Robust principal component analysis is applied to low-frequency image components, and regional variance estimation is applied to high-frequency components. Experiments reveal that the method is effective for multifocus, visible-light, and infrared image fusion. Compared with traditional algorithms, the new algorithm not only increases the amount of preserved information and clarity but also improves robustness.

Clustering of metabolic and cardiovascular risk factors in the polycystic ovary syndrome: a principal component analysis.

Science.gov (United States)

Stuckey, Bronwyn G A; Opie, Nicole; Cussons, Andrea J; Watts, Gerald F; Burke, Valerie

2014-08-01

Polycystic ovary syndrome (PCOS) is a prevalent condition with heterogeneity of clinical features and cardiovascular risk factors that implies multiple aetiological factors and possible outcomes. To reduce a set of correlated variables to a smaller number of uncorrelated and interpretable factors that may delineate subgroups within PCOS or suggest pathogenetic mechanisms. We used principal component analysis (PCA) to examine the endocrine and cardiometabolic variables associated with PCOS defined by the National Institutes of Health (NIH) criteria. Data were retrieved from the database of a single clinical endocrinologist. We included women with PCOS (N = 378) who were not taking the oral contraceptive pill or other sex hormones, lipid lowering medication, metformin or other medication that could influence the variables of interest. PCA was performed retaining those factors with eigenvalues of at least 1.0. Varimax rotation was used to produce interpretable factors. We identified three principal components. In component 1, the dominant variables were homeostatic model assessment (HOMA) index, body mass index (BMI), high density lipoprotein (HDL) cholesterol and sex hormone binding globulin (SHBG); in component 2, systolic blood pressure, low density lipoprotein (LDL) cholesterol and triglycerides; in component 3, total testosterone and LH/FSH ratio. These components explained 37%, 13% and 11% of the variance in the PCOS cohort respectively. Multiple correlated variables from patients with PCOS can be reduced to three uncorrelated components characterised by insulin resistance, dyslipidaemia/hypertension or hyperandrogenaemia. Clustering of risk factors is consistent with different pathogenetic pathways within PCOS and/or differing cardiometabolic outcomes. Copyright © 2014 Elsevier Inc. All rights reserved.

Principal component analysis and neurocomputing-based models for total ozone concentration over different urban regions of India

Science.gov (United States)

Chattopadhyay, Goutami; Chattopadhyay, Surajit; Chakraborthy, Parthasarathi

2012-07-01

The present study deals with daily total ozone concentration time series over four metro cities of India namely Kolkata, Mumbai, Chennai, and New Delhi in the multivariate environment. Using the Kaiser-Meyer-Olkin measure, it is established that the data set under consideration are suitable for principal component analysis. Subsequently, by introducing rotated component matrix for the principal components, the predictors suitable for generating artificial neural network (ANN) for daily total ozone prediction are identified. The multicollinearity is removed in this way. Models of ANN in the form of multilayer perceptron trained through backpropagation learning are generated for all of the study zones, and the model outcomes are assessed statistically. Measuring various statistics like Pearson correlation coefficients, Willmott's indices, percentage errors of prediction, and mean absolute errors, it is observed that for Mumbai and Kolkata the proposed ANN model generates very good predictions. The results are supported by the linearly distributed coordinates in the scatterplots.

Semantics Desensitization: A Paradigmatic Intervention Approach to Anxiety Disorders.

Science.gov (United States)

Hekmat, Hamid; And Others

1984-01-01

Assigned speech-anxious clients (N=30) to one of the following treatment conditions: (1) semantic desensitization; (2) attention placebo and (3) waiting list control. Results indicated that semantic desensitization therapy reduced both the affective and behavioral components of anxiety as compared to the two controls. (LLL)

Reduced sensitivity of the N400 and late positive component to semantic congruity and word repetition in left temporal lobe epilepsy.

Science.gov (United States)

Olichney, John M; Riggins, Brock R; Hillert, Dieter G; Nowacki, Ralph; Tecoma, Evelyn; Kutas, Marta; Iragui, Vicente J

2002-07-01

We studied 14 patients with well-characterized refractory temporal lobe epilepsy (TLE), 7 with right temporal lobe epilepsy (RTE) and 7 with left temporal lobe epilepsy (LTE), on a word repetition ERP experiment. Much prior literature supports the view that patients with left TLE are more likely to develop verbal memory deficits, often attributable to left hippocampal sclerosis. Our main objectives were to test if abnormalities of the N400 or Late Positive Component (LPC, P600) were associated with a left temporal seizure focus, or left temporal lobe dysfunction. A minimum of 19 channels of EEG/EOG data were collected while subjects performed a semantic categorization task. Auditory category statements were followed by a visual target word, which were 50% "congruous" (category exemplars) and 50% "incongruous" (non-category exemplars) with the preceding semantic context. These auditory-visual pairings were repeated pseudo-randomly at time intervals ranging from approximately 10-140 seconds later. The ERP data were submitted to repeated-measures ANOVAs, which showed the RTE group had generally normal effects of word repetition on the LPC and the N400. Also, the N400 component was larger to incongruous than congruous new words, as is normally the case. In contrast, the LTE group did not have statistically significant effects of either word repetition or congruity on their ERPs (N400 or LPC), suggesting that this ERP semantic categorization paradigm is sensitive to left temporal lobe dysfunction. Further studies are ongoing to determine if these ERP abnormalities predict hippocampal sclerosis on histopathology, or outcome after anterior temporal lobectomy.

Semantic Blogging : Spreading the Semantic Web Meme

OpenAIRE

Cayzer, Steve

2004-01-01

This paper is about semantic blogging, an application of the semantic web to blogging. The semantic web promises to make the web more useful by endowing metadata with machine processable semantics. Blogging is a lightweight web publishing paradigm which provides a very low barrier to entry, useful syndication and aggregation behaviour, a simple to understand structure and decentralized construction of a rich information network. Semantic blogging builds upon the success and clear network valu...

Semantic Service Design for Collaborative Business Processes in Internetworked Enterprises

Science.gov (United States)

Bianchini, Devis; Cappiello, Cinzia; de Antonellis, Valeria; Pernici, Barbara

Modern collaborating enterprises can be seen as borderless organizations whose processes are dynamically transformed and integrated with the ones of their partners (Internetworked Enterprises, IE), thus enabling the design of collaborative business processes. The adoption of Semantic Web and service-oriented technologies for implementing collaboration in such distributed and heterogeneous environments promises significant benefits. IE can model their own processes independently by using the Software as a Service paradigm (SaaS). Each enterprise maintains a catalog of available services and these can be shared across IE and reused to build up complex collaborative processes. Moreover, each enterprise can adopt its own terminology and concepts to describe business processes and component services. This brings requirements to manage semantic heterogeneity in process descriptions which are distributed across different enterprise systems. To enable effective service-based collaboration, IEs have to standardize their process descriptions and model them through component services using the same approach and principles. For enabling collaborative business processes across IE, services should be designed following an homogeneous approach, possibly maintaining a uniform level of granularity. In the paper we propose an ontology-based semantic modeling approach apt to enrich and reconcile semantics of process descriptions to facilitate process knowledge management and to enable semantic service design (by discovery, reuse and integration of process elements/constructs). The approach brings together Semantic Web technologies, techniques in process modeling, ontology building and semantic matching in order to provide a comprehensive semantic modeling framework.

An application of principal component analysis to the clavicle and clavicle fixation devices

OpenAIRE

Daruwalla, Zubin J; Courtis, Patrick; Fitzpatrick, Clare; Fitzpatrick, David; Mullett, Hannan

2010-01-01

Abstract Background Principal component analysis (PCA) enables the building of statistical shape models of bones and joints. This has been used in conjunction with computer assisted surgery in the past. However, PCA of the clavicle has not been performed. Using PCA, we present a novel method that examines the major modes of size and three-dimensional shape variation in male and female clavicles and suggests a method of grouping the clavicle into size and shape categories. Materials and method...

Biomedical semantics in the Semantic Web.

Science.gov (United States)

Splendiani, Andrea; Burger, Albert; Paschke, Adrian; Romano, Paolo; Marshall, M Scott

2011-03-07

The Semantic Web offers an ideal platform for representing and linking biomedical information, which is a prerequisite for the development and application of analytical tools to address problems in data-intensive areas such as systems biology and translational medicine. As for any new paradigm, the adoption of the Semantic Web offers opportunities and poses questions and challenges to the life sciences scientific community: which technologies in the Semantic Web stack will be more beneficial for the life sciences? Is biomedical information too complex to benefit from simple interlinked representations? What are the implications of adopting a new paradigm for knowledge representation? What are the incentives for the adoption of the Semantic Web, and who are the facilitators? Is there going to be a Semantic Web revolution in the life sciences?We report here a few reflections on these questions, following discussions at the SWAT4LS (Semantic Web Applications and Tools for Life Sciences) workshop series, of which this Journal of Biomedical Semantics special issue presents selected papers from the 2009 edition, held in Amsterdam on November 20th.

Semantically Enhanced Online Configuration of Feedback Control Schemes.

Science.gov (United States)

Milis, Georgios M; Panayiotou, Christos G; Polycarpou, Marios M

2018-03-01

Recent progress toward the realization of the "Internet of Things" has improved the ability of physical and soft/cyber entities to operate effectively within large-scale, heterogeneous systems. It is important that such capacity be accompanied by feedback control capabilities sufficient to ensure that the overall systems behave according to their specifications and meet their functional objectives. To achieve this, such systems require new architectures that facilitate the online deployment, composition, interoperability, and scalability of control system components. Most current control systems lack scalability and interoperability because their design is based on a fixed configuration of specific components, with knowledge of their individual characteristics only implicitly passed through the design. This paper addresses the need for flexibility when replacing components or installing new components, which might occur when an existing component is upgraded or when a new application requires a new component, without the need to readjust or redesign the overall system. A semantically enhanced feedback control architecture is introduced for a class of systems, aimed at accommodating new components into a closed-loop control framework by exploiting the semantic inference capabilities of an ontology-based knowledge model. This architecture supports continuous operation of the control system, a crucial property for large-scale systems for which interruptions have negative impact on key performance metrics that may include human comfort and welfare or economy costs. A case-study example from the smart buildings domain is used to illustrate the proposed architecture and semantic inference mechanisms.

Principal component analysis of 1/fα noise

International Nuclear Information System (INIS)

Gao, J.B.; Cao Yinhe; Lee, J.-M.

2003-01-01

Principal component analysis (PCA) is a popular data analysis method. One of the motivations for using PCA in practice is to reduce the dimension of the original data by projecting the raw data onto a few dominant eigenvectors with large variance (energy). Due to the ubiquity of 1/f α noise in science and engineering, in this Letter we study the prototypical stochastic model for 1/f α processes--the fractional Brownian motion (fBm) processes using PCA, and find that the eigenvalues from PCA of fBm processes follow a power-law, with the exponent being the key parameter defining the fBm processes. We also study random-walk-type processes constructed from DNA sequences, and find that the eigenvalue spectrum from PCA of those random-walk processes also follow power-law relations, with the exponent characterizing the correlation structures of the DNA sequence. In fact, it is observed that PCA can automatically remove linear trends induced by patchiness in the DNA sequence, hence, PCA has a similar capability to the detrended fluctuation analysis. Implications of the power-law distributed eigenvalue spectrum are discussed

Modelling Monthly Mental Sickness Cases Using Principal ...

African Journals Online (AJOL)

The methodology was principal component analysis (PCA) using data obtained from the hospital to estimate regression coefficients and parameters. It was found that the principal component regression model that was derived was good predictive tool. The principal component regression model obtained was okay and this ...

Identifikasi Wajah Manusia untuk Sistem Monitoring Kehadiran Perkuliahan menggunakan Ekstraksi Fitur Principal Component Analysis (PCA

Directory of Open Access Journals (Sweden)

Cucu Suhery

2017-04-01

Full Text Available Berbagai sistem monitoring presensi yang ada memiliki kekurangan dan kelebihan masing-masing, dan perlu  untuk terus dikembangkan sehingga memudahkan dalam proses pengolahan datanya. Pada penelitian ini dikembangkan suatu sistem monitoring presensi menggunakan deteksi wajah manusia yang diintegrasikan dengan basis data menggunakan bahasa pemrograman Python dan library opencv. Akuisisi data citra dilakukan dengan ponsel android, kemudian citra tersebut dideteksi dan dipotong sehingga hanya didapat bagian wajah saja.  Deteksi wajah menggunakan metode Haar-Cascade Classifier, kemudian ekstraksi fitur dilakukan menggunakan metode Principal Component Analysis (PCA. Hasil dari PCA diberi label sesuai dengan data manusia yang ada pada basis data. Semua citra yang telah memiliki nilai PCA dan tersimpan di basis data akan dicari kemiripannya dengan citra wajah pada proses pengujian menggunakan metoda Euclidian Distance. Pada penelitian ini basis data yang digunakan yaitu MySQL. Hasil deteksi citra wajah pada proses pelatihan memiliki tingkat keberhasilan 100% dan hasil identifikasi wajah pada proses pengujian memiliki tingkat keberhasilan 90%..   Kata kunci— android, haar-cascade classifier, principal component analysis, euclidian distance, MySQL, sistem monitoring presensi, deteksi wajah

Trend extraction of rail corrugation measured dynamically based on the relevant low-frequency principal components reconstruction

International Nuclear Information System (INIS)

Li, Yanfu; Liu, Hongli; Ma, Ziji

2016-01-01

Rail corrugation dynamic measurement techniques are critical to guarantee transport security and guide rail maintenance. During the inspection process, low-frequency trends caused by rail fluctuation are usually superimposed on rail corrugation and seriously affect the assessment of rail maintenance quality. In order to extract and remove the nonlinear and non-stationary trends from original mixed signals, a hybrid model based ensemble empirical mode decomposition (EEMD) and modified principal component analysis (MPCA) is proposed in this paper. Compared with the existing de-trending methods based on EMD, this method first considers low-frequency intrinsic mode functions (IMFs) thought to be underlying trend components that maybe contain some unrelated components, such as white noise and low-frequency signal itself, and proposes to use PCA to accurately extract the pure trends from the IMFs containing multiple components. On the other hand, due to the energy contribution ratio between trends and mixed signals is prior unknown, and the principal components (PCs) decomposed by PCA are arranged in order of energy reduction without considering frequency distribution, the proposed method modifies traditional PCA and just selects relevant low-frequency PCs to reconstruct the trends based on the zero-crossing numbers (ZCN) of each PC. Extensive tests are presented to illustrate the effectiveness of the proposed method. The results show the proposed EEMD-PCA-ZCN is an effective tool for trend extraction of rail corrugation measured dynamically. (paper)

Preserved musical semantic memory in semantic dementia.

Science.gov (United States)

Weinstein, Jessica; Koenig, Phyllis; Gunawardena, Delani; McMillan, Corey; Bonner, Michael; Grossman, Murray

2011-02-01

To understand the scope of semantic impairment in semantic dementia. Case study. Academic medical center. A man with semantic dementia, as demonstrated by clinical, neuropsychological, and imaging studies. Music performance and magnetic resonance imaging results. Despite profoundly impaired semantic memory for words and objects due to left temporal lobe atrophy, this semiprofessional musician was creative and expressive in demonstrating preserved musical knowledge. Long-term representations of words and objects in semantic memory may be dissociated from meaningful knowledge in other domains, such as music.

Characterization of soil chemical properties of strawberry fields using principal component analysis

Directory of Open Access Journals (Sweden)

Gláucia Oliveira Islabão

2013-02-01

Full Text Available One of the largest strawberry-producing municipalities of Rio Grande do Sul (RS is Turuçu, in the South of the State. The strawberry production system adopted by farmers is similar to that used in other regions in Brazil and in the world. The main difference is related to the soil management, which can change the soil chemical properties during the strawberry cycle. This study had the objective of assessing the spatial and temporal distribution of soil fertility parameters using principal component analysis (PCA. Soil sampling was based on topography, dividing the field in three thirds: upper, middle and lower. From each of these thirds, five soil samples were randomly collected in the 0-0.20 m layer, to form a composite sample for each third. Four samples were taken during the strawberry cycle and the following properties were determined: soil organic matter (OM, soil total nitrogen (N, available phosphorus (P and potassium (K, exchangeable calcium (Ca and magnesium (Mg, soil pH (pH, cation exchange capacity (CEC at pH 7.0, soil base (V% and soil aluminum saturation(m%. No spatial variation was observed for any of the studied soil fertility parameters in the strawberry fields and temporal variation was only detected for available K. Phosphorus and K contents were always high or very high from the beginning of the strawberry cycle, while pH values ranged from very low to very high. Principal component analysis allowed the clustering of all strawberry fields based on variables related to soil acidity and organic matter content.

Principal Component Analysis to Explore Climatic Variability and Dengue Outbreak in Lahore

Directory of Open Access Journals (Sweden)

Syed Afrozuddin Ahmed

2014-08-01

Full Text Available Normal 0 false false false EN-US X-NONE X-NONE Various studies have reported that global warming causes unstable climate and many serious impact to physical environment and public health. The increasing incidence of dengue incidence is now a priority health issue and become a health burden of Pakistan.  In this study it has been investigated that spatial pattern of environment causes the emergence or increasing rate of dengue fever incidence that effects the population and its health. Principal component analysis is performed for the purpose of finding if there is/are any general environmental factor/structure which could be affected in the emergence of dengue fever cases in Pakistani climate. Principal component is applied to find structure in data for all four periods i.e. 1980 to 2012, 1980 to 1995 and 1996 to 2012.  The first three PCs for the period (1980-2012, 1980-1994, 1995-2012 are almost the same and it represent hot and windy weather. The PC1s of all dengue periods are different to each other. PC2 for all period are same and it is wetness in weather. PC3s are different and it is the combination of wetness and windy weather. PC4s for all period show humid but no rain in weather. For climatic variable only minimum temperature and maximum temperature are significantly correlated with daily dengue cases.  PC1, PC3 and PC4 are highly significantly correlated with daily dengue cases 

Semantic Contours in Tracks Based on Emotional Tags

DEFF Research Database (Denmark)

Petersen, Michael Kai; Hansen, Lars Kai; Butkus, Andrius

2009-01-01

Outlining a high level cognitive approach to how we select media based on affective user preferences, we model the latent semantics of lyrics as patterns of emotional components. Using a selection of affective last.fm tags as top-down emotional buoys, we apply LSA latent semantic analysis to bottom......-up represent the correlation of terms and song lyrics in a vector space that reflects the emotional context. Analyzing the resulting patterns of affective components, by comparing them against last.fm tag clouds describing the corresponding songs, we propose that it might be feasible to automatically generate...

THE STUDY OF THE CHARACTERIZATION INDICES OF FABRICS BY PRINCIPAL COMPONENT ANALYSIS METHOD

OpenAIRE

HRISTIAN Liliana; OSTAFE Maria Magdalena; BORDEIANU Demetra Lacramioara; APOSTOL Laura Liliana

2017-01-01

The paper was pursued to prioritize the worsted fabrics type, for the manufacture of outerwear products by characterization indeces of fabrics, using the mathematical model of Principal Component Analysis (PCA). There are a number of variables with a certain influence on the quality of fabrics, but some of these variables are more important than others, so it is useful to identify those variables to a better understanding the factors which can lead the improving of the fabrics quality. A s...

SemVisM: semantic visualizer for medical image

Science.gov (United States)

Landaeta, Luis; La Cruz, Alexandra; Baranya, Alexander; Vidal, María.-Esther

2015-01-01

SemVisM is a toolbox that combines medical informatics and computer graphics tools for reducing the semantic gap between low-level features and high-level semantic concepts/terms in the images. This paper presents a novel strategy for visualizing medical data annotated semantically, combining rendering techniques, and segmentation algorithms. SemVisM comprises two main components: i) AMORE (A Modest vOlume REgister) to handle input data (RAW, DAT or DICOM) and to initially annotate the images using terms defined on medical ontologies (e.g., MesH, FMA or RadLex), and ii) VOLPROB (VOlume PRObability Builder) for generating the annotated volumetric data containing the classified voxels that belong to a particular tissue. SemVisM is built on top of the semantic visualizer ANISE.1

The functional connectivity of semantic task changes in the recovery from stroke aphasia

Science.gov (United States)

Lu, Jie; Wu, Xia; Yao, Li; Li, Kun-Cheng; Shu, Hua; Dong, Qi

2007-03-01

Little is known about the difference of functional connectivity of semantic task between the recovery aphasic patients and normal subject. In this paper, an fMRI experiment was performed in a patient with aphasia following a left-sided ischemic lesion and normal subject. Picture naming was used as semantic activation task in this study. We compared the preliminary functional connectivity results of the recovery aphasic patient with the normal subject. The fMRI data were separated by independent component analysis (ICA) into 90 components. According to our experience and other papers, we chose a region of interest (ROI) of semantic (x=-57, y=15, z=8, r=11mm). From the 90 components, we chose one component as the functional connectivity of the semantic ROI according to one criterion. The criterion is the mean value of the voxels in the ROI. So the component of the highest mean value of the ROI is the functional connectivity of the ROI. The voxel with its value higher than 2.4 was thought as activated (pgyrus and inferior/middle temporal gyrus are larger than the ones of normal. The activated area of the right inferior frontal gyrus is smaller than the ones of normal. The functional connectivity of stroke aphasic patient under semantic condition is different with the normal one. The focus of the stroke aphasic patient can affect the functional connectivity.

Soft Sensor of Vehicle State Estimation Based on the Kernel Principal Component and Improved Neural Network

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Haorui Liu

2016-01-01

Full Text Available In the car control systems, it is hard to measure some key vehicle states directly and accurately when running on the road and the cost of the measurement is high as well. To address these problems, a vehicle state estimation method based on the kernel principal component analysis and the improved Elman neural network is proposed. Combining with nonlinear vehicle model of three degrees of freedom (3 DOF, longitudinal, lateral, and yaw motion, this paper applies the method to the soft sensor of the vehicle states. The simulation results of the double lane change tested by Matlab/SIMULINK cosimulation prove the KPCA-IENN algorithm (kernel principal component algorithm and improved Elman neural network to be quick and precise when tracking the vehicle states within the nonlinear area. This algorithm method can meet the software performance requirements of the vehicle states estimation in precision, tracking speed, noise suppression, and other aspects.

Development of motion image prediction method using principal component analysis

International Nuclear Information System (INIS)

Chhatkuli, Ritu Bhusal; Demachi, Kazuyuki; Kawai, Masaki; Sakakibara, Hiroshi; Kamiaka, Kazuma

2012-01-01

Respiratory motion can induce the limit in the accuracy of area irradiated during lung cancer radiation therapy. Many methods have been introduced to minimize the impact of healthy tissue irradiation due to the lung tumor motion. The purpose of this research is to develop an algorithm for the improvement of image guided radiation therapy by the prediction of motion images. We predict the motion images by using principal component analysis (PCA) and multi-channel singular spectral analysis (MSSA) method. The images/movies were successfully predicted and verified using the developed algorithm. With the proposed prediction method it is possible to forecast the tumor images over the next breathing period. The implementation of this method in real time is believed to be significant for higher level of tumor tracking including the detection of sudden abdominal changes during radiation therapy. (author)

Semantic Web technologies for the big data in life sciences.

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Wu, Hongyan; Yamaguchi, Atsuko

2014-08-01

The life sciences field is entering an era of big data with the breakthroughs of science and technology. More and more big data-related projects and activities are being performed in the world. Life sciences data generated by new technologies are continuing to grow in not only size but also variety and complexity, with great speed. To ensure that big data has a major influence in the life sciences, comprehensive data analysis across multiple data sources and even across disciplines is indispensable. The increasing volume of data and the heterogeneous, complex varieties of data are two principal issues mainly discussed in life science informatics. The ever-evolving next-generation Web, characterized as the Semantic Web, is an extension of the current Web, aiming to provide information for not only humans but also computers to semantically process large-scale data. The paper presents a survey of big data in life sciences, big data related projects and Semantic Web technologies. The paper introduces the main Semantic Web technologies and their current situation, and provides a detailed analysis of how Semantic Web technologies address the heterogeneous variety of life sciences big data. The paper helps to understand the role of Semantic Web technologies in the big data era and how they provide a promising solution for the big data in life sciences.

Semantic Fields to Improve Business: the hotels case

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Joan-Francesc Fondevila-Gascón

2016-12-01

Full Text Available The decision-making from a tourist depends on the social media experience. For tourists, the importance of qualitative sources (for example, comments in forums of websites, blogs and social networks: Internet technologies is increasing for tourist enterprises. A representative percentage of tourists choose destinations thanks to the opinions of other users. In this article, we use the methodology of sentiment analysis and opinion mining to capture keywords and linking messages with a singular semantic field to find the principal concepts of online comments collected in Booking and TripAdvisor opinion platforms for tourists staying in hotels. We conclude that hotels find in the semantic fields a tool for observing internal strengths and weaknesses and external opportunities and threats.

[Principal component analysis and cluster analysis of inorganic elements in sea cucumber Apostichopus japonicus].

Science.gov (United States)

Liu, Xiao-Fang; Xue, Chang-Hu; Wang, Yu-Ming; Li, Zhao-Jie; Xue, Yong; Xu, Jie

2011-11-01

The present study is to investigate the feasibility of multi-elements analysis in determination of the geographical origin of sea cucumber Apostichopus japonicus, and to make choice of the effective tracers in sea cucumber Apostichopus japonicus geographical origin assessment. The content of the elements such as Al, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Mo, Cd, Hg and Pb in sea cucumber Apostichopus japonicus samples from seven places of geographical origin were determined by means of ICP-MS. The results were used for the development of elements database. Cluster analysis(CA) and principal component analysis (PCA) were applied to differentiate the sea cucumber Apostichopus japonicus geographical origin. Three principal components which accounted for over 89% of the total variance were extracted from the standardized data. The results of Q-type cluster analysis showed that the 26 samples could be clustered reasonably into five groups, the classification results were significantly associated with the marine distribution of the sea cucumber Apostichopus japonicus samples. The CA and PCA were the effective methods for elements analysis of sea cucumber Apostichopus japonicus samples. The content of the mineral elements in sea cucumber Apostichopus japonicus samples was good chemical descriptors for differentiating their geographical origins.

Geographic distribution of suicide and railway suicide in Belgium, 2008-2013: a principal component analysis.

Science.gov (United States)

Strale, Mathieu; Krysinska, Karolina; Overmeiren, Gaëtan Van; Andriessen, Karl

2017-06-01

This study investigated the geographic distribution of suicide and railway suicide in Belgium over 2008--2013 on local (i.e., district or arrondissement) level. There were differences in the regional distribution of suicide and railway suicides in Belgium over the study period. Principal component analysis identified three groups of correlations among population variables and socio-economic indicators, such as population density, unemployment, and age group distribution, on two components that helped explaining the variance of railway suicide at a local (arrondissement) level. This information is of particular importance to prevent suicides in high-risk areas on the Belgian railway network.

Semantic Advertising

OpenAIRE

Zamanzadeh, Ben; Ashish, Naveen; Ramakrishnan, Cartic; Zimmerman, John

2013-01-01

We present the concept of Semantic Advertising which we see as the future of online advertising. Semantic Advertising is online advertising powered by semantic technology which essentially enables us to represent and reason with concepts and the meaning of things. This paper aims to 1) Define semantic advertising, 2) Place it in the context of broader and more widely used concepts such as the Semantic Web and Semantic Search, 3) Provide a survey of work in related areas such as context matchi...

Kernel principal component analysis residual diagnosis (KPCARD): An automated method for cosmic ray artifact removal in Raman spectra

International Nuclear Information System (INIS)

Li, Boyan; Calvet, Amandine; Casamayou-Boucau, Yannick; Ryder, Alan G.

2016-01-01

A new, fully automated, rapid method, referred to as kernel principal component analysis residual diagnosis (KPCARD), is proposed for removing cosmic ray artifacts (CRAs) in Raman spectra, and in particular for large Raman imaging datasets. KPCARD identifies CRAs via a statistical analysis of the residuals obtained at each wavenumber in the spectra. The method utilizes the stochastic nature of CRAs; therefore, the most significant components in principal component analysis (PCA) of large numbers of Raman spectra should not contain any CRAs. The process worked by first implementing kernel PCA (kPCA) on all the Raman mapping data and second accurately estimating the inter- and intra-spectrum noise to generate two threshold values. CRA identification was then achieved by using the threshold values to evaluate the residuals for each spectrum and assess if a CRA was present. CRA correction was achieved by spectral replacement where, the nearest neighbor (NN) spectrum, most spectroscopically similar to the CRA contaminated spectrum and principal components (PCs) obtained by kPCA were both used to generate a robust, best curve fit to the CRA contaminated spectrum. This best fit spectrum then replaced the CRA contaminated spectrum in the dataset. KPCARD efficacy was demonstrated by using simulated data and real Raman spectra collected from solid-state materials. The results showed that KPCARD was fast ( 1 million) Raman datasets. - Highlights: • New rapid, automatable method for cosmic ray artifact correction of Raman spectra. • Uses combination of kernel PCA and noise estimation for artifact identification. • Implements a best fit spectrum replacement correction approach.

A Principal Component Analysis of Skills and Competencies Required of Quantity Surveyors: Nigerian Perspective

OpenAIRE

Oluwasuji Dada, Joshua

2014-01-01

The purpose of this paper is to examine the intrinsic relationships among sets of quantity surveyors’ skill and competence variables with a view to reducing them into principal components. The research adopts a data reduction technique using factor analysis statistical technique. A structured questionnaire was administered among major stakeholders in the Nigerian construction industry. The respondents were asked to give rating, on a 5 point Likert scale, on skills and competencies re...

Linearization of the Principal Component Analysis method for radiative transfer acceleration: Application to retrieval algorithms and sensitivity studies

International Nuclear Information System (INIS)

Spurr, R.; Natraj, V.; Lerot, C.; Van Roozendael, M.; Loyola, D.

2013-01-01

Principal Component Analysis (PCA) is a promising tool for enhancing radiative transfer (RT) performance. When applied to binned optical property data sets, PCA exploits redundancy in the optical data, and restricts the number of full multiple-scatter calculations to those optical states corresponding to the most important principal components, yet still maintaining high accuracy in the radiance approximations. We show that the entire PCA RT enhancement process is analytically differentiable with respect to any atmospheric or surface parameter, thus allowing for accurate and fast approximations of Jacobian matrices, in addition to radiances. This linearization greatly extends the power and scope of the PCA method to many remote sensing retrieval applications and sensitivity studies. In the first example, we examine accuracy for PCA-derived UV-backscatter radiance and Jacobian fields over a 290–340 nm window. In a second application, we show that performance for UV-based total ozone column retrieval is considerably improved without compromising the accuracy. -- Highlights: •Principal Component Analysis (PCA) of spectrally-binned atmospheric optical properties. •PCA-based accelerated radiative transfer with 2-stream model for fast multiple-scatter. •Atmospheric and surface property linearization of this PCA performance enhancement. •Accuracy of PCA enhancement for radiances and bulk-property Jacobians, 290–340 nm. •Application of PCA speed enhancement to UV backscatter total ozone retrievals

Evidence for age-associated disinhibition of the wake drive provided by scoring principal components of the resting EEG spectrum in sleep-provoking conditions.

Science.gov (United States)

Putilov, Arcady A; Donskaya, Olga G

2016-01-01

Age-associated changes in different bandwidths of the human electroencephalographic (EEG) spectrum are well documented, but their functional significance is poorly understood. This spectrum seems to represent summation of simultaneous influences of several sleep-wake regulatory processes. Scoring of its orthogonal (uncorrelated) principal components can help in separation of the brain signatures of these processes. In particular, the opposite age-associated changes were documented for scores on the two largest (1st and 2nd) principal components of the sleep EEG spectrum. A decrease of the first score and an increase of the second score can reflect, respectively, the weakening of the sleep drive and disinhibition of the opposing wake drive with age. In order to support the suggestion of age-associated disinhibition of the wake drive from the antagonistic influence of the sleep drive, we analyzed principal component scores of the resting EEG spectra obtained in sleep deprivation experiments with 81 healthy young adults aged between 19 and 26 and 40 healthy older adults aged between 45 and 66 years. At the second day of the sleep deprivation experiments, frontal scores on the 1st principal component of the EEG spectrum demonstrated an age-associated reduction of response to eyes closed relaxation. Scores on the 2nd principal component were either initially increased during wakefulness or less responsive to such sleep-provoking conditions (frontal and occipital scores, respectively). These results are in line with the suggestion of disinhibition of the wake drive with age. They provide an explanation of why older adults are less vulnerable to sleep deprivation than young adults.

A Principal Component Analysis of 39 Scientific Impact Measures

Science.gov (United States)

Bollen, Johan; Van de Sompel, Herbert

2009-01-01

Background The impact of scientific publications has traditionally been expressed in terms of citation counts. However, scientific activity has moved online over the past decade. To better capture scientific impact in the digital era, a variety of new impact measures has been proposed on the basis of social network analysis and usage log data. Here we investigate how these new measures relate to each other, and how accurately and completely they express scientific impact. Methodology We performed a principal component analysis of the rankings produced by 39 existing and proposed measures of scholarly impact that were calculated on the basis of both citation and usage log data. Conclusions Our results indicate that the notion of scientific impact is a multi-dimensional construct that can not be adequately measured by any single indicator, although some measures are more suitable than others. The commonly used citation Impact Factor is not positioned at the core of this construct, but at its periphery, and should thus be used with caution. PMID:19562078

Principal Component Analysis of Process Datasets with Missing Values

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Kristen A. Severson

2017-07-01

Full Text Available Datasets with missing values arising from causes such as sensor failure, inconsistent sampling rates, and merging data from different systems are common in the process industry. Methods for handling missing data typically operate during data pre-processing, but can also occur during model building. This article considers missing data within the context of principal component analysis (PCA, which is a method originally developed for complete data that has widespread industrial application in multivariate statistical process control. Due to the prevalence of missing data and the success of PCA for handling complete data, several PCA algorithms that can act on incomplete data have been proposed. Here, algorithms for applying PCA to datasets with missing values are reviewed. A case study is presented to demonstrate the performance of the algorithms and suggestions are made with respect to choosing which algorithm is most appropriate for particular settings. An alternating algorithm based on the singular value decomposition achieved the best results in the majority of test cases involving process datasets.

A principal component analysis of 39 scientific impact measures.

Directory of Open Access Journals (Sweden)

Johan Bollen

Full Text Available BACKGROUND: The impact of scientific publications has traditionally been expressed in terms of citation counts. However, scientific activity has moved online over the past decade. To better capture scientific impact in the digital era, a variety of new impact measures has been proposed on the basis of social network analysis and usage log data. Here we investigate how these new measures relate to each other, and how accurately and completely they express scientific impact. METHODOLOGY: We performed a principal component analysis of the rankings produced by 39 existing and proposed measures of scholarly impact that were calculated on the basis of both citation and usage log data. CONCLUSIONS: Our results indicate that the notion of scientific impact is a multi-dimensional construct that can not be adequately measured by any single indicator, although some measures are more suitable than others. The commonly used citation Impact Factor is not positioned at the core of this construct, but at its periphery, and should thus be used with caution.

Principal components analysis based control of a multi-dof underactuated prosthetic hand

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Magenes Giovanni

2010-04-01

Full Text Available Abstract Background Functionality, controllability and cosmetics are the key issues to be addressed in order to accomplish a successful functional substitution of the human hand by means of a prosthesis. Not only the prosthesis should duplicate the human hand in shape, functionality, sensorization, perception and sense of body-belonging, but it should also be controlled as the natural one, in the most intuitive and undemanding way. At present, prosthetic hands are controlled by means of non-invasive interfaces based on electromyography (EMG. Driving a multi degrees of freedom (DoF hand for achieving hand dexterity implies to selectively modulate many different EMG signals in order to make each joint move independently, and this could require significant cognitive effort to the user. Methods A Principal Components Analysis (PCA based algorithm is used to drive a 16 DoFs underactuated prosthetic hand prototype (called CyberHand with a two dimensional control input, in order to perform the three prehensile forms mostly used in Activities of Daily Living (ADLs. Such Principal Components set has been derived directly from the artificial hand by collecting its sensory data while performing 50 different grasps, and subsequently used for control. Results Trials have shown that two independent input signals can be successfully used to control the posture of a real robotic hand and that correct grasps (in terms of involved fingers, stability and posture may be achieved. Conclusions This work demonstrates the effectiveness of a bio-inspired system successfully conjugating the advantages of an underactuated, anthropomorphic hand with a PCA-based control strategy, and opens up promising possibilities for the development of an intuitively controllable hand prosthesis.

The Role of Simple Semantics in the Process of Artificial Grammar Learning.

Science.gov (United States)

Öttl, Birgit; Jäger, Gerhard; Kaup, Barbara

2017-10-01

This study investigated the effect of semantic information on artificial grammar learning (AGL). Recursive grammars of different complexity levels (regular language, mirror language, copy language) were investigated in a series of AGL experiments. In the with-semantics condition, participants acquired semantic information prior to the AGL experiment; in the without-semantics control condition, participants did not receive semantic information. It was hypothesized that semantics would generally facilitate grammar acquisition and that the learning benefit in the with-semantics conditions would increase with increasing grammar complexity. Experiment 1 showed learning effects for all grammars but no performance difference between conditions. Experiment 2 replicated the absence of a semantic benefit for all grammars even though semantic information was more prominent during grammar acquisition as compared to Experiment 1. Thus, we did not find evidence for the idea that semantics facilitates grammar acquisition, which seems to support the view of an independent syntactic processing component.

Differentiation of perceptual and semantic subsequent memory effects using an orthographic paradigm.

Science.gov (United States)

Kuo, Michael C C; Liu, Karen P Y; Ting, Kin Hung; Chan, Chetwyn C H

2012-11-27

This study aimed to differentiate perceptual and semantic encoding processes using subsequent memory effects (SMEs) elicited by the recognition of orthographs of single Chinese characters. Participants studied a series of Chinese characters perceptually (by inspecting orthographic components) or semantically (by determining the object making sounds), and then made studied or unstudied judgments during the recognition phase. Recognition performance in terms of d-prime measure in the semantic condition was higher, though not significant, than that of the perceptual condition. The between perceptual-semantic condition differences in SMEs at P550 and late positive component latencies (700-1000ms) were not significant in the frontal area. An additional analysis identified larger SME in the semantic condition during 600-1000ms in the frontal pole regions. These results indicate that coordination and incorporation of orthographic information into mental representation is essential to both task conditions. The differentiation was also revealed in earlier SMEs (perceptual>semantic) at N3 (240-360ms) latency, which is a novel finding. The left-distributed N3 was interpreted as more efficient processing of meaning with semantically learned characters. Frontal pole SMEs indicated strategic processing by executive functions, which would further enhance memory. Copyright © 2012 Elsevier B.V. All rights reserved.

Derivation of the reduced reaction mechanisms of ozone depletion events in the Arctic spring by using concentration sensitivity analysis and principal component analysis

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L. Cao

2016-12-01

Full Text Available The ozone depletion events (ODEs in the springtime Arctic have been investigated since the 1980s. It is found that the depletion of ozone is highly associated with an auto-catalytic reaction cycle, which involves mostly the bromine-containing compounds. Moreover, bromide stored in various substrates in the Arctic such as the underlying surface covered by ice and snow can be also activated by the absorbed HOBr. Subsequently, this leads to an explosive increase of the bromine amount in the troposphere, which is called the “bromine explosion mechanism”. In the present study, a reaction scheme representing the chemistry of ozone depletion and halogen release is processed with two different mechanism reduction approaches, namely, the concentration sensitivity analysis and the principal component analysis. In the concentration sensitivity analysis, the interdependence of the mixing ratios of ozone and principal bromine species on the rate of each reaction in the ODE mechanism is identified. Furthermore, the most influential reactions in different time periods of ODEs are also revealed. By removing 11 reactions with the maximum absolute values of sensitivities lower than 10 %, a reduced reaction mechanism of ODEs is derived. The onsets of each time period of ODEs in simulations using the original reaction mechanism and the reduced reaction mechanism are identical while the maximum deviation of the mixing ratio of principal bromine species between different mechanisms is found to be less than 1 %. By performing the principal component analysis on an array of the sensitivity matrices, the dependence of a particular species concentration on a combination of the reaction rates in the mechanism is revealed. Redundant reactions are indicated by principal components corresponding to small eigenvalues and insignificant elements in principal components with large eigenvalues. Through this investigation, aside from the 11 reactions identified as

Study of Seasonal Variation in Groundwater Quality of Sagar City (India by Principal Component Analysis

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Hemant Pathak

2011-01-01

Full Text Available Groundwater is one of the major resources of the drinking water in Sagar city (India.. In this study 15 sampling station were selected for the investigations on 14 chemical parameters. The work was carried out during different months of the pre-monsoon, monsoon and post-monsoon seasons in June 2009 to June 2010. The multivariate statistics such as principal component and cluster analysis were applied to the datasets to investigate seasonal variations in groundwater quality. Principal axis factoring has been used to observe the mode of association of parameters and their interrelationships, for evaluating water quality. Average value of BOD, COD, ammonia and iron was high during entire study period. Elevated values of BOD and ammonia in monsoon, slightly more value of BOD in post-monsoon, BOD, ammonia and iron in pre-monsoon period reflected contribution on temporal effect on groundwater. Results of principal component analysis evinced that all the parameters equally and significantly contribute to groundwater quality variations. Factor 1 and factor 2 analysis revealed the DO value deteriorate due to organic load (BOD/Ammonia in different seasons. Hierarchical cluster analysis grouped 15 stations into four clusters in monsoon, five clusters in post-monsoon and five clusters in pre-monsoon with similar water quality features. Clustered group at monsoon, post-monsoon and pre-monsoon consisted one station exhibiting significant spatial variation in physicochemical composition. The anthropogenic nitrogenous species, as fallout from modernization activities. The study indicated that the groundwater sufficiently well oxygenated and nutrient-rich in study places.

Type-specific proactive interference in patients with semantic and phonological STM deficits.

Science.gov (United States)

Harris, Lara; Olson, Andrew; Humphreys, Glyn

2014-01-01

Prior neuropsychological evidence suggests that semantic and phonological components of short-term memory (STM) are functionally and neurologically distinct. The current paper examines proactive interference (PI) from semantic and phonological information in two STM-impaired patients, DS (semantic STM deficit) and AK (phonological STM deficit). In Experiment 1 probe recognition tasks with open and closed sets of stimuli were used. Phonological PI was assessed using nonword items, and semantic and phonological PI was assessed using words. In Experiment 2 phonological and semantic PI was elicited by an item recognition probe test with stimuli that bore phonological and semantic relations to the probes. The data suggested heightened phonological PI for the semantic STM patient, and exaggerated effects of semantic PI in the phonological STM case. The findings are consistent with an account of extremely rapid decay of activated type-specific representations in cases of severely impaired phonological and semantic STM.

Enhancement of Jahani (Firouzabad salt dome lithological units, using principal components analysis

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Houshang Pourcaseb

2016-04-01

Full Text Available In this study, principal components analysis was used to investigate lithological characteristics of Jahani salt dome, Firouzabad. The spectral curves of rocks in the study area show that the evaporate rocks have the highest reflectance at specified wavelengths. The highest reflection has been seen in gypsum and white salt, while minimal reflection can be observed in the igneous rocks from the region. The results show that PCs have significantly low information. It is clear that PC1 shows more information in the highest variance while PC2 has less information. Regional geological map and field controls show compatibility between the enhanced zones and outcrops in the field.

Semantator: annotating clinical narratives with semantic web ontologies.

Science.gov (United States)

Song, Dezhao; Chute, Christopher G; Tao, Cui

2012-01-01

To facilitate clinical research, clinical data needs to be stored in a machine processable and understandable way. Manual annotating clinical data is time consuming. Automatic approaches (e.g., Natural Language Processing systems) have been adopted to convert such data into structured formats; however, the quality of such automatically extracted data may not always be satisfying. In this paper, we propose Semantator, a semi-automatic tool for document annotation with Semantic Web ontologies. With a loaded free text document and an ontology, Semantator supports the creation/deletion of ontology instances for any document fragment, linking/disconnecting instances with the properties in the ontology, and also enables automatic annotation by connecting to the NCBO annotator and cTAKES. By representing annotations in Semantic Web standards, Semantator supports reasoning based upon the underlying semantics of the owl:disjointWith and owl:equivalentClass predicates. We present discussions based on user experiences of using Semantator.

BANK CAPITAL AND MACROECONOMIC SHOCKS: A PRINCIPAL COMPONENTS ANALYSIS AND VECTOR ERROR CORRECTION MODEL

Directory of Open Access Journals (Sweden)

Christian NZENGUE PEGNET

2011-07-01

Full Text Available The recent financial turmoil has clearly highlighted the potential role of financial factors on amplification of macroeconomic developments and stressed the importance of analyzing the relationship between banks’ balance sheets and economic activity. This paper assesses the impact of the bank capital channel in the transmission of schocks in Europe on the basis of bank's balance sheet data. The empirical analysis is carried out through a Principal Component Analysis and in a Vector Error Correction Model.

A stable systemic risk ranking in China's banking sector: Based on principal component analysis

Science.gov (United States)

Fang, Libing; Xiao, Binqing; Yu, Honghai; You, Qixing

2018-02-01

In this paper, we compare five popular systemic risk rankings, and apply principal component analysis (PCA) model to provide a stable systemic risk ranking for the Chinese banking sector. Our empirical results indicate that five methods suggest vastly different systemic risk rankings for the same bank, while the combined systemic risk measure based on PCA provides a reliable ranking. Furthermore, according to factor loadings of the first component, PCA combined ranking is mainly based on fundamentals instead of market price data. We clearly find that price-based rankings are not as practical a method as fundamentals-based ones. This PCA combined ranking directly shows systemic risk contributions of each bank for banking supervision purpose and reminds banks to prevent and cope with the financial crisis in advance.

Formalization in Component Based Development

DEFF Research Database (Denmark)

Holmegaard, Jens Peter; Knudsen, John; Makowski, Piotr

2006-01-01

We present a unifying conceptual framework for components, component interfaces, contracts and composition of components by focusing on the collection of properties or qualities that they must share. A specific property, such as signature, functionality behaviour or timing is an aspect. Each aspect...... may be specified in a formal language convenient for its purpose and, in principle, unrelated to languages for other aspects. Each aspect forms its own semantic domain, although a semantic domain may be parameterized by values derived from other aspects. The proposed conceptual framework is introduced...

Application of principal component analysis and information fusion technique to detect hotspots in NOAA/AVHRR images of Jharia coalfield, India - article no. 013523

Energy Technology Data Exchange (ETDEWEB)

Gautam, R.S.; Singh, D.; Mittal, A. [Indian Institute of Technology Roorkee, Roorkee (India)

2007-07-01

Present paper proposes an algorithm for hotspot (sub-surface fire) detection in NOAA/AVHRR images in Jharia region of India by employing Principal Component Analysis (PCA) and fusion technique. Proposed technique is very simple to implement and is more adaptive in comparison to thresholding, multi-thresholding and contextual algorithms. The algorithm takes into account the information of AVHRR channels 1, 2, 3, 4 and vegetation indices NDVI and MSAVI for the required purpose. Proposed technique consists of three steps: (1) detection and removal of cloud and water pixels from preprocessed AVHRR image and screening out the noise of channel 3, (2) application of PCA on multi-channel information along with vegetation index information of NOAA/AVHRR image to obtain principal components, and (3) fusion of information obtained from principal component 1 and 2 to classify image pixels as hotspots. Image processing techniques are applied to fuse information in first two principal component images and no absolute threshold is incorporated to specify whether particular pixel belongs to hotspot class or not, hence, proposed method is adaptive in nature and works successfully for most of the AVHRR images with average 87.27% detection accuracy and 0.201% false alarm rate while comparing with ground truth points in Jharia region of India.

Preliminary study of soil permeability properties using principal component analysis

Science.gov (United States)

Yulianti, M.; Sudriani, Y.; Rustini, H. A.

2018-02-01

Soil permeability measurement is undoubtedly important in carrying out soil-water research such as rainfall-runoff modelling, irrigation water distribution systems, etc. It is also known that acquiring reliable soil permeability data is rather laborious, time-consuming, and costly. Therefore, it is desirable to develop the prediction model. Several studies of empirical equations for predicting permeability have been undertaken by many researchers. These studies derived the models from areas which soil characteristics are different from Indonesian soil, which suggest a possibility that these permeability models are site-specific. The purpose of this study is to identify which soil parameters correspond strongly to soil permeability and propose a preliminary model for permeability prediction. Principal component analysis (PCA) was applied to 16 parameters analysed from 37 sites consist of 91 samples obtained from Batanghari Watershed. Findings indicated five variables that have strong correlation with soil permeability, and we recommend a preliminary permeability model, which is potential for further development.

Mapping brain activity in gradient-echo functional MRI using principal component analysis

Science.gov (United States)

Khosla, Deepak; Singh, Manbir; Don, Manuel

1997-05-01

The detection of sites of brain activation in functional MRI has been a topic of immense research interest and many technique shave been proposed to this end. Recently, principal component analysis (PCA) has been applied to extract the activated regions and their time course of activation. This method is based on the assumption that the activation is orthogonal to other signal variations such as brain motion, physiological oscillations and other uncorrelated noises. A distinct advantage of this method is that it does not require any knowledge of the time course of the true stimulus paradigm. This technique is well suited to EPI image sequences where the sampling rate is high enough to capture the effects of physiological oscillations. In this work, we propose and apply tow methods that are based on PCA to conventional gradient-echo images and investigate their usefulness as tools to extract reliable information on brain activation. The first method is a conventional technique where a single image sequence with alternating on and off stages is subject to a principal component analysis. The second method is a PCA-based approach called the common spatial factor analysis technique (CSF). As the name suggests, this method relies on common spatial factors between the above fMRI image sequence and a background fMRI. We have applied these methods to identify active brain ares during visual stimulation and motor tasks. The results from these methods are compared to those obtained by using the standard cross-correlation technique. We found good agreement in the areas identified as active across all three techniques. The results suggest that PCA and CSF methods have good potential in detecting the true stimulus correlated changes in the presence of other interfering signals.

Kernel Principal Component Analysis and its Applications in Face Recognition and Active Shape Models

OpenAIRE

Wang, Quan

2012-01-01

Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and feature extraction. Kernel PCA is the nonlinear form of PCA, which better exploits the complicated spatial structure of high-dimensional features. In this paper, we first review the basic ideas of PCA and kernel PCA. Then we focus on the reconstruction of pre-images for kernel PCA. We also give an introduction on how PCA is used in active shape models (ASMs), and discuss how kernel PCA can be applied ...

Registration of dynamic dopamine D{sub 2}receptor images using principal component analysis

Energy Technology Data Exchange (ETDEWEB)

Acton, P.D.; Ell, P.J. [Institute of Nuclear Medicine, University College London Medical School, London (United Kingdom); Pilowsky, L.S.; Brammer, M.J. [Institute of Psychiatry, De Crespigny Park, London (United Kingdom); Suckling, J. [Clinical Age Research Unit, Kings College School of Medicine and Dentistry, London (United Kingdom)

1997-11-01

This paper describes a novel technique for registering a dynamic sequence of single-photon emission tomography (SPET) dopamine D{sub 2}receptor images, using principal component analysis (PCA). Conventional methods for registering images, such as count difference and correlation coefficient algorithms, fail to take into account the dynamic nature of the data, resulting in large systematic errors when registering time-varying images. However, by using principal component analysis to extract the temporal structure of the image sequence, misregistration can be quantified by examining the distribution of eigenvalues. The registration procedures were tested using a computer-generated dynamic phantom derived from a high-resolution magnetic resonance image of a realistic brain phantom. Each method was also applied to clinical SPET images of dopamine D {sub 2}receptors, using the ligands iodine-123 iodobenzamide and iodine-123 epidepride, to investigate the influence of misregistration on kinetic modelling parameters and the binding potential. The PCA technique gave highly significant (P <0.001) improvements in image registration, leading to alignment errors in x and y of about 25% of the alternative methods, with reductions in autocorrelations over time. It could also be applied to align image sequences which the other methods failed completely to register, particularly {sup 123}I-epidepride scans. The PCA method produced data of much greater quality for subsequent kinetic modelling, with an improvement of nearly 50% in the {chi}{sup 2}of the fit to the compartmental model, and provided superior quality registration of particularly difficult dynamic sequences. (orig.) With 4 figs., 2 tabs., 26 refs.

Application of empirical orthogonal functions or principal component analysis to environmental variability data

International Nuclear Information System (INIS)

Carvajal Escobar, Yesid; Marco Segura, Juan B

2005-01-01

An EOF analysis or principal component analysis (PC) was made for monthly precipitation (1972-1998) using 50 stations, and for monthly rate of flow (1951-2000) at 8 stations in the Valle del Cauca state, Colombia. Previously, we had applied 5 measures in order to verify the convenience of the analysis. These measures were: i) evaluation of significance level of correlation between variables; II) the kaiser-Meyer-Oikin (KMO) test; III) the Bartlett sphericity test; (IV) the measurement of sample adequacy (MSA), and v) the percentage of non-redundant residues with absolute values>0.05. For the selection of the significant PCS in every set of variables we applied seven criteria: the graphical method, the explained variance percentage, the mean root, the tests of Velicer, Bartlett, Broken Stich and the cross validation test. We chose the latter as the best one. It is robust and quantitative. Precipitation stations were divided in three homogeneous groups, applying a hierarchical cluster analysis, which was verified through the geographic method and the discriminate analysis for the first four EOFs of precipitation. There are many advantages to the EOF method: reduction of the dimensionality of multivariate data, calculation of missing data, evaluation and reduction of multi-co linearity, building of homogeneous groups, and detection of outliers. With the first four principal components we can explain 60.34% of the total variance of monthly precipitation for the Valle del Cauca state, and 94% of the total variance for the selected records of rates of flow

Functional Principal Component Analysis and Randomized Sparse Clustering Algorithm for Medical Image Analysis

Science.gov (United States)

Lin, Nan; Jiang, Junhai; Guo, Shicheng; Xiong, Momiao

2015-01-01

Due to the advancement in sensor technology, the growing large medical image data have the ability to visualize the anatomical changes in biological tissues. As a consequence, the medical images have the potential to enhance the diagnosis of disease, the prediction of clinical outcomes and the characterization of disease progression. But in the meantime, the growing data dimensions pose great methodological and computational challenges for the representation and selection of features in image cluster analysis. To address these challenges, we first extend the functional principal component analysis (FPCA) from one dimension to two dimensions to fully capture the space variation of image the signals. The image signals contain a large number of redundant features which provide no additional information for clustering analysis. The widely used methods for removing the irrelevant features are sparse clustering algorithms using a lasso-type penalty to select the features. However, the accuracy of clustering using a lasso-type penalty depends on the selection of the penalty parameters and the threshold value. In practice, they are difficult to determine. Recently, randomized algorithms have received a great deal of attentions in big data analysis. This paper presents a randomized algorithm for accurate feature selection in image clustering analysis. The proposed method is applied to both the liver and kidney cancer histology image data from the TCGA database. The results demonstrate that the randomized feature selection method coupled with functional principal component analysis substantially outperforms the current sparse clustering algorithms in image cluster analysis. PMID:26196383

Life, Information, Entropy, and Time: Vehicles for Semantic Inheritance.

Science.gov (United States)

Crofts, Antony R

2007-01-01

Attempts to understand how information content can be included in an accounting of the energy flux of the biosphere have led to the conclusion that, in information transmission, one component, the semantic content, or "the meaning of the message," adds no thermodynamic burden over and above costs arising from coding, transmission and translation. In biology, semantic content has two major roles. For all life forms, the message of the genotype encoded in DNA specifies the phenotype, and hence the organism that is tested against the real world through the mechanisms of Darwinian evolution. For human beings, communication through language and similar abstractions provides an additional supra-phenotypic vehicle for semantic inheritance, which supports the cultural heritages around which civilizations revolve. The following three postulates provide the basis for discussion of a number of themes that demonstrate some important consequences. (i) Information transmission through either pathway has thermodynamic components associated with data storage and transmission. (ii) The semantic content adds no additional thermodynamic cost. (iii) For all semantic exchange, meaning is accessible only through translation and interpretation, and has a value only in context. (1) For both pathways of semantic inheritance, translational and copying machineries are imperfect. As a consequence both pathways are subject to mutation and to evolutionary pressure by selection. Recognition of semantic content as a common component allows an understanding of the relationship between genes and memes, and a reformulation of Universal Darwinism. (2) The emergent properties of life are dependent on a processing of semantic content. The translational steps allow amplification in complexity through combinatorial possibilities in space and time. Amplification depends on the increased potential for complexity opened by 3D interaction specificity of proteins, and on the selection of useful variants by

Semantic content-based recommendations using semantic graphs.

Science.gov (United States)

Guo, Weisen; Kraines, Steven B

2010-01-01

Recommender systems (RSs) can be useful for suggesting items that might be of interest to specific users. Most existing content-based recommendation (CBR) systems are designed to recommend items based on text content, and the items in these systems are usually described with keywords. However, similarity evaluations based on keywords suffer from the ambiguity of natural languages. We present a semantic CBR method that uses Semantic Web technologies to recommend items that are more similar semantically with the items that the user prefers. We use semantic graphs to represent the items and we calculate the similarity scores for each pair of semantic graphs using an inverse graph frequency algorithm. The items having higher similarity scores to the items that are known to be preferred by the user are recommended.

Application of Principal Component Analysis in Prompt Gamma Spectra for Material Sorting

Energy Technology Data Exchange (ETDEWEB)

Im, Hee Jung; Lee, Yun Hee; Song, Byoung Chul; Park, Yong Joon; Kim, Won Ho

2006-11-15

For the detection of illicit materials in a very short time by comparing unknown samples' gamma spectra to pre-programmed material signatures, we at first, selected a method to reduce the noise of the obtained gamma spectra. After a noise reduction, a pattern recognition technique was applied to discriminate the illicit materials from the innocuous materials in the noise reduced data. Principal component analysis was applied for a noise reduction and pattern recognition in prompt gamma spectra. A computer program for the detection of illicit materials based on PCA method was developed in our lab and can be applied to the PGNAA system for the baggage checking at all ports of entry at a very short time.

Integrative sparse principal component analysis of gene expression data.

Science.gov (United States)

Liu, Mengque; Fan, Xinyan; Fang, Kuangnan; Zhang, Qingzhao; Ma, Shuangge

2017-12-01

In the analysis of gene expression data, dimension reduction techniques have been extensively adopted. The most popular one is perhaps the PCA (principal component analysis). To generate more reliable and more interpretable results, the SPCA (sparse PCA) technique has been developed. With the "small sample size, high dimensionality" characteristic of gene expression data, the analysis results generated from a single dataset are often unsatisfactory. Under contexts other than dimension reduction, integrative analysis techniques, which jointly analyze the raw data of multiple independent datasets, have been developed and shown to outperform "classic" meta-analysis and other multidatasets techniques and single-dataset analysis. In this study, we conduct integrative analysis by developing the iSPCA (integrative SPCA) method. iSPCA achieves the selection and estimation of sparse loadings using a group penalty. To take advantage of the similarity across datasets and generate more accurate results, we further impose contrasted penalties. Different penalties are proposed to accommodate different data conditions. Extensive simulations show that iSPCA outperforms the alternatives under a wide spectrum of settings. The analysis of breast cancer and pancreatic cancer data further shows iSPCA's satisfactory performance. © 2017 WILEY PERIODICALS, INC.

Topographical characteristics and principal component structure of the hypnagogic EEG.

Science.gov (United States)

Tanaka, H; Hayashi, M; Hori, T

1997-07-01

The purpose of the present study was to identify the dominant topographic components of electroencephalographs (EEG) and their behavior during the waking-sleeping transition period. Somnography of nocturnal sleep was recorded on 10 male subjects. Each recording, from "lights-off" to 5 minutes after the appearance of the first sleep spindle, was analyzed. The typical EEG patterns during hypnagogic period were classified into nine EEG stages. Topographic maps demonstrated that the dominant areas of alpha-band activity moved from the posterior areas to anterior areas along the midline of the scalp. In delta-, theta-, and sigma-band activities, the differences of EEG amplitude between the focus areas (the dominant areas) and the surrounding areas increased as a function of EEG stage. To identify the dominant topographic components, a principal component analysis was carried out on a 12-channel EEG data set for each of six frequency bands. The dominant areas of alpha 2- (9.6-11.4 Hz) and alpha 3- (11.6-13.4 Hz) band activities moved from the posterior to anterior areas, respectively. The distribution of alpha 2-band activity on the scalp clearly changed just after EEG stage 3 (alpha intermittent, < 50%). On the other hand, alpha 3-band activity became dominant in anterior areas after the appearance of vertex sharp-wave bursts (EEG stage 7). For the sigma band, the amplitude of extensive areas from the frontal pole to the parietal showed a rapid rise after the onset of stage 7 (the appearance of vertex sharp-wave bursts). Based on the results, sleep onset process probably started before the onset of sleep stage 1 in standard criteria. On the other hand, the basic sleep process may start before the onset of sleep stage 2 or the manually scored spindles.

Semantic Web Technologies for the Adaptive Web

DEFF Research Database (Denmark)

Dolog, Peter; Nejdl, Wolfgang

2007-01-01

Ontologies and reasoning are the key terms brought into focus by the semantic web community. Formal representation of ontologies in a common data model on the web can be taken as a foundation for adaptive web technologies as well. This chapter describes how ontologies shared on the semantic web...... provide conceptualization for the links which are a main vehicle to access information on the web. The subject domain ontologies serve as constraints for generating only those links which are relevant for the domain a user is currently interested in. Furthermore, user model ontologies provide additional...... means for deciding which links to show, annotate, hide, generate, and reorder. The semantic web technologies provide means to formalize the domain ontologies and metadata created from them. The formalization enables reasoning for personalization decisions. This chapter describes which components...

The Purification Method of Matching Points Based on Principal Component Analysis

Directory of Open Access Journals (Sweden)

DONG Yang

2017-02-01

Full Text Available The traditional purification method of matching points usually uses a small number of the points as initial input. Though it can meet most of the requirements of point constraints, the iterative purification solution is easy to fall into local extreme, which results in the missing of correct matching points. To solve this problem, we introduce the principal component analysis method to use the whole point set as initial input. And thorough mismatching points step eliminating and robust solving, more accurate global optimal solution, which intends to reduce the omission rate of correct matching points and thus reaches better purification effect, can be obtained. Experimental results show that this method can obtain the global optimal solution under a certain original false matching rate, and can decrease or avoid the omission of correct matching points.

Neural Network for Principal Component Analysis with Applications in Image Compression

Directory of Open Access Journals (Sweden)

Luminita State

2007-04-01

Full Text Available Classical feature extraction and data projection methods have been extensively investigated in the pattern recognition and exploratory data analysis literature. Feature extraction and multivariate data projection allow avoiding the "curse of dimensionality", improve the generalization ability of classifiers and significantly reduce the computational requirements of pattern classifiers. During the past decade a large number of artificial neural networks and learning algorithms have been proposed for solving feature extraction problems, most of them being adaptive in nature and well-suited for many real environments where adaptive approach is required. Principal Component Analysis, also called Karhunen-Loeve transform is a well-known statistical method for feature extraction, data compression and multivariate data projection and so far it has been broadly used in a large series of signal and image processing, pattern recognition and data analysis applications.

Independent principal component analysis for simulation of soil water content and bulk density in a Canadian Watershed

Directory of Open Access Journals (Sweden)

Alaba Boluwade

2016-09-01

Full Text Available Accurate characterization of soil properties such as soil water content (SWC and bulk density (BD is vital for hydrologic processes and thus, it is importance to estimate θ (water content and ρ (soil bulk density among other soil surface parameters involved in water retention and infiltration, runoff generation and water erosion, etc. The spatial estimation of these soil properties are important in guiding agricultural management decisions. These soil properties vary both in space and time and are correlated. Therefore, it is important to find an efficient and robust technique to simulate spatially correlated variables. Methods such as principal component analysis (PCA and independent component analysis (ICA can be used for the joint simulations of spatially correlated variables, but they are not without their flaws. This study applied a variant of PCA called independent principal component analysis (IPCA that combines the strengths of both PCA and ICA for spatial simulation of SWC and BD using the soil data set from an 11 km2 Castor watershed in southern Quebec, Canada. Diagnostic checks using the histograms and cumulative distribution function (cdf both raw and back transformed simulations show good agreement. Therefore, the results from this study has potential in characterization of water content variability and bulk density variation for precision agriculture.

Structured Sparse Principal Components Analysis With the TV-Elastic Net Penalty.

Science.gov (United States)

de Pierrefeu, Amicie; Lofstedt, Tommy; Hadj-Selem, Fouad; Dubois, Mathieu; Jardri, Renaud; Fovet, Thomas; Ciuciu, Philippe; Frouin, Vincent; Duchesnay, Edouard

2018-02-01

Principal component analysis (PCA) is an exploratory tool widely used in data analysis to uncover the dominant patterns of variability within a population. Despite its ability to represent a data set in a low-dimensional space, PCA's interpretability remains limited. Indeed, the components produced by PCA are often noisy or exhibit no visually meaningful patterns. Furthermore, the fact that the components are usually non-sparse may also impede interpretation, unless arbitrary thresholding is applied. However, in neuroimaging, it is essential to uncover clinically interpretable phenotypic markers that would account for the main variability in the brain images of a population. Recently, some alternatives to the standard PCA approach, such as sparse PCA (SPCA), have been proposed, their aim being to limit the density of the components. Nonetheless, sparsity alone does not entirely solve the interpretability problem in neuroimaging, since it may yield scattered and unstable components. We hypothesized that the incorporation of prior information regarding the structure of the data may lead to improved relevance and interpretability of brain patterns. We therefore present a simple extension of the popular PCA framework that adds structured sparsity penalties on the loading vectors in order to identify the few stable regions in the brain images that capture most of the variability. Such structured sparsity can be obtained by combining, e.g., and total variation (TV) penalties, where the TV regularization encodes information on the underlying structure of the data. This paper presents the structured SPCA (denoted SPCA-TV) optimization framework and its resolution. We demonstrate SPCA-TV's effectiveness and versatility on three different data sets. It can be applied to any kind of structured data, such as, e.g., -dimensional array images or meshes of cortical surfaces. The gains of SPCA-TV over unstructured approaches (such as SPCA and ElasticNet PCA) or structured approach

Electrocortical N400 Effects of Semantic Satiation

Directory of Open Access Journals (Sweden)

Kim Ströberg

2017-12-01

Full Text Available Semantic satiation is characterised by the subjective and temporary loss of meaning after high repetition of a prime word. To study the nature of this effect, previous electroencephalography (EEG research recorded the N400, an ERP component that is sensitive to violations of semantic context. The N400 is characterised by a relative negativity to words that are unrelated vs. related to the semantic context. The semantic satiation hypothesis predicts that the N400 should decrease with high repetition. However, previous findings have been inconsistent. Because of these inconsistent findings and the shortcomings of previous research, we used a modified design that minimises confounding effects from non-semantic processes. We recorded 64-channel EEG and analysed the N400 in a semantic priming task in which the primes were repeated 3 or 30 times. Critically, we separated low and high repetition trials and excluded response trials. Further, we varied the physical features (letter case and format of consecutive primes to minimise confounding effects from perceptual habituation. For centrofrontal electrodes, the N400 was reduced after 30 repetitions (vs. 3 repetitions. Explorative source reconstructions suggested that activity decreased after 30 repetitions in bilateral inferior temporal gyrus, the right posterior section of the superior and middle temporal gyrus, right supramarginal gyrus, bilateral lateral occipital cortex, and bilateral lateral orbitofrontal cortex. These areas overlap broadly with those typically involved in the N400, namely middle temporal gyrus and inferior frontal gyrus. The results support the semantic rather than the perceptual nature of the satiation effect.

The MMI Semantic Framework: Rosetta Stones for Earth Sciences

Science.gov (United States)

Rueda, C.; Bermudez, L. E.; Graybeal, J.; Alexander, P.

2009-12-01

Semantic interoperability—the exchange of meaning among computer systems—is needed to successfully share data in Ocean Science and across all Earth sciences. The best approach toward semantic interoperability requires a designed framework, and operationally tested tools and infrastructure within that framework. Currently available technologies make a scientific semantic framework feasible, but its development requires sustainable architectural vision and development processes. This presentation outlines the MMI Semantic Framework, including recent progress on it and its client applications. The MMI Semantic Framework consists of tools, infrastructure, and operational and community procedures and best practices, to meet short-term and long-term semantic interoperability goals. The design and prioritization of the semantic framework capabilities are based on real-world scenarios in Earth observation systems. We describe some key uses cases, as well as the associated requirements for building the overall infrastructure, which is realized through the MMI Ontology Registry and Repository. This system includes support for community creation and sharing of semantic content, ontology registration, version management, and seamless integration of user-friendly tools and application programming interfaces. The presentation describes the architectural components for semantic mediation, registry and repository for vocabularies, ontology, and term mappings. We show how the technologies and approaches in the framework can address community needs for managing and exchanging semantic information. We will demonstrate how different types of users and client applications exploit the tools and services for data aggregation, visualization, archiving, and integration. Specific examples from OOSTethys (http://www.oostethys.org) and the Ocean Observatories Initiative Cyberinfrastructure (http://www.oceanobservatories.org) will be cited. Finally, we show how semantic augmentation of web

Total sleep deprivation does not significantly degrade semantic encoding.

Science.gov (United States)

Honn, K A; Grant, D A; Hinson, J M; Whitney, P; Van Dongen, Hpa

2018-01-17

Sleep deprivation impairs performance on cognitive tasks, but it is unclear which cognitive processes it degrades. We administered a semantic matching task with variable stimulus onset asynchrony (SOA) and both speeded and self-paced trial blocks. The task was administered at the baseline and 24 hours later after 30.8 hours of total sleep deprivation (TSD) or matching well-rested control. After sleep deprivation, the 20% slowest response times (RTs) were significantly increased. However, the semantic encoding time component of the RTs remained at baseline level. Thus, the performance impairment induced by sleep deprivation on this task occurred in cognitive processes downstream of semantic encoding.

Detecting 3-D rotational motion and extracting target information from the principal component analysis of scatterer range histories

CSIR Research Space (South Africa)

Nel, W

2009-10-01

Full Text Available to estimate the 3-D position of scatterers as a by-product of the analysis. The technique is based on principal component analysis of accurate scatterer range histories and is shown only in simulation. Future research should focus on practical application....

Autobiographically significant concepts: more episodic than semantic in nature? An electrophysiological investigation of overlapping types of memory.

Science.gov (United States)

Renoult, Louis; Davidson, Patrick S R; Schmitz, Erika; Park, Lillian; Campbell, Kenneth; Moscovitch, Morris; Levine, Brian

2015-01-01

A common assertion is that semantic memory emerges from episodic memory, shedding the distinctive contexts associated with episodes over time and/or repeated instances. Some semantic concepts, however, may retain their episodic origins or acquire episodic information during life experiences. The current study examined this hypothesis by investigating the ERP correlates of autobiographically significant (AS) concepts, that is, semantic concepts that are associated with vivid episodic memories. We inferred the contribution of semantic and episodic memory to AS concepts using the amplitudes of the N400 and late positive component, respectively. We compared famous names that easily brought to mind episodic memories (high AS names) against equally famous names that did not bring such recollections to mind (low AS names) on a semantic task (fame judgment) and an episodic task (recognition memory). Compared with low AS names, high AS names were associated with increased amplitude of the late positive component in both tasks. Moreover, in the recognition task, this effect of AS was highly correlated with recognition confidence. In contrast, the N400 component did not differentiate the high versus low AS names but, instead, was related to the amount of general knowledge participants had regarding each name. These results suggest that semantic concepts high in AS, such as famous names, have an episodic component and are associated with similar brain processes to those that are engaged by episodic memory. Studying AS concepts may provide unique insights into how episodic and semantic memory interact.

A development framework for semantically interoperable health information systems.

Science.gov (United States)

Lopez, Diego M; Blobel, Bernd G M E

2009-02-01

Semantic interoperability is a basic challenge to be met for new generations of distributed, communicating and co-operating health information systems (HIS) enabling shared care and e-Health. Analysis, design, implementation and maintenance of such systems and intrinsic architectures have to follow a unified development methodology. The Generic Component Model (GCM) is used as a framework for modeling any system to evaluate and harmonize state of the art architecture development approaches and standards for health information systems as well as to derive a coherent architecture development framework for sustainable, semantically interoperable HIS and their components. The proposed methodology is based on the Rational Unified Process (RUP), taking advantage of its flexibility to be configured for integrating other architectural approaches such as Service-Oriented Architecture (SOA), Model-Driven Architecture (MDA), ISO 10746, and HL7 Development Framework (HDF). Existing architectural approaches have been analyzed, compared and finally harmonized towards an architecture development framework for advanced health information systems. Starting with the requirements for semantic interoperability derived from paradigm changes for health information systems, and supported in formal software process engineering methods, an appropriate development framework for semantically interoperable HIS has been provided. The usability of the framework has been exemplified in a public health scenario.

Characterization of deep aquifer dynamics using principal component analysis of sequential multilevel data

Directory of Open Access Journals (Sweden)

D. Kurtzman

2012-03-01

Full Text Available Two sequential multilevel profiles were obtained in an observation well opened to a 130-m thick, unconfined, contaminated aquifer in Tel Aviv, Israel. While the general profile characteristics of major ions, trace elements, and volatile organic compounds were maintained in the two sampling campaigns conducted 295 days apart, the vertical locations of high concentration gradients were shifted between the two profiles. Principal component analysis (PCA of the chemical variables resulted in a first principal component which was responsible for ∼60% of the variability, and was highly correlated with depth. PCA revealed three distinct depth-dependent water bodies in both multilevel profiles, which were found to have shifted vertically between the sampling events. This shift cut across a clayey bed which separated the top and intermediate water bodies in the first profile, and was located entirely within the intermediate water body in the second profile. Continuous electrical conductivity monitoring in a packed-off section of the observation well revealed an event in which a distinct water body flowed through the monitored section (v ∼ 150 m yr⁻¹. It was concluded that the observed changes in the profiles result from dominantly lateral flow of water bodies in the aquifer rather than vertical flow. The significance of this study is twofold: (a it demonstrates the utility of sequential multilevel observations from deep wells and the efficacy of PCA for evaluating the data; (b the fact that distinct water bodies of 10 to 100 m vertical and horizontal dimensions flow under contaminated sites, which has implications for monitoring and remediation.

Principal Component Analysis: Most Favourite Tool in Chemometrics

Indian Academy of Sciences (India)

GENERAL ARTICLE. Principal ... Chemometrics is a discipline that combines mathematics, statis- ... workers have used PCA for air quality monitoring [8]. ..... J S Verbeke, Handbook of Chemometrics and Qualimetrics, Elsevier, New York,.

Geospatial semantic web

CERN Document Server

Zhang, Chuanrong; Li, Weidong

2015-01-01

This book covers key issues related to Geospatial Semantic Web, including geospatial web services for spatial data interoperability; geospatial ontology for semantic interoperability; ontology creation, sharing, and integration; querying knowledge and information from heterogeneous data source; interfaces for Geospatial Semantic Web, VGI (Volunteered Geographic Information) and Geospatial Semantic Web; challenges of Geospatial Semantic Web; and development of Geospatial Semantic Web applications. This book also describes state-of-the-art technologies that attempt to solve these problems such as WFS, WMS, RDF, OWL, and GeoSPARQL, and demonstrates how to use the Geospatial Semantic Web technologies to solve practical real-world problems such as spatial data interoperability.

An Efficient Data Compression Model Based on Spatial Clustering and Principal Component Analysis in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Yihang Yin

2015-08-01

Full Text Available Wireless sensor networks (WSNs have been widely used to monitor the environment, and sensors in WSNs are usually power constrained. Because inner-node communication consumes most of the power, efficient data compression schemes are needed to reduce the data transmission to prolong the lifetime of WSNs. In this paper, we propose an efficient data compression model to aggregate data, which is based on spatial clustering and principal component analysis (PCA. First, sensors with a strong temporal-spatial correlation are grouped into one cluster for further processing with a novel similarity measure metric. Next, sensor data in one cluster are aggregated in the cluster head sensor node, and an efficient adaptive strategy is proposed for the selection of the cluster head to conserve energy. Finally, the proposed model applies principal component analysis with an error bound guarantee to compress the data and retain the definite variance at the same time. Computer simulations show that the proposed model can greatly reduce communication and obtain a lower mean square error than other PCA-based algorithms.

An Efficient Data Compression Model Based on Spatial Clustering and Principal Component Analysis in Wireless Sensor Networks.

Science.gov (United States)

Yin, Yihang; Liu, Fengzheng; Zhou, Xiang; Li, Quanzhong

2015-08-07

Wireless sensor networks (WSNs) have been widely used to monitor the environment, and sensors in WSNs are usually power constrained. Because inner-node communication consumes most of the power, efficient data compression schemes are needed to reduce the data transmission to prolong the lifetime of WSNs. In this paper, we propose an efficient data compression model to aggregate data, which is based on spatial clustering and principal component analysis (PCA). First, sensors with a strong temporal-spatial correlation are grouped into one cluster for further processing with a novel similarity measure metric. Next, sensor data in one cluster are aggregated in the cluster head sensor node, and an efficient adaptive strategy is proposed for the selection of the cluster head to conserve energy. Finally, the proposed model applies principal component analysis with an error bound guarantee to compress the data and retain the definite variance at the same time. Computer simulations show that the proposed model can greatly reduce communication and obtain a lower mean square error than other PCA-based algorithms.

Application of principal component analysis to multispectral imaging data for evaluation of pigmented skin lesions

Science.gov (United States)

Jakovels, Dainis; Lihacova, Ilze; Kuzmina, Ilona; Spigulis, Janis

2013-11-01

Non-invasive and fast primary diagnostics of pigmented skin lesions is required due to frequent incidence of skin cancer - melanoma. Diagnostic potential of principal component analysis (PCA) for distant skin melanoma recognition is discussed. Processing of the measured clinical multi-spectral images (31 melanomas and 94 nonmalignant pigmented lesions) in the wavelength range of 450-950 nm by means of PCA resulted in 87 % sensitivity and 78 % specificity for separation between malignant melanomas and pigmented nevi.

A stock market forecasting model combining two-directional two-dimensional principal component analysis and radial basis function neural network.

Science.gov (United States)

Guo, Zhiqiang; Wang, Huaiqing; Yang, Jie; Miller, David J

2015-01-01

In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron.

Lexical-Semantic Processing and Reading: Relations between Semantic Priming, Visual Word Recognition and Reading Comprehension

Science.gov (United States)

Nobre, Alexandre de Pontes; de Salles, Jerusa Fumagalli

2016-01-01

The aim of this study was to investigate relations between lexical-semantic processing and two components of reading: visual word recognition and reading comprehension. Sixty-eight children from private schools in Porto Alegre, Brazil, from 7 to 12 years, were evaluated. Reading was assessed with a word/nonword reading task and a reading…

Design and validation of a morphing myoelectric hand posture controller based on principal component analysis of human grasping.

Science.gov (United States)

Segil, Jacob L; Weir, Richard F ff

2014-03-01

An ideal myoelectric prosthetic hand should have the ability to continuously morph between any posture like an anatomical hand. This paper describes the design and validation of a morphing myoelectric hand controller based on principal component analysis of human grasping. The controller commands continuously morphing hand postures including functional grasps using between two and four surface electromyography (EMG) electrodes pairs. Four unique maps were developed to transform the EMG control signals in the principal component domain. A preliminary validation experiment was performed by 10 nonamputee subjects to determine the map with highest performance. The subjects used the myoelectric controller to morph a virtual hand between functional grasps in a series of randomized trials. The number of joints controlled accurately was evaluated to characterize the performance of each map. Additional metrics were studied including completion rate, time to completion, and path efficiency. The highest performing map controlled over 13 out of 15 joints accurately.

Semantic Multimedia

NARCIS (Netherlands)

S. Staab; A. Scherp; R. Arndt; R. Troncy (Raphael); M. Grzegorzek; C. Saathoff; S. Schenk; L. Hardman (Lynda)

2008-01-01

htmlabstractMultimedia constitutes an interesting field of application for Semantic Web and Semantic Web reasoning, as the access and management of multimedia content and context depends strongly on the semantic descriptions of both. At the same time, multimedia resources constitute complex objects,

Nonlinear fitness-space-structure adaptation and principal component analysis in genetic algorithms: an application to x-ray reflectivity analysis

International Nuclear Information System (INIS)

Tiilikainen, J; Tilli, J-M; Bosund, V; Mattila, M; Hakkarainen, T; Airaksinen, V-M; Lipsanen, H

2007-01-01

Two novel genetic algorithms implementing principal component analysis and an adaptive nonlinear fitness-space-structure technique are presented and compared with conventional algorithms in x-ray reflectivity analysis. Principal component analysis based on Hessian or interparameter covariance matrices is used to rotate a coordinate frame. The nonlinear adaptation applies nonlinear estimates to reshape the probability distribution of the trial parameters. The simulated x-ray reflectivity of a realistic model of a periodic nanolaminate structure was used as a test case for the fitting algorithms. The novel methods had significantly faster convergence and less stagnation than conventional non-adaptive genetic algorithms. The covariance approach needs no additional curve calculations compared with conventional methods, and it had better convergence properties than the computationally expensive Hessian approach. These new algorithms can also be applied to other fitting problems where tight interparameter dependence is present

Sensor Failure Detection of FASSIP System using Principal Component Analysis

Science.gov (United States)

Sudarno; Juarsa, Mulya; Santosa, Kussigit; Deswandri; Sunaryo, Geni Rina

2018-02-01

In the nuclear reactor accident of Fukushima Daiichi in Japan, the damages of core and pressure vessel were caused by the failure of its active cooling system (diesel generator was inundated by tsunami). Thus researches on passive cooling system for Nuclear Power Plant are performed to improve the safety aspects of nuclear reactors. The FASSIP system (Passive System Simulation Facility) is an installation used to study the characteristics of passive cooling systems at nuclear power plants. The accuracy of sensor measurement of FASSIP system is essential, because as the basis for determining the characteristics of a passive cooling system. In this research, a sensor failure detection method for FASSIP system is developed, so the indication of sensor failures can be detected early. The method used is Principal Component Analysis (PCA) to reduce the dimension of the sensor, with the Squarred Prediction Error (SPE) and statistic Hotteling criteria for detecting sensor failure indication. The results shows that PCA method is capable to detect the occurrence of a failure at any sensor.

Principal components analysis of protein structure ensembles calculated using NMR data

International Nuclear Information System (INIS)

Howe, Peter W.A.

2001-01-01

One important problem when calculating structures of biomolecules from NMR data is distinguishing converged structures from outlier structures. This paper describes how Principal Components Analysis (PCA) has the potential to classify calculated structures automatically, according to correlated structural variation across the population. PCA analysis has the additional advantage that it highlights regions of proteins which are varying across the population. To apply PCA, protein structures have to be reduced in complexity and this paper describes two different representations of protein structures which achieve this. The calculated structures of a 28 amino acid peptide are used to demonstrate the methods. The two different representations of protein structure are shown to give equivalent results, and correct results are obtained even though the ensemble of structures used as an example contains two different protein conformations. The PCA analysis also correctly identifies the structural differences between the two conformations

An analytics of electricity consumption characteristics based on principal component analysis

Science.gov (United States)

Feng, Junshu

2018-02-01

Abstract . More detailed analysis of the electricity consumption characteristics can make demand side management (DSM) much more targeted. In this paper, an analytics of electricity consumption characteristics based on principal component analysis (PCA) is given, which the PCA method can be used in to extract the main typical characteristics of electricity consumers. Then, electricity consumption characteristics matrix is designed, which can make a comparison of different typical electricity consumption characteristics between different types of consumers, such as industrial consumers, commercial consumers and residents. In our case study, the electricity consumption has been mainly divided into four characteristics: extreme peak using, peak using, peak-shifting using and others. Moreover, it has been found that industrial consumers shift their peak load often, meanwhile commercial and residential consumers have more peak-time consumption. The conclusions can provide decision support of DSM for the government and power providers.

PSG: Peer-to-Peer semantic grid framework architecture

Directory of Open Access Journals (Sweden)

Amira Soliman

2011-07-01

Full Text Available The grid vision, of sharing diverse resources in a flexible, coordinated and secure manner, strongly depends on metadata. Currently, grid metadata is generated and used in an ad-hoc fashion, much of it buried in the grid middleware code libraries and database schemas. This ad-hoc expression and use of metadata causes chronic dependency on human intervention during the operation of grid machinery. Therefore, the Semantic Grid is emerged as an extension of the grid in which rich resource metadata is exposed and handled explicitly, and shared and managed via grid protocols. The layering of an explicit semantic infrastructure over the grid infrastructure potentially leads to increase interoperability and flexibility. In this paper, we present PSG framework architecture that offers semantic-based grid services. PSG architecture allows the explicit use of semantics and defining the associated grid services. PSG architecture is originated from the integration of Peer-to-Peer (P2P computing with semantics and agents. Ontologies are used in annotating each grid component, developing users/nodes profiles and organizing framework agents. While, P2P is responsible for organizing and coordinating the grid nodes and resources.

Latent semantics as cognitive components

DEFF Research Database (Denmark)

Petersen, Michael Kai; Mørup, Morten; Hansen, Lars Kai

2010-01-01

Cognitive component analysis, defined as an unsupervised learning of features resembling human comprehension, suggests that the sensory structures we perceive might often be modeled by reducing dimensionality and treating objects in space and time as linear mixtures incorporating sparsity...... emotional responses can be encoded in words, we propose a simplified cognitive approach to model how we perceive media. Representing song lyrics in a vector space of reduced dimensionality using LSA, we combine bottom-up defined term distances with affective adjectives, that top-down constrain the latent......, which we suggest might function as cognitive components for perceiving the underlying structure in lyrics....

The facilitation effect of associative and semantic relatedness in word recognition

Directory of Open Access Journals (Sweden)

Jakić Milena

2011-01-01

Full Text Available In this study we addressed three issues concerning semantic and associative relatedness between two words and how they prime each other. The first issue is whether there is a priming effect of semantic relatedness over and above the effect of associative relatedness. The second issue is how difference in semantic overlap between two words affects priming. In order to specify the semantic overlap we introduce five relation types that differ in number of common semantic components. Three relation types (synonyms, antonyms and hyponyms represent semantic relatedness while two relation types represent associative relatedness, with negligible or no semantic relatedness. Finally, the third issue addressed in this study is whether there is a symmetric priming effect if we swap the position of prime and target, i.e. whether the direction of relatedness between two words affects priming. In two lexical decision experiments we presented five types of word pairs. In both experiments we obtained stronger facilitation for pairs that were both semantically and associatively related. Closer inspection showed that larger semantic overlap between words is paralleled by greater facilitation effect. The effects did not change when prime and target swap their position, indicating that the observed facilitation effects are symmetrical. This outcome complies with predictions of distributed models of memory.

New Role of Thermal Mapping in Winter Maintenance with Principal Components Analysis

Directory of Open Access Journals (Sweden)

Mario Marchetti

2014-01-01

Full Text Available Thermal mapping uses IR thermometry to measure road pavement temperature at a high resolution to identify and to map sections of the road network prone to ice occurrence. However, measurements are time-consuming and ultimately only provide a snapshot of road conditions at the time of the survey. As such, there is a need for surveys to be restricted to a series of specific climatic conditions during winter. Typically, five to six surveys are used, but it is questionable whether the full range of atmospheric conditions is adequately covered. This work investigates the role of statistics in adding value to thermal mapping data. Principal components analysis is used to interpolate between individual thermal mapping surveys to build a thermal map (or even a road surface temperature forecast, for a wider range of climatic conditions than that permitted by traditional surveys. The results indicate that when this approach is used, fewer thermal mapping surveys are actually required. Furthermore, comparisons with numerical models indicate that this approach could yield a suitable verification method for the spatial component of road weather forecasts—a key issue currently in winter road maintenance.

Progress Towards Improved Analysis of TES X-ray Data Using Principal Component Analysis

Science.gov (United States)

Busch, S. E.; Adams, J. S.; Bandler, S. R.; Chervenak, J. A.; Eckart, M. E.; Finkbeiner, F. M.; Fixsen, D. J.; Kelley, R. L.; Kilbourne, C. A.; Lee, S.-J.;

Semantic Meaning in Attitudinal Lexemes in the Domain of Kesenangan (Joy in Indonesian: An Analysis of Meaning Components and Lexical Relation

Directory of Open Access Journals (Sweden)

Prima Gusti Yanti

2017-04-01

Full Text Available The attitudinal lexeme on the domain of kesenangan in Indonesia language has not shown such clear meaning relationship, for both the common and diagnostic meaning of the lexemes. Those lexemes have such circular definitions, confusing upon their use. This study is conducted using a qualitative research approach employing content analysis technique. The aim of this study is to find out lexical relation and semantic meaning in attitudinal lexeme in the domain of kesenangan (joy in Indonesian language. Data is collected from seven Indonesian dictionaries, two magazines, five newspapers, and six literary works. All data is analyzed using a component analysis in the semantic theory. The research findings show that fourteen (14 lexemes (senang, nikmat, enak, puas, asyik, sukacita, ria, bangga, lega, bahagia, gembira, girang, riang, and ceria of attitudinal lexemes are related with the domain of kesenangan. The result shows that hyponymy and synonymy lexical relations occur in the domain of kesenangan. Synonymy relation consists of near-synonymy and propositional synonymy. In this case, absolute synonymy is not found.

Varieties of semantic 'access' deficit in Wernicke's aphasia and semantic aphasia.

Science.gov (United States)

Thompson, Hannah E; Robson, Holly; Lambon Ralph, Matthew A; Jefferies, Elizabeth

2015-12-01

Comprehension deficits are common in stroke aphasia, including in cases with (i) semantic aphasia, characterized by poor executive control of semantic processing across verbal and non-verbal modalities; and (ii) Wernicke's aphasia, associated with poor auditory-verbal comprehension and repetition, plus fluent speech with jargon. However, the varieties of these comprehension problems, and their underlying causes, are not well understood. Both patient groups exhibit some type of semantic 'access' deficit, as opposed to the 'storage' deficits observed in semantic dementia. Nevertheless, existing descriptions suggest that these patients might have different varieties of 'access' impairment-related to difficulty resolving competition (in semantic aphasia) versus initial activation of concepts from sensory inputs (in Wernicke's aphasia). We used a case series design to compare patients with Wernicke's aphasia and those with semantic aphasia on Warrington's paradigmatic assessment of semantic 'access' deficits. In these verbal and non-verbal matching tasks, a small set of semantically-related items are repeatedly presented over several cycles so that the target on one trial becomes a distractor on another (building up interference and eliciting semantic 'blocking' effects). Patients with Wernicke's aphasia and semantic aphasia were distinguished according to lesion location in the temporal cortex, but in each group, some individuals had additional prefrontal damage. Both of these aspects of lesion variability-one that mapped onto classical 'syndromes' and one that did not-predicted aspects of the semantic 'access' deficit. Both semantic aphasia and Wernicke's aphasia cases showed multimodal semantic impairment, although as expected, the Wernicke's aphasia group showed greater deficits on auditory-verbal than picture judgements. Distribution of damage in the temporal lobe was crucial for predicting the initially 'beneficial' effects of stimulus repetition: cases with

Use of a Principal Components Analysis for the Generation of Daily Time Series.

Science.gov (United States)

Dreveton, Christine; Guillou, Yann

2004-07-01

A new approach for generating daily time series is considered in response to the weather-derivatives market. This approach consists of performing a principal components analysis to create independent variables, the values of which are then generated separately with a random process. Weather derivatives are financial or insurance products that give companies the opportunity to cover themselves against adverse climate conditions. The aim of a generator is to provide a wider range of feasible situations to be used in an assessment of risk. Generation of a temperature time series is required by insurers or bankers for pricing weather options. The provision of conditional probabilities and a good representation of the interannual variance are the main challenges of a generator when used for weather derivatives. The generator was developed according to this new approach using a principal components analysis and was applied to the daily average temperature time series of the Paris-Montsouris station in France. The observed dataset was homogenized and the trend was removed to represent correctly the present climate. The results obtained with the generator show that it represents correctly the interannual variance of the observed climate; this is the main result of the work, because one of the main discrepancies of other generators is their inability to represent accurately the observed interannual climate variance—this discrepancy is not acceptable for an application to weather derivatives. The generator was also tested to calculate conditional probabilities: for example, the knowledge of the aggregated value of heating degree-days in the middle of the heating season allows one to estimate the probability if reaching a threshold at the end of the heating season. This represents the main application of a climate generator for use with weather derivatives.

Principal component analysis of MSBAS DInSAR time series from Campi Flegrei, Italy

Science.gov (United States)

Tiampo, Kristy F.; González, Pablo J.; Samsonov, Sergey; Fernández, Jose; Camacho, Antonio

2017-09-01

Because of its proximity to the city of Naples and with a population of nearly 1 million people within its caldera, Campi Flegrei is one of the highest risk volcanic areas in the world. Since the last major eruption in 1538, the caldera has undergone frequent episodes of ground subsidence and uplift accompanied by seismic activity that has been interpreted as the result of a stationary, deeper source below the caldera that feeds shallower eruptions. However, the location and depth of the deeper source is not well-characterized and its relationship to current activity is poorly understood. Recently, a significant increase in the uplift rate has occurred, resulting in almost 13 cm of uplift by 2013 (De Martino et al., 2014; Samsonov et al., 2014b; Di Vito et al., 2016). Here we apply a principal component decomposition to high resolution time series from the region produced by the advanced Multidimensional SBAS DInSAR technique in order to better delineate both the deeper source and the recent shallow activity. We analyzed both a period of substantial subsidence (1993-1999) and a second of significant uplift (2007-2013) and inverted the associated vertical surface displacement for the most likely source models. Results suggest that the underlying dynamics of the caldera changed in the late 1990s, from one in which the primary signal arises from a shallow deflating source above a deeper, expanding source to one dominated by a shallow inflating source. In general, the shallow source lies between 2700 and 3400 m below the caldera while the deeper source lies at 7600 m or more in depth. The combination of principal component analysis with high resolution MSBAS time series data allows for these new insights and confirms the applicability of both to areas at risk from dynamic natural hazards.

Ecological Safety Evaluation of Land Use in Ji’an City Based on the Principal Component Analysis

Institute of Scientific and Technical Information of China (English)

2010-01-01

According to the ecological safety evaluation index data of land-use change in Ji’an City from 1999 to 2008,positive treatment on selected reverse indices is conducted by Reciprocal Method.Meanwhile,Index Method is used to standardize the selected indices,and Principal Component Analysis is applied by using year as a unit.FB is obtained,which is related with the ecological safety of land-use change from 1999 to 2008.According to the scientific,integrative,hierarchical,practical and dynamic principles,ecological safety evaluation index system of land-use change in Ji’an City is established.Principal Component Analysis and evaluation model are used to calculate four parameters,including the natural resources safety index of land use,the socio-economic safety indicators of land use,the eco-environmental safety index of land use,and the ecological safety degree of land use in Ji’an City.Result indicates that the ecological safety degree of land use in Ji’an City shows a slow upward trend as a whole.At the same time,ecological safety degree of land-use change is relatively low in Ji’an City with the safety value of 0.645,which is at a weak safety zone and needs further monitoring and maintenance.

Fault diagnosis of main coolant pump in the nuclear power station based on the principal component analysis

International Nuclear Information System (INIS)

Feng Junting; Xu Mi; Wang Guizeng

2003-01-01

The fault diagnosis method based on principal component analysis is studied. The fault character direction storeroom of fifteen parameters abnormity is built in the simulation for the main coolant pump of nuclear power station. The measuring data are analyzed, and the results show that it is feasible for the fault diagnosis system of main coolant pump in the nuclear power station

InterFace: A software package for face image warping, averaging, and principal components analysis.

Science.gov (United States)

Kramer, Robin S S; Jenkins, Rob; Burton, A Mike

2017-12-01

We describe InterFace, a software package for research in face recognition. The package supports image warping, reshaping, averaging of multiple face images, and morphing between faces. It also supports principal components analysis (PCA) of face images, along with tools for exploring the "face space" produced by PCA. The package uses a simple graphical user interface, allowing users to perform these sophisticated image manipulations without any need for programming knowledge. The program is available for download in the form of an app, which requires that users also have access to the (freely available) MATLAB Runtime environment.

Generative Semantics.

Science.gov (United States)

King, Margaret

The first section of this paper deals with the attempts within the framework of transformational grammar to make semantics a systematic part of linguistic description, and outlines the characteristics of the generative semantics position. The second section takes a critical look at generative semantics in its later manifestations, and makes a case…

A Principal Component Analysis (PCA Approach to Seasonal and Zooplankton Diversity Relationships in Fishing Grounds of Mannar Gulf, India

Directory of Open Access Journals (Sweden)

Selvin J. PITCHAIKANI

2017-06-01

Full Text Available Principal component analysis (PCA is a technique used to emphasize variation and bring out strong patterns in a dataset. It is often used to make data easy to explore and visualize. The primary objective of the present study was to record information of zooplankton diversity in a systematic way and to study the variability and relationships among seasons prevailed in Gulf of Mannar. The PCA for the zooplankton seasonal diversity was investigated using the four seasonal datasets to understand the statistical significance among the four seasons. Two different principal components (PC were segregated in all the seasons homogeneously. PCA analyses revealed that Temora turbinata is an opportunistic species and zooplankton diversity was significantly different from season to season and principally, the zooplankton abundance and its dynamics in Gulf of Mannar is structured by seasonal current patterns. The factor loadings of zooplankton for different seasons in Tiruchendur coastal water (GOM is different compared with the Southwest coast of India; particularly, routine and opportunistic species were found within the positive and negative factors. The copepods Acrocalanus gracilis and Acartia erythrea were dominant in summer and Southwest monsoon due to the rainfall and freshwater discharge during the summer season; however, these species were replaced by Temora turbinata during Northeast monsoon season.

A SEMantic and EPisodic Memory Test (SEMEP) Developed within the Embodied Cognition Framework: Application to Normal Aging, Alzheimer's Disease and Semantic Dementia.

Science.gov (United States)

Vallet, Guillaume T; Hudon, Carol; Bier, Nathalie; Macoir, Joël; Versace, Rémy; Simard, Martine

2017-01-01

Embodiment has highlighted the importance of sensory-motor components in cognition. Perception and memory are thus very tightly bound together, and episodic and semantic memories should rely on the same grounded memory traces. Reduced perception should then directly reduce the ability to encode and retrieve an episodic memory, as in normal aging. Multimodal integration deficits, as in Alzheimer's disease, should lead to more severe episodic memory impairment. The present study introduces a new memory test developed to take into account these assumptions. The SEMEP (SEMantic-Episodic) memory test proposes to assess conjointly semantic and episodic knowledge across multiple tasks: semantic matching, naming, free recall, and recognition. The performance of young adults is compared to healthy elderly adults (HE), patients with Alzheimer's disease (AD), and patients with semantic dementia (SD). The results show specific patterns of performance between the groups. HE commit memory errors only for presented but not to be remembered items. AD patients present the worst episodic memory performance associated with intrusion errors (recall or recognition of items never presented). They were the only group to not benefit from a visual isolation (addition of a yellow background), a method known to increase the distinctiveness of the memory traces. Finally, SD patients suffer from the most severe semantic impairment. To conclude, confusion errors are common across all the elderly groups, whereas AD was the only group to exhibit regular intrusion errors and SD patients to show severe semantic impairment.

A SEMantic and EPisodic Memory Test (SEMEP Developed within the Embodied Cognition Framework: Application to Normal Aging, Alzheimer's Disease and Semantic Dementia

Directory of Open Access Journals (Sweden)

Guillaume T. Vallet

2017-09-01

Full Text Available Embodiment has highlighted the importance of sensory-motor components in cognition. Perception and memory are thus very tightly bound together, and episodic and semantic memories should rely on the same grounded memory traces. Reduced perception should then directly reduce the ability to encode and retrieve an episodic memory, as in normal aging. Multimodal integration deficits, as in Alzheimer's disease, should lead to more severe episodic memory impairment. The present study introduces a new memory test developed to take into account these assumptions. The SEMEP (SEMantic-Episodic memory test proposes to assess conjointly semantic and episodic knowledge across multiple tasks: semantic matching, naming, free recall, and recognition. The performance of young adults is compared to healthy elderly adults (HE, patients with Alzheimer's disease (AD, and patients with semantic dementia (SD. The results show specific patterns of performance between the groups. HE commit memory errors only for presented but not to be remembered items. AD patients present the worst episodic memory performance associated with intrusion errors (recall or recognition of items never presented. They were the only group to not benefit from a visual isolation (addition of a yellow background, a method known to increase the distinctiveness of the memory traces. Finally, SD patients suffer from the most severe semantic impairment. To conclude, confusion errors are common across all the elderly groups, whereas AD was the only group to exhibit regular intrusion errors and SD patients to show severe semantic impairment.

Optimisation of a machine learning algorithm in human locomotion using principal component and discriminant function analyses

OpenAIRE

Bisele, M; Bencsik, M; Lewis, MGC; Barnett, CT

2017-01-01

Assessment methods in human locomotion often involve the description of normalised graphical profiles and/or the extraction of discrete variables. Whilst useful, these approaches may not represent the full complexity of gait data. Multivariate statistical methods, such as Principal Component Analysis (PCA) and Discriminant Function Analysis (DFA), have been adopted since they have the potential to overcome these data handling issues. The aim of the current study was to develop and optimise a ...

Simulation of an industrial wastewater treatment plant using artificial neural networks and principal components analysis

Directory of Open Access Journals (Sweden)

Oliveira-Esquerre K.P.

2002-01-01

Full Text Available This work presents a way to predict the biochemical oxygen demand (BOD of the output stream of the biological wastewater treatment plant at RIPASA S/A Celulose e Papel, one of the major pulp and paper plants in Brazil. The best prediction performance is achieved when the data are preprocessed using principal components analysis (PCA before they are fed to a backpropagated neural network. The influence of input variables is analyzed and satisfactory prediction results are obtained for an optimized situation.

Age-related effects on perceptual and semantic encoding in memory.

Science.gov (United States)

Kuo, M C C; Liu, K P Y; Ting, K H; Chan, C C H

2014-03-07

This study examined the age-related subsequent memory effect (SME) in perceptual and semantic encoding using event-related potentials (ERPs). Seventeen younger adults and 17 older adults studied a series of Chinese characters either perceptually (by inspecting orthographic components) or semantically (by determining whether the depicted object makes sounds). The two tasks had similar levels of difficulty. The participants made studied or unstudied judgments during the recognition phase. Younger adults performed better in both conditions, with significant SMEs detected in the time windows of P2, N3, P550, and late positive component (LPC). In the older group, SMEs were observed in the P2 and N3 latencies in both conditions but were only detected in the P550 in the semantic condition. Between-group analyses showed larger frontal and central SMEs in the younger sample in the LPC latency regardless of encoding type. Aging effect appears to be stronger on influencing perceptual than semantic encoding processes. The effects seem to be associated with a decline in updating and maintaining representations during perceptual encoding. The age-related decline in the encoding function may be due in part to changes in frontal lobe function. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

Relationship Structures and Semantic Type Assignments of the UMLS Enriched Semantic Network

Science.gov (United States)

Zhang, Li; Halper, Michael; Perl, Yehoshua; Geller, James; Cimino, James J.

2005-01-01

Objective: The Enriched Semantic Network (ESN) was introduced as an extension of the Unified Medical Language System (UMLS) Semantic Network (SN). Its multiple subsumption configuration and concomitant multiple inheritance make the ESN's relationship structures and semantic type assignments different from those of the SN. A technique for deriving the relationship structures of the ESN's semantic types and an automated technique for deriving the ESN's semantic type assignments from those of the SN are presented. Design: The technique to derive the ESN's relationship structures finds all newly inherited relationships in the ESN. All such relationships are audited for semantic validity, and the blocking mechanism is used to block invalid relationships. The mapping technique to derive the ESN's semantic type assignments uses current SN semantic type assignments and preserves nonredundant categorizations, while preventing new redundant categorizations. Results: Among the 426 newly inherited relationships, 326 are deemed valid. Seven blockings are applied to avoid inheritance of the 100 invalid relationships. Sixteen semantic types have different relationship structures in the ESN as compared to those in the SN. The mapping of semantic type assignments from the SN to the ESN avoids the generation of 26,950 redundant categorizations. The resulting ESN contains 138 semantic types, 149 IS-A links, 7,303 relationships, and 1,013,876 semantic type assignments. Conclusion: The ESN's multiple inheritance provides more complete relationship structures than in the SN. The ESN's semantic type assignments avoid the existing redundant categorizations appearing in the SN and prevent new ones that might arise due to multiple parents. Compared to the SN, the ESN provides a more accurate unifying semantic abstraction of the UMLS Metathesaurus. PMID:16049233

Real time damage detection using recursive principal components and time varying auto-regressive modeling

Science.gov (United States)

Krishnan, M.; Bhowmik, B.; Hazra, B.; Pakrashi, V.

2018-02-01

In this paper, a novel baseline free approach for continuous online damage detection of multi degree of freedom vibrating structures using Recursive Principal Component Analysis (RPCA) in conjunction with Time Varying Auto-Regressive Modeling (TVAR) is proposed. In this method, the acceleration data is used to obtain recursive proper orthogonal components online using rank-one perturbation method, followed by TVAR modeling of the first transformed response, to detect the change in the dynamic behavior of the vibrating system from its pristine state to contiguous linear/non-linear-states that indicate damage. Most of the works available in the literature deal with algorithms that require windowing of the gathered data owing to their data-driven nature which renders them ineffective for online implementation. Algorithms focussed on mathematically consistent recursive techniques in a rigorous theoretical framework of structural damage detection is missing, which motivates the development of the present framework that is amenable for online implementation which could be utilized along with suite experimental and numerical investigations. The RPCA algorithm iterates the eigenvector and eigenvalue estimates for sample covariance matrices and new data point at each successive time instants, using the rank-one perturbation method. TVAR modeling on the principal component explaining maximum variance is utilized and the damage is identified by tracking the TVAR coefficients. This eliminates the need for offline post processing and facilitates online damage detection especially when applied to streaming data without requiring any baseline data. Numerical simulations performed on a 5-dof nonlinear system under white noise excitation and El Centro (also known as 1940 Imperial Valley earthquake) excitation, for different damage scenarios, demonstrate the robustness of the proposed algorithm. The method is further validated on results obtained from case studies involving

Life, Information, Entropy, and Time: Vehicles for Semantic Inheritance

OpenAIRE

Crofts, Antony R.

2007-01-01

Attempts to understand how information content can be included in an accounting of the energy flux of the biosphere have led to the conclusion that, in information transmission, one component, the semantic content, or “the meaning of the message,” adds no thermodynamic burden over and above costs arising from coding, transmission and translation. In biology, semantic content has two major roles. For all life forms, the message of the genotype encoded in DNA specifies the phenotype, and hence ...

Semantic modeling and structural synthesis of onboard electronics protection means as open information system

Science.gov (United States)

Zhevnerchuk, D. V.; Surkova, A. S.; Lomakina, L. S.; Golubev, A. S.

2018-05-01

The article describes the component representation approach and semantic models of on-board electronics protection from ionizing radiation of various nature. Semantic models are constructed, the feature of which is the representation of electronic elements, protection modules, sources of impact in the form of blocks with interfaces. The rules of logical inference and algorithms for synthesizing the object properties of the semantic network, imitating the interface between the components of the protection system and the sources of radiation, are developed. The results of the algorithm are considered using the example of radiation-resistant microcircuits 1645RU5U, 1645RT2U and the calculation and experimental method for estimating the durability of on-board electronics.

Principal component analysis of the CT density histogram to generate parametric response maps of COPD

Science.gov (United States)

Zha, N.; Capaldi, D. P. I.; Pike, D.; McCormack, D. G.; Cunningham, I. A.; Parraga, G.

2015-03-01

Pulmonary x-ray computed tomography (CT) may be used to characterize emphysema and airways disease in patients with chronic obstructive pulmonary disease (COPD). One analysis approach - parametric response mapping (PMR) utilizes registered inspiratory and expiratory CT image volumes and CT-density-histogram thresholds, but there is no consensus regarding the threshold values used, or their clinical meaning. Principal-component-analysis (PCA) of the CT density histogram can be exploited to quantify emphysema using data-driven CT-density-histogram thresholds. Thus, the objective of this proof-of-concept demonstration was to develop a PRM approach using PCA-derived thresholds in COPD patients and ex-smokers without airflow limitation. Methods: Fifteen COPD ex-smokers and 5 normal ex-smokers were evaluated. Thoracic CT images were also acquired at full inspiration and full expiration and these images were non-rigidly co-registered. PCA was performed for the CT density histograms, from which the components with the highest eigenvalues greater than one were summed. Since the values of the principal component curve correlate directly with the variability in the sample, the maximum and minimum points on the curve were used as threshold values for the PCA-adjusted PRM technique. Results: A significant correlation was determined between conventional and PCA-adjusted PRM with 3He MRI apparent diffusion coefficient (p<0.001), with CT RA950 (p<0.0001), as well as with 3He MRI ventilation defect percent, a measurement of both small airways disease (p=0.049 and p=0.06, respectively) and emphysema (p=0.02). Conclusions: PRM generated using PCA thresholds of the CT density histogram showed significant correlations with CT and 3He MRI measurements of emphysema, but not airways disease.

Personal semantics: Is it distinct from episodic and semantic memory? An electrophysiological study of memory for autobiographical facts and repeated events in honor of Shlomo Bentin.

Science.gov (United States)

Renoult, Louis; Tanguay, Annick; Beaudry, Myriam; Tavakoli, Paniz; Rabipour, Sheida; Campbell, Kenneth; Moscovitch, Morris; Levine, Brian; Davidson, Patrick S R

2016-03-01

Declarative memory is thought to consist of two independent systems: episodic and semantic. Episodic memory represents personal and contextually unique events, while semantic memory represents culturally-shared, acontextual factual knowledge. Personal semantics refers to aspects of declarative memory that appear to fall somewhere in between the extremes of episodic and semantic. Examples include autobiographical knowledge and memories of repeated personal events. These two aspects of personal semantics have been studied little and rarely compared to both semantic and episodic memory. We recorded the event-related potentials (ERPs) of 27 healthy participants while they verified the veracity of sentences probing four types of questions: general (i.e., semantic) facts, autobiographical facts, repeated events, and unique (i.e., episodic) events. Behavioral results showed equivalent reaction times in all 4 conditions. True sentences were verified faster than false sentences, except for unique events for which no significant difference was observed. Electrophysiological results showed that the N400 (which is classically associated with retrieval from semantic memory) was maximal for general facts and the LPC (which is classically associated with retrieval from episodic memory) was maximal for unique events. For both ERP components, the two personal semantic conditions (i.e., autobiographical facts and repeated events) systematically differed from semantic memory. In addition, N400 amplitudes also differentiated autobiographical facts from unique events. Autobiographical facts and repeated events did not differ significantly from each other but their corresponding scalp distributions differed from those associated with general facts. Our results suggest that the neural correlates of personal semantics can be distinguished from those of semantic and episodic memory, and may provide clues as to how unique events are transformed to semantic memory. Copyright © 2015 Elsevier

Concealed semantic and episodic autobiographical memory electrified.

Science.gov (United States)

Ganis, Giorgio; Schendan, Haline E

2012-01-01

Electrophysiology-based concealed information tests (CIT) try to determine whether somebody possesses concealed information about a crime-related item (probe) by comparing event-related potentials (ERPs) between this item and comparison items (irrelevants). Although the broader field is sometimes referred to as "memory detection," little attention has been paid to the precise type of underlying memory involved. This study begins addressing this issue by examining the key distinction between semantic and episodic memory in the autobiographical domain within a CIT paradigm. This study also addresses the issue of whether multiple repetitions of the items over the course of the session habituate the brain responses. Participants were tested in a 3-stimulus CIT with semantic autobiographical probes (their own date of birth) and episodic autobiographical probes (a secret date learned just before the study). Results dissociated these two memory conditions on several ERP components. Semantic probes elicited a smaller frontal N2 than episodic probes, consistent with the idea that the frontal N2 decreases with greater pre-existing knowledge about the item. Likewise, semantic probes elicited a smaller central N400 than episodic probes. Semantic probes also elicited a larger P3b than episodic probes because of their richer meaning. In contrast, episodic probes elicited a larger late positive complex (LPC) than semantic probes, because of the recent episodic memory associated with them. All these ERPs showed a difference between probes and irrelevants in both memory conditions, except for the N400, which showed a difference only in the semantic condition. Finally, although repetition affected the ERPs, it did not reduce the difference between probes and irrelevants. These findings show that the type of memory associated with a probe has both theoretical and practical importance for CIT research.

Concealed semantic and episodic autobiographical memory electrified

Science.gov (United States)

Ganis, Giorgio; Schendan, Haline E.

2013-01-01

Electrophysiology-based concealed information tests (CIT) try to determine whether somebody possesses concealed information about a crime-related item (probe) by comparing event-related potentials (ERPs) between this item and comparison items (irrelevants). Although the broader field is sometimes referred to as “memory detection,” little attention has been paid to the precise type of underlying memory involved. This study begins addressing this issue by examining the key distinction between semantic and episodic memory in the autobiographical domain within a CIT paradigm. This study also addresses the issue of whether multiple repetitions of the items over the course of the session habituate the brain responses. Participants were tested in a 3-stimulus CIT with semantic autobiographical probes (their own date of birth) and episodic autobiographical probes (a secret date learned just before the study). Results dissociated these two memory conditions on several ERP components. Semantic probes elicited a smaller frontal N2 than episodic probes, consistent with the idea that the frontal N2 decreases with greater pre-existing knowledge about the item. Likewise, semantic probes elicited a smaller central N400 than episodic probes. Semantic probes also elicited a larger P3b than episodic probes because of their richer meaning. In contrast, episodic probes elicited a larger late positive complex (LPC) than semantic probes, because of the recent episodic memory associated with them. All these ERPs showed a difference between probes and irrelevants in both memory conditions, except for the N400, which showed a difference only in the semantic condition. Finally, although repetition affected the ERPs, it did not reduce the difference between probes and irrelevants. These findings show that the type of memory associated with a probe has both theoretical and practical importance for CIT research. PMID:23355816

A Unified Approach to Functional Principal Component Analysis and Functional Multiple-Set Canonical Correlation.

Science.gov (United States)

Choi, Ji Yeh; Hwang, Heungsun; Yamamoto, Michio; Jung, Kwanghee; Woodward, Todd S

2017-06-01

Functional principal component analysis (FPCA) and functional multiple-set canonical correlation analysis (FMCCA) are data reduction techniques for functional data that are collected in the form of smooth curves or functions over a continuum such as time or space. In FPCA, low-dimensional components are extracted from a single functional dataset such that they explain the most variance of the dataset, whereas in FMCCA, low-dimensional components are obtained from each of multiple functional datasets in such a way that the associations among the components are maximized across the different sets. In this paper, we propose a unified approach to FPCA and FMCCA. The proposed approach subsumes both techniques as special cases. Furthermore, it permits a compromise between the techniques, such that components are obtained from each set of functional data to maximize their associations across different datasets, while accounting for the variance of the data well. We propose a single optimization criterion for the proposed approach, and develop an alternating regularized least squares algorithm to minimize the criterion in combination with basis function approximations to functions. We conduct a simulation study to investigate the performance of the proposed approach based on synthetic data. We also apply the approach for the analysis of multiple-subject functional magnetic resonance imaging data to obtain low-dimensional components of blood-oxygen level-dependent signal changes of the brain over time, which are highly correlated across the subjects as well as representative of the data. The extracted components are used to identify networks of neural activity that are commonly activated across the subjects while carrying out a working memory task.

What Klein’s semantic gradient does and does not really show: decomposing Stroop interference into task and informational conflict components

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Yulia eLevin

2016-02-01

Full Text Available The present study suggests that the idea that Stroop interference originates from multiple components may gain theoretically from integrating two independent frameworks. The first framework is represented by the well-known notion of semantic gradient of interference and the second one is the distinction between two types of conflict – the task and the informational conflict – giving rise to the interference (Goldfarb & Henik, 2007; McLeod & MacDonald, 2000. The proposed integration led to the conclusion that two (i.e., orthographic and lexical components of the four theoretically distinct components represent task conflict, and the other two (i.e., indirect and direct informational conflict components represent informational conflict. The four components were independently estimated in a series of experiments. The results confirmed the contribution of task conflict (estimated by a robust orthographic component and of informational conflict (estimated by a strong direct informational conflict component to Stroop interference. However, the performed critical review of the relevant literature (see General Discussion, as well as the results of the experiments reported, showed that the other two components expressing each type of conflict (i.e., the lexical component of task conflict and the indirect informational conflict were small, and unstable. The present analysis refines our knowledge of the origins of Stroop interference by providing evidence that each type of conflict has its major and minor contributions. The implications for cognitive control of an automatic reading process are also discussed.

Trust estimation of the semantic web using semantic web clustering

Science.gov (United States)

Shirgahi, Hossein; Mohsenzadeh, Mehran; Haj Seyyed Javadi, Hamid

2017-05-01

Development of semantic web and social network is undeniable in the Internet world these days. Widespread nature of semantic web has been very challenging to assess the trust in this field. In recent years, extensive researches have been done to estimate the trust of semantic web. Since trust of semantic web is a multidimensional problem, in this paper, we used parameters of social network authority, the value of pages links authority and semantic authority to assess the trust. Due to the large space of semantic network, we considered the problem scope to the clusters of semantic subnetworks and obtained the trust of each cluster elements as local and calculated the trust of outside resources according to their local trusts and trust of clusters to each other. According to the experimental result, the proposed method shows more than 79% Fscore that is about 11.9% in average more than Eigen, Tidal and centralised trust methods. Mean of error in this proposed method is 12.936, that is 9.75% in average less than Eigen and Tidal trust methods.

A novel normalization method based on principal component analysis to reduce the effect of peak overlaps in two-dimensional correlation spectroscopy

Science.gov (United States)

Wang, Yanwei; Gao, Wenying; Wang, Xiaogong; Yu, Zhiwu

2008-07-01

Two-dimensional correlation spectroscopy (2D-COS) has been widely used to separate overlapped spectroscopic bands. However, band overlap may sometimes cause misleading results in the 2D-COS spectra, especially if one peak is embedded within another peak by the overlap. In this work, we propose a new normalization method, based on principal component analysis (PCA). For each spectrum under discussion, the first principal component of PCA is simply taken as the normalization factor of the spectrum. It is demonstrated that the method works well with simulated dynamic spectra. Successful result has also been obtained from the analysis of an overlapped band in the wavenumber range 1440-1486 cm -1 for the evaporation process of a solution containing behenic acid, methanol, and chloroform.

Mapping ash properties using principal components analysis

Science.gov (United States)

Pereira, Paulo; Brevik, Eric; Cerda, Artemi; Ubeda, Xavier; Novara, Agata; Francos, Marcos; Rodrigo-Comino, Jesus; Bogunovic, Igor; Khaledian, Yones

2017-04-01

In post-fire environments ash has important benefits for soils, such as protection and source of nutrients, crucial for vegetation recuperation (Jordan et al., 2016; Pereira et al., 2015a; 2016a,b). The thickness and distribution of ash are fundamental aspects for soil protection (Cerdà and Doerr, 2008; Pereira et al., 2015b) and the severity at which was produced is important for the type and amount of elements that is released in soil solution (Bodi et al., 2014). Ash is very mobile material, and it is important were it will be deposited. Until the first rainfalls are is very mobile. After it, bind in the soil surface and is harder to erode. Mapping ash properties in the immediate period after fire is complex, since it is constantly moving (Pereira et al., 2015b). However, is an important task, since according the amount and type of ash produced we can identify the degree of soil protection and the nutrients that will be dissolved. The objective of this work is to apply to map ash properties (CaCO3, pH, and select extractable elements) using a principal component analysis (PCA) in the immediate period after the fire. Four days after the fire we established a grid in a 9x27 m area and took ash samples every 3 meters for a total of 40 sampling points (Pereira et al., 2017). The PCA identified 5 different factors. Factor 1 identified high loadings in electrical conductivity, calcium, and magnesium and negative with aluminum and iron, while Factor 3 had high positive loadings in total phosphorous and silica. Factor 3 showed high positive loadings in sodium and potassium, factor 4 high negative loadings in CaCO3 and pH, and factor 5 high loadings in sodium and potassium. The experimental variograms of the extracted factors showed that the Gaussian model was the most precise to model factor 1, the linear to model factor 2 and the wave hole effect to model factor 3, 4 and 5. The maps produced confirm the patternd observed in the experimental variograms. Factor 1 and 2

Comparison of common components analysis with principal components analysis and independent components analysis: Application to SPME-GC-MS volatolomic signatures.

Science.gov (United States)

Bouhlel, Jihéne; Jouan-Rimbaud Bouveresse, Delphine; Abouelkaram, Said; Baéza, Elisabeth; Jondreville, Catherine; Travel, Angélique; Ratel, Jérémy; Engel, Erwan; Rutledge, Douglas N

2018-02-01

The aim of this work is to compare a novel exploratory chemometrics method, Common Components Analysis (CCA), with Principal Components Analysis (PCA) and Independent Components Analysis (ICA). CCA consists in adapting the multi-block statistical method known as Common Components and Specific Weights Analysis (CCSWA or ComDim) by applying it to a single data matrix, with one variable per block. As an application, the three methods were applied to SPME-GC-MS volatolomic signatures of livers in an attempt to reveal volatile organic compounds (VOCs) markers of chicken exposure to different types of micropollutants. An application of CCA to the initial SPME-GC-MS data revealed a drift in the sample Scores along CC2, as a function of injection order, probably resulting from time-related evolution in the instrument. This drift was eliminated by orthogonalization of the data set with respect to CC2, and the resulting data are used as the orthogonalized data input into each of the three methods. Since the first step in CCA is to norm-scale all the variables, preliminary data scaling has no effect on the results, so that CCA was applied only to orthogonalized SPME-GC-MS data, while, PCA and ICA were applied to the "orthogonalized", "orthogonalized and Pareto-scaled", and "orthogonalized and autoscaled" data. The comparison showed that PCA results were highly dependent on the scaling of variables, contrary to ICA where the data scaling did not have a strong influence. Nevertheless, for both PCA and ICA the clearest separations of exposed groups were obtained after autoscaling of variables. The main part of this work was to compare the CCA results using the orthogonalized data with those obtained with PCA and ICA applied to orthogonalized and autoscaled variables. The clearest separations of exposed chicken groups were obtained by CCA. CCA Loadings also clearly identified the variables contributing most to the Common Components giving separations. The PCA Loadings did not

SEMANTIC SEGMENTATION OF BUILDING ELEMENTS USING POINT CLOUD HASHING

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M. Chizhova

2018-05-01

Full Text Available For the interpretation of point clouds, the semantic definition of extracted segments from point clouds or images is a common problem. Usually, the semantic of geometrical pre-segmented point cloud elements are determined using probabilistic networks and scene databases. The proposed semantic segmentation method is based on the psychological human interpretation of geometric objects, especially on fundamental rules of primary comprehension. Starting from these rules the buildings could be quite well and simply classified by a human operator (e.g. architect into different building types and structural elements (dome, nave, transept etc., including particular building parts which are visually detected. The key part of the procedure is a novel method based on hashing where point cloud projections are transformed into binary pixel representations. A segmentation approach released on the example of classical Orthodox churches is suitable for other buildings and objects characterized through a particular typology in its construction (e.g. industrial objects in standardized enviroments with strict component design allowing clear semantic modelling.

Semantic dysfunction in women with schizotypal personality disorder.

Science.gov (United States)

Niznikiewicz, Margaret A; Shenton, Martha E; Voglmaier, Martina; Nestor, Paul G; Dickey, Chandlee C; Frumin, Melissa; Seidman, Larry J; Allen, Christopher G; McCarley, Robert W

2002-10-01

This study examined whether early or late processes in semantic networks were abnormal in women with a diagnosis of schizotypal personality disorder. The N400 component of the EEG event-related potentials was used as a probe of semantic processes. Word pairs were presented with short and long stimulus-onset asynchronies to investigate, respectively, early and late semantic processes in 16 women with schizotypal personality disorder and 15 normal female comparison subjects. Event-related potentials were recorded in response to the last words in a pair. With the short stimulus-onset asynchrony, the N400 amplitude was less negative in the schizotypal personality disorder group than in the normal comparison group. No group differences were found with the long stimulus-onset asynchrony. The finding of a less negative than normal N400 amplitude with the short stimulus-onset asynchrony in women with schizotypal personality disorder supports the hypothesis that persons with this disorder evince an overactivation of semantic networks. The absence of group differences with the long stimulus-onset asynchrony, which is primarily sensitive to processes involved in context integration, suggests that in this group of schizotypal personality disorder subjects, additional demands on working memory may be necessary to bring out the semantic dysfunction.

A Familiar Pattern? Semantic Memory Contributes to the Enhancement of Visuo-Spatial Memories

Science.gov (United States)

Riby, Leigh M.; Orme, Elizabeth

2013-01-01

In this study we quantify for the first time electrophysiological components associated with incorporating long-term semantic knowledge with visuo-spatial information using two variants of a traditional matrix patterns task. Results indicated that the matrix task with greater semantic content was associated with enhanced accuracy and RTs in a…

Rationalization of paclitaxel insensitivity of yeast β-tubulin and human βIII-tubulin isotype using principal component analysis

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Das Lalita

2012-08-01

Full Text Available Abstract Background The chemotherapeutic agent paclitaxel arrests cell division by binding to the hetero-dimeric protein tubulin. Subtle differences in tubulin sequences, across eukaryotes and among β-tubulin isotypes, can have profound impact on paclitaxel-tubulin binding. To capture the experimentally observed paclitaxel-resistance of human βIII tubulin isotype and yeast β-tubulin, within a common theoretical framework, we have performed structural principal component analyses of β-tubulin sequences across eukaryotes. Results The paclitaxel-resistance of human βIII tubulin isotype and yeast β-tubulin uniquely mapped on to the lowest two principal components, defining the paclitaxel-binding site residues of β-tubulin. The molecular mechanisms behind paclitaxel-resistance, mediated through key residues, were identified from structural consequences of characteristic mutations that confer paclitaxel-resistance. Specifically, Ala277 in βIII isotype was shown to be crucial for paclitaxel-resistance. Conclusions The present analysis captures the origin of two apparently unrelated events, paclitaxel-insensitivity of yeast tubulin and human βIII tubulin isotype, through two common collective sequence vectors.

Empirical research on financial capability evaluation of A-share listed companies in the securities industry based on principal component analysis

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Xiuping Wang

2017-11-01

Full Text Available Based on the relevant financial data indicators of A-share markets of Shanghai and Shenzhen in 2009, with all of 29 listed companies in the securities industry as the research objects, this paper selects 10variables that can fully reflect the financial capability indicators and uses the principal component analysis to carry out the empirical research on the financial capability. The research results show that the comprehensive financial capability of listed companies in A-share securities industry must be focused on the following four capabilities, investment and income, profit, capital composition and debt repayment and cash flow indicators. In addition, the principal component analysis can effectively evaluate the financial capability of listed companies in A-share securities industry, and solve the problems in the previous analysis methods, such as excessive indicators, information overlapping and so on.

Aerodynamic multi-objective integrated optimization based on principal component analysis

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Jiangtao HUANG

2017-08-01

Full Text Available Based on improved multi-objective particle swarm optimization (MOPSO algorithm with principal component analysis (PCA methodology, an efficient high-dimension multi-objective optimization method is proposed, which, as the purpose of this paper, aims to improve the convergence of Pareto front in multi-objective optimization design. The mathematical efficiency, the physical reasonableness and the reliability in dealing with redundant objectives of PCA are verified by typical DTLZ5 test function and multi-objective correlation analysis of supercritical airfoil, and the proposed method is integrated into aircraft multi-disciplinary design (AMDEsign platform, which contains aerodynamics, stealth and structure weight analysis and optimization module. Then the proposed method is used for the multi-point integrated aerodynamic optimization of a wide-body passenger aircraft, in which the redundant objectives identified by PCA are transformed to optimization constraints, and several design methods are compared. The design results illustrate that the strategy used in this paper is sufficient and multi-point design requirements of the passenger aircraft are reached. The visualization level of non-dominant Pareto set is improved by effectively reducing the dimension without losing the primary feature of the problem.

Personality disorders in substance abusers: Validation of the DIP-Q through principal components factor analysis and canonical correlation analysis

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Hesse Morten

2005-05-01

Full Text Available Abstract Background Personality disorders are common in substance abusers. Self-report questionnaires that can aid in the assessment of personality disorders are commonly used in assessment, but are rarely validated. Methods The Danish DIP-Q as a measure of co-morbid personality disorders in substance abusers was validated through principal components factor analysis and canonical correlation analysis. A 4 components structure was constructed based on 238 protocols, representing antagonism, neuroticism, introversion and conscientiousness. The structure was compared with (a a 4-factor solution from the DIP-Q in a sample of Swedish drug and alcohol abusers (N = 133, and (b a consensus 4-components solution based on a meta-analysis of published correlation matrices of dimensional personality disorder scales. Results It was found that the 4-factor model of personality was congruent across the Danish and Swedish samples, and showed good congruence with the consensus model. A canonical correlation analysis was conducted on a subset of the Danish sample with staff ratings of pathology. Three factors that correlated highly between the two variable sets were found. These variables were highly similar to the three first factors from the principal components analysis, antagonism, neuroticism and introversion. Conclusion The findings support the validity of the DIP-Q as a measure of DSM-IV personality disorders in substance abusers.

Nonlinear Denoising and Analysis of Neuroimages With Kernel Principal Component Analysis and Pre-Image Estimation

DEFF Research Database (Denmark)

Rasmussen, Peter Mondrup; Abrahamsen, Trine Julie; Madsen, Kristoffer Hougaard

2012-01-01

We investigate the use of kernel principal component analysis (PCA) and the inverse problem known as pre-image estimation in neuroimaging: i) We explore kernel PCA and pre-image estimation as a means for image denoising as part of the image preprocessing pipeline. Evaluation of the denoising...... procedure is performed within a data-driven split-half evaluation framework. ii) We introduce manifold navigation for exploration of a nonlinear data manifold, and illustrate how pre-image estimation can be used to generate brain maps in the continuum between experimentally defined brain states/classes. We...

Improvement of User's Accuracy Through Classification of Principal Component Images and Stacked Temporal Images

Institute of Scientific and Technical Information of China (English)

Nilanchal Patel; Brijesh Kumar Kaushal

2010-01-01

The classification accuracy of the various categories on the classified remotely sensed images are usually evaluated by two different measures of accuracy, namely, producer's accuracy (PA) and user's accuracy (UA). The PA of a category indicates to what extent the reference pixels of the category are correctly classified, whereas the UA ora category represents to what extent the other categories are less misclassified into the category in question. Therefore, the UA of the various categories determines the reliability of their interpretation on the classified image and is more important to the analyst than the PA. The present investigation has been performed in order to determine ifthere occurs improvement in the UA of the various categories on the classified image of the principal components of the original bands and on the classified image of the stacked image of two different years. We performed the analyses using the IRS LISS Ⅲ images of two different years, i.e., 1996 and 2009, that represent the different magnitude of urbanization and the stacked image of these two years pertaining to Ranchi area, Jharkhand, India, with a view to assessing the impacts of urbanization on the UA of the different categories. The results of the investigation demonstrated that there occurs significant improvement in the UA of the impervious categories in the classified image of the stacked image, which is attributable to the aggregation of the spectral information from twice the number of bands from two different years. On the other hand, the classified image of the principal components did not show any improvement in the UA as compared to the original images.

Verbal fluency in children with intellectual disability: Influence of basic executive components

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Gligorović Milica

2014-01-01

Full Text Available Phonemic and semantic fluency tasks are frequently used to differentiate executive control roles and the integrity of lexical-semantic representation. The main goal of this study is to determine the influence of basic executive components on phonemic and semantic productivity in children with mild intellectual disability. The sample consisted of 95 children with unspecified mild intellectual disability (MID, ages 10-13.11. Phonemic fluency was assessed by the Controlled Oral Word Association Test (COWAT, while semantic fluency was assessed by the Category Naming Test (CNT. Cognitive flexibility was assessed by Wisconsin Card Sorting Test (WCST and Trail Making Test (TMT. Number Manipulation Task (NMT was used for the verbal working memory assessment, while Day/Night Stroop Task was used for the assessment of inhibitory control. The results analysis showed that all of the assessed EF components significantly affect phonemic productivity. Semantic productivity significantly depends on WCST and TMT performance. Verbal working memory and inhibitory control do not significantly contribute to semantic productivity. The results of our study indicate that the discrepancy between phonemic and semantic productivity in children with MID could be directly associated with the basic executive functions components.

Semantic Desktop

Science.gov (United States)

Sauermann, Leo; Kiesel, Malte; Schumacher, Kinga; Bernardi, Ansgar

In diesem Beitrag wird gezeigt, wie der Arbeitsplatz der Zukunft aussehen könnte und wo das Semantic Web neue Möglichkeiten eröffnet. Dazu werden Ansätze aus dem Bereich Semantic Web, Knowledge Representation, Desktop-Anwendungen und Visualisierung vorgestellt, die es uns ermöglichen, die bestehenden Daten eines Benutzers neu zu interpretieren und zu verwenden. Dabei bringt die Kombination von Semantic Web und Desktop Computern besondere Vorteile - ein Paradigma, das unter dem Titel Semantic Desktop bekannt ist. Die beschriebenen Möglichkeiten der Applikationsintegration sind aber nicht auf den Desktop beschränkt, sondern können genauso in Web-Anwendungen Verwendung finden.

PM10 and gaseous pollutants trends from air quality monitoring networks in Bari province: principal component analysis and absolute principal component scores on a two years and half data set

Science.gov (United States)

2014-01-01

Background The chemical composition of aerosols and particle size distributions are the most significant factors affecting air quality. In particular, the exposure to finer particles can cause short and long-term effects on human health. In the present paper PM10 (particulate matter with aerodynamic diameter lower than 10 μm), CO, NOx (NO and NO2), Benzene and Toluene trends monitored in six monitoring stations of Bari province are shown. The data set used was composed by bi-hourly means for all parameters (12 bi-hourly means per day for each parameter) and it’s referred to the period of time from January 2005 and May 2007. The main aim of the paper is to provide a clear illustration of how large data sets from monitoring stations can give information about the number and nature of the pollutant sources, and mainly to assess the contribution of the traffic source to PM10 concentration level by using multivariate statistical techniques such as Principal Component Analysis (PCA) and Absolute Principal Component Scores (APCS). Results Comparing the night and day mean concentrations (per day) for each parameter it has been pointed out that there is a different night and day behavior for some parameters such as CO, Benzene and Toluene than PM10. This suggests that CO, Benzene and Toluene concentrations are mainly connected with transport systems, whereas PM10 is mostly influenced by different factors. The statistical techniques identified three recurrent sources, associated with vehicular traffic and particulate transport, covering over 90% of variance. The contemporaneous analysis of gas and PM10 has allowed underlining the differences between the sources of these pollutants. Conclusions The analysis of the pollutant trends from large data set and the application of multivariate statistical techniques such as PCA and APCS can give useful information about air quality and pollutant’s sources. These knowledge can provide useful advices to environmental policies in

Statistical techniques applied to aerial radiometric surveys (STAARS): principal components analysis user's manual

International Nuclear Information System (INIS)

Koch, C.D.; Pirkle, F.L.; Schmidt, J.S.

1981-01-01

A Principal Components Analysis (PCA) has been written to aid in the interpretation of multivariate aerial radiometric data collected by the US Department of Energy (DOE) under the National Uranium Resource Evaluation (NURE) program. The variations exhibited by these data have been reduced and classified into a number of linear combinations by using the PCA program. The PCA program then generates histograms and outlier maps of the individual variates. Black and white plots can be made on a Calcomp plotter by the application of follow-up programs. All programs referred to in this guide were written for a DEC-10. From this analysis a geologist may begin to interpret the data structure. Insight into geological processes underlying the data may be obtained

Semantic heterogeneity: comparing new semantic web approaches with those of digital libraries

OpenAIRE

Krause, Jürgen

2008-01-01

To demonstrate that newer developments in the semantic web community, particularly those based on ontologies (simple knowledge organization system and others) mitigate common arguments from the digital library (DL) community against participation in the Semantic web. The approach is a semantic web discussion focusing on the weak structure of the Web and the lack of consideration given to the semantic content during indexing. The points criticised by the semantic web and ontology approaches ar...

Semantic framework for mapping object-oriented model to semantic web languages.

Science.gov (United States)

Ježek, Petr; Mouček, Roman

2015-01-01

The article deals with and discusses two main approaches in building semantic structures for electrophysiological metadata. It is the use of conventional data structures, repositories, and programming languages on one hand and the use of formal representations of ontologies, known from knowledge representation, such as description logics or semantic web languages on the other hand. Although knowledge engineering offers languages supporting richer semantic means of expression and technological advanced approaches, conventional data structures and repositories are still popular among developers, administrators and users because of their simplicity, overall intelligibility, and lower demands on technical equipment. The choice of conventional data resources and repositories, however, raises the question of how and where to add semantics that cannot be naturally expressed using them. As one of the possible solutions, this semantics can be added into the structures of the programming language that accesses and processes the underlying data. To support this idea we introduced a software prototype that enables its users to add semantically richer expressions into a Java object-oriented code. This approach does not burden users with additional demands on programming environment since reflective Java annotations were used as an entry for these expressions. Moreover, additional semantics need not to be written by the programmer directly to the code, but it can be collected from non-programmers using a graphic user interface. The mapping that allows the transformation of the semantically enriched Java code into the Semantic Web language OWL was proposed and implemented in a library named the Semantic Framework. This approach was validated by the integration of the Semantic Framework in the EEG/ERP Portal and by the subsequent registration of the EEG/ERP Portal in the Neuroscience Information Framework.

Programming the semantic web

CERN Document Server

Segaran, Toby; Taylor, Jamie

2009-01-01

With this book, the promise of the Semantic Web -- in which machines can find, share, and combine data on the Web -- is not just a technical possibility, but a practical reality Programming the Semantic Web demonstrates several ways to implement semantic web applications, using current and emerging standards and technologies. You'll learn how to incorporate existing data sources into semantically aware applications and publish rich semantic data. Each chapter walks you through a single piece of semantic technology and explains how you can use it to solve real problems. Whether you're writing

Portable XRF and principal component analysis for bill characterization in forensic science.

Science.gov (United States)

Appoloni, C R; Melquiades, F L

2014-02-01

Several modern techniques have been applied to prevent counterfeiting of money bills. The objective of this study was to demonstrate the potential of Portable X-ray Fluorescence (PXRF) technique and the multivariate analysis method of Principal Component Analysis (PCA) for classification of bills in order to use it in forensic science. Bills of Dollar, Euro and Real (Brazilian currency) were measured directly at different colored regions, without any previous preparation. Spectra interpretation allowed the identification of Ca, Ti, Fe, Cu, Sr, Y, Zr and Pb. PCA analysis separated the bills in three groups and subgroups among Brazilian currency. In conclusion, the samples were classified according to its origin identifying the elements responsible for differentiation and basic pigment composition. PXRF allied to multivariate discriminate methods is a promising technique for rapid and no destructive identification of false bills in forensic science. Copyright © 2013 Elsevier Ltd. All rights reserved.

Factors affecting medication adherence in community-managed patients with hypertension based on the principal component analysis: evidence from Xinjiang, China

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Zhang YJ

2018-05-01

Full Text Available Yuji Zhang,* Xiaoju Li,* Lu Mao, Mei Zhang, Ke Li, Yinxia Zheng, Wangfei Cui, Hongpo Yin, Yanli He, Mingxia Jing Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, China *These authors contributed equally to this work Purpose: The analysis of factors affecting the nonadherence to antihypertensive medications is important in the control of blood pressure among patients with hypertension. The purpose of this study was to assess the relationship between factors and medication adherence in Xinjiang community-managed patients with hypertension based on the principal component analysis.Patients and methods: A total of 1,916 community-managed patients with hypertension, selected randomly through a multi-stage sampling, participated in the survey. Self-designed questionnaires were used to classify the participants as either adherent or nonadherent to their medication regimen. A principal component analysis was used in order to eliminate the correlation between factors. Factors related to nonadherence were analyzed by using a χ2-test and a binary logistic regression model.Results: This study extracted nine common factors, with a cumulative variance contribution rate of 63.6%. Further analysis revealed that the following variables were significantly related to nonadherence: severity of disease, community management, diabetes, and taking traditional medications.Conclusion: Community management plays an important role in improving the patients’ medication-taking behavior. Regular medication regimen instruction and better community management services through community-level have the potential to reduce nonadherence. Mild hypertensive patients should be monitored by community health care providers. Keywords: hypertension, medication adherence, factors, principal component analysis, community management, China

Location and characterisation of pollution sites by principal ...

African Journals Online (AJOL)

Location and characterisation of pollution sites by principal component analysis of trace contaminants in a slightly polluted seasonal river: a case study of the Arenales River (Salta, Argentina) ... Keywords: trace element contamination, water quality, principal component analysis, Arenales River, Salta, Argentina ...

Proteome comparison for discrimination between honeydew and floral honeys from botanical species Mimosa scabrella Bentham by principal component analysis.

Science.gov (United States)

Azevedo, Mônia Stremel; Valentim-Neto, Pedro Alexandre; Seraglio, Siluana Katia Tischer; da Luz, Cynthia Fernandes Pinto; Arisi, Ana Carolina Maisonnave; Costa, Ana Carolina Oliveira

2017-10-01

Due to the increasing valuation and appreciation of honeydew honey in many European countries and also to existing contamination among different types of honeys, authentication is an important aspect of quality control with regard to guaranteeing the origin in terms of source (honeydew or floral) and needs to be determined. Furthermore, proteins are minor components of the honey, despite the importance of their physiological effects, and can differ according to the source of the honey. In this context, the aims of this study were to carry out protein extraction from honeydew and floral honeys and to discriminate these honeys from the same botanical species, Mimosa scabrella Bentham, through proteome comparison using two-dimensional gel electrophoresis and principal component analysis. The results showed that the proteome profile and principal component analysis can be a useful tool for discrimination between these types of honey using matched proteins (45 matched spots). Also, the proteome profile showed 160 protein spots in honeydew honey and 84 spots in the floral honey. The protein profile can be a differential characteristic of this type of honey, in view of the importance of proteins as bioactive compounds in honey. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Semantic metrics

OpenAIRE

Hu, Bo; Kalfoglou, Yannis; Dupplaw, David; Alani, Harith; Lewis, Paul; Shadbolt, Nigel

2006-01-01

In the context of the Semantic Web, many ontology-related operations, e.g. ontology ranking, segmentation, alignment, articulation, reuse, evaluation, can be boiled down to one fundamental operation: computing the similarity and/or dissimilarity among ontological entities, and in some cases among ontologies themselves. In this paper, we review standard metrics for computing distance measures and we propose a series of semantic metrics. We give a formal account of semantic metrics drawn from a...

On a syntactic-semantic model with the locative case

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Antonić Ivana

2008-01-01

Full Text Available The topic of this paper is a syntactic-semantic model whose distinctive element is the locative case with the preposition U (IN and the relevant feature (+ human being. This model is realized in three different variants - with the intransitive (A or transitive verb (B, where the nominative in the function of subject and the locative indicate different (B1 or the same (B2 referents. Furthermore, the verb belongs to a semantic class which denotes emerging, stimulation, duration, fading away, diminishing or change in the intensity, in principle, of any phenomenon, and concretely in this model such verbs appear in the collocational link with the nouns implying man's psychological, physiological or mental states, feelings or mood. With an adequate analytic procedure, all the three variants of this model are approached from the syntactic-semantic and pragmatic perspective. The paper points to the causative semantics of these structures, reduced to the metalinguistic formula 'make that X V', which confirms that the semantics of these verb-noun collocational links, syntactically speaking, condenses a complex two-member sentential structure represented by the semantically deficient verb (= causative component in the basic, matrix structure, and the complement clause with the conjunction DA (THAT and the basic verb. And precisely from this semantic feature there follows that the notion in the locative case semantically, actually, represents the BEARER of a physiological, physiological or mental state, feeling, mood, so that it represents the GRAMMATICAL SUBJECT of the corresponding basic subordinated predication whose exponent, actually, is the grammatical subject in the structure with the intransitive verb (or with the syntactically-semantically intransitive verb structure, that is the object in the structure with the transitive verb. Two possible semantic interpretations of this model are presented: the one related to the referential pointing to the

Use of Principal Components Analysis and Kriging to Predict Groundwater-Sourced Rural Drinking Water Quality in Saskatchewan.

Science.gov (United States)

McLeod, Lianne; Bharadwaj, Lalita; Epp, Tasha; Waldner, Cheryl L

2017-09-15

Groundwater drinking water supply surveillance data were accessed to summarize water quality delivered as public and private water supplies in southern Saskatchewan as part of an exposure assessment for epidemiologic analyses of associations between water quality and type 2 diabetes or cardiovascular disease. Arsenic in drinking water has been linked to a variety of chronic diseases and previous studies have identified multiple wells with arsenic above the drinking water standard of 0.01 mg/L; therefore, arsenic concentrations were of specific interest. Principal components analysis was applied to obtain principal component (PC) scores to summarize mixtures of correlated parameters identified as health standards and those identified as aesthetic objectives in the Saskatchewan Drinking Water Quality Standards and Objective. Ordinary, universal, and empirical Bayesian kriging were used to interpolate arsenic concentrations and PC scores in southern Saskatchewan, and the results were compared. Empirical Bayesian kriging performed best across all analyses, based on having the greatest number of variables for which the root mean square error was lowest. While all of the kriging methods appeared to underestimate high values of arsenic and PC scores, empirical Bayesian kriging was chosen to summarize large scale geographic trends in groundwater-sourced drinking water quality and assess exposure to mixtures of trace metals and ions.

Principles of Linguistic Composition Below and Beyond the Clause—Elements of a semantic combinatorial system

DEFF Research Database (Denmark)

Bundgaard, Peer

2006-01-01

beyond the scope of the clause. To this end it exposes two major principles of semantic combination that are active through all levels of linguistic composition: viz. frame-schematic structure and narrative structure. These principles are considered as being components of a semantic combinatorial system...

Trajectory modeling of gestational weight: A functional principal component analysis approach.

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Menglu Che

Full Text Available Suboptimal gestational weight gain (GWG, which is linked to increased risk of adverse outcomes for a pregnant woman and her infant, is prevalent. In the study of a large cohort of Canadian pregnant women, our goals are to estimate the individual weight growth trajectory using sparsely collected bodyweight data, and to identify the factors affecting the weight change during pregnancy, such as prepregnancy body mass index (BMI, dietary intakes and physical activity. The first goal was achieved through functional principal component analysis (FPCA by conditional expectation. For the second goal, we used linear regression with the total weight gain as the response variable. The trajectory modeling through FPCA had a significantly smaller root mean square error (RMSE and improved adaptability than the classic nonlinear mixed-effect models, demonstrating a novel tool that can be used to facilitate real time monitoring and interventions of GWG. Our regression analysis showed that prepregnancy BMI had a high predictive value for the weight changes during pregnancy, which agrees with the published weight gain guideline.

Association test based on SNP set: logistic kernel machine based test vs. principal component analysis.

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Yang Zhao

Full Text Available GWAS has facilitated greatly the discovery of risk SNPs associated with complex diseases. Traditional methods analyze SNP individually and are limited by low power and reproducibility since correction for multiple comparisons is necessary. Several methods have been proposed based on grouping SNPs into SNP sets using biological knowledge and/or genomic features. In this article, we compare the linear kernel machine based test (LKM and principal components analysis based approach (PCA using simulated datasets under the scenarios of 0 to 3 causal SNPs, as well as simple and complex linkage disequilibrium (LD structures of the simulated regions. Our simulation study demonstrates that both LKM and PCA can control the type I error at the significance level of 0.05. If the causal SNP is in strong LD with the genotyped SNPs, both the PCA with a small number of principal components (PCs and the LKM with kernel of linear or identical-by-state function are valid tests. However, if the LD structure is complex, such as several LD blocks in the SNP set, or when the causal SNP is not in the LD block in which most of the genotyped SNPs reside, more PCs should be included to capture the information of the causal SNP. Simulation studies also demonstrate the ability of LKM and PCA to combine information from multiple causal SNPs and to provide increased power over individual SNP analysis. We also apply LKM and PCA to analyze two SNP sets extracted from an actual GWAS dataset on non-small cell lung cancer.

Inquisitive semantics and pragmatics

NARCIS (Netherlands)

Groenendijk, J.; Roelofsen, F.; Larrazabal, J.M.; Zubeldia, L.

2009-01-01

This paper starts with an informal introduction to inquisitive semantics. After that, we present a formal definition of the semantics, and introduce the basic semantic notions of inquisitiveness and informativeness, in terms of wich we define the semantic categories of questions, assertions, and

Personal semantics: at the crossroads of semantic and episodic memory.

Science.gov (United States)

Renoult, Louis; Davidson, Patrick S R; Palombo, Daniela J; Moscovitch, Morris; Levine, Brian

2012-11-01

Declarative memory is usually described as consisting of two systems: semantic and episodic memory. Between these two poles, however, may lie a third entity: personal semantics (PS). PS concerns knowledge of one's past. Although typically assumed to be an aspect of semantic memory, it is essentially absent from existing models of knowledge. Furthermore, like episodic memory (EM), PS is idiosyncratically personal (i.e., not culturally-shared). We show that, depending on how it is operationalized, the neural correlates of PS can look more similar to semantic memory, more similar to EM, or dissimilar to both. We consider three different perspectives to better integrate PS into existing models of declarative memory and suggest experimental strategies for disentangling PS from semantic and episodic memory. Copyright © 2012 Elsevier Ltd. All rights reserved.

Latitude-Time Total Electron Content Anomalies as Precursors to Japan's Large Earthquakes Associated with Principal Component Analysis

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Jyh-Woei Lin

2011-01-01

Full Text Available The goal of this study is to determine whether principal component analysis (PCA can be used to process latitude-time ionospheric TEC data on a monthly basis to identify earthquake associated TEC anomalies. PCA is applied to latitude-time (mean-of-a-month ionospheric total electron content (TEC records collected from the Japan GEONET network to detect TEC anomalies associated with 18 earthquakes in Japan (M≥6.0 from 2000 to 2005. According to the results, PCA was able to discriminate clear TEC anomalies in the months when all 18 earthquakes occurred. After reviewing months when no M≥6.0 earthquakes occurred but geomagnetic storm activity was present, it is possible that the maximal principal eigenvalues PCA returned for these 18 earthquakes indicate earthquake associated TEC anomalies. Previously PCA has been used to discriminate earthquake-associated TEC anomalies recognized by other researchers, who found that statistical association between large earthquakes and TEC anomalies could be established in the 5 days before earthquake nucleation; however, since PCA uses the characteristics of principal eigenvalues to determine earthquake related TEC anomalies, it is possible to show that such anomalies existed earlier than this 5-day statistical window.

Functional neuroimaging of semantic and episodic musical memory.

Science.gov (United States)

Platel, Hervé

2005-12-01

The distinction between episodic and semantic memory has become very popular since it was first proposed by Tulving in 1972. So far, very few neuropsychological, psychophysical, and imaging studies have related to the mnemonic aspects of music, notably on the long-term memory features, and practically nothing is known about the functional anatomy of long-term memory for music. Numerous functional imaging studies have shown that retrieval from semantic and episodic memory is subserved by distinct neural networks. For instance, the HERA model (hemispheric encoding/retrieval asymmetry) ascribes to the left prefrontal cortex a preferential role in the encoding process of episodic material and the recall of semantic information, while the right prefrontal cortex would preferentially operate in the recall of episodic information. However, these results were essentially obtained with verbal and visuo-spatial material. We have done a study to determine the neural substrates underlying the semantic and episodic components of music using familiar and nonfamiliar melodic tunes. Two distinct patterns of activations were found: bilateral activation of the middle and superior frontal areas and precuneus for episodic memory, and activation of the medial and orbital frontal cortex bilaterally, left angular gyrus, and the anterior part of the left middle and superior temporal gyri for semantic memory. We discuss these findings in light of the available neuropsychological data obtained in brain-damaged subjects and functional neuroimaging studies.

SSWAP: A Simple Semantic Web Architecture and Protocol for semantic web services.

Science.gov (United States)

Gessler, Damian D G; Schiltz, Gary S; May, Greg D; Avraham, Shulamit; Town, Christopher D; Grant, David; Nelson, Rex T

2009-09-23

SSWAP (Simple Semantic Web Architecture and Protocol; pronounced "swap") is an architecture, protocol, and platform for using reasoning to semantically integrate heterogeneous disparate data and services on the web. SSWAP was developed as a hybrid semantic web services technology to overcome limitations found in both pure web service technologies and pure semantic web technologies. There are currently over 2400 resources published in SSWAP. Approximately two dozen are custom-written services for QTL (Quantitative Trait Loci) and mapping data for legumes and grasses (grains). The remaining are wrappers to Nucleic Acids Research Database and Web Server entries. As an architecture, SSWAP establishes how clients (users of data, services, and ontologies), providers (suppliers of data, services, and ontologies), and discovery servers (semantic search engines) interact to allow for the description, querying, discovery, invocation, and response of semantic web services. As a protocol, SSWAP provides the vocabulary and semantics to allow clients, providers, and discovery servers to engage in semantic web services. The protocol is based on the W3C-sanctioned first-order description logic language OWL DL. As an open source platform, a discovery server running at http://sswap.info (as in to "swap info") uses the description logic reasoner Pellet to integrate semantic resources. The platform hosts an interactive guide to the protocol at http://sswap.info/protocol.jsp, developer tools at http://sswap.info/developer.jsp, and a portal to third-party ontologies at http://sswapmeet.sswap.info (a "swap meet"). SSWAP addresses the three basic requirements of a semantic web services architecture (i.e., a common syntax, shared semantic, and semantic discovery) while addressing three technology limitations common in distributed service systems: i.e., i) the fatal mutability of traditional interfaces, ii) the rigidity and fragility of static subsumption hierarchies, and iii) the

Impact of Semantic Relatedness on Associative Memory: An ERP Study

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Pierre Desaunay

2017-06-01

Full Text Available Encoding and retrieval processes in memory for pairs of pictures are thought to be influenced by inter-item similarity and by features of individual items. Using Event-Related Potentials (ERP, we aimed to identify how these processes impact on both the early mid-frontal FN400 and the Late Positive Component (LPC potentials during associative retrieval of pictures. Twenty young adults undertook a sham task, using an incidental encoding of semantically related and unrelated pairs of drawings. At test, we conducted a recognition task in which participants were asked to identify target identical pairs of pictures, which could be semantically related or unrelated, among new and rearranged pairs. We observed semantic (related and unrelated pairs and condition effects (old, rearranged and new pairs on the early mid-frontal potential. First, a lower amplitude was shown for identical and rearranged semantically related pairs, which might reflect a retrieval process driven by semantic cues. Second, among semantically unrelated pairs, we found a larger negativity for identical pairs, compared to rearranged and new ones, suggesting additional retrieval processing that focuses on associative information. We also observed an LPC old/new effect with a mid-parietal and a right occipito-parietal topography for semantically related and unrelated old pairs, demonstrating a recollection phenomenon irrespective of the degree of association. These findings suggest that associative recognition using visual stimuli begins at early stages of retrieval, and differs according to the degree of semantic relatedness among items. However, either strategy may ultimately lead to recollection processes.

Laboratory spectroscopy of meteorite samples at UV-vis-NIR wavelengths: Analysis and discrimination by principal components analysis

Science.gov (United States)

Penttilä, Antti; Martikainen, Julia; Gritsevich, Maria; Muinonen, Karri

2018-02-01

Meteorite samples are measured with the University of Helsinki integrating-sphere UV-vis-NIR spectrometer. The resulting spectra of 30 meteorites are compared with selected spectra from the NASA Planetary Data System meteorite spectra database. The spectral measurements are transformed with the principal component analysis, and it is shown that different meteorite types can be distinguished from the transformed data. The motivation is to improve the link between asteroid spectral observations and meteorite spectral measurements.

Application of time series analysis on molecular dynamics simulations of proteins: a study of different conformational spaces by principal component analysis.

Science.gov (United States)

Alakent, Burak; Doruker, Pemra; Camurdan, Mehmet C

2004-09-08

Time series analysis is applied on the collective coordinates obtained from principal component analysis of independent molecular dynamics simulations of alpha-amylase inhibitor tendamistat and immunity protein of colicin E7 based on the Calpha coordinates history. Even though the principal component directions obtained for each run are considerably different, the dynamics information obtained from these runs are surprisingly similar in terms of time series models and parameters. There are two main differences in the dynamics of the two proteins: the higher density of low frequencies and the larger step sizes for the interminima motions of colicin E7 than those of alpha-amylase inhibitor, which may be attributed to the higher number of residues of colicin E7 and/or the structural differences of the two proteins. The cumulative density function of the low frequencies in each run conforms to the expectations from the normal mode analysis. When different runs of alpha-amylase inhibitor are projected on the same set of eigenvectors, it is found that principal components obtained from a certain conformational region of a protein has a moderate explanation power in other conformational regions and the local minima are similar to a certain extent, while the height of the energy barriers in between the minima significantly change. As a final remark, time series analysis tools are further exploited in this study with the motive of explaining the equilibrium fluctuations of proteins. Copyright 2004 American Institute of Physics

SemanticOrganizer: A Customizable Semantic Repository for Distributed NASA Project Teams

Science.gov (United States)

Keller, Richard M.; Berrios, Daniel C.; Carvalho, Robert E.; Hall, David R.; Rich, Stephen J.; Sturken, Ian B.; Swanson, Keith J.; Wolfe, Shawn R.

2004-01-01

SemanticOrganizer is a collaborative knowledge management system designed to support distributed NASA projects, including diverse teams of scientists, engineers, and accident investigators. The system provides a customizable, semantically structured information repository that stores work products relevant to multiple projects of differing types. SemanticOrganizer is one of the earliest and largest semantic web applications deployed at NASA to date, and has been used in diverse contexts ranging from the investigation of Space Shuttle Columbia's accident to the search for life on other planets. Although the underlying repository employs a single unified ontology, access control and ontology customization mechanisms make the repository contents appear different for each project team. This paper describes SemanticOrganizer, its customization facilities, and a sampling of its applications. The paper also summarizes some key lessons learned from building and fielding a successful semantic web application across a wide-ranging set of domains with diverse users.

Semantics, pragmatics, and formal thought disorders in people with schizophrenia.

Science.gov (United States)

Salavera, Carlos; Puyuelo, Miguel; Antoñanzas, José L; Teruel, Pilar

2013-01-01

The aim of this study was to analyze how formal thought disorders (FTD) affect semantics and pragmatics in patients with schizophrenia. The sample comprised subjects with schizophrenia (n = 102) who met the criteria for the disorder according to the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition Text Revision. In the research process, the following scales were used: Positive and Negative Syndrome Scale (PANSS) for psychopathology measurements; the Scale for the Assessment of Thought, Language, and Communication (TLC) for FTD, Word Accentuation Test (WAT), System for the Behavioral Evaluation of Social Skills (SECHS), the pragmatics section of the Objective Criteria Language Battery (BLOC-SR) and the verbal sections of the Wechsler Adults Intelligence Scale (WAIS) III, for assessment of semantics and pragmatics. The results in the semantics and pragmatics sections were inferior to the average values obtained in the general population. Our data demonstrated that the more serious the FTD, the worse the performances in the Verbal-WAIS tests (particularly in its vocabulary, similarities, and comprehension sections), SECHS, and BLOC-SR, indicating that FTD affects semantics and pragmatics, although the results of the WAT indicated good premorbid language skills. The principal conclusion we can draw from this study is the evidence that in schizophrenia the superior level of language structure seems to be compromised, and that this level is related to semantics and pragmatics; when there is an alteration in this level, symptoms of FTD appear, with a wide-ranging relationship between both language and FTD. The second conclusion is that the subject's language is affected by the disorder and rules out the possibility of a previous verbal impairment.

Contact- and distance-based principal component analysis of protein dynamics

Energy Technology Data Exchange (ETDEWEB)

Ernst, Matthias; Sittel, Florian; Stock, Gerhard, E-mail: stock@physik.uni-freiburg.de [Biomolecular Dynamics, Institute of Physics, Albert Ludwigs University, 79104 Freiburg (Germany)

2015-12-28

To interpret molecular dynamics simulations of complex systems, systematic dimensionality reduction methods such as principal component analysis (PCA) represent a well-established and popular approach. Apart from Cartesian coordinates, internal coordinates, e.g., backbone dihedral angles or various kinds of distances, may be used as input data in a PCA. Adopting two well-known model problems, folding of villin headpiece and the functional dynamics of BPTI, a systematic study of PCA using distance-based measures is presented which employs distances between C{sub α}-atoms as well as distances between inter-residue contacts including side chains. While this approach seems prohibitive for larger systems due to the quadratic scaling of the number of distances with the size of the molecule, it is shown that it is sufficient (and sometimes even better) to include only relatively few selected distances in the analysis. The quality of the PCA is assessed by considering the resolution of the resulting free energy landscape (to identify metastable conformational states and barriers) and the decay behavior of the corresponding autocorrelation functions (to test the time scale separation of the PCA). By comparing results obtained with distance-based, dihedral angle, and Cartesian coordinates, the study shows that the choice of input variables may drastically influence the outcome of a PCA.

Varieties of semantic ‘access’ deficit in Wernicke’s aphasia and semantic aphasia

Science.gov (United States)

Robson, Holly; Lambon Ralph, Matthew A.; Jefferies, Elizabeth

2015-01-01

Comprehension deficits are common in stroke aphasia, including in cases with (i) semantic aphasia, characterized by poor executive control of semantic processing across verbal and non-verbal modalities; and (ii) Wernicke’s aphasia, associated with poor auditory–verbal comprehension and repetition, plus fluent speech with jargon. However, the varieties of these comprehension problems, and their underlying causes, are not well understood. Both patient groups exhibit some type of semantic ‘access’ deficit, as opposed to the ‘storage’ deficits observed in semantic dementia. Nevertheless, existing descriptions suggest that these patients might have different varieties of ‘access’ impairment—related to difficulty resolving competition (in semantic aphasia) versus initial activation of concepts from sensory inputs (in Wernicke’s aphasia). We used a case series design to compare patients with Wernicke’s aphasia and those with semantic aphasia on Warrington’s paradigmatic assessment of semantic ‘access’ deficits. In these verbal and non-verbal matching tasks, a small set of semantically-related items are repeatedly presented over several cycles so that the target on one trial becomes a distractor on another (building up interference and eliciting semantic ‘blocking’ effects). Patients with Wernicke’s aphasia and semantic aphasia were distinguished according to lesion location in the temporal cortex, but in each group, some individuals had additional prefrontal damage. Both of these aspects of lesion variability—one that mapped onto classical ‘syndromes’ and one that did not—predicted aspects of the semantic ‘access’ deficit. Both semantic aphasia and Wernicke’s aphasia cases showed multimodal semantic impairment, although as expected, the Wernicke’s aphasia group showed greater deficits on auditory-verbal than picture judgements. Distribution of damage in the temporal lobe was crucial for predicting the initially �

Real-time detection of organic contamination events in water distribution systems by principal components analysis of ultraviolet spectral data.

Science.gov (United States)

Zhang, Jian; Hou, Dibo; Wang, Ke; Huang, Pingjie; Zhang, Guangxin; Loáiciga, Hugo

2017-05-01

The detection of organic contaminants in water distribution systems is essential to protect public health from potential harmful compounds resulting from accidental spills or intentional releases. Existing methods for detecting organic contaminants are based on quantitative analyses such as chemical testing and gas/liquid chromatography, which are time- and reagent-consuming and involve costly maintenance. This study proposes a novel procedure based on discrete wavelet transform and principal component analysis for detecting organic contamination events from ultraviolet spectral data. Firstly, the spectrum of each observation is transformed using discrete wavelet with a coiflet mother wavelet to capture the abrupt change along the wavelength. Principal component analysis is then employed to approximate the spectra based on capture and fusion features. The significant value of Hotelling's T 2 statistics is calculated and used to detect outliers. An alarm of contamination event is triggered by sequential Bayesian analysis when the outliers appear continuously in several observations. The effectiveness of the proposed procedure is tested on-line using a pilot-scale setup and experimental data.

Principal Component Analysis of Chinese Porcelains from the Five Dynasties to the Qing Dynasty

Science.gov (United States)

Yap, C. T.; Hua, Younan

1992-10-01

This is a study of the possibility of identifying antique Chinese porcelains according to the period or dynasty, using major and minor chemical components (SiO2 , Al2O3 , Fe2O3 , K2O, Na2O, CaO and MgO) from the body of the porcelain. Principal component analysis is applied to published data on 66 pieces of Chinese procelains made in Jingdezhen during the Five Dynasties and the Song, Yuan, Ming and Qing Dynasties. It is shown that porcelains made during the Five Dynasties and the Yuan (or Ming) and Qing Dynasties can be segregated completely without any overlap. However, there is appreciable overlap between the Five Dynasties and the Song Dynasty, some overlap between the Song and Ming Dynasties and also between the Yuan and Ming Dynasties. Interestingly, Qing procelains are well separated from all the others. The percentage of silica in the porcelain body decreases and that of alumina increases with recentness with the exception of the Yuan and Ming Dynasties, where this trend is reversed.

Use of Geochemistry Data Collected by the Mars Exploration Rover Spirit in Gusev Crater to Teach Geomorphic Zonation through Principal Components Analysis

Science.gov (United States)

Rodrigue, Christine M.

2011-01-01

This paper presents a laboratory exercise used to teach principal components analysis (PCA) as a means of surface zonation. The lab was built around abundance data for 16 oxides and elements collected by the Mars Exploration Rover Spirit in Gusev Crater between Sol 14 and Sol 470. Students used PCA to reduce 15 of these into 3 components, which,…

Word-embeddings Italian semantic spaces: A semantic model for psycholinguistic research

Directory of Open Access Journals (Sweden)

Marelli Marco

2017-01-01

Full Text Available Distributional semantics has been for long a source of successful models in psycholinguistics, permitting to obtain semantic estimates for a large number of words in an automatic and fast way. However, resources in this respect remain scarce or limitedly accessible for languages different from English. The present paper describes WEISS (Word-Embeddings Italian Semantic Space, a distributional semantic model based on Italian. WEISS includes models of semantic representations that are trained adopting state-of-the-art word-embeddings methods, applying neural networks to induce distributed representations for lexical meanings. The resource is evaluated against two test sets, demonstrating that WEISS obtains a better performance with respect to a baseline encoding word associations. Moreover, an extensive qualitative analysis of the WEISS output provides examples of the model potentialities in capturing several semantic phenomena. Two variants of WEISS are released and made easily accessible via web through the SNAUT graphic interface.

Semantically Interoperable XML Data.

Science.gov (United States)

Vergara-Niedermayr, Cristobal; Wang, Fusheng; Pan, Tony; Kurc, Tahsin; Saltz, Joel

2013-09-01

XML is ubiquitously used as an information exchange platform for web-based applications in healthcare, life sciences, and many other domains. Proliferating XML data are now managed through latest native XML database technologies. XML data sources conforming to common XML schemas could be shared and integrated with syntactic interoperability. Semantic interoperability can be achieved through semantic annotations of data models using common data elements linked to concepts from ontologies. In this paper, we present a framework and software system to support the development of semantic interoperable XML based data sources that can be shared through a Grid infrastructure. We also present our work on supporting semantic validated XML data through semantic annotations for XML Schema, semantic validation and semantic authoring of XML data. We demonstrate the use of the system for a biomedical database of medical image annotations and markups.

Semantically Interoperable XML Data

Science.gov (United States)

Vergara-Niedermayr, Cristobal; Wang, Fusheng; Pan, Tony; Kurc, Tahsin; Saltz, Joel

2013-01-01

XML is ubiquitously used as an information exchange platform for web-based applications in healthcare, life sciences, and many other domains. Proliferating XML data are now managed through latest native XML database technologies. XML data sources conforming to common XML schemas could be shared and integrated with syntactic interoperability. Semantic interoperability can be achieved through semantic annotations of data models using common data elements linked to concepts from ontologies. In this paper, we present a framework and software system to support the development of semantic interoperable XML based data sources that can be shared through a Grid infrastructure. We also present our work on supporting semantic validated XML data through semantic annotations for XML Schema, semantic validation and semantic authoring of XML data. We demonstrate the use of the system for a biomedical database of medical image annotations and markups. PMID:25298789

Semantics of Kinship Terms in Tamil from the Semantic Typology Point of View

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Анна Александровна Смирнитская

2016-12-01

Full Text Available In this article the author examines the lexical-semantic group “kinship terms” in Tamil, applying the attainments of modern semantic typology and the theory of semantic derivation. The kinship terms describing nuclear and extended family are explored. The “semantic shift” relation between two different meanings is established if such relation is realized by synchronous polysemy in one lexeme, semantic derivation, diachronic semantic change, cognates or some other means. The starting point of the study is the typological data from the DatSemShift catalogue of semantic shifts in languages of the world developed by a group of researchers under the guidance of Anna A. Zalizniak in the Institute of Linguistics, RAS. We verify the presence of semantic shifts described in the Database in Tamil. Also, we propose new semantic shifts specific only for this language. We confirm the presence of semantic relation of the studied type among the meanings with English “labels”: father - parents, girl - daughter, to deliver (a child - parents, - child, old woman - wife, owner - wife and others. The data also allows the assumption that the same relation exists between the meanings: old - grandfather, earth - mother, son - courage, unripe - son and others. The meanings of this field are the sources of semantic movements to abstract notions, lexicon of posession, forms of address and others; in addition many inner semantic relations inside this field are revealed. The meanings covering the nuclear part of the kinship system participate in universal semantic shifts described in the DatSemShift catalogue, while the meanings from collateral branches of this bifurcative kinship system (uncle, aunt turn out to be incomparable with kinship terms from indo-european lineal systems. Their meanings can be included in the DatSemShift catalogue only with an indication of system specifics. The information about semantic shifts can be useful for

INCREMENTAL PRINCIPAL COMPONENT ANALYSIS BASED OUTLIER DETECTION METHODS FOR SPATIOTEMPORAL DATA STREAMS

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A. Bhushan

2015-07-01

Full Text Available In this paper, we address outliers in spatiotemporal data streams obtained from sensors placed across geographically distributed locations. Outliers may appear in such sensor data due to various reasons such as instrumental error and environmental change. Real-time detection of these outliers is essential to prevent propagation of errors in subsequent analyses and results. Incremental Principal Component Analysis (IPCA is one possible approach for detecting outliers in such type of spatiotemporal data streams. IPCA has been widely used in many real-time applications such as credit card fraud detection, pattern recognition, and image analysis. However, the suitability of applying IPCA for outlier detection in spatiotemporal data streams is unknown and needs to be investigated. To fill this research gap, this paper contributes by presenting two new IPCA-based outlier detection methods and performing a comparative analysis with the existing IPCA-based outlier detection methods to assess their suitability for spatiotemporal sensor data streams.

State and group dynamics of world stock market by principal component analysis

Science.gov (United States)

Nobi, Ashadun; Lee, Jae Woo

2016-05-01

We study the dynamic interactions and structural changes by a principal component analysis (PCA) to cross-correlation coefficients of global financial indices in the years 1998-2012. The variances explained by the first PC increase with time and show a drastic change during the crisis. A sharp change in PC coefficient implies a transition of market state, a situation which occurs frequently in the American and Asian indices. However, the European indices remain stable over time. Using the first two PC coefficients, we identify indices that are similar and more strongly correlated than the others. We observe that the European indices form a robust group over the observation period. The dynamics of the individual indices within the group increase in similarity with time, and the dynamics of indices are more similar during the crises. Furthermore, the group formation of indices changes position in two-dimensional spaces due to crises. Finally, after a financial crisis, the difference of PCs between the European and American indices narrows.

Semantic Role Labeling

CERN Document Server

Palmer, Martha; Xue, Nianwen

2011-01-01

This book is aimed at providing an overview of several aspects of semantic role labeling. Chapter 1 begins with linguistic background on the definition of semantic roles and the controversies surrounding them. Chapter 2 describes how the theories have led to structured lexicons such as FrameNet, VerbNet and the PropBank Frame Files that in turn provide the basis for large scale semantic annotation of corpora. This data has facilitated the development of automatic semantic role labeling systems based on supervised machine learning techniques. Chapter 3 presents the general principles of applyin

Application of Principal Component Analysis in Assessment of Relation Between the Parameters of Technological Quality of Wheat Grains Treated with Inert Dusts Against Rice Weevil (Sitophilus oryzae L.

Directory of Open Access Journals (Sweden)

Marija Bodroža-Solarov

2011-01-01

Full Text Available Quality parameters of several wheat grain lots (low vitreous and high vitreous grains,non-infested and infested with rice weevils, (Sitophilus oryzae L. treated with inert dusts(natural zeolite, two diatomaceous earths originating from Serbia and a commercial productProtect-It® were investigated. Principal component analysis (PCA was used to investigatethe classification of treated grain lots and to assess how attributes of technological qualitycontribute to this classification. This research showed that vitreousness (0.95 and test weight(0.93 contributed most to the first principal component whereas extensigraph area (-0.76contributed to the second component. The determined accountability of the total variabilityby the first component was around 55%, while with the second it was 18%, which meansthat those two dimensions together account for around 70% of total variability of the observedset of variables. Principal component analysis (PCA of data set was able to distinguishamong the various treatments of wheat lots. It was revealed that inert dust treatments producedifferent effects depending on the degree of endosperm vitreousness.

Representations for Semantic Learning Webs: Semantic Web Technology in Learning Support

Science.gov (United States)

Dzbor, M.; Stutt, A.; Motta, E.; Collins, T.

2007-01-01

Recent work on applying semantic technologies to learning has concentrated on providing novel means of accessing and making use of learning objects. However, this is unnecessarily limiting: semantic technologies will make it possible to develop a range of educational Semantic Web services, such as interpretation, structure-visualization, support…

Combining multiple regression and principal component analysis for accurate predictions for column ozone in Peninsular Malaysia

Science.gov (United States)

Rajab, Jasim M.; MatJafri, M. Z.; Lim, H. S.

2013-06-01

This study encompasses columnar ozone modelling in the peninsular Malaysia. Data of eight atmospheric parameters [air surface temperature (AST), carbon monoxide (CO), methane (CH4), water vapour (H2Ovapour), skin surface temperature (SSKT), atmosphere temperature (AT), relative humidity (RH), and mean surface pressure (MSP)] data set, retrieved from NASA's Atmospheric Infrared Sounder (AIRS), for the entire period (2003-2008) was employed to develop models to predict the value of columnar ozone (O3) in study area. The combined method, which is based on using both multiple regressions combined with principal component analysis (PCA) modelling, was used to predict columnar ozone. This combined approach was utilized to improve the prediction accuracy of columnar ozone. Separate analysis was carried out for north east monsoon (NEM) and south west monsoon (SWM) seasons. The O3 was negatively correlated with CH4, H2Ovapour, RH, and MSP, whereas it was positively correlated with CO, AST, SSKT, and AT during both the NEM and SWM season periods. Multiple regression analysis was used to fit the columnar ozone data using the atmospheric parameter's variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to acquire subsets of the predictor variables to be comprised in the linear regression model of the atmospheric parameter's variables. It was found that the increase in columnar O3 value is associated with an increase in the values of AST, SSKT, AT, and CO and with a drop in the levels of CH4, H2Ovapour, RH, and MSP. The result of fitting the best models for the columnar O3 value using eight of the independent variables gave about the same values of the R (≈0.93) and R2 (≈0.86) for both the NEM and SWM seasons. The common variables that appeared in both regression equations were SSKT, CH4 and RH, and the principal precursor of the columnar O3 value in both the NEM and SWM seasons was SSKT.

Diagnose Test-Taker's Profile in Terms of Core Profile Patterns: Principal Component (PC) vs. Profile Analysis via MDS (PAMS) Approaches.

Science.gov (United States)

Kim, Se-Kang; Davison, Mark L.

A study was conducted to examine how principal components analysis (PCA) and Profile Analysis via Multidimensional Scaling (PAMS) can be used to diagnose individuals observed score profiles in terms of core profile patterns identified by each method. The standardization sample from the Wechsler Intelligence Scale for Children, Third Edition…

Topographical gradients of semantics and phonology revealed by temporal lobe stimulation.

Science.gov (United States)

Miozzo, Michele; Williams, Alicia C; McKhann, Guy M; Hamberger, Marla J

2017-02-01

Word retrieval is a fundamental component of oral communication, and it is well established that this function is supported by left temporal cortex. Nevertheless, the specific temporal areas mediating word retrieval and the particular linguistic processes these regions support have not been well delineated. Toward this end, we analyzed over 1000 naming errors induced by left temporal cortical stimulation in epilepsy surgery patients. Errors were primarily semantic (lemon → "pear"), phonological (horn → "corn"), non-responses, and delayed responses (correct responses after a delay), and each error type appeared predominantly in a specific region: semantic errors in mid-middle temporal gyrus (TG), phonological errors and delayed responses in middle and posterior superior TG, and non-responses in anterior inferior TG. To the extent that semantic errors, phonological errors and delayed responses reflect disruptions in different processes, our results imply topographical specialization of semantic and phonological processing. Specifically, results revealed an inferior-to-superior gradient, with more superior regions associated with phonological processing. Further, errors were increasingly semantically related to targets toward posterior temporal cortex. We speculate that detailed semantic input is needed to support phonological retrieval, and thus, the specificity of semantic input increases progressively toward posterior temporal regions implicated in phonological processing. Hum Brain Mapp 38:688-703, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Developing a semantic web model for medical differential diagnosis recommendation.

Science.gov (United States)

Mohammed, Osama; Benlamri, Rachid

2014-10-01

In this paper we describe a novel model for differential diagnosis designed to make recommendations by utilizing semantic web technologies. The model is a response to a number of requirements, ranging from incorporating essential clinical diagnostic semantics to the integration of data mining for the process of identifying candidate diseases that best explain a set of clinical features. We introduce two major components, which we find essential to the construction of an integral differential diagnosis recommendation model: the evidence-based recommender component and the proximity-based recommender component. Both approaches are driven by disease diagnosis ontologies designed specifically to enable the process of generating diagnostic recommendations. These ontologies are the disease symptom ontology and the patient ontology. The evidence-based diagnosis process develops dynamic rules based on standardized clinical pathways. The proximity-based component employs data mining to provide clinicians with diagnosis predictions, as well as generates new diagnosis rules from provided training datasets. This article describes the integration between these two components along with the developed diagnosis ontologies to form a novel medical differential diagnosis recommendation model. This article also provides test cases from the implementation of the overall model, which shows quite promising diagnostic recommendation results.

UML 2 Semantics and Applications

CERN Document Server

Lano, Kevin

2009-01-01

A coherent and integrated account of the leading UML 2 semantics work and the practical applications of UML semantics development With contributions from leading experts in the field, the book begins with an introduction to UML and goes on to offer in-depth and up-to-date coverage of: The role of semantics Considerations and rationale for a UML system model Definition of the UML system model UML descriptive semantics Axiomatic semantics of UML class diagrams The object constraint language Axiomatic semantics of state machines A coalgebraic semantic framework for reasoning about interaction des

Principal components analysis of an evaluation of the hemiplegic subject based on the Bobath approach.

Science.gov (United States)

Corriveau, H; Arsenault, A B; Dutil, E; Lepage, Y

1992-01-01

An evaluation based on the Bobath approach to treatment has previously been developed and partially validated. The purpose of the present study was to verify the content validity of this evaluation with the use of a statistical approach known as principal components analysis. Thirty-eight hemiplegic subjects participated in the study. Analysis of the scores on each of six parameters (sensorium, active movements, muscle tone, reflex activity, postural reactions, and pain) was evaluated on three occasions across a 2-month period. Each time this produced three factors that contained 70% of the variation in the data set. The first component mainly reflected variations in mobility, the second mainly variations in muscle tone, and the third mainly variations in sensorium and pain. The results of such exploratory analysis highlight the fact that some of the parameters are not only important but also interrelated. These results seem to partially support the conceptual framework substantiating the Bobath approach to treatment.

Automotive Exterior Noise Optimization Using Grey Relational Analysis Coupled with Principal Component Analysis

Science.gov (United States)

Chen, Shuming; Wang, Dengfeng; Liu, Bo

This paper investigates optimization design of the thickness of the sound package performed on a passenger automobile. The major characteristics indexes for performance selected to evaluate the processes are the SPL of the exterior noise and the weight of the sound package, and the corresponding parameters of the sound package are the thickness of the glass wool with aluminum foil for the first layer, the thickness of the glass fiber for the second layer, and the thickness of the PE foam for the third layer. In this paper, the process is fundamentally with multiple performances, thus, the grey relational analysis that utilizes grey relational grade as performance index is especially employed to determine the optimal combination of the thickness of the different layers for the designed sound package. Additionally, in order to evaluate the weighting values corresponding to various performance characteristics, the principal component analysis is used to show their relative importance properly and objectively. The results of the confirmation experiments uncover that grey relational analysis coupled with principal analysis methods can successfully be applied to find the optimal combination of the thickness for each layer of the sound package material. Therefore, the presented method can be an effective tool to improve the vehicle exterior noise and lower the weight of the sound package. In addition, it will also be helpful for other applications in the automotive industry, such as the First Automobile Works in China, Changan Automobile in China, etc.

Application of Principal Component Analysis (PCA) to Reduce Multicollinearity Exchange Rate Currency of Some Countries in Asia Period 2004-2014

Science.gov (United States)

Rahayu, Sri; Sugiarto, Teguh; Madu, Ludiro; Holiawati; Subagyo, Ahmad

2017-01-01

This study aims to apply the model principal component analysis to reduce multicollinearity on variable currency exchange rate in eight countries in Asia against US Dollar including the Yen (Japan), Won (South Korea), Dollar (Hong Kong), Yuan (China), Bath (Thailand), Rupiah (Indonesia), Ringgit (Malaysia), Dollar (Singapore). It looks at yield…

Automatic scatter detection in fluorescence landscapes by means of spherical principal component analysis

DEFF Research Database (Denmark)

Kotwa, Ewelina Katarzyna; Jørgensen, Bo Munk; Brockhoff, Per B.

2013-01-01

In this paper, we introduce a new method, based on spherical principal component analysis (S‐PCA), for the identification of Rayleigh and Raman scatters in fluorescence excitation–emission data. These scatters should be found and eliminated as a prestep before fitting parallel factor analysis...... models to the data, in order to avoid model degeneracies. The work is inspired and based on a previous research, where scatter removal was automatic (based on a robust version of PCA called ROBPCA) and required no visual data inspection but appeared to be computationally intensive. To overcome...... this drawback, we implement the fast S‐PCA in the scatter identification routine. Moreover, an additional pattern interpolation step that complements the method, based on robust regression, will be applied. In this way, substantial time savings are gained, and the user's engagement is restricted to a minimum...

Post annealing performance evaluation of printable interdigital capacitive sensors by principal component analysis

KAUST Repository

Zia, Asif Iqbal

2015-06-01

The surface roughness of thin-film gold electrodes induces instability in impedance spectroscopy measurements of capacitive interdigital printable sensors. Post-fabrication thermodynamic annealing was carried out at temperatures ranging from 30 °C to 210 °C in a vacuum oven and the variation in surface morphology of thin-film gold electrodes was observed by scanning electron microscopy. Impedance spectra obtained at different temperatures were translated into equivalent circuit models by applying complex nonlinear least square curve-fitting algorithm. Principal component analysis was applied to deduce the classification of the parameters affected due to the annealing process and to evaluate the performance stability using mathematical model. Physics of the thermodynamic annealing was discussed based on the surface activation energies. The post anneal testing of the sensors validated the achieved stability in impedance measurement. © 2001-2012 IEEE.

Post annealing performance evaluation of printable interdigital capacitive sensors by principal component analysis

KAUST Repository

Zia, Asif Iqbal; Mukhopadhyay, Subhas Chandra; Yu, Paklam; Al-Bahadly, Ibrahim H.; Gooneratne, Chinthaka Pasan; Kosel, Jü rgen

2015-01-01

The surface roughness of thin-film gold electrodes induces instability in impedance spectroscopy measurements of capacitive interdigital printable sensors. Post-fabrication thermodynamic annealing was carried out at temperatures ranging from 30 °C to 210 °C in a vacuum oven and the variation in surface morphology of thin-film gold electrodes was observed by scanning electron microscopy. Impedance spectra obtained at different temperatures were translated into equivalent circuit models by applying complex nonlinear least square curve-fitting algorithm. Principal component analysis was applied to deduce the classification of the parameters affected due to the annealing process and to evaluate the performance stability using mathematical model. Physics of the thermodynamic annealing was discussed based on the surface activation energies. The post anneal testing of the sensors validated the achieved stability in impedance measurement. © 2001-2012 IEEE.

Meinongian Semantics and Artificial Intelligence

Directory of Open Access Journals (Sweden)

William J. Rapaport

2013-12-01

Full Text Available This essay describes computational semantic networks for a philosophical audience and surveys several approaches to semantic-network semantics. In particular, propositional semantic networks (exemplified by SNePS are discussed; it is argued that only a fully intensional, Meinongian semantics is appropriate for them; and several Meinongian systems are presented.

Geospatial Semantics and the Semantic Web

CERN Document Server

Ashish, Naveen

2011-01-01

The availability of geographic and geospatial information and services, especially on the open Web has become abundant in the last several years with the proliferation of online maps, geo-coding services, geospatial Web services and geospatially enabled applications. The need for geospatial reasoning has significantly increased in many everyday applications including personal digital assistants, Web search applications, local aware mobile services, specialized systems for emergency response, medical triaging, intelligence analysis and more. Geospatial Semantics and the Semantic Web: Foundation

Improving power output of inertial energy harvesters by employing principal component analysis of input acceleration

Science.gov (United States)

Smilek, Jan; Hadas, Zdenek

2017-02-01

In this paper we propose the use of principal component analysis to process the measured acceleration data in order to determine the direction of acceleration with the highest variance on given frequency of interest. This method can be used for improving the power generated by inertial energy harvesters. Their power output is highly dependent on the excitation acceleration magnitude and frequency, but the axes of acceleration measurements might not always be perfectly aligned with the directions of movement, and therefore the generated power output might be severely underestimated in simulations, possibly leading to false conclusions about the feasibility of using the inertial energy harvester for the examined application.

Infrared and visible image fusion based on robust principal component analysis and compressed sensing

Science.gov (United States)

Li, Jun; Song, Minghui; Peng, Yuanxi

2018-03-01

Current infrared and visible image fusion methods do not achieve adequate information extraction, i.e., they cannot extract the target information from infrared images while retaining the background information from visible images. Moreover, most of them have high complexity and are time-consuming. This paper proposes an efficient image fusion framework for infrared and visible images on the basis of robust principal component analysis (RPCA) and compressed sensing (CS). The novel framework consists of three phases. First, RPCA decomposition is applied to the infrared and visible images to obtain their sparse and low-rank components, which represent the salient features and background information of the images, respectively. Second, the sparse and low-rank coefficients are fused by different strategies. On the one hand, the measurements of the sparse coefficients are obtained by the random Gaussian matrix, and they are then fused by the standard deviation (SD) based fusion rule. Next, the fused sparse component is obtained by reconstructing the result of the fused measurement using the fast continuous linearized augmented Lagrangian algorithm (FCLALM). On the other hand, the low-rank coefficients are fused using the max-absolute rule. Subsequently, the fused image is superposed by the fused sparse and low-rank components. For comparison, several popular fusion algorithms are tested experimentally. By comparing the fused results subjectively and objectively, we find that the proposed framework can extract the infrared targets while retaining the background information in the visible images. Thus, it exhibits state-of-the-art performance in terms of both fusion effects and timeliness.

Principal component analysis of air particulate data from the industrial area of islamabad, pakistan

International Nuclear Information System (INIS)

Waheed, S.; Siddique, N.; Daud, M.

2008-01-01

A Gent air sampler was used to collect 72 pairs of size fractionated coarse and fine (PM/sub 10/ and PM/sub 2.5/) particulate mass samples from the industrial zone (sector I-9) of Islamabad. These samples were analyzed for their elemental composition using Instrumental Neutron Activation Analysis (INAA). Principal component analysis (PCA), which can be used for source apportionment of quantified elemental data, was used to interpret the data. Graphical representations of loadings were used to explain the data through grouping of the elements from same source. The present work shows well defined elemental fingerprints of suspended soil and road dust, industry, motor vehicle exhaust and tyres, and coal and refuses combustions for the studied locality of Islamabad. (author)

Graph Mining Meets the Semantic Web

Energy Technology Data Exchange (ETDEWEB)

Lee, Sangkeun (Matt) [ORNL; Sukumar, Sreenivas R [ORNL; Lim, Seung-Hwan [ORNL

2015-01-01

The Resource Description Framework (RDF) and SPARQL Protocol and RDF Query Language (SPARQL) were introduced about a decade ago to enable flexible schema-free data interchange on the Semantic Web. Today, data scientists use the framework as a scalable graph representation for integrating, querying, exploring and analyzing data sets hosted at different sources. With increasing adoption, the need for graph mining capabilities for the Semantic Web has emerged. We address that need through implementation of three popular iterative Graph Mining algorithms (Triangle count, Connected component analysis, and PageRank). We implement these algorithms as SPARQL queries, wrapped within Python scripts. We evaluate the performance of our implementation on 6 real world data sets and show graph mining algorithms (that have a linear-algebra formulation) can indeed be unleashed on data represented as RDF graphs using the SPARQL query interface.

Support vector machine and principal component analysis for microarray data classification