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Subject = principal component analysis;
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Displaying Results 1  14 of 14 on page 1 of 1
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24h electrical load data — a sequential or partitioned time series?
(2003)
Fay, Damien; Ringwood, John; Condon, Marissa; Kelly, Michael
24h electrical load data — a sequential or partitioned time series?
(2003)
Fay, Damien; Ringwood, John; Condon, Marissa; Kelly, Michael
Abstract:
Variations in electrical load are, among other things, hour of the day dependent, introducing a dilemma for the forecaster: whether to partition the data and use a separate model for each hour of the day (the parallel approach), or use a single model (the sequential approach). This paper examines which approach is appropriate for forecasting hourly electrical load in Ireland. It is found that, with the exception of some hours of the day, the sequential approach is superior. The final solution however, uses a combination of linear sequential and parallel neural models in a multitime scale formulation.
http://mural.maynoothuniversity.ie/9504/
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24Hour Electrical Load Data  A Time Series or a Set of Independent Points?
(2001)
Fay, Damien; Ringwood, John; Condon, Marissa; Kelly, Michael
24Hour Electrical Load Data  A Time Series or a Set of Independent Points?
(2001)
Fay, Damien; Ringwood, John; Condon, Marissa; Kelly, Michael
Abstract:
The paper investigates whether a time series or a set of independent points is a more appropriate description of 24hour Irish electrical load data. A set of independent points means that the load at each hour of the day is independent from the load at any other hour. The data is first split into 24 series, one for each hour of the day i.e. a 1am 2am 3am series etc. These are called parallel series. The linear crosscorrelation's of the parallel series are used to indicate independence. While the loads at 9am and 6pm to 8pm appear independent the remaining loads are highly intercorrelated. This suggests that 24hour electrical load data has a dual nature. Two techniques are used to test this hypothesis. The first technique models each parallel series using neural networks. This technique is found to be computationally expensive. The second technique uses a hybrid technique called the Multi Time Scale (MTS) technique. This models 24hour electrical load data as a time series th...
http://mural.maynoothuniversity.ie/1967/
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Classification of Human Poses using a Vision based Technique
(2007)
Kelly, Dan; Olivo, Paolo; Markham, Charles; McDonald, John; Caulfield, Brian; Fitzgeral...
Classification of Human Poses using a Vision based Technique
(2007)
Kelly, Dan; Olivo, Paolo; Markham, Charles; McDonald, John; Caulfield, Brian; Fitzgerald, Diarmuid
Abstract:
This paper presents work being carried out to estimate human pose using vision based methods. The data acquisition system uses an active marker technique synchronized with a three camera stereo vision system. The locations of the markers are then used to reconstruct a skeleton representation of the human pose. PCA and clustering techniques are used to classify the pose.
http://mural.maynoothuniversity.ie/8348/
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Filtered Gaussian Processes for Learning with Large DataSets
(2005)
Shi, Jian Qing; MurraySmith, Roderick; Titterington, D. Mike; Pearlmutter, Barak A.
Filtered Gaussian Processes for Learning with Large DataSets
(2005)
Shi, Jian Qing; MurraySmith, Roderick; Titterington, D. Mike; Pearlmutter, Barak A.
Abstract:
Kernelbased nonparametric models have been applied widely over recent years. However, the associated computational complexity imposes limitations on the applicability of those methods to problems with large datasets. In this paper we develop a filtering approach based on a Gaussian process regression model. The idea is to generate a smalldimensional set of filtered data that keeps a high proportion of the information contained in the original large dataset. Model learning and prediction are based on the filtered data, thereby decreasing the computational burden dramatically.
http://mural.maynoothuniversity.ie/2511/
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Generating realistic, animated human gestures in order to model, analyse and recognize Irish Sign Language
(2009)
Péporté, Michèle
Generating realistic, animated human gestures in order to model, analyse and recognize Irish Sign Language
(2009)
Péporté, Michèle
Abstract:
The aim of this thesis is to generate a gesture recognition system which can recognize several signs of Irish Sign Language (ISL). This project is divided into three parts. The first part provides background information on ISL. An overview of the ISL structure is a prerequisite to identifying and understanding the difficulties encountered in the development of a recognition system. The second part involves the generation of a data repository: synthetic and realtime video. Initially the synthetic data is created in a 3D animation package in order to simplify the creation of motion variations of the animated signer. The animation environment in our implementation allows for the generation of different versions of the same gesture with slight variations in the parameters of the motion. Secondly a database of ISL realtime video was created. This database contains 1400 different signs, including motion variation in each gesture. The third part details step by step my novel classificati...
http://doras.dcu.ie/14887/
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Handshape recognition using principal component analysis and convolutional neural networks applied to sign language
(2018)
Oliveira, Marlon
Handshape recognition using principal component analysis and convolutional neural networks applied to sign language
(2018)
Oliveira, Marlon
Abstract:
Handshape recognition is an important problem in computer vision with significant societal impact. However, it is not an easy task, since hands are naturally deformable objects. Handshape recognition contains open problems, such as low accuracy or low speed, and despite a large number of proposed approaches, no solution has been found to solve these open problems. In this thesis, a new image dataset for Irish Sign Language (ISL) recognition is introduced. A deeper study using only 2D images is presented on Principal Component Analysis (PCA) in two stages. A comparison between approaches that do not need features (known as endtoend) and featurebased approaches is carried out. The dataset was collected by filming six human subjects performing ISL handshapes and movements. Frames from the videos were extracted. Afterwards the redundant images were filtered with an iterative image selection process that selects the images which keep the dataset diverse. The accuracy of PCA can be i...
http://doras.dcu.ie/22191/
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Independent Subspace Analysis using Locally Linear Embedding
(2003)
Fitzgerald, Derry; Lawlor, Bob; Coyle, Eugene
Independent Subspace Analysis using Locally Linear Embedding
(2003)
Fitzgerald, Derry; Lawlor, Bob; Coyle, Eugene
Abstract:
While Independent Subspace Analysis provides a means of blindly separating sound sources from a single channel signal, it does have a number of problems. In particular the amount of information required for separation of sources varies with the signal. This is as a result of the variancebased nature of Principal Component Analysis, which is used for dimensional reduction in the Independent Subspace Analysis algorithm. In an attempt to overcome this problem the use of a nonvariance based dimensional reduction method, Locally Linear Embedding, is proposed. Locally Linear Embedding is a geometry based dimensional reduction technique. The use of this approach is demonstrated by its application to single channel source separation, and its merits discussed.
http://mural.maynoothuniversity.ie/694/
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Labelfree discrimination analysis of dedifferentiated vascular smooth muscle cells, mesenchymal stem cells and their vascular and osteogenic progeny using vibrational spectroscopy
(2018)
Molony, Claire; McIntyre, Jennifer; Maguire, Adrian; Hakimjavadi, Roya; Burtenshaw, Den...
Labelfree discrimination analysis of dedifferentiated vascular smooth muscle cells, mesenchymal stem cells and their vascular and osteogenic progeny using vibrational spectroscopy
(2018)
Molony, Claire; McIntyre, Jennifer; Maguire, Adrian; Hakimjavadi, Roya; Burtenshaw, Denise; Casey, Gillian; Luca, Mariana Di; Hennelly, Bryan M.; Byrne, Hugh J.; Cahill, Paul A.
Abstract:
The accumulation of vascular smooth muscle (SMC)like cells and stem cellderived myogenic and osteogenic progeny contributes significantly to arteriosclerotic disease. This study established whether labelfree vibrational spectroscopy can discriminate dedifferentiated ‘synthetic’ SMCs from undifferentiated stem cells and their myogenic and osteogenic progeny in vitro, compared with conventional immunocytochemical and genetic analyses. TGFβ1 and Jagged1induced myogenic differentiation of CD44+ mesenchymal stem cells was confirmed in vitro by immunocytochemical analysis of specific SMC differentiation marker expression (αactin, calponin and myosin heavy chain 11), an epigenetic histone mark (H3K4me2) at the myosin heavy chain 11 locus, promoter transactivation and mRNA transcript levels. Osteogenic differentiation was confirmed by alizarin red staining of calcium deposition. Fourier Transform Infrared (FTIR) maps facilitated initial screening and discrimination while Raman spect...
http://mural.maynoothuniversity.ie/13132/
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Matrix Factorisation Techniques for Endpoint Detection in Plasma Etching
(2008)
Ragnoli, Emanuele; McLoone, Seamus; Ringwood, John; Macgerailt, N.
Matrix Factorisation Techniques for Endpoint Detection in Plasma Etching
(2008)
Ragnoli, Emanuele; McLoone, Seamus; Ringwood, John; Macgerailt, N.
Abstract:
Advanced data mining techniques such as variable selection through matrix factorization have been intensively applied in the last ten years in the area of plasmaetch point detection using optimal emission spectroscopy (OES). OES data sets are enormous, consisting of measurements of over 2000 wavelength recorded at sample rates of 1  3 Hertz, and consequently, these techniques are needed in order to generate compact representations of the relevant process characteristics. To date, the main technique employed in this regard has been PCA (Principal Components Analysis), a matrix factorisation technique which generates linear combinations of the original variables that best capture the information in the data (in terms of variance explained). Recently, an alternative matrix factorisation technique, NonNegative Matrix Factorisation (NMF) [1], has been gaining increasing attention in the fields of image feature extraction and blind source separation due to its tendency to yield sparse ...
http://mural.maynoothuniversity.ie/8838/
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Maximizing Positive Porfolio Diversification
(2014)
Maguire, Phil; Moser, Philippe; O'Reilly, Kieran; McMenamin, Conor; Kelly, Robert;...
Maximizing Positive Porfolio Diversification
(2014)
Maguire, Phil; Moser, Philippe; O'Reilly, Kieran; McMenamin, Conor; Kelly, Robert; Maguire, Rebecca
Abstract:
In this article we introduce a new strategy for optimal diversification which combines elements of Diversified Risk Parity [1], [2] and Diversification Ratio [3], with emphasis on positive risk premiums. The Uncorrelated Positive Bets strategy involves the identification of reliable, independent sources of randomness and the quantification of their positive risk premium.We use principal component analysis to identify the most significant sources of randomness contributing to the market and then apply the Randomness Deficiency Coefficient metric [4] and principal portfolio positivity to identify a set of reliable uncorrelated positive bets. Portfolios are then optimized by maximizing their diversified positive risk premium. We contrast the performance of a range of diversification strategies for a portfolio held for a twoyear outofsample period with a 30 stock constraint. In particular, we introduce the notion of diversification inefficiency to explain why diversification strategi...
http://mural.maynoothuniversity.ie/6526/
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NonLinear Approaches for the Classification of Facial Expressions at Varying Degrees of Intensity
(2007)
Reilly, Jane; Ghent, John; McDonald, John
NonLinear Approaches for the Classification of Facial Expressions at Varying Degrees of Intensity
(2007)
Reilly, Jane; Ghent, John; McDonald, John
Abstract:
The research discussed in this paper documents a comparative analysis of two nonlinear dimensionality reduction techniques for the classification of facial expressions at varying degrees of intensity. These nonlinear dimensionality reduction techniques are Kernel Principal Component Analysis (KPCA) and Locally Linear Embedding (LLE). The approaches presented in this paper employ psychological tools, computer vision techniques and machine learning algorithms. In this paper we concentrate on comparing the performance of these two techniques when combined with Support Vector Machines (SVMs) at the task of classifying facial expressions across the full expression intensity range from nearneutral to extreme facial expression. Receiver Operating Characteristic (ROC) curve analysis is employed as a means of comprehensively comparing the results of these techniques.
http://mural.maynoothuniversity.ie/8345/
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Nonlinearity reduction of manifolds using Gaussian blur for handshape recognition based on multidimensional grids
(2013)
Farouk, Mohamed; Sutherland, Alistair; Shoukry, Amin A.
Nonlinearity reduction of manifolds using Gaussian blur for handshape recognition based on multidimensional grids
(2013)
Farouk, Mohamed; Sutherland, Alistair; Shoukry, Amin A.
Abstract:
This paper presents a handshape recognition algorithm based on using multidimensional grids (MDGs) to divide the feature space of a set of hand images. Principal Component Analysis (PCA) is used as a feature extraction and dimensionality reduction method to generate eigenspaces from example images. Images are blurred by convolving with a Gaussian kernel as a low pass filter. Image blurring is used to reduce the nonlinearity in the manifolds within the eigenspaces where MDG structure can be used to divide the spaces linearly. The algorithm is invariant to linear transformations like rotation and translation. Computer generated images for different handshapes in Irish Sign Language are used in testing. Experimental results show accuracy and performance of the proposed algorithm in terms of blurring level and MDG size.
http://doras.dcu.ie/19305/
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Principal component pyramids using image blurring for nonlinearity reduction in hand shape recognition
(2015)
Farouk, Mohamed
Principal component pyramids using image blurring for nonlinearity reduction in hand shape recognition
(2015)
Farouk, Mohamed
Abstract:
The thesis presents four algorithms using a multistage hierarchical strategy for hand shape recognition. The proposed multistage hierarchy analyzes new patterns by projecting them into the different levels of a data pyramid, which consists of different principal component spaces. Image blurring is used to reduce the nonlinearity in manifolds generated by a set of example images. Flattening the space helps in classifying different hand shapes more accurately. Four algorithms using different pattern recognition techniques are proposed. The first algorithm is based on using perpendicular distance to measure the distance between new patterns and the nearest manifold. The second algorithm is based on using supervised multidimensional grids. The third algorithm uses unsupervised multidimensional grids to cluster the space into cells of similar objects. The fourth algorithm is based on training a set of simple architecture multilayer neural networks at the different levels of the pyramid ...
http://doras.dcu.ie/20432/
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State Estimation for the VASIMR Plasma Engine
(2008)
Lynn, Shane; Ringwood, John; Del Valle Gamboa, Juan Ignacio
State Estimation for the VASIMR Plasma Engine
(2008)
Lynn, Shane; Ringwood, John; Del Valle Gamboa, Juan Ignacio
Abstract:
This paper presents work on the application of virtual metrology techniques to the VAriable Specific Impulse Magnetoplasma Rocket (VASMIR) engine. The work concentrates on the estimation of internal temperatures of the rocket using state space models and Optical Emission Spectroscopy (OES). These estimations are useful as direct thermal measurements will not be available in the final system design.
http://mural.maynoothuniversity.ie/1937/
Displaying Results 1  14 of 14 on page 1 of 1
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Institution
Dublin City University (4)
Maynooth University (10)
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Book chapter (4)
Conference item (2)
Journal article (4)
Other (4)
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Peerreviewed (9)
Nonpeerreviewed (1)
Unknown (4)
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2018 (2)
2015 (1)
2014 (1)
2013 (1)
2009 (1)
2008 (2)
2007 (2)
2005 (1)
2003 (2)
2001 (1)
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