Number Of Data Samples Research Articles

Practical Conditions for Effectiveness of the Universum Learning

Many applications of machine learning involve analysis of sparse high-dimensional data, in which the number of input features is larger than the number of data samples. Standard inductive learning methods may not be sufficient for such data, and this provides motivation for nonstandard learning settings. This paper investigates a new learning methodology called learning through contradictions or Universum support vector machine (U-SVM). U-SVM incorporates a priori knowledge about application data, in the form of additional Universum samples, into the learning process. This paper investigates possible advantages of U-SVM versus standard SVM, and describes the practical conditions necessary for the effectiveness of the U-SVM. These conditions are based on the analysis of the univariate histograms of projections of training samples onto the normal direction vector of (standard) SVM decision boundary. Several empirical comparisons are presented to illustrate the practical utility of the proposed approach.

Numerical modelling of elastic behaviour of concrete reinforced with steel short fies in plane stress conditions

This work describes a numerical model of fie reinforced concrete elastic behaviour implemented using the finite elements method (Hughes, 2000). In structures made of this material, each point is formed by steel fies embedded into a simple concrete matrix. The reinforced concrete is represented inside a finite element as an orthotropic material having random material direction based on the vanishing diameter fie model (Dvorak and Bahei-el-Din, 1982) and the mixing theory modified for short length reinforcement (Oller, 2003). Statistical analysis consisted of repeating the problem's numerical simulation where the direction of fies was modified by a random function to set up a sampling database from the results and measure their variability. A sensitivity study of finite element size and the number of sampling data was then carried out in terms of total strain energy. Finite element size and sampling data are recommended. The average structural response of a reinforced concrete beam with different quantities of steel fies where minimum data dispersion was observed is given as an example of applying the above.

A Semidefinite Relaxation Approach to Spreading Sequence Estimation for DS-SS Signals

SUMMARY In non-cooperative scenarios, the estimation of direct sequence spread spectrum (DS-SS) signals has to be done in a blind manner. In this letter, we consider the spreading sequence estimation problem for DS-SS signals. First, the maximum likelihood estimate (MLE) of spreading sequence is derived, then a semidefinite relaxation (SDR) approach is proposed to cope with the exponential complexity of performing MLE. Simulation results demonstrate that the proposed approach provides significant performance improvements compared to existing methods, especially in the case of low numbers of data samples and low signal-to-noise ratio

Covariance-based approaches to aeroacoustic noise source analysis

In this paper, several covariance-based approaches are proposed for aeroacoustic noise source analysis under the assumptions of a single dominant source and all observers contaminated solely by uncorrelated noise. The Cramér-Rao Bounds (CRB) of the unbiased source power estimates are also derived. The proposed methods are evaluated using both simulated data as well as data acquired from an airfoil trailing edge noise experiment in an open-jet aeroacoustic facility. The numerical examples show that the covariance-based algorithms significantly outperform an existing least-squares approach and provide accurate power estimates even under low signal-to-noise ratio (SNR) conditions. Furthermore, the mean-squared-errors (MSEs) of the so-obtained estimates are close to the corresponding CRB especially for a large number of data samples. The experimental results show that the power estimates of the proposed approaches are consistent with one another as long as the core analysis assumptions are obeyed.

A New Method of Neurofuzzy Network Based on Variable Precision Rough

In view of the current application deficiency of neuro-fuzzy network, a new optimal method of neurofuzzy network based on variable precision rough set is presented and its application in complex systems modeling is discussed. This method takes the β classification accuracy of variable precision rough set theory as information function to select the condition attribute, and then modeling data are discredited through selecting a proper precision to forms a decision table. Finally, the significant attributes and the key attribute values are extract from the decision table by using reduction algorithm based on variable precision, and are map pad into the fuzzy rules.It simplifies the fuzzy rules and therefore optimize the structure of neuro- fuzzy network effectively, reducing the training time of neural network greatly and improving the precision of training. This method has been applied to the modeling of non-linear time-delay system with a large number of sam-pling data, the validity and feasibility of this method is demonstrated by an example of modeling.

Complete neighborhood preserving embedding for face recognition

Neighborhood preserving embedding (NPE) is a linear approximation to the locally linear embedding algorithm which can preserve the local neighborhood structure on the data manifold. However, in typical face recognition where the number of data samples is smaller than the dimension of data space, it is difficult to directly apply NPE to high dimensional matrices because of computational complexity. Moreover, in such case, NPE often suffers from the singularity problem of eigenmatrix, which makes the direct implementation of the NPE algorithm almost impossible. In practice, principal component analysis or singular value decomposition is applied as a preprocessing step to attack these problems. Nevertheless, this strategy may discard dimensions that contain important discriminative information and the eigensystem computation of NPE could be unstable. Towards a practical dimensionality reduction method for face data, we develop a new scheme in this paper, namely, the complete neighborhood preserving embedding (CNPE). CNPE transforms the singular generalized eigensystem computation of NPE into two eigenvalue decomposition problems. Moreover, a feasible and effective procedure is proposed to alleviate the computational burden of high dimensional matrix for typical face image data. Experimental results on the ORL face database and the Yale face database show that the proposed CNPE algorithm achieves better performance than other feature extraction methods, such as Eigenfaces, Fisherfaces and NPE, etc.

An incremental dimensionality reduction method on discriminant information for pattern classification

From the view of classification, linear discriminant analysis (LDA) is a proper dimensionality reduction method which finds an optimal linear transformation that maximizes the class separability. However it is difficult to apply LDA in under sampled problems where the number of data samples is smaller than the dimensionality of data space, due to the singularity of scatter matrices caused by high-dimensionality. In order to make LDA applicable, we propose a new dimensionality reduction algorithm called discriminant multidimensional mapping (DMM), which combines the advantages of multidimensional scaling (MDS) and LDA. DMM is effective for small sample datasets with high-dimensionality. Its superiority is given from theoretical point of view. Then we extend DMM for large datasets and datasets with non-linear manifold respectively, and get two algorithms: landmark DMM (LDMM) and geodesic–metric discriminant mapping (GDM). The performances of these algorithms are also shown by preliminary numerical experiments.

Behavior Detection Using Confidence Intervals of Hidden Markov Models

Markov models are commonly used to analyze real-world problems. Their combination of discrete states and stochastic transitions is suited to applications with deterministic and stochastic components. Hidden Markov models (HMMs) are a class of Markov models commonly used in pattern recognition. Currently, HMMs recognize patterns using a maximum-likelihood approach. One major drawback with this approach is that data observations are mapped to HMMs without considering the number of data samples available. Another problem is that this approach is only useful for choosing between HMMs. It does not provide a criterion for determining whether or not a given HMM adequately matches the data stream. In this paper, we recognize complex behaviors using HMMs and confidence intervals. The certainty of a data match increases with the number of data samples considered. Receiver operating characteristic curves are used to find the optimal threshold for either accepting or rejecting an HMM description. We present one example using a family of HMMs to show the utility of the proposed approach. A second example using models extracted from a database of consumer purchases provides additional evidence that this approach can perform better than existing techniques.

The Road Pollution Impact on Zea mays: Inductive Modeling and Qualitative Assessment

The present paper describes the results of experiments with measurements of heavy metals concentrations in soil, leaves and roots of Zea mays cultivated near the major road in the area Araxos, Greece. The chain of ecological interactions is decomposed into individual subsystems with sufficiently small number of data samples describing concentrations of elements. The GMDH-based regression models are obtained and analyzed to assess different aspects of interactions within the chain “soil-roots-leaves”. The paper also represents a qualitative assessment technique to simplify analysis of the results, as well as to generalize the modeling results for different subsystems to the whole system.

Independent Component Analysis for Magnetic Resonance Image Analysis

Independent component analysis (ICA) has recently received considerable interest in applications of magnetic resonance (MR) image analysis. However, unlike its applications to functional magnetic resonance imaging (fMRI) where the number of data samples is greater than the number of signal sources to be separated, a dilemma encountered in MR image analysis is that the number of MR images is usually less than the number of signal sources to be blindly separated. As a result, at least two or more brain tissue substances are forced into a single independent component (IC) in which none of these brain tissue substances can be discriminated from another. In addition, since the ICA is generally initialized by random initial conditions, the final generated ICs are different. In order to resolve this issue, this paper presents an approach which implements the over-complete ICA in conjunction with spatial domain-based classification so as to achieve better classification in each of ICA-demixed ICs. In order to demonstrate the proposed over-complete ICA, (OC-ICA) experiments are conducted for performance analysis and evaluation. Results show that the OC-ICA implemented with classification can be very effective, provided the training samples are judiciously selected.

810 媒質表面温度を用いた温度分布推定法に関する研究(OS24.一般セッション(3) 医療分野の計算力学/コンピュータシミュレーション,オーガナイズドセッション)

It is important to know the temperature field of a human body during Hyperthermia. Heat transfer experiments using a phantom model are conducted and by the use of these experimental data, inverse analysis are applied to estimate thermophysical properties and temperature field of the phantom model. It is shown that results of the inverse analysis agree well with the experimental data and the accuracy of the estimation is increased as the number of sampling data used for the inverse analysis is increased.

Method and apparatus for acoustic source tracking using a horizontal line array

An apparatus for processing passive acoustic signals received on a horizontal line array that were either emitted from an underwater object or echo returned from an object, is proposed to track the motion (bearing change and range change) of an object (target) relative to the receiver horizontal line array. Adaptive array processing for a moving object is biased for a moving source when the number of data samples is limited by the stationariness condition. Motion compensation can be carried out in the beam domain by beam shifting for a bearing changing object and frequency shifting for a range changing object. The method includes receiving acoustic signals from the target, determining the beam covariance matrices, determining the target bearing rate and range rate, processing the beam covariance matrices by compensating for the target motion, and producing a beam power plot versus time. Interference signal is suppressed when the interference source does not have the same motion (bearing and range rate) as the target. The method does not need detailed environmental acoustic information of the sound channel normally required to model the sound propagation.

Image segmentation using active contours driven by the Bhattacharyya gradient flow.

This paper addresses the problem of image segmentation by means of active contours, whose evolution is driven by the gradient flow derived from an energy functional that is based on the Bhattacharyya distance. In particular, given the values of a photometric variable (or of a set thereof), which is to be used for classifying the image pixels, the active contours are designed to converge to the shape that results in maximal discrepancy between the empirical distributions of the photometric variable inside and outside of the contours. The above discrepancy is measured by means of the Bhattacharyya distance that proves to be an extremely useful tool for solving the problem at hand. The proposed methodology can be viewed as a generalization of the segmentation methods, in which active contours maximize the difference between a finite number of empirical moments of the "inside" and "outside" distributions. Furthermore, it is shown that the proposed methodology is very versatile and flexible in the sense that it allows one to easily accommodate a diversity of the image features based on which the segmentation should be performed. As an additional contribution, a method for automatically adjusting the smoothness properties of the empirical distributions is proposed. Such a procedure is crucial in situations when the number of data samples (supporting a certain segmentation class) varies considerably in the course of the evolution of the active contour. In this case, the smoothness properties of the empirical distributions have to be properly adjusted to avoid either over- or underestimation artifacts. Finally, a number of relevant segmentation results are demonstrated and some further research directions are discussed.

A practical randomization approach of deterministic equation to determine probabilistic fatigue and fracture behaviours based on small experimental data sets

This paper outlines a new technique to address the paucity of data in determining fatigue and fracture performances based on reliability concepts. Two new randomized models of time-dependent processes are presented for estimating the P–a–t and P–S–N curves, by using a randomization approach of deterministic equations, dealing with small sample numbers of data. The confidence level formulations for these curves are also given. The concepts are then applied for the determination of the P–a–t and P–S–N curves. Two sets of fatigue and fracture tests for these curves are conducted to validate the presented method, demonstrating the practical use of the proposed technique.

A comparison of generalized linear discriminant analysis algorithms

Linear discriminant analysis (LDA) is a dimension reduction method which finds an optimal linear transformation that maximizes the class separability. However, in undersampled problems where the number of data samples is smaller than the dimension of data space, it is difficult to apply LDA due to the singularity of scatter matrices caused by high dimensionality. In order to make LDA applicable, several generalizations of LDA have been proposed recently. In this paper, we present theoretical and algorithmic relationships among several generalized LDA algorithms and compare their computational complexities and performances in text classification and face recognition. Towards a practical dimension reduction method for high dimensional data, an efficient algorithm is proposed, which reduces the computational complexity greatly while achieving competitive prediction accuracies. We also present nonlinear extensions of these LDA algorithms based on kernel methods. It is shown that a generalized eigenvalue problem can be formulated in the kernel-based feature space, and generalized LDA algorithms are applied to solve the generalized eigenvalue problem, resulting in nonlinear discriminant analysis. Performances of these linear and nonlinear discriminant analysis algorithms are compared extensively.

Data Pattern Estimation with Movement of the Center of Gravity

In the rule based modeling, data partitioning plays crucial role because partitioned sub data set implies particular information of the given data set or system. In this paper, we present an empirical study result of the data pattern estimation to find underlying data patterns of the given data. Presented method performs crisp type clustering with given n number of data samples by means of the sequential agglomerative hierarchical nested model (SAHN). In each sequence, the average value of the sum of all inter-distance between centroid and data point. In the sequel, compute the derivation of the weighted average distance to observe a pattern distribution. For the final step, after overall clustering process is completed, weighted average distance value is applied to estimate range of the number of clusters in given dataset. The proposed estimation method and its result are considered with the use of FCM demo data set in MATLAB fuzzy logic toolbox and Box and Jenkins's gas furnace data

Phase shifting interferometry using a robust parameter estimation method

The paper proposes a maximum-likelihood (ML) method based on spectral estimation theory for the estimation of phase distribution in interferometry in the presence of nonsinusoidal waveforms, noise, and the miscalibration of the piezoelectric device. The proposed method also allows the use of arbitrary phase steps. ML estimators are asymptotically efficient for large number of data samples. The method is complemented well by the incorporation of global search algorithm known as Probabilistic Global Search Lausanne for minimizing the ML function. The performance of the proposed method is studied in the presence of noise.

3D in vivo arthrokinematics of the ulnohumeral joint with active pronation-supination

The functional method identifies the hip joint centre (HJC) as the centre of rotation of the femur relative to the pelvis during an ad hoc movement normally recorded using stereophotogrammetry. This method may be used for the direct determination of subject-specific HJC coordinates or for creating a database from which regression equations may be derived that allow for the prediction of those coordinates. In order to contribute to the optimization of the functional method, the effects of the following factors were investigated: the algorithm used to estimate the HJC coordinates from marker coordinates, the type and amplitude of the movement of the femur relative to the pelvis, marker cluster location and dimensions, and the number of data samples. This was done using a simulation approach which, in turn, was validated using experiments made on a physical analogue of the pelvis and femur system. The algorithms used in the present context were classified and, in some instances, modified in order to optimize both accuracy and computation time, and submitted to a comparative evaluation. The type of movement that allowed for the most accurate results consisted of several flexion-extension/abduction-adduction movements performed on vertical planes of different orientations, followed by a circumduction movement. The accuracy of the HJC estimate improved, with an increasing rate, as a function of the amplitude of these movements. A sharp improvement was found as the number of the photogrammetric data samples used to describe the movement increased up to 500. For optimal performance with the recommended algorithms, markers were best located as far as possible from each other and with their centroid as close as possible to the HJC. By optimizing the analytical and experimental protocol, HJC location error not caused by soft tissue artefacts may be reduced by a factor of ten with a maximal expected value for such error of approximately 1 mm.

Low Cost BIST for Static and Dynamic Testing of ADCs

This paper presents a low cost test method for the static and dynamic characterization of analog-to-digital converters. The method is suitable for implementation in a SoC environment, as a built-in self test (BIST) solution. In the proposed approach, noise is used as the test signal. Theory of operation and practical results demonstrating the effectiveness of the method for INL, DNL, THD and SINAD characterization are presented. The BIST surface overhead caused by the noise generator is only 7.4% of the ADC total area. The reduced number of data samples required allows a reduction of about 7.5× in test time, in comparison to the histogram method.

An optimized protocol for hip joint centre determination using the functional method

The functional method identifies the hip joint centre (HJC) as the centre of rotation of the femur relative to the pelvis during an ad hoc movement normally recorded using stereophotogrammetry. This method may be used for the direct determination of subject-specific HJC coordinates or for creating a database from which regression equations may be derived that allow for the prediction of those coordinates. In order to contribute to the optimization of the functional method, the effects of the following factors were investigated: the algorithm used to estimate the HJC coordinates from marker coordinates, the type and amplitude of the movement of the femur relative to the pelvis, marker cluster location and dimensions, and the number of data samples. This was done using a simulation approach which, in turn, was validated using experiments made on a physical analogue of the pelvis and femur system. The algorithms used in the present context were classified and, in some instances, modified in order to optimize both accuracy and computation time, and submitted to a comparative evaluation. The type of movement that allowed for the most accurate results consisted of several flexion-extension/abduction-adduction movements performed on vertical planes of different orientations, followed by a circumduction movement. The accuracy of the HJC estimate improved, with an increasing rate, as a function of the amplitude of these movements. A sharp improvement was found as the number of the photogrammetric data samples used to describe the movement increased up to 500. For optimal performance with the recommended algorithms, markers were best located as far as possible from each other and with their centroid as close as possible to the HJC. By optimizing the analytical and experimental protocol, HJC location error not caused by soft tissue artefacts may be reduced by a factor of ten with a maximal expected value for such error of approximately 1 mm.