Search results for "Pattern recognition"
showing 10 items of 2301 documents
Means of 2D and 3D Shapes and Their Application in Anatomical Atlas Building
2015
This works deals with the concept of mean when applied to 2D or 3D shapes and with its applicability to the construction of digital atlases to be used in digital anatomy. Unlike numerical data, there are several possible definitions of the mean of a shape distribution and procedures for its estimation from a sample of shapes. Most popular definitions are based in the distance function or in the coverage function, each with its strengths and limitations. Closely related to the concept of mean shape is the concept of atlas, here understood as a probability or membership map that tells how likely is that a point belongs to a shape drawn from the shape distribution at hand. We devise a procedur…
Diagnosis of Incipient Bearing Faults using Convolutional Neural Networks
2019
The majority of faults occurring in rotating electrical machinery is attributed to bearings. To reduce downtime, it is desired to apply various diagnostic methods so that bearing degradation can be detected in good time prior to a complete failure. The work presented in this paper utilizes a data-driven machine learning approach based on convolutional neural networks (CNNs) in order to diagnose different types of bearing faults. A one-dimensional CNN is trained on vibration signals and compared to a two-dimensional CNN trained in time-frequency domain using continuous wavelet transform (CWT). The proposed method is demonstrated on data collected from run-to-failure tests.The results show th…
Improving Pattern Recognition Based Pharmacological Drug Selection Through ROC Analysis
2004
The design of new medical drugs is a very complex process in which combinatorial chemistry techniques are used. The goal consists of discriminating between molecular compounds exhibiting or not certain pharmacological activities. Different machine learning approaches have been recently applied to different drug design problems leading to competitive results in pointing at particular compounds with high probability of exhibiting activity. The present work first deeps into the natural trade-off between accuracy in the much less populated active group and false alarm rate which could lead to too many expensive laboratory tests. Preliminary results show how different classification techniques a…
Cue combination in a combined feature contrast detection and figure identification task
2006
AbstractTarget figures defined by feature contrast in spatial frequency, orientation or both cues had to be detected in Gabor random fields and their shape had to be identified in a dual task paradigm. Performance improved with increasing feature contrast and was strongly correlated among both tasks. Subjects performed significantly better with combined cues than with single cues. The improvement due to cue summation was stronger than predicted by the assumption of independent feature specific mechanisms, and increased with the performance level achieved with single cues until it was limited by ceiling effects. Further, cue summation was also strongly correlated among tasks: when there was …
Mode-superposition correction method for deterministic and stochastic analysis of structural systems
2001
The role played by the modal analysis in the framework of structural dynamics is fundamental from both deterministic and stochastic point of view. However the accuracy obtained by means of the classical modal analysis is not always satisfactory. Therefore it is clear the importance of methods able to correct the modal response in such a way to obtain the required accuracy. Many methods have been proposed in the last years but they are meaningful only when the forcing function is expressed by an analytical function. Moreover in stochastic analysis they fail for white noise excitation. In the paper a method able to give a very accurate response for both deterministic and stochastic input is p…
A perturbation approach for the response of dynamically modified structural systems
1998
The problem of the structural analysis under changes of dynamical parameters is of particular interest. This is due to the fact that often the real structures are different from the predicted ones. In this paper, an unconditionally stable step-by-step procedure, able to evaluate the deterministic response of linear structures with modifications, is presented. The proposed procedure requires the evaluation of the transition matrix, which is the fundamental operator of the step-by-step solution, by means of a perturbation approach. This technique overcomes the difficulties connected with the evaluation of the eigenproperties of the modified structures usually required to obtain the transition…
Vector representation of non-standard spellings using dynamic time warping and a denoising autoencoder
2017
The presence of non-standard spellings in Twitter causes challenges for many natural language processing tasks. Traditional approaches mainly regard the problem as a translation, spell checking, or speech recognition problem. This paper proposes a method that represents the stochastic relationship between words and their non-standard versions in real vectors. The method uses dynamic time warping to preprocess the non-standard spellings and autoencoder to derive the vector representation. The derived vectors encode word patterns and the Euclidean distance between the vectors represents a distance in the word space that challenges the prevailing edit distance. After training the autoencoder o…
On the metric properties of dynamic time warping
1987
Recently, some new and promising methods have been proposed to reduce the number of Dynamic Time Warping (DTW) computations in Isolated Word Recognition. For these methods to be properly applicable, the verification of the Triangle Inequality (TI) by the DTW-based Dissimilarity Measure utilized seems to be an important prerequisite.
Trading off accuracy for efficiency by randomized greedy warping
2016
Dynamic Time Warping (DTW) is a widely used distance measure for time series data mining. Its quadratic complexity requires the application of various techniques (e.g. warping constraints, lower-bounds) for deployment in real-time scenarios. In this paper we propose a randomized greedy warping algorithm for finding similarity between time series instances. We show that the proposed algorithm outperforms the simple greedy approach and also provides very good time series similarity approximation consistently, as compared to DTW. We show that the Randomized Time Warping (RTW) can be used in place of DTW as a fast similarity approximation technique by trading some classification accuracy for ve…
On the use of a metric-space search algorithm (AESA) for fast DTW-based recognition of isolated words
1988
The approximating and eliminating search algorithm (AESA) presented was recently introduced for finding nearest neighbors in metric spaces. Although the AESA was originally developed for reducing the time complexity of dynamic time-warping isolated word recognition (DTW-IWR), only rather limited experiments had been previously carried out to check its performance in this task. A set of experiments aimed at filling this gap is reported. The main results show that the important features reflected in previous simulation experiments are also true for real speech samples. With single-speaker dictionaries of up to 200 words, and for most of the different speech parameterizations, local metrics, a…