Mahalanobis distance

About: Mahalanobis distance is a research topic. Over the lifetime, 4616 publications have been published within this topic receiving 95294 citations.

Structural damage detection based on stochastic subspace identification and statistical pattern recognition: I. Theory

Abstract: One of the key issues in vibration-based structural health monitoring is to extract the damage-sensitive but environment-insensitive features from sampled dynamic response measurements and to carry out the statistical analysis of these features for structural damage detection. A new damage feature is proposed in this paper by using the system matrices of the forward innovation model based on the covariance-driven stochastic subspace identification of a vibrating system. To overcome the variations of the system matrices, a non-singularity transposition matrix is introduced so that the system matrices are normalized to their standard forms. For reducing the effects of modeling errors, noise and environmental variations on measured structural responses, a statistical pattern recognition paradigm is incorporated into the proposed method. The Mahalanobis and Euclidean distance decision functions of the damage feature vector are adopted by defining a statistics-based damage index. The proposed structural damage detection method is verified against one numerical signal and two numerical beams. It is demonstrated that the proposed statistics-based damage index is sensitive to damage and shows some robustness to the noise and false estimation of the system ranks. The method is capable of locating damage of the beam structures under different types of excitations. The robustness of the proposed damage detection method to the variations in environmental temperature is further validated in a companion paper by a reinforced concrete beam tested in the laboratory and a full-scale arch bridge tested in the field.

A Multi-stage Online Signature Verification System

Abstract: This paper presents a new three-stage verification system which is based on three types of features: global features; local features of the corner points; and function features that contain information of each point of the signatures. The first verification stage implements a parameter-based method, in which the Mahalanobis distance is used as a dissimilarity measure between the signatures. The second verification stage involves corner extraction and corner matching. It also performs signature segmentation. The third verification stage implements a function-based method, which is based on an elastic matching algorithm establishing a point-to-point correspondence between the compared signatures. By combining the three different types of verification, a high security level can be reached. According to our experiments, the rates of false rejection and false acceptance are, respectively, 5.8% and 0%.

Multi-component medicine evaluation method

Abstract: A method for evaluation of a multi-component medicine by judging the degree of difference of the multi-component medicine to be evaluated from a group of multi-component medicines selected as a reference group by using a Mahalanobis distance obtained by combining the 3D-HPLC fingerprint data of a multi-component medicine with fingerprint data of 3D-HPLC of other multi-component medicines of the same kind forming a reference group, allocating variable axes in the MT method to the number of multi-component medicine and the elution time or detection wavelength of the fingerprint data, obtaining a unit space from the signal strength, and obtaining the Mahalanobis distance of the multi-component medicine from the unit space.

The Races of Maize: II. Use of Multivariate Analysis of Variance to Measure Morphological Similarity 1

Abstract: he racial means and the residual covariance matrix from the multivariate analysis of variance of an experiment based on a randomized block design involving 15 races of maize (Zea mays L.) from southeastern South America were used to calculate generalized distances between the races. Sixteen characters commonly used in taxonomic studies of the races of maize were employed. The effects of transformations designed to eliminate some of the heterogeneity among the withinrace, within-row covariance matrices were studied, and the effects of within-plot sampling were investigated. It was shown that the use of transformations had very little effect on the relative distances (and hence that highly significant heterogeneity — as measured by the multivariate extension of Bartlett's test — had little effect on the analysis of the non-transformed data). Similarly, essentially the same relative distances were obtained when only a single plant from each race was used per block (eight blocks were used). In all cases the distances obtained were relatively very similar (i.e., Spearman rank correlation coefficient between 0.91 and 0.99) to the distances obtained from the commonly employed Mahalanobis’ generalized distance technique.

Facial Gesture Recognition Using Correlation And Mahalanobis Distance

Abstract: Augmenting human computer interaction with automated analysis and synthesis of facial expressions is a goal towards which much research effort has been devoted recently. Facial gesture recognition is one of the important component of natural human-machine interfaces; it may also be used in behavioural science, security systems and in clinical practice. Although humans recognise facial expressions virtually without effort or delay, reliable expression recognition by machine is still a challenge. The face expression recognition problem is challenging because different individuals display the same expression differently. This paper presents an overview of gesture recognition in real time using the concepts of correlation and Mahalanobis distance.We consider the six universal emotional categories namely joy, anger, fear, disgust, sadness and surprise.