• Title/Summary/Keyword: Mahalanobis distance

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Modeling of Strength of High Performance Concrete with Artificial Neural Network and Mahalanobis Distance Outlier Detection Method (신경망 이론과 Mahalanobis Distance 이상치 탐색방법을 이용한 고강도 콘크리트 강도 예측 모델 개발에 관한 연구)

  • Hong, Jung-Eui
    • Journal of the Society of Korea Industrial and Systems Engineering
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    • v.33 no.4
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    • pp.122-129
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    • 2010
  • High-performance concrete (HPC) is a new terminology used in concrete construction industry. Several studies have shown that concrete strength development is determined not only by the water-to-cement ratio but also influenced by the content of other concrete ingredients. HPC is a highly complex material, which makes modeling its behavior a very difficult task. This paper aimed at demonstrating the possibilities of adapting artificial neural network (ANN) to predict the comprresive strength of HPC. Mahalanobis Distance (MD) outlier detection method used for the purpose increase prediction ability of ANN. The detailed procedure of calculating Mahalanobis Distance (MD) is described. The effects of outlier compared with before and after artificial neural network training. MD outlier detection method successfully removed existence of outlier and improved the neural network training and prediction performance.

Performance Improvement of Microphone Array Speech Recognition Using Features Weighted Mahalanobis Distance (가중특징 Mahalanobis거리를 이용한 마이크 어레이 음석인식의 성능향상)

  • Nguyen, Dinh Cuong;Chung, Hyun-Yeol
    • The Journal of the Acoustical Society of Korea
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    • v.29 no.1E
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    • pp.45-53
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    • 2010
  • In this paper, we present the use of the Features Weighted Mahalanobis Distance (FWMD) in improving the performance of Likelihood Maximizing Beamforming (Limabeam) algorithm in speech recognition for microphone array. The proposed approach is based on the replacement of the traditional distance measure in a Gaussian classifier with adding weight for different features in the Mahalanobis distance according to their distances after the variance normalization. By using Features Weighted Mahalanobis Distance for Limabeam algorithm (FWMD-Limabeam), we obtained correct word recognition rate of 90.26% for calibrate Limabeam and 87.23% for unsupervised Limabeam, resulting in a higher rate of 3% and 6% respectively than those produced by the original Limabearn. By implementing a HM-Net speech recognition strategy alternatively, we could save memory and reduce computation complexity.

Sound Quality evaluation of the interior noise for the driving vehicle using Mahalanobis Distance (Mahalanobis Distance 를 이용한 주행중 차량 실내소음의 음질평가)

  • Park, Sang-Gil;Kim, Ho-San;Bae, Chul-Yong;Lee, Bong-Hyun;Oh, Jae-Eung
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 2007.11a
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    • pp.318-321
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    • 2007
  • Since human listening is very sensitive to sound, a subjective index of a sound quality is required. Therefore, in the analysis for each situation, the sound evaluation is composed with sound quality factor. Many researchers spends their effort to make a more reliable and more accurate of sound in term of sound quality index for various system noise. The previous methods to evaluation of the SQ about vehicle interior noise are linear regression analysis of subjective SQ metrics by statistics and the estimation of the subjective SQ values by neural network. But these are so depended on jury test very much that they result in many difficulties. So, to reduce jury test weight, we suggested a new method using Mahalanobis distance for SQ evaluation. Threrefore, in this study Mahalanobis distance for the vehicle interior noise was derived using the objective SQ except jury test. Finnaly, the results of the SQ evaluation was analyzed discrimination between reference and abnormal group.

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Sound Quality Evaluation of Interior Noise of Driving Vehicle Using Mahalanobis Distance (Mahalanobis Distance를 이용한 주행 중 차량 실내소음의 음질평가)

  • Park, Sang-Gil;Lee, Hae-Jin;Bae, Chul-Yong;Lee, Bong-Hyun;Oh, Jae-Eung
    • Transactions of the Korean Society for Noise and Vibration Engineering
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    • v.18 no.1
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    • pp.57-60
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    • 2008
  • Since human listening is very sensitive to sound, for evaluating of a sound quality is required. Therefore, in the analysis for each situation, the sound evaluation is composed with sound quality factor. My researchers spends their effort to make a more reliable and more accurate of sound in term of sound quality index for various system noise. The previous methods to evaluation of the SQ about vehicle interior noise are linear regression analysis of subjective SQ metrics by statistics and the estimation of the subjective SQ values by neural network. But these are highly dependent on jury test and have many difficulties due to various environmental factors. So, to reduce jury test weight. we suggested a new method using Mahalanobis distance for SQ evaluation. Threrefore, in this study Mahalanobis distance for the vehicle interior noise was derived using the objective SQ except jury test. Finnaly, the results of the SQ evaluation was analyzed discrimination between reference and abnormal group.

Target Identification using the Mahalanobis Distance and Geometric Parameters (마할라노비스 거리와 기하학적 파라메터에 의한 표적의 인식)

  • 이준웅;권인소
    • Journal of Institute of Control, Robotics and Systems
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    • v.5 no.7
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    • pp.814-820
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    • 1999
  • We propose a target identification algorithm for visual tracking. Target identification is realized by finding out corresponding line segments to the hypothesized model segments of the target. The key idea is the combination of the Mahalanobis distance with the geometrical relationship between model segments and extracted line segments.

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A Fault Diagnosis on the Rotating Machinery Using Mahalanobis Distance (마할라노비스 거리를 이용한 회전기기의 이상진단)

  • Park, Sang-Gil;Park, Won-Sik;Jung, Jae-Eun;Lee, You-Yub;Oh, Jae-Eung
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.32 no.7
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    • pp.556-560
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    • 2008
  • As higher reliability and accuracy on production facilities are required to detect incipient faults, a diagnostic system for predictive maintenance of the facility is highly recommended. In this paper, we present a study on the application of vibration signals to diagnose faults for a Rotating Machinery using the Mahalanobis Distance-Taguchi System. RMS, Crest Factor and Kurtosis that is known as the Statistical Methods and the spectrum analysis are used to diagnose faults as parameters of Mahalanobis distance.

A Study on the Optimal Mahalanobis Distance for Speech Recognition

  • Lee, Chang-Young
    • Speech Sciences
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    • v.13 no.4
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    • pp.177-186
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    • 2006
  • In an effort to enhance the quality of feature vector classification and thereby reduce the recognition error rate of the speaker-independent speech recognition, we employ the Mahalanobis distance in the calculation of the similarity measure between feature vectors. It is assumed that the metric matrix of the Mahalanobis distance be diagonal for the sake of cost reduction in memory and time of calculation. We propose that the diagonal elements be given in terms of the variations of the feature vector components. Geometrically, this prescription tends to redistribute the set of data in the shape of a hypersphere in the feature vector space. The idea is applied to the speech recognition by hidden Markov model with fuzzy vector quantization. The result shows that the recognition is improved by an appropriate choice of the relevant adjustable parameter. The Viterbi score difference of the two winners in the recognition test shows that the general behavior is in accord with that of the recognition error rate.

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Local Influence on Misclassification Probability

  • Kim, Myung-Geun
    • Journal of the Korean Statistical Society
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    • v.25 no.1
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    • pp.145-151
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    • 1996
  • The local behaviour of the surface formed by the perturbed maximum likelihood estimator of the squared Mahalanobis distance is investigated. The study of the local behaviour allows a simultaneous perturbation on the samples of interest and it is effective in identifying influential observations.

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Sound Quality Evaluation of the Level D Noise for the vehicle using Mahalanobis Distance (Mahalanobis Distance 를 이용한 차량 D 단 소음의 음질 평가)

  • Park, Sang-Gil;Park, Won-Sik;Sim, Hyoun-Jin;Lee, Jung-Youn;Oh, Jae-Eung
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 2007.11a
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    • pp.311-317
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    • 2007
  • The reduction of the Vehicle interior noise has been the main interest of NVH engineers. The driver's perception on the vehicle noise is affected largely by psychoacoustic characteristic of the noise as well as the SPL. The previous methods to evaluation of the SQ about vehicle interior noise are linear regression analysis of subjective SQ metrics by statistics and the estimation of the subjective SQ values by neural network. But these are so depended on jury test very much that they result in many difficulties. So, to reduce jury test weight, we suggested a new method using Mahalanobis distance for SQ evaluation. And, optimal characteristic values influenced on the result of the SQ evaluation were derived by signal to noise ratio(SN ratio) of the Taguchi method. Finally, the new method to evaluate SQ is constructed using Mahalanobis-Taguchi system(MTS). Furthermore, the MTS method for SQ evaluation was compared by the result of SQ grade table at the previous study and their virtues and faults introduced.

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