• The Korean Journal of Applied Statistics
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• v.37 no.4
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• pp.455 -465
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• 2024

SMOTE (synthetic minority over-sampling technique) has been used the most as a solution to the problem of imbalanced data. SMOTE selects the nearest neighbor based on Euclidean distance. However, Euclidean distance has the disadvantage of not considering the correlation between variables. In particular, the Mahalanobis distance has the advantage of considering the covariance of variables. But if there are outliers, they usually influence calculating the Mahalanobis distance. To solve this problem, we use the Mahalanobis distance by estimating the covariance matrix using MCD (minimum covariance determinant). Then apply Mahalanobis distance based on MCD to SMOTE to create new data. Therefore, we showed that in most cases this method provided high performance indicators for classifying imbalanced data.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.8
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• pp.573-580
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• 2020

One of challengeable problems of multi-agent systems is a positioning control. Augmented Cucker-Smale model is using for controlling position and velocity of the multi-agent system. The original model applies same coefficients to all agents in same group, so that does not consider characteristic of each agent. To enhance performance of the original model, this paper transforms original coefficients to Mahalanobis distance coefficients that reflects an initial distribution of multi-agent systems and applies statistical degrees of freedom. This paper not only confirms tendency of enhanced performance of the suggested model by using monte-carlo simulation, but also additionally compares trajectory of the original model with the suggested model to confirm coefficients of Mahalanobis distance performing correctly.

• Journal of the Korean Institute of Telematics and Electronics
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• v.21 no.3
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• pp.1-5
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• 1984

It is investigated that Mahalanobis distances are invariant under a proper transformation and interest MDs are distributed as non-central chi-square while it is well known that intraset MDs are distributed as central chi-square. And their moments have been expressed as simple polynomials whose coefficients satisfy straightforward recursive relations.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.3
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• pp.747-754
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• 2014

In spite of its usefulness for health monitoring of structures on steady external load, the statistical pattern recognition technology (SPRT), based on Mahalanobis distance theory (MDT), is not good enough for the health monitoring of structures on large variability external load like earthquake. Damage is usually determined by the difference between the average measured value of undamaged structure and the measure value of damaged one. So when external variability gets larger, the difference gets bigger along, which is thus easily mistaken for a damage. This paper aims to overcome the problem and develop an improved Mahalanobis distance theory (IMDT), that is, a SPRT with revised MDT in order to decrease external variability so that we will be able to continue to monitor the structure on uncertain external variability. This method is experimentally tested to see if it precisely evaluates the health of a cable-stayed bridge on each general random load and earthquake load. As a result, the IMDT is found to be valid in locating structural damage made by damaged cables by means of data from undamaged cables. So it is proved to be effectively applicable to the health monitoring of bridges on external load of variability.

• 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.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.1 s.307
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• pp.9-16
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• 2006

RDA(Relational Discriminant Analysis) is a way of finding classifiers based on the dissimilarity measures among the prototypes extracted from feature vectors instead of the feature vectors themselves. Therefore, the accuracy of the RDA classifier is dependent on the methods of selecting prototypes and measuring proximities. In this paper we propose to utilize PRS(Prototype Reduction Schemes) and Mahalanobis distances to devise a method of increasing classification accuracies. Our experimental results demonstrate that the proposed mechanism increases the classification accuracy compared with the conventional approaches for samples involving real-life data sets as well as artificial data sets.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.325-326
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• 2012

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.12
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• pp.174-182
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• 2015

In this paper, we developed the small size RGB sensor that recognizes a long distance using a low-cost color sensor. Light receiving portion of the sensor was used as a camera lens for far distance recognition, and illuminating unit was increased the strength of the light by using a high-power white LED and a lens mounted on the reflector. RGB color recognition algorithm consists of the learning process and the realtime recognition process. We obtain a normalized RGB color reference data in the learning process using the specimens painted with target colors, and classifies the three colors using the Mahalanobis distance in recognition process. We apply the developed the RGB color recognition sensor to a prototype of the part classification system and evaluate the performance of its.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.5
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• pp.677-682
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• 2010

This paper addresses a method of estimating roughly the human pose by comparing Haar-wavelet value which is learned in face detection technology using AdaBoost algorithm. We also presents its application to face recognition. The learned weak classifier is used to a Haar-wavelet robust to each pose's feature by comparing the coefficients during the process of face detection. The Mahalanobis distance is used to measure the matching degree in Haar-wavelet selection. When a facial image is detected using the selected Haar-wavelet, the pose is estimated. The proposed pose estimation can be used to improve face recognition speed. Experiments are conducted to evaluate the performance of the proposed method for pose estimation.

• Journal of Korean Society of Disaster and Security
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• v.10 no.2
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• pp.1-6
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• 2017

In the previous research, the research on the development of subway disaster detection system that discovers the disaster early warning of the subway station disaster and the evacuation to the passengers based on the Internet of things. This paper as a follow-up study analyzes the sensor data installed in the station in real time to quickly detect the disaster. In particular, we developed a statistical methodology based on the Mahalanobis distance in consideration of the environment that varies depending on the installation location of the sensor during initial system construction.