• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.145-148
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• 1996

In moving object tracking based on the visual sensory feedback, a prerequisite is to determine which feature or which object is to be tracked and then the feature or the object identification precedes the tracking. In this paper, we focus on the object identification not image feature identification. The 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 geometrica relationship between model segments and extracted line segments. We demonstrate the robustness and feasibility of the proposed target identification algorithm by a moving vehicle identification and tracking in the video traffic surveillance system over images of a road scene.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1057-1060
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• 2004

We propose new rotation moment invariants based on multiresolution filter bank techniques. The multiresolution pyramid motivates our simple but efficient feature selection procedure based on the fuzzy C-mean clustering, combined with the Mahalanobis distance. The procedure verifies an impact of random noise as well as an interesting and less known impact of noise due to spatial transformations. The recognition accuracy of the proposed techniques has been tested with the preceding moment invariants as well as with some wavelet based schemes. The numerical experiments, with more than 30,000 images, demonstrate a tangible accuracy increase of about 3% for low noise, 8% for the average noise and 15% for high level noise.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.770-773
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• 2007

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, it presents 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 Computing Science and Engineering
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• v.7 no.4
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• pp.272-284
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• 2013

In this paper, we propose a new framework for anomaly detection in medical wireless sensor networks, which are used for remote monitoring of patient vital signs. The proposed framework performs sequential data analysis on a mini gateway used as a base station to detect abnormal changes and to cope with unreliable measurements in collected data without prior knowledge of anomalous events or normal data patterns. The proposed approach is based on the Mahalanobis distance for spatial analysis, and a kernel density estimator for the identification of abnormal temporal patterns. Our main objective is to distinguish between faulty measurements and clinical emergencies in order to reduce false alarms triggered by faulty measurements or ill-behaved sensors. Our experimental results on both real and synthetic medical datasets show that the proposed approach can achieve good detection accuracy with a low false alarm rate (less than 5.5%).

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.6
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• pp.619-623
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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, it presents 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.

• 한국HCI학회:학술대회논문집
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• 2007.02c
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• pp.189-193
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• 2007

필적 감정은 개인의 고유한 필적 개성을 이용하여 임의의 두 필기 문장 또는 텍스트가 동일인에 의해 작성되었는지를 판별하는 기술로 유서대필 및 보안수사, 서명의 검증, 범죄 수사 등에 활용되어지고 있다. 이러한 작업은 감정 전문가의 판단기준에 의해 필적의 유사성을 판별하기 때문에 객관성 결여 및 과도한 소요 시간, 과도한 처리비용의 문제를 내포하게 된다. 이러한 문제를 해결하여 판별의 객관성과 업무의 신속한 처리를 가능하게 하기 본 논문에서는 컴퓨터를 통한 패턴 분석을 적용하여 두 필적의 유사성을 판별하는 방법을 본 논문에서는 제안한다. 이를 위하여 본 논문은 학습단계와 자동분석단계로 나뉘며, 학습단계에서는 입력된 문서영상에서 필적의 영역을 추출한 후, 특징을 추출하고 DTW연산을 통하여 학습을 한다. 자동분석단계에서는 대조할 문서영상에서의 특징을 추출하고 입력된 문서영상과 대조할 문서영상간의 마할라노비스 거리(Mahalanobis Distance)를 구하여 서명 및 필적에 대한 유사도를 도출한다. 실험은 4명의 필적을 이용하여 비교하였으며, 우수한 결과를 보였다.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.3
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• pp.334-341
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• 1998

This paper proposes an algorithm to obtain the time-to-contact between an observer and a target and surface orientation of the target. These two physical elements are computed from the image deformation of a known shape, which is extracted by supervised classification of detected line segments based on MAP and Mahalanobis distance. The proposed algorithm was applied to the natural outdoor traffic scene and would contribute to the development for a collision avoidance system.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.1
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• pp.22-28
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• 2013

Robotic GMA (Gas Metal Arc) welding process is one of widely acceptable metal joining process. The heat and mass inputs are coupled and transferred by the weld arc to the molten weld pool and by the molten metal that is being transferred to the weld pool. The amount and distribution of the input energy are basically controlled by the obvious and careful choices of welding process parameters in order to accomplish the optimal bead geometry and the desired quality of the weldment. To make effective use of automated and robotic GMA welding, it is imperative to predict online faults for bead geometry and welding quality with respect to welding parameters, applicable to all welding positions and covering a wide range of material thickness. MD (Mahalanobis Distance) technique was employed for investigating and modeling the GMA welding process and significance test techniques were applied for the interpretation of the experimental data. To successfully accomplish this objective, two sets of experiment were performed with different welding parameters; the welded samples from SM 490A steel flats. First, a set of weldments without any faults were generated in a number of repeated sessions in order to be used as references. The experimental results of current and voltage waveforms were used to predict the magnitude of bead geometry and welding quality, and to establish the relationships between weld process parameters and online welding faults. Statistical models developed from experimental results which can be used to quantify the welding quality with respect to process parameters in order to achieve the desired bead geometry based on weld quality criteria.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.3
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• pp.246-252
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• 2016

Since thermal power plant (TPP) equipment is operated under very high pressure and temperature, failures of the equipment give rise to severe losses of life and property. To prevent the losses, fault detection method is, therefore, absolutely necessary to identify abnormal operating conditions of the equipment in advance. In this paper, we present Mahalanobis distance (MD) based fault detection method for steam boiler tube in TPP. In the MD-based method, it is supposed that abnormal data samples are far away from normal samples. Using multivariate samples collected from normal target system, mean vector and covariance matrix are calculated and threshold value of MD is decided. In a test phase, after calculating the MDs between the mean vector and test samples, alarm signals occur if the MDs exceed the predefined threshold. To demonstrate the performance, a failure case due to boiler tube leakage in 200MW TPP is employed. The experimental results show that the presented method can perform early detection of boiler tube leakage successfully.

• 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.