• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.1226-1228
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• 2011

본 논문에서는 유사 시퀀스 매칭에서 미리 버림 계산의 효율적인 방법을 제안한다. 미리 버림은 유사 시퀀스 매칭에서 유클리디안 거리 계산 도중 거리 계산 값이 허용치보다 큰 경우 나머지 거리 계산을 하지 않는 방법이다. 기존의 방법은 시퀀스 첫 엔트리를 시작으로 하여 유클리디안 거리 계산을 진행한다. 이 방법은 데이터 고려 없이 계산이 진행되기 때문에 데이터의 특성에 따라 효과가 크게 다른 점을 보인다. 본 논문에서는 미리 버림의 효과를 최대화 시키기 위해 유클리디안 거리 계산 시작점을 오프셋이라 정의하고, 이를 데이터 특성에 맞게 조절하는 방법을 제안한다. 실험 결과, 제안한 오프셋 조절 미리 버림 방법이 대용량의 데이터 베이스 기반 시스템에서 기존 기법에 비해 좋은 성능 향상시킨 것으로 나타났다.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.08a
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• pp.235-237
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• 1998

벡터양자화기의 부호화 단계에서 계산량을 줄이는 새로운 알고리즘을 제안한다. 벡터양자화기의 부호화는 주어진 입력벡터에 가장 가까운 코드워드를 찾는 것인데 모든 코드워드와 거리계산을 필요로 하기 때문에 많은 계산량이 소요되믈 효율적인 알고리즘이 필요하다. 본 논문에서는 입력벡터와 코드워드와의 유클리디안 거리계산 대신에 벡터 차의 절대값 합을 이용하여 주어진 입력벡터에 최단거리의 코드워드가 될 수 없는 코드워드를 제외함으로써 유클리디안 거리계산을 최소화하여 계산량을 줄이는 알고리즘을 제안된 방법을 고정 소수점 연산을 이용한 DSP 칩에 효과적이며 이는 실험 결과를 통하여 확증할 수 있다.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.4
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• pp.53-63
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• 2014

Moving context determination is an important issue to be resolved in a mobile computing environment. This paper presents a method for recognizing and classifying a mobile user's moving context by Euclidean distance similarity. In the proposed method, basic data are gathered using Global Positioning System (GPS) and accelerometer sensors, and by using the data, the system decides which moving situation the user is in. The decided situation is one of the four categories: stop, walking, run, and moved by a car. In order to evaluate the effectiveness and feasibility of the proposed scheme, we have implemented applications using several variations of Euclidean distance similarity on the Android system, and measured the accuracies. Experimental results show that the proposed system achieves more than 90% accuracy.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.08a
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• pp.302-305
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• 1998

음소의 확률적 분포를 이용하는 음소 HMM 모델을 결정하기 위한 여러 가지 거리 측정방법에 대한 연구이다. 음소 HMM 모델 결정을 위해서 LPC 계수를 이용하고, 거리 측정자를 LPC 계수, LPC 스첵트럼, LPC 켑스트럼 등의 파라미터를 이용하고, 또한 양자화 과정은 k-means 와 LBG 알고리즘을 혼합한 하이브리드 알고리듬을 사용하였다. LPC 코드북을 구성하기 위해 세 가지 파라미터를 유클리디안 거리로 거리측정에 이용하였다. 이렇게 양자화한 파라미터의 평균과 분산을 구하고, 양자화한 파라미터 코드북의 확률갑승ㄹ 비교해 한국어 음소 HMM 모델 결정을 위한 거리 측정 파라미터를 비교하였으며, 그 결과 LPC 계수를 주파수 영역으로 변환하여 유클리디안 거리를 이용한 코드북의 분산이 작으므로 상대적으로 높은 확률을 가짐을 보았다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.4
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• pp.875-885
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• 2010

CBR (Case-Based Reasoning) is a technique to infer the relationships between existing data and case data, and the method to calculate similarity and Euclidean distance is mostly frequently being used. However, since those methods compare all the existing and case data, it also has a demerit that it takes much time for data search and filtering. Therefore, to solve this problem, various researches have been conducted. This paper suggests the method of SE(Speed Euclidean-distance) calculation that utilizes the patterns discovered in the existing process of computing similarity and Euclidean distance. Because SE calculation applies the patterns and weight found during inputting new cases and enables fast data extraction and short operation time, it can enhance computing speed for temporal or spatial restrictions and eliminate unnecessary computing operation. Through this experiment, it has been found that the proposed method improves performance in various computer environments or processing rate more efficiently than the existing method that extracts data using similarity or Euclidean method does.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.6
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• pp.1293-1299
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• 2012

In this paper, a real-time vehicle detection and tracking algorithms is proposed. The vehicle detection could be processed using GMM (Gaussian Mixture Model) algorithm and mathematical morphological processing with HD CCTV camera images. The vehicle tracking based on separated vehicle object was performed using Euclidean distance between detected object. In more detail, background could be estimated using GMM from CCTV input image signal and then object could be separated from difference image of the input image and background image. At the next stage, candidated objects were reformed by using mathematical morphological processing. Finally, vehicle object could be detected using vehicle size informations dependent on distance and vehicle type in tunnel. The vehicle tracking performed using Euclidean distance between the objects in the video frames. Through computer simulation using recoded real video signal in tunnel, it is shown that the proposed system works well.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.1
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• pp.65-71
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• 2006

There has been equipped wireless network infrastructure making possible to contact mobile computing at buildings, university, airport etc. Due to increase of mobile user dramatically, it raises interest about application and importance of LBS. The purpose of this study is to develop an indoor positioning system which is position of mobile users using Wireless LAN signal strength. We present Euclidean distance model and Bayesian inference model for analyzing position determination. The experimental results showed that the positioning of Bayesian inference model is more accurate than that of Euclidean distance model. In case of static target, the positioning accuracy of Bayesian inference model is within 2 m and increases when the number of cumulative tracking points increase. We suppose, however, Bayesian inference model using 5- cumulative tracking points is the most optimized thing, to decrease operation rate of mobile instruments and distance error of tracking points by movement of mobile user.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.10
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• pp.949-954
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• 2010

Computing the rotation-invariant distance between image time-series is a time-consuming process that incurs a lot of Euclidean distances for all possible rotations. In this paper we propose an innovative solution that significantly reduces the number of Euclidean distances using the triangular inequality. To this end, we first present the notion of self rotation distance and show that, by using the self rotation distance with the triangular inequality, we can prune many unnecessary distance computations. We next present that only one self-rotation is enough for all self-rotation distances required. Experimental results show that our self rotation distance-based methods outperform the existing methods by up to an order of magnitude.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.343-348
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• 2014

Face recognition system has advanctage to automatically recognize a person without causing repulsion at deteciton process. However, the face recognition system has a drawback to show lower perfomance according to illumination variation unlike the other biometric systems using fingerprint and iris. Therefore, this paper proposed a robust face recogntion method against illumination varition by slective fusion technique using both visible and infrared faces based on fuzzy linear disciment analysis(fuzzy-LDA). In the first step, both the visible image and infrared image are divided into four bands using wavelet transform. In the second step, Euclidean distance is calculated at each subband. In the third step, recognition rate is determined at each subband using the Euclidean distance calculated in the second step. And then, weights are determined by considering the recognition rate of each band. Finally, a fusion face recognition is performed and robust recognition results are obtained.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.3
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• pp.79-86
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• 2009

This study proposed a method of detecting the facial area by calculating Euclidian distances among skin color elements and extracting the characteristics of the face. The proposed algorithm is composed of light calibration and face detection. The light calibration process performs calibration for the change of light. The face detection process extracts the area of skin color by calculating Euclidian distances to the input images using as characteristic vectors color and chroma in 20 skin color sample images. From the extracted facial area candidate, the eyes were detected in space C of color model CMY, and the mouth was detected in space Q of color model YIQ. From the extracted facial area candidate, the facial area was detected based on the knowledge of an ordinary face. When an experiment was conducted with 40 color images of face as input images, the method showed a face detection rate of 100%.