• 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 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.

• 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.

• 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%.

• Journal of the Korea Convergence Society
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• v.10 no.9
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• pp.31-36
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• 2019

Music therapy, a field that convergence music and treatment, which play a fundamental role in personality formation, possesses diverse and complex treatment methods. Music therapists in charge of music therapy may experience the same phenomenon as countertransference in consultation with clients. In addition, experiencing psychological burnout, there are many difficulties in reaching the final goal of music therapy. In this paper, we provide a collaborative filtering-based music therapy consultation data recommendation model for smooth music therapy consultation with clients who visited for music therapy. The proposed model grasps the similarity between the conventional consultation data and the new consultant data through the euclidean distance algorithm. This is to recommend similar consultation materials. Since music therapists can provide optimal consultation materials for consultants who need music therapy, smooth consultation is expected.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2005.11a
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• pp.205-208
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• 2005

본 논문은 특징 가중치 벡터를 적용하여 능동형태 모델(Active Shape Model)기반에서 눈동자의 움직임 추적 속도를 향상시키는 방법을 제안한다. 일반적인 능동형태 모델에서는 객체 추적을 위한 PDM 구성을 위해 각 특징점 구성 벡터의 유클리디안 거리의 최소 값으로 Training Set정렬 과정을 수행한다. 이 과정에서 적절하지 못한 샘플 영상으로 인해 안정된 PDM을 구성하지 못하는 문제점이 발생한다. 이러한 문제점을 해결하기 위하여 본 논문에 서는 형태를 구성하는 특징점마다 가중치를 부여하는 벡터를 작성하고, 최소자승근사법으로 최유사 특징점 벡터를 산출하기 위한 선형방정식을 구상하였다. 이로 인해 안정된 PDM을 구성할 수 있었으며, 눈동자 추적실험을 통해 형태적 움직임을 보정하는 실험을 수행하였다. 실험결과 기존의 능동형태 모델에 비해 반복연산의 횟수가 줄어들고, 다양한 형태로 나타나는 눈동자의 움직임 추적에 보다 안정적인 결과를 얻을 수 있었다.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.460-465
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• 2006

This paper presents a new fuzzy learning rule which considers the Euclidean distances between the input vector and the prototypes of classes. The new fuzzy learning rule is integrated into the supervised IAFC neural network 4. This neural network is stable and plastic. We used iris data to compare the performance of the supervised IAFC neural network 4 with the performances of back propagation neural network and LVQ algorithm.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.265-267
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• 2002

본 논문에서는 적응적 정규화 자연기울기 학습법과 자연 프루닝(pruning) 방법의 결합을 통하여 일반화 성능이 우수만 신경망을 구성하고자 한다. 먼저 적응적 정규화 자연기울기 학습을 통하여 신경망의 가중치를 최적화 시키고, 자연 프루닝에 의하여 신경망의 구조를 단순화 시킨다. 이러한 모델들 중 최적의 모델은 베이시안 정보 기준에 의해 선택함으로써 일반화 성능이 우수만 신경망을 구성하는 방법을 제안한다 벤치마크 (benchmark) 데이터로 제안하는 방법과 유클리디안(Euclidean) 거리에 기반한 결합 방법과 자연 프루닝만을 적용한 방법을 비교함으로써 우수성을 검증한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.332-334
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• 2021

As fatal accidents due to industrial accidents and deaths due to civil accidents were pointed out as social problems, the Act on Punishment of Serious Accidents Occurred in the Workplace was enacted to ensure the safety of citizens and to prevent serious accidents in advance. Effort is required. In this paper, we propose a distance prediction model in relation to the case where an operator is hit by heavy equipment such as a forklift. For the data, actual forklift trucks and workers roaming environments were directly captured by CCTV, and it was conducted based on the Euclidean distance. It is thought that it will be possible to learn YOLO-v4 by directly building a data-set at the industrial site, and then implement a model that predicts the distance and determines whether it is a dangerous situation, which can be used as basic data for a comprehensive risk situation judgment model.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.36T no.1
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• pp.93-102
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• 1999

In this paper, we propose a data model and a retrieval method for content-based video query After partitioning a video into frame sets of same length which is called video-window, each video-window can be mapped to a point in a multidimensional space. A video can be represented a trajectory by connection of neighboring video-window in a multidimensional space. The similarity between two video-windows is defined as the euclidean distance of two points in multidimensional space, and the similarity between two video segments of arbitrary length is obtained by comparing corresponding trajectory. A new retrieval method with filtering and refinement step if developed, which return correct results and makes retrieval speed increase by 4.7 times approximately in comparison to a method without filtering and refinement step.