• The Journal of the Acoustical Society of Korea
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• v.11 no.1E
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• pp.15-22
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• 1992

잡음이 있는 환경하에서는 음성 인식의 성능이 현저하게 떨어지게 된다. 본 논문에서는 이렇나 잡음의 영향에 강한 거리척도를 제안하고자 한다. 우리는 잡음이 더해진 음성신호의 특징벡터를 깨끗한 음성신호의 특징벡터가 FIR 시스템을 거쳐 변형된 것이라고 가정한다. 여기서 FIR 시스템은 잡음의 영 향을 모델링한 것이라고 할 수 있다. 미지의 FIR 시스템 계수잡음의 영향을 모델링한 것이라고 할 수 있다. 미지의 FIR 시스템계수들은 RLS 적응 알고리즘을 이용하여 구한다. 제안된 거리척도는 적응 여파 기의 예측 오차에 관한 식으로 표시되어진다. 여러 가지 적응 여파기의 구조중 단일 채널 일차 FIR 구 조가 가장 좋은 음성 인식 성능을 보이며, 이 경우 효과적인 거리척도 알고리즘을 구할 수 있다. 여러 가지 신호대 잡음비에 관하여 화자독립 격리단어 인식 실험을 DTW 알고리즘을 이용하여 수행하여 본 결과 제안된 거리척도가 거의 모든 신호대 잡음비에 대하여 우수한 성능을 보였다.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.5_6
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• pp.288-296
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• 2004

This paper proposes a new mesh simplification algorithm using differential error metric. Many simplification algorithms make use of a distance error metric, but it is hard to measure an accurate geometric error for the high-curvature region even though it has a small distance error measured in distance error metric. This paper proposes a new differential error metric that results in unifying a distance metric and its first and second order differentials, which become tangent vector and curvature metric. Since discrete surfaces may be considered as piecewise linear approximation of unknown smooth surfaces, theses differentials can be estimated and we can construct new concept of differential error metric for discrete surfaces with them. For our simplification algorithm based on iterative edge collapses, this differential error metric can assign the new vertex position maintaining the geometry of an original appearance. In this paper, we clearly show that our simplified results have better quality and smaller geometry error than others.

• Journal of Korea Multimedia Society
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• v.14 no.6
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• pp.711-718
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• 2011

The k-Nearest Neighbor classifier that does not require a training phase is appropriate for a variable number of classes problem like face recognition, Recently distance metric learning methods that is trained with a given data set have reported the significant improvement of the kNN classifier. However, the performance of a distance metric learning method is variable for each application, In this paper, we focus on the face recognition and compare the performance of the state-of-the-art distance metric learning methods, Our experimental results on the public face databases demonstrate that the Mahalanobis distance metric based on PCA is still competitive with respect to both performance and time complexity in face recognition.

• The Journal of the Acoustical Society of Korea
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• v.19 no.2
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• pp.40-45
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• 2000

In speech corpus generation and speech recognition, it is sometimes needed to segment the input speech data without any prior knowledge. A method to accomplish this kind of segmentation, often called as blind segmentation, or acoustic segmentation, is to find boundaries which minimize the Euclidean distances among the feature vectors of each segments. However, the use of this metric alone is prone to errors because of the fluctuations or variations of the feature vectors within a segment. In this paper, we introduce the principal component analysis method to take the trend of feature vectors into consideration, so that the proposed distance measure be the distance between feature vectors and their projected points on the principal components. The proposed distance measure is applied in the LBDP(level building dynamic programming) algorithm for an experimentation of continuous speech segmentation. The result was rather promising, resulting in 3-6% reduction in deletion rate compared to the pure Euclidean measure.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.367-375
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• 2023

This study developed a new distance metric for vessel trajectories, applicable to marine traffic control services in the Korean coastal waters. The proposed metric is designed through the weighted summation of the traditional Hausdorff distance, which measures the similarity between spatiotemporal data and incorporates the differences in the average Speed Over Ground (SOG) and the variance in Course Over Ground (COG) between two trajectories. To validate the effectiveness of this new metric, a comparative analysis was conducted using the actual Automatic Identification System (AIS) trajectory data, in conjunction with an agglomerative clustering algorithm. Data visualizations were used to confirm that the results of trajectory clustering, with the new metric, reflect geographical distances and the distribution of vessel behavioral characteristics more accurately, than conventional metrics such as the Hausdorff distance and Dynamic Time Warping distance. Quantitatively, based on the Davies-Bouldin index, the clustering results were found to be superior or comparable and demonstrated exceptional efficiency in computational distance calculation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.451-454
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• 2002

본 논문에서는 개인의 적성을 판단하는 문제를 처리하기 위한 가중치 퍼지추론 알고리즘을 제시하고, 지식표현을 위해 퍼지 집합 이론과 퍼지 생성 규칙들을 이용하였다. 거리척도에 서는 퍼지값이 높은 구간의 척도를 낮은 구간의 척도에 비례하여 유사성을 구하였다. 또한, 가중치를 정량화한 값과 척도값을 연산하여 유사성을 나타냈고, 추출된 항목과 규칙과의 가능성을 구하였다. 여기서, 결과는 수검자들이 응답한 값들에 따라 임의의 직업군이 적당한 지를 나타내기 위해 확신도로 해석하였다.

• Journal of Cadastre & Land InformatiX
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• v.46 no.2
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• pp.43-55
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• 2016

This study estimated land prices using instance-based learning. A k-nearest neighbor method was utilized among various instance-based learning methods, and the 10 distance metrics including Euclidean distance were calculated in k-nearest neighbor estimation. One distance metric prediction which shows the best predictive performance would be normally chosen as final estimate out of 10 distance metric predictions. In contrast to this practice, an ensemble technique which combines multiple predictions to obtain better performance was applied in this study. We applied the gradient boosting algorithm, a sort of residual-fitting model to our data in ensemble combining. Sales price data of farm lands in Haenam-gun, Jeolla Province were used to demonstrate advantages of instance-based learning as well as an ensemble technique. The result showed that the ensemble prediction was more accurate than previous 10 distance metric predictions.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.2
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• pp.66-69
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• 1997

In this paper a fuzzy similarity measure is proposed. The proposed fuzzy similarity measure considers the relative distance between data and cluster centers in addition to the Euclidean distance to decide the degree of similarity. The boundary of a cluster center is constracted on the competitive region and expanded on the less competitive region. This result shows the possibility of using relative distance as a similarity measure.

• Bulletin of the Korean Space Science Society
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• 2003.10a
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• pp.23-23
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• 2003

천체의 거리는 다양한안 방법을 사용하여 직접 혹 간접적으로 측정되어왔다. 특히 우주배경복사를 측정하는 인공위성 관측의 정밀도가 혁명적으로 향상됨에 따라 우주의 나이는 수 % 이내의 정밀도로 결정할 수 있는 수준으로 발전하였다. 우주의 규모가 구체적으로 정의되는 가운데 우주의 절대적인 크기를 제시하기 위하여 천체의 거리를 측정하는데 사용되는 표준등불로 세페이드 변광성 이외에 식쌍성이 새롭게 대두되었다. 이 논문에서는 식쌍성이 거리척도의 표준등불이 될 수 잇는지 검증하기 위하여 광도곡선과 시선속도곡선이 잘 알려진 알골형 쌍성 식쌍성 RY Aqr, RX Gem, RS Vul을 선정하여 거리를 산출하였다. 별을 선정한 기준은 2색 이상의 광도곡선이 발표되고, 이중 분광쌍성으로 시선속도곡선이 각 성분별로 잘 관측되어 발표되고, IUE 관측 자료가 있는 알골형 쌍성이다. 거리 산출과정에서 간접적으로 유추하여 얻는 인자를 줄이기 위하여, 광도곡선으로부터 별의 상대적인 크기를 구하고, 시선속도곡선으로부터 공전궤도의 장반경을 구하고, 별의 에너지 분포 곡선으로부터 별의 온도를 측정하였다. 위 3종류의 관측 결과를 종합하여 식쌍성의 물리적 인자와 거리를 구하였다. 이와 같은 방법으로 구한 거리는 히파크러스를 이용하여 관측한 시차와 비교하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.6 no.3
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• pp.249-254
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• 2005

In this paper, we analysis the recognition performance of PCA/LDA by distance measures. We are adapt to ORL face database with the fourteen distance measures. In case of PCA, it has high performance for the manhattan distance and the weighted SSE distance to face recognition, In case of PCA/LDA, it has high performance for the angle-based distance and the modified SSE distance. Also, PCA/LDA is better than PCA for reduction of dimension. Therefore, the PCA/LDA method and the angle-based distance have the most performance and a few dimension for face recognition with ORL face database.