• Development and Reproduction
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• v.21 no.4
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• pp.435-440
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• 2017

The author performed PCR-based genetic platform to measure the hierarchical dendrogram of Euclidean genetic distances of Korean scallop populations (KSP), particularly for Chlamys farreri, which was further compared with those of the Chinese scallop populations (CSP), by employing the with specifically designed oligonucleotide primer sets. The scallop is economically and ecologically very important bivalves in South Korea. Relatively, individuals of KSP population were fairly distantly related to that of CSP population, as shown in the hierarchical dendrogram of Euclidean genetic distances. Comparatively, individuals of KSP population were fairly distantly related to that of CSP population. Thus analysis of genetic difference between scallop populations could provide important statistics for fishery and aquaculture. Overall the results showed specific and/or conserved genetic loci between scallop populations. Information on the genetic distance of the bivalve would be helpful to understand scallop expansion or conservation in the coastal regions of South Korea. Specific markers developed by the author will be useful for the analysis of scallop population genetics and distribution in coastal region.

• Proceedings of the Korean Statistical Society Conference
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• 2002.05a
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• pp.45-48
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• 2002

Consider the mean distance of Brownian motion on Riemannian manifolds. We obtain the first three terms of the asymptotic expansion of the mean distance by means of Stochastic Differential Equation(SDE) for Brownian motion on Riemannian manifold. This method proves to be much simpler for further expansion than the methods developed by Liao and Zheng(1995). Our expansion gives the same characterizations as the mean exit time from a small geodesic ball with regard to Euclidean space and the rank 1 symmetric spaces.

• The Pure and Applied Mathematics
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• v.10 no.3
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• pp.193-198
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• 2003

Let X and Y be n-dimensional Euclidean spaces with $n\;{\geq}\;3$. In this paper, we generalize a classical theorem of Bookman and Quarles by proving that if a mapping, from a half space of X into Y, preserves a distance $\rho$, then the restriction of f to a subset of the half space is an isometry.

• Journal of Internet Computing and Services
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• v.15 no.4
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• pp.119-124
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• 2014

Blind adaptive systems based on the Euclidean distance (ED) between the distribution function of the output samples and that of a set of random symbols generated at the receiver matching with the distribution function of the transmitted symbol points estimate the ED at each iteration time to examine its convergence state or its minimum ED value. The problem is that this ED estimation obtained by block?data processing requires a heavy calculation burden. In this paper, a recursive ED estimation method is proposed that reduces the computational complexity by way of utilizing the relationship between the current and previous states of the datablock. The relationship provides a ground that the currently estimated ED value can be used for the estimation of the next ED without the need for processing the whole new data block. From the simulation results the proposed recursive ED estimation shows the same estimation values as that of the conventional method, and in the aspect of computational burden, the proposed method requires only O(N) at each iteration time while the conventional block?processing method does $O(N^2)$.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.10A
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• pp.1563-1570
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• 1999

This paper relates to the soft decision method with erasure technique in digital terrestrial television broadcasting system. The proposed decoder use the CSI derived from using the pilots in receiver. The active real(I) and imaginary(Q) data are transferred to the branch metric calculation block that decides the Euclidean distance for the soft decision decoding and also the estimated CSI values are transferred to the same block. After calculating the Euclidean distance for the soft decision decoding, the Euclidean distance of branch metric is multiplied by CSI. To do so, new branch metric values that consider each carrier state information are obtained. We simulated this method in better performance of about 0.15dB to 0.17dB and 2.2dB to 2.9dB in Rayleigh channel than that of the conventional soft decision Viterbi decoding with or without bit interleaver where the constellation is QPSK, 16-QAM and 64-QAM.

• Journal of Digital Contents Society
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• v.10 no.3
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• pp.423-431
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• 2009

There are various image retrieval methods using shape, color and texture features. One of the most active area is using shape and color information. A number of shape representations have been suggested to recognize shapes even under affine transformation. There are many kinds of method for shape recognition, the well-known method is Fourier descriptors and moment invariant. The other method is CSS(Curvature Scale Space). The maxima of curvature scale space image have already been used to represent 2-D shapes in different applications. Because preexistence CSS exists several problems, in this paper we use improved CSS method for retrieval image. There are two kinds of method, One is using RGB color information feature and the other is using HSI color information feature. In this paper we used HSI color model to represent color histogram before, then use it as comparison measure. The similarity is measured by using Euclidean distance and for reduce search time and accuracy, We use DTW for measure similarity. Compare with the result of using Euclidean distance, we can find efficiency elevated.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.561-566
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• 2021

The optimum kernel size for error-distribution estimation with given error samples cannot be used in the weight adjustment of minimum Euclidean distance between error distributions (MED) algorithms. In this paper, a new adaptive kernel estimation method for convergence enhancement of MED algorithms is proposed. The proposed method uses the average rate of change in error power with respect to a small interval of the kernel width for weight adjustment of the MED learning algorithm. The proposed kernel adjustment method is applied to experiments in communication channel compensation, and performance improvement is demonstrated. Unlike the conventional method yielding a very small kernel calculated through optimum estimation of error distribution, the proposed method converges to an appropriate kernel size for weight adjustment of the MED algorithm. The experimental results confirm that the proposed kernel estimation method for MED can be considered a method that can solve the sensitivity problem from choosing an appropriate kernel size for the MED algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1637-1643
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• 2022

In recent years, the demand for image-processing technology in digital marketing has increased due to the expansion and diversification of the digital market, such as video, security, and machine intelligence. Noise-processing is essential for image-correction and reconstruction, especially in the case of sensitive noises, such as in CT, MRI, X-ray, and scanners. The two main salt and pepper noises have been actively studied, but the details and edges are still unsatisfactory and tend to blur when there is a lot of noise. Therefore, this paper proposes an algorithm that applies a weight-based Euclidean distance equation to the partial mask and uses only the non-noisy pixels that are the most similar to the original as effective pixels. The proposed algorithm determines the type of filter based on the state of the internal pixels of the designed partial mask and the degree of mask deterioration, which results in superior noise cancellation even in highly damaged environments.

• Journal of Broadcast Engineering
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• v.9 no.1
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• pp.2-7
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• 2004

In this paper, we proposed the content-based music information retrieval technique using some MPEG-7 low-level descriptors. Especially, pitch information and timbral features can be applied in music genre classification, music retrieval, or QBH(Query By Humming) because these can be modeling the stochasticpattern or timbral information of music signal. In this work, we restricted the music domain as O.S.T of movie or soap opera to apply broadcasting system. That is, the user can retrievalthe information of the unknown music using only an audio clip with a few seconds extracted from video content when background music sound greeted user's ear. We proposed the audio feature set organized by MPEG-7 descriptors and distance function by vector distance or ratio computation. Thus, we observed that the feature set organized by pitch information is superior to timbral spectral feature set and IFCR(Intra-Feature Component Ratio) is better than ED(Euclidean Distance) as a vector distance function. To evaluate music recognition, k-NN is used as a classifier

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.7B
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• pp.595-605
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• 2012

DV-Hop is one of the well known range-free localization algorithms. The algorithm works well in case of isotropic network since the sensor and anchor nodes are placed in the entire area. However, it results in large errors in case of anisotropic networks where the hop count between nodes is not linearly proportional to the Euclidean distance between them. Hence, we proposed a novel range-free algorithm for anisotropic networks to improve the localization accuracy. In the paper, the Euclidean distance between anchor node and unknown node is estimated by the average hop distance calculated at each hop count with hop count and distance information between anchor nodes. By estimating the unknown location of nodes with the estimated distance estimated by the average hop distance calculated at each hop, the localization accuracy is improved. Simulation results show that the proposed algorithm has more accuracy than DV-Hop.