• Kyungpook Mathematical Journal
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• v.62 no.4
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• pp.773-785
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• 2022

In this paper, we propose the notion of a distance between n points, called a g-metric, which is a further generalized G-metric. Indeed, it is shown that the g-metric with dimension 2 is the ordinary metric and the g-metric with dimension 3 is equivalent to the G-metric.

• 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.

• Bulletin of the Korean Mathematical Society
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• v.53 no.2
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• pp.399-410
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• 2016

In this paper, we study the relations between the Thurston metric and the hyperbolic metric on a closed surface of genus $g{\geq}2$. We show a rigidity result which says if there is an inequality between the marked length spectra of these two metrics, then they are isotopic. We obtain some inequalities on length comparisons between these metrics. Besides, we show certain distance distortions under conformal graftings, with respect to the $Teichm{\ddot{u}}ller$ metric, the length spectrum metric and Thurston's asymmetric metrics.

• Journal of the Korean Mathematical Society
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• v.51 no.6
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• pp.1189-1207
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• 2014

A partial metric, also called a nonzero self-distance, is motivated by experience from computer science. Besides a lot of properties of partial metric analogous to those of metric, fixed point theorems in partial metric spaces have been studied recently. We establish several kinds of extended fixed point theorems in ordered partial metric spaces with higher dimension under generalized notions of mixed monotone mappings.

• The Pure and Applied Mathematics
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• v.18 no.1
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• pp.13-29
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• 2011

In this paper, we introduce the new concept of ${\omega}-D^*$-distance on a $D^*$-metric space and prove a non-convex minimization theorem which improves the result of Caristi[1], ${\'{C}}iri{\'{c}}$[2], Ekeland[4], Kada et al.[5] and Ume[8, 9].

• Nonlinear Functional Analysis and Applications
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• v.27 no.3
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• pp.663-677
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• 2022

This present paper extends some fixed point theorems in rectangular b-metric spaces using subadditive altering distance and establishing the existence and uniqueness of fixed point for Kannan type mappings. Non-trivial examples are further provided to support the hypotheses of our results.

• Proceedings of the Acoustical Society of Korea Conference
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• 1987.11a
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• pp.89-92
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• 1987

In a template-matchig-based speech recognition syste, excessive weight given to perceptually unimportant spectral variations is undesirable for discriminating among acoustically similar words. By introducing a simple threshold-type nonlinearity applied to the distance metric, the word recognition performance can be improved for a vocabulary with similar sounding words, without modifying the system structure.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.742-762
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• 2015

In this study, we propose a distance metric learning approach called discriminant metric learning (DML) for face verification, which addresses a binary-class problem for classifying whether or not two input images are of the same subject. The critical issue for solving this problem is determining the method to be used for measuring the distance between two images. Among various methods, the large margin nearest neighbor (LMNN) method is a state-of-the-art algorithm. However, to compensate the LMNN's entangled data distribution due to high levels of appearance variations in unconstrained environments, DML's goal is to penalize violations of the negative pair distance relationship, i.e., the images with different labels, while being integrated with LMNN to model the distance relation between positive pairs, i.e., the images with the same label. The likelihoods of the input images, estimated using DML and LMNN metrics, are then weighted and combined for further analysis. Additionally, rather than using the k-nearest neighbor (k-NN) classification mechanism, we propose a verification mechanism that measures the correlation of the class label distribution of neighbors to reduce the false negative rate of positive pairs. From the experimental results, we see that DML can modify the relation of negative pairs in the original LMNN space and compensate for LMNN's performance on faces with large variances, such as pose and expression.

• 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.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.112-114
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• 2011

On multimedia databases, in order to realize the fast access method, indexing methods for the multidimension data space are used. However, since it is a premise to use the Euclid distance as the distance measure, this method lacks in flexibility. On the other hand, there are metric indexing methods which require only to satisfy distance axiom. Since metric indexing methods can also apply for distance measures other than the Euclid distance, these methods have high flexibility. This paper proposes an improved method of VP-tree which is one of the metric indexing methods. VP-tree follows the node which suits the search range from a route node at searching. And distances between a query and all objects linked from the leaf node which finally arrived are computed, and it investigates whether each object is contained in the search range. However, search speed will become slow if the number of distance calculations in a leaf node increases. Therefore, we paid attention to the candidates selection method using the triangular inequality in a leaf node. As the improved methods, we propose a method to use the nearest neighbor object point for the query as the datum point of the triangular inequality. It becomes possible to make the search range smaller and to cut down the number of times of distance calculation by these improved methods. From evaluation experiments using 10,000 image data, it was found that our proposed method could cut 5%~12% of search time of the traditional method.