• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3041-3063
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• 2018

Community search aims at finding a meaningful community that contains the query node and also maximizes (minimizes) a goodness metric. This problem has recently drawn intense research interest. However, most metric-based algorithms tend to include irrelevant subgraphs in the identified community. Apart from the user-defined metric algorithm, how can we search the natural community that the query node belongs to? In this paper, we propose a novel community search algorithm based on the concept of the k-hop and local distance dynamics model, which can naturally capture a community that contains the query node. The basic idea is to envision the nodes that k-hop away from the query node as an adaptive local dynamical system, where each node only interacts with its local topological structure. Relying on a proposed local distance dynamics model, the distances among nodes change over time, where the nodes sharing the same community with the query node tend to gradually move together, while other nodes stay far away from each other. Such interplay eventually leads to a steady distribution of distances, and a meaningful community is naturally found. Extensive experiments show that our community search algorithm has good performance relative to several state-of-the-art algorithms.

• Journal of Computing Science and Engineering
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• v.4 no.3
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• pp.225-239
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• 2010

This paper introduces a new approach to modeling relative distance among nodes under a variety of communication rates, due to node's mobility in Mobile Ad-hoc Networks (MANETs). When mobile nodes move to another location, the relative distance of communicating nodes will directly affect the data rate of transmission. The larger the distance between two communicating nodes is, the lower the rate that they can use for transferring data will be. The connection certainty of a link also changes because a node may move closer to or farther away out of the communication range of other nodes. Therefore, the stability of a route is related to connection entropy. Taking into account these issues, this paper proposes a new routing metric for MANETs. The new metric considers both link weight and route stability based on connection entropy. The problem of determining the best route is subsequently formulated as the minimization of an object function formed as a linear combination of the link weight and the connection uncertainty of that link. The simulation results show that the proposed routing metric improves end-to-end throughput and reduces the percentage of link breakages and route reparations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.360-365
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• 1995

We shall define the usual fuzzy distance between two fuzzy points in R, the set of all real, numbers, using the usual distance between two points in R. Applying the notion of this usual fuzzy distance, we construct the usual fuzzy topology for R, introduce the notions of lower, stationary and upper cover and obtain the fuzzy Heine-Borel theorem.

• Nonlinear Functional Analysis and Applications
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• v.28 no.2
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• pp.395-405
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• 2023

In this manuscript, we establish the concept of 𝓗(ω, θ)-contraction which based on modified ω distance mappings which introduced by Alegre and Marin [4] in 2016 and 𝓗 simulation functions which introduced by Bataihah et.al. [14] in 2020 and we employ our contraction to prove the existence and uniqueness some new fixed point results. On the other hand, we create some examples and an application to show the importance of our results.

• Genomics & Informatics
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• v.18 no.1
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• pp.2.1-2.11
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• 2020

In this paper, we propose a new approach to detecting outliers in a set of segmented genomes of the flu virus, a data set with a heterogeneous set of sequences. The approach has the following computational phases: feature extraction, which is a mapping into feature space, alignment-free distance measure to measure the distance between any two segmented genomes, and a mapping into distance space to analyze a quantum of distance values. The approach is implemented using supervised and unsupervised learning modes. The experiments show robustness in detecting outliers of the segmented genome of the flu virus.

• The Journal of the Acoustical Society of Korea
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• v.31 no.4
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• pp.266-272
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• 2012

Speaker segmentation is a process of automatically detecting the speaker boundary points in the audio data. Speaker segmentation methods are divided into two categories depending on whether they use a prior knowledge or not: One is the model-based segmentation and the other is the metric-based segmentation. In this paper, we introduce the eigenvoice-based speaker weight distance method and compare it with the representative metric-based methods. Also, we employ and compare the Euclidean and cosine similarity functions to calculate the distance between speaker weight vectors. And we verify that the speaker weight distance method is computationally very efficient compared with the method directly using the distance between the speaker adapted models constructed by the eigenvoice technique.

• Journal of the Korea Society of Computer and Information
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• v.12 no.6
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• pp.147-152
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• 2007

Face detection is a crucial part of the face recognition system. It determines the performance of the whole recognition system. Hausdorff distance metric has been used in face detection and recognition with good results. It defines the distance metric based only on the geometric similarity between two sets or points. However, not only the geometry but also the local patterns around the points are available in most cases. In this paper a new Hausdorff distance measure is proposed that makes hybrid use of the similarity of the geometry and the local patterns around the points. Several experiments shows that the new method outperforms the conventional method.

• Communications for Statistical Applications and Methods
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• v.31 no.3
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• pp.309-322
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• 2024

Identifying algae in water is important for managing algal blooms which have great impact on drinking water supply systems. There have been various microscopic approaches developed for algae classification. Many of them are based on the morphological features of algae. However, there have seldom been mathematical frameworks for comparing the shape of algae, represented as a planar continuous curve obtained from an image. In this work, we describe a recent framework for computing shape distance between two different algae based on the elastic metric and a novel functional representation called the square root velocity function (SRVF). We further introduce statistical procedures for multiple shapes of algae including computing the sample mean, the sample covariance, and performing the principal component analysis (PCA). Based on the shape distance, we classify six algal species in watersheds experiencing algal blooms, including three cyanobacteria (Microcystis, Oscillatoria, and Anabaena), two diatoms (Fragilaria and Synedra), and one green algae (Pediastrum). We provide and compare the classification performance of various distance-based and model-based methods. We additionally compare elastic shape distance to non-elastic distance using the nearest neighbor classifiers.

• Journal of Korea Multimedia Society
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• v.15 no.2
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• pp.215-225
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• 2012

In multimedia database, a multidimensional space-based indexing has been used to increase search efficiency. However, this method is inefficient in terms of ubiquity because it uses Euclidean distance as a scale of distance calculation. On the contrary, a metric space-based indexing method, in which metric axiom is prerequisite is widely available because a metric scale other than Euclidean distance could be used. This paper is attempted to propose a way of improving VP-tree, one of the metric space indexing methods. The VP-tree calculates the distance with an object which is ultimately linked to the a leaf node depending on the node fit for the search range from a root node and examines if it is appropriate with the search range. Because search speed decreases as the number of distance calculations at the leaf node increases, however, this paper has proposed a method which uses the latest interface on query object as the base point of trigonometric inequality for improvement after focusing on the trigonometric inequality-based range compression method in a leaf node. This improvement method would be able to narrow the search range and reduce the number of distance calculations. According to a system performance test using 10,000 video data, the new method reduced search time for similar videos by 5-12%, compared to a conventional method.

• Kyungpook Mathematical Journal
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• v.55 no.1
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• pp.205-218
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• 2015

In this paper, we introduce the concept of generalized c-distance on a cone metric space and prove some common fixed point and coincidence point results by using this notion. Our results generalize and extend several well known comparable results in the literature.