• The Journal of Korean Institute of Communications and Information Sciences
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• v.13 no.5
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• pp.379-389
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• 1988

In this paper, an algorithm for obtaining 2-state switching failure function and a terminal pair reliability evaluation method in a communication network are proposed. The communication network is modeled by a graph. By using the sequence of matrix transformations for the graph, minimal cut-set matrix representing all minimal cut-sets which completely interrupt the communication path is determined and 2-state switching failure function is then obtained from the minimal cut-set matrix. The terminal pari reliability of the communication network is evaluated by corresponding the random variables to 2-state switching failure function. Illustrative examples are provided to demonstrate the algorithm. A computer program evaluating the terminal pair reliability in a complex and large network has also been developed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.5
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• pp.1976-1997
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• 2016

This paper investigates the problem of co-tier interference mitigation for dynamic small- cell networks, in which the load of each small-cell varies with the number of active associated small-cell users (SUs). Due to the fact that most small-cell base stations (SBSs) are deployed in an ad-hoc manner, the problem of reducing co-tier interference caused by dynamic loads in a distributed fashion is quite challenging. First, we propose a new distributed channel allocation method for small-cells with dynamic loads and define a dynamic interference graph. Based on this approach, we formulate the problem as a dynamic interference graph game and prove that the game is a potential game and has at least one pure strategy Nash equilibrium (NE) point. Moreover, we show that the best pure strategy NE point minimizes the expectation of the aggregate dynamic co-tier interference in the small-cell network. A distributed dynamic learning algorithm is then designed to achieve NE of the game, in which each SBS is unaware of the probability distributions of its own and other SBSs' dynamic loads. Simulation results show that the proposed approach can mitigate dynamic co-tier interference effectively and significantly outperform random channel selection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5191-5211
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• 2016

It is essential to satisfy class-specific QoS constraints to provide broadband services for new generation networks. The present study proposes a QoS-driven multicast scheme for wireless networks in which the transmission rate and end-to-end delay are assumed to be bounded during a multiple multicast session. A distributed algorithm was used to identify a cost-efficient sub-graph between the source and destination which can satisfy QoS constraints of a multicast session. The model was then modified as to be applied for wireless networks in which satisfying interference constraints is the main challenge. A discrete power control scheme was also applied for the QoS-aware multicast model to accommodate the effect of transmission power level based on link capacity requirements. We also proposed random power allocation (RPA) and gradient power allocation (GPA) algorithms to efficient resource distribution each of which has different time complexity and optimality levels. Experimental results confirm that the proposed power allocation techniques decrease the number of unavailable links between intermediate nodes in the sub-graph and considerably increase the chance of finding an optimal solution.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.684-690
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• 2007

We describe our method to predict the direction of conditional probabilistic dependencies between clusters of random variables. Selected variables called 'gateway variables' are used to predict the conditional probabilistic dependency relations between clusters. The direction of conditional probabilistic dependencies between clusters are predicted by finding directed acyclic graph (DAG)-shaped dependency structure between the gateway variables. We show that our method shows meaningful prediction results in determining directions of conditional probabilistic dependencies between clusters.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.11
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• pp.23-32
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• 2018

The purpose of this study is to establish a walking energy weighted ERAM model that can predict the pedestrian volume by the connection structure of the vertical and horizontal spaces within a three-dimensional building. The process of building a walking-energy weighted ERAM model is as follows. First, the spatial graph was used to reproduce three-dimensional buildings with vertical and horizontal spatial connection structures. Second, the walking energy was measured on the spatial graph. Third, ERAM model was used to apply weights with spatial connection properties in random walking environment, and the walking energy weights were applied to the ERAM model to calculate the walk energy weighted ERAM values and visualize the distribution of pedestrian flow. To verify the validation of the established model, existing and proposed spatial analysis models were compared to real space. The results of this study are as follows : The model proposed in this study showed as much elaborated estimation of pedestrian traffic flow in real space as in traditional spatial analysis models, and also it showed much higher level of forecasting pedestrian traffic flow in real space than existing models.

• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.381-394
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• 2016

Graphical model represents conditional dependencies between variables as a graph with nodes and edges. It is widely used in various fields including physics, economics, and biology to describe complex association. Conditional dependencies can be estimated from a inverse covariance matrix, where zero off-diagonal elements denote conditional independence of corresponding variables. This paper proposes a efficient BCDR (block coordinate descent with random permutation) algorithm using graphics processing units and random permutation for the CONCORD (convex correlation selection method) based on the BCD (block coordinate descent) algorithm, which estimates a inverse covariance matrix based on pseudo-likelihood. We conduct numerical studies for two network structures to demonstrate the efficiency of the proposed algorithm for the CONCORD in terms of computation times.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.438-441
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• 2007

Recently, due to molecular biology and engineering technology, DNA microarray makes people watch thousands of genes and the state of variation from the tissue samples of living body. With DNA Microarray, it is possible to construct a genetic group that has similar expression patterns and grasp the progress and variation of gene. This paper practices Cluster Analysis which purposes the discovery of biological subgroup or class by using gene expression information. Hence, the purpose of this paper is to predict a new class which is unknown, open leukaemia data are used for the experiment, and MCL (Markov CLustering) algorithm is applied as an analysis method. The MCL algorithm is based on probability and graph flow theory. MCL simulates random walks on a graph using Markov matrices to determine the transition probabilities among nodes of the graph. If you look at closely to the method, first, MCL algorithm should be applied after getting the distance by using Euclidean distance, then inflation and diagonal factors which are tuning modulus should be tuned, and finally the threshold using the average of each column should be gotten to distinguish one class from another class. Our method has improved the accuracy through using the threshold, namely the average of each column. Our experimental result shows about 70% of accuracy in average compared to the class that is known before. Also, for the comparison evaluation to other algorithm, the proposed method compared to and analyzed SOM (Self-Organizing Map) clustering algorithm which is divided into neural network and hierarchical clustering. The method shows the better result when compared to hierarchical clustering. In further study, it should be studied whether there will be a similar result when the parameter of inflation gotten from our experiment is applied to other gene expression data. We are also trying to make a systematic method to improve the accuracy by regulating the factors mentioned above.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.134-142
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• 2012

This paper analyze an LDPC code replication method suitable for cloud computing. First, we determine the number of blocks suitable for cloud computing through analysis of the performance for the file availability and storage overhead. Also we determine the type of LDPC code appropriate for cloud computing through the performance for three types of LDPC codes. Finally we present the graph random generation method and the comparing method of each generated LDPC code's performance by the iterative decoding process. By the simulation, we confirmed the best graph's regularity is left-regular or least left-regular. Also, we confirmed the best graph's total number of edges are minimum value or near the minimum value.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4946-4960
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• 2020

In a device-to-device (D2D) underlaid cellular network, there exist two types of co-channel interference. One type is inter-layer interference caused by spectrum reuse between D2D transmitters and cellular users (CUEs). Another type is intra-layer interference caused by spectrum sharing among D2D pairs. To mitigate the inter-layer interference, we first derive the interference limited area (ILA) to protect the coverage probability of cellular users by modeling D2D users' location as a Poisson point process, where a D2D transmitter is allowed to reuse the spectrum of the CUE only if the D2D transmitter is outside the ILA of the CUE. To coordinate the intra-layer interference, the spectrum sharing criterion of D2D pairs is derived based on the (signal-to-interference ratio) SIR requirement of D2D communication. Based on this criterion, D2D pairs are allowed to share the spectrum when one D2D pair is far from another sufficiently. Furthermore, to maximize the energy efficiency of the system, a resource allocation scheme is proposed according to weighted graph coloring theory and the proposed ILA restriction. Simulation results show that our proposed scheme provides significant performance gains over the conventional scheme and the random allocation scheme.

• The KIPS Transactions:PartA
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• v.9A no.3
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• pp.387-398
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• 2002

As whole genome sequences of many organisms have been revealed by small-scale genome projects, the intensive research on individual genes and their functions has been performed. However on-memory algorithms are inefficient to analysis of whole genome sequences, since the size of individual whole genome is from several million base pairs to hundreds billion base pairs. In order to effectively manipulate the huge sequence data, it is necessary to use the indexed data structure for external memory. In this paper, we introduce a workbench system for analysis and visualization of whole genome sequence using string B-tree that is suitable for analysis of huge data. This system consists of two parts : analysis query part and visualization part. Query system supports various transactions such as sequence search, k-occurrence, and k-mer analysis. Visualization system helps biological scientist to easily understand whole structure and specificity by many kinds of visualization such as whole genome sequence, annotation, CGR (Chaos Game Representation), k-mer, and RWP (Random Walk Plot). One can find the relations among organisms, predict the genes in a genome, and research on the function of junk DNA using our workbench.