• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3258-3273
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• 2021

Malware is a severe threat to the computing system and there's a long history of the battle between malware detection and anti-detection. Most traditional detection methods are based on static analysis with signature matching and dynamic analysis methods that are focused on sensitive behaviors. However, the usual detections have only limited effect when meeting the development of malware, so that the manual update for feature sets is essential. Besides, most of these methods match target samples with the usual feature database, which ignored the characteristics of the sample itself. In this paper, we propose a new malware detection method that could combine the features of a single sample and the general features of malware. Firstly, a structure of Directed Cyclic Graph (DCG) is adopted to extract features from samples. Then the sensitivity of each API call is computed with Markov Chain. Afterward, the graph is merged with the chain to get the final features. Finally, the detectors based on machine learning or deep learning are devised for identification. To evaluate the effect and robustness of our approach, several experiments were adopted. The results showed that the proposed method had a good performance in most tests, and the approach also had stability with the development and growth of malware.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.8
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• pp.954-957
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• 2016

In cases of military communication plan, it often requires to find a proper solution for frequency assignment within feasible time. Minimizing the number of used resources are related to cost issue, hence it is a critical objective. When the dual polar antenna is used, the performance can be much developed by exploiting the polarization separation. In this paper, we propose an algorithm that assigns polarizations and frequencies within complexity of $O(N^2)$ based on the graph matching theory. We have verified that the proposed algorithm shows almost twice performance relative to the uni-polar frequency assignment algorithms and it approaches very closely to its theoretical optima.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1243-1258
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• 2017

A new medical materials scheduling system and its modeling method for the complex rescue are presented. Different from other similar system, first both the BeiDou Satellite Communication System (BSCS) and the Special Fiber-optic Communication Network (SFCN) are used to collect the rescue requirements and the location information of disaster areas. Then all these messages will be displayed in a special medical software terminal. After that the bipartite graph models are utilized to compute the optimal scheduling of medical materials. Finally, all these results will be transmitted back by the BSCS and the SFCN again to implement a fast guidance of medical rescue. The sole drug scheduling issue, the multiple drugs scheduling issue, and the backup-scheme selection issue are all utilized: the Kuhn-Munkres algorithm is used to realize the optimal matching of sole drug scheduling issue, the spectral clustering-based method is employed to calculate the optimal distribution of multiple drugs scheduling issue, and the similarity metric of neighboring matrix is utilized to realize the estimation of backup-scheme selection issue of medical materials. Many simulation analysis experiments and applications have proved the correctness of proposed technique and system.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.4
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• pp.285-292
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• 2013

This study proposes a network matching method based on a network division technique. The proposed method generates polygons surrounded by links of the original network dataset, and detects corresponding polygon group pairs using a intersection-based graph clustering. Then corresponding sub-network pairs are obtained from the polygon group pairs. To perform the geometric correction between them, the Iterative Closest Points algorithm is applied to the nodes of each corresponding sub-networks pair. Finally, Hausdorff distance analysis is applied to find link pairs of networks. To assess the feasibility of the algorithm, we apply it to the networks from the KTDB center and commercial CNS company. In the experiments, several Hausdorff distance thresholds from 3m to 18m with 3m intervals are tested and, finally, we can get the F-measure of 0.99 when using the threshold of 15m.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.1-6
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• 2022

This paper deals with the maximum cardinality matching(MCM) problem. The augmenting path technique is well known in MCM. MCM is obtained by $O({\sqrt{n}}m)$ time complexity augmenting path algorithm for the general graph, and O(m log n) algorithm for the bipartite graph. On the other hand, this paper suggests O(n) linear time algorithm. The proposed algorithm based on the basic principle of as possible as largest selected inter-vertex edges in order to obtain the MCM. This paper simply selects edge {u,𝜐} that the minimum degree vertex u and minimum degree vertex 𝜐 in N_G(u) 𝜈(G)=k times iteration. For various general and bipartite graphs experimental data, this algorithm can be get the 𝜈(G) exactly.

• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.63-83
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• 2023

Many existing studies have found significant correlations between green spaces, including urban parks, and children's happiness. Furthermore, it was implied that the area/proximity of the urban park would be effective in enhancing infancy happiness. However, inferring causal effects from observed data requires appropriate adjustment of confounding variables, and from this perspective, the causal relationship between the area of urban parks and children's happiness has not been well understood. The causal effect of urban parks on children's happiness was estimated in this study using data from the panel study on Korean children. As methods for adjusting confounding variables, regression adjustment using a regression method, weighting method, and matching method were used, and key concepts of each method were described before the analysis results. Confounders were chosen for the analysis using a directed acyclic graph. In contrast to previous research, the analysis found no significant causal relationship between the size of the city park and children's happiness.

• Journal of Korea Game Society
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• v.12 no.1
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• pp.123-132
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• 2012

There are many of games using smart devices. Voice recognition is can be useful way for input. In the game, voice have to be quickly recognized, at the same time it have to be manipulated promptly as well. In this study, we developed the optimized real-time phoneme recognition using max flow matching that it can be efficiently used in the game field. Firstly, voice wavelength is transformed to FFT, secondly, transformed value is made by a graph in Z plane, thirdly, data is extracted in specific area, and then data is saved in database. After all the value is recognized using weighted bipartite max flow matching. This way would be useful method in game or robot field when researchers hope to recognize the fast voice recognition.

• Journal of Information Processing Systems
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• v.14 no.4
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• pp.961-979
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• 2018

For many years, matching in a bipartite graph has been widely used in various assignment problems, such as stable marriage problem (SMP). As an application of bipartite matching, the problem of stable marriage is defined over equally sized sets of men and women to identify a stable matching in which each person is assigned a partner of opposite gender according to their preferences. The classical SMP proposed by Gale and Shapley uses preference lists for each individual (men and women) which are infeasible in real world applications for a large populace of men and women such as matrimonial websites. In this paper, we have proposed an enhancement to the SMP by computing a weighted score for the users registered at matrimonial websites. The proposed enhancement has been formulated into profit maximization of matrimonial websites in terms of their ability to provide a suitable match for the users. The proposed formulation to maximize the profits of matrimonial websites leads to a combinatorial optimization problem. We have proposed greedy and genetic algorithm based approaches to solve the proposed optimization problem. We have shown that the proposed genetic algorithm based approaches outperform the existing Gale-Shapley algorithm on the dataset crawled from matrimonial websites.

• Journal of KIISE:Databases
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• v.32 no.5
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• pp.498-508
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• 2005

A shape data is used one oi most important feature for image retrieval as data to reflect meaning of image. Especially, structural feature of shape is widely studied because it represents primitive properties of shape and relation information between basic units well. However, most structural features of shape have the problem that it is not able to guarantee an efficient search time because the features are expressed as graph or tree. In order to solve this problem, we generate curvature-based morphological graph, End design key to cluster shapes from this graph. Proposed this graph have contour features and morphological features of a shape. Shape retrieval is accomplished by stages. We reduce a search space through clustering, and determine total similarity value through pattern matching of external curvature. Various experiments show that our approach reduces computational complexity and retrieval cost.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.5
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• pp.892-898
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• 2012

Rough classification methods are used to improving the recognition speed in many character recognition problems. In this case, some irreversible result can occur by an error in rough classification. Methods for duplicating each model in several classes are used in order to reduce this risk. But the errors by rough classfication can not be completely ruled out by these methods. In this paper, an recognition method is proposed to increase speed that matches models selectively without any increase in error. This method constructs a model graph using similarity between models. Then a search process begins from a particular point in the model graph. In this process, matching of unnecessary models are reduced that are not similar to the input pattern. In this paper, the proposed method is applied to the recognition problem of handwriting numbers and upper/lower cases of English alphabets. In the experiments, the proposed method was compared with the basic method that matches all models with input pattern. As a result, the same recognition rate, which has shown as the basic method, was obtained by controlling the out-degree of the model graph and the number of maintaining candidates during the search process thereby being increased the recognition speed to 2.45 times.