• Korean Journal of Microbiology
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• v.39 no.2
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• pp.67-75
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• 2003

One large topic in comparative genomics is to predict functional annotation by classifying protein sequences. Computational approaches for function prediction include protein structure prediction, sequence alignment and domain prediction or binding site prediction. This paper is on another computational approach searching for sets of homologous sequences from sequence similarity graph. Methods based on similarity graph do not need previous knowledges about sequences, but largely depend on the researcher's subjective threshold settings. In this paper, we propose a genome sequence clustering method of iterative testing and graph decomposition, and a simple method to calculate a strict threshold having biochemical meaning. Proposed method was applied to known bacterial genome sequences and the result was shown with the BAG algorithm's. Result clusters are lacking some completeness, but the confidence level is very high and the method does not need user-defined thresholds.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.703-722
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• 2016

Passive wireless devices have increasing civilian and military applications, especially in the scenario with wearable devices and Internet of Things. In this paper, we study indoor localization of a target equipped with radio-frequency identification (RFID) device in ultra-wideband (UWB) wireless networks. With known room layout, deterministic multipath components, including the line-of-sight (LOS) signal and the reflected signals via multipath propagation, are employed to locate the target with one transmitter and a single inaccurate receiver. A factor graph corresponding to the joint posterior position distribution of target and receiver is constructed. However, due to the mixed distribution in the factor node of likelihood function, the expressions of messages are intractable by directly applying belief propagation on factor graph. To this end, we approximate the messages by Gaussian distribution via minimizing the Kullback-Leibler divergence (KLD) between them. Accordingly, a parametric message passing algorithm for indoor passive localization is derived, in which only the means and variances of Gaussian distributions have to be updated. Performance of the proposed algorithm and the impact of critical parameters are evaluated by Monte Carlo simulations, which demonstrate the superior performance in localization accuracy and the robustness to the statistics of multipath channels.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.1
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• pp.48-52
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• 2010

In this paper, we introduce a new path planning algorithm which combines the merits of a visibility graph algorithm and an adaptive cell decomposition. We quantize a given map with empty cells, blocked cells, and mixed cells, then find the optimal path on the quantized map using a visibility graph algorithm. For reducing the number of the quantized cells we use the quad-tree technique which is used in an adaptive cell decomposition, and for improving the performance of the visibility checking in making a visibility graph we propose a new visibility checking method which uses the property of the quad-tree instead of the well-known rotational sweep-line algorithm. For the more efficient visibility checking, we propose two additional improvements for our suggested method. Both of them are used for reducing the visited cells in the quad-tree. The experiments for a performance comparison of our algorithm with other well-known algorithms show that our proposed method is superior to others.

• Journal of Korea Society of Industrial Information Systems
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• v.10 no.2
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• pp.26-31
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• 2005

In this paper, we present the document summarizers which are simpler and more condense than the existing ones generally used in the web search engines. This method is a statistic-based summarization method using the concept of the complete graph. We suppose that each sentence as a vertex and the similarity between two sentences as a link of the graph. We compare this summarizer with those of Clustering and MMR techniques which are well-known as the good summarization methods. For the comparison, we use FScore using the summarization results generated by human subjects. Our experimental results verify the accuracy of this method, being about $30\%$ better than the others.

• Journal of KIISE:Databases
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• v.29 no.4
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• pp.274-284
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• 2002

Queries on XML are based on paths in the data graph, which is represented as an edge labeled graph model. All proposed query languages for XML express queries using regular expressions to traverse arbitrary paths in the data graph. A meaningful query usually has several regular path expressions in it, but much of recent research is more concerned with optimizing a single path expression. In this paper, we present an efficient technique to process multiple path expressions in a query. We developed a data structure named as the path identifier(PID) to identify whether two given nodes lie on the fame path in the data graph or not, and utilized the PID for efficient processing of multiple path expressions. We implement our technique and present preliminary performance results.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.475-478
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• 2019

웹어플리케이션의 발전에 따라 자바스크립트 런타임 플랫폼인 Node.js 의 사용도 증가하고 있다. 개발자들은 Node.js 의 다양한 모듈을 활용하여 프로그래밍을 하게 되는데, Node.js 모듈 보안의 중요성에 비하여 모듈 취약점 분석은 충분히 이루어지지 않고 있다. 본 논문에서는 소스코드의 구조를 트리 형태로 표현하는 Abstract Syntax Tree 와 소스코드의 실행 흐름 및 변수의 흐름을 그래프로 나타내는 Control Flow Graph/Data Flow Graph 가 Node.js 모듈 취약점 분석에 효율적으로 활용될 수 있음을 서술하고자 한다. Node.js 모듈은 여러 스크립트 파일로 나누어져 있다는 점과 사용자의 입력이 분명하다는 특징이 있다. 또한 자바스크립트 언어를 사용하므로 선언된 변수들의 타입에 따라 적용되는 범위인 scope 가 다르게 적용된다는 특징이 있다. 본 논문에서는 이러한 Node.js 모듈의 특징을 고려하여 Abstract Syntax Tree 및 Control Flow Graph/Data Flow Graph 을 어떻게 생성하고 취약점 분석에 활용할 것인지에 대한 방법론을 제안하고, 실제 분석에 활용할 수 있는 코드 구현을 통하여 구체화시키고자 한다.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.11
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• pp.876-880
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• 2009

A control flow graph(CFG) is an essential data structure for program analyses based on graph theory or control-/data- flow analyses. Esterel is an imperative synchronous language and its synchronous parallelism makes it difficult to construct a CFG of an Esterel program. In this work, we present a method to construct over-approximated CFGs for Esterel. Our method is very intuitive and generated CFGs include not only exposed paths but also invisible ones. Though the CFGs may contain some inexecutable paths due to complex combinations of parallelism and exception handling, they are very useful for other program analyses.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.677-682
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• 2006

Multi-domain design is difficult because such systems tend to be complex and include a mixtures of electrical, mechanical, hydraulic, and thermal components. To design an optimal system, unified and automated procedure with efficient search technique is required. This paper introduces design method for multi-domain system to obtain design solutions automatically, combining bond graph which is domain independent modeling tool and genetic programming which is well recognized as a powerful tool for open-ended search. The suggested design methodology has been applied for design of electric fitter, electric printer drive, and and pump system as a proof of concept for this approach.

• Communications for Statistical Applications and Methods
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• v.26 no.3
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• pp.261-272
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• 2019

In this paper, we propose a procedure to build a prediction interval of the sum of dependent binary random variables over a graph to account for the dependence among binary variables. Our main interest is to find a prediction interval of the weighted sum of dependent binary random variables indexed by a graph. This problem is motivated by the prediction problem of various elections including Korean National Assembly and US presidential election. Traditional and popular approaches to construct the prediction interval of the seats won by major parties are normal approximation by the CLT and Monte Carlo method by generating many independent Bernoulli random variables assuming that those binary random variables are independent and the success probabilities are known constants. However, in practice, the survey results (also the exit polls) on the election are random and hardly independent to each other. They are more often spatially correlated random variables. To take this into account, we suggest a spatial auto-regressive (AR) model for the surveyed success probabilities, and propose a residual based bootstrap procedure to construct the prediction interval of the sum of the binary outcomes. Finally, we apply the procedure to building the prediction intervals of the number of legislative seats won by each party from the exit poll data in the $19^{th}$ and $20^{th}$ Korea National Assembly elections.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.24-32
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• 2021

Recently, ransomware attacks have been infected through various channels such as e-mail, phishing, and device hacking, and the extent of the damage is increasing rapidly. However, existing known malware (static/dynamic) analysis engines are very difficult to detect/block against novel ransomware that has evolved like Advanced Persistent Threat (APT) attacks. This work proposes a method for modeling ransomware malicious behavior based on graph databases and detecting novel multi-complex malicious behavior for ransomware. Studies confirm that pattern detection of ransomware is possible in novel graph database environments that differ from existing relational databases. Furthermore, we prove that the associative analysis technique of graph theory is significantly efficient for ransomware analysis performance.