• Microbiology and Biotechnology Letters
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• v.39 no.2
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• pp.119-125
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• 2011

The Egr-1 gene is known to be a transcription factor for activating the expression of many tumor-repressing genes. In this study, three strains activating the promoter of the Egr-1 gene were selected, through the use of Egr-1 luciferase reporter assay and western blotting, from amongst approximately 3,800 oligotrophic bacteria isolated from the cultivated soils of various regions within Korea. These strains were identified as Pseudomonas koreensis on the basis of phylogenetic tree analysis of their 16S ribosomal DNA sequences and biochemical characteristics analyses using a variety of commercial kits (API 20NE, ID 32GN, API ZYM kits). In addition, we discovered that these strains produced anti-bacterial activity against Bacillus subtilis, Staphylococcus aureus and Listeria monocytogenes.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.23 no.1
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• pp.61-71
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• 2010

The attributes related to the health condition of RC deck slabs are analyzed to help us identify and rate the safety level of the bridges in this study. According to the related reports the state assessment for the outward aspects of bridges is the important and critical part for rating the overall structural safety. In this respect, the careful identification for the various state attributes make the field inspection and structural diagnosis very effective. This study analyzes the influence of the state attributes on evaluation classes and the relationship of them by the inductive reasoning, which raise the understanding and performance for evaluation work, and support the logical approach for the state assessment. ID3 algorithm applied to the case set which is constructed from the field reports indicates the main attributes and the precedence governing the assessment, and derives the decision hierarchy for the state assessment.

• Journal of Korea Multimedia Society
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• v.8 no.4
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• pp.525-535
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• 2005

A password-based authenticated tripartite key exchange protocol based on A. Joux's protocol was proposed. By using encryption scheme with shared password, we can resolve man-in-the-middle attack and lack of authentication problems. We also suggested a scheme to avoid the offline dictionary attack to which symmetric encryption schemes are vulnerable. The proposed protocol does not require a trusted party which is required in certificate or identity based authentication schemes. Therefore in a ad hoc network which is difficult to install network infrastructure, the proposed protocol would be very useful. The proposed protocol is more efficient in computation aspect than any existing password-based authenticated tripartite key exchange protocols. When it is used as a base line protocol of tree based group key exchange protocol, the computational weak points of the proposed protocol are compensated.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.5
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• pp.839-850
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• 2014

As the social and financial damages caused by APT attack such as 3.20 cyber terror are increased, the technical solution against APT attack is required. It is, however, difficult to protect APT attack with existing security equipments because the attack use a zero-day malware persistingly. In this paper, we propose a host based anomaly detection method to overcome the limitation of the conventional signature-based intrusion detection system. First, we defined 39 features to identify between normal and abnormal behavior, and then collected 8.7 million feature data set that are occurred during running both malware and normal executable file. Further, each process is represented as 83-dimensional vector that profiles the frequency of appearance of features. the vector also includes the frequency of features generated in the child processes of each process. Therefore, it is possible to represent the whole behavior information of the process while the process is running. In the experimental results which is applying C4.5 decision tree algorithm, we have confirmed 2.0% and 5.8% for the false positive and the false negative, respectively.

• Journal of Species Research
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• v.8 no.4
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• pp.319-336
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• 2019

During an investigation of unrecorded prokaryotic species in Republic of Korea, a total of 53 bacterial strains belonging to the class Gammaproteobacteria were isolated from soil, seawater, tidal flats, rhizosphere, salt ponds, beach sand, urine, manure, sediment, and animal intestine (Russian grayling butterfly [Hipparchia autonoe], mouse [Mus musculus], and sea bass [Lateolabrax japonicus]). Strains were identified to species using the 16S rRNA gene sequence, showing high similarity (>98.7%) with the closest bacterial species and forming a robust clade in the neighbor-joining phylogenetic tree. The 53 strains of Gammaproteobacteria in this study have not been report previously in Korea. Therefore, we describe 27 genera of 16 families in 7 orders: 13 strains in the order Alteromonadales, 1 strain in the order Chromatiales, 11 strains in the order Enterobacterales, 7 strains in the order Oceanospirillales, 10 strains in the order Pseudomonadales, 8 strains in the order Vibrionales, and 3 strains in the order Xanthomonadales. Gram reaction, strain ID, isolation source, and morphological and basic biochemical characteristics are described for each species.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.27-30
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• 2018

We design an expert system for tomato smart farm using decision trees and construct a control system with decision structure similar to that of farmers by using the data generated by factors that vary depending on the surrounding environment of each house. At present, Smart farm's control system does not control itself like the way farmers have done so far. Therefore, the dependency of smart farm control system is still not high. Direct intervention by farmers is indispensable for environmental control based on surrounding environment such as sensor value in smart farm. Therefore, we aimed to design a controller that incorporates decision trees into the expert system to make a system similar to the decision making of farmers. Prior to controlling the equipment in the house, it automatically selects the most direct effect among the various environmental factors, and then builds an expert system for complex control by including criteria for decision making by farmers. This study focused on deriving results using data without using heavy tools. Data is coming out of many smart farms at present. We expect this to be a standard for a methodology that allows farmers to access quickly and easily and reduce direct intervention.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.1-17
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• 2015

In this paper, we report what we have observed with regards to a prediction model for the military based on enlisted men's internal(cumulative records) and external data(SNS data). This work is significant in the military's efforts to supervise them. In spite of their effort, many commanders have failed to prevent accidents by their subordinates. One of the important duties of officers' work is to take care of their subordinates in prevention unexpected accidents. However, it is hard to prevent accidents so we must attempt to determine a proper method. Our motivation for presenting this paper is to mate it possible to predict accidents using enlisted men's internal and external data. The biggest issue facing the military is the occurrence of accidents by enlisted men related to maladjustment and the relaxation of military discipline. The core method of preventing accidents by soldiers is to identify problems and manage them quickly. Commanders predict accidents by interviewing their soldiers and observing their surroundings. It requires considerable time and effort and results in a significant difference depending on the capabilities of the commanders. In this paper, we seek to predict accidents with objective data which can easily be obtained. Recently, records of enlisted men as well as SNS communication between commanders and soldiers, make it possible to predict and prevent accidents. This paper concerns the application of data mining to identify their interests, predict accidents and make use of internal and external data (SNS). We propose both a topic analysis and decision tree method. The study is conducted in two steps. First, topic analysis is conducted through the SNS of enlisted men. Second, the decision tree method is used to analyze the internal data with the results of the first analysis. The dependent variable for these analysis is the presence of any accidents. In order to analyze their SNS, we require tools such as text mining and topic analysis. We used SAS Enterprise Miner 12.1, which provides a text miner module. Our approach for finding their interests is composed of three main phases; collecting, topic analysis, and converting topic analysis results into points for using independent variables. In the first phase, we collect enlisted men's SNS data by commender's ID. After gathering unstructured SNS data, the topic analysis phase extracts issues from them. For simplicity, 5 topics(vacation, friends, stress, training, and sports) are extracted from 20,000 articles. In the third phase, using these 5 topics, we quantify them as personal points. After quantifying their topic, we include these results in independent variables which are composed of 15 internal data sets. Then, we make two decision trees. The first tree is composed of their internal data only. The second tree is composed of their external data(SNS) as well as their internal data. After that, we compare the results of misclassification from SAS E-miner. The first model's misclassification is 12.1%. On the other hand, second model's misclassification is 7.8%. This method predicts accidents with an accuracy of approximately 92%. The gap of the two models is 4.3%. Finally, we test if the difference between them is meaningful or not, using the McNemar test. The result of test is considered relevant.(p-value : 0.0003) This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of enlisted men's data. Additionally, various independent variables used in the decision tree model are used as categorical variables instead of continuous variables. So it suffers a loss of information. In spite of extensive efforts to provide prediction models for the military, commanders' predictions are accurate only when they have sufficient data about their subordinates. Our proposed methodology can provide support to decision-making in the military. This study is expected to contribute to the prevention of accidents in the military based on scientific analysis of enlisted men and proper management of them.