• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.43-54
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• 2005

This paper proposes a novel texture segmentation method using Bayesian image segmentation method and SOM(Self Organization feature Map). Multi-scale wavelet coefficients are used as the input of SOM, and likelihood and a posterior probability for observations are obtained from trained SOMs. Texture segmentation is performed by a posterior probability from trained SOMs and MAP(Maximum A Posterior) classification. And the result of texture segmentation is improved by context information. This proposed segmentation method shows better performance than segmentation method by HMT(Hidden Markov Tree) model. The texture segmentation results by SOM and multi-sclae Bayesian image segmentation technique called HMTseg also show better performance than by HMT and HMTseg.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.605-616
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• 2021

While malwares must be accurately identifiable from arbitrary programs, existing studies using classification techniques have limitations that they can only be applied to limited samples. In this work, we propose a method to utilize API call frequency to detect and classify malware families from arbitrary programs. Our proposed method defines a rule that checks whether the call frequency of a particular API exceeds the threshold, and identifies a specific family by utilizing the rate information on the corresponding rules. In this paper, decision tree algorithm is applied to define the optimal threshold that can accurately identify a particular family from the training set. The performance measurements using 4,443 samples showed 85.1% precision and 91.3% recall rate for family detection, 97.7% precision and 98.1% reproduction rate for classification, which confirms that our method works to distinguish malware families effectively.

• Journal of Korea Multimedia Society
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• v.13 no.2
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• pp.237-248
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• 2010

When scenes in the real world are perceived for the purpose of computer/robot vision fields, there are great deals of texture based patterns in them. This paper introduces a texture feature representation on a coordinate system in which many different patterns can be represented with a mathematical model (Gabor function). The representation of texture features of each pattern on the coordinate system results in the high performance/competence of texture pattern classification. A decision tree algorithm is used to classify pattern data represented on the proposed coordinate system. The experimental results for the texture pattern classification show that the proposed method is better than previous researches.

• The Journal of Information Systems
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• v.26 no.3
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• pp.273-293
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• 2017

Purpose The purpose of this study is to introduce a framework that helps Muslims to determine whether a food can be consumed. It can complement existing Halal food classification services having a difficulty of constructing Halal food database. Design/methodology/approach The proposed framework includes two components. First, OCR(Optical Character Recognition) technique is utilized to read the food additive information. Second, machine learning methods were used to trained and predicted to determine whether a food can be consumed using the provided information. Findings Among the compared machine learning methods, SVM(Support Vector Machine), DT(Decision Tree), and NB(Naive Bayes), SVM with linear kernel and DT had excellent performance in the Halal food classification. The framework which adopting the proposed framework will enhance the tourism experiences of Muslim tourists who consider keeping the Islamic law most importantly. Furthermore, it can eventually contribute to the enhancement of smart tourism ecosystem.

• Journal of the Korean Society of Safety
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• v.36 no.5
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• pp.52-60
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• 2021

As the prevention of fatal accidents is considered an essential part of social responsibilities, both government and individual have devoted efforts to mitigate the unsafe conditions and behaviors that facilitate accidents. Several studies have analyzed the factors that cause fatal accidents and compared them to those of non-fatal accidents. However, studies on mathematical and systematic analysis techniques for identifying the features of fatal accidents are rare. Recently, various industrial fields have employed machine learning algorithms. This study aimed to apply machine learning algorithms for the classification of fatal and non-fatal accidents based on the features of each accident. These features were obtained by text mining literature on accidents. The classification was performed using four machine learning algorithms, which are widely used in industrial fields, including logistic regression, decision tree, neural network, and support vector machine algorithms. The results revealed that the machine learning algorithms exhibited a high accuracy for the classification of accidents into the two categories. In addition, the importance of comparing similar cases between fatal and non-fatal accidents was discussed. This study presented a method for classifying accidents using machine learning algorithms based on the reports on previous studies on accidents.

• Journal of the Korea Safety Management & Science
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• v.4 no.4
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• pp.15-26
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• 2002

The quantitative evaluation method of the safety management cost was suggested to prevent a gas accident as a major industrial accident. In a gas governor station, process risk assessments such as the fault tree analysis(FTA) and the consequence analysis were performed. Based on process risk assessments, potential accident costs were estimated and the cost-benefit analysis(CBA) was performed. From the cost-benefit analysis for five classification items of safety management cost, the order of the cost/benefit ratio was estimated.

• Communications for Statistical Applications and Methods
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• v.11 no.3
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• pp.553-562
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• 2004

In this short communication we discuss the bias problems of CART in split variable selection and suggest a method to reduce the variable selection bias. Penalties proportional to the number of categories or distinct values are applied to the splitting criteria of CART. The results of empirical comparisons show that the proposed modification of CART reduces the bias in variable selection.

• Journal of Korean Society of Forest Science
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• v.98 no.3
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• pp.324-330
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• 2009

The objective of this study is to estimate the carbon sequestration in forest stands using KOMPSAT-2 imagery. For estimating the amount of carbon sequestration, the stand biomass of forest was estimated with the total weight, which was the sum of individual tree weight. Individual tree volumes could be estimated by the crown width extracted from KOMPSAT-2 imagery. In particular, the carbon conversion index and the ratio of the $CO_2$ molecular weight to the C atomic weight, reported in the Intergovernmental Panel on Climate Change (IPCC) guideline, was used to convert the stand biomass into the amount of carbon sequestration. Thereafter, the KOMPSAT-2 imagery was classified with the segment based classification (SBC) method in order to quantify carbon sequestration by tree species. This approach, estimating the amount of carbon sequestration for certain species in stand, can be available to extend plot-based carbon sequestration to stand-based carbon sequestration.

• Journal of Cadastre & Land InformatiX
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• v.45 no.1
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• pp.225-240
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• 2015

The effect of passing pedestrians'characteristics on locational goodwill was empirically modeled and tested. The theoretical basis for the study was central place theory, bid rent and, agglomeration theory, and demand externality theory. The data included information on goodwill, retail rents and passing pedestrians' characteristics in 100 retail trade areas in Seoul. The empirical model was tested with the sample of 1,307 retail units in Seoul, South Korea. The data set was analyzed with the Classification and Regression Tree software. As the results, using the regression tree method, the variables does affect locational goodwill in the each retail trade area were the volume of pedestrians around 2:00 pm on weekdays, volume of pedestrians around 4:00 pm on weekdays, and volume of pedestrians around 8:00 pm on weekdays. In summary, not only the economic base in the retail trade area but also the volume of passing pedestrians should be considered to determine the locational goodwill.

• Korean Journal of Microbiology
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• v.23 no.3
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• pp.235-240
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• 1985

A total of 10 main characters of 16 species of family Rhodospirllaceae were phenetically and cladistically analyzed by Farris' method. The obtained phenogram and cladistic tree were compared with Bergey's manual and other papers. The results supported that the system of 5 subgroups (genera) is available in family Rhodospirllaceae and indicated that close affinities between Rhodospirllum tenue and Rhodopseudomonas gelatinosa and between Rhodomicrobium vannielii and other species of genus Rhodopseudomonas were proved.