• Journal of the Earthquake Engineering Society of Korea
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• v.20 no.4
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• pp.235-243
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• 2016

Korea is part of a region of low to moderate seismicity located inside the Eurasian plate with bedrock located at depths less than 30 m. However, the spectral acceleration obtained from site response analyses based on the geologic conditions of inland areas of the Korean peninsula are significantly different from the current Korean seismic code. Therefore, suitable site classification scheme and design response spectra based on local site conditions in the Korean peninsula are required to produce reliable estimates of earthquake ground motion. In this study, site-specific response analyses were performed at more than 300 sites with at least 100 sites at each site categories of $S_C$, $S_D$, and $S_E$ as defined in the current seismic code in Korea. The process of creating a huge database of input parameters - such as shear wave velocity profiles, normalized shear modulus reduction curves, damping curves, and input earthquake motions - for site response analyses were described. The response spectra and site coefficients obtained from site response analyses were compared with those proposed for the site categories in the current code. Problems with the current seismic design code were subsequently discussed, and the development and verifications of new site classification system and corresponding design response spectra are detailed in companion papers (II-development of new site categories and design response spectra and III-Verifications)

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.135-143
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• 2022

In order to convert Korean folk paintings called Minhwa, a part of traditional Korean heritage, into DBs, it is necessary to design a classification system suitable for the characteristics of folk paintings. A classification system and the generating of unique codes are required to classify and save them. To realize this, a basic classification system was created by listing objects depicted in folk paintings, and keywords were extracted by reclassifying them for each object. In order to assign a unique code to each piece, we organize the English names of each Minhwa since the English names of the folk painting contain the names of objects. The code name is extracted by applying the order of nouns and consonant priority rules in English names and attaching five Arabic numerals. These codes are later assigned to each image file stored in the database and are input together with the keyword. The Minhwa DB constructed in this way enables storage and search centered on objects and keywords and the intuitive inferring of the type of object from the code name.

• Journal of Korea Multimedia Society
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• v.20 no.11
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• pp.1759-1767
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• 2017

Although Open API has been invigorated by advancements in the software industry, diverse types of malicious code have also increased. Thus, many studies have been carried out to discriminate the behaviors of malicious code based on API data, and to determine whether malicious code is included in a specific executable file. Existing methods detect malicious code by analyzing signature data, which requires a long time to detect mutated malicious code and has a high false detection rate. Accordingly, in this paper, we propose a method that analyzes and detects malicious code using association rule mining and an Naive Bayes classification. The proposed method reduces the false detection rate by mining the rules of malicious and normal code APIs in the PE file and grouping patterns using the DHP(Direct Hashing and Pruning) algorithm, and classifies malicious and normal files using the Naive Bayes.

• Korean Journal of Construction Engineering and Management
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• v.2 no.4 s.8
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• pp.80-91
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• 2001

Considering that the classified items in the work section level can have an applicability when those items are being used to cost and specification information with consistency, the work section classification code should be applied as an Integrated code system. Our construction industry is using three work section classification systems for civil engineering projects, such as integrated construction information classification system, standard method of measurement and guide of project specification. And each standard construction specification is also using different work section classification systems. This study suggests a methodology to integrate the code systems in construction specifications with civil engineering standard method of measurement. And the methodology suggested in this study was applied to a web-based prototype system with practical specification codes.

• Journal of the Korea Society for Simulation
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• v.18 no.1
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• pp.63-70
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• 2009

This project presents a classification methodology for malicious codes in Windows OS (Operating System) environment, develops a test classification system. Thousands of malicious codes are brought in every day. In a result, classification system is needed to analyzers for supporting information which newly brought malicious codes are a new species or a variety. This system provides the similarity for analyzers to judge how much a new species or a variety is different to the known malicious code. It provides to save time and effort, to less a faulty analysis. This research includes the design of classification system and test system. We classify the malicious codes to 9 groups and then 9 groups divide the clusters according to the each property. This system provides the similarity for analyzers to save time and effort. It is used prospect system of malicious code in the future.

• Journal of the Korean Operations Research and Management Science Society
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• v.13 no.1
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• pp.31-38
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• 1988

This paper presents a methodology for the development of a part classification and coding system suited to each individual company. When coding a group of parts for a specific company by a general purpose part classification & coding system like OPITZ system, it is frequently observed that we use only a small subset of total available code numbers. Such sparsity in the actual occurrences of code numbers implies that we can design a better system which uses digits of the system more parsimoniously. A 2-dimensional fuzzy ISODATA algorithm is developed to extract the important characteristics for the classification from the set of given parts. Based on the extracted characteristics nd the distances between fuzzy clustering cenetroids, a company-unique classification and coding system can be developed. An example case study for a medium sized machine shop is presented.

• The Journal of the Korean life insurance medical association
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• v.27 no.1
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• pp.21-23
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• 2008

The 5th revision of Korean Classification of Diseases(KCD) became effective on January 1, 2008. It has reflected the changes made to the tenth revision of International Classification of Diseases (ICD-10) between 1998 and 2005 and the suggestions of academic and related societies in Korea. Two important alterations seem to have a major implication in the insurance industry. One would be the official introduction of a Korean version of International Classification of Diseases for Oncology, third edition(ICD-O-3). The borderline ovarian tumor is classified as a borderline neoplasm, which was classified as a malignant neoplasm in the previous edition of International Classification of Diseases for Oncology. The other would be the appearance of non-C-code malignant neoplasm for the diseases, such as polycythemia vera, newly classified as a malignant neoplasm by the current edition of International Classification of Diseases for Oncology. The National Office of Statistics(NSO) adopted the way of implementation used in the Australian Modification of International Classification of Diseases(ICD-10-AM), instead of assigning them into corresponding C code. Overall, the changes made in this revision doesn't seem to have a serious impact on the insurance industry since it has only reflected updates made to ICD-10.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.273-280
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• 2020

Automatic Static Analysis Tools help developers to quickly find potential defects in source code with less effort. However, the tools reports a large number of false positive warnings which do not have to fix. In our study, we proposed an artificial neural network-based warning classification method using topic models of source code blocks. We collect revisions for fixing bugs from software change management (SCM) system and extract code blocks modified by developers. In deep learning stage, topic distribution values of the code blocks and the binary data that present the warning removal in the blocks are used as input and target data in an simple artificial neural network, respectively. In our experimental results, our warning classification model based on neural network shows very high performance to predict label of warnings such as true or false positive.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1807-1823
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• 2016

Local Binary Pattern (LBP) and its variants have powerful discriminative capabilities but most of them just consider each LBP code independently. In this paper, we propose sub oriented histograms of LBP for smoke detection and image classification. We first extract LBP codes from an image, compute the gradient of LBP codes, and then calculate sub oriented histograms to capture spatial relations of LBP codes. Since an LBP code is just a label without any numerical meaning, we use Hamming distance to estimate the gradient of LBP codes instead of Euclidean distance. We propose to use two coordinates systems to compute two orientations, which are quantized into discrete bins. For each pair of the two discrete orientations, we generate a sub LBP code map from the original LBP code map, and compute sub oriented histograms for all sub LBP code maps. Finally, all the sub oriented histograms are concatenated together to form a robust feature vector, which is input into SVM for training and classifying. Experiments show that our approach not only has better performance than existing methods in smoke detection, but also has good performance in texture classification.

• Journal of the Korean Society of Marine Environment & Safety
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• v.30 no.4
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• pp.332-339
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• 2024

Safety is essential for organizations operating in high-risk environments, such as the Navy. Effective safety management requires continuous improvement and supplementation, commonly achieved through the PDCA (Plan-Do-Check-Act) cycle. Despite reinforced safety regulations and expanded education, safety accidents persist in the Navy, indicating a need to enhance the safety accident analysis and classification system. This study aims to analyze and identify the shortcomings of the current Navy safety accident classification system to establish a more effective framework. By doing so, we will be able to register the results of safety accidents, identify their root causes, and propose a 12-digit Navy safety accident classification code. This code will contribute to the development of mid- to long-term safety management policies.