• Earthquakes and Structures
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• v.15 no.1
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• pp.1-8
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• 2018

A new site classification system and site coefficients based on local site conditions in Korea were developed and implemented as a part of minimum design load requirements for general seismic design. The new site classification system adopted bedrock depth and average shear wave velocity of soil above the bedrock as parameters for site classification. These code provisions were passed through a public hearing process before it was enacted. The public hearing process recommended to modify the naming of site classes and adjust the amplification factors so that the level of short-period amplification is suitable for economical seismic design. In this paper, the new code provisions were assessed using dynamic centrifuge tests and by comparing the design response spectra (DRS) with records from 2016 Gyeongju earthquake, the largest earthquake in history of instrumental seismic observation in Korea. The dynamic centrifuge tests were performed to simulate the representative Korean site conditions, such as shallow depth to bedrock and short-period amplification characteristics, and the results corroborated with the new DRS. The Gyeongju earthquake records also showed good agreement with the DRS. In summary, the new code provisions are reliable for representing the site amplification characteristic of shallow bedrock condition in Korea.

• Proceedings of the KSR Conference
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• 2003.10b
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• pp.259-264
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• 2003

Failure code system must include data of maintenance history, classification of failure, affective range and situation when failure occur. But the present failure code system have used a simple code system for classification to include only merchandise and tools. Advantageously, expansional standard code system that will be developed, it make that users can take steps of standardized overhaul and inspection as proposal maintain contents when failure occur or something wrong in vehicle of urban transit. Standardized failure codes must be developed and used that manufacturing companies and urban transit operating companies in order to give effect to maintenance works.

• Proceeding of Spring/Autumn Annual Conference of KHA
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• 2008.11a
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• pp.469-474
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• 2008

In the last decade many advanced home network services have been provided by construction industry, and many researches on the area have been carried out. One of the problems we are confronted with is that we do not have a standard classification scheme for home network services yet. In this paper a more stable classification scheme is suggested after comparative analysis of many different schemes in previous researches. More commonly highly attended service categories in the scheme is selected, and those are examined in terms of how commonly they are supplied by construction industry. In the later part of this paper the current home network building certification code is reviewed if the code complies with the highly attended categories in the classification scheme suggested in this paper. Some ideas to improve the current certification code is suggested by adding or optionally adding the highly attended categories.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.255-256
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• 1998

In this paper, we propose the multi-axial classification system using parallel coding method that is systemic and flexible properties for representing dental clinical behavior. The methodology and organization of this thesis as follows. First, an analysis of other classification systems. Second, the domain of medical behavior and axises using selected elements was were determined. Third, the new code system is constructed of these common factors in properties of prediction of hierarchy, brevity, simplicity, flexibility and mnemonic usage. Finally, the framework of classification system for dental was made using multi-axial code system. The result of the this study, the eight bases axis of multi-axial code system is composed and can be basic information of research for construction of classification system of all medical domain.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.454-475
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• 2018

This paper presents a novel Android malware classification model planned to classify and categorize Android malicious code at Drebin dataset. The amount of malicious mobile application targeting Android based smartphones has increased rapidly. In this paper, Restricted Boltzmann Machine and Deep Belief Network are used to classify malware into families of Android application. A texture-fingerprint based approach is proposed to extract or detect the feature of malware content. A malware has a unique "image texture" in feature spatial relations. The method uses information on texture image extracted from malicious or benign code, which are mapped to uncompressed gray-scale according to the texture image-based approach. By studying and extracting the implicit features of the API call from a large number of training samples, we get the original dynamic activity features sets. In order to improve the accuracy of classification algorithm on the features selection, on the basis of which, it combines the implicit features of the texture image and API call in malicious code, to train Restricted Boltzmann Machine and Back Propagation. In an evaluation with different malware and benign samples, the experimental results suggest that the usability of this method---using Deep Belief Network to classify Android malware by their texture images and API calls, it detects more than 94% of the malware with few false alarms. Which is higher than shallow machine learning algorithm clearly.

• Journal of Digestive Cancer Research
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• v.9 no.2
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• pp.57-59
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• 2021

Carcinoid tumor is called as neuroendocrine tumor and is classified into neuroendocrine tumor Grade 1, neuroendocrine tumor Grade 2, and neuroendocrine carcinoma based on the differentiation of tumors. Recently, the incidence of rectal carcinoid tumor has been increasing probably due to the increased interest on screening colonoscopy and the advancement of endoscopic imaging technology. As the rectal carcinoid shows a wide range of clinical characteristics such as metastasis and long-term prognosis depending on the size and histologic features, it is a challenge to give a consistent diagnostic code in patients with the rectal carcinoid. If the rectal carcinoid tumor is less than 1 cm in size, it can be given as the code of definite malignancy or the code of uncertain malignant potential according to International Classification of Diseases for Oncology (ICD-O) by World Health Organization (WHO). Because patients get different amount of benefit from the insurance company based on different diagnostic codes, this inconsistent coding system has caused a significant confusion in the clinical practice. In 2019, WHO updated ICD-O and Statistics Korea subsequently changed Korean Standard Classification of Diseases (KCD) including the code of rectal carcinoid tumors. This review will summarize what has been changed in recent ICD-O and KCD system regarding the rectal carcinoid tumor and surmise its clinical implication.

• Journal of Korean Society of Transportation
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• v.20 no.7
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• pp.167-175
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• 2002

The objective of this study is to reveal a shipper's preference for road freight transport according to commodity classification code. The shipper's preference in freight transport can be obtained by using value of travel-time savings. The characteristics of freight are so various that the shipper's preference also appear widely different. In these days, there were few attempts to estimate value of freight travel-time savings in Korea. but most of them included only rail or marine freight transport so it couldn't obtain unique travel-time savings for road freight transport. In this study the value of travel-time savings of road freight transport was estimated according to commodity classification code. Revealed preference method and associated binominal logit models were applied to estimate the value of travel-time savings in transit from a Seoul metropolitan commodity flow survey data in 1998. Data sets were segmented by commodity classification code and nineteen binominal legit models were estimated according to segmented groups. The results of this study showed that the value of freight travel-time savings varied wide ranges from 16,441 won to 66,769 won per hour a vehicle along with commodity classification code.

• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.23-30
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• 2021

An Automated Industry and Occupation Coding System assigns statistical classification code to the enormous amount of natural language data collected from people who write about their industry and occupation. Unlike previous studies that applied information retrieval, we propose a system that does not need an index database and gives proper code regardless of the level of classification. Also, we show our model, which utilized KoBERT that achieves high performance in natural language downstream tasks with deep learning, outperforms baseline. Our method achieves 95.65%, 91.51%, and 97.66% in Occupation/Industry Code Classification of Population and Housing Census, and Industry Code Classification of Census on Basic Characteristics of Establishments. Moreover, we also demonstrate future improvements through error analysis in the respect of data and modeling.

• Journal of KIISE:Software and Applications
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• v.33 no.10
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• pp.886-895
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• 2006

Fingerprint classification reduces the number of matches required in automated fingerprint identification systems by categorizing fingerprints into a predefined class. Support vector machines (SVMs), widely used in pattern classification, have produced a high accuracy rate when performing fingerprint classification. In order to effectively apply SVMs to multi-class fingerprint classification systems, we propose a novel method in which SVMs are generated with the one-vs-all (OVA) scheme and dynamically ordered with $na{\ddot{i}}ve$ Bayes classifiers. More specifically, it uses representative fingerprint features such as the FingerCode, singularities and pseudo ridges to train the OVA SVMs and $na{\ddot{i}}ve$ Bayes classifiers. The proposed method has been validated on the NIST-4 database and produced a classification accuracy of 90.8% for 5-class classification. Especially, it has effectively managed tie problems usually occurred in applying OVA SVMs to multi-class classification.

• Korean Journal of Computational Design and Engineering
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• v.21 no.3
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• pp.325-333
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• 2016

This paper aims to classify objects and their properties represented in Korea Building Act sentences for applying to BIM-enabled automated code compliance checking task. In order to conduct automated code compliance checking, it is necessary to develop translation process of converting the building act sentences into computer-executable forms. However, since Korea building act sentences are written in natural language, some of requirements are ambiguous to translate explicitly. In this regard, the building act sentences regarding building permit requirements are analyzed focusing on the regulation-specific objects and related properties representation from noun phrases within the scope of this paper. From 1977 building act sentences and attached reference regulations, 1200 regulation-specific objects and about 220 related properties are extracted and classified. In the application for the classification, object-property database is implemented and some of application using the database and the regulation-specific classification is suggested to support to generate rule set written in computable codes.