• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.65-70
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• 2006

For grouping the direction of improvement in the survey industry of Korea Standard Industry Classification, watching for internal survey industry, It has a purpose to present the direction of improvement. On the based of UN International Standard industrial classification, survey industry classification of KSIC has not been focused at the special quality of survey industry which is growing fast. Standard Industry Classification of foreign survey has rapidly adapting to survey industry development as detailed and specialized on purpose. thus, Korea's survey KSIC is in urgency to specialize and detail at the field of survey industry as well.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.221-241
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• 2018

As we enter the knowledge society, the importance of information as a new form of capital is being emphasized. The importance of information classification is also increasing for efficient management of digital information produced exponentially. In this study, we tried to automatically classify and provide tailored information that can help companies decide to make technology commercialization. Therefore, we propose a method to classify information based on Korea Standard Industry Classification (KSIC), which indicates the business characteristics of enterprises. The classification of information or documents has been largely based on machine learning, but there is not enough training data categorized on the basis of KSIC. Therefore, this study applied the method of calculating similarity between documents. Specifically, a method and a model for presenting the most appropriate KSIC code are proposed by collecting explanatory texts of each code of KSIC and calculating the similarity with the classification object document using the vector space model. The IPC data were collected and classified by KSIC. And then verified the methodology by comparing it with the KSIC-IPC concordance table provided by the Korean Intellectual Property Office. As a result of the verification, the highest agreement was obtained when the LT method, which is a kind of TF-IDF calculation formula, was applied. At this time, the degree of match of the first rank matching KSIC was 53% and the cumulative match of the fifth ranking was 76%. Through this, it can be confirmed that KSIC classification of technology, industry, and market information that SMEs need more quantitatively and objectively is possible. In addition, it is considered that the methods and results provided in this study can be used as a basic data to help the qualitative judgment of experts in creating a linkage table between heterogeneous classification systems.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.27 no.4
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• pp.398-422
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• 2017

Objectives: The major aim of this study is to construct the database of retrospective exposure assessment for crystalline silica through reviews of literatures in South Korea. Methods: Airborne concentrations of crystalline silica were collected using an academic information search engine, Research Information Service System(RISS), operated by the Korea Education & Research Information Service(KERIS). The key words used for the literature search were 'silica', 'crystalline silica', 'cristobalite', 'quartz' and 'tridymite'. A total number of 18 published documents with the information of crystalline silica level in air or bulk samples were selected and used to estimate retrospective exposures to crystalline silica. Weighted arithmetic mean(WAM) calculated across studies was summarized by industry type. Industries were classified according to Korea Standard Industrial Classification(KSIC) using information provided in the literature. Results: A total of 2,131 individual air sampling data measured from 1987 to 2012 were compiled. Compiled individual measurement data consisted of 827 respirable crystalline silica (RCS), 31 total crystalline silica(TCS), 24 crystalline silica(CS), 778 respirable dust(RD) and 471 total dust(TD). Most of RCS measurements(68.9%) were collected from 'cast of metals(KSIC 243)'. Comparing industry types, 'mining coal and lignite(KISC 051)' showed the highest WAM concentration of RCS, $0.14mg/m^3$, followed by $0.11mg/m^3$ of 'manufacture of other non-metallic mineral products(KSIC 239)', $0.108mg/m^3$ of 'manufacture of ceramic ware(KSIC 232)', $0.098mg/m^3$ of 'heavy construction(KSIC 412)' and $0.062mg/m^3$ of 'cast of metals(KSIC 243)'. In terms of crystalline silica contents in airborne dust, 'manufacture of other non-metallic mineral products(KSIC 239)' showed the highest value of 7.3%(wt/wt), followed by 6.8% of 'manufacture of ceramic ware(KSIC 232)', 5.8% of 'mining of iron ores(KSIC 061)', 4.9% of 'cast of metals(KSIC 243)' and 4.5% of 'heavy construction(KSIC 412)'. WAM concentrations of RCS had no consistent trends over time from 1994 ($0.26mg/m^3$) to 2012 ($0.12mg/m^3$). Conclusion: The data set related RCS exposure level by industries can be used to determine not only the possibility of retrospective exposure to RCS, but also to evaluate the level of quantitative retrospective exposure to RCS.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.701-712
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• 2013

The e-learning industry status survey statistic provides information about the actual conditions of supply and demand of the e-learning industries. NIPA (National IT Industry Promotion Agency) has published the annual report of the survey results since 2004. Due to the 9th version of the KSIC (Korean standard industrial classification) revised in 2008, a refinement of the sampling design for the survey becomes necessary, especially that for the business demand sector. This article, based on the 9th revision of the KSIC, constructs a stratification of the target population used for the e-learning industry status survey on the business demand sector. Classification of strata in the business population is based on the industrial type and employment scale of business. Under the stratified population, we design a sampling scheme by using the power allocation method that enables us to satisfy a target coefficient of variation of each industrial stratum. In order to secure an accurate survey results based on the proposed sampling design, we consider the problem of calculating the design weights, derivation of parameter estimators, and formulas of their standard errors.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.2
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• pp.71-80
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• 2014

Standard Industrial classification is a key factor of technology spillover effect. It is the result of the empirical study that is the IC(industrial classification) which influences the technology spillover effect by way of interaction term, or moderating effect combing independent variables and moderators. As relatively high technology industry is more important than the low counterpart in R&D management system. And the result of the study says that Government should support SME's considering the IC moderating effect and different subsidies which is appropriated to the SME's IC(industrial classification). This way of Government subsidy will improve the efficiency of industrial policy effect of SME's.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.1
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• pp.129-138
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• 2017

The aim of this paper is to suggest methodology for local governments when discovering locally customized future promising industries with regard to policies of central government, regional competencies, and industrial promising. Firstly, key industries by region specified in '5-years regional industrial development master plan(2014)' were utilized. Secondly, science and technology competency by region was calculated with analyzing patent data in each key industries. Thirdly, industrial promising was verified by calculating Knowledge Stock and Activity Index based on measuring industry-IPC linkage. Based on the methodology proposed above, case study(case of Busan city) was done. Finally, 7 core industries and 94 candidates of future promising industries were extracted on the basis of 5 digit of KSIC subdivision. The methodology is expected to contribute local governments to establish evidence-based, efficient, and future-oriented local R&D roadmapping.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.24 no.3
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• pp.345-352
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• 2014

Objectives: This study is aimed to describe the current situation about urinary biomarker N-methylformamide(NMF) for workers exposed to N,N-dimethylformamide(DMF) according to industrial classification. Materials: Special health examination records of the workers who had undergone urinary biological monitoring in 2013 were collected. The numbers and percentage of workers, whose urinary NMF values were above the limit of detection(LOD) and above the biological exposure index(BEI) were calculated. Health relatedness with DMF as judged by their doctors was also described. All description was classified according to the $9^{th}$ Korean Standard Industrial Classification(KSIC). Results: It appeared that most workers exposed to DMF belong to manufacturing section(80.7%). The geometric mean(GM) values of urinary NMF were 6.25 mg/L, 3.54, and 3.86 for the manufacturing section, professional, scientific and technical activities section, and for the construction section respectively. In detail, it seemed that division of textiles(except apparel) (GM 7.51 mg/L), division of leather, luggage and footwear(11.59 mg/L), and division of rubber and plastic products(6.89 mg/L) were highly exposed to DMF with a high percentage of workers with urinary NMF values above BEI. This was probably due to the effect of skin absorption that the division of leather, luggage and footwear showed the highest urine NMF GM. Conclusions: It seemed that workers in manufacture industries such as textile, leather, luggage, footwear, rubber and plastic products were highly exposed to DMF. So, efforts should be focused on those industries in order to effectively diminish worker's exposure. Further studies to compare DMF air-monitoring with bio-monitoring according to industrial classification should be considered.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.516-518
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• 2020

통계청에서는 지역별고용조사, 인구총조사 등 다양한 조사를 실시하고 있다. 이러한 조사에서는 응답자의 사업체명, 사업체가 주로 하는 일, 응딥자가 한 일, 부서 및 직책 정보 등을 조사해서 조사되어진 자료를 토대로 한국 표준 산업분류 형태로 코드를 부여해 주고 있다. 각 조사에서는 자연어 형태로 입력을 받아서 자료처리 기간에 코딩작업을 하는 조사가 있고 조사원이 입력을 하면서 자동코딩시스템을 이용해서 산업분류 코드를 입력하는 방식도 있다. 본 연구에서는 전자의 방법을 자동화하는 것에 초점을 두었다. 딥러닝 알고리즘을 이용해서 기존에 코드부여가 완료된 자료를 가지고 실험을 해본 결과 조사된 모든 항목을 사용했을 때에는 CNN이 81.36%로 가장 좋은 성능을 보였고, 항목을 2가지로 (사업체가 주로 하는 일/응딥자가 한 일) 줄였을 경우 전체적으로 더 좋은 성능을 보였다. 그 중에 CNN-LSTM이 85.91%로 가장 좋은 성능을 보였다.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.1
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• pp.61-69
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• 2016

Understanding load patterns and customer classification is a basic step in analyzing the behavior of electricity consumers. To achieve that, there have been many researches about clustering customers' daily load data. Nowadays, the deployment of advanced metering infrastructure (AMI) and big-data technologies make it easier to study customers' load data. In this paper, we study load clustering from the view point of yearly and daily load pattern. We compare four clustering methods; K-means clustering, hierarchical clustering (average & Ward's method) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). We also discuss the relationship between clustering results and Korean Standard Industrial Classification that is one of possible labels for customers' load data. We find that hierarchical clustering with Ward's method is suitable for clustering load data and KSIC can be well characterized by daily load pattern, but not quite well by yearly load pattern.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.541-544
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• 2022

재난안전분야 연구 및 기술개발을 위한 현황분석 및 동향파악을 위해 연구개발활동의 주요 성과물인 특허정보의 활용은 매우 중요하다. 본 논문에서는 재난안전분야 국내 특허문헌을 대상으로 산업분야별 현황 및 특성을 분석하였다. 분석연구를 위해 재난안전분야 키워드를 포함하고 표준산업분류 매핑이 가능한 국내 특허정보를 식별하여 데이터셋으로 사용하였다. 분석 결과, 표준산업분류 체계의 산업분야 레벨별 특허 분포 현황 및 출원기관 분포 현황과 산업분야별 핵심 키워드가 자세히 파악되었다. 연구결과는 국가 재난대응을 위한 지능형 위기경보 체계 등을 개발하기 위한 정보 자원으로 활용이 기대되며, 향후 논문, 보고서를 통합한 포괄적인 재난안전분야 문헌 분석 연구가 필요하다.