• 시스템엔지니어링워크숍
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• s.4
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• pp.135-140
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• 2004

Defense analysis & evaluation includes menace analysis, validation analysis, problem analysis, scientific technical analysis, technical trad-off analysis, alternative analysis, cost analysis, etc. Reliable related data is required to perform these analysis activities efficiently. but in case of these defense analysis & evaluation data acquisition system, the data is insufficient and scattered about each organization. the data of database system is also not utilized sufficiently. abroad technical data is also low level data such as catalog or military officer's collection. therefore, this paper propose defense analysis & evaluation data acquisition system by systems engineering process. we also propose construction method of data acquisition system.

• Journal of the Korean Society of Systems Engineering
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• v.1 no.2
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• pp.69-76
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• 2005

Defense analysis & evaluation includes menace analysis, validation analysis, problem analysis, scientific technical analysis, technical trade-off analysis, alternative analysis, cost analysis, etc. Reliable related data is required to perform these analysis activities efficiently. but in case of these defense analysis & evaluation data acquisition system, the data is insufficient and scattered about each organization. The data of database system is also not utilized sufficiently. Abroad technical data is also low level data such as catalog or military officer's collection. Therefore, this paper propose defense analysis & evaluation data acquisition system by systems engineering process. we also propose construction method of data acquisition system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.1-10
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• 2018

Current evaluation practices for IT projects suffer from several problems, which include the difficulty of self-explanation for the evaluation results and the improperly scaled scoring system. This study aims to develop a methodology of opinion mining to extract key factors for the causal relationship analysis and to assess the feasibility of quantifying evaluation scores from text comments using opinion mining based on big data analysis. The research has been performed on the domain of publicly procured IT proposal evaluations, which are managed by the National Procurement Service. Around 10,000 sets of comments and evaluation scores have been gathered, most of which are in the form of digital data but some in paper documents. Thus, more refined form of text has been prepared using various tools. From them, keywords for factors and polarity indicators have been extracted, and experts on this domain have selected some of them as the key factors and indicators. Also, those keywords have been grouped into into dimensions. Causal relationship between keyword or dimension factors and evaluation scores were analyzed based on the two research models-a keyword-based model and a dimension-based model, using the correlation analysis and the regression analysis. The results show that keyword factors such as planning, strategy, technology and PM mostly affects the evaluation result and that the keywords are more appropriate forms of factors for causal relationship analysis than the dimensions. Also, it can be asserted from the analysis that evaluation scores can be composed or calculated from the unstructured text comments using opinion mining, when a comprehensive dictionary of polarity for Korean language can be provided. This study may contribute to the area of big data-based evaluation methodology and opinion mining for IT proposal evaluation, leading to a more reliable and effective IT proposal evaluation method.

• Journal of the Korean Society of Tobacco Science
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• v.22 no.2
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• pp.184-189
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• 2000

This study was conducted to develop the objective evaluation method for tobacco smoke. The evaluation was carried out by using the data of cut or blended tobacco components, smoke components, electric nose system (ENS), and sensory test. By using the statistical methods, such as cluster analysis, discriminant analysis, factor analysis, correlation analysis, and multiple regression analysis, the relationship among the data of tobacco, smoke, ENS, and sensory evaluation was studied. By the results of cluster analysis, the data from smoke analysis by GC and ENS were able to select the difference of tobacco leaf characteristics. As the results of discriminant analysis, grouping by the components of tobacco leaves and smoke was possible and the results of GC analysis of smoke could be used for discrimination of tobacco leaves. In the results of factor analysis, nicotine, tar, CO, puff No and pH in the smoke were the factors effecting on the tobacco leaf characteristics. From the correlation analysis, aroma, taste, irritation, and smoke volume of sensory test had high relation to tar, p-cresol threonolatone, levoglucosane, and quinic acid- ${\gamma}$ -lactone of smoke. The ENS data showed high efficiency for discriminant analysis and cluster analysis, but it was not good for factor analysis, and correlation analysis. It was possible to estimate tobacco leaves and their blending characteristics by the analytical data of tobacco leaves, smoke, ENS, and sensory test results. By the multiple regression analysis, some correlation among selected chemical components and sensory evaluation were found. This study strongly indicated that the some chemical analysis data was available for the objective evaluation of tobacco sensory attributes.

• Journal of the Korean Society of Clothing and Textiles
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• v.40 no.6
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• pp.1034-1044
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• 2016

This study seeks a method to objectively evaluate sensibility based on Big Data in the field of design. In order to do so, this study examined the sensibility responses on design factors for the public through a network analysis of texts displayed in social media. 'Hanbok', a formal clothing that represents Korea, was selected as the subject for the research methodology. We then collected 47,677 keywords related to Hanbok from 12,000 posts on Naver blogs from January $1^{st}$ to December $31^{st}$ 2015 and that analyzed using social matrix (a Big Data analysis software) rather than using previous survey methods. We also derived 56 key-words related to design elements and sensibility responses of Hanbok. Centrality analysis and CONCOR analysis were conducted using Ucinet6. The visualization of the network text analysis allowed the categorization of the main design factors of Hanbok with evaluation terms that mean positive, negative, and neutral sensibility responses. We also derived key evaluation factors for Hanbok as fitting, rationality, trend, and uniqueness. The evaluation terms extracted based on natural language processing technologies of atypical data have validity as a scale for evaluation and are expected to be suitable for utilization in an index for sensibility evaluation that supplements the limits of previous surveys and statistical analysis methods. The network text analysis method used in this study provides new guidelines for the use of Big Data involving sensibility evaluation methods in the field of design.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.2
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• pp.71-80
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• 2010

This thesis is purposed on developing security product co-evaluation statistics administrate program which is can administrate or analysis CC accreditation product using by DEVS modeling via portal site of member of CCRA. Via developing security product evaluation statistics administrate program, it can analysis the trend of all countries of the world in many ways, and noticed the ways of evaluation and accreditation of most countries via scheme analysis. Except this, it can analysis the situation of accreditation trend of any countries via data analysis of ICCC 2009. Also, For trend analysis to evaluation technique of CCRA member, it analyzed up to date technology and policy of the evaluation organization and the Certification Authority of most countries. And it peformed analysis the most trend of information security of evaluation authorization in CCRA member countries. In this program, It provide the function of trend statistics analysis which can statically analyzed the evaluation accreditation trends of most countries and automatical statistics by categorization ( by Product, Class and statistics in national) and report creation functions which can easily extraction and use the needed data. It has been updated the related informations until latest accredited product using by CC(Common Criteria) portal home page's data.

• The Journal of Bigdata
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• v.1 no.1
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• pp.53-60
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• 2016

Data of central government and local government was collected automatically from the public data portal. And we did the multidimensional analysis based on various perspective like file format and present condition of public data. To complete this work, we constructed Data Warehouse based on the other countries' evaluation index case. Finally, the result from service level evaluation by using multidimensional analysis was used to display each area, establishment, fields.

• Journal of Web Engineering
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• v.20 no.3
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• pp.795-816
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• 2021

The existing industry evaluation method utilizes the method of collecting the structured information such as the financial information of the companies included in the relevant industry and deriving the industrial evaluation index through the statistical analysis model. This method takes a long time to calculate the structured data and cause the time delay problem. In this paper, to solve this time delay problem, we derive monthly industry-specific interest and likability as a time series data type, which is a new industry evaluation indicator based on unstructured data. In addition, we propose a method to predict the industrial risk index, which is used as an important factor in industrial evaluation, based on derived industry-specific interest and likability time series data.

• Journal of the Korean Society of Clothing and Textiles
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• v.33 no.1
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• pp.139-148
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• 2009

In this study, we tried to verify the effectiveness of 3D scans of clothed participants from 20 to 24 year old men in fit analysis process. Carrying out fit analysis, we used 3D scan data and direct fit evaluation with the basic garments made as semi-fitted bodice pattern for men. The result of fit evaluation through 3D scan data showed the expert rated fitting items more positive than direct fit evaluation. Even though, generally there was a no significant difference in the response between 3D and direct fit analysis. Only there was a significant difference in the response to the specific place such as the center front line, neckline, shoulder seam, and etc. There was a no significant difference between the result of 3D scan and direct fit evaluation in the subjects' group. Also the result of assessment of 3D targeting consumers showed very positive and interested in using 3D scans for fit analysis. The ability to rotate the 3D scans for a variety of views proved to be a very effective process to analyze fit. Moreover, digital data is easily accessed at any locations and any time. Fit analysis using 3D scans could be great tool for not only fit evaluation in research but also better fitting in apparel industry.

• Journal of the Korean Data and Information Science Society
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• v.16 no.2
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• pp.349-357
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• 2005

Principal component analysis(PCA) was carried out to evaluate the water quality with the monitering data collected from 1997 to 2003 along the coastal area of Ulsan, Korea. To enhance evaluation and to complement descriptive power of traditional PCA, optimal scaling was applied to transform the original data into optimally scaled data. Cluster analysis was also applied to classify the monitering stations according to their characteristics of water quality.