• Journal of Digital Convergence
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• v.15 no.1
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• pp.247-253
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• 2017

Feature selection or variable selection is a data mining scheme for selecting highly relevant features with target concept from high dimensional data. It decreases dimensionality of data, and makes it easy to analyze clusters or classification. A feature selection scheme requires an evaluation function. Most of current evaluation functions are based on statistics or information theory, and they can evaluate only for single feature (one-dimensional data). However, features have interactions between them, and require evaluation function for multi-dimensional data for efficient feature selection. In this study, we propose modification of FSDD evaluation function for utilizing evaluation of multiple features using extended distance function. Original FSDD is just possible for single feature evaluation. Proposed approach may be expected to be applied on other single feature evaluation method.

• Journal of the Korean Society for information Management
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• v.40 no.3
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• pp.77-98
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• 2023

Large Language Models (LLMs) are becoming the major trend in the natural language processing field. These models were built based on research data, but information such as types, limitations, and risks of using research data are unknown. This research would present how to analyze and evaluate the LLMs that were built with research data: LLaMA or LLaMA base models such as Alpaca of Stanford, Vicuna of the large model systems organization, and ChatGPT from OpenAI from the perspective of research data. This quality evaluation focuses on the validity, functionality, and reliability of Data Quality Management (DQM). Furthermore, we adopted the Holistic Evaluation of Language Models (HELM) to understand its evaluation criteria and then discussed its limitations. This study presents quality evaluation criteria for LLMs using research data and future development directions.

• Korean Institute of Interior Design Journal
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• v.20 no.5
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• pp.143-151
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• 2011

Since it is important to understand data acquiring characteristics through relationship between spatial types and spatial elements and apply it to spatial plans for smooth communication between designer and user of space, the conclusions gained from analysis of data acquiring characteristics of spatial elements through image evaluation by types of interior space can be summarized as in the followings: First, for the amount of acquired data by types of interior space, it shows that the acquired amount of data is to change by types and data acquiring method (phrase and image) even though the spatial elements are same. Second, for the data acquiring process of spatial types by gender, it shows that there is a big difference in acquiring of data according to the evaluation method by phrase and image. Third, for the amount of acquired data of spatial types by gender, it shows that there is a difference between male and female, which is by "classic ${\rightarrow}$ modern ${\rightarrow}$ natural" in case of male and "classic ${\rightarrow}$ natural ${\rightarrow}$ modern" in case of female. regarding both of phrase and image. Fourth, for the evaluation by gender, it shows that there is a deviation in the value of difference according to the elements by which data acquiring characteristics evaluate space. It is considered that this deviation characteristic is in need of reflection in the process of spatial evaluation. This study analyzed data acquiring characteristics of space user's spatial elements through image evaluation by types of space to understand how data acquiring would be changed of spatial elements according to type and gender. Through this study, it expects to make clear that, when a designer is planning a certain space, if the space can be a space for the user by understanding of which elements should be exposed to users by types to acquire more data.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.167-174
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• 2006

In this paper, the applicability of strain data to a strain-energy-based damage evaluation methodology in detecting damage in a beam-like structure is demonstrated. For the purpose of this study, one of the premier damage evaluation methodology based on modal amplitudes, the damage index method, is expanded to accomodate strain data, and the numerical and experimental verifications are conducted using numerical and experimental data. To compare the relative performance of damage detection, the damage evaluation using acceleration data is also performed for the same damage scenarios. The experimental strain and acceleration data are extracted from laboratory static and dynamic tests. The numerical and experimental studies show that the strain data as well as acceleration data can be utilized in detecting damage.

• 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 Clothing and Textiles
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• v.25 no.6
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• pp.1091-1099
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• 2001

Evaluation on jean products were varied although they were the identical jeans. Therefore, we could confirm the bias by price. brand, and store when consumer evaluating jean products. The various consumer characteristics also provided effects evaluation on jean products. An evaluation on jean products is very subjective and the degrees depending on these three cues could be varied by consumer's characteristics. For empirical study, experiments by the subjects among students of ewha womans university were done by using jeans as stimulus. Data was collected by a questionnaire made up by a researcher based on the theoretical and pretest. Data was analyzed by ANOVA, factor analysis, grouping analysis, F-test, and etc. 571 data were analysed out of the 600 data. Cues such as price, brand, and store affected significantly the evaluation of jeans. The most important cue of all three was store, then price, and then brand. These three cues affected the evaluation of jean products separately and together. The result of the study was that the consumers characteristics mediated the effects of extrinsic cues like price, brand, and store on jean products evaluation. Consumer's characteristics like prior knowledge and shopping orientation mediated the effects of price, and store cue on jean products evaluation.

• Proceedings of the Korean Nuclear Society Conference
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• 2005.05a
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• pp.956-957
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• 2005

• Proceedings of the Korean Nuclear Society Conference
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• 2005.10a
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• pp.805-806
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• 2005

• Journal of Animal Science and Technology
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• v.65 no.6
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• pp.1180-1193
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• 2023

Most carcass and meat quality traits are moderate to highly heritable, indicating that they can be improved through selection. Genetic evaluation for these types of traits is performed using performance data obtained from commercial and progeny testing evaluation. The performance data from commercial farms are available in large volume, however, some drawbacks have been observed. The drawback of the commercial data is mainly due to sorting of animals based on live weight prior to slaughter, and this could lead to bias in the genetic evaluation of later measured traits such as carcass traits. The current study has two components to address the drawback of the commercial data. The first component of the study aimed to estimate genetic parameters for carcass and meat quality traits in Korean Hanwoo cattle using a large sample size of industry-based carcass performance records (n = 469,002). The second component of the study aimed to describe the impact of sorting animals into different contemporary groups based on an early measured trait and then examine the effect on the genetic evaluation of subsequently measured traits. To demonstrate our objectives, we used real performance data to estimate genetic parameters and simulated data was used to assess the bias in genetic evaluation. The results of our first study showed that commercial data obtained from slaughterhouses is a potential source of carcass performance data and useful for genetic evaluation of carcass traits to improve beef cattle performance. However, we observed some harvesting effect which leads to bias in genetic evaluation of carcass traits. This is mainly due to the selection of animal based on their body weight before arrival to slaughterhouse. Overall, the non-random allocation of animals into a contemporary group leads to a biased estimated breeding value in genetic evaluation, the severity of which increases when the evaluation traits are highly correlated.

• Corrosion Science and Technology
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• v.13 no.2
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• pp.62-69
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• 2014

A huge number of carbon steel piping components installed in the secondary system of nuclear power plants are exposed to aging mechanisms such as FAC (Flow-Accelerated Corrosion), Cavitation, Flashing, and LDIE (Liquid Droplet Impingement Erosion). Those aging mechanisms can lead to thinning of the piping components. To manage the wall thinning degradation, most of utilities in the world predict the wall thinning rate based on the computational program such as CHECWORKS, COMSY, and BRT-CICERO, evaluate the UT (Ultrasonic Test) data, and determine next inspection timing, repair or replacement, if needed. There are several evaluation methods, such as band, blanket, and strip methods, commonly used for determining the wear of piping components from single UT inspection data. It has been identified that those single UT evaluation methods not only do not consider the manufacturing features of pipes, but also may exclude the data of the most thinned point when determining the representative wear rate of piping components. This paper describes a newly developed single UT evaluation method, E-Cross method, for solving above problems and introduces application examples for several pipes and elbows. It was identified that the E-Cross method using the length and width of UT data excluded the most thinned points appropriate as the single UT evaluation method for thinned piping components.