• Proceedings of the Korean Society of Computer Information Conference
• /
• 2023.01a
• /
• pp.209-210
• /
• 2023

본 연구에서는 OTT 개인화 추천 서비스에서 5요인 성격이론을 적용하여 사용자들의 정보 프라이버시 염려에 관한 영향을 미치는 요인을 파악하고 프라이버시 염려와 개인정보 제공의도와의 관계에 관한 가설을 도출하였다. OTT 개인화 추천 서비스의 정보 프라이버시 염려에 영향을 미치는 요인으로 성격이론인 친화성, 정서적 불안정성, 성실성, 외향성, 경험에 대한 개방성 다섯 가지 요인을 도출하였으며, OTT 추천 서비스의 특성인 추천서비스의 정확성, 추천서비스의 다양성, 추천 서비스의 신기성 세 가지 요인을 도출하였다. 본 연구는 5요인 성격이론을 OTT 개인화 추천서비스 연구에 적용하였다는 데 의의가 있을 뿐만 아니라, OTT 기업들이 사용자의 정보 프라이버시 염려 행동을 이해하는 데에 도움을 줄 것으로 기대한다.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.60 no.12
• /
• pp.2215-2220
• /
• 2011

The accurate short-term load forecasting is essential for the efficient power system operation and the system marginal price decision of the electricity market. So far, errors of load forecasting for Chuseok Holiday are very big compared with forecasting errors for the other special days. In order to improve the accuracy of load forecasting for Chuseok Holiday, selection of input data, the daily normalized load patterns and load forecasting model are investigated. The efficient data selection and daily normalized load pattern based on fuzzy linear regression model is proposed. The proposed load forecasting method for Chuseok Holiday is tested in recent 5 years from 2006 to 2010, and improved the accuracy of the load forecasting compared with the former research.

• The Journal of Information Systems
• /
• v.19 no.3
• /
• pp.1-12
• /
• 2010

Personality is comprehensive nature of the mood and attitude of people, most clearly revealed in the interaction with other people. This study is a analysis on personality type to information system security and control from financial institute employee. Based on 'The Big Five' personality model, this study develops hypothetical causal relationships of potential organization member's personality and their information system security and control. Research hypotheses are empirically tested with data collected from 901 employees. Results show that employees of high level security mind are the owner of conscientious and emotional stable personality and the employees of high level control mind are the owner of agreeable and emotional stable personality. Therefore the owner of agreeable and stable personality is higher security and control than others.

• Design & Manufacturing
• /
• v.17 no.1
• /
• pp.27-32
• /
• 2023

In this paper, we intend to predict the mass of the spiral using CAE and machine learning. First, We generated 125 data for the experiment through a complete factor design of 3 factors and 5 levels. Next, the data were derived by performing a molding analysis through CAE, and the machine learning process was performed using a machine learning tool. To select the optimal model among the models learned using the learning data, accuracy was evaluated using RMSE. The evaluation results confirmed that the Support Vector Machine had a good predictive performance. To evaluate the predictive performance of the predictive model, We randomly generated 10 non-overlapping data within the existing injection molding condition level. We compared the CAE and support vector machine results by applying random data. As a result, good performance was confirmed with a MAPE value of 0.48%.

• Korea Journal of Hospital Management
• /
• v.23 no.1
• /
• pp.41-50
• /
• 2018

The purpose of this research is to evaluate Brand Value by using the K-BPI(Korea Brand Power Index) of Korea Management Association which is based on consumer awareness, as well as to identify how Brand Value which is composed of top of awareness, unaided awareness, aided awareness, image, possibility of purchasing, preference, affects on the Market Share perceived by consumers. This research subjects were 10 hospitals with more than 1,000 beds in Seoul and Gyeonggi-do, and survey subjects were 20 or older adults living in the metropolitan area of Korea. Using K-BPI for measuring Brand Value and used calculation of Market Share according to consumer preference model for measuring Market Share. The major results of this research are as follows: First, this research identified that the top 5 hospitals of largest hospitals in metropolitan area measured by using K-BPI and Market Share were same hospitals as Big 4 hospitals of previous research evaluating the comprehensive competitiveness of hospitals and also same as hospitals that appeared recently. Second, Big 5 hospitals ranked first to fifth in both Brand Value and Market Share. To identify the relationship between K-BPI items(top of awareness, unaided awareness, aided awareness, image, availability, preference) and Market Share, multiple linear regression was used by dividing 5 upper and 5 lower group of hospitals per each. The group of 5 upper hospitals had a significant effect on Market Share, with 'top of awareness', 'unaided awareness', 'aided awareness'. The group of 5 lower hospitals had a significant effect on Market Share with 'unaided awareness', 'aided awareness'. The results of this study and hospitals of the first to third hospitals published in the K-BPI press release reported by KMAC in 2017, and the previous studies evaluating the comprehensive competitiveness hospitals, all had one thing in common that Big 4 hospitals ranked high position. This suggests that evaluation of Brand Value also can be a evaluation measure of hospital. A new competitiveness of hospital is expected by managing brand awareness to have a brand competitiveness and by securing intrinsic Market Share of consumer to reach hospital use ultimately.

• Geophysics and Geophysical Exploration
• /
• v.9 no.4
• /
• pp.299-303
• /
• 2006

Geologic noise, especially located at shallow depth, can be a great obstacle in the interpretation of geophysical data. Thus, it is important to suppress geologic noise in order to accurately detect major anomalous bodies in the survey area. In the inversion of geophysical data, model parameters at shallow depth, which have small size and low contrast of physical property, can be regarded as one of geologic noise. The least-squares method with smoothness constraint has been widely used in the inversion of geophysical data. The method imposes a big penalty on the large model parameter, while a small penalty on the small model parameter. Therefore, it is not easy to suppress small anomalous boies. In this study, we developed a new inversion scheme which can effectively suppress geologic noise by imposing a big penalty on the slowly varying model parameter and a small penalty on the largely varying model parameter. We call the method MTE (main-target emphasizing) inversion. Applying the method to the inversion of 2.5D small loop EM data, we can ensure that it is effective in suppressing small anomalous boies and emphasizing major anomalous bodies in the survey area.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.05a
• /
• pp.134-137
• /
• 2016

Big data analysis refers the ability to store, manage and analyze collected data from an existing database management tool. In addition, extract value from large amounts of structured or unstructured data set and means the technology to analyze the results. Meta-analysis is a statistical integration method that delivers an opportunity to overview the entire result of integrating and analyzing many quantitative research results. Meta-analysis is sometimes expressed as an analysis of another analysis. Commonly, factors of metabolic syndrome can be defined as abdominal obesity, high triglycerides, low high density lipoprotein cholesterol, elevated blood pressure, and elevated fasting glucose. This study will find meaningful mediator variables for criterion variables that affect before and after the metabolic syndrome studies, on the basis of the results of a meta-analysis. We reviewed a total of 5 studies related to metabolic syndrome published in Korea between 2000 and 2016, where a cause and effect relationship is established between variables that are specified in the conceptual model of this study.

• The Journal of Bigdata
• /
• v.5 no.1
• /
• pp.11-27
• /
• 2020

In this paper, we developed a big data platform to store, process, and analyze effectively on such education longitudinal study data. And it was applied to the Seoul Education Longitudinal Study(SELS) to confirm its usefulness. The developed platform consists of data preprocessing unit and data analysis unit. The data preprocessing unit 1) masking, 2) converts each item into a factor 3) normalizes / creates dummy variables 4) data derivation, and 5) data warehousing. The data analysis unit consists of OLAP and data mining(DM). In the multidimensional analysis, OLAP is performed after selecting a measure and designing a schema. The DM process involves variable selection, research model selection, data modification, parameter tuning, model training, model evaluation, and interpretation of the results. The data warehouse created through the preprocessing process on this platform can be shared by various researchers, and the continuous accumulation of data sets makes further analysis easier for subsequent researchers. In addition, policy-makers can access the SELS data warehouse directly and analyze it online through multi-dimensional analysis, enabling scientific decision making. To prove the usefulness of the developed platform, SELS data was built on the platform and OLAP and DM were performed by selecting the mathematics academic achievement as a measure, and various factors affecting the measurements were analyzed using DM techniques. This enabled us to quickly and effectively derive implications for data-based education policies.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.23 no.1
• /
• pp.34-45
• /
• 2021

Weather is one of the important factors in the agricultural industry as it affects the price, production, and quality of crops. Upland crops are directly exposed to the natural environment because they are mainly grown in mountainous areas. Therefore, it is necessary to provide accurate weather for upland crops. This study examined the effectiveness of 12 forest soil factors to interpolate the weather in mountainous areas. The daily temperature and precipitation were collected by the Korea Meteorological Administration between January 2009 and December 2018. The Generalized Additive Model (GAM), Kriging, and Random Forest (RF) were considered to interpolate. For evaluating the interpolation performance, automatic weather stations were used as training data and automated synoptic observing systems were used as test data for cross-validation. Unfortunately, the forest soil factors were not significant to interpolate the weather in the mountainous areas. GAM with only geography aspects showed that it can interpolate well in terms of root mean squared error and mean absolute error. The significance of the factors was tested at the 5% significance level in GAM, and the climate zone code (CLZN_CD) and soil water code B (SIBFLR_LAR) were identified as relatively important factors. It has shown that CLZN_CD could help to interpolate the daily average and minimum daily temperature for upland crops.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.15 no.11
• /
• pp.6565-6575
• /
• 2014

Hamilton's Markov-switching model [5] was extended to the simultaneous equations model. A framework for an instrumental variable interpretation of full information maximum likelihood (FIML) by Hausman [4] can be used to deal with the problem of simultaneous equations based on the Hamilton filter [5]. A comparison of the proposed FIML Markov-switching model with the LIML Markov-switching models [1,2,3] revealed the LIML Markov-switching models to be a special case of the proposed FIML Markov-switching model, where all but the first equation were just identified. Moreover, the proposed Markov-switching model is a general form in simultaneous equations and covers a broad class of models that could not be handled previously. Excess sensitivity of marginal propensity to consume with big shocks, such as housing bubble bursts in 2008, can be determined by applying the proposed model to Campbell and Mankiw's consumption function [6], and allowing for the possibility of structural breaks in the sensitivity of consumption growth to income growth.