• The Research Journal of the Costume Culture
• /
• v.25 no.4
• /
• pp.433-447
• /
• 2017

This study reveals the components of college women's shopping orientations and compares the attributes of shoes accordingly. This study attempts to investigate the needs of consumers in the target market of young women by comparing the importance of shoe attributes with their shopping orientations and to provide basic data for efficient marketing strategies which could increase sales. Data was collected using a questionnaire survey. Of a total of 330 questionnaires, 319 were used for statistical analysis. The survey was carried out from July to August 2016. The 17 shopping orientation-related questions and 13 questions about shoe purchase attributes were measured using a five-point Likert Scale. SPSS 23 was used to carry out: descriptives, factor analysis, reliability analysis, cluster analysis, ANOVA, and Duncan's test. Shopping orientations were divided between brand orientation, pleasure orientation, trend orientation and utilitarian orientation. Shoe attributes were categorized into ostentation value, product value, economic value and aesthetic value. College women were divided into the following groups: active shopping, passive shopping, rational shopping and conforming shopping. According to the comparison of the importance of shoe attributes by consumer type among college women, a significant difference by group was found in ostentation value and aesthetic value only. Furthermore, the average scores on the importance of product value and economic value were very high without significant differences between groups. The study results would be available as basic data to help improving the visual image of shoes and product quality for brands targeting young women in the fashion industry.

• Fashion & Textile Research Journal
• /
• v.6 no.3
• /
• pp.321-328
• /
• 2004

The purpose of this study were to investigate the differences of shopping value, promotional affects and product characteristics between buyers and non-buyers, and to examine the most influenced variable on purchasing behaviors of fashion products in CATV home-shopping. We collected data from 595 consumers related to CATV home-shopping in Busan. Data were analyzed by factor analysis, t-test, ${\chi}^2$-test and discriminant analysis. Results showed that there are significant differences between buyers and non-buyers of shopping value demographic characteristics, promotional affects and product characteristics. Especially, shopping value perceived by consumers(e.g., practical value and hedonic value) and product characteristics(e.g., response of others) discriminated whether consumers purchased the fashion products of CATV home-shopping or not. We discussed the implications of results to encourage the purchasing behavior of fashion products in CATV home-shopping.

• Knowledge Management Research
• /
• v.11 no.1
• /
• pp.65-81
• /
• 2010

This paper explores the determinants of the economic value of patents using a survival time analysis. The analysis is based on renewal information of about 250,000 patents filed from 1984 to 2005 in the Korea Intellectual Property Office. A patent right is valid only when its owner pays yearly maintenance fees. Failure to pay causes patent rights to be lapsed. We use the fact that more valued patents live longer and the lengths of their renewals can be closely related to their value. The value can be affected not only by its own technological aspects such as quality and breadth, but also by characteristics of its owners such as innovativeness and age. This paper presents patent-specific and firm-specific characteristics which influence patent value. The result of analysis implies that patent value depends on both the technological contents of the patent and general capabilities of a firm.

• Anatomy and Cell Biology
• /
• v.55 no.1
• /
• pp.72-78
• /
• 2022

The purpose of this study was to analyze the accuracy of intra oral scanner (IOS) to confirm the applicability of IOS for the recording and analysis of tooth morphology in forensics. The less damaged mandible specimen with many teeth remaining was scanned three times using three types of intraoral scanners (CS3600, i500, and Trios3). For quantitative comparisons of the scanned images produced by these intraoral scanners, root mean square (RMS) values were computed using a three-dimensional analysis program and a one-way ANOVA was conducted with Tukey HSD (honestly significant difference) as a post-hoc analysis (α=0.05). The repeatability of the full scan data was highest with the i500 (0.14±0.03 mm), and the post-hoc analysis confirmed significant differences between the CS3600 and the i500 outcomes (P-value=0.003). The repeatability of the partial scan data for the teeth in the mandible was highest with the i500 (0.08±0.02 mm), and the post-hoc analysis confirmed significant differences between the CS3600 and the i500 (P-value=0.016). The precision of the full scan data was highest with the i500 (0.16±0.01 mm) but the differences were not statistically significant (P-value=0.091). Meanwhile, the precision of the partial scan data for the teeth in the mandible was highest with the Trios3 (0.22±0.02 mm), but the differences were not statistically significant (P-value=0.762). Considering that the scanning of other areas of the oral cavity in addition to the teeth is important in forensic odontology, the i500 scanner appears to be the most appropriate intraoral scanner for human identification. However, as the scope of oral scanning is generally limited to teeth in the practice of dentistry, additional discussions of how to apply the IOS in forensic odontology are needed. Ultimately, the results here can contribute to the overall discussion of the forensic applicability dental data produced by intraoral scanners.

• Journal of Sensor Science and Technology
• /
• v.15 no.6
• /
• pp.433-441
• /
• 2006

Because of the low power and low rate of a sensor network, outlier is frequently occurred in the time series data of sensor network. In this paper, we suggest periodic pattern analysis that is applied to the time series data of sensor network and predict outlier that exist in the time series data of sensor network. A periodic pattern is minimum period of time in which trend of values in data is appeared continuous and repeated. In this paper, a quantization and smoothing is applied to the time series data in order to analyze the periodic pattern and the fluctuation of each adjacent value in the smoothed data is measured to be modified to a simple data. Then, the periodic pattern is abstracted from the modified simple data, and the time series data is restructured according to the periods to produce periodic pattern data. In the experiment, the machine learning is applied to the periodic pattern data to predict outlier to see the results. The characteristics of analysis of the periodic pattern in this paper is not analyzing the periods according to the size of value of data but to analyze time periods according to the fluctuation of the value of data. Therefore analysis of periodic pattern is robust to outlier. Also it is possible to express values of time attribute as values in time period by restructuring the time series data into periodic pattern. Thus, it is possible to use time attribute even in the general machine learning algorithm in which the time series data is not possible to be learned.

• The Journal of Society for e-Business Studies
• /
• v.22 no.1
• /
• pp.123-146
• /
• 2017

The information that consumers share in the SNS (Social Networking Service) has a great influence on the purchase of consumers. Therefore, it is necessary to pay attention to new research methodology and advertising strategy using Social Big Data. In this context, the purpose of this study is to quantitatively analyze customer value through Social Big Data. In this study, we analyzed the value structure of consumers for the three smartphone brands through text mining and positive/negative image analysis. Analysis result, it was possible to distinguish the emotional aspects (sensitivity) and rational aspects (rationality) for customer value per brand. In the case of the Galaxy S7 and iPhone 6S, emotional aspects were important before the launch, but the rational aspects was important after release date. On the other hand, in the case of the LG G5, emotional aspects were important before and after launch. We can propose two core advertising strategies based on analyzed consumer value. When developing advertising strategy in the case of the Galaxy S7, there is a need to emphasize the rational aspects of product attributes and differentiated functions. In the case of the LG G5, it is necessary to consider the emotional aspects of happiness, excitement, pleasure, and fun that are felt by using products in advertising strategy. As a result, this study will provide a good standard for actual advertising strategy through consumer value analysis. Advertising strategies are primarily driven by intuition or experience. Therefore, it is important to develop advertising strategies by analyzing consumer value through social big data analysis.

• KIEAE Journal
• /
• v.7 no.6
• /
• pp.23-28
• /
• 2007

Limited fossil fuels and unstable energy supply are considered as one of the critical problems in architecture requiring large amounts of energy. In order to this challenge, environment-friendly architecture design is required. Clear data should be prepared to apply solar energy to architecture aggressively and properly. This study used FS statistical analysis data regarding average daily solar radiation of Seoul observed over 20 years to find out standard year and standard daily solar radiation. This study also aims to compare and evaluate an appropriate method of selecting a standard year which is too close to measurement value through comparison and analysis with daily solar radiation acquired by applying overseas researchers' suggesting weight factor. As a result, the data nearest to measurement value of daily solar radiation was UK CIBSE TRY(TYPE 2) displaying 0.100in t-statistic index. For UK CIBSE TRY(TYPE 2), weight factor was applied to three climatic elements except relative humidity. TYPE 1 and TYPE 3 recorded 0.343 and 0.367, respectively, showing higher record of t-statistic than TYPE 2. TYPE 1 was calculated through FS statistical value of single data about daily solar radiation with other climatic elements excluded. For TYPE 3, relative humidity was added to TYPE 2. In particular, since TYPE 2 was closer to the measurement value compared to the others, it is necessary to consider relationship with other climate elements if other climate elements are added.

• Communications for Statistical Applications and Methods
• /
• v.23 no.6
• /
• pp.555-562
• /
• 2016

Interval censored data often occur in an observational study where the subject is followed periodically. Instead of observing an exact failure time, two inspection times that include it are available. There are several methods to analyze interval censored failure time data (Sun, 2006). However, in the presence of competing risks, few methods have been suggested to estimate covariate effect on interval censored competing risk data. A sub-distribution hazard model is a commonly used regression model because it has one-to-one correspondence with a cumulative incidence function. Alternatively, Klein and Andersen (2005) proposed a pseudo-value approach that directly uses the cumulative incidence function. In this paper, we consider an extension of the pseudo-value approach into the interval censored data to estimate regression coefficients. The pseudo-values generated from the estimated cumulative incidence function then become response variables in a generalized estimating equation. Simulation studies show that the suggested method performs well in several situations and an HIV-AIDS cohort study is analyzed as a real data example.

• Korea Trade Review
• /
• v.46 no.6
• /
• pp.191-209
• /
• 2021

In this paper, various time series analysis models and machine learning models are presented for long-term prediction of export growth rate, and the prediction performance is compared and reviewed by RMSE and MAE. Export growth rate is one of the major economic indicators to evaluate the economic status. And It is also used to predict economic forecast. The export growth rate may have a negative (-) value as well as a positive (+) value. Therefore, Instead of using the ReLU function, which is often used for time series prediction of deep learning models, the PReLU function, which can have a negative (-) value as an output value, was used as the activation function of deep learning models. The time series prediction performance of each model for three types of data was compared and reviewed. The forecast data of long-term prediction of export growth rate was deduced by three forecast methods such as a fixed forecast method, a recursive forecast method and a rolling forecast method. As a result of the forecast, the traditional time series analysis model, ARDL, showed excellent performance, but as the time period of learning data increases, the performance of machine learning models including LSTM was relatively improved.

• Journal of the Korea Safety Management & Science
• /
• v.3 no.2
• /
• pp.113-121
• /
• 2001

Today, facilities are composed of many complex components or parts. Because of this characteristics, the frequency of failures is decreasing, but the strength of failures is increasing; therefore, the failure analysis about many complex components or parts was needed. In the former research about Fault Tree Analysis, failure data of similar facilities have been used for forecasting about target system or components, but in case that the system or components for forecasting failure is new or qualitative and quantitative data are given simultaneously, there are many difficulty in using Fault Tree Analysis with this incorrect failure data. Therefore, this paper deal with the Fault Tree Analysis method which be applied with Fuzzy theory in above case. In case that , therefore, if there is no the correct failure data, it is represented a system or components as qualitative variable. subsequently, it converted to the quantitative value using fuzzy theory, and the values used as the value for failure forecast.