• Convergence Security Journal
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• v.11 no.3
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• pp.19-24
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• 2011

Software failure time presented in the literature exhibit either constant monotonic increasing or monotonic decreasing, For data analysis of software reliability model, data scale tools of trend analysis are developed. The methods of trend analysis are arithmetic mean test and Laplace trend test. Trend analysis only offer information of outline content. In this paper, we discuss forecasting failure time case of failure time censoring. In this study, time series analys is used in the simple moving average and weighted moving averages, exponential smoothing method for predict the future failure times, Empirical analysis used interval failure time for the prediction of this model. Model selection using the mean square error was presented for effective comparison.

• The Journal of the Korea Contents Association
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• v.19 no.6
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• pp.359-367
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• 2019

As a strategy for strengthening local competitiveness, efficient use of regional assets is becoming more and more important. The key to regional identity and competitiveness is local assets. The purpose of this study is to derive the priority region for development by evaluating local assets. The analysis methods used in this study are Geographic Information System analysis, Big Data Trend analysis, and Analytic Hierarchy Process analysis. To assess the potential of local assets, the preference of assets, historical value, cluster of resources, wide-area transport accessibility, and population density were set as analysis indicators and itemized weights were applied using AHP to reflect the importance of each item. As a result of analyzing Yeongju city in Gyeongsangbuk-do, eight major points such as Buseoksa Temple, Sosu Seowon, Huibangsa Temple, Punggi Hot Spring Resort, Punggi Station, National Center for Forest Therapy, Yeongju east region and Museom Village were derived.

• Journal of Fashion Business
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• v.6 no.1
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• pp.1-19
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• 2002

The purpose of this study was to suggest the most appropriate design concepts for sportive fashion product based on the analysis of consumers' acceptance of the recent sportive fashion trends according to their lifestyle. The subjects consisted of 295 males and females, between 17 and 35 year-old. A self-report questionnaire with 4 stimuli was employed for data gathering, and the data were analyzed by the methods of frequency, factor analysis, cluster analysis, and Pearson's correlation coefficient. The results of this study were summarized as follows: For the First, the recent sportive fashion trends were categorized into four groups; 'street- sportive' trend, 'futuristic-sportive' trend, 'ethnic-sportive' trend and 'urban-utility sportive' trend. Secondly, based on the result of cluster analysis on consumers' lifestyle, total four consumer groups were identified; 'pursuing sense' group, 'pursuing culture' group, 'pursuing utility' group, and 'indifference' group. Thirdly, the consumers relatively preferred two sportive styles among the four groups, typically representing 'urban-utility' trend and 'street' trend. The typical 'urban-utility' style was particularly preferred by the consumers who desired to express themselves as urban, modern, and luxurious. The typical 'street' style was preferred by the consumers who tried to express themselves as active and fashionable. Finally, preferences of the sportive trends according to consumers' lifestyle were interpreted as follows: the 'pursuing sense' group relatively preferred 'urbanutility' style and 'street' style, the 'pursuing culture' group preferred 'street' style and the 'pursuing utility' group preferred 'urban-utility' style, meaningwhile the 'indifference' group preferred 'street' style and 'urban- utility' style.

• Korean Institute of Interior Design Journal
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• v.20 no.1
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• pp.51-60
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• 2011

Analysis of trend by various information is systematically approached by strategy differentiating in Interior Design. At the present, trend is approached by intuitive viewpoint without systematic strategy and analysis system about interior coordination activity. And, it is not still established specific systematic architecture of the interior coordination by logical and academical approach. This Study set the goal at overall understanding about the Trend that shifting fast and offering objective data. Therefore, I approached the semiology-oriented process as the most suitable academical system on analysis of interior coordination trend. Object target of analysis was investigated to three domestic and overseas exhibitions announced from 2007 to 2008. These analysis was based on the context and text from the life style and the major determinant theme of the age of each exhibition. Also, it was arranged color, material and texture by the related expression system with topics and theme keywords. And it'll be considered as utilizing the code of specific application in interior coordination which is from the investigating about exhibition. Therefore, this study will be expected to help in meaning transmission and methodology establishment by more beneficial objective system, when designer work the interior coordination practically through the establishment of systematic viewpoint about interior coordination.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.439-447
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• 2021

Due to the frequent emergence of global abnormal climates, related studies on meteorological change is being actively proceed. However, the research on trend analysis using weather data accumulated over a long period of time was insufficient. In this study, the trend of temperature time series data accumulated from automated surface observing system (ASOS) for 40 years was analyzed by using a non-parametric analysis method. As a result of the Mann-Kendall test on the annual average temperature and seasonal average temperature time series data in South Korea, it has shown that an upward trend exists. In addition, the result of calculating the Sen's slope, which can determine the degree of tendency before and after the searched change point by applying the Pettitt test, recent data after the fluctuation point confirmed that the tendency of temperature rise was even greater.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.65-82
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• 2015

Recently, there has been a surge of interest in finding core issues and analyzing emerging trends for the future. This represents efforts to devise national strategies and policies based on the selection of promising areas that can create economic and social added value. The existing studies, including those dedicated to the discovery of future promising fields, have mostly been dependent on qualitative research methods such as literature review and expert judgement. Deriving results from large amounts of information under this approach is both costly and time consuming. Efforts have been made to make up for the weaknesses of the conventional qualitative analysis approach designed to select key promising areas through discovery of future core issues and emerging trend analysis in various areas of academic research. There needs to be a paradigm shift in toward implementing qualitative research methods along with quantitative research methods like text mining in a mutually complementary manner. The change is to ensure objective and practical emerging trend analysis results based on large amounts of data. However, even such studies have had shortcoming related to their dependence on simple keywords for analysis, which makes it difficult to derive meaning from data. Besides, no study has been carried out so far to develop core issues and analyze emerging trends in special domains like the aviation industry. The change used to implement recent studies is being witnessed in various areas such as the steel industry, the information and communications technology industry, the construction industry in architectural engineering and so on. This study focused on retrieving aviation-related core issues and emerging trends from overall research papers pertaining to aviation through text mining, which is one of the big data analysis techniques. In this manner, the promising future areas for the air transport industry are selected based on objective data from aviation-related research papers. In order to compensate for the difficulties in grasping the meaning of single words in emerging trend analysis at keyword levels, this study will adopt topic analysis, which is a technique used to find out general themes latent in text document sets. The analysis will lead to the extraction of topics, which represent keyword sets, thereby discovering core issues and conducting emerging trend analysis. Based on the issues, it identified aviation-related research trends and selected the promising areas for the future. Research on core issue retrieval and emerging trend analysis for the aviation industry based on big data analysis is still in its incipient stages. So, the analysis targets for this study are restricted to data from aviation-related research papers. However, it has significance in that it prepared a quantitative analysis model for continuously monitoring the derived core issues and presenting directions regarding the areas with good prospects for the future. In the future, the scope is slated to expand to cover relevant domestic or international news articles and bidding information as well, thus increasing the reliability of analysis results. On the basis of the topic analysis results, core issues for the aviation industry will be determined. Then, emerging trend analysis for the issues will be implemented by year in order to identify the changes they undergo in time series. Through these procedures, this study aims to prepare a system for developing key promising areas for the future aviation industry as well as for ensuring rapid response. Additionally, the promising areas selected based on the aforementioned results and the analysis of pertinent policy research reports will be compared with the areas in which the actual government investments are made. The results from this comparative analysis are expected to make useful reference materials for future policy development and budget establishment.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.1
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• pp.64-73
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• 2015

Recent emergence of spatial big data attracts the attention of various research groups. This paper analyzes the research trend on spatial big data by text mining the related Scopus DB. We apply topic model and network analysis to the extracted abstracts of articles related to spatial big data. It was observed that optics, astronomy, and computer science are the major areas of spatial big data analysis. The major topics discovered from the articles are related to mobile/cloud/smart service of spatial big data in urban setting. Trends of discovered topics are provided over periods along with the results of topic network. We expect that uncovered areas of spatial big data research can be further explored.

• Journal of the Korean Society of Clothing and Textiles
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• v.26 no.8
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• pp.1254-1264
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• 2002

The purpose of this study was to suggest a direction for golfwear design targeting young female golfers, based on the analysis of their preference of recent sportswear trend. Total 197 respondents, female golfers, were sampled for data gathering, who were asked to answer to the self-report questionnaire with the stimuli of five trendy golfwear styles [i.e., representing recent sportswear trend]. In summary, the results of this study were as follows: 1) The female golfers showed significant difference in their preference of recent sportswear-trend sues, according to their age or marital status. 2) Among the five trendy styles, each of which corresponded to five themes in 2002 S/S sportswear trend, the style of "Retro Chic", a type of retrospective trend theme, was most favored. 3) The female golfers in their twenties residing in Kangnam, were found to have ① relatively higher fashion leadership, and ② higher preference of maximal and kitsch trends, when compared with the rest of the respondents. 4) The most influential design elements in each trendy style, dominating the golfers' preference, varied with the feature of trend themes. 5) Based on the analysis of the young female golfers′ preferences of recent sportswear trend, a direction for golfwear design were suggested.

• Korean Journal of Remote Sensing
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• v.33 no.1
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• pp.25-35
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• 2017

Spatial downscaling with fine resolution auxiliary variables has been widely applied to predict precipitation at fine resolution from coarse resolution satellite-based precipitation products. The spatial downscaling framework is usually based on the decomposition of precipitation values into trend and residual components. The fine resolution auxiliary variables contribute to the estimation of the trend components. The main focus of this study is on quantitative analysis of impacts of trend component estimates on predictive performance in spatial downscaling. Two regression models were considered to estimate the trend components: multiple linear regression (MLR) and geographically weighted regression (GWR). After estimating the trend components using the two models,residual components were predicted at fine resolution grids using area-to-point kriging. Finally, the sum of the trend and residual components were considered as downscaling results. From the downscaling experiments with time-series Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation data, MLR-based downscaling showed the similar or even better predictive performance, compared with GWR-based downscaling with very high explanatory power. Despite very high explanatory power of GWR, the relationships quantified from TRMM precipitation data with errors and the auxiliary variables at coarse resolution may exaggerate the errors in the trend components at fine resolution. As a result, the errors attached to the trend estimates greatly affected the predictive performance. These results indicate that any regression model with high explanatory power does not always improve predictive performance due to intrinsic errors of the input coarse resolution data. Thus, it is suggested that the explanatory power of trend estimation models alone cannot be always used for the selection of an optimal model in spatial downscaling with fine resolution auxiliary variables.

• Proceedings of the Korean Reliability Society Conference
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• 2000.11a
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• pp.365-371
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• 2000

The problem of trend change in the failure rate is great interest in the reliability and survival analysis. In this paper we develop a test statistic for testing whether or not the failure rate changes its trend using random censored data. The asymptotic normality of the test statistic is established. We discuss the efficiency values of loss due to censoring.