• Knowledge Management Research
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• v.16 no.2
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• pp.29-45
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• 2015

The number of studies on social media has increased rapidly worldwide. There has been, however, little effort to assess the achievements and limitations of social media studies up to present. The purpose of this study is to provide a systematical viewpoint on social media research published by scholarly business-related journals. Our analysis focus on research topic, functionality, targeted media types, methodology, and characteristics. User research receives the most attention, followed by effect research in terms of developmental models of media research agenda. The major methodological trend is online survey. Facebook and twitter are the two most popular mediums studied in literature, and social media is mainly characterized as information sharing as well as relationship building. Two recommendations are suggested in ways to strengthen social media research: more various topics and application areas, and the rigor and diversity of research methods.

• Asian Journal of Atmospheric Environment
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• v.11 no.4
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• pp.235-253
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• 2017

This study was conducted for analyzing the contribution factors on ozone concentrations and its long term trends in each major city and province in Korea through several statistical methods such as simple linear regression, generalized linear model, KZ-filer, correlation matrix, Kringing method, and cluster analysis. The overall ozone levels in South Korea have been consistently increasing over the past 10 years. The ozone concentrations in Seoul, the biggest city in Korea, are the lowest in all areas with the highest increasing ratio for $95^{th}%$ ozone. It is thought that the active photochemical reaction could affect the higher ozone concentration increase. On the other hand, the ozone concentrations in Jeju are the highest in Korea with the highest increasing ratio for $5^{th}%$, $33^{th}%$, and $50^{th}%$ ozone. It is also thought that the weak $NO_x$ titration could be the reason of higher ozone concentrations in Jeju. In case of Jeju, transport related factors is the major factor affecting the ozone trend. Thus, it is assumed that the variation of ozone trend of Asian region affecting the ozone trend in Jeju, where domestic ozone photochemical reaction is less active than urban area. It is thought that the photochemical reaction plays the role of increasing of ozone concentrations in the urban area, even though the LRT affected on the increase of ozone concentrations in non-urban area.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.35-44
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• 2020

In this study, we analyze how stock trend determination affects trend prediction accuracy. In stock markets, successful investment requires accurate stock price trend prediction. Therefore, a volume of research has been conducted to improve the trend prediction accuracy. For example, information extracted from SNS (social networking service) and news articles by text mining algorithms is used to enhance the prediction accuracy. Moreover, various machine learning algorithms have been utilized. However, stock trend determination has not been properly analyzed, and conventionally used methods have been employed repeatedly. For this reason, we formulate the trend determination as a moving average-based procedure and analyze its impact on stock trend prediction accuracy. The analysis reveals that trend determination makes prediction accuracy vary as much as 47% and that prediction accuracy is proportional to and inversely proportional to reference window size and target window size, respectively.

• Journal of KIISE:Software and Applications
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• v.36 no.3
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• pp.236-243
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• 2009

With advances in topic detection and tracking(TDT), automatic trend analysis from a collection of time-stamped documents, like patents, news papers, and blog pages, is a challenging research problem. Past research in this area has mainly focused on showing a trend line over time of a given concept by measuring the strength of trend-associated term frequency information. for detection of emerging trends, either a simple criterion such as frequency change was used, or an overall comparison was made against a training data. We note that in order to show most salient trends detected among many possibilities, it is critical to devise a ranking function. To this end, we define four properties(change, persistency, stability and volume) of trend lines drawn from frequency information, to quantify various aspects of trends, and propose a method by which trend lines can be ranked. The properties are examined individually and in combination in a series of experiments for their validity using the ranking algorithm. The results show that a judicious combination of the four properties is a better indicator for salient trends than any single criterion used in the past for ranking or detecting emerging trends.

• Fisheries and Aquatic Sciences
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• v.26 no.11
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• pp.639-648
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• 2023

The sea surface temperature (SST) and ocean heat content in the Korea Waters are gradually increased. Especially the increasing trend of annual mean SST in the Korea Water is higher about 2.6 times than the global mean during past 55 years (1968-2022). Before 2010s, the increasing trend of SST was led by winter season in the Korea Waters. However, this pattern was clearly changed after 2010s. The increasing trend of SST during summer is higher about 3.9 times than during winter after 2010s. We examine the long-term variations of several ocean and climate factors to understand the reasons for the long-term pattern changes of SST between summer and winter in recent. Tsushima warm current was significantly strengthened in summer compare to winter during past 33 years (1986-2018). The long-term patterns of Siberian High and East Asian Winter Monsoon were definitely changed before and after early- or mid-2000s. The intensities of those two climate factors was changed to the increasing trend or weakened decreasing trend from the distinctive decreasing trend. In addition, the extreme weather condition like the heatwave days and cold spell days in the Korea significantly increased since mid- or late-2000s. From these results, we can consider that the occurrences of frequent and intensified marine heatwaves during summer and marine cold spells during winter in the Korea Waters might be related with the long-term pattern change of SST, which should be caused by the long-term change of climate factors and advection heat, in a few decade.

• Atmosphere
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• v.15 no.2
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• pp.101-118
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• 2005

Atmospheric ozone changes temporally and spatially according to both anthropogenic and natural causes. It is essential to quantify the natural contributions to total ozone variations for the estimation of trend caused by anthropogenic processes. The aims of this study are to understand the intrinsic natural variability of long-term total ozone changes and to estimate more reliable ozone trend caused by anthropogenic ozone-depleting materials. For doing that, long-term time series for Seoul of monthly total ozone which were measured from both ground-based Dobson Spectrophotometer (Beck #124)(1985-2004) and satellite TOMS (1979-1984) are analyzed for selected period, after dividing the whole period (1979~2004) into two periods; the former period (1979~1991) and the latter period (1992~2004). In this study, ozone trends for the time series are calculated using multiple regression models with explanatory natural oscillations for the Arctic Oscillation(AO), North Atlantic Oscillation(NAO), North Pacific Oscillation(NPO), Pacific Decadal Oscillation(PDO), Quasi Biennial Oscillation(QBO), Southern Oscillation(SO), and Solar Cycle(SC) including tropopause pressure(TROPP). Using the developed models, more reliable anthropogenic ozone trend is estimated than previous studies that considered only QBO and SC as natural oscillations (eg; WMO, 1999). The quasi-anthropogenic ozone trend in Seoul is estimated to -0.12 %/decade during the whole period, -2.39 %/decade during the former period, and +0.10 %/decade during the latter period, respectively. Consequently, the net forcing mechanism of the natural oscillations on the ozone variability might be noticeably different in two time intervals with positive forcing for the former period (1979-1991) and negative forcing for the latter period (1992-2004). These results are also found to be consistent with those analyzed from the data observed at ground stations (Sapporo, Tateno) of Japan. In addition, the recent trend analyses for Seoul show positive change-in-trend estimates of +0.75 %/decade since 1997 relative to negative trend of -1.49 %/decade existing prior to 1997, showing -0.74 %/decade for the recent 8-year period since 1997. Also, additional supporting evidence for a slowdown in ozone depletion in the upper stratosphere has been obtained by Newchurch et al.(2003).

• Culinary science and hospitality research
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• v.14 no.3
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• pp.45-57
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• 2008

The purpose of this study is to initiate a systematic approach to maximize profits through continuous development of menu and build a strong image of Western restaurants located inside hotels by identifying their guests' knowledge and concern and menu selection behavior in well being trend. Findings from the analysis are as follows. First, among the Western menu selection behavior, organic grain and seafood, seasonal event menu, less spicy and more natural cooking methods are favored as the most important consideration. Second, customers' knowledge and concern in well being trend and menu selection behavior were found to be statistically significant. Third, customers' awareness in health and obesity were found to be statistically significant to the concern in well being trend. Fourth, demographical characteristics of customers such as gender, marital status, age, income level and education were tested for their relationships with knowledge and concern in well being trend.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.2
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• pp.65-73
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• 2013

In the simulation output analysis, bootstrap method is an applicable resampling technique to insufficient data which are not significant statistically. The moving block bootstrap, the stationary bootstrap, and the threshold bootstrap are typical bootstrap methods to be used for autocorrelated time series data. They are nonparametric methods for stationary time series data, which correctly describe the original data. In the simulation output analysis, however, we may not use them because of the non-stationarity in the data set caused by the trend such as increasing or decreasing. In these cases, we can get rid of the trend by differencing the data, which guarantees the stationarity. We can get the bootstrapped data from the differenced stationary data. Taking a reverse transform to the bootstrapped data, finally, we get the pseudo-samples for the original data. In this paper, we introduce the applicability of bootstrap methods to the time series data having trend, and then verify it through the statistical analyses.

• Journal of Hydrogen and New Energy
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• v.19 no.1
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• pp.90-102
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• 2008

Hydrogen storage is widely recognized as a critical enabling technology for the successful commercialization. There are a few different approaches for hydrogen storage technology. In this paper, characteristics of hydrogen storage technologies were analyzed from the literature survey. Also, The technology trend of hydrogen production was scrutinized based on patent analysis. In patent analysis the search range was limited to the open patents issued from 1996 to 2006. The technology trend of hydrogen storage was assessed by classifying each patent based on the publishing year, country, and the type of storage technology.

• The Journal of the Korea Contents Association
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• v.20 no.10
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• pp.702-714
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• 2020

Stock investment is a personal investment technique that has gathered tremendous interest since the reduction in interest rates and tax exemption. However, it is risky especially for those who do not have expert knowledge on stock volatility. Therefore, it is well understood that accurate stock trend prediction can greatly help stock investment, giving birth to a volume of research work in the field. In order to compare different research works and to optimize hyper-parameters for prediction models, it is required to have an evaluation standard that can accurately assess performances of prediction models. However, little research has been done in the area, and conventionally used methods have been employed repeatedly without being rigorously validated. For this reason, we first analyze performance evaluation of stock trend prediction with respect to performance metrics and data composition, and propose a fair evaluation method based on prediction disparity ratio.