• Advances in environmental research
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• v.6 no.4
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• pp.265-279
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• 2017

The climate change has made adverse effects on land surface temperature for many regions of the world. Several climatic studies focused on different downscaling techniques for climatological parameters of different regions. For statistical downscaling of any hydrological parameters, conventional Neural Network Models were used in common. However, it seems that in any modeling study, uncertainty is a vital aspect when making any predictions about the performance. In this paper, Gamma Test is performed to determine the data length selection for training to minimize the uncertainty in model development. Another measure to improve the data quality and model development are wavelet transforms. Hence, Gamma Test with Wavelet decomposed Feedforward Neural Network (GT-WNN) model is developed and tested for downscaled land surface temperature of Patna Urban, Bihar. The results of GT-WNN model are compared with GT-FFNN and conventional Feedforward Neural Network (FFNN) model. The effectiveness of the developed models is illustrated by Root Mean Square Error and Coefficient of Correlation. Results showed that GT-WNN outperformed the GT-FFNN and conventional FFNN in downscaling the land surface temperature. The land surface temperature is forecasted for a period of 2015-2044 with GT-WNN model for Patna Urban in Bihar. In addition, the significance of the probable changes in the land surface temperature is also found through Mann-Kendall (M-K) Test for Summer, Winter, Monsoon and Post Monsoon seasons. Results showed an increasing surface temperature trend for summer and winter seasons and no significant trend for monsoon and post monsoon season over the study area for the period between 2015 and 2044. Overall, the M-K test analysis for the annual data shows an increasing trend in the land surface temperature of Patna Urban.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.2
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• pp.146-153
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• 2011

Model based machine condition diagnosis methods are generally using a normal and many failure models which need sufficient data to train the models. However, data, especially for failure modes of interest, is very hard to get in real applications. So their industrial applications are either severely limited or impossible when the failure models cannot be trained. In this paper, continuous hidden Markov model(CHMM) with only a normal model has been suggested as a very promising machine condition diagnosis method which can be easily used for industrial applications. Generally hidden Markov model also uses many pattern models to recognize specific patterns and the recognition results of CHMM show the likelihood trend of models. By observing this likelihood trend of a normal model, it is possible to detect failures. This method has been successively applied to arc weld defect diagnosis. The result shows CHMM's big potential as a machine condition monitoring method.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1B
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• pp.55-64
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• 2008

The evidence of changes in the climate system is obvious in the world. Nevertheless, at the current techniques for flood frequency analysis, the flood distribution can not reflect climate change or long-term climate cycles. Using a linear regression and a Mann-Kendall test, trends in annual maximum precipitation and flood data for several major gauging sites were evaluated. Moreover, this research considered incorporating flood trends by climate change effects in flood frequency analyses. For five rainfall gauging sites (Seoul, Incheon, Ulleungdo, Jeonju, and Gangneung), upward trends were observed in all gauged annual maximum precipitation records but they were not statistically significant. For three streamflow gauging sites (Andong Dam, Soyanggang Dam, and Daecheong Dam), upward trends were also observed in all gauged annual maximum flood records, but only the flood at Andong Dam was statistically significant. A log-normal trend model was introduced to reflect the observed linear trends in annual maximum flood series and applied to estimate flood frequency and risk for Andong Dam and Soyanggang Dam. As results, when the target year was 2005, 50-year floods of the log-normal trend model were 41% and 21% larger then those of a log-normal model for Andong Dam and Soyanggang Dam, respectively. Moreover, the estimated floods of the log-normal trend model increases as the target year increases.

• Convergence Security Journal
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• v.8 no.3
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• pp.33-39
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• 2008

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 exponentially weighted moving average chart, in measuring failure time. In control, exponentially weighted moving average chart's uses are efficiency case of analysis with knowing information, Using real software failure time, we are proposed to use exponentially weighted moving average chart and comparative analysis of software failure time.

• Journal of Environmental Science International
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• v.11 no.11
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• pp.1141-1147
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• 2002

Based on the tide gauge data from the Permanent Service for Meau Sea Level (PSMSL) collected at 23 locations in the Korean coast, the long-term sea-level trend was computed using a simple linear regression fit over the recorded length of the monthly mean sea-level data. The computed sea-level trend was also corrected for the vertical land movement due to post glacial rebound(PGR) using the ICE-4G(VM2) model output. It was found that the PGR-corrected sea-level trend near Korea was 2.310 $\pm$ 2.220 mm/yr, which is higher than the global average at 1.0∼2.0mm/yr, as assessed by the Intergovernmental Panel on Climate Change(IPCC). The regional distribution of the long-term sea-level trend near Korea revealed that the South Sea had the largest sea-level rise followed by the West Sea and East Sea, respectively, supporting the results of the previous study by Seo et al. However, due to the relatively short record period and large spatial variability, the sea-level trend from the tide gauge data for the Korean coast could be biased with a steric sea-level rise by the global warming during the 20th century.

• The Research Journal of the Costume Culture
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• v.13 no.1
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• pp.75-86
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• 2005

The objectives of this study were to investigate differences between image perceptions according to gender, place of residence, and fashion trend; and to examine how the image of preferred clothing was evaluated in each given area of fashion trend. Subjects were 386 schoolchildren (boys:196, girls:190) in Seoul, Daejeon, and Jinju, Korea. Based on a quasi-experiment study, a survey was conducted with a questionnaire providing different clothing images of fashion trend. Stimuli were 5 colored photo pictures of a girl wearing clothing according to fashion trend. The clothing used in the study met requirements of 2004 S/S trend of children's clothing. The high valued clothing sold in three target places were used. There was a significant difference in image perceptions between two sexes. Girls showed more positive attitude in image perceptions toward fashionable clothing in most areas than boys. Children from smaller towns evaluated the model clothing more fashionable. Schoolchildren preferred sporty clothing to the other fashionable clothing. In view of trend, romantic clothing normally viewed less dynamic were evaluated preferable clothing when children viewed the clothing active. Sports-wears were considered fashionable when they viewed the clothing neat and vigorous.

• Journal of Korea Port Economic Association
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• v.39 no.2
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• pp.129-141
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• 2023

This study analyzes the dynamic characteristics of daily freight rates of dry bulk and tanker shipping markets and their forecasting accuracy by using the error correction models. In order to calculate the error terms from the co-integrated time series, this study uses the common stochastic trend model (CSTM model) and vector error correction model (VECM model). First, the error correction model using the error term from the CSTM model yields more appropriate results of adjustment speed coefficient than one using the error term from the VECM model. Furthermore, according to the adjusted determination coefficients (adjR²), the error correction model of CSTM-model error term shows more model fitness than that of VECM-model error term. Second, according to the criteria of mean absolute error (MAE) and mean absolute scaled error (MASE) which measure the forecasting accuracy, the results show that the error correction model with CSTM-model error term produces more accurate forecasts than that of VECM-model error term in the 12 cases among the total 15 cases. This study proposes the analysis and forecast tasks 1) using both of the CSTM-model and VECM-model error terms at the same time and 2) incorporating additional data of commodity and energy markets, and 3) differentiating the adjustment speed coefficients based the sign of the error term as the future research topics.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.44
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• pp.257-261
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• 1997

Patents often serve as leading indicators of technological change. This patenting activity reflected R&D (Research & Development) of new technology. The purpose of this study is to set up a forecasting model that anticipate the number of domestic patent applications and the number of patents granted relating to R&D inputs (R&D expenditure, R&D manpower) at the level of three industrial sectors in Korea : electrical-electronic, machinery, chemical etc. In this study, forecasting models were used trend extrapolation and a set of regressions. Both Theil's inequality coefficient and MAE(Mean Absolute Error) were utilized to test the precision of predicted value. The patent data and the R&D data were based on Indicators of Industrial Technology data throught 1980 to 1996. The major results obtained in this study are as follows (1) The regression model is more useful for forecasting the trends of the number of patent applications and patents granted than the trend extrapolation method. (2) The variance of Theil's inequality is smaller in patent applications than in patent granted.

• 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.

• Atmosphere
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• v.18 no.3
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• pp.199-206
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• 2008

This study employs a structural time series method in order to model and estimate stochastic trend of surface temperatures of the globe, Northern Hemisphere, and Northeast Asia ($20^{\circ}N{\sim}60^{\circ}N$, $100^{\circ}E{\sim}150^{\circ}E$). For this study the reanalysis data CRUTEM3 (CRU/Hadley Centre gridded land-surface air temperature Version 3) is used. The results show that in these three regions range from $0.268^{\circ}C$ to $0.336^{\circ}C$ in 1997, whereas these vary from $0.423^{\circ}C$ to $0.583^{\circ}C$ in 2007. The annual mean temperature over Northeast Asia has increased by $0.031^{\circ}C$ in 2007 compared to 1997. The climate change in surface temperatures over Northeast Asia is slightly higher than that over the Northern Hemisphere.