• International Commerce and Information Review
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• v.16 no.3
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• pp.73-96
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• 2014

The purpose of this research is to evaluate a short-term export demand forecasting model reflecting individual passenger vehicle brands and market characteristics by using Vector Autoregressive (VAR) models that are based on multivariate time-series model. The short-term export demand forecasting model was created by discerning theoretical potential factors that affect the short-term export demand of individual passenger vehicle brands. Quarterly short-term export demand forecasting model for two Korean small vehicle brands (Accent and Avante) were created by using VAR model. Predictive value at t+1 quarter calculated with the forecasting models for each passenger vehicle brand and the actual amount of sales were compared and evaluated by altering subject period by one quarter. As a result, RMSE % of Accent and Avante was 4.3% and 20.0% respectively. They amount to 3.9 days for Accent and 18.4 days for Avante when calculated per daily sales amount. This shows that the short-term export demand forecasting model of this research is highly usable in terms of prediction and consistency.

• Journal of Environmental Science International
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• v.21 no.4
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• pp.517-533
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• 2012

This study calculated wind speed at the height of 10 m using a disaster prediction model(Florida Public Hurricane Loss Model, FPHLM) that was developed and used in the United States. Using its distributions, a usable information of surface wind was produced for the purpose of disaster prevention when the typhoon attack. The advanced research version of the WRF (Weather Research and Forecasting) was used in this study, and two domains focusing on South Korea were determined through two-way nesting. A horizontal time series and vertical profile analysis were carried out to examine whether the model provided a resonable simulation, and the meteorological factors, including potential temperature, generally showed the similar distribution with observational data. We determined through comparison of observations that data taken at 700 hPa and used as input data to calculate wind speed at the height of 10 m for the actual terrain was suitable for the simulation. Using these results, the wind speed at the height of 10 m for the actual terrain was calculated and its distributions were shown. Thus, a stronger wind occurred in coastal areas compared to inland areas showing that coastal areas are more vulnerable to strong winds.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.1B
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• pp.21-27
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• 2011

When execute runoff forecasting, can not remove perfectly uncertainty of forecasting results. But, reduce uncertainty by various techniques analysis. This study applied various forecasting techniques for runoff prediction's accuracy elevation in Gongju basin. statics techniques is ESP, Period Average & Moving average, Exponential Smoothing, Winters, Auto regressive moving average process. Authoritativeness estimation with results of runoff forecasting by each techniques used MAE (Mean Absolute Error), RMSE (Root Mean Squared Error), RRMSE (Relative Root Mean Squared Error), Mean Absolute Percentage Error (MAPE), TIC (Theil Inequality Coefficient). Result that use MAE, RMSE, RRMSE, MAPE, TIC and confirm improvement effect of runoff forecasting, ESP techniques than the others displayed the best result.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.17
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• pp.7991-7995
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• 2015

Background: To identify the epidemiological characteristics of colorectal cancer mortality in China during the period of 1991-2011, and forecast the future five-year trend. Materials and Methods: Mortality data for colorectal cancer in China from 1991 to 2011 was used to describe epidemiological characteristics in terms of age group, gender, and rural/urban residence. Trend surface analysis was performed to analyze the geographical distribution of colorectal cancer. Four models including curve estimation, time series modeling, gray modeling and joinpoint regression were applied to forecast the trends for the future five years. Results: Since 1991 the colorectal cancer mortality rate increased yearly, and our results showed that the trend would continue to increase in the ensuing 5 years. The mortality rate in males was higher than that of females and the rate in urban areas was higher than in rural areas. The mortality rate was relatively low for individuals less than 60 years of age, but increased dramatically afterwards. People living in the northeastern China provinces or in eastern China had a higher mortality rate for colorectal cancer than those living in middle or western China provinces. Conclusions: The steadily increasing mortality of colorectal cancer in China will become a substantial public health burden in the foreseeable future. For this increasing trend to be controlled, further efforts should concentrate on educating the general public to increase prevention and early detection by screening. More effective prevention and management strategies are needed in higher mortality areas (Eastern parts of China) and high-risk populations (60+ years old).

• Journal of Korea Water Resources Association
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• v.30 no.1
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• pp.45-54
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• 1997

The correct estimation of the daily or hourly urban water demand is required for the efficient management and operation of the water supply facilities. The prediction of water supply demand are regression model and time series method, the optimum ARIMA (Auto Regressive Integrated Moving Average) model was sought for the daily urban water demand estimation in this paper. The data used for this study were obtained from the city of Kwangju Korea. The raw data used in this study were rearranged 15, 30, 60, 90 days for the purpose of analysis. The statistical analysis was applied to the data to obtain the ARIMA model. As a result, the parameters determining the ARIMA model was obtained. The accuracy of the model was 2% of water supply. The developed model was found to be useful for the practical operation and management of the water supply facilities.

• Journal of Biomedical Engineering Research
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• v.24 no.3
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• pp.151-158
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• 2003

Numerous studies of short-term, beat-to-beat variability in cardiovascular signals have used linear analysis techniques. However, no study has been done about the appropriateness of linear techniques or the comparison between linearities and nonlinearities in short-term, beat-to-beat variability. This paper aims to verify the appropriateness of linear techniques by investigating nonlinearities in short-term, beat-to-beat variability. We compared linear autoregressive moving average(ARMA) with nonlinear neural network(NN) models for predicting current instantaneous heart rate(HR) and mean arterial blood pressure(BP) from past HRs and BPs. To evaluate these models. we used HR and BP time series from the MIMIC database. Experimental results indicate that NN-based nonlinearities do not play a significant role and suggest that 10 technique provides adequate characterization of the system dynamics responsible for generating short-term, beat-to-beat variability.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.395-406
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• 2017

The purpose of this study is to identify the pattern of daily electricity demand through clustering and classification. The hourly data was collected by KPS (Korea Power Exchange) between 2008 and 2012. The time trend was eliminated for conducting the pattern of daily electricity demand because electricity demand data is times series data. We have considered k-means clustering, Gaussian mixture model clustering, and functional clustering in order to find the optimal clustering method. The classification analysis was conducted to understand the relationship between external factors, day of the week, holiday, and weather. Data was divided into training data and test data. Training data consisted of external factors and clustered number between 2008 and 2011. Test data was daily data of external factors in 2012. Decision tree, random forest, Support vector machine, and Naive Bayes were used. As a result, Gaussian model based clustering and random forest showed the best prediction performance when the number of cluster was 8.

• Journal of Communications and Networks
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• v.16 no.4
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• pp.407-414
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• 2014

The Internet is a highly distributed and complex system consisting of billion devices and has become the field of various kinds of conflicts during the last two decades. As a matter of fact, various actors utilise the Internet for illicit purposes, such as for performing distributed denial of service attacks (DDoS) and for spreading various types of aggressive malware. Despite the fact that numerous services provide information regarding the threat level of the Internet, they are mostly based on information acquired by their sensors or on offline statistical sampling of various security applications (antivirus software, intrusion detection systems, etc.). This paper introduces proactive threat observatory system (PROTOS), an open-source early warning system that does not require a commercial license and is capable of estimating the threat level across the Internet. The proposed system utilises both a global and a local approach, and is thus able to determine whether a specific host is under an imminent threat, as well as to provide an estimation of the malicious activity across the Internet. Apart from these obvious advantages, PROTOS supports a large-scale installation and can be extended even further to improve the effectiveness by incorporating prediction and forecasting techniques.

• Journal of Korean Clinical Nursing Research
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• v.26 no.2
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• pp.265-273
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• 2020

Purpose: The purpose of this study was to test the predictive validity of the Fall Assessment Scale-Korean version (FAS-K) and to find the most appropriate cutoff score to screen high-risk fall groups in adult patients in general hospitals in Korea. Methods: We performed a prospective evaluation study in medical and surgical ward patients at two major general hospitals in Seoul. Data were collected from Nov. 1, 2018 to Feb. 28, 2019, nurses performed 651 observation series. The researcher measured the fall risk assessment score by applying FAS-K, MFS (Morse Fall Scale), and JHFRAT (Johns Hopkins Hospital Fall Risk Assessment tool) to the patients twice a week between 10 am and 12 noon. Data were analyzed using Pearson's corelation coefficients, and the sensitivity, specificity, predictive value, and the area under the curve (AUC) of the three tools. Results: The FAS-K was positively correlated with the MFS (r=.70, p<.001) and the JHFRAT (r=.82, p<.001). According to the receiver operating characteristics (ROC) curve analysis of the FAS-K, sensitivity, specificity, and positive and negative prediction values were 85.3%, 49.4%, 8.5%, and 98.4%, respectively, when the FAS-K score was 4. Therefore, the cut-off score of the FAS-K to identify groups with high fall risk was 4. Conclusion: The FAS-K is a valid tool for measuring fall risk in adult inpatients. In addition, the FAS-K score, 4, can be used to identify high-risk fall groups and know specific points in time to provide active interventions to prevent falls.

• Journal of Environmental Science International
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• v.11 no.7
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• pp.687-696
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• 2002

Air quality monitoring data and meteorology data which had collected from 1995. 1. to 1999. 2. in six areas of Daegu, Manchondong, Bokhyundong, Deamyungdong, Samdukdong, Leehyundong and Nowondong, were investigated to determine the distribution and characteristic of ozone. A equation of multiple regression was suggested after time series analysis of contribution factor and meteorology factor were investigated during the day which had high concentration of ozone. The results show the following; First, 63.6% of high ozone concentration days, more than 60 ppb of ozone concentration, were in May, June and September. The percentage of each area showed that; Manchondong 14.4%, Bokhyundong 15.4%, Deamyungdong 15.6%, Samdukdong 15.6%, Leehyundong 17.3% and Nowondong 21.6%. Second, correlation coefficients of ozone, $SO_2$, TSP, $NO_2$ and CO showed negative relationship; the results were respectively -0.229, -0.074, -0.387, -0.190(p<0.01), and humidity were -0.677. but temperature, amount of radiation and wind speed had positive relationship; the results were respectively 0.515, 0.509, 0.400(p<0.01). Third, $R^2$ of equation of multiple regression at each area showed that; Nowondong 45.4%, Lee hyundong 77.9%, Samdukdong 69.9%, Daemyungdong 78.8%, Manchondong 88.6%, Bokhyundong 77.6%. Including 1 hour prior ozone concentration, $R^2$ of each area was significantly increased; Nowondong 75.2%, Leehyundong 89.3%, Samdukdong 86.4%, Daemyungdong 88.6%, Manchondong 88.6%, Bokhyundong 88.0%. Using equation of multiple regression, There were some different $R^2$ between predicted value and observed value; Nowondong 48%, Leehyundong 77.5%, Samdukdong 58%, Daemyungdong 73.4%, Manchondong 77.7%, Bokhyundong 75.1%. $R^2$ of model including 1 hour prior ozone concentration was higher than equation of current day; Nowondong 82.5%, Leehyundong 88.3%, Samdukdong 80.7%, Daemyungdong 82.4%, Manchondong 87.6%, Bokhyundong 88.5%.