• Journal of Korean Society for Atmospheric Environment
• /
• v.17 no.3
• /
• pp.251-258
• /
• 2001

Indoor and outdoor nitrogen dioxide(NO$_2$) concentrations of 122 houses were measured and compared with measurements of personal NO$_2$ exposure simultaneously . Time activity patterns were used to determine the impacts on NO$_2$ exposure assessment and time weighed average model to estimate the personal NO$_2$ exposure. Most people spent their times more than 80% of indoor and more than 50% in home, respectively. Personal NO$_2$ esposure was found to be significantly associated with both indoor NO$_2$ concentration(r=0.70) and outdoor NO$_2$ concentration (r=0.68). Using time weighted average model, personal NO$_2$ exposure was estimated with NO$_2$ measurements in indoor home, indoor workplace and outdoor home. The estimated NO$_2$ measurements were significantly correlated with measured personal exposures(r=0.69, N=122). For the difference between measured and estimated NO$_2$ exposures by multiple regression analysis showed that NO$_2$ concentrations in near workplace and other outdoors of no NO$_2$ measurements affected the personal NO$_2$ exposures(p=0.023).

• Journal of Environmental Health Sciences
• /
• v.33 no.5
• /
• pp.359-364
• /
• 2007

The aim of this study were to assess the personal exposure to volatile organic compounds (VOCs) and to estimate the personal exposure using time-weighted average model. Three target VOCs (benzene, toluene, xylene) were analyzed in personal exposure samples and residential indoor, residential outdoor and workplace indoor microenvironments samples in the iron mill 30 workers during working 5 days. Personal exposure to VOCs significantly correlated with workplace concentration p<0.05), suggesting workplace had strong source and major contribution to personal exposure. Personal exposure could be estimated with time activity pattern and time weighted average (TWA) model of residential indoor and workplace concentrations measured. Time weighted mean microenvironments concentrations were close approximately of personal exposure concentrations. Total exposure for participants can be estimated by TWA with microenvironments measurements and time activity pattern.

• Journal of Environmental Health Sciences
• /
• v.26 no.4
• /
• pp.58-64
• /
• 2000

Indoor air quality tends to be the dominant contributor to personal exposure, because most people spend over 80% of their time indoors. In this study, indoor and outdoor NO$_2$ concentrations were measured and compared with simultaneously personal exposures of 21 university students in weekday and weekend. House characteristics and activity pattern were used to determine the impacts of these factors on personal exposure. Since university students spent most of their times in indoor, their NO$_2$ exposure was associated with indoor NO$_2$ level rather than outdoor NO$_2$ level both weekday and weekend in spite of different time activity. Using time-weighted average model, NO$_2$ exposures of university students were estimated by NO$_2$ measurements in indoor home, indoor school, and outdoor home levels. Estimated NO$_2$ personal exposures were significantly correlated with measured NO$_2$ personal exposures($r^2$=0.87). However, estimated personal NO$_2$ exposures by time-weighted average model were underestimated, comparing with the measured personal NO$_2$ exposure. Using multiple regression analysis, effect of personal NO$_2$ exposure for transportation was confirmed.

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

• Atmosphere
• /
• v.21 no.2
• /
• pp.209-220
• /
• 2011

A new consensus algorithm for the prediction of tropical cyclone track has been developed. Conventional consensus is a simple average of a few fixed models that showed the good performance in track prediction for the past few years. Meanwhile, the consensus in this study is a weighted average of a few models that may change for every individual forecast time. The models are selected as follows. The first step is to find the analogous past tropical cyclone tracks to the current track. The next step is to evaluate the model performances for those past tracks. Finally, we take the weighted average of the selected models. More weight is given to the higher performance model. This new algorithm has been named as DYTRAP (DYnamic data-base Typhoon tRAck Prediction) in the sense that the data base is used to find the analogous past tracks and the effective models for every individual track prediction case. DYTRAP has been applied to all 2009 tropical cyclone track prediction. The results outperforms those of all models as well as all the official forecasts of the typhoon centers. In order to prove the real usefulness of DYTRAP, it is necessary to apply the DYTRAP system to the real time prediction because the forecast in typhoon centers usually uses 6-hour or 12-hour-old model guidances.

• Journal of radiological science and technology
• /
• v.24 no.2
• /
• pp.19-22
• /
• 2001

The purpose of this study is to evaluate time course of signal enhancement on Gadomer-17 enhance MRI, and to correlate the size of enhanced area with that of the infarct area on 2'3'5'-triphenyl tetrazolium chloride(TTC) histochemical examination for the assessment of myocardial viability in reperfused Myocardial Infarction in a cat model. Tan cats(average weight: 3.8 kg) which had undergone 90 minutes of occlusion of the LAD followed by 90 minutes of reperfusion underwent MR T2-weighted imaging, and T1-weighted imaging, enhanced T1-weighted imaging. We used 1.5T Magneton Vision MRI system(Siemens, Erlangen, Germany). Signal intensities were measured in the enhanced and non-enhanced areas of enhanced T1-weighted imaging. and TTC histochemical staining the size of the abnormal signal area on each image was compared with that of the infarct area. Maximum enhancement was detected during a $40{\sim}60$ minute period with an average enhancement of $168{\pm}9.9%$ of normal myocardium. TTC staining revealed that the size of the high signal area on T2-weighted images and of the enhanced area on enhanced T1-weighted images was greater than that of the infarct area($T2=48.1%{\pm}3.7$, enhanced $T1=47.2%{\pm}2.6$, TTC $staining=38.7%{\pm}3.1$ ; p<0.05). In reperfused Myocardial Infarction in a cat model, enhanced MR imaging delineates reversibly and irreversibly damaged myocardium, with a strong enhancement and a broad temporal window. We may therefore expect that enhanced MR image is useful for demonstrating myocardial injury.

• Atmosphere
• /
• v.24 no.3
• /
• pp.365-378
• /
• 2014

The limits of S-band dual-polarization radars in Korea are not reflected on the recent weather forecasts of Korea Meteorological Administration and furthermore, they are only utilized for rainfall estimations and hydrometeor classification researches. Therefore, this study applied four merging methods [SA (Simple Average), WA (Weighted Average), SSE (Sum of Squared Error), TV (Time-varying mergence)] to the QPE (Quantitative Precipitation Estimation) model [called RAR (Radar-AWS Rainfall) calculation system] using single-polarization radars and S-band dual-polarization radar in order to improve the accuracy of the rainfall estimation of the RAR calculation system. As a result, the merging results of the WA and SSE methods, which are assigned different weights due to the accuracy of the individual model, performed better than the popular merging method, the SA (Simple Average) method. In particular, the results of TVWA (Time-Varying WA) and TVSSE (Time-Varying SSE), which were weighted differently due to the time-varying model error and standard deviation, were superior to the WA and SSE. Among of all the merging methods, the accuracy of the TVWA merging results showed the best performance. Therefore, merging the rainfalls from the RAR calculation system and S-band dual-polarization radar using the merging method proposed by this study enables to improve the accuracy of the quantitative rainfall estimation of the RAR calculation system. Moreover, this study is worthy of the fundamental research on the active utilization of dual-polarization radar for weather forecasts.

• Proceedings of the Korean Environmental Health Society Conference
• /
• 2004.12a
• /
• pp.74-74
• /
• 2004

• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.4
• /
• pp.563-568
• /
• 2007

This paper presents a methodology for predicting nonlinear time series based on the neural network with weighted fuzzy membership functions (NEWFM). The degree of classification intensity is obtained by bounded sum of weighted fuzzy membership functions extracted by NEWFM, then weighted average defuzzification is used for predicting nonlinear time series. The experimental results demonstrate that NEWFM has the classification capability of 92.22% against the target class of GDP. The time series created by NEWFM model has a relatively close approximation to the GDP which is a typical business cycle indicator, and has been proved to be a useful indicator which has the turning point forecasting capability of average 12 months in the peak point and average 6 months in the trough point during 5th to 8th cyclical period. In addition, NEWFM measures the efficiency of the economic indexes by the feature selection and enables the users to forecast with reduced numbers of 7 among 10 leading indexes while improving the classification rate from 90% to 92.22%.

• Communications for Statistical Applications and Methods
• /
• v.7 no.3
• /
• pp.773-788
• /
• 2000

Statistical process control (SPC) and engineering process control (EPC) are based on different strategies for processes quality improvement. SPC reduces process variability by detecting and eliminating special causes of process variation. while EPC reduces process variability by adjusting compensatory variables to keep the quality variable close to target. Recently there has been needs for a process control proceduce which combines the tow strategies. This paper considers a combined scheme which simultaneously applies SPC and EPC techniques to reduce the variation of a process. The process model under consideration is an integrated moving average(IMA) process with a step shift. The EPC part of the scheme adjusts the process back to target at every fixed monitoring intervals, which is referred to a repeated adjustment scheme. The SPC part of the scheme uses an exponentially weighted moving average(EWMA) of observed deviation from target to detect special causes. A Markov chain model is developed to relate the scheme's expected cost per unit time to the design parameters of he combined control scheme. The expected cost per unit time is composed of off-target cost, adjustment cost, monitoring cost, and false alarm cost.