• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.12
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• pp.6602-6609
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• 2013

This study is expected to provide background data for establishing mitigation measures for malodor and for comparing complex odor criteria. The impact of malodor at the afflicted locations was analyzed using Industrial Source Complex Short Term 3 (ISCST3) model, which was recommended by the EPA. The Odor Emission Rates (ODR) for piglets and hogs were predicted based on the average, minimum, and maximum emission rates as classification. The forecasting result of the complex odor modelling of pigsty showed that tolerance limit was exceeded at an adjacent administration building, but tolerance limit was not surpassed at an afflicted location which was within 185m from the pigsty. The ISCST3 modelling of the satisfactory ODR for tolerance limit was accomplished at the administration building. From the prediction of this modelling, maximum emission rates based on 1hr at administration building were 10.59~52.93, 19.05~31.76, and 10.59 $OU/m^3/s/m^2$ at emission rates of 50%, 30%, and 10%. This emission rate was slightly higher than the tolerance limit of 10.00 $OU/m^3/s/m^2$. However, it was inferred that the tolerance limit could be satisfied if the emission rate of 10% was controlled.

• Journal of Korean Society on Water Environment
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• v.26 no.3
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• pp.460-467
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• 2010

For D-WTP's sedimentation basin and distribution reservoir, and water tap the predictive models proposed tentatively herein included the models for estimating TTHM concentration in precipitated water, for treated water and for tap water, and the estimated correlation formula between treated water's TTHM concentration and tap water. As for TTHM-concentration predictive model in sedimentation water, the coefficient of determination is 0.866 for best-fitted short-term $DOC{\times}UV_{254}$ based Model (TTHM). As for $HAA_5$-concentration predictive model in sedimentation water, the coefficient of determination is 0.947 for the suitable $UV_{254}$-based model ($HAA_5$). In case of the predictive model in treated water, the coefficient of determination is 0.980 for best-fitted $DOC{\times}UV_{254}$ based model (TTHM) using coagulated waters, while the coefficient of determination is 0.983 for best-fitted $DOC{\times}UV_{254}$ based model ($HAA_5$) using coagulated waters, which described the $HAA_5$ concentration well. However, the predictive model for tap water could not be compatible with the one for treated water, only except for possibility inducing correlation formula for prediction, [i.e., the correlation formula between TTHM concentration and tap water was verified as TTHM (tap water) = $1.162{\times}TTHM$ (treated water), while $HAA_5$ (tap water) = $0.965{\times}HAA_5$ (treated water).] The correlation analysis between DOC and $KMnO_4$ consumption by process resulted in higher relationship with filtrated water, showing that its regression is $DOC=0.669{\times}KMnO_4$ consumption - 0.166 with 0.689 of determination coefficient. By substituting it to the existing DOC-based model ($HAA_5$) for treated water, the consequential model formula was made as follows; $HAA_5=8.35(KMnO_4\;consumption{\times}0.669-0.166)^{0.701}(Cl_2)^{0.577}t^{0.150}0.9216^{(pH-7.5)}1.022^{(Temp-20^{\circ}C)}$

• Journal of Korean Society of Transportation
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• v.18 no.5
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• pp.83-97
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• 2000

The traffic management schemes through traffic signal control and information provision could be effective when the link-level data and trip-level data were used simultaneously in analysis Procedures. But, because the trip-level data. such as origin, destination and departure time, can not be obtained through the existing surveillance systems directly. It is needed to estimate it using the link-level data which can be obtained easily. Therefore the objective of this study is to develop the model to estimate O-D demand using only the link flows in highway network as a real time. The methodological approaches in this study are kalman filer, least-square method and normalized least-square method. The kalman filter is developed in the basis of the bayesian update. The normalized least-square method is developed in the basis of the least-square method and the natural constraint equation. These three models were experimented using two kinds of simulated data. The one has two abrupt changing Patterns in traffic flow rates The other is a 24 hours data that has three Peak times in a day Among these models, kalman filer has Produced more accurate and adaptive results than others. Therefore it is seemed that this model could be used in traffic demand management. control, travel time forecasting and dynamic assignment, and so forth.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.1
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• pp.1-14
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• 2013

Annual average daily traffic (AADT) serves the important basic data in transportation sector. Despite of its importance, AADT is estimated through permanent traffic counts (PTC) at limited locations because of constraints in budget and so on. At most of locations, AADT is estimated using short-term traffic counts (STC). Though many studies have been carried out at home and abroad in an effort to enhance the accuracy of AADT estimate, the method to simplify average STC data has been adopted because of application difficulty. A typical model for estimating AADT is an adjustment factor application model which applies the monthly or weekly adjustment factors at PTC points (or group) with similar traffic pattern. But this model has the limit in determining the PTC points (or group) with similar traffic pattern with STC. Because STC represents usually 24-hour or 48-hour data, it's difficult to forecast a 365-day traffic variation. In order to improve the accuracy of traffic volume prediction, this study used the geostatistical approach called co-kriging and according to their reports. To compare results, using 3 methods : using adjustment factor in same section(method 1), using grouping method to apply adjustment factor(method 2), cokriging model using previous year's traffic data which is in a high spatial correlation with traffic volume data as a secondary variable. This study deals with estimating AADT considering time and space so AADT estimation is more reliable comparing other research.

• Journal of Korea Water Resources Association
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• v.51 no.6
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• pp.503-514
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• 2018

In recent, the hydrological regime of the Mekong river is changing drastically due to climate change and haphazard watershed development including dam construction. Information of hydrologic feature like streamflow of the Mekong river are required for water disaster prevention and sustainable water resources development in the river sharing countries. In this study, runoff simulations at the Kratie station of the lower Mekong river are performed using SWAT (Soil and Water Assessment Tool), a physics-based hydrologic model, and LSTM (Long Short-Term Memory), a data-driven deep learning algorithm. The SWAT model was set up based on globally-available database (topography: HydroSHED, landuse: GLCF-MODIS, soil: FAO-Soil map, rainfall: APHRODITE, etc) and then simulated daily discharge from 2003 to 2007. The LSTM was built using deep learning open-source library TensorFlow and the deep-layer neural networks of the LSTM were trained based merely on daily water level data of 10 upper stations of the Kratie during two periods: 2000~2002 and 2008~2014. Then, LSTM simulated daily discharge for 2003~2007 as in SWAT model. The simulation results show that Nash-Sutcliffe Efficiency (NSE) of each model were calculated at 0.9(SWAT) and 0.99(LSTM), respectively. In order to simply simulate hydrological time series of ungauged large watersheds, data-driven model like the LSTM method is more applicable than the physics-based hydrological model having complexity due to various database pressure because it is able to memorize the preceding time series sequences and reflect them to prediction.

• The Korean Journal of Pain
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• v.24 no.2
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• pp.81-86
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• 2011

Background: Facet joint disease plays a major role in axial low-back pain. Few diagnostic tests and imaging methods for identifying this condition exist. Single photon emission computed tomography (SPECT) is reported that it has a high sensitivity and specificity in diagnosing facet disease. We prospectively evaluated the use of bone scintigraphy with SPECT for the identification of patients with low back pain who would benefit from medial branch block. Methods: SPECT was performed on 33 patients clinically suspected of facet joint disease. After SPECT, an ultrasound guided medial branch block was performed on all patients. On 28 SPECT-positive patients, medial branch block was performed based on the SPECT findings. On 5 negative patients, medial branch block was performed based on clinical findings. For one month, we evaluated the patients using the visual analogue scale (VAS) and Oswestry disability index. SigmaStat and paired t-tests were used to analyze patient data and compare results. Results: Of the 33 patients, the ones who showed more than 50% reduction in VAS score were assigned 'responders'. SPECT positive patients showed a better response to medial branch blocks than negative patients, but no changes in the Oswestry disability index were seen. Conclusions: SPECT is a sensitive tool for the identification of facet joint disease and predicting the response to medial branch block.

• Korean Journal of Clinical Laboratory Science
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• v.50 no.2
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• pp.126-135
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• 2018

This study examined whether the difference in the middle cerebral artery (MCA) velocities can predict the prognosis of stroke and whether the prognostic impact differs among stroke subtypes. Transient ischemic attack (TIA) or acute ischemic stroke patients, who underwent a routine evaluation and transcranial Doppler (TCD), were included in this study. The MCA asymmetry index was calculated using the relative percentage difference in the mean flow velocity (MFV) between the left and right MCA: (|RMCA MFV-LMCA MFV|/mean MCA MFV)${\times}100$. The stroke subtypes were determined using the TOAST classification. Poor functional outcomes were defined as a mRS score ${\geq}3$ at 3 months after the onset of stroke. A total of 988 patients were included, of whom 157 (15.9%) had a poor functional outcome. Multivariable analysis showed that only the MCA asymmetry index was independently associated with a poor functional outcome. ROC curve analysis showed that adding the MCA asymmetry index to the prediction model improved the discrimination of a poor functional outcome from acute ischemic stroke (from 88.6% [95% CI, 85.2~91.9] to 89.2% [95% CI, 85.9~92.5]). The MCA asymmetry index has an independent prognostic value for predicting a poor short-term functional outcome after an acute cerebral infarction. Therefore, TCD may be useful for predicting a poor functional outcome in patients with acute ischemic stroke.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.4
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• pp.143-154
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• 2012

As demand of water resources and attentions to changes in climate (e.g., due to ENSO) increase, long/short term prediction of precipitation is getting necessary in water planning. This research evaluated the ability of MM5 to predict precipitation in the Tampa Bay region over 23 year period from 1986 to 2008. Additionally MM5 results were statistically bias-corrected using observation data at 33 stations over the study area using CDF-mapping approach and evaluated comparing to raw results for each ENSO phase (i.e., El Ni$\tilde{n}$o and La Ni$\tilde{n}$a). The bias-corrected model results accurately reproduced the monthly mean point precipitation values. Areal average daily/monthly precipitation predictions estimated using block-kriging algorithm showed fairly high accuracy with mean error of daily precipitation, 0.8 mm and mean error of monthly precipitation, 7.1 mm. The results evaluated according to ENSO phase showed that the accuracy in model output varies with the seasons and ENSO phases. Reasons for low predictions skills and alternatives for simulation improvement are discussed. A comprehensive evaluation including sensitivity to physics schemes, boundary conditions reanalysis products and updating land use maps is suggested to enhance model performance. We believe that the outcome of this research guides to a better implementation of regional climate modeling tools in water management at regional/seasonal scale.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.6
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• pp.77-90
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• 2015

An increasing number of vehicles is causing various traffic problems such as chronic congestion of highways and air pollution. Local governments have been managing traffic by constructing systems such as Intelligent Transport Systems (ITS) and Advanced Traffic Management Systems (ATMS) to relieve such problems, but construction of an infrastructure-based traffic system is insufficient in resolving chronic traffic problems. A more sophisticated system with enhanced operational management capabilities added to the existing facilities is necessary at this point. As traffic patterns of the urban traffic flow is time-specific due to the different vehicle populations throughout the time of the day, a local network-wide signal operation plan that can manage such situation-specific traffic patterns is deemed to be necessary. Therefore, this study is conducted for the purpose of establishment of a plan for contextual signal control management through signal optimization at the network level after setting the Frame Signal in accordance to the traffic patterns gathered from the short-term traffic forecast data as a means to mitigate the problems with existing standardized signal operations.

• Journal of Korea Water Resources Association
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• v.42 no.3
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• pp.213-225
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• 2009

A stationary Markov chain model is a stochastic process with the Markov property. Having the Markov property means that, given the present state, future states are independent of the past states. The Markov chain model has been widely used for water resources design as a main tool. A main assumption of the stationary Markov model is that statistical properties remain the same for all times. Hence, the stationary Markov chain model basically can not consider the changes of mean or variance. In this regard, a primary objective of this study is to develop a model which is able to make use of exogenous variables. The regression based link functions are employed to dynamically update model parameters given the exogenous variables, and the model parameters are estimated by canonical correlation analysis. The proposed model is applied to daily rainfall series at Seoul station having 46 years data from 1961 to 2006. The model shows a capability to reproduce daily and seasonal characteristics simultaneously. Therefore, the proposed model can be used as a short or mid-term prediction tool if elaborate GCM forecasts are used as a predictor. Also, the nonstationary Markov chain model can be applied to climate change studies if GCM based climate change scenarios are provided as inputs.