• Journal of Korean Society of Water and Wastewater
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• v.26 no.6
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• pp.861-869
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• 2012

The chlorine's residual concentration prevents the regrowth of microorganism in water transport along the pipeline system. Precise prediction of chlorine concentration is important in determining disinfectant injection for the water distribution system. In this study, a pilot scale water distribution system was designed and fabricated to measure the temporal variation of chlorine concentration for three flow conditions (V = 0.88, 1.33, 1.95 m/s). Various kinetic models were applied to identify the relationship between hydraulic condition and chlorine decay. Genetic Algorithm (GA) was integrated into five kinetic models and time series of chlorine were used to calibrate parameters. Model fitness was compared by Root Mean Square Error (RMSE) between measurement and prediction. Limited first order model and Parallel first order showed good fitness for prediction of chlorine concentration.

• Journal of Environmental Science International
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• v.10 no.3
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• pp.187-194
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• 2001

For the purpose of predicting air pollutants concentration in Pusan coastal urban, we used an Eulerian model of flow and dispersion/chemistry/deposition process considering SST effects which estimate through POM. The results of air quality model including emission from various sources show that the seasonal variation pattern of respective pollutants was affected by the seasonal SST fields and local circulation. Horizontal deviation of diurnal SST was 2.5~4K, especially large gradients in coastal region. Through numerical simulation of wind fields we predicted that local circulation prevailed during daytime in summer and nighttime in winter. So high concentration distribution showed toward inland in spring and summer seasons, while high concentration distribution showed at inland near coast in autumn and winter.

• Journal of Korean Society for Atmospheric Environment
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• v.11 no.2
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• pp.145-152
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• 1995

Predictability of a Gaussian model, ISCST2 was assessed by scaling up wind tunnel experiments with a 1/3,000 terrain model to the real scale. Concentration profiles obtained from the flat-terrain experiment in the neutral condition were estimated to be in agreement with the calculated ones from ISCST2 in the stability class A, but the difference between the two was still large. Concentration profiles from the mountainous-terrain experiments were better fitted to the calculated ones primarily because in the experiment, concentration behind the source was raised due to the effect of a hill in the upstream side. Model prediction was improved with including the downwash effect of buildings and the hill, but overall concentration profiles were not much different from a typical Gaussian profile. While concentration profiles in the experiments were changed with local flows by varying the wind direction and the topography, those from the Gaussian modeling were mot freely changed together with these variations.

• The Korean Journal of Physiology and Pharmacology
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• v.20 no.1
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• pp.111-117
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• 2016

Vagal nerve activity has been known to play a crucial role in the induction and maintenance of atrial fibrillation (AF). However, it is unclear how the distribution and concentration of local acetylcholine (ACh) promotes AF. In this study, we investigated the effect of the spatial distribution and concentration of ACh on fibrillation patterns in an in silico human atrial model. A human atrial action potential model with an ACh-dependent $K^+$ current ($I_{KAch}$) was used to examine the effect of vagal activation. A simulation of cardiac wave dynamics was performed in a realistic 3D model of the atrium. A model of the ganglionated plexus (GP) and nerve was developed based on the "octopus hypothesis". The pattern of cardiac wave dynamics was examined by applying vagal activation to the GP areas or randomly. AF inducibility in the octopus hypothesis-based GP and nerve model was tested. The effect of the ACh concentration level was also examined. In the single cell simulation, an increase in the ACh concentration shortened $APD_{90}$ and increased the maximal slope of the restitution curve. In the 3D simulation, a random distribution of vagal activation promoted wavebreaks while ACh secretion limited to the GP areas did not induce a noticeable change in wave dynamics. The octopus hypothesis-based model of the GP and nerve exhibited AF inducibility at higher ACh concentrations. In conclusion, a 3D in silico model of the GP and parasympathetic nerve based on the octopus model exhibited higher AF inducibility with higher ACh concentrations.

• Journal of Biomedical Engineering Research
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• v.24 no.1
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• pp.45-53
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• 2003

Based on the 4-compartmental pharmacokinetic model developed in PART1, target-controlled infusion(TCI) pump system was designed and evaluated. The TCI system consists of digital board including microcontroller and digital signal process(DSP), analog board, motor-driven actuator, user friendly interface, power management and controller. It provides two modes according to the drugs: plasma target concentration and effect target concentration. Anaesthetist controls the depth of anaesthesia for patients by adjusting the required concentration to maintain both plasma and effect site in drug concentration. The data estimated in DSP include infusion rate, initial load dose, and rotation number of motor encoder. During TCI operation, plasma concentration. effect site concentration, awaken concentration, context-sensitive decrement time and system error information are displayed in real time. Li-ion battery guarantees above 2 hours without power line failure. For high reliability of the system, two microprocessors were used to perform independent functions for both pharmacokinetic algorithm and motor control strategy.

• Fire Science and Engineering
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• v.32 no.1
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• pp.7-15
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• 2018

The prediction performance of combustion models in the Fire Dynamics Simulator (FDS) were evaluated by comparing with experiment for compartment propane gas fires. The mixture fraction model in the FDS v5.5.3 and Eddy Dissipation Concept (EDC) model in the FDS v6.6.3 were adopted in the simulations. Four chemical reaction mechanisms, such as 1-step Mixing Controlled, 2-step Mixing Controlled, 3-step Mixing Controlled and 3-step Mixed (Mixing Controlled + finite chemical reactions) reactions, were implemented in the EDC model. The simulation results with each combustion model showed similar level for the temperature inside the compartment. The prediction performance of FDS with each combustion model showed significant differences for the CO concentration while no distinguished differences were identified for the $O_2$ and $CO_2$ concentrations. The EDC 3-step Mixing Controlled largely over-predicted the CO concentration obtained by experiment and the mixture fraction model under-predicted the experiment slightly. The EDC 3-step Mixed showed the best prediction performance for the CO concentration and the EDC 2-step Mixing Controlled also predicted the CO concentration reasonably. The EDC 1-step Mixing Controlled significantly under-predict the experimental CO concentration when the previously suggested CO yield was adopted. The FDS simulation with the EDC 1-step Mixing Controlled showed difficulties in predicting the $CO_2$ concentration when the CO yield was modified to predict the CO concentration reasonably.

• Journal of Korea Port Economic Association
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• v.17 no.2
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• pp.61-87
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• 2001

The purpose of this paper is to analyze the location, population growth. and cargo concentration of Korean port-cities. In the location theory, Sommer (1976) and McGee (1967) models are newly introduced, as are the Rimmer (1967), Bird (1965), Hoyle (1981) models. which were already introduced in previous studies from Korea. Analysis of population growth in the Korean port-cities is conducted using data from 1966 to 1998. Rimmer and Hoyle's concentration models are used to measure cargo concentration from 1966 to 2000. The main results of this paper are as follows: First, Korean ports are concentrated on the East Sea, the Southern Sea, and the West Sea. Their locations are closely related with the hinterland. the inland city, and growth of port-cities. In considering the foreign countrys' cases, Korean port-cities are similar to the models of Bird and Hoyle. Second, the populations of Ulsan and Pohang grew at the fastest rate in 1966-1998, while the port cities in the Honam and Jeiu region grew at much lower ratios. Most port cities are located near large industrial complexes. Third the growth rates of Gwangyang, Daesan, Pohang, Pyungtaeg, and Samchunpo increased, while those of Busan. Mukho, Masan, Mogpo, Yeosu, and Sokcho declined. Of particular note, the growth rate of Busan remained negative after the late 1980s. Fourth. empirical results using the Rimmer (1967) model indicate that Gwangyang, Daesan, Pyungtag, and Pohang have shown the concentration. But the deconcentration was shown from the Busan, Mukho, Janghang, Gunsan, Mogpo, Yeosu, Masan, Sokcho. and Jeju. Fifth, the concentration of ports located in West coast region has shown the mixed results between concentration and deconcentration except the concentration of early 1970s and 1990s. The concentration of ports located in East coast region has shown the concentration before the middle of 1980s. And deconcentration after the middle of 1980s have appeared. The Southern coast region has shown the continuous deconcentration except the partial concentration of early 1986. and 1991. Planners of Korean ports should find out the factors of concentration and deconcentration of each ports and should determine factors such as investment priority level. size and scope in order to ensure the balanced development of regional ports and port-cities.

• Journal of Korean Society of Water and Wastewater
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• v.36 no.4
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• pp.239-248
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• 2022

The prediction of algal bloom is an important field of study in algal bloom management, and chlorophyll-a concentration(Chl-a) is commonly used to represent the status of algal bloom. In, recent years advanced machine learning algorithms are increasingly used for the prediction of algal bloom. In this study, XGBoost(XGB), an ensemble machine learning algorithm, was used to develop a model to predict Chl-a in a reservoir. The daily observation of water quality data and climate data was used for the training and testing of the model. In the first step of the study, the input variables were clustered into two groups(low and high value groups) based on the observed value of water temperature(TEMP), total organic carbon concentration(TOC), total nitrogen concentration(TN) and total phosphorus concentration(TP). For each of the four water quality items, two XGB models were developed using only the data in each clustered group(Model 1). The results were compared to the prediction of an XGB model developed by using the entire data before clustering(Model 2). The model performance was evaluated using three indices including root mean squared error-observation standard deviation ratio(RSR). The model performance was improved using Model 1 for TEMP, TN, TP as the RSR of each model was 0.503, 0.477 and 0.493, respectively, while the RSR of Model 2 was 0.521. On the other hand, Model 2 shows better performance than Model 1 for TOC, where the RSR was 0.532. Explainable artificial intelligence(XAI) is an ongoing field of research in machine learning study. Shapley value analysis, a novel XAI algorithm, was also used for the quantitative interpretation of the XGB model performance developed in this study.

• Geotechnical Engineering
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• v.11 no.2
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• pp.121-138
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• 1995

The Langmuir competitive model and the IAS(ideal adsorption solution) model were eveluated and compared in a multisolute adsorption study using five organic compounds (phenol, 2, 4-dichlorophenol, 2, 4, 6-trichlorophenot brucine, and thiourea) and two soils. The chemicals were evaluated individually and in mixtures. In general, the IfS model predicted the equilibrium concentration of a chemical in a mixture better than the Langmuir model. The Langmuir model underestimated the sorption of phenol when the concentration of another compound in a mixture with phenol was high. Neither of the models predicted satisfactorily the equilibrium concentration of thiourea in the mixtures. Thiourea is an aliphatic compound while the other four chemicals are aromatic compounds.

• Journal of Korean Society for Atmospheric Environment
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• v.27 no.3
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• pp.358-366
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• 2011

The purpose of this study was to analyze the monthly and seasonal PM10 data using the Autoregressive Error (ARE) model at the southern part of the Gyeonggi-Do, Pyeongtaek monitoring site in Korea. In the ARE model, six meteorological variables and four pollution variables are used as the explanatory variables. The six meteorological variables are daily maximum temperature, wind speed, amount of cloud, relative humidity, rainfall, and global radiation. The four air pollution variables are sulfur dioxide ($SO_2$), nitrogen dioxide ($NO_2$), carbon monoxide (CO), and ozone ($O_3$). The result shows that monthly ARE models explained about 17~49% of the PM10 concentration. However, the ARE model could be improved if we add the more explanatory variables in the model.