• Journal of Korean Society on Water Environment
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• v.24 no.6
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• pp.784-792
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• 2008

Greater focus must be placed on ensuring that the water quality model (WQM) reflects the objective of its application and the characteristics of the water environment properly before it is selected. In the development or application of WQM, various factors influencing the model predictions should be reviewed so that it can perform more properly and reasonably based on scientific theory. This study reviewed the characteristic of existing WQM and the domestic river environment to find the requirements of the model application for TMDLs management in Korea. In this study, a water quality model, QUAL-NIER, was developed based on the USEPA's QUAL2E. The core structure and reaction scheme of the model was established followed by the formulation of equations according to the scheme with some supplements on the reaction mechanisms which are necessary for domestic rivers. Algorithms on the equations were set up and programmed to form a computer-based model. The developed model, QUAL-NIER was applied to the main stem of the Nakdong river. The model was calibrated and verified to data measured in 2004. The model results displayed good agrement with the field measurements for both calibration and verification. From this study, it was concluded that the developed QUAL-NIER model was very powerful with regard to the water quality simulation in domestic rivers.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.1
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• pp.9-20
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• 2019

The primary objective of this study was to analyze the delivery ratio using Hydrological Simulation Program - Fortran (HSPF) in Okdong-cheon watershed. Model parameters related to hydrology and water quality were calibrated and validated by comparing model predictions with the 8-day interval filed data collected for ten years from the Korea Ministry of Environment. The results indicated that hydrology and water quality parameters appeared to be reasonably comparable to the field data. The pollutant delivery loads of the watershed in 2015 were simulated using the HSPF model. The delivery ratios of each subwatershed were also estimated by the simple ratio calculation of pollutant discharge load and pollutant delivery load. Coefficients of the regression equation between the delivery ratio and specific discharge were also computed using the delivery ratio. Based on the results, multiple regression analysis was performed using the discharge and the physical characteristics of the subwatershed such as the area. The equation of delivery ratio derived in this study is only for the Okdong-cheon watershed, so the larger studies are needed to apply the findings to other watersheds.

• Computers and Concrete
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• v.21 no.6
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• pp.697-703
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• 2018

Compressive strength is one of the most important engineering properties of concrete, and testing of the compressive strength of concrete specimens is often costly and time consuming. In order to provide the time for concrete form removal, re-shoring to slab, project scheduling and quality control, it is necessary to predict the concrete strength based upon the early strength data. However, concrete compressive strength is affected by many factors, such as quality of raw materials, water cement ratio, ratio of fine aggregate to coarse aggregate, age of concrete, compaction of concrete, temperature, relative humidity and curing of concrete. The concrete compressive strength is a quite nonlinear function that changes depend on the materials used in the concrete and the time. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of concrete compressive strength. The training of fuzzy system was performed by a hybrid method of gradient descent method and least squares algorithm, and the subtractive clustering algorithm (SCA) was utilized for optimizing the number of fuzzy rules. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed ANFIS model. Further, predictions from three models (the back propagation neural network model, the statistics model, and the ANFIS model) were compared with the experimental data. The results show that the proposed ANFIS model is a feasible, efficient, and accurate tool for predicting the concrete compressive strength.

• Journal of Communications and Networks
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• v.13 no.2
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• pp.132-141
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• 2011

IEEE 802.15.4 networks are a feasible platform candidate for connecting all health-care-related equipment dispersed across a hospital room to collect critical time-sensitive data about patient health state, such as the heart rate and blood pressure. To meet the quality of service requirements of health-care systems, this paper proposes a multi-priority queue system that differentiates between various types of frames. The effect of the proposed system on the average delay and throughput is explored herein. By employing different contention window parameters, as in IEEE 802.11e, this multi-queue system prioritizes frames on the basis of priority classes. Performance under both saturated and unsaturated traffic conditions was evaluated using a novel analytical model that comprehensively integrates two legacy models for 802.15.4 and 802.11e. To improve the accuracy, our model also accommodates the transmission retries and deferment algorithms that significantly affect the performance of IEEE 802.15.4. The multi-queue scheme is predicted to separate the average delay and throughput of two different classes by up to 48.4% and 46%, respectively, without wasting bandwidth. These outcomes imply that the multi-queue system should be employed in health-care systems for prompt allocation of synchronous channels and faster delivery of urgent information. The simulation results validate these model's predictions with a maximum deviation of 7.6%.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.19 no.8
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• pp.563-568
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• 2007

The air-side particulate fouling in the heat exchangers of HVAC applications degrades the performances of cooling capacity, pressure drop across a heat exchanger, and indoor air quality. Indoor and outdoor air contaminants foul heat exchangers. An empirical modeling equation has been derived from the experimental results using accelerated tests and it showed good predictions of the fouling characteristics of the slitted finned tube heat exchangers. However the modeling equation predicts only the fouling characteristics of new heat exchangers and it can not predicts fouling characteristics obtained from actual field data which contains the effect of hydrophilicity deterioration. Therefore an modified modeling equation is proposed and it shows good predictions of the actual fouling characteristics of finned-tube heat exchangers.

• Journal of Powder Materials
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• v.27 no.5
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• pp.365-372
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• 2020

Predicting the quality of materials after they are subjected to plasma sintering is a challenging task because of the non-linear relationships between the process variables and mechanical properties. Furthermore, the variables governing the sintering process affect the microstructure and the mechanical properties of the final product. Therefore, an artificial neural network modeling was carried out to correlate the parameters of the spark plasma sintering process with the densification and hardness values of Ti-6Al-4V alloys dispersed with nano-sized TiN particles. The relative density (%), effective density (g/㎤), and hardness (HV) were estimated as functions of sintering temperature (℃), time (min), and composition (change in % TiN). A total of 20 datasets were collected from the open literature to develop the model. The high-level accuracy in model predictions (>80%) discloses the complex relationships among the sintering process variables, product quality, and mechanical performance. Further, the effect of sintering temperature, time, and TiN percentage on the density and hardness values were quantitatively estimated with the help of the developed model.

• Preventive Nutrition and Food Science
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• v.13 no.4
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• pp.348-353
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• 2008

Aerobic bacterial growth on Korean pan.fried meat patties as a primary quality deterioration factor was modeled as a function of temperature to estimate microbial spoilage on a real.time basis under dynamic storage conditions. Bacteria counts in the stretch.wrapped foods held at constant temperatures of 0, 5, 10 and $15^{\circ}C$ were measured throughout storage. The bootstrapping method was applied to generate many resampled data sets of mean microbial counts, which were then used to estimate the parameters of the microbial growth model of Baranyi & Roberts in the form of differential equations. The temperature functions of the primary model parameters were set up with confidence limits. Incorporating the temperature dependent parameters into the differential equations of bacterial growth could produce predictions closely representing the experimental data under constant and fluctuating temperature conditions.

• Journal of Korean Society on Water Environment
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• v.26 no.1
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• pp.111-115
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• 2010

Beaches in estuaries, bays, and harbors are frequently contaminated with indicators of human pathogens such as fecal indicator bacteria. Tracking down the sources of contamination at these enclosed beaches is complicated by the many point and non-point sources that could potentially degrade water quality along the shore. A mathematical framework was developed to test quantitative relationships between fecal indicator bacteria concentration in ankle depth water at enclosed beaches, the loading rate of fecal indicator bacteria from non-point sources located along the shore, physical characteristics of the beach that affect the transport of fecal indicator bacteria across the beach boundary layer, and a background concentration of fecal indicator bacteria attributable to point sources of fecal pollution that impact water quality over a large region of the embayment. Field measurements of fecal indicator bacteria concentrations and water turbulence at an enclosed beach were generally consistent with predictions and assumptions of the mathematical model, and demonstrated its utility for assessing waste load of non-point sources, such as runoff, bather shedding, bird droppings, and tidal washing of contaminated sediments.

• Proceedings of the KSEEG Conference
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• 2004.12a
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• pp.9-28
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• 2004

Concern about arsenic is increasing throughout the world, including areas of the United States. Elevated levels of arsenic above current drinking-water regulations in ground and surface water can be the result of purely natural phenomena, but often are due to anthropogenic activities, such as mining and agriculture. The current study correlates arsenic speciation in acid mine drainage and mining influenced water with the important water-chemistry properties Eh, pH, and iron(III) concentration. The results show that arsenic speciation is generally in equilibrium with iron chemistry in low pH AMD, which is often not the case in other natural-water matrices. High pH mine waters and groundwater do not 짐ways hold to the redox predictions as well as low pH AMD samples. The oxidation and precipitation of oxyhydroxides depletes iron from some systems, and this also affects arsenite and arsenate concentrations differently through sorption processes.

• Journal of Korean Society of Water and Wastewater
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• v.31 no.4
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• pp.289-296
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• 2017

Pulp and paper industry produces large volumes of wastewater and residual sludge waste, resulting in many issues in relation to wastewater treatment and sludge disposal. Contaminants in pulp and paper wastewater include effluent solids, sediments, chemical oxygen demand (COD), and biological oxygen demand (BOD), which should be treated by wastewater treatment processes such as coagulation and biological treatment. However, few works have been attempted to predict the treatment efficiency of pulp and paper wastewater. Accordingly, this study presented empirical models based on experimental data in laboratory-scale coagulation tests and compared them with statistical models such as artificial neural network (ANN). Results showed that the water quality parameters such as turbidity, suspended solids, COD, and UVA can be predicted using either linear or expoential regression models. Nevertheless, the accuracies for turbidity and UVA predictions were relatively lower than those for SS and COD. On the other hand, ANN showed higher accuracies than the emprical models for all water parameters. However, it seems that two kinds of models should be used together to provide more accurate information on the treatment efficiency of pulp and paper wastewater.