• Journal of Korean Society of Disaster and Security
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• v.12 no.2
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• pp.25-34
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• 2019

The efficiency of sewage treatment was analyzed by statistical method based on the water quality and operation data of the sewage treatment plant applying the oxidation method. The obtained water quality data were pH, temperature, BOD, SS, T-N, ${NH_4}^+-N$, and T-P of influent and discharge water. Data analysis was performed by correlation analysis, ANOVA analysis, and cluster analysis. As a result of the statistical analysis, the influent flow rate in the sewage treatment plant was the highest in summer. The average inflow flow rate was $3.000m^3/s$. According to Box plot results, COD, and T-P concentrations of effluents were not significantly different from season to season. The Pearson correlation analysis showed strong positive correlation between BOD, COD, T-N, and T-P in influent flow. Seasonal BOD and T-N concentrations were highest in winter and COD and T-P in seasonal influences. BOD showed a strong negative correlation with the water temperature, but showed a positive correlation with other operating factors such as HRT, SRT and C/N. The higher the influent temperature, the lower the BOD concentration. Therefore, retention time was shortened and BOD treatment efficiency was lowered. It was found that T-N had a higher retention time and a higher concentration than DO concentration. On the other hand, T-P did not show a significant correlation with operating factors.

• Food Engineering Progress
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• v.21 no.2
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• pp.132-137
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• 2017

The purpose of this study was to optimize dough properties using response surface methodology (RSM) and to demonstrate the performances of dough prepared under optimized conditions. Dough mixed with yeast, margarine, salt, sugar and wheat flour was prepared by fermentation process. Hardness, cohesiveness and springiness of dough were selected as critical quality attributes. The critical formulations (yeast and water) and process (fermentation time) variables were selected as critical input variables based on preliminary experiment. Box-Behnken design (BBD) was used as RSM. As a result, the quardratic, the squared and the linear model respectively provided the most appropriate fit ($R^2$>90) and had no significant lack of fit (p>0.05) on critical quality attributes (hardness, cohesiveness and springiness). The accurate prediction of dough characteristics was possible from the selected models. It was confirmed by validation that a good correlation was obtained between the actual and predicted values. In conclusion, the methodologies using RSM in this study might be applicable to the optimization of fermented foods containing various wheat flour and yeast.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.67 no.4
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• pp.274-284
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• 2022

To use kenaf (Hibiscus cannabinus L.) as a fiber and livestock feed, a high-yielding variety needs to be identified. For this, accurate phenotyping of plant height is required for this breeding purpose due to the strong relationship between plant height and yield. Plant height can be estimated using RGB images from unmanned aerial vehicles (UAV-RGB) and photogrammetry based on Structure from Motion (SfM) algorithms. In kenaf, accurate measurement of height is limited because kenaf stems have high flexibility and its height is easily affected by wind, growing up to 3 ~ 4 m. Therefore, we aimed to identify a method suitable for the accurate estimation of plant height of kenaf and investigate the feasibility of using the UAV-RGB-derived plant height map. Height estimation derived from UAV-RGB was improved using multi-point calibration against the five different wooden structures with known heights (30, 60, 90, 120, and 150 cm). Using the proposed method, we analyzed the variation in temporal height of 23 kenaf cultivars. Our results demontrated that the actual and estimated heights were reliably comparable with the coefficient of determination (R²) of 0.80 and a slope of 0.94. This method enabled the effective identification of cultivars with significantly different heights at each growth stages.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.39 no.12
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• pp.927-934
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• 2015

In this study, we numerically investigated the concentration characteristics of a linear Fresnel reflector system that can drive a solar thermal absorption refrigeration system to be installed in Saudi Arabia. Using an optical modeling program based on the Monte Carlo ray-tracing method, we simulated the concentrated solar flux, concentration efficiency, and concentrated solar energy on four representative days of the year - the vernal equinox, summer solstice, autumnal equinox, and winter solstice. Except the winter solstice, the concentrations were approximately steady from 9 AM to 15 PM, and the concentration efficiencies exceed 70%. Moreover, the maximum solar flux around the solar receiver center changes only within the range of $13.0{\sim}14.6kW/m^2$. When we investigated the effects of the receiver installation height, reflector width, and reflector gap, the optimal receiver installation height was found to be 5 m. A smaller reflector width had a greater concentration efficiency. However, the design of the reflector width should be based on the capacity of the refrigeration system because it dominantly affects the concentrated solar energy. The present study was an essential prerequisite for thermal analyses of the solar receiver. Thus, an optical-thermal integration study in the future will assist with the performance prediction and design of the entire system.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.20 no.5
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• pp.482-490
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• 2008

For a coastal or harbor structure design, one of the most important environmental factors is the appropriate extreme highest tide level condition. Especially, the information of extreme highest tide level distribution is essential for reliability design. In this paper, 23 set of extreme highest tide level data obtained from National Oceanographic Research Institute(NORI) were analyzed for extreme highest tide levels. The probability distributions considered in this research were Generalized Extreme Value(GEV), Gumbel, and Weibull distribution. For each of these distributions, three parameter estimation methods, i.e. the method of moments, maximum likelihood and probability weighted moments, were applied. Chi-square and Kolmogorov-Smirnov goodness-offit tests were performed, and the assumed distribution was accepted at the confidence level 95%. Gumbel distribution which best fits to the 22 tidal station was selected as the most probable parent distribution, and optimally estimated parameters and extreme highest tide level with various return periods were presented. The extreme values of Incheon, Cheju, Yeosu, Pusan, and Mukho, which estimated by Shim et al.(1992) are lower than that of this result.

• Korean Journal of Construction Engineering and Management
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• v.7 no.6
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• pp.175-184
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• 2006

This study focused on developing Life Cycle Cost(LCC) analysis model for selecting sludge collectors in wastewater treatment system and applying the model to a case study. Cost items are examined through literature review and historical data of a facility. Analysis period, discount rate, energy cost escalation ratio are assumed to reasonable level. Monetary evaluation is performed using historical data and estimations from vendors. Sensitive analysis is executed using Monte Carlo Simulation for assumed factors. Interviews with operators, vendors, constructors, managers are conducted to define factors which indicates ease of maintenance, ease of delivery, technical performance, efficiency, environmental friendship. Factors are representing technical and social factors. Results from LCC analysis and qualitative analysis are evaluate together with Weighted Matrix Evaluation Methods for optimum alternative of sludge collectors.

• Journal of Korean Society of Environmental Engineers
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• v.29 no.1
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• pp.62-67
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• 2007

It was investigated to develop the technology for simultaneous removal of $SO_2/NO$ in flue gas using liquid homogeneous catalyst. Test was carried out using a bench scale and a pilot scale experiment. The investigation led to the following results: 1) Removal efficiency of $SO_2$ gas showed good results regardless of operating condition. Removal efficiency of NO gas, however, proportionally increased with higher packing height, lower concentration and larger injection rate of catalyst 2) The optimum design parameters for simultaneous removal of $SO_2/NO$ gas using Fe(II)-EDTA catalyst were as follow: HTU(height of transfer unit) = 0.5 m, liquid gas ratio = 20 $L/m^3$, NTU (number of transfer unit) = 3 stages, cross dimension of scrubber=0.025 $m^2$ 3) The removal efficiencies of $SO_2$ and NO were 95% and 81%, repletely. 4) The high HTU is advantageous on removal of the NO, but the excessive HTU diminishes operating efficiency. Consequently, it is important to decide the HTU of optimum.

• Korean Chemical Engineering Research
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• v.46 no.3
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• pp.610-618
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• 2008

It is not easy to compare the treatment processes and find an optimum operating condition by the experiments due to influent conditions, treatment processes, various operational conditions and complex factors in real wastewater treatment system and also need a lot of time and costs. In this paper, the activated sludge models are applied to four principal biological wastewater treatment processes, $A_2O$(anaerobic/anoxic/oxic process), Bardenpho(4 steps), VIP(Virginia Initiative Plant) and UCT(University of Cape Town), and are used to compare their environmental and economic assessment for four key processes. In order to evaluate each processes, a new assessment index which can compare the efficiency of treatment performances in various processes is proposed, which considers both environmental and economic cost. It shows that the proposed index can be used to select the optimum processes among the candidate treatment processes as well as to find the optimum condition in each process. And it can find the change of economic and environmental index under the changes of influent flowrate and aerobic reaction size and predict the optimum index under various operation conditions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.1
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• pp.468-473
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• 2016

The aim of this study was to measure the opening force of an access door of a pressurized smoke control zone and verify the reliability of the opening force. For the access door opening force, the opening load of the access door was measured before and after pressurized air had entered the smoke control zone. The reliability of the measured values was verified using the Anderson Darling's statistical analysis method of the Minitab Program. Because the analyzed P values were greater than 0.05 except for some floors before and after the operation of the smoke control equipment, the opening force was found to have 95% reliability. The normal distribution of the measured values showed no relationship with the operation of the smoke control equipment and the precision of the force gauge was believed to be reliable. The major factors for the optimal design of the pressurized smoke control equipment include the precision and reliability of the force gauge, the correct posture of the measuring person, and the same conditions for access doors. Therefore, a digital force gauge is believed to be suitable for measuring the opening force of the access door of a pressurized smoke control zone. In addition, standardization of the posture of a measuring person, the setup of the initial opening force of an access door, etc., are major variables for effective measurements of the door opening force of an access door.

• Smart Media Journal
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• v.8 no.1
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• pp.74-81
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• 2019

Recently, machine learning technology has had a significant impact on society, particularly in the medical, manufacturing, marketing, finance, broadcasting, and agricultural aspects of human lives. In this paper, we study how to apply machine learning techniques to foods, which have the greatest influence on the human survival. In the field of Smart Farm, which integrates the Internet of Things (IoT) technology into agriculture, we focus on optimizing the crop growth environment by monitoring the growth environment in real time. KT Smart Farm Solution 2.0 has adopted machine learning to optimize temperature and humidity in the greenhouse. Most existing smart farm businesses mainly focus on controlling the growth environment and improving productivity. On the other hand, in this study, we are studying how to apply machine learning with respect to harvest time so that we will be able to harvest fruits of the highest quality and ship them at an excellent cost. In order to apply machine learning techniques to the field of smart farms, it is important to acquire abundant voluminous data. Therefore, to apply accurate machine learning technology, it is necessary to continuously collect large data. Therefore, the color, value, internal temperature, and moisture of greenhouse-grown fruits are collected and secured in real time using color, weight, and temperature/humidity sensors. The proposed FPSML provides an architecture that can be used repeatedly for a similar fruit crop. It allows for a more accurate harvest time as massive data is accumulated continuously.