• Journal of Agricultural Extension & Community Development
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• v.20 no.2
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• pp.511-533
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• 2013

The study used a seasonal ARIMA model to forecast the number of tourists to Kyeongju foreign in a uni-variable time series. Time series monthly data for the investigation were collected ranging from 1995 to 2010. A total of 192 observations were used for data analysis. The date showed that a big difference existed between on-season and off-season of the number of foreign tourists in Kyeongju. In the forecast multiplicative seasonal ARIMA(1,1,0) $(4,0,0)_{12}$ model was found the most appropriate model. Results show that the number of tourists was 694 thousands in 2011, 715 thousands in 2012, 725 thousands in 2013, 738 thousands in 2014, and 884 thousands in 2015. It was suggested that the grasping of the Kyeongju forecast model was very important in respect of how experts in tourism development, policy makers or planners would establish marketing strategies to allocate services in Kyeongju as a tourist destination and provide tourism facilities efficiently.

• Journal of the Korean earth science society
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• v.26 no.7
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• pp.706-713
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• 2005

Subsurface oceanic data (Z20; Depth of $20^{\circ}C$ isotherm and WWV; Warm Water Volume) from the tropical Pacific Ocean from 1980 to 2004 were utilized to examine upper ocean variations in relation to E1 Nino. Time series analysis using EOF, composite, and cross-correlation methods indicated that there are significant time delays between subsurface oceanic parameters and the Nino3.4 SST. It implied that Z20 and WWV would be more reliable predictors of El Nino events. Based on analyzed results, we also constructed neural network model to predict the Nino3.4 SST from 1996 to 2004. The forecasting skills for the model using WWV were statistically higher than that using the trade wind except for short range forecasting less than 3 months. This model greatly predicted SST than any other previous statistical model, especially at lead times of 5 to 8 months.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.318-324
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• 2000

Ultrasonic and magnetic resonance imaging systems are used to visualize the interior states of biological objects. These nondestructive methods have many advantages but too much expensive. And they do not give exact color information and may miss some details. If it is allowed to destruct some biological objects to get the interior and exterior information, constructing 3D image from the series of the sliced sectional images gives more useful information with relatively low cost. In this paper, PC based automatic 3D model generator was developed. The system was composed of three modules. One is the object handling and image acquisition module, which feeds and slices objects sequentially and maintains the paraffin cool to be in solid state and captures the sectional image consecutively. The second is the system control and interface module, which controls actuators for feeding, slicing, and image capturing. And the last is the image processing and visualization module, which processes a series of acquired sectional images and generates 3D graphic model. The handling module was composed of the gripper, which grasps and feeds the object and the cutting device, which cuts the object by moving cutting edge forward and backward. Sliced sectional images were acquired and saved in the form of bitmap file. The 3D model was generated to obtain the volumetric information using these 2D sectional image files after being segmented from the background paraffin. Once 3-D model was constructed on the computer, user could manipulate it with various transformation methods such as translation, rotation, scaling including arbitrary sectional view.

• Agricultural and Biosystems Engineering
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• v.1 no.2
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• pp.95-99
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• 2000

Nondestructive methods such as ultrasonic and magnetic resonance imaging systems have many advantages but still much expensive. And they do not give exact color information and may miss some details. If it is allowed to destruct some biological objects to get interior and exterior informations, constructing 3D image form a series of slices sectional images gives more useful information with relatively low cost. In this paper, a PC based automatic 3D model generator was developed. The system was composed of three modules. The first module was the object handling and image acquisition module, which fed and sliced the object sequentially and maintains the paraffine cool to be in solid state and captures the sectional image consecutively. The second one was the system control and interface module, which controls actuators for feeding, slicing, and image capturing. And the last was the image processing and visualization module, which processed a series of acquired sectional images and generated 3D volumetric model. Handling module was composed of the gripper, which grasped and fed the object and the cutting device, which cuts the object by moving cutting edge forward and backward. sliced sectional images were acquired and saved in a form of bitmap file. 2D sectional image files were segmented from the background paraffine and utilized to generate the 3D model. Once 3-D model was constructed on the computer, user could manipulated it with various transformation methods such as translation, rotation, scaling including arbitrary sectional view.

• Journal of Microbiology
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• v.40 no.2
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• pp.119-124
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• 2002

Experiments for the characterization of inactivation were performed in a series of batch processes with the total coliform used as a general indicator organism based on the chlorine residuals as a disinfectant. The water samples were taken from the outlet of a settling basin in a conventional surface water treat- ment system that is provided with the raw water drawn from the mid-stream of the Han River, The inactivation of total coliform was experimentally analysed for the dose of disinfectants contact time, filtration and mixing intensity. The curves obtained from a series of batch processes were shaped with a general tailing-off and biphasic mode of inactivation, i.e. a sharp loss of bacterial viability within 15 min followed by an extended phase. In order to observe the effect of carry-over suspended solids on chlorine consumption and disinfection efficiency, the water samples were filtered, prior to inoculation with coliforms, with membranes of both 2.5$\mu$m and 11.0 $\mu$m pore size, and with a sand tilter of 1.0 mm in effective size and of 1.4 in uniformity coefficient. As far as the disinfection efficiency is concerned, there were no significant differences. The parameters estimated by the models of Chick-Wat-son, Hom and Selleck from our experimental data obtained within 120 min are: log(N/N$\_$0/)=-0.16CT with n=1, leg(N/N$\_$0/)=-0.71C$\^$0.87/ with n 1 for the Chick-Watson model, log (N/N$\_$0/)=-1.87C$\^$0.47/ T$\^$0.36/ for the Hom model, log (MHo)=-2.13log (1+CT/0.11) for the Selleck model. It is notable that among the models reviewed with regard to the experimental data obtained, the Selleck model appeared to most closely resemble the total coliform survival curve.

• Korean Journal of Soil Science and Fertilizer
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• v.48 no.4
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• pp.246-254
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• 2015

This study explored the effect of rainfall pattern and soil characteristics on water management in rice paddy fields, using a soil water balance model, BUDGET. In two sites with different soil textural group, coarse loamy soil (Gangseo series) and fine soil (Hwadong series), respectively, we have monitored daily decrease of water depth, percolation rate, and groundwater table. The observed evapotranspiration (ET) was obtained from differences between water depth decrease and percolation rate. The root mean square difference values between observed and BUDGET-estimated ET ranged between 10% and 20% of the average observed ET. This is comparable to the measurement uncertainty, suggesting that the BUDGET model can provide reliable ET estimation for rice fields. In BUDGET model of this study, irrigation requirement was determined as minimum water need for maintaining water-saturated soil surface, assuming 100 mm of bund height and no lateral loss of water. The model results showed different water balance and irrigation requirement with the different soil profile and indicated that minimum percolation rate by plow pan could determine the irrigation requirement of rice paddy field. For the condition of different rainfall distribution, the results presented different irrigation period and amounts, representing the importance of securing water for irrigation against different rainfall pattern.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.7
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• pp.307-314
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• 2022

Recently, researches using deep learning-based models are being actively conducted to replace statistical-based time series forecast techniques to predict electric power demand. The result of analyzing the researches shows that the performance of the LSTM-based prediction model is acceptable, but it is not sufficient for long-term regional-wide power demand prediction. In this paper, we propose a WaveNet deep learning model to predict electric power demand 24-hour-ahead with temperature data in order to achieve the prediction accuracy better than MAPE value of 2% which statistical-based time series forecast techniques can present. First of all, we illustrate a delated causal one-dimensional convolutional neural network architecture of WaveNet and the preprocessing mechanism of the input data of electric power demand and temperature. Second, we present the training process and walk forward validation with the modified WaveNet. The performance comparison results show that the prediction model with temperature data achieves MAPE value of 1.33%, which is better than MAPE Value (2.33%) of the same model without temperature data.

• Journal of Korean Society of Water and Wastewater
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• v.35 no.6
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• pp.425-436
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• 2021

In this study, chemically enhanced steam cleaning(CESC) was applied as a novel and efficient method for the control of organic and inorganic fouling in ceramic membrane filtration. The constant filtration regression model and the resistance in series model(RISM) were used to investigate the membrane fouling mechanisms. For total filtration, the coefficient of determination(R²) with an approximate value of 1 was obtained in the intermediate blocking model which is considered as the dominant contamination mechanism. In addition, most of the coefficient values showed similar values and this means that the complex fouling was formed during the filtration period. In the RISM, R _c/R _f increased about 4.37 times in chemically enhanced steam cleaning compared to physical backwashing, which implies that the internal fouling resistance was converted to cake layer resistance, so that the membrane fouling hardly to be removed by physical backwashing could be efficiently removed by chemically enhanced steam cleaning. The results of flux recovery rate showed that high-temperature steam may loosen the structure of the membrane cake layer due to the increase in diffusivity and solubility of chemicals and finally enhance the cleaning effect. As a consequence, it is expected that chemically enhanced steam cleaning can drastically improve the efficiency of membrane filtration process when the characteristics of the foulant are identified.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.4
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• pp.495-501
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• 2024

SWAT-K (Soil and Water Assessment Tool-Korea) model is a long-term runoff model using a soil-centered water balance equation. Soil is crucial for simulating hydrological components, requiring a database (usersoil.dbf) with soil series attribute information. Since the soil property information estimated by soil transfer functions developed overseas does not reflect the characteristics of domestic soil, the Korea Institute of Civil Engineering and Building Technology has established the soil database, which incorporates the results of domestic soil surveys and research from the National Institute of Agricultural Sciences. This study provides a more detailed description of the hydrological component simulation process using soil property information and revises and supplements the previously established soil database to operate in the latest SWAT model. Additionally, by providing this database through the integrated water management platform, it is expected to be utilized not only in the SWAT-K model but also in various watershed hydrological models developed considering soil characteristics.

• Journal of IKEEE
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• v.28 no.3
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• pp.451-457
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• 2024

This paper proposes a method for predicting agricultural product prices by utilizing various variables such as price, climate factors, demand, and import volume as data, and applying the Long Short-Term Memory (LSTM) model. The analysis of prediction performance using the LSTM model, which learns the long-term dependencies of time series data, showed that integrating diverse data improved performance compared to traditional methods. Furthermore, even when predicting without price data as a dependent variable, meaningful results were achieved using only independent variables, indicating the potential for further model development. Moreover, it was found that using a multi-variable model could further enhance prediction performance, suggesting that this complex approach is effective in improving the accuracy of cabbage price predictions.