• Journal of Environmental Impact Assessment
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• v.13 no.4
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• pp.153-164
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• 2004

An integration study of time-variable small watershed and stream models (USEPA's SWMM and WASP5) was performed for impact assessment of urbanization on water environment. The study area, the Kyoungan Stream, the tributary of Paldang Lake, was divided into 111 subbasins, based on the topographic condition, land use, and drainage system. RUNOFF block of SWMM was applied to estimate runoff flow and quality. EXTRAN block computed daily and hourly flow according to simulated runoff flow, water supply, and drainage data. SWMM was connected to WASP5 by transforming output file of SWMM into input file of WASP5. The nonpoint source loads and flow data of SWMM were imported to WASP5. The stream was divided into 45 segments based on the watershed delineation. The study included three water quality parameters, BOD, TN, and TP. The validate models were used to examine the impact of urbanization on stream flow and water quality.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.588-591
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• 2003

This paper presents a new short-term load forecasting system using data mining. Since the electric load has very different pattern according to the day, it definitely gives rise to the forecasting error if only one forecasting model is used. Thus, to resolve this problem, the fuzzy model-based classifier and predictor are proposed for the forecasting of the hourly electric load. The proposed classifier is the multi-input and multi-output fuzzy system of which the consequent part is composed of the Bayesian classifier. The proposed classifier attempts to categorize the input electric load into Monday, Tuesday$\sim$Friday, Saturday, and Sunday electric load, Then, we construct the Takagi-Sugeno (T-S) fuzzy model-based predictor for each class. The parameter identification problem is converted into the generalized eigenvalue problem (GEVP) by formulating the linear matrix inequalities (LMIs). Finally, to show the feasibility of the proposed method, this paper provides the short-term load forecasting example.

• Journal of Biomedical Engineering Research
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• v.24 no.5
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• pp.465-471
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• 2003

The purpose of this research was to evaluate chronic stress using physiological parameters. Wistar rats were exposed to the sound stress for 14 days. Biosignals were acquired hourly. To develop a fuzzy inference system which can integrate physiological parameters. the parameters of the system were adjusted by the adaptive neuro-fuzzy inference system. Of the training dataset, input dataset was the physiological parameters from the biosignals and output dataset was the target values from the cortisol production. Physiological parameters were integrated using the fuzzy inference system. then 24-hour results were analyzed by the Cosinor method. Chronic stress was evaluated from the degree of circadian rhythm disturbance. Suppose that the degree of stress for initial rest period is 1. Then. the degree of stress after 14-day sound stress increased to 1.37, and increased to 1.47 after the 7-day recovery period. That is, the rat was exposed to 37%-increased amount of stress by the 14-day sound and did not recover after the 7-day recovery period.

• Journal of Advanced Navigation Technology
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• v.22 no.5
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• pp.478-484
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• 2018

This study predicts solar radiation, solar radiation, and solar power generation using hourly weather data such as temperature, precipitation, wind direction, wind speed, humidity, cloudiness, sunshine and solar radiation. I/O pattern in supervised learning is the most important factor in prediction, but it must be determined by repeated experiments because humans have to decide. This study proposed four input and output patterns for solar and sunrise prediction. In addition, we predicted solar power generation using the predicted solar and solar radiation data and power generation data of Youngam solar power plant in Jeollanamdo. As a experiment result, the model 4 showed the best prediction results in the sunshine and solar radiation prediction, and the RMSE of sunshine was 1.5 times and the sunshine RMSE was 3 times less than that of model 1. As a experiment result of solar power generation prediction, the best prediction result was obtained for model 4 as well as sunshine and solar radiation, and the RMSE was reduced by 2.7 times less than that of model 1.

• Korean Journal of Agricultural and Forest Meteorology
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• v.3 no.3
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• pp.156-162
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• 2001

In order to monitor local climatic information, twelve automated weather stations (AWS) were installed in alpine area by the Alpine Agricultural Experiment Station, Rural Development Administration (RDA), at the field of major crop located in around highland area, and collected data from 1993 to 2000. Hourly measurements of air and soil temperature (underground 10 cm,20 cm), relative humidity, wind speed and direction, precipitation, solar radiation and leaf wetness were automatically performed and the data could be collected through a public phone line. Datalogger was selected as CR10X (Campbell scientific, LTD, USA) out of consideration for sensers' compatibility, economics, endurance and conveniences. All AWS in alpine area were combined for net work and daily climatic data were analyzed in text and graphic file by program (Chumsungdae, LTD) on 1 km $\times$ 1 km grid tell basis. In this analysis system, important multi-functionalities, monitoring and analysis of local climatic information in alpine area was emphasized. The first objective was to obtain the output of a real time data from AWS. Secondly, daily climatic normals for each grid tell were calculated from geo-statistical relationships based on the climatic records of existing weather stations as well as their topographical informations. On 1 km $\times$ 1 km grid cell basis, real time climatic data from the automated weather stations and daily climatic normals were analyzed and graphed. In the future, if several simulation models were developed and connected with this system it would be possible to precisely forecast crop growth and yield or plant disease and pest by using climatic information in alpine area.

• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.3
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• pp.149-163
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• 2004

Evapotranspiration (ET) is a critical component of the hydrologic cycle which influences economic activities as well as the natural ecosystem. While there have been numerous studies on ET estimation for homogeneous areas using point measurements of meteorological variables, monitoring of spatial ET has not been possible at landscape - or watershed - scales. We propose a site-specific application of the land surface model, which is enabled by spatially interpolated input data at the desired resolution. Gyunggi Province of South Korea was divided into a regular grid of 10 million cells with 30m spacing and hourly temperature, humidity, wind, precipitation and solar irradiance were estimated for each grid cell by spatial interpolation of synoptic weather data. Topoclimatology models were used to accommodate effects of topography in a spatial interpolation procedure, including cold air drainage on nocturnal temperature and solar irradiance on daytime temperature. Satellite remote sensing data were used to classify the vegetation type of each grid cell, and corresponding spatial attributes including soil texture, canopy structure, and phenological features were identified. All data were fed into a standalone version of SiB2(Simple Biosphere Model 2) to simulate latent heat flux at each grid cell. A computer program was written for data management in the cell - based SiB2 operation such as extracting input data for SiB2 from grid matrices and recombining the output data back to the grid format. ET estimates at selected grid cells were validated against the actual measurement of latent heat fluxes by eddy covariance measurement. We applied this system to obtain the spatial ET of the study area on a continuous basis for the 2001-2003 period. The results showed a strong feasibility of using spatial - data driven land surface models for operational monitoring of regional ET.

• Journal of the Korea Convergence Society
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• v.9 no.3
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• pp.53-59
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• 2018

In this paper, we propose a sequence mining based manufacturing process using a decision model in cognitive factory. The proposed model is a method to increase the production efficiency by applying the sequence mining decision model in a small scale production process. The data appearing in the production process is composed of the input variables. And the output variable is composed the production rate and the defect rate per hour. We use the GSP algorithm and the REPTree algorithm to generate rules and models using the variables with high significance level through t-test. As a result, the defect rate are improved by 0.38% and the average hourly production rate was increased by 1.89. This has a meaning results for improving the production efficiency through data mining analysis in the small scale production of the cognitive factory.

• Journal of Energy Engineering
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• v.28 no.4
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• pp.82-93
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• 2019

In this study, a new model is proposed to improve the problem of the decline of predict rate of heat demand on a particular date, such as a public holiday for the conventional heat demand forecasting system. The proposed model was the Four Season Mixed Heat Demand Prediction Neural Network Model, which showed an increase in the forecast rate of heat demand, especially for each type of forecast date (weekday/weekend/holiday). The proposed model was selected through the following process. A model with an even error for each type of forecast date in a particular season is selected to form the entire forecast model. To avoid shortening learning time and excessive learning, after each of the four different models that were structurally simplified were learning and a model that showed optimal prediction error was selected through various combinations. The output of the model is the hourly 24-hour heat demand at the forecast date and the total is the daily total heat demand. These forecasts enable efficient heat supply planning and allow the selection and utilization of output values according to their purpose. For daily heat demand forecasts for the proposed model, the overall MAPE improved from 5.3~6.1% for individual models to 5.2% and the forecast for holiday heat demand greatly improved from 4.9~7.9% to 2.9%. The data in this study utilized 34 months of heat demand data from a specific apartment complex provided by the Korea District Heating Corp. (January 2015 to October 2017).

• Tuberculosis and Respiratory Diseases
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• v.55 no.5
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• pp.506-515
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• 2003

Background : A decreased level of serum arginine vasopressin(AVP) and an increased sensitivity to an exogenous AVP is expected in patients with septic shock who often require a high infusion rate of catecholamines. The goal of the study was to determine whether an exogenous AVP infusion to the patients with septic shock would achieve a significant decrement in infusion rate of catecholamine vasopressors while maintaining hemodynamic stability and adequate urine output. Method : Eight patients with septic shock who require a high infusion rate of norepinephrine had received a trial of 4-hour AVP infusion with simultaneous titration of norepinephrine. Hemodynamic parameters and urine output were monitored during the AVP infusion and the monitoring continued up to 4 hours after the AVP infusion had stopped. Results : Mean arterial pressure showed no significant changes during the study period(p=0.197). Norepinephrine infusion rate significantly decreased with concurrent AVP administration(p=0.001). However, beneficial effects had disappeared after the AVP infusion was stopped. In addition, hourly urine output showed no significant changes throughout the trials(p=0.093). Conclusion : Concurrent AVP infusion achieved the catecholamine vasopressor sparing effect in the septic shock patients, but there was no evidence of the improvement of renal function. Further study may be indicated to determine whether AVP infusion would provide an organ-protective effect to the septic shock patients.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.20 no.3
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• pp.168-174
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• 2010

South Korea's industrial injuries are decreasing overall in the last 32 years. Nevertheless, the fatal occupational injury rate is still higher than in developed countries. This study was conducted to help prevention strategies of occupational injuries for the Republic of Korea. Fatal occupational injury rates were obtained from "Industrial Accident Analysis"of the Korean Ministry of Labor. Poisson regression was used to assess time trends. Socioeconomic indicators were obtained from the Korea Labor Institute and the Statistics Korea. Fatal occupational injury rates were adjusted by year, and Pearson correlation analysis was used to assess the relationship between the socio-economic indicators and occupational injuries. In 1975, fatal occupational injury rate was 54.8 per 100,000 workers. With somewhat up and down, it was decreased to 21.0 in 2006. An annual rate of change for the years 1975-2006 was - 1.83%, and for the years 2002-2006 was -5.02%. As economic growth rate, paricipation rate for the age less than 25 and hours of work per week or year increased, fatal occupational injury rate also increased. Conversely, as GDP per capita, paricipation rate or employment rate for female, paricipation rate for the age 25 or more, hourly compensation costs for production workers and services output as percent of GDP increased, fatal occupational injury rate decreased. By the development of safety techniques and the adoption of more legislative constraints, developed economy reduce occupational injuries. Conversely, economic growth may raise occupational injuries. Therefore, prevention strategies are needed to manage both of them. We need to make an effort to prevent occupational injuries due to not only sexual differences, but also job differences between male and female. Preventive strategies are needed to consider the characteristics of younger workers. Addition to wage, other appropriate variables for work condition should be considered together. Extending work hours is need to be regulated with systemic methods.