• Korean Journal of Remote Sensing
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• 제19권5호
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• pp.341-349
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• 2003

The so-called B-method, a simplified surface energy budget, permits calculation of daily actual evapotranspiration (ET) using remotely sensed data, such as NOAA-AVHRR. Even if the use of satellite data allows estimation of the albedo and surface temperature, this model requires meteorological data measured at ground-level to obtain the other inputs. In addition, a difficulty may be occurred by the difference of temporal scales between the net radiation in daily scale and instantaneous measurement at midday of the surface and air temperatures because the data covered whole day are necessary to obtain accumulated daily net radiation. In order to solve these problems, this study attempted a modification of B-method through an extension of hourly ET value calculated using a complete instantaneous inputs. The estimation of the daily apparent ET from newly proposed system showed a root mean square error of 0.26 mm/day as compared the output obtained from the classical model. It is evident that this may offer more rapid estimation and reduced data volume.

• Magazine of the Korean Society of Agricultural Engineers
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• 제36권2호
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• pp.88-95
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• 1994

This paper is to evaluate the applicability of neural networks to the estimation of evapotranspiration. Two neural networks were developed to forecast daily evapotranspiration of the rice crop with back-propagation and counter-propagation algorithm. The neural network trained by back-propagation algorithm with delta learning rule is a three-layer network with input, hidden, and output layers. The other network with counter-propagation algorithm is a four-layer network with input, normalizing, competitive, and output layers. Training neural networks was conducted using daily actual evapotranspiration of rice crop and daily climatic data such as mean temperature, sunshine hours, solar radiation, relative humidity, and pan evaporation. During the training, neural network parameters were calibrated. The trained networks were applied to a set of field data not used in the training. The created response of the back-propagation network was in good agreement with desired values and showed better performances than the counter-propagation network did. Evaluating the neural network performance indicates that the back-propagation neural network may be applied to the estimation of evapotranspiration of the rice crop. This study does not provide with a conclusive statement as to the ability of a neural network to evapotranspiration estimating. More detailed study is required for better understanding and evaluating the behavior of neural networks.

• The Transactions of The Korean Institute of Electrical Engineers
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• 제60권4호
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• pp.717-725
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• 2011

In the power system with an electric storage system that can increase utilization rate of the source of such new renewable energy, this paper introduces the approach on the daily unit commitment scheduling that determines simultaneously optimum operational condition and output of thermal generators and electric storage device. The unit commitment is one of the most important issues in economic operation and security of short-term operational plan of the power system. It is to determine on/off status of generator to minimize operational cost during the given period. The committed generator should satisfy various operational limitation such as estimated demand by system, spinning reserve condition within minimum operational cost. In order to determine on/off or charge/discharge/idle condition and output level of units and electric storage system, the MILP(Mixed Integer Linear Programming) is suggested. The proposed approach is the mixed method between LP(Linear Programming) and IP(integer programming) which seeks the value of real number and integer that maximize or minimize function objective within given condition. The daily unit commitment problem with the electric storage system is applied to MILP algorithm through linearization and formulation process. The proposed approach is applied to the test system.

• The Journal of Korean Society for Radiation Therapy
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• 제17권2호
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• pp.105-111
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• 2005

Purpose : Daily Q.A is the important step which must be preceded in a radiation treatment. Specially, radiation output measurement and laser alignment, SSD indicator related to a patient set-up recurrence must be confirmed for a reasonable radiation treatment. Daily Q.A proceeds correctness and a prompt way, and needs an objective measurement basis. Manufacture of the device which can facilitate confirmation of output measurement and appliances check at one time was requested. Materials and Methods : Produced the phantom formal daily check device which can confirm a lot of appliances check (output measurement and laser alignment. field size, SSD indicator) with one time of set up at a time, and measurement observed a linear accelerator (4 machine) for four months and evaluated efficiency. Results : We were able to confirm an laser alignment, field size, SSD indicator check at the same time, and out put measurement was possible with the same set up, so daily Q.A time was reduced, and we were able to confirm an objective basis about each item measurement. As a result of having measured for four months, output measurement within ${\pm}2%$, and measured laser alignment, field size, SSD indicator in range within ${\pm}1mm$. Conclusion : We can enforce output measurement and appliances check conveniently, and time was reduced and was able to raise efficiency of business. We were able to bring a cost reduction by substitution expensive commercialized equipment. Further It is necessary to makes a product as strong and slight materials, and improve convenience of use.

• Clinical and Experimental Pediatrics
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• 제59권4호
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• pp.202-204
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• 2016

A 15-year-old boy, who was diagnosed with Alport syndrome and end-stage renal disease, received a renal transplant from a living-related donor. On postoperative day 1, his daily urine output was 10,000 mL despite normal graft function. His laboratory findings including urine, serum osmolality, and antidiuretic hormone levels showed signs similar to central diabetes insipidus, so he was administered desmopressin acetate nasal spray. After administering the desmopressin, urine specific gravity and osmolality increased abruptly, and daily urine output declined to the normal range. The desmopressin acetate was tapered gradually and discontinued 3 months later. Graft function was good, and urine output was maintained within the normal range without desmopressin 20 months after the transplantation. We present a case of a massive polyuria due to transient deficiency of antidiuretic hormone with the necessity of desmopressin therapy immediately after kidney transplantation in a pediatric patient.

• Journal of The Korean Society of Agricultural Engineers
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• 제63권6호
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• pp.49-60
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• 2021

BCSA (Bias-Correction and Stochastic Analog) is a statistical downscaling technique designed to effectively correct the systematic errors of GCM (General Circulation Model) output and reproduce basic statistics and spatial variability of the observed precipitation filed. In this study, the applicability of BCSA was evaluated using the ASOS observation data over South Korea, which belongs to the monsoon climatic zone with large spatial variability of rainfall and different rainfall characteristics. The results presented the reproducibility of temporal and spatial variability of daily precipitation in various manners. As a result of comparing the spatial correlation with the observation data, it was found that the reproducibility of various climate indices including the average spatial correlation (variability) of rainfall events in South Korea was superior to the raw GCM output. In addition, the needs of future related studies to improve BCSA, such as supplementing algorithms to reduce calculation time, enhancing reproducibility of temporal rainfall patterns, and evaluating applicability to other meteorological factors, were pointed out. The results of this study can be used as the logical background for applying BCSA for reproducing spatial details of the rainfall characteristic over the Korean Peninsula.

• Atmosphere
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• 제20권2호
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• pp.73-89
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• 2010

An updateable model output statistics (UMOS) system for daily maximum and minimum temperature ($T_M$ and $T_m$) over South Korea based on the Canadian UMOS system were developed and validated. RDAPS (regional data assimilation and prediction system) and KWRF (Korea WRF) which have quite different physics and dynamics were used for the development of UMOS system. The 20 most frequently selected potential predictors for each season, station, and forecast projection time from the 68 potential predictors of the MOS system, were used as potential predictors of the UMOS system. The UMOS equations were developed through the weighted blending of the new and old model data, with weights chosen to emphasize the new model data while including enough old model data to ensure stable equations and a smooth transition of dependency from the old model to the new model. The UMOS equations are being updated by every 7 days. The validation results of $T_M$ and $T_m$ showed that seasonal mean bias, RMSE, and correlation coefficients for the total forecast projection times are -0.41-0.17 K, 1.80-2.46 K, and 0.80-0.97, respectively. The performance is slightly better in autumn and winter than in spring and summer. Also the performance of UMOS system are clearly dependent on location, better at the coastal region than inland area. As in the MOS system, the performance of UMOS system is degraded as the forecast day increases.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 한국농업기계학회 1993년도 Proceedings of International Conference for Agricultural Machinery and Process Engineering
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• pp.1273-1281
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• 1993

Estimation of daily and seasonal evaportranspiration is essential for water resource planning irrigation feasibility study, and real-time irrigation water management . This paper is to evaluate the applicability of neural networks to the estimation of evapotranspiration . A neural network was developed to forecast daily evapotranspiration of the rice crop. It is a three-layer network with input, hidden , and output layers. Back-propagation algorithm with delta learning rule was used to train the neural network. Training neural network wasconducted usign daily actural evapotranspiration of rice crop and daily climatic data such as mean temperature, sunshine hours, solar radiation, relative humidity , and pan evaporation . During the training, neural network parameters were calibrated. The trained network was applied to a set of field data not used in the training . The created response of the neural network was in good agreement with desired values. Evaluating the neural networ performance indicates that neural network may be applied to the estimation of evapotranspiration of the rice crop.

• Water Engineering Research
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• 제5권3호
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• pp.133-146
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• 2004

Soil and water assessment tool (SWAT) was simulated based on the default parameters and a priori soil parameter estimation method in Bocheong watershed of Korea. The performance of the model was tested against the measured daily runoff data for 5 years between 1993 and 1997. The sensitivity analysis of SWAT model parameters was conducted to identify the most sensitive model parameters affecting the model output. The results of SWAT simulation indicate that the overall performance of SWAT in calculating daily runoff is reasonably acceptable. However, there is a problem in estimating the low flow components of streamflow since the low flow components simulated by SWAT are significantly different from the measured low flow. The sensitivity analysis with SWAT points out that soil related parameters are the most sensitive parameters affecting surface and ground water balance components and groundwater flow related parameters exhibit negligible sensitivity.

• International conference on construction engineering and project management
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.595-596
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• 2015

Growing demand for traditional Korean housing, modernized Korean traditional housing (Hanok) was developed as a way of providing Hanok for the public. However, the standard cost data for Hanok has limitations, as it was developed based on single mock-up project actually constructed and verified by another mock-up Hanok. In order to meet these research objectives, daily report composition which is easy-to-use for on-site workers and managers and also easy-to-accumulate standard cost data was developed first. Secondly, access to the system was made easy through a web server. Finally, an automated calculation formula was inserted to allow the last inputted data to be automatically included for adjustment of standard costs. This system was designed from an industry perspective so that any unspecified and nonprofessional users can easily use. For the users, it has an advantage that on-site workers are provided with a daily report system through web server and also they are able to complete such reports through simple input and output without any additional forms.