• Journal of agriculture & life science
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• v.44 no.6
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• pp.91-99
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• 2010

We used a multiplex PCR primer set composed of 11 microsatellite (MS) markers and two sexing markers for gender detection. Genomic DNA extracted from hair roots of 3,510 Hanwoo were genotyped. Based on the 11MS markers, no animals had identical genotypes(TGLA227, BM2113, TGLA53, ETF10, SPS115, TGLA122, ETH3, ETH225, BM1824 and INRA23). The expected probability of identity among genotypes of random individuals (PI), the probability of identity among genotypes from random half-sibs ($PI_{half-sibs}$) and among genotypes of random individuals, and the probability of identity among genotypes from random sibs ($PI_{sibs}$) were estimated as $1.31{\times}10^{-23}$, $2.52{\times}10^{-16}$and $1.09{\times}10^{-6}$, respectively using the API-CALC program, version 1.0. We successfully completed the genotype analysis of 3,510 Hanwoo with a 3.93% genotyping failure rate. It was revealed that extracting DNA from the hair root was a time-efficient and cost-effective method to collect specimens for DNA isolation from live animals. This method also minimized stress for the animals during specimen collection. Among the hair roots from the back, belly, upper tail and lower tail, 5~13 hair roots of the lower tail led to the best genotype analysis results. Finally, we established a 96-well-format method of DNA preparation applicable for high- throughput genotype analysis.

• Korean Journal of Remote Sensing
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• v.24 no.6
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• pp.551-558
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• 2008

It is well known that the protein content of rice grain is an indicator of taste of cooked rice in the countries where people as the staple food. Ground-based optical sensing over the crop canopy would provide information not only on the mass of plant body which reflects the light, but also on the crop nitrogen content which is closely related to the greenness of plant leaves. The vegetation index has been related to crop variables such as biomass, leaf nitrogen, plant cover, and chlorophyll in cereals. The objective of this study was to investigate the correlation between GNDVI and NDVI values, and grain protein content at different dates and to estimate the grain protein content using G(NDVI) values. We measured Green normalized difference vegetation index [$GNDVI=({\rho}0.80{\mu}m-{\rho}0.55{\mu}m)/({\rho}0.80{\mu}m+{\rho}0.55{\mu}m)$] and [$GNDVI=({\rho}0.80{\mu}m-{\rho}0.68{\mu}m)/({\rho}0.80{\mu}m+{\rho}0.68{\mu}m)$] by using two different active sensors. The study was conducted during the rice growing season for three years from 2005 through 2007 at the experimental plots of National Institute of Agricultural Science and Technology. The experiments were carried out by randomized complete block design with the application of four levels of nitrogen fertilizers(0, 70, 100, 130kg N/ha) and the same amount of phosphorous and potassium content of the fertilizers. After heading stage, relationships between GNDVI of rice canopy and grain protein content showed the highly positive correlation at different dates for three years. GNDVI values showed higher correlation coefficients than that of NDVI during growing season in 2005-07. The correlation between GNDVI values at different dates and grain protein contents was highly correlated at early July. We attempted to estimate the grain protein content at harvesting stage using GNDVI values from early July for three years. The determination coefficients of the linear model by GNDVI values were 0.9l and the measured and estimated grain protein content at harvesting stage using GNDVI values highly correlated($R^2=0.96^{***}$). Results from this study show that GNDVI appeared very effective to estimate leaf nitrogen and grain protein content of rice canopy.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.111-121
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• 2019

Evapotranspiration (ET) of vegetation is one of the major components of the hydrologic cycle, and its accurate estimation is important for hydrologic water balance, irrigation management, crop yield simulation, and water resources planning and management. For agricultural crops, ET is often calculated in terms of a short or tall crop reference, such as well-watered, clipped grass (reference crop evapotranspiration, $ET_o$). The Penman-Monteith equation recommended by FAO (FAO 56-PM) has been accepted by researchers and practitioners, as the sole $ET_o$ method. However, its accuracy is contingent on high quality measurements of four meteorological variables, and its use has been limited by incomplete and/or inaccurate input data. Therefore, this study evaluated the applicability of Backpropagation Neural Network (BPNN) model for estimating $ET_o$ from less meteorological data than required by the FAO 56-PM. A total of six meteorological inputs, minimum temperature, average temperature, maximum temperature, relative humidity, wind speed and solar radiation, were divided into a series of input groups (a combination of one, two, three, four, five and six variables) and each combination of different meteorological dataset was evaluated for its level of accuracy in estimating $ET_o$. The overall findings of this study indicated that $ET_o$ could be reasonably estimated using less than all six meteorological data using BPNN. In addition, it was shown that the proper choice of neural network architecture could not only minimize the computational error, but also maximize the relationship between dependent and independent variables. The findings of this study would be of use in instances where data availability and/or accuracy are limited.

• The Journal of Korean Society for Radiation Therapy
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• v.30 no.1_2
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• pp.177-183
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• 2018

Purpose : Volumetric Modulated Arc Therapy(VMAT) has the advantage of uniformly and precisely irradiating the tumor to the shape of the tumor while reducing the risk of radiation damage to normal tissues. such as brain cancer, head and neck cancer and prostate cancer, It is being used for treatment. The purpose of this study is to evaluate the usefulness of the Jaw-Tracking technique(JTT) in VMAT for brain and head and neck cancer. Materials and Methods : We selected eight patients with brain and head and neck cancer(4 Brain, 4 head and neck) who were treated with the VMAT treatment technique. Contouring information of the patient's tumor and normal organ was fused to the Rando phantom using the deformable registration of Velocity(Varian, USA). A treatment plan was developed using the Varian Eclipse(ver 15.5, Varian, USA) with the same patient actual beam parameters except for the use of jaw-tracking. As the evaluation index, the maximum dose and mean dose of target and OAR were compared and a portal dosimetry was performed for the treatment plan verification. Results : When using JTT, the relative dose of OAR decreased by 5.24 % and the maximum dose by 7.05 %, respectively, compared with the Static-Jaw technique(SJT). In the various OARs, the mean dose and maximum dose reduction ranges ranged from 0.01 to 3.16 Gy and from 0.12 to 6.27 Gy, respectively. In the case of the target, the maximum dose of GTV, CTV, PTV decreased by 0.17 %, 0.43 %, and 0.37 % in JTT, and the mean dose decreased by 0.24 %, 0.47 % and 0.47 %, respectively. Gamma analysis The JTT and SJT passing rates were $98{\pm}1.73%$ and $97{\pm}1.83%$ on the basis of 3 % / 3 mm, respectively. Comparing the doses of all OARs applied to the experiment, it was found that the use of JTT resulted in a significant decrease in dose due to additional jaw shielding besides MLC than SJT. Conclusion : In radiation therapy using VMAT treatment plan, we can apply JTT in the case of adjacent tumor and normal organs such as brain cancer and head and neck cancer, and in radiotherapy required large field and high energy caused increase leakage dose through MLC. It is considered that the target dose of PTV can be increased by lowering the dose of normal tissue surrounding the tumor.

• Korean Journal of Plant Resources
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• v.32 no.5
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• pp.519-542
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• 2019

Twenty soybean cultivars developed recently were assessed using 27 insertion and deletion (InDel) markers derived from dense variation blocks (dVBs) of soybean genome. The objective of this study is to identify the distinctness and genetic relationships among a total of 169 soybean accessions including new cultivars. The genetic homology between 149 accessions in the soybean barcode system and 20 new cultivars was 61.3% on average with the range from 25.9% to 96.3%, demonstrating the versatile application of these markers for cultivars identification. The phylogenic analysis revealed four subgroups related to their usage. The 80% of cultivars for vegetable and early maturity and the 65.9% of cultivars for bean sprouts were clustered in subgroup I-2 and II-2, respectively, indicating of the limited gene pools of their crossing parents in breeding. On the other hands, the cultivars for soy sauce and tofu with considerable gene flow by genome reshuffling were distributed evenly to several subgroups, I-1 (44.4%), I-2 (26.4%) and II-2 (23.6%). We believe that the 27 InDel markers specific to dVBs can be used not only for cultivar identification and genetic diversity, but also in breeding purposes such as introduction of genetic resources and selection of breeding lines with target traits.

• Geophysics and Geophysical Exploration
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• v.1 no.1
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• pp.25-30
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• 1998

Time-lapse crosswell seismic data, recorded before and after the cavity filling, showed that the filling increased the velocity at a known cavity zone in an old mine site in Inchon area. The seismic response depicted on the tomogram and in conjunction with the geologic data from drillings imply that the size of the cavity may be either small or filled by debris. In this study, I attempted to evaluate the filling effect by analyzing velocity measured from the time-lapse tomograms. The data acquired by a downhole airgun and 24-channel hydrophone system revealed that there exists measurable amounts of source statics. I presented a methodology to estimate the source statics. The procedure for this method is: 1) examine the source firing-time for each source, and remove the effect of irregular firing time, and 2) estimate the residual statics caused by inaccurate source positioning. This proposed multi-step inversion may reduce high frequency numerical noise and enhance the resolution at the zone of interest. The multi-step inversion with different starting models successfully shows the subtle velocity changes at the small cavity zone. The inversion procedure is: 1) conduct an inversion using regular sized cells, and generate an image of gross velocity structure by applying a 2-D median filter on the resulting tomogram, and 2) construct the starting velocity model by modifying the final velocity model from the first phase. The model was modified so that the zone of interest consists of small-sized grids. The final velocity model developed from the baseline survey was as a starting velocity model on the monitor inversion. Since we expected a velocity change only in the cavity zone, in the monitor inversion, we can significantly reduce the number of model parameters by fixing the model out-side the cavity zone equal to the baseline model.

• Geophysics and Geophysical Exploration
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• v.8 no.1
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• pp.67-72
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• 2005

To investigate the feasibility of a new concept of storing Liquefied Natural Gas (LNG) in a lined hard rock cavern, and to develop essential technologies for constructing underground LNG storage facilities, a small pilot plant storing liquid nitrogen (LN2) has been constructed at the Korea Institute of Geoscience and Mineral Resources (KIGAM). The LN2 stored in the cavern will subject the host rock around the cavern to very low temperatures, which is expected to cause the development of an ice ring and the change of ground condition around the storage cavern. To investigate and monitor changes in ground conditions at this pilot plant site, geophysical, hydrogeological, and rock mechanical investigations were carried out. In particular, geophysical methods including borehole radar and three-dimensional (3D) resistivity surveys were used to identify and monitor the development of an ice ring, and other possible changes in ground conditions resulting from the very low temperature of LN2 in the storage tank. We acquired 3D resistivity data before and after storing the LN2, and the results were compared. From the 3D images obtained during the three phases of the resistivity monitoring survey, we delineated zones of distinct resistivity changes that are closely related to the storage of LN2. In these results, we observed a decrease in resistivity at the eastern part of the storage cavern. Comparing the hydrogeological data and Joint patterns around the storage cavern, we interpret this change in resistivity to result from changes in the groundwater flow pattern. Freezing of the host rock by the very low temperature of LN2 causes a drastic change in the hydrogeological conditions and groundwater flow patterns in this pilot plant.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1135-1147
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• 2021

In order to geometrically correct high-resolution satellite imagery, the sensor modeling process that restores the geometric relationship between the satellite sensor and the ground surface at the image acquisition time is required. In general, high-resolution satellites provide RPC (Rational Polynomial Coefficient) information, but the vendor-provided RPC includes geometric distortion caused by the position and orientation of the satellite sensor. GCP (Ground Control Point) is generally used to correct the RPC errors. The representative method of acquiring GCP is field survey to obtain accurate ground coordinates. However, it is difficult to find the GCP in the satellite image due to the quality of the image, land cover change, relief displacement, etc. By using image maps acquired from various sensors as reference data, it is possible to automate the collection of GCP through the image matching algorithm. In this study, the RPC of KOMPSAT-3A satellite image was corrected through the extracted matching point using the UAV (Unmanned Aerial Vehichle) imagery. We propose a pre-porocessing method for the extraction of matching points between the UAV imagery and KOMPSAT-3A satellite image. To this end, the characteristics of matching points extracted by independently applying the SURF (Speeded-Up Robust Features) and the phase correlation, which are representative feature-based matching method and area-based matching method, respectively, were compared. The RPC adjustment parameters were calculated using the matching points extracted through each algorithm. In order to verify the performance and usability of the proposed method, it was compared with the GCP-based RPC correction result. The GCP-based method showed an improvement of correction accuracy by 2.14 pixels for the sample and 5.43 pixelsfor the line compared to the vendor-provided RPC. In the proposed method using SURF and phase correlation methods, the accuracy of sample was improved by 0.83 pixels and 1.49 pixels, and that of line wasimproved by 4.81 pixels and 5.19 pixels, respectively, compared to the vendor-provided RPC. Through the experimental results, the proposed method using the UAV imagery presented the possibility as an alternative to the GCP-based method for the RPC correction.

• Journal of Mushroom
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• v.19 no.3
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• pp.210-215
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• 2021

For sterilization of microorganisms of the Listeria genus contaminating enoki mushroom, pilot mushroom grower equipped with air sterilization devices were developed. Sterilization experiments were performed using physical and chemical treatments. Internal temperature and humidity were controlled, maintaining 6.62℃±0.30 in the upper shelves, 6.46℃±0.24 in the middle shelves, and 6.48℃±0.25 in the lower shelves. Humidities were 79.97%±4.42, 79.43%±4.06, and 79.94±4.30%, respectively, with a temperature setting of 6.5℃, and a relative humidity of 75%. A suitable enoki mushroom cultivation stage for air sterilizer application was during the growth stage, with temperature in the 6.5~8.5℃ range, and humidity of 70~80%. At these same internal conditions, the ozone concentration in the mushroom cultivator was found to be 160 ppb during ion-cluster generator operation. After physical sterilization, the Listeria innocua survival rate was 0.1 to 0.9% using ion cluster sterilization, and 9.3 to 10.6% using UV air sterilization. The Listeria innocua survival rates on different materials were 9.3~10.6% on the metal specimen, and 9.9~16.2% on the plastic wrapper. The survival rate was particularly high on the rough side of the plastic wrapper. Ion cluster air sterilization is a labor-saving and effective method for suppressing the occurrence of Listeria bacteria on mushroom growers walls and shelves. For the plastic wrapper, chemical sterilization is more effective than physical sterilization.

• Journal of Korea Water Resources Association
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• v.54 no.10
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• pp.795-805
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• 2021

In this study, after developing an LSTM-based deep learning model for estimating daily runoff in the Soyang River Dam basin, the accuracy of the model for various combinations of model structure and input data was investigated. A model was built based on the database consisting of average daily precipitation, average daily temperature, average daily wind speed (input up to here), and daily average flow rate (output) during the first 12 years (1997.1.1-2008.12.31). The Nash-Sutcliffe Model Efficiency Coefficient (NSE) and RMSE were examined for validation using the flow discharge data of the later 12 years (2009.1.1-2020.12.31). The combination that showed the highest accuracy was the case in which all possible input data (12 years of daily precipitation, weather temperature, wind speed) were used on the LSTM model structure with 64 hidden units. The NSE and RMSE of the verification period were 0.862 and 76.8 m³/s, respectively. When the number of hidden units of LSTM exceeds 500, the performance degradation of the model due to overfitting begins to appear, and when the number of hidden units exceeds 1000, the overfitting problem becomes prominent. A model with very high performance (NSE=0.8~0.84) could be obtained when only 12 years of daily precipitation was used for model training. A model with reasonably high performance (NSE=0.63-0.85) when only one year of input data was used for model training. In particular, an accurate model (NSE=0.85) could be obtained if the one year of training data contains a wide magnitude of flow events such as extreme flow and droughts as well as normal events. If the training data includes both the normal and extreme flow rates, input data that is longer than 5 years did not significantly improve the model performance.