• Journal of Embryo Transfer
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• v.17 no.2
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• pp.153-162
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• 2002

In order to develop the breeding techniques to increase Hanwoo (Bos taurus coreanae) population, the present study was performed 1) to establish the treatment protocol on reproductive disorders with GnRH or PGF／sub 2$\alpha$／, 2) to improve intraovarian PG $F_{2}$$\alpha$／ administration for reducing open period. Among total of 43 diagnosed, high percentage of cows (41.9％, 18 cows) were diagnosed as silent heat, followed by inactive ovaries (32.6％, 14 cows), ovarian cysts (9.3％, 4 cows), persistent corpus luteum (7.0％, 3 cows), endometriosis (4.7％, 2 cows), pyometra (2.3％, 1 cow) and luteal cysts (2.3％, 1 cow). To treat silent heat, 18 cows were administrated with 25 mg PGF／sub 2$\alpha$／, heat-detected, artificially inseminated and monitored pregnancy. All treated cows were heat-detected and 16 cows (88％) were successfully pregnant. With 200 $\mu\textrm{g}$ GnRH treatment, 7 cows (70％) with inactive ovaries and 3 cows (75％) with ovarian cysts were successfully pregnant. Administration with 25mg PGF／sub 2$\alpha$／, successfully treated 3 cows (100％) with persistent corpus luteum and 1 cow (100％) with luteal cysts, followed by 100％ pregnancy rate. With the combined treatment of 25 mg PG $F_{2}$$\alpha$／and antibiotics, 2 cows (100％) with endometriosis were effectively treated and got pregnant after. artificial insemination (AI). In order to reduce open period, 5 mg PGF／sub 2$\alpha$／ was administrated intraovarian to 20 days cows after delivery, heat-detected, artificially inseminated and monitored pregnancy. In the first experiment, in order to recover uterus, 5mg PGF／sub 2$\alpha$／were administrated, followed by administration of 5mg PGF／sub 2$\alpha$／ at the interval of 14 days. As results, 74％ (17／23 cows) of pregnancy rate after AI. In order to further reduce the open period, 5 mg PGF／sub 2$\alpha$／was administrated at the interval of 11 days without the period of uterus recovery, resulted in 94％ (16／17 cows) pregnancy rate. In conclusion, these results showed that PGF／sub 2$\alpha$／ and GnRH treatment were effective hormonal treatment resume in Hanwoo with various reproductive disorders. In addition, modified protocol of intraovarian PGF／sub 2$\alpha$／ administration could be the effective method for reducing the open period.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.363-373
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• 2020

During last decades numerous studies generating orthoimage have been carried out. Traditional methods require exterior orientation parameters of aerial images and precise 3D object modeling data and DTM (Digital Terrain Model) to detect and recover occlusion areas. Furthermore, it is challenging task to automate the complicated process. In this paper, we proposed a new concept of true orthoimage generation using DL (Deep Learning). DL is rapidly used in wide range of fields. In particular, GAN (Generative Adversarial Network) is one of the DL models for various tasks in imaging processing and computer vision. The generator tries to produce results similar to the real images, while discriminator judges fake and real images until the results are satisfied. Such mutually adversarial mechanism improves quality of the results. Experiments were performed using GAN-based Pix2Pix model by utilizing IR (Infrared) orthoimages, intensity from LiDAR data provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) through the ISPRS (International Society for Photogrammetry and Remote Sensing). Two approaches were implemented: (1) One-step training with intensity data and high resolution orthoimages, (2) Recursive training with intensity data and color-coded low resolution intensity images for progressive enhancement of the results. Two methods provided similar quality based on FID (Fréchet Inception Distance) measures. However, if quality of the input data is close to the target image, better results could be obtained by increasing epoch. This paper is an early experimental study for feasibility of DL-based true orthoimage generation and further improvement would be necessary.

• Korean Journal of Animal Reproduction
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• v.24 no.1
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• pp.19-33
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• 2000

Eighty dairy farms (38 farms responded) in Palmerston North area of New Zealand were questioned by mail questionnaire on: 1) general characteristics, 2) milk yield and feed supplementary, 3) reproductive efficiencies (12 questions) and 4) reproductive disorders (12 questions) from February to July, 1998. Among those 4 items, the reproductive efficiencies (3) and disorders (4), various diseases and culling rates were surveyed and analyzed for Korean dairy farmers (especially in Cheju island) and compared with New Zealand. The results are as follows: 1. Fifteen farms in 38 dairy farms relied entirely on artificial insemination, the rest of 23 dairy farms (60.5%) raised 5∼6 bulls to increase conception rate. The dairy farmers in Palmerston North used artificial insemination from Oct 4th to Dec 10th for 42.8 days, and then used bulls from that point to coming Jan 10th for 41.4 days. The submission rate within 3, 6 and 10 weeks following the initiation of AI season was 84.7, 93.9 and 97.9% respectively. 2. The average age of heifers at the first estrus, pregnancy and calving was 11.0, 18.0 and 24.7 months respectively, and an average 1.4 estrus cycles were required for conception. The intervals of estrus recurrence and the following conception after calving were 38 and 68 days respectively. 3. Among inseminated cows, calving, abortion and empty cow was 90.9, 1.6 and 7.4% respectively. Calving rate decreased according to increasing farm size, while the number of empty cows decreased. 4. Stillbirth, retained placenta and delivery abnormalities were 5.3, 3.7 and 5.5% respectively, not different depend on herd size. 5. The incidence of milk fever, grass tetany, and ketosis was 3.6, 3.0 and 1.0%, respectively. The delivery abnormality and mastitis treated with medicine were 3.1 and 6.7%, but decreased according to farm size. Lameness was 8.6% on average, but over 10% in farms which has more than 400 milking cows. 6. Among the culled cows (15.5% of the total), those culled due to an old age, lameness and other diseases were 2.9, 1.8 and 4.3% respectively and those culled due to low milk production, reproductive abnormality reduced with farm size. 7. Compared with the data collected in Korea, the reproductive efficiency was better, and lameness, metabolic problem and culling rate were higher in New Zealand

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1159-1172
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• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.6
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• pp.60-74
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• 2018

This study was to investigate the pedestrian's thermal environments in the North Sidewalk of E-W Street during summer heatwave. We carried out detailed measurements with four human-biometeorological stations on Dongjin Street, Jinju, Korea ($N35^{\circ}10.73{\sim}10.75^{\prime}$, $E128^{\circ}55.90{\sim}58.00^{\prime}$, elevation: 50m). Two of the stations stood under one row street tree and hedge(One-Tree), two row street tree and hedge (Two-Tree), one of the stations stood under shelter and awning(Shelter), while the other in the sun (Sunlit). The measurement spots were instrumented with microclimate monitoring stations to continuously measure microclimate, radiation from the six cardinal directions at the height of 1.1m so as to calculate the Universal Thermal Climate Index (UTCI) from 24th July to 21th August 2018. The radiant temperature of sidewalk's elements were measured by the reflective sphere and thermal camera at 29th July 2018. The analysis results of 9 day's 1 minute term human-biometeorological data absorbed by a man in standing position from 10am to 4pm, and 1 day's radiant temperature of sidewalk elements from 1:16pm to 1:35pm, showed the following. The shading of street tree and shelter were mitigated heat stress by the lowered UTCI at mid and late summer's daytime, One-Tree and Two-Tree lowered respectively 0.4~0.5 level, 0.5~0.8 level of the heat stress, Shelter lowered respectively 0.3~1.0 level of the heat stress compared with those in the Sunlit. But the thermal environments in the One-Tree, Two-Tree and Shelter during the heat wave supposed to user "very strong heat stress" while those in the Sunlit supposed to user "very strong heat stres" and "exterme heat stress". The main heat load temperature compared with body temperature ($37^{\circ}C$) were respectively $7.4^{\circ}C{\sim}21.4^{\circ}C$ (pavement), $14.7^{\circ}C{\sim}15.8^{\circ}C$ (road), $12.7^{\circ}C$ (shelter canopy), $7.0^{\circ}C$ (street funiture), $3.5^{\circ}C{\sim}6.4^{\circ}C$ (building facade). The main heat load percentage were respectively 34.9%~81.0% (pavement), 9.6%~25.2% (road), 24.8% (shelter canopy), 14.1%~15.4% (building facade), 5.7% (street facility). Reducing the radiant temperature of the pavement, road, building surfaces by shading is the most effective means to achieve outdoor thermal comfort for pedestrians in sidewalk. Therefore, increasing the projected canopy area and LAI of street tree through the minimal training and pruning, building dense roadside hedge are essential for pedestrians thermal comfort. In addition, thermal liner, high reflective materials, greening etc. should be introduced for reducing the surface temperature of shelter and awning canopy. Also, retro-reflective materials of building facade should be introduced for the control of reflective sun radiation. More aggressively pavement watering should be introduced for reducing the surface temperature of sidewalk's pavement.