• Korean Journal of Remote Sensing
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• v.25 no.1
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• pp.31-44
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• 2009

The objective of this study was to measure backscattering coefficients of paddy rice using a L-, C-, and X-band scatterometer system with full polarization and various angles during the rice growth period and to relate backscattering coefficients to rice growth parameters. Radar backscattering measurements of paddy rice field using multifrequency (L, C, and X) and full polarization were conducted at an experimental field located in National Academy of Agricultural Science (NAAS), Suwon, Korea. The scatterometer system consists of dual-polarimetric square horn antennas, HP8720D vector network analyzer ($20\;MHz{\sim}20\;GHz$), RF cables, and a personal computer that controls frequency, polarization and data storage. The backscattering coefficients were calculated by applying radar equation for the measured at incidence angles between $20^{\circ}$ and $60^{\circ}$ with $5^{\circ}$ interval for four polarization (HH, VV, HV, VH), respectively. We measured the temporal variations of backscattering coefficients of the rice crop at L-, C-, X-band during a rice growth period. In three bands, VV-polarized backscattering coefficients were higher than hh-polarized backscattering coefficients during rooting stage (mid-June) and HH-polarized backscattering coefficients were higher than VV-, HV/VH-polarized backscattering coefficients after panicle initiation stage (mid-July). Cross polarized backscattering coefficients in X-band increased towards the heading stage (mid-Aug) and thereafter saturated, again increased near the harvesting season. Backscattering coefficients of range at X-band were lower than that of L-, C-band. HH-, VV-polarized ${\sigma}^{\circ}$ steadily increased toward panicle initiation stage and thereafter decreased, and again increased near the harvesting season. We plotted the relationship between backscattering coefficients with L-, C-, X-band and rice growth parameters. Biomass was correlated with L-band hh-polarization at a large incident angle. LAI (Leaf Area Index) was highly correlated with C-band HH- and cross-polarizations. Grain weight was correlated with backscattering coefficients of X-band VV-polarization at a large incidence angle. X-band was sensitive to grain maturity during the post heading stage.

• Journal of Practical Agriculture & Fisheries Research
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• v.16 no.1
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• pp.193-206
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• 2014

The objectives of the project are to increase farmers' income through GAP and to reduce the loss of agricultural produce, for which the Korean partner takes a role of transferring needed technologies to the project site. To accomplish the project plan, it is set to implement the project with six components: construction of buildings, installation of agricultural facilities, establishment of demonstration farms, dispatching experts, conducting training program in Korea and provision of equipments. The Project Management Committee and the Project Implementation Team are consisted of Korean experts and senior officials from Department of Agriculture, Myanmar that managed the project systematically to ensure the success of the project. The process of the project are; the ceremony of laying the foundation and commencing the construction of training center in April, 2012. The Ribbon Cutting Ceremony for the completion of GAP Training Center was successfully held under PMC (MOAI, GAPI/ARDC) arrangement in SAl, Naypyitaw on June 17, 2012. The Chairman of GAPI, Dr. Sang Mu Lee, Director General U Kyaw Win of DOA, officials and staff members from Korea and Myanmar, teachers and students from SAl attended the ceremony. The team carried out an inspection and fixing donors' plates on donated project machineries, agro-equipments, vehicles, computers and printer, furniture, tools and so forth. Demonstration farm for paddy rice, fruits and vegetables was laid out in April, 2012. Twenty nine Korean rice varieties and many Korean vegetable varieties were introduced into GAP Project farm to check the suitability of the varieties under Myanmar growing conditions. Paddy was cultivated three times in DAR and twice in SAl. In June 2012, vinyl houses were started to be constructed for raising seedlings and finished in December 2012. Fruit orchard for mango, longan and dragon fruit was established in June, 2012. Vegetables were grown until successful harvest and the harvested produce was used for panel testing and distribution in January 2013. Machineries for postharvest handling systems were imported in November 2012. Setting the washing line for vegetables were finished and the system as run for testing in June 2013. New water tanks, pine lines, pump house and electricity were set up in October 2013.

• Journal of Haehwa Medicine
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• v.9 no.1
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• pp.513-545
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• 2000

I. Introduction Bi(痺) means blocking. It can reach at the joints or muscles or whole body and make pains. Numbness and movement disorders. BiJeung can be devided into SilBi and HeoBi. In SilBi there are PungHanSeupBi, YeolBi and WanBi. In HeoBi, there are GiHyeolHeoBi, EumHeoBi and YangHeoBi. The common principle for the treatment of BiJeung is devision of the chronic stage and the acute stage. In the acute stage, BiJeung is usually cured easily but in the chronic stage, it is difficult. In the terminal stage, BiJeung can reach at the internal organs. BiJeung is one kind of symptoms making muscles, bones and jonts feel pain, numbness or edema. For example it can be gout or SLE etc. Many famous doctors studied medical science by their fathers or teachers. So the history of medical science is long. So I studied ${\ll}Bijeungjujip{\gg}$. II. Final Decision 1. BanSuMun(斑秀文) thought that BiJeung can be cured by blocking of blood stream. So he insisted that the important thing to cure BiJeung is to improve the blood stream. He usually used DangGuiSaYeokTang(當歸四逆湯), DangGuiJakYakSanHapORyeongSan, DoHong-SaMulTang(桃紅四物湯), SaMyoSanHapHeuiDongTang and HwangGiGyeJiOMulTang. 2. JangGeonBu(張健夫) focused on soothing muscles and improving blood seam. So he used many herbs like WiRyeongSeon(威靈仙), GangHwal(羌活), DokHwal(獨活), WooSeul(牛膝), etc. Especially he pasted wastes of the boiled herbs. 3. OSeongNong(吳聖農) introduced four rules to treat arthritis. So he usually used SeoGak-SanGaGam(犀角散加減), BoYanHwanOTang(補陽還五湯), ODuTang(烏頭湯), HwangGiGyeJiOMulTang. 4. GongJiSin thought disk hernia as one kind of BiJeung. And he said that Pung can hurt upper limbs and Seup can hurt lower limbs. He used to use GyeJiJakYakJiMoTang(桂枝芍藥知母湯). 5. LoJiJeong(路志正) introduced four principles to treat BiJeung. He used BangPungTang(防風湯), DaeJinGuTang) for PungBi(風痺), OPaeTang(烏貝湯) for HanBi(寒痺), YukGunJaTang(六君子湯) for SeupBi(濕痺) and SaMyoTang(四妙湯), SeonBiTang(宣痺湯), BaekHoGaGyeTang(白虎加桂湯) for YeolBi(熱痺). 6. GangChunHwa(姜春華) discussed herbs. He said SaengJiHwang(生地黃) is effective for PungSeupBi and WiRyungSun(威靈仙) is effective for the joints pain. He usually used SipJeonDaeBoTang(十全大補湯), DangGuiDaeBoTang(當歸大補湯), YoukGunJaTang(六君子湯) and YukMiJiHwanTang(六味地黃湯). 7. DongGeonHwa(董建華) said that the most important thing to treat BiJeung is how to use herbs. He usually used CheonO(川烏), MaHwang(麻黃) for HanBi, SeoGak(犀角) for YeolBi, BiHae) or JamSa(蠶沙) for SeupBi, SukJiHwang(熟地黃) or Vertebrae of Pigs for improving the function of kidney and liver, deer horn or DuChung(杜沖) for improving strength of body and HwangGi(黃?) or OGaPi(五加皮) for improving the function of heart. 8. YiSuSan(李壽山) devided BiJeung into two types(PungHanSeupBi, PungYeolSeupBi). And he used GyeJiJakYakJiMoTang(桂枝芍藥知母湯) for the treatment of gout. And he liked to use HwanGiGyeJiOMulTangHapSinGiHwan 枝五物湯合腎氣丸) for the treat ment of WanBi(頑痺). 9. AnDukHyeong(顔德馨) made YongMaJeongTongDan(龍馬定痛丹)-(MaJeonJa(馬錢子) 30g, JiJaChung 3g, JiRyong(地龍) 3g, JeonGal(全蝎) 3g, JuSa(朱砂) 0.3g) 10. JangBaekYou(張伯臾) devided BiJeung into YeolBi and HanBi. And he focused on improving blood stream. 11. JinMuO(陳茂梧) introduced anti-wind and dampness prescription(HoJangGeun(虎杖根) 15g, CheonChoGeun 15g, SangGiSaeng(桑寄生) 15g, JamSa(蠶絲) 15g, JeMaJeonJa(制馬錢子) 3g). 12. YiChongBo(李總甫) explained basic prescriptions to treat BiJeung. He used SinJeongChuBiEum(新定推痺陰) for HaengBi(行痺), SinJeongHwaBiSan(新定化痺散) for TongBi(痛痺), SinJeongGaeBiTang(新定開痺湯) for ChakBi(着痺), SinJeongCheongBiEum(新定淸痺飮) for SeupYeolBi(濕熱痺), SinRyeokTang(腎瀝湯) for PoBi(胞痺), ORyeongSan for BuBi(腑痺), OBiTang(五痺湯) for JangBi(臟痺), SinChakTang(腎着湯) for SingChakByeong(腎着病). 13. HwangJeonGeuk(黃傳克) used SaMu1SaDeungHapJe(四物四藤合制) for the treatment of a acute arthritis, PalJinHpPalDeungTang(八珍合八藤湯) or BuGyeJiHwangTangHapTaDeungTang(附桂地黃湯合四藤湯) for the chronic stage and ByeolGapJeungAekTongRakEum(鱉甲增液通絡飮) for EumHeo(陰虛) 14. GaYeo(柯與參) used HwalRakJiTongTang(活絡止痛湯) for shoulder ache, SoJongJinTongHwalRakTank(消腫鎭痛活絡湯) for YeolBi(熱痺), LiGwanJeolTang(利關節湯) for ChakBi(着痺), SinBiTang(腎痺湯) for SinBi(腎痺) and SamGyoBoSinHwan(三膠補腎丸) for back ache. 15. JangGilJin(蔣길塵) liked to use hot-character herbs and insects. And he used SeoGeunLipAnTang(舒筋立安湯) as basic prescription. 16. RyuJangGeol(留章杰) used GuMiGangHwalTang(九味羌活湯) and BangPungTang(防風湯) at the acute stage, ODuTang(烏頭湯) or GyeJiJakYakJiMoTang(桂枝芍藥知母湯) for HanBi of internal organs, YangHwaHaeEungTang(陽和解凝湯) for HanBi, DokHwalGiSaengTang(獨活寄生湯), EuiYiInTang(薏苡仁湯) for SeupBi, YukGunJaTang(六君子湯) for GiHeoBi(氣虛痺) and SeongYouTang(聖兪湯) for HyeolHeoBi(血虛痺). 17. YangYuHak(楊有鶴) liked to use SoGyeongHwalHyelTang(疏經活血湯) and he would rather use DoIn(桃仁), HongHwa(紅花), DangGui(當歸), CheonGung(川芎) than insects. 18. SaHongDo(史鴻濤) made RyuPungSeupTang(類風濕湯)-((HwangGi 200g, JinGu 20g, BangGi(防己) 15g, HongHwa(紅花) 15g, DoIn(桃仁) 15g, CheongPungDeung(靑風藤) 20g, JiRyong(地龍) 15g, GyeJi(桂枝) 15g, WoSeul(牛膝) 15g, CheonSanGap(穿山甲) 15g, BaekJi(白芷) 15g, BaekSeonPi(白鮮皮) 15g, GamCho(甘草) 15g).

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.123-139
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• 2019

The Korean NPL market was formed by the government and foreign capital shortly after the 1997 IMF crisis. However, this market is short-lived, as the bad debt has started to increase after the global financial crisis in 2009 due to the real economic recession. NPL has become a major investment in the market in recent years when the domestic capital market's investment capital began to enter the NPL market in earnest. Although the domestic NPL market has received considerable attention due to the overheating of the NPL market in recent years, research on the NPL market has been abrupt since the history of capital market investment in the domestic NPL market is short. In addition, decision-making through more scientific and systematic analysis is required due to the decline in profitability and the price fluctuation due to the fluctuation of the real estate business. In this study, we propose a prediction model that can determine the achievement of the benchmark yield by using the NPL market related data in accordance with the market demand. In order to build the model, we used Korean NPL data from December 2013 to December 2017 for about 4 years. The total number of things data was 2291. As independent variables, only the variables related to the dependent variable were selected for the 11 variables that indicate the characteristics of the real estate. In order to select the variables, one to one t-test and logistic regression stepwise and decision tree were performed. Seven independent variables (purchase year, SPC (Special Purpose Company), municipality, appraisal value, purchase cost, OPB (Outstanding Principle Balance), HP (Holding Period)). The dependent variable is a bivariate variable that indicates whether the benchmark rate is reached. This is because the accuracy of the model predicting the binomial variables is higher than the model predicting the continuous variables, and the accuracy of these models is directly related to the effectiveness of the model. In addition, in the case of a special purpose company, whether or not to purchase the property is the main concern. Therefore, whether or not to achieve a certain level of return is enough to make a decision. For the dependent variable, we constructed and compared the predictive model by calculating the dependent variable by adjusting the numerical value to ascertain whether 12%, which is the standard rate of return used in the industry, is a meaningful reference value. As a result, it was found that the hit ratio average of the predictive model constructed using the dependent variable calculated by the 12% standard rate of return was the best at 64.60%. In order to propose an optimal prediction model based on the determined dependent variables and 7 independent variables, we construct a prediction model by applying the five methodologies of discriminant analysis, logistic regression analysis, decision tree, artificial neural network, and genetic algorithm linear model we tried to compare them. To do this, 10 sets of training data and testing data were extracted using 10 fold validation method. After building the model using this data, the hit ratio of each set was averaged and the performance was compared. As a result, the hit ratio average of prediction models constructed by using discriminant analysis, logistic regression model, decision tree, artificial neural network, and genetic algorithm linear model were 64.40%, 65.12%, 63.54%, 67.40%, and 60.51%, respectively. It was confirmed that the model using the artificial neural network is the best. Through this study, it is proved that it is effective to utilize 7 independent variables and artificial neural network prediction model in the future NPL market. The proposed model predicts that the 12% return of new things will be achieved beforehand, which will help the special purpose companies make investment decisions. Furthermore, we anticipate that the NPL market will be liquidated as the transaction proceeds at an appropriate price.