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[CE-0002] Genotype study for heading date using GWAS and genomic selection in wheat core-set
Changhyun Choi(Changhyun Choi),Myoung Hui Lee(Myoung Hui Lee),Urim Kim(Urim Kim),Jin-Kyung Cha(Jin-Kyung Cha),Kyeong-Min Kim(Kyeong-Min Kim),Chon-Sik Kang(Chon-Sik Kang),Jiyoung Son(Jiyoung Son),Jong- 한국육종학회 2022 한국육종학회 공동학술발표집 Vol.2022 No.-
Optimum Baseline of a Single-Pass In-SAR System to Generate the Best DEM in Tidal Flats
Choi, Changhyun,Kim, Duk-jin IEEE 2018 IEEE journal of selected topics in applied earth o Vol.11 No.3
<P>Continuous monitoring of topographic heights and changes in tidal flats is challenging, as it is generally difficult to observe topographic changes from on-site measurements or remote sensing techniques with high resolution and high accuracy. In this regard, an interferometric synthetic aperture radar (In-SAR) can be an effective tool to generate precise digital elevation models (DEMs) and detect large-scale topographic changes. Nevertheless, utilizing the In-SAR to detect topographic changes in tidal flats is not practical because the average slope of tidal flats is usually less than 5°, and the overall spatial and temporal variations of height are not significant. Therefore, the accuracy of In-SAR DEMs must be high to detect meaningful topographic changes. In order to minimize the error of In-SAR DEMs, height of ambiguity and random phase deviation of interferograms should be taken into account. These two factors are related to incidence angle and baseline. We simulated topographic error levels in tidal flats for a single-pass In-SAR system such as TanDEM-X. Phase error of interferograms was derived using the relationship between In-SAR coherence and the probability density function of phase deviation. Signal-to-noise ratio and geometric decorrelation were formulated by the function of baseline, incidence angle, and surface slope. The simulation results show that the height error of the DEM was minimized to lower than 15 cm when the baseline was 1500 m with an incidence angle of 29° in the TanDEM-X system. Finally, the validation of simulation results was carried out by comparing them with TanDEM-X DEM accuracies in tidal flats.</P>
Room-temperature NO<sub>2</sub> sensor based on electrochemically etched porous silicon
Choi, Myung Sik,Na, Han Gil,Mirzaei, Ali,Bang, Jae Hoon,Oum, Wansik,Han, Seungmin,Choi, Sun-Woo,Kim, Mooshob,Jin, Changhyun,Kim, Sang Sub,Kim, Hyoun Woo Elsevier 2019 Journal of Alloys and Compounds Vol.811 No.-
<P><B>Abstract</B></P> <P>With high-performance room-temperature gas sensors being in great demand from an energy-saving standpoint, in this study, we fabricated porous silicon (PS) sensors by electrochemically etching at different times (30, 60, and 90 min). The porous nature of the etched PSs was studied using scanning electron microscopy, and subsequently gas sensors were fabricated. NO<SUB>2</SUB> sensing studies showed that the highest gas performance can be obtained at room temperature (30 °C). Furthermore, the PS sensor etched for 60 min had the best performance among the sensors, which is related to its higher surface area and high enough initial resistance. In particular for the PS sensor etched for 60 min, the response (R<SUB>a</SUB>/R<SUB>g</SUB>) to 10 ppm NO<SUB>2</SUB> was 9.56, which was much higher than other interfering gases, demonstrating its high selectivity towards NO<SUB>2</SUB> gas. This study reveals the need for optimization of electrochemical etching to realize gas sensors based on PS working at room temperature.</P> <P><B>Highlights</B></P> <P> <UL> <LI> We fabricated porous silicon (PS) sensors for room-temperature NO<SUB>2</SUB> sensing, by electrochemically etching at different times. </LI> <LI> The PS sensor etched for 60 min had the best performance among the sensors. </LI> <LI> The response of PS sensor to 10 ppm NO<SUB>2</SUB> was 9.56, demonstrating its high selectivity towards NO<SUB>2</SUB> gas. </LI> </UL> </P>
Flood Simulation and Economic Analysis Considering Stormwater Drainage Pumping Stations
Choi, Changhyun,Kim, Yonsoo,Kim, Soojun,Kim, Kyungtae,Kim, Hung Soo 한국방재학회 2015 한국방재학회 학술발표대회논문집 Vol.14 No.-
Global climate change has made natural disasters become large-scale, diversified, and concentrated, causing social and economic damages one after another. The frequency of natural disasters and the scale of subsequent damages are continuously increasing and more than 90% of damages are caused by rainfall, wind speed, and snowfall. In particular, flood damage accounts for the greatest percentage of damage, which prompts us to prepare necessary preventive measures. Flood damage caused by river inundation often occurred in the past. Recently, however, inland flood damages, such as the inundation of Gangnam Station in 2011 and 2012, caused by urbanization have been increasing, which makes it necessary to simulate flood inundation and calculate the damages while considering the effect of inland inundation as well as river inundation. Accordingly, this study analyzed the inland inundation reduction effect through a simulation of flood inundation utilizing flood pumping stations in Anyang River. An economic analysis was also conducted. In addition, this study prepared/compared the flow inundation map based on the existence and operation of flood pumping stations during a localized heavy rainfall event and tried to analyze the economic feasibility by comparing the expected flood damage and the cost of flood pumping stations using Multi-dimensional Flood Damage Assessment. Through this study, results will be provided as reference data for qualitative flood inundation simulation and economic analysis of the basin where natural discharge of direct runoff is impossible and compulsive drainage is taking place.
빅 데이터 분석 기법을 이용한 풍수해 복원탄력성 지표 개발 및 평가: (2) 복원탄력성 평가
최창현(Choi Changhyun),김연수(Kim Yonsoo),김종성(Kim Jongsung),김동현(Kim Donghyun),김정욱(Kim Jungwook),김형수(Kim Hung Soo) 한국방재학회 2018 한국방재학회지 Vol.18 No.4
본 연구에서는 빅 데이터 분석 기법을 이용하여 풍수해 복원탄력성을 평가할 수 있는 방안을 제시하였다. 적정 지표 선정을 위해 빅 데이터 분석 기법이 적용된 풍수해 복원탄력성 지표에 표준화 방법 및 요인분석을 적용하였고, TF-IDF (Term Frequency-Inverse Document Frequency)를 이용하여 각 지표별 가중치를 산정하였다. 본 연구에서 개발된 풍수해 복원탄력성 평가 방안을 이용하여 안양천 유역의 시군구별 풍수해 복원탄력성을 평가하였고, 이를 지역안전도 평가 결과와 비교 및 검토하였다. 개발된 연구 성과는 기존의 재난관리 분야에 적용이 미비하였던 빅 데이터 분석 기법의 활용 방안을 제시하였고, 기후변화로 인해 자연재난의 강도 및 빈도가 증가하고 있는 상황에서 효율적인 재난관리를 실시하기 위한 기초자료로 활용할 수 있을 것으로 기대된다. In this study, we proposed a method to evaluate the resilience of storm and flood using big data analysis. Standardization method and factor analysis were applied to storm and flood resilience indicators with big data analysis technique for indicator selection. And the weights for each indicator were calculated using the TF-IDF (Term Frequency-Inverse Document Frequency). The storm and flood resilience was evaluated municipality of city, town, and county in Anyang river basin using the storm and flood resilience evaluation method developed in this study and compared with the result of the regional safety assessment. The results of this research suggested the application methodology of big data analysis techniques which were not applied to the existing disaster management field. And it is expected that it will be used as basic data for effective disaster management in the situation where the intensity and frequency of natural disasters are increasing due to climate change.
최창현(Choi, Changhyun),김종성(Kim, Jongsung),김정환(Kim, Jeonghwan),김한용(Kim, Hanyong),이우주(Lee, Woojoo),김형수(Kim, Hung Soo) 한국방재학회 2017 한국방재학회논문집 Vol.17 No.3
본 연구에서는 한강권역을 대상으로 선형회귀모형, 일반화선형모형, 주성분 회귀모형, 인공신경망 모형과 같은 통계적 모형을 적용하여 호우피해예측함수를 개발하였다. 학습용 데이터(1994∼2011년)로부터 개발된 함수를 평가용 데이터(2012∼2015년)에 적용하고, 실제 호우피해액과 예측 호우 피해액을 비교하여 예측력을 평가하였다. 평가결과 NRMSE는 10.61∼13.89%로 나타났으며, 일반화선형모형에 벌점화를 통한 축소추정법을 적용한 함수에서 가장 좋은 예측력을 나타냈다. 본 연구에서 개발된호우피해예측함수를 활용하여 재난 피해 발생 전 피해규모와 영향을 신속하게 추정한다면, 예방 및 대비 차원의 재난관리에 유용하게 활용될 수 있을 것이다. In this study, we develop heavy rain damage prediction functions for Han river basin by using statistical models such as linear regression model, generalized linear model, principal component regression model, artificial neural network model. The prediction functions were estimated from the training data (1994 to 2011) and evaluated by the test data (2012 to 2015). Their performances were assessed by comparing observed heavy rain damages and predicted damages. Specifically, the NRMSE was 10.61~13.89%. A generalized linear model based on penalized likelihood method showed the best prediction performance. This heavy rain damage prediction function developed in this study can be used not only for estimati