• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.917-919
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• 2003

This paper demonstrates that RADARSAT ScanSAR data can be an important data source of radar remote sensing for monitoring crop systems and estimation of rice yield for large areas in tropic and sub-tropical regions. Experiments were carried out to show the effectiveness of RADARSAT ScanSAR data for rice yield estimation in whole province of Guangdong, South China. A methodology was developed to deal with a series of issues in extracting rice information from the ScanSAR data, such as topographic influences, levels of agro-management, irregular distribution of paddy fields and different rice cropping systems. A model was provided for rice yield estimation based on the relationship between the backscatter coefficient of multi-temporal SAR data and the biomass of rice.

• 한국농공학회논문집
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• 제46권5호
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• pp.89-98
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• 2004

The amount of rice yield reduction due to inundation should be estimated to analyse economic efficiency of the farmland drainage improvement projects because those projects are generally promoted to mitigate flood inundation damage to rice in Korea. Estimation of rice yield reduction will also provide information on the flood risk performance to farmers. This study presented the relationships between inundated durations and rice yield reduction rates for different rice growth stages from the observed data collected from 1966 to 2000 in Korea, and developed the rice yield reduction estimation model (RYREM). RYREM was applied to the test watershed for estimating the rice yield reduction rates and the amount of expected average annual rice yield reduction by the rainfalls with 48 hours duration, 10, 20, 50, 100, 200 years return periods.

• 한국태양에너지학회 논문집
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• 제30권6호
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• pp.95-101
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• 2010

This paper presents performance estimation approach of grid-connected photovoltaic(PV) system to predict energy yield from irradiance to PV system using normalized yield model for changing meteorological conditions. The accuracy and validity of proposed performance estimation method is identified by compared measured with estimated yield using monitored data. These results will indicate that it is useful to estimate various loss factors causing the system performance obstruction and enhance the lifetime yield of PV system.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1993년도 Proceedings of International Conference for Agricultural Machinery and Process Engineering
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• pp.383-392
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• 1993

Identification of rice paddy fields and estimation of their areas from the images taken by LANDSAT-5/TM were attempted. The results were verified by aerial photographs and also by ground observations. Changes of the spectral characteristics of rice plants were measured with a portable spectroradiometer during the growth period. Analyzing these characteristics, an index was developed for evaluating the growth and the yield of rice . Applying the index to the data observed by LANDSAT-5.TM on Sep. 26, 1986, Oct .20, 1989 and Sep, 21, 1990, it was confirmed that the estimated derived from the index agreed with actual values. The results well demonstrated its feasibility for evaluating the yield of rice by a satellite like LANDSAT-5/TM.

• 산업경영시스템학회지
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• 제32권3호
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• pp.99-109
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• 2009

Negative binomial yield model for semiconductor manufacturing consists of two parameters which are the average number of defects per die and the clustering parameter. Estimating the clustering parameter is quite complex because the parameter has not clear closed form. In this paper, a Bayesian approach using Markov Chain Monte Carlo is proposed to estimate the clustering parameter. To find an appropriate estimation method for the clustering parameter, two typical estimators, the method of moments estimator and the maximum likelihood estimator, and the proposed Bayesian estimator are compared with respect to the mean absolute deviation between the real yield and the estimated yield. Experimental results show that both the proposed Bayesian estimator and the maximum likelihood estimator have excellent performance and the choice of method depends on the purpose of use.

• 전자공학회논문지A
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• 제30A권2호
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• pp.46-52
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• 1993

The estimation of yield for semiconductor devices requires not only establishment of critical area but also a new parameter of process defect density that contains inspection mean defect density related cleanness of manufacure process line, minimum feature size and the total number of mask process. We estimate the repaired yield of memory devide, leads the semiconductor technique, repaired by redundancy scheme in relation with defect density distribution function, and we confirm the repaired yield for different devices as this model. This shows the possibility of the yield estimation as statistical analysis for the condition of device related cleanness of manufacture process line, design and manufacture process.

• 한국측량학회지
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• 제34권4호
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• pp.383-390
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• 2016

Remote sensing data has been widely used in the estimation of crop yields by employing statistical methods such as regression model. Machine learning, which is an efficient empirical method for classification and prediction, is another approach to crop yield estimation. This paper described the corn yield estimation in Iowa State using four machine learning approaches such as SVM (Support Vector Machine), RF (Random Forest), ERT (Extremely Randomized Trees) and DL (Deep Learning). Also, comparisons of the validation statistics among them were presented. To examine the seasonal sensitivities of the corn yields, three period groups were set up: (1) MJJAS (May to September), (2) JA (July and August) and (3) OC (optimal combination of month). In overall, the DL method showed the highest accuracies in terms of the correlation coefficient for the three period groups. The accuracies were relatively favorable in the OC group, which indicates the optimal combination of month can be significant in statistical modeling of crop yields. The differences between our predictions and USDA (United States Department of Agriculture) statistics were about 6-8 %, which shows the machine learning approaches can be a viable option for crop yield modeling. In particular, the DL showed more stable results by overcoming the overfitting problem of generic machine learning methods.

• The Journal of Asian Finance, Economics and Business
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• 제7권11호
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• pp.23-31
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• 2020

This paper investigates the impact of monetary policy independence shock on bond yield by allowing for heterogeneous coefficients in the model based on panel data for 19 developing countries using quarterly data from 1991 to 2016. First, we estimate the model using conventional panel VAR estimation with the assumption of homogeneous coefficients across countries. Second, by performing Chow and Roy-Zellner tests to check the homogeneity assumption, we find that the assumption does not hold in the model. Third, we apply a mean-group estimation for panel VAR as a solution for heterogeneity panel model. The results reveal that central bank independence is effective in reducing bond yield with the maximum at period 6 after the shock. Shock one standard deviation bond yield has a negative effect on consumption and investment. We determine that central bank independence has a contradictory effect on real activity; a negative effect on consumption but a positive influence on investment for the first two years after the shock. Additionally, we split our sample into three groups to make the subgroups pool. Our empirical result shows that monetary policy independence shock reduces bond yield. Meanwhile, the response of economic activity to bond yield varies for all three groups.

• 한국농공학회논문집
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• 제53권2호
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• pp.19-26
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• 2011

SATEEC ArcView GIS system was developed using the Universal Soil Loss Equation (USLE) and sediment delivery ratio (SDR) modules. In addition, time-variant R and C modules and $R_5$ module were developed and integrated into the SATEEC system in recent years. The SATEEC ArcView GIS 2.1 system is a simple-to-use system which can estimate soil erosion and sediment yield spatially and temporarily using only USLE input data, DEM, and daily rainfall dataset. In this study, the SATEEC 2.1 system was used to evaluate the effects of USLE LS input data considering slope length segmentation on soil erosion and sediment yield estimation. Use of USLE LS with slope length segmentation due to roads in the watershed, soil erosion estimation decreased by 24.70 %. However, the estimated sediment yield using SATEEC GA-SDR matched measured sediment values in both scenarios (EI values of 0.650 and EI 0.651 w/o and w/flow segmentation). This is because the SATEEC GA-SDR module estimates lower SDR in case of greater soil erosion estimation (without flow length segmentation) and greater SDR in case of lower soil erosion estimation (with flow length segmentation). This indicates that the SATEEC soil erosion need to be estimated with care for accurate estimation of SDR at a watershed scale and for accurate evaluation of BMPs in the watershed.

• 한국측량학회지
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• 제34권5호
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• pp.525-534
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• 2016

쌀은 오랜 기간 동안 남한 지역의 주식임과 동시에 농부들의 주 수입원이며, 농업 분야 관련 정책 수립을 위한 수학적인 쌀 생산량 추정 모델의 구축이 필요하다. 본 연구의 목적은 (1) 쌀 생산량 추정을 위한 회선신경망 모델의 구축과, (2) 최고의 성능을 보이는 회선신경망의 파라미터를 결정하는 것과, (3) 인공신경망 모델과의 비교를 통해 회선신경망의 성능을 평가하는 것이다. 각 모델의 입력데이터로는 2000~2013년도의 4~9월까지에 해당하는 기상자료와 MODIS 위성자료를 사용하였으며, 정확도 평가를 위해 교차 검증을 실시하였다. 회선신경망과 인공신경망은 쌀 생산 표본점을 대상으로 각각 36.10kg/10a, 48.61kg/10a와 시군구 지역을 대상으로 각각 31.30kg/10a, 39.31kg/10a의 RMSE를 보였다. 회선신경망 모델은 인공신경망 모델보다 우수한 성능을 보였으며, 본 연구를 통해 쌀 생산량 추정 분야에 대한 회선신경망 모델의 적용 가능성을 확인할 수 있었다.