• Proceedings of the KSRS Conference
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• 2003.11a
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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.

• Journal of The Korean Society of Agricultural Engineers
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• v.46 no.5
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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.

• Journal of the Korean Solar Energy Society
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• v.30 no.6
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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.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.3
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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.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.30A no.2
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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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.4
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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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• v.7 no.11
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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.

• Journal of The Korean Society of Agricultural Engineers
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• v.53 no.2
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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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.5
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• pp.525-534
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• 2016

In South Korea, paddy rice has been consumed over the entire region and it is the main source of income for farmers, thus mathematical model for the estimation of rice yield is required for such decision-making processes in agriculture. The objectives of our study are to: (1) develop rice yield estimation model using Convolutional Neural Networks(CNN), (2) choose hyper-parameters for the model which show the best performance and (3) investigate whether CNN model can effectively predict the rice yield by the comparison with the model using Artificial Neural Networks(ANN). Weather and MODIS(The MOderate Resolution Imaging Spectroradiometer) products from April to September in year 2000~2013 were used for the rice yield estimation models and cross-validation was implemented for the accuracy assessment. The CNN and ANN models showed Root Mean Square Error(RMSE) of 36.10kg/10a, 48.61kg/10a based on rice points, respectively and 31.30kg/10a, 39.31kg/10a based on 'Si-Gun-Gu' districts, respectively. The CNN models outperformed ANN models and its possibility of application for the field of rice yield estimation in South Korea was proved.