• Magazine of the Korean Society of Agricultural Engineers
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• v.39 no.6
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• pp.31-40
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• 1997

Potential impacts for unfavourable weather conditions and the assessment of the magnitudes of their adverse effects on crop yields were studied. EPIC model was investigated for its capability on crop yield predictions for rice and soybean. Weather generationmodel was used to generate long-term climatic data. The model was verified with ohserved climate data of Suwon city. Fifty years weather data including abnormal conditions were generated and used for crop yield simulation by EPIC model. Crop yield probability function was derived from simulated crop yield data, which followed normal distribution. Probable crop yield reductions due to abnormal weather conditions were also analyzed.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.48 no.4
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• pp.316-321
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• 2003

Analysis of breeding gains in grain yield has been intensively conducted in wheat, barley, oat, maize, and soybean. Such information is limited in rice. The objective of this study was to compare the breeding gains and cultural gains contributed to yield gains of Korean rice varieties since early 1900s. Two sets of yield data were used for analysis; the historical yield data of 1908 for old japonica cultivars, and present yield data in the years from 1996 to 1998 for the six cultivars, consisting of previous two old cultivars and four contemporary cultivars. The old cultivars were two native cultivars, Jodongi and Damageum, while contemporary cultivars were two premium quality japonica cultivars, Hwaseongbyeo and Dongjinbyeo, and two Tongil-type cultivars, high yielding cultivars developed from indica/japonica hybridization, Milyang23 and Dasanbyeo. The yield differences of old cultivars between the experiments in 1908 and the experiments from 1996 to 1998 were estimated as cultural gains (1.84 tons $\textrm{ha}^{-1}$) due to the improvement of cultivation technology. Yield differences between the old cultivars and contemporary cultivars were considered total yield gains during the periods. These were 2.51 tons $\textrm{ha}^{-1}$ for japonica cultivars and 3.81 tons $\textrm{ha}^{-1}$ for Tongil-type cultivars. From these data, the genetic gain of 0.67 tons $\textrm{ha}^{-1}$ and 1.97 tons $\textrm{ha}^{-1}$ were estimated for japonica cultivars and Tongil-type cultivars respectively. The ratio between cultural gain and genetic gain appeared to be 2.7:1 for japonica cultivars and 1:1 for Tongil-type cultivars. This analysis clearly showed the higher genetic contribution in Tongil-type cultivars than in japonica cultivars, suggesting a guideline to be used when planning new yield improvement programs. Additional implication has emerged when a better yield response to modem cultivation technology was found in one of the old cultivars, suggesting the combined improvement between breeding and cultural improvement is necessary for attaining the maximum yield capacity of a crop.

• Korean Journal of Soil Science and Fertilizer
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• v.46 no.4
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• pp.231-236
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• 2013

This study was conducted to investigate the yield response of soybean to drought stress in 1984 and 1986 at the experiment field of the National Academy of Agricultural Science using experiment plots with different soil water tension and fertilizer levels. The average yield response factor (YRF) of soybean to evapotranspiration (ET) calculated as [(Ya/Ym)/(ETa/ETm)], where Ya, average yield; Ym, maximum yield; ETa, average ET; and ETm, maximum ET, was 0.91 with the range from 0.74 to 1.16. Relationship between yield index (YI=[Ya/Ym]) and evapotranspiration index (ETI=[ETa/PET]) was $YI=0.87{\cdot}(ETI)+0.09$. Relationship between YI and the maximum soil water tension (Hmax) was $YI=1.23-0.23{\cdot}{\log}$ (Hmax). Relationship between YI and the days of drought stressed (Dr) was $YI=0.877{\cdot}{\exp}$ ($-0.01{\cdot}Dr$). The relation between YI and fertilizer level (F) was $YI=-0.21{\cdot}F2+0.36{\cdot}F+0.33$, under very serious drought condition as the maximum soil water tension was 0.3 MPa.

• 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.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2009.10a
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• pp.277-280
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• 2009

This paper deals with the performance evaluation of Barlat's and BBC yield criteria by the directional variation prediction of the yield stresses and the R-values. for the evaluation of yield criteria, three kinds of Aluminum alloys and two kinds of steels were selected and their material properties are from Stoughton and Yoon's work. The experimental data required for the parameter evaluation included the uniaxial yield stresses and R-values (width-to-thickness strain ratio in uniaxial tension) measured in rolling direction, diaganol direction and the transverse direction, the equibiaxial yield stress and the R-value of equibiaxial tension. The optimization method, the Downhill Simplex method, was selected for the coefficient identification of Barlat91, Barlat97 and Barlat2000 yield criteria. Yield surface shapes, yield stress and R-value directionalities of Barlat's and BBC yield criteria were investigated and compared with the experimental data. Barlat2000 and BBC yield criteria were extremely qualified for the shape of the yield surface and the directionality of the yield stresses and the R-values.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.430-433
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• 2005

In this study, weight of evidence(WOE) technique based on the bayesian method was applied to estimate the groundwater yield characteristics in the Pocheon area in Kyungki-do. The ground water preservation depends on many hydrogeologic factors that include hydrologic data, landuse data, topographic data, geological map and other natural materials, even with man-made things. All these data can be digitally collected and managed by GIS database. In the applied technique of WOE, The prior probabilities were estimated as the factors that affect the yield on lineament, geology, drainage pattern or river system density, landuse and soil. We calculated the value of the Weight W+, W- of each factor and estimated the contrast value of it. Results by the ground water yield characteristic calculations were presented in the form of posterior probability map to the consideration of in-situ samples. It is concluded that this technique is regarded as one of the effective technique for the feasibility mapping related to detection of groundwater bearing zones and its spatial pattern.

• Journal of Information Technology Applications and Management
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• v.15 no.1
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• pp.243-260
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• 2008

To prevent low yields in the semiconductor industry is crucial to the success of that industry. However, to prevent low yields is difficult because of too many factors to affect yield variation and their complex relation in the semiconductor manufacturing process. This study presents a new efficient detection methodology for detecting abnormal yields including high and low yields, which can forecast the yield level of a production unit (namely a lot) based on yield-related feature variables' behaviors. In the methodology, we use C5.0 to identify the yield-related feature variables that are the combination of correlated process variables associated with yield, use SOM (Self-Organizing Map) neural networks to extract and classify significant patterns of past abnormal yield lots and finally use C5.0 to generate classification rules for detecting abnormal yield lot. We illustrate the effectiveness of our methodology using a semiconductor manufacturing company's field data.

• Journal of Forest and Environmental Science
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• v.28 no.4
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• pp.205-211
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• 2012

Yield tables are a frequently used data base for regional timber resource forecasting. A normal yield table is based on two independent variables, age and site (species constant), and applies to fully stocked (or normal) stands while empirical yield tables are based on average rather than fully stocked stands. Normal and empirical yield tables essentially have many limitations. The limitations of normal and empirical yield tables led to the development of variable density yield tables. Mathematical models for estimating timber yields are usually developed by fitting a suitable equation to observed data. The model is then used to predict yields for conditions resembling those of the original data set. It may be accurate for the specific conditions, but of unproven accuracy or even entirely useless in other circumstances. Thus, these models tend to be specific rather than general and require validation before applying to other areas. Dalbergia sissoo forms a major portion of irrigated plantations in the hot desert of India and is an important timber tree species where stem wood is primarily used as timber. Variable density yield model is not available for this species which is very crucial in long-term planning for managing the plantations on a sustained basis. Thus, the objective of this study was to develop variable density yield model based on the data collected from 30 sample plots of D. sissoo laid out in IGNP area of Rajasthan State (India) and measured annually for 5 years. The best approximating model was selected based on the fit statistics among the models tested in the study. The model develop was evaluated based on quantitative and qualitative statistical criteria which showed that the model is statistically sound in prediction. The model can be safely applied on D. sissooo plantations in the study area or areas having similar conditions.

• Journal of Environmental Science International
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• v.25 no.10
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• pp.1433-1444
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• 2016

South Korea is a maritime nation, surrounded by water on three sides; hence, it is important to preserve in a sustainable manner. Most areas, especially those bordering the East Sea, have been suffering from severe coastal erosion. Information on the sediment yield of a river basin is an important requirement for water resources development and management. In Korea, data on suspended sediment yield are limited owing to a lack of logistic support for systematic sediment sampling activities. This paper presents an integrated approach to estimate the sediment yield for ungauged coastal basins by using a soil erosion model and a sediment delivery rate model in a geographic information system (GIS)-based platform. For applying the sediment yield model, a basin specific parameter was validated on the basis of field data, that, ranging from 0.6 to 1.2 for the 19 gauging stations. The calculated specific sediment yield ranged from 17 to $181t/km^2.yr$ in the various basin sizes of Korea. We obtained reasonable sediment yield values when comparing the measured data trends around the world with those in Korean basins.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.168-173
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• 2022

As the function of a product is advanced and the process is refined, the yield in the fine manufacturing process becomes an important variable that determines the cost and quality of the product. Since a fine manufacturing process generally produces a product through many steps, it is difficult to find which process or equipment has a defect, and thus it is practically difficult to ensure a high yield. This paper presents the system architecture of how to build a smart manufacturing system to analyze the big data of the manufacturing plant, and the equipment factor analysis methodology to increase the yield of products in the smart manufacturing system. In order to improve the yield of the product, it is necessary to analyze the defect factor that causes the low yield among the numerous factors of the equipment, and find and manage the equipment factor that affects the defect factor. This study analyzed the key factors of abnormal equipment that affect the yield of products in the manufacturing process using the data mining technique. Eventually, a methodology for finding key factors of abnormal equipment that directly affect the yield of products in smart manufacturing systems is presented. The methodology presented in this study was applied to the actual manufacturing plant to confirm the effect of key factors of important facilities on yield.