• 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.

• 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.44 no.2
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• pp.166-170
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• 1999

The identification of quantitative trait loci (QTL) has the potential to enhance the efficiency of im- proving food processing traits of soybean. In this study, 92 restriction fragment length polymorphism (RFLP) loci and two morphological markers (W$_1$ and T) were used to identify QTL associated with food processing traits of soybean for sprout in 83 F$_2$-derived lines from a cross of ＇Pureun＇ x ＇Jinpum 2＇. The genetic map consisted of 76 loci which covered about 760 cM and converged into 20 linkage groups. Eighteen markers remained unlinked. Phenotypic data were collected for hypocotyl length, abnormal seedling rate, and sprout yield seven days after seed germination at 2$0^{\circ}C$. Based on the single-factor analysis of variance, eight independent markers were associated with hypocotyl length. Four of seven markers associated with abnormal seedling rate were identified as independent. Seven loci were associated with sprout yield. For three different traits, much of genetic variation was explained by the identified QTL in this population. Several RFLP markers in linkage group (LG) Bl were detected as being associated with three traits, providing a genetic explanation for the biological correlation of sprout yield with hypocotyl length (r=OA07＊＊＊) and with abnormal seedling rate (r=-406＊＊＊).

• Journal of The Korean Society of Grassland and Forage Science
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• v.41 no.4
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• pp.287-294
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• 2021

The objective of this study was conducted to calculate the damage of whole crop maize in accordance with abnormal climate using the forage yield prediction model through machine learning. The forage yield prediction model was developed through 8 machine learning by processing after collecting whole crop maize and climate data, and the experimental area was selected as Gyeonggi-do. The forage yield prediction model was developed using the DeepCrossing (R²=0.5442, RMSE=0.1769) technique of the highest accuracy among machine learning techniques. The damage was calculated as the difference between the predicted dry matter yield of normal and abnormal climate. In normal climate, the predicted dry matter yield varies depending on the region, it was found in the range of 15,003~17,517 kg/ha. In abnormal temperature, precipitation, and wind speed, the predicted dry matter yield differed according to region and abnormal climate level, and ranged from 14,947 to 17,571, 14,986 to 17,525, and 14,920 to 17,557 kg/ha, respectively. In abnormal temperature, precipitation, and wind speed, the damage was in the range of -68 to 89 kg/ha, -17 to 17 kg/ha, and -112 to 121 kg/ha, respectively, which could not be judged as damage. In order to accurately calculate the damage of whole crop maize need to increase the number of abnormal climate data used in the forage yield prediction model.

• 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.

• Journal of Animal Science and Technology
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• v.63 no.1
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• pp.77-90
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• 2021

This study aimed to assess the impact of abnormal climate events on the production of Italian ryegrass (IRG), such as autumn low-temperature, severe winter cold and spring droughts in the central inland, southern inland and southern coastal regions. Seasonal climatic variables, including temperature, precipitation, wind speed, relative humidity, and sunshine duration, were used to set the abnormal climate events using principal component analysis, and the abnormal climate events were distinguished from normal using Euclidean-distance cluster analysis. Furthermore, to estimate the impact caused by abnormal climate events, the dry matter yield (DMY) of IRG between abnormal and normal climate events was compared using a t-test with 5% significance level. As a result, the impact to the DMY of IRG by abnormal climate events in the central inland of Korea was significantly large in order of severe winter cold, spring drought, and autumn low-temperature. In the southern inland regions, severe winter cold was also the most serious abnormal event. These results indicate that the severe cold is critical to IRG in inland regions. Meanwhile, in the southern coastal regions, where severe cold weather is rare, the spring drought was the most serious abnormal climate event. In particular, since 2005, the frequency of spring droughts has tended to increase. In consideration of the trend and frequency of spring drought events, it is likely that drought becomes a NEW NORMAL during spring in Korea. This study was carried out to assess the impact of seasonal abnormal climate events on the DMY of IRG, and it can be helpful to make a guideline for its vulnerability.

• Journal of the Korea Society of Computer and Information
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• v.4 no.4
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• pp.244-250
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• 1999

This study aimed to analyze the effect of the change in CP class of a firm on the abnormal yield of its stock price. As a result, it was found that the change in CP class of a firm had an effect on the abnormal yield. That is. the abnormal yield rose when the class of CP rose while it dropped when the class of CP dropped. And it was analyzed that the class of CP in the firm in which its current net gain was great while it dropped in the firm in which the current net gain was small. And it was found that the CP class of the firm with the high debt to equity ratio rose when the CP class of the firm changed, whereas it rose in the firm with the low debt to equity ratio. But it was found that the size of majority shareholders equity rate in a firm, the size of corporate value of the firm, the size of cash flow of the firm and the size of the burden of financial costs of the firm were not related to the abnormal yield of its stock price. This study has its significance in analyzing the effect of the information on the change in CP class of the firm on the capital market. But it has its limitations in the sample firm and the selection of the point in time of disclosure.

• Journal of Climate Change Research
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• v.4 no.4
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• pp.317-330
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• 2013

The yield of rice production is affected severely by abnormal weather events, such as flood, drought, high temperature etc. The objective of this paper is to assess impacts of abnormal weather events on rice production, using a panel model which analyzes both cross-section data and ti- me series data. Abnormal weather is defined as the weather event which goes beyond the range of ${\pm}2{\sigma}$ from the average of a weather factor. The result of an analysis on impacts of high temperature on rice production showed that the yield of rice was decreased 5.8% to 16.3% under the conditions of extremely high temperature, and it was decreased 8.8 to 20.8% under the conditions of both extremely high and heavy rain. Adaptation strategies, development of new varieties enduring high temperature and heavy rain, adaptation of crop insurance, modernization of irrigation facilities are needed to minimize the impacts of abnormal weather on rice production, and to stabilize farmers' income.

• Journal of The Korean Society of Grassland and Forage Science
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• v.43 no.2
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• pp.88-94
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• 2023

This study was conducted to analyze and compare the dry matter yield of Italian ryegrass (IRG) cultivated under monoculture and mixed culture system to recommend suitable varieties that can be cultivated. Italian ryegrass cultivars, Green Fram (GF, extremely early-maturing), Kowinearly (KE, early-maturing), Kowinmaster (KM, mild-maturing), and Hwasan 104 (H104, late-maturing), were used for mono or mixed cultivation. The average monthly temperature in Cheonan over the past 30 years tended to be similar, but that in November and March are judged to be abnormal weather. The dry matter yield of GF+H104 was significantly higher during harvest than that of GF (p<0.05). The dry matter yields of KE and KE+KM were significantly higher during harvest than the output standards of KE and KM. There was no significant difference between the dry matter yield of H104 and KM (p>0.05), but KM had the highest yield of 16,763.1 kg/ha. Analysis showed that the highest dry matter yield during IRG harvest was obtained under monoculture and KE+KM mixed culture. Because the occurrence frequency of abnormal weather such as drought during spring is increasing recently, it is judged that IRG cultivation using early and middle growth is necessary to prepare for abnormal weather.

• Journal of Environmental Science International
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• v.24 no.6
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• pp.769-776
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• 2015

This study was conducted to determine the effects of high temperature and deficit irrigation on growth and yield of hot pepper. Hot pepper was subjected to four irrigation treatments: fully irrigation (FI), 10, 20, and 30 days deficit irrigation (DI) combination with high temperature treatment. Control plants were grown natural environment and conventional culture methods. The plant height treated with high temperature was significantly higher than that of control plant. At FI combination with high temperature treatment, growth parameters such as stem diameter, leaf area, fresh and dry weight were the greatest. The yield was the greatest (2,036 kg/10a) under control, DI combination with high temperature treatment decreased by approximately 42% compare with FI combination with high temperature treatment. The number of abnormal fruits was approximately 38/plant under control, which was the smallest and that of 30 days DI combination with high temperature was higher 3.3 times compare with control. Flower abscission and calcium deficiency induced by DI treatments, especially those physiological disorder promoted by increasing DI treatments period. Results indicated that yield of hot pepper reduced by DI treatments, these results suggest that the growers should irrigate to proper soil moisture for preventing reduction of total fruit yield.