• Journal of Information Technology Applications and Management
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• 제15권1호
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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.

• 한국농공학회지
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• 제39권6호
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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.

• 한국작물학회지
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• 제44권2호
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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＊＊＊).

• 한국초지조사료학회지
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• 제41권4호
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• pp.287-294
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• 2021

본 연구는 기계학습을 통한 수량예측모델을 이용하여 이상기상에 따른 WCM의 DMY 피해량을 산출하기 위한 목적으로 수행하였다. 수량예측모델은 WCM 데이터 및 기상 데이터를 수집 후 가공하여 8가지 기계학습을 통해 제작하였으며 실험지역은 경기도로 선정하였다. 수량예측모델은 기계학습 기법 중 정확성이 가장 높은 DeepCrossing (R²=0.5442, RMSE=0.1769) 기법을 통해 제작하였다. 피해량은 정상기상 및 이상기상의 DMY 예측값 간 차이로 산출하였다. 정상기상에서 WCM의 DMY 예측값은 지역에 따라 차이가 있으나 15,003~17,517 kg/ha 범위로 나타났다. 이상기온, 이상강수량 및 이상풍속에서 WCM의 DMY 예측값은 지역 및 각 이상기상 수준에 따라 차이가 있었으며 각각 14,947~17,571 kg/ha, 14,986~17,525 kg/ha 및 14,920~17,557 kg/ha 범위로 나타났다. 이상기온, 이상강수량 및 이상풍속에서 WCM의 피해량은 각각 -68~89 kg/ha, -17~17 kg/ha 및 -112~121 kg/ha 범위로 피해로 판단할 수 없는 수준이었다. WCM의 정확한 피해량을 산출하기 위해서는 수량예측모델에 이용하는 이상기상 데이터 수의 증가가 필요하다.

• 반도체디스플레이기술학회지
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• 제21권4호
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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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• 제63권1호
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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.

• 한국컴퓨터정보학회논문지
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• 제4권4호
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• pp.244-250
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• 1999

본 연구는 기업의 CP 등급변화가 주가의 비정상수익률에 미치는 영향을 분석하였다. 분석결과 기업의 CP 등급 변화는 주가의 비정상수익률에 영향을 미치는 것으로 나타났다. 즉, CP의 등급이 상승한 경우에는 비정상수익률이 향상되고, CP의 등급이 하락하면 비정상수익률이 하락하는 것으로 나타났다. 그리고 기업의 당기순이익이 큰 기업이 등급이 상승되고, 당기순이익이 적은 기업은 등급이 하락하는 것으로 분석되었다. 또한 기업의 CP 등급 변화시 부채비율이 큰 기업이 등급이 상승되고, 부채비율이 낮은 기업은 등급이 상승하는 것으로 나타났다. 그러나 기업의 주주지분율의 크기. 기업의 기업가치의 크기, 기업의 현금흐름의 크기, 기업의 금융비융 부담률의 크기는 주식의 비정상수익률과 관계가 없는 것으로 나타났다. 본 연구는 기업의 CP 변화에 관한 정보가 자본시장에 미치는 영향을 분석한 것으로 의의가 있다. 그러나 표본기업과 공시시점의 선정에 한계가 있다.

• 한국기후변화학회지
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• 제4권4호
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• pp.317-330
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• 2013

홍수와 가뭄, 고온 등 이상기상의 영향으로 쌀 단수가 감소할 수 있다. 본 연구의 목적은 등숙기 이상기상이 쌀 단수에 미치는 영향을 파악하는 것이며, 이를 위하여 횡단면 자료와 시계열 자료를 모두 이용할 수 있는 패널모형을 이용하였다. 본 연구에서는 기상요소의 평균값을 기준으로 ${\pm}2{\sigma}$의 범위를 벗어날 때를 이상기상으로 정의하였다. 분석결과를 보면, 이상고온이 발생하였을 때 쌀 단수가 5.8~16.3% 감소, 이상고온과 폭우가 동시에 발생하였을 때 8.8~20.8% 감소하는 것으로 나타났다. 이상기상으로 인한 쌀 생산량 감소를 최소화하고, 농가의 소득안정을 위하여 고온과 폭우에 강한 신품종 개발, 농업용 수리시설의 현대화, 농작물보험 채택 등의 적응전략이 필요하다.

• 한국초지조사료학회지
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• 제43권2호
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• pp.88-94
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• 2023

본 연구는 이탈리안 라이그라스(IRG)를 단파 및 혼파에 따라 건물수량의 차이를 비교 분석하여 이상기상 발생 시 적합한 품종을 추천하기 위하여 실시하였다. 천안의 평균온도와 천안의 30년 간 평균온도는 비슷한 경향이었으나, 11월과 3월은 이상기상으로 판단된다. IRG 품종은 Green Fram(GF, 극조생), Kowinearly(KE, 조생), Kowinmaster(KM, 중생), Hwasan 104(H104, 만생)로 단파 또는 혼파하였다. GF 출수 기준으로 수확 시 GF+H104의 건물수량이 유의적으로 가장 높게 나타났다(p<0.05). KE와 KM 출수기준으로 수확 시 KE 및 KE+KM의 건물수량이 유의적으로 높게 나타났다. H104 출수기준으로 수확 시 건물수량은 처리간에 유의적인 차이는 없었으나(p>0.05). KM이 16,763.1 kg/ha로 가장 높았다. IRG 수확시기의 건물수량을 비교하였을 때 KE, KM의 단파 및 혼파에서 가장 높았다. 최근 봄 가뭄 등 이상기상의 발생 빈도가 높아지고 있으므로 이상기상에 대비하기 위하여 조생 및 중생을 이용한 IRG 혼파재배가 필요한 것으로 판단된다.

• 한국환경과학회지
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• 제24권6호
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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.