• Convergence Security Journal
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• v.22 no.2
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• pp.99-104
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• 2022

This study provided a prediction model for solar energy production in Yeongam province, Jeollanam-do. The model was derived from the correlation between climate changes and solar power production in Yeongam province, Jeollanam-do, and presented a prediction of solar power generation through the regression analysis of 6 parameters related to weather and solar power generation. The data used in this study were the weather and photovoltaic production data from January in 2016 to December in 2019 provided by public data. Based on the data, the machine learning technique was used to analyzed the correlation between weather change and solar energy production and derived to the prediction model. The model showed that the photovoltaic production can be categorized by the three-stage production index and will be used as an important barometer in the agriculture activity and the use of photovoltaic electricity.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.139-145
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• 1996

A forecast on the past output data sets of small bar steels is very important information to make a decision on the future production quantities. In many cases, however, it has been mainly determined by experience (or rule of thumb). In this paper, past basic data sets of each small bar steels are statistically analyzed by some graphical and statistical forecasting methods. This work is mainly done by SAS. Among various quantitative forecasting methods in SAS, STEPAR forecasting method was best performed to the above data sets. By the method, the future production quantities of each small bar steels are forecasted. As a result of this statistical analysis, 95% confidence intervals for future forecast quantities are very wide. To improve this problem, a suitable systematic database system, integrated management system of demand-production-inventory and integrated computer system should be required.

• Smart Media Journal
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• v.11 no.5
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• pp.9-16
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• 2022

Recently, agricultural sites are automating into digital agricultural smart farms by applying technologies such as big data and Internet of Things (IoT). These smart farms aim to increase production and improve crop quality by measuring the environment of crops, investigating and processing data. Production prediction is an important study in smart farm digital agriculture, which is a high-tech agriculture, and it is necessary to analyze environmental data using big data and further standardized research to manage the quality of growth information data. In this paper, environmental and production data collected from smart farm strawberry farms were analyzed and studied. Based on regression analysis, crop production prediction models were analyzed using Ridge Regression, LightGBM, and XGBoost. Among the three models, the optimal model was XGBoost, and R2 showed 82.5 percent explanatory power. As a result of the study, the correlation between the amount of positive fluid absorption and environmental data was confirmed, and significant results were obtained for the production prediction study. In the future, it is expected to contribute to the prevention of environmental pollution and reduction of sheep through the management of sheep by studying the amount of sheep absorption, such as information on the growing environment of crops and the ingredients of sheep.

• Smart Media Journal
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• v.11 no.4
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• pp.19-29
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• 2022

Recently, with the development of IoT technology, the number of farms using smart farms is increasing. Smart farms monitor the environment and optimise internal environment automatically to improve crop yield and quality. For optimized crop cultivation, researches on predict crop productivity are actively studied, by using collected agricultural digital data. However, most of the existing studies are based on statistical models based on existing statistical data, and thus there is a problem with low prediction accuracy. In this paper, we use various predition models for predicting the production and sales profits, and compare the performance results through models by using the agricultural digital data collected in the facility horticultural smart farm. The models that compared the performance are multiple linear regression, support vector machine, artificial neural network, recurrent neural network, LSTM, and ConvLSTM. As a result of performance comparison, ConvLSTM showed the best performance in R2 value and RMSE value.

• Proceedings of the Korea Technology Innovation Society Conference
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• 1999.11c
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• pp.331-340
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• 1999

국제원유가격의 변동은 세계 각국의 경제에 상당한 영향을 미치고 있다. 이러한 원유가격의 변동을 정확히 예측하기 위해서는 원유가격 변동요인의 정립이 필히 요구된다. 본 연구에서는 전통적으로 원유가격의 중요한 변동요인으로 알려져 있는 OPEC의 원유생산량과 걸프전쟁 이후 주요한 국제원유가격 변동요인으로 알려져 있는 OPEC의 원유생산량과 걸프 전쟁 이후 주요한 국제원유가격 변동요인으로 주목받고 있는 미국의 원유재고량의 영향과 역할을 공적분(Cointegration) 모형과 오차수정모형(Error-Correction Model)을 통해 분석하였다. 분석결과, 원유생산량과 더불어 원유재고량도 원유가격의 중요한 변동요인으로 작용함을 알 수 있었다. 장·단기 탄력성의 경우, 원유생산량의 생산탄력성은 단기에 비해 장기에 더 탄력적으로 나타났으며 장기에는 원유재고량의 변동이 생산량의 변동보다 오히려 원유가격에 더 큰 영향을 미치는 것으로 나타났다. 또한, 원유가격은 첫해에서 나타난 불균형을 대략 12%의 조정속도로, 장기균형으로 조정됨을 알 수 있었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.405-408
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• 2022

반도체 제조업은 정해진 시간 내에 최고의 품질의 반도체를 대량 생산해 내는 것을 목표로 한다. 생산량을 높이기 위해 유휴 시간을 최소화하는 연구가 꾸준히 진행 중이며 가장 대표적인 유휴 시간은 예방 정비이다. 예방 정비는 설비의 문제가 발생하기 전 예방하는 작업으로 품질 향상에 높은 영향을 미치는 작업인 반면 생산량이 크게 떨어지는 작업이다. 이 작업 시간을 최소화하기 위하여 작업 후 복구되는 시간에서 중복되는 작업을 최소화하는 방법을 선택한다. 샘플 테스트를 반복하며 조율해 나가던 작업을 연구 모델을 이용해 종말점 설정 값의 예측한 값을 바로 적용하여 최소한의 샘플 테스트를 거쳐 신뢰 구간 달성 후 생산에 재 합류하는 것을 목표로 한다. 설비에서 수집된 데이터를 학습하여 종말점 설정 값 예측 모델에 대하여 연구한다. 연구 모델을 사용한 예측 결과가 신뢰 구간에 포함되어 샘플 테스트 개수를 줄이는데 유효한 효과가 있음을 확인한다.

• Journal of Korean Society of Forest Science
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• v.99 no.4
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• pp.479-486
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• 2010

We analyzed the relationships between the daily yield and quality of pine-mushroom to predict the annual production pattern and unit price of the mushroom with the records of pine-mushroom trade at Yeongdeok forestry cooperative's market for nine years (2000~2008). Although there were some exceptions due to extreme drought or extraordinary temperature, the production ratio of high quality (first and second grade) was more than 50% in early stage and decreased, while that of low quality (pileus opened and defected ones) showed increasing pattern after the production reached in peak. The ratio of high quality and that of low quality were reversed 1~9 days before the mushroom production reached the acme of daily yield, which allowed us to predict that the mushroom production would be decreased when the ratio of low quality overcomes that of high quality. The ratio of high quality preceded about 3~4 days prior to that of daily yield, and the mushroom yield showed significant correlations with the ratio of high quality mushroom prior to 3~4 days of the day with the coefficient larger than 0.5 (r=0.51 for 3 days and r=0.54 for 4 days). Thus, we concluded that the analysis of grade distribution of pine-mushroom at the market may provide a significant clue to predict production pattern of the mushroom. In addition, the price of high quality pine-mushroom showed clear negative correlations with the yield. Thus, the analysis may take a good role for the trading of pine-mushroom with providing information for predicting the price of pine-mushroom.

• Proceedings of the Korea Technology Innovation Society Conference
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• 1999.11a
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• pp.333-340
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• 1999

국제원유가격의 변동은 세계 각국의 경제에 상당한 영향을 미치고 있다. 이러한 원유가격의 변동을 정확히 예측하기 위해서는 원유가격 변동요인의 정립이 필히 요구된다. 본 연구에서는 전통적으로 원유가격의 중요한 변동요인으로 알려져 있는 OPEC의 원유생산량과 걸프전쟁 이후 주요한 국제원유가격 변동요인으로 주목받고 있는 미국의 원유재고량의 영향과 역할을 공적분(Cointegration) 모형과 오차수정모형(Error-Correction Model)을 통해 분석하였다. 분석결과, 원유생산량과 더불어 원유재고량도 원유가격의 중요한 변동요인으로 작용함을 알 수 있었다. 장·단기 탄력성의 경우, 원유생산량의 생산탄력성은 장기에 비해 단기에 더 탄력적으로 나타났으며 원유재고량의 재고탄력성은 단기에 비해 장기에 더 탄력적으로 나타났으며 장기에는 원유재고량의 변동이 생산량의 변동보다 오히려 원유가격에 더 큰 영향을 미치는 것으로 나타났다. 또한, 원유가격은 첫해에서 나타난 불균형을 대략 12%의 조정속도로, 장기균형으로 조정됨을 알 수 있었다.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.329-341
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• 2000

In Korea, rice is the major crop because rice cultivation serves both cultural and socio-economic purpose. For this reason, rice production survey is very important to the J\linistry of Agriculture and Forestry. Especially. the result of the rice production esimation is used for the agricultural policies. Then, we suggest the method of esimating rice production by using statistical model.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.873-885
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• 2016

Onions are grown in a few specific regions of Korea that depend on the climate and the regional characteristic of the production area. Therefore, when onion yields are to be estimated, it is reasonable to use a statistical model in which both the climate and the region are considered simultaneously. In this paper, using a spatial panel regression model, we predicted onion yields with the different weather conditions of the regions. We used the spatial auto regressive (SAR) model that reflects the spatial lag, and panel data of several climate variables for 13 main onion production areas from 2006 to 2015. The spatial weight matrix was considered for the model by the threshold value method and the nearest neighbor method, respectively. Autocorrelation was detected to be significant for the best fitted model using the nearest neighbor method. The random effects model was chosen by the Hausman test, and the significant climate variables of the model were the cumulative duration time of sunshine (January), the average relative humidity (April), the average minimum temperature (June), and the cumulative precipitation (November).