• 대한안전경영과학회지
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• 제9권3호
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• pp.111-118
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• 2007

What is CRM(Customer Relationship Management) means that planning, executing, and re-accessing the marketing strategy based on the customer character by analyzing the material related to customers. That is CRM is a strategy of customer service on the base of data. In the case of the telecommunications and a newspaper, there are restricted application of CRM, because they are provided services by paying a given amount of money within a given period of time. This paper develops CRM model(chum prediction model) that can apply to a newspaper. For model-building, real data were used which were collected from one of the major a newspaper company in Korea. Also, this paper verifies the efficient result.

• 한국항공운항학회지
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• 제28권2호
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• pp.29-35
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• 2020

In this paper, we established a prediction model of fuel consumption at the aircraft's taxi operation. To look for countermeasures to reduce fuel consumption and carbon emissions, Airbus A380's actual ground taxi data was used. As a result, the number of stops or turnings during the taxi operation was not related to fuel consumption. It was confirmed that the amount of fuel consumption in the taxi operation was the taxi time and the thrust change. It can be confirmed that ground control optimization, which is the result of close cooperation between the control organization and the airline, is absolutely necessary to reduce taxi time and minimize the occurrence of thrust change events.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2015년도 제46회 하계학술대회
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• pp.611-612
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• 2015

Prediction algorithm of the energy storage system in accordance with the load pattern can cause economic loss in case of a failure prediction. In this paper, we compare the electricity charge between industrial power system with ESS - this case's operation is based on Non-prediction operation method. - and without ESS. In addition, we derive the payback period.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2003년도 춘계학술대회
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• pp.400-409
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• 2003

Collaborative filtering (CF) recommendation is a knowledge sharing technology for distribution of opinions and facilitating contacts in network society between people with similar interests. The main concerns of the CF algorithm are about prediction accuracy, speed of response time, problem of data sparsity, and scalability. In general, the efforts of improving prediction algorithms and lessening response time are decoupled. We propose a three-step CF recommendation model which is composed of profiling, inferring, and predicting steps while considering prediction accuracy and computing speed simultaneously. This model combines a CF algorithm with two machine learning processes, SOM (Self-Organizing Map) and CBR (Case Based Reasoning) by changing an unsupervised clustering problem into a supervised user preference reasoning problem, which is a novel approach for the CF recommendation field. This paper demonstrates the utility of the CF recommendation based on SOM cluster-indexing CBR with validation against control algorithms through an open dataset of user preference.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2020년도 가을 학술논문 발표대회
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• pp.111-112
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• 2020

In the case of, the ignition of flammable external materials by the radiant flame and the accompanying fire in the upper layer are occurring every year, and in the case of the Flashover prediction formula, the limit is reached through the surface area of the space and the factor. Predicts heat release rate. In this study, the critical heat release rate of each prediction formula was calculated based on the ISO 9705 model.

• Computers and Concrete
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• 제32권5호
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• pp.513-525
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• 2023

Recently, research on predicting the behavior of reinforced concrete (RC) columns using machine learning methods has been actively conducted. However, most studies have focused on predicting the ultimate strength of RC columns using a regression algorithm. Therefore, this study develops a successive machine learning process for predicting multiple nonlinear behaviors of rectangular RC columns. This process consists of three stages: single machine learning, bagging ensemble, and stacking ensemble. In the case of strength prediction, sufficient prediction accuracy is confirmed even in the first stage. In the case of displacement, although sufficient accuracy is not achieved in the first and second stages, the stacking ensemble model in the third stage performs better than the machine learning models in the first and second stages. In addition, the performance of the final prediction models is verified by comparing the backbone curves and hysteresis loops obtained from predicted outputs with actual experimental data.

• 한국IT서비스학회지
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• 제13권3호
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• pp.221-233
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• 2014

Because people's interest of the stock market has been increased with the development of economy, a lot of studies have been going to predict fluctuation of stock prices. Latterly many studies have been made using scientific and technological method among the various forecasting method, and also data using for study are becoming diverse. So, in this paper we propose stock prices prediction models using sentiment analysis and machine learning based on news articles and SNS data to improve the accuracy of prediction of stock prices. Stock prices prediction models that we propose are generated through the four-step process that contain data collection, sentiment dictionary construction, sentiment analysis, and machine learning. The data have been collected to target newspapers related to economy in the case of news article and to target twitter in the case of SNS data. Sentiment dictionary was built using news articles among the collected data, and we utilize it to process sentiment analysis. In machine learning phase, we generate prediction models using various techniques of classification and the data that was made through sentiment analysis. After generating prediction models, we conducted 10-fold cross-validation to measure the performance of they. The experimental result showed that accuracy is over 80% in a number of ways and F1 score is closer to 0.8. The result can be seen as significantly enhanced result compared with conventional researches utilizing opinion mining or data mining techniques.

• 한국해안·해양공학회논문집
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• 제34권3호
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• pp.47-57
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• 2022

본 연구에서는 진해만의 단일 정점 장기 모니터링 자료를 사용하여 LSTM 모형을 이용한 DO 농도 예측 및 결정 트리 모형을 이용한 빈산소수괴 발생 예측 연구를 수행하였다. LSTM을 이용한 DO 농도 예측 결과, Hidden node의 수가 증가할수록 모형의 복잡도가 증가하여 많은 Epoch을 요구하는 모습을 보였으며, 예측 시간 간격이 증가할수록 긴 Sequence length에서 높은 정확도를 보였다. 결정 트리를 이용한 빈산소수괴 발생 예측 결과, 30 day 예측에서 빈산소수괴 미발생 예측 정확도는 6 6 .1%로 발생 예측 정확도의 37.5%보다 상대적으로 높게 나타났다. 이는 결정 트리 모형이 DO 농도를 관측값보다 고평가하여 나타난 결과로 판단된다.

• Journal of the Korean Statistical Society
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• 제13권2호
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• pp.107-113
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• 1984

In a linear regression model the idependent variables are frequently subject to measurement errors. For this case, the problem of estimating unknown parameters has been extensively discussed in the literature while very few has been concerned with the effect of measurement errors on prediction. This paper investigates the behavior of the predicted values of the dependent variable in terms of the average mean square error of prediction (AMSEP). AMSEP may be used as a criterion for selecting an appropriate estimation method, for designing an estimation experiment, and for developing cost-effective future sampling schemes.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 1999년도 연약지반처리위원회 학술세미나
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• pp.62-70
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• 1999

Artesian pressure exists in Yangsan site, the maximum value of which has been measured as high as 5 t/m$^2$. This paper deals with the prediction of consolidation settlement for the site with artesian pressure. The consolidation settlement at the site has been accelerated using vertical band drains. Since the artesian pressure gives lower effective stress than a static condition, its effect should be considered in the settlement prediction. This case study shows that the prediction of settlement and pore pressure dissipation agrees well with the measurements, when considering the artesian effect.