• Journal of the Korean Society for Aviation and Aeronautics
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• v.28 no.2
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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.

• Proceedings of the KIEE Conference
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• 2015.07a
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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.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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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.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.11a
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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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• v.32 no.5
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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.

• Journal of Information Technology Services
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• v.13 no.3
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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.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.3
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• pp.47-57
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• 2022

We carried out studies on prediction in concentration of dissolved oxygen (DO) with LSTM model and prediction in occurrence of hypoxia water mass (HWM) with decision tree. As results of study on prediction in DO concentration, a large number of Hidden node caused high complexity of model and required enough Epoch. And it was high accuracy in long Sequence length as prediction time step increased. The results of prediction in occurrence of HWM showed that the accuracy of nonHWM case was 66.1% in 30 day prediction, it was higher than 37.5% of HWM case. The reason is that the decision tree might overestimate DO concentration.

• Journal of the Korean Statistical Society
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• v.13 no.2
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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.

• Proceedings of the Korean Geotechical Society Conference
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• 1999.02a
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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.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.1
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• pp.1-4
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• 2013

Mines are classified as the attack, defense and protect mine depending on laying position. In case of the defense and protect mine for protecting the major ports, it is important to predict that mines are laid position for a safe maneveuring of friendly ships. Furthermore, the marine environment affects mines laying position. Therfore, this paper is studied on a prediction of mines laying position through the prediction of the marine environment.