• Journal of Korea Water Resources Association
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• v.52 no.4
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• pp.279-289
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• 2019

Unlike other natural disasters, drought is a reoccurring and region-wide phenomenon after being triggered by a prolonged precipitation deficiency. Considering that remote sensing products provide consistent temporal and spatial measurements of precipitation, this study developed a remote sensing data-based drought outlook model. The meteorological drought was defined by the Standardized Precipitation Index (SPI) achieved from PERSIANN_CDR, TRMM 3B42 and GPM IMERG images. Bayesian networks were employed in this study to combine the historical drought information and dynamical prediction products in advance of drought outlook. Drought outlook was determined through a decision-making model considering the current drought condition and forecasted condition from the Bayesian networks. Drought outlook condition was classified by four states such as no drought, drought occurrence, drought persistence, and drought removal. The receiver operating characteristics (ROC) curve analysis were employed to measure the relative outlook performance with the dynamical prediction production, Multi-Model Ensemble (MME). The ROC analysis indicated that the proposed outlook model showed better performance than the MME, especially for drought occurrence and persistence of 2- and 3-month outlook.

• Tunnel and Underground Space
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• v.30 no.6
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• pp.540-550
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• 2020

Machine learning has been actively used in the field of automation due to the development and establishment of AI technology. The important thing in utilizing machine learning is that appropriate algorithms exist depending on data characteristics, and it is needed to analysis the datasets for applying machine learning techniques. In this study, advance rate is predicted using geotechnical and machine data of TBM tunnel section passing through the soil ground below the stream. Although there were no problems of application of statistical technology in the linear regression model, the coefficient of determination was 0.76. While, the ensemble model and support vector machine showed the predicted performance of 0.88 or higher. it is indicating that the model suitable for predicting advance rate of the EPB Shield TBM was the support vector machine in the analyzed dataset. As a result, it is judged that the suitability of the prediction model using data including mechanical data and ground information is high. In addition, research is needed to increase the diversity of ground conditions and the amount of data.

• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.118-129
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• 2022

The purpose of this report is to study a strategic model of promotion activities through various analysis and sales forecasting by selecting wearable products for domestic online companies and collecting sales data. For data analysis, various algorithms are used for analysis and the results are selected as the optimal model. The gradation boosting model, which is selected as the best result, will allow nine independent variables to be entered, including promotion type, price, amount, gender, model, company, grade, sales date, and region, when predicting dependent variables through supervised learning. In this study, the review values set as dependent variables for each type of sales promotion were studied in more detail through the ensemble analysis technique, and the main purpose is to analyze and predict them. The purpose of this study is to study the grades. As a result of the analysis, the evaluation result is 95% of AUC, and F1 is about 93%. In the end, it was confirmed that among the types of sales promotion activities, value-added benefits affected the number of reviews and review grades, and that major variables affected the review and review grades.

• Information Systems Review
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• v.22 no.4
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• pp.21-39
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• 2020

Recently, deep learning has shown high performance in various applications such as pattern analysis and image classification. Especially known as a difficult task in the field of machine learning research, stock market forecasting is an area where the effectiveness of deep learning techniques is being verified by many researchers. This study proposed a deep learning Convolutional Neural Network (CNN) model to predict the direction of stock prices. We then used the feature selection method to improve the performance of the model. We compared the performance of machine learning classifiers against CNN. The classifiers used in this study are as follows: Logistic Regression, Decision Tree, Neural Network, Support Vector Machine, Adaboost, Bagging, and Random Forest. The results of this study confirmed that the CNN showed higher performancecompared with other classifiers in the case of feature selection. The results show that the CNN model effectively predicted the stock price direction by analyzing the embedded values of the financial data

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Economic and Environmental Geology
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• v.46 no.4
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• pp.313-327
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• 2013

In the study, three-dimensional geostatistical simulations on McMurray Formation which is the largest oil sand reservoir in Athabasca area, Canada were performed, and the optimal site for steam assisted gravity drainage (SAGD) was selected based on the predictions. In the selection, the factors related to the vertical extendibility of steam chamber were considered as the criteria for an optimal site. For the predictions, 110 borehole data acquired from the study area were analyzed in the Markovian transition probability (TP) framework and three-dimensional distributions of the composing media were predicted stochastically through an existing TP based geostatistical model. The potential of a specific medium at a position within the prediction domain was estimated from the ensemble probability based on the multiple realizations. From the ensemble map, the cumulative thickness of the permeable media (i.e. Breccia and Sand) was analyzed and the locations with the highest potential for SAGD applications were delineated. As a supportive criterion for an optimal SAGD site, mean vertical extension of a unit permeable media was also delineated through transition rate based computations. The mean vertical extension of a permeable media show rough agreement with the cumulative thickness in their general distribution. However, the distributions show distinctive disagreement at a few locations where the cumulative thickness was higher due to highly alternating juxtaposition of the permeable and the less permeable media. This observation implies that the cumulative thickness alone may not be a sufficient criterion for an optimal SAGD site and the mean vertical extension of the permeable media needs to be jointly considered for the sound selections.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.29 no.4
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• pp.206-216
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• 2017

The temporal distribution of the turbulence kinetic energy (TKE) and the vertical component of Reynolds stresses ($-{\bar{u^{\prime}w^{\prime}}}$) was measured during one wave period under high wave energy conditions. The wave data were obtained at Hujeong Beach in the east coast of Korea at January 14~18 of 2017 when an extratropical cyclone was developed in the East Sea. Among the whole thousands of waves measured during the period, hundreds of regular waves that had with similar pattern were selected for the analysis in order to give three representing mean wave patterns using the ensemble average technique. The turbulence properties were then estimated based on the selected wave data. It is interesting to find out that $-{\bar{u^{\prime}w^{\prime}}}$ has one clear peak near the time of flow reversal while TKE has two peaks at the corresponding times of maximum cross-shore velocity magnitudes. The distinguished pattern of Reynolds stress indicates that vertical fluxes of such properties as suspended sediments may be enhanced at the time when the horizontal flow direction is reversed to disturb the flows, supporting the turbulence convection process proposed by Nielsen (1992). The characteristic patterns of turbulence properties are examined using the CADMAS-SURF Reynolds-Averaged Navier-Stokes (RANS) model. Although the model can reasonably simulate the distribution of TKE pattern, it fails to produce the $-{\bar{u^{\prime}w^{\prime}}}$ peak at the time of flow reversal, which indicates that the application of RANS model is limited in the prediction of some turbulence properties such as Reynolds stresses.

• Journal of Environmental Impact Assessment
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• v.27 no.6
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• pp.562-581
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• 2018

This study was designed to predict the changes in species richness of plants under the climate change in South Korea. The target species were selected based on the Plants Adaptable to Climate Change in the Korean Peninsula. Altogether, 89 species including 23 native plants, 30 northern plants, and 36 southern plants. We used the Species Distribution Model to predict the potential habitat of individual species under the climate change. We applied ten single-model algorithms and the pre-evaluation weighted ensemble method. And then, species richness was derived from the results of individual species. Two representative concentration pathways (RCP 4.5 and RCP 8.5) were used to simulate the species richness of plants in 2050 and 2070. The current species richness was predicted to be high in the national parks located in the Baekdudaegan mountain range in Gangwon Province and islands of the South Sea. The future species richness was predicted to be lower in the national park and the Baekdudaegan mountain range in Gangwon Province and to be higher for southern coastal regions. The average value of the current species richness showed that the national park area was higher than the whole area of South Korea. However, predicted species richness were not the difference between the national park area and the whole area of South Korea. The difference between current and future species richness of plants could be the disappearance of a large number of native and northern plants from South Korea. The additional reason could be the expansion of potential habitat of southern plants under climate change. However, if species dispersal to a suitable habitat was not achieved, the species richness will be reduced drastically. The results were different depending on whether species were dispersed or not. This study will be useful for the conservation planning, establishment of the protected area, restoration of biological species and strategies for adaptation of climate change.

• Journal of Biosystems Engineering
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• v.25 no.2
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• pp.97-106
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• 2000

To develop the grain moisture meter using microwave free space transmission technique, a 10.5GHz microwave signal with the power of 11mW generated by an oscillar with a dielectric resonator is transmitted to an isolator and radiated from a transmitting $2{\times}2$ microstrip patch array antenna into the sample holder filled with the 12 to 26%w.b. of Korean Hwawung paddy rice. the microwave signal, attenuated through the grain with moisture, is collected by a receiving $2{\times}2$ microstrip patch array antenna and detected using a Shottky diode with excellent high frequency characteristic. A pair of light and simple microstrip patch array antenna for measurement of grain moisture content is designed and implemented on atenflon substrate with trleative dielectric constant of 2.6 and thickness of 0.54 by using Ensemble ver. 4.02 software. The aperture of microstrip patch arrays is 41 mm width and 24mm high. The characteristics of microstrip patch antenna such as grain. return loss, and bandwidth are 11.35dBi, -38dB and 0.35GHz($50^{\circ}$ at far-field pattern of E and H plane. The width of the sample holder is large enough to cover the signal between the antennas temperature and bulk density respectively. The calibration model for measurement of grain moisture content is proposed to reduce the effects of fluectuations in bulk density and temperature which give serious errors for the measurements . From the results of regression analysis using the statistically analysis method, the moisture content of grain samples (MC(%)) is expressed in terms of the output voltage(v), temperature (t), and bulk density of samples(${\rho}b$)as follows ;$$MC(%)\;=\;(-3.9838{\times}10^{-8}{\times}v^{3}+8.023{\times}10^{-6}{\times}v^{2}-0.0011{\times}v-0.0004{\times}t+0.1706){\frac{1}{{\rho}b}}{\times}100$ Its determination coefficient, standard error of prediction(SEP) and bias were found to be 0.9855, 0.479%w.b. and -0.0.369 %w.b. respectively between measured and predicted moisture contents of the grain samples.

• The Journal of Bigdata
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• v.5 no.2
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• pp.53-67
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• 2020

This study employees a supervised learning prediction model to detect nonconformity in advance of processed food manufacturing and processing businesses. The study was conducted according to the standard procedure of machine learning, such as definition of objective function, data preprocessing and feature engineering and model selection and evaluation. The dependent variable was set as the number of supervised inspection detections over the past five years from 2014 to 2018, and the objective function was to maximize the probability of detecting the nonconforming companies. The data was preprocessed by reflecting not only basic attributes such as revenues, operating duration, number of employees, but also the inspections track records and extraneous climate data. After applying the feature variable extraction method, the machine learning algorithm was applied to the data by deriving the company's risk, item risk, environmental risk, and past violation history as feature variables that affect the determination of nonconformity. The f1-score of the decision tree, one of ensemble models, was much higher than those of other models. Based on the results of this study, it is expected that the official food control for food safety management will be enhanced and geared into the data-evidence based management as well as scientific administrative system.