• Journal of The Korean Astronomical Society
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• v.52 no.4
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• pp.133-144
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• 2019

We develop forecast models of daily probabilities of major flares (M- and X-class) based on empirical relationships between photospheric magnetic parameters and daily flaring rates from May 2010 to April 2018. In this study, we consider ten magnetic parameters characterizing size, distribution, and non-potentiality of vector magnetic fields from Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) and Geostationary Operational Environmental Satellites (GOES) X-ray flare data. The magnetic parameters are classified into three types: the total unsigned parameters, the total signed parameters, and the mean parameters. We divide the data into two sets chronologically: 70% for training and 30% for testing. The empirical relationships between the parameters and flaring rates are used to predict flare occurrence probabilities for a given magnetic parameter value. Major results of this study are as follows. First, major flare occurrence rates are well correlated with ten parameters having correlation coefficients above 0.85. Second, logarithmic values of flaring rates are well approximated by linear equations. Third, using total unsigned and signed parameters achieved better performance for predicting flares than the mean parameters in terms of verification measures of probabilistic and converted binary forecasts. We conclude that the total quantity of non-potentiality of magnetic fields is crucial for flare forecasting among the magnetic parameters considered in this study. When this model is applied for operational use, it can be used using the data of 21:00 TAI with a slight underestimation of 2-6.3%.

• International Journal of Contents
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• v.15 no.4
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• pp.65-73
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• 2019

Social media is a massive dataset in which individuals' thoughts are freely recorded. So there have been a variety of efforts to analyze it and to understand the social phenomenon. In this study, Twitter was used to define the moments when negative perceptions of the Korean Meteorological Administration (KMA) were displayed and the reasons people were dissatisfied with the KMA. Machine learning methods were used for sentiment analysis to automatically train the implied awareness on Twitter which mentioned the KMA July-October 2011-2014. The trained models were used to validate sentiments on Twitter 2015-2016, and the frequency of negative sentiments was compared with the satisfaction of forecast users. It was found that the frequency of the negative sentiments increased before satisfaction decreased sharply. And the tweet keywords and the news headlines were qualitatively compared to analyze the cause of negative sentiments. As a result, it was revealed that the individual caused the increase in the monthly negative sentiments increase in 2016. This study represents the value of sentiment analysis that can complement user satisfaction surveys. Also, combining Twitter and news headlines provided the idea of analyzing the causes of dissatisfaction that are difficult to identify with only satisfaction surveys. The results contribute to improving user satisfaction with weather services by efficiently managing changes in satisfaction.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.1
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• pp.156-161
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• 1992

In this research a monitoring system for turning using NC information and the current of feed motor as a monitoring signal was developed. The overall system consists of modules such as learning process, NC data transmission, generation of forecast information, signal acquisition, monitoring and post process. In the learning process, the reference data and the cutting force equation necessary for monitoring are obtained from the accumulated monitoring results. In the generation of forecast information, the information of forecasted cutting forces is acquired from the cutting force equation and NC program and appended to each NC block as a monitor code. Reliability of monitoring is improved by using the monitor code in the real-time monitoring. Monitoring module is divided into two parts : the off-line monitoring where errors of NC program are checked and the on-line monitoring where the level of motor current is monitored during cutting operations. If the actual current level exceeds the limit value provided by the monitor code in the level monitoring, it is recognized as abnormal. In the event of abnormal status, the post processor sends the emergency stop signal to NC controller to stop the operation. Actual experiments have shown that the developed monitoring system works well.

• Journal of Environmental Science International
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• v.32 no.8
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• pp.595-611
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• 2023

The purpose of this study is to improve the daily prediction results of PM2.5 from the air quality diagnosis and evaluation system operated by the Busan Institute of Health and Environment in real time. The air quality diagnosis and evaluation system is based on the photochemical numerical model, CMAQ (Community multiscale air quality modeling system), and includes a 3-day forecast at the end of the model's calculation. The photochemical numerical model basically has limitations because of the uncertainty of input data and simplification of physical and chemical processes. To overcome these limitations, this study applied DNN (Deep Neural Network), a deep learning technique, to the results of the numerical model. As a result of applying DNN, the r of the model was significantly improved. The r value for GFS (Global forecast system) and UM (Unified model) increased from 0.77 to 0.87 and 0.70 to 0.83, respectively. The RMSE (Root mean square error), which indicates the model's error rate, was also significantly improved (GFS: 5.01 to 6.52 ug/m³ , UM: 5.76 to 7.44 ug/m³ ). The prediction results for each concentration grade performed in the field also improved significantly (GFS: 74.4 to 80.1%, UM: 70.0 to 77.9%). In particular, it was confirmed that the improvement effect at the high concentration grade was excellent.

• Journal of the Korea Institute of Building Construction
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• v.18 no.6
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• pp.621-631
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• 2018

The repair and maintenance cost of domestic public rental housing is an issue of considerable interest and growing financial concern. This paper suggests a quantity-based model as an alternative method for predicting costs, instead of the conventional model which is based on actual cost data. Furthermore, this paper provides a forecast of the repair costs incurred each year during the multi family house's maintenance phase (40 years). The recently changed the long-term repair plan and quality-improved interior materials were considered into the research. In order to estimate the cost of maintenance work, 5 sample apartments were selected and analyzed. The repair and maintenance cost from the case studies was converted to cost per household and per floor area for general use. On the other hand, the net present value method was applied to reflect the effect of time. We expect that the results will help to establish expenditure plans that are more effective for public rental housing in the maintenance stage.

• 한국공간정보시스템학회:학술대회논문집
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• 2005.05a
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• pp.351-354
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• 2005

NASA's remote sensing market analysis and forecast projects 3.3 billion dollars in 2005. On the other hand, a market research firm expected that world commercial remote sensing imagery, GIS software, Data, and value-added services will generate 8.3 billion dollar in 2007. It has been widely believed that geo-spatial information and service market is growing rapidly and has a huge potential, but it is not clearly understood where it is now and will be heading in the future. Also, it could be a significant question to answer where the industry in Korea fits the best in the world business trends and where it should be strategically heading to take a large market share. Furthermore, if it could be worth being considered as a Korean strategic industry for the future. Instead of pursuing direct answers to the questions, the author will start reviewing general business practices, major business transactions such as merging and acquisitions (M&A's) and initial public offerings (IPO's), and research on market capitalization and revenue of major companies. Throughout the study, a list of common grounds in the market was uncovered and realized as follows: (1) value-added data matters in geo-spatial information and service market; (2) private sector grows faster; (3) characteristics of multi-national industry; and (4) Dependency on major industry. Based on the findings, the author presents a list of recommendations as conclusions.

• Journal of Advanced Navigation Technology
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• v.22 no.6
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• pp.491-499
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• 2018

Runway visual range (RVR), one of the important indicators of aircraft takeoff and landing, is affected by meteorological conditions such as temperature, humidity, etc. It is important to estimate the RVR at the time of arrival in advance. This study estimated the RVR of the local airport after 1 hour by upgrading the RVR estimation model using the proposed deep learning network. To this end, the advancement of the estimation model was carried out by changing the time interval of the meteorological data (temperature, humidity, wind speed, RVR) as input value and the linear conversion of the results. The proposed method generates estimation model based on the past measured meteorological data and estimates the RVR after 1 hour and confirms its validity by comparing with measured RVR after 1 hour. The proposed estimation model could be used for the RVR after 1 hour as reference in small airports in regions which do not forecast the RVR.

• Journal of the Korean earth science society
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• v.23 no.7
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• pp.587-596
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• 2002

A super ensemble model was developed for the seasonal prediction of regional precipitation in Korea using the lag correlated large scale predictors, based on the empirical orthogonal function (EOF) analysis and multiple linear regression model. The predictability of this model was also evaluated by cross-validation. Correlation between the predicted and the observed value obtained from the super ensemble model showed 0.73 in spring, 0.61 in summer, 0.69 in autumn and 0.75 in winter. The predictability of categorical forecasting was also evaluated based on the three classes such as above normal, near normal and below normal that are clearly defined in terms of a priori specified by threshold values. Categorical forecasting by the super ensemble model has a hit rate with a range from 0.42 to 0.74 in seasonal precipitation.

• Journal of the Korea Computer Graphics Society
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• v.23 no.4
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• pp.21-29
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• 2017

Previous visual analytics researches has focused on reducing the uncertainty of predicted results using a variety of interactive visual data exploration techniques. The main purpose of the interactive search technique is to reduce the quality difference of the predicted results according to the level of the decision maker by understanding the relationship between the variables and choosing the appropriate model to predict the unknown variables. However, it is difficult to create a predictive model which forecast time series data whose overall trends is unknown such as youth physical growth data. In this paper, we pro pose a novel predictive analysis technique to forecast the physical growth value in small pieces of time series data with un certain trends. This model estimates the distribution of data at a particular point in time. We also propose a visual analytics system that minimizes the possible uncertainties in predictive modeling process.

• The Korean Journal of Applied Statistics
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• v.35 no.5
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• pp.603-617
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• 2022

Following the September 11, 2001 terrorist attacks, the United States declared war on terror and invaded Afghanistan and Iraq, winning quickly. However, interest in analyzing terrorist activities has developed as a result of a significant amount of time being spent on the post-war stabilization effort, which failed to minimize the number of terrorist activities that occurred later. Based on terrorist data from 2003 to 2010, this study utilized a Bayesian hierarchical model to forecast the terrorist threat in 2011. The model depicts spatiotemporal dependence with predictors such as population and religion by autonomous district. The military commander in charge of the region can utilize the forecast value based on the our model to prevent terrorism by deploying forces efficiently.