• The Korean Journal of Applied Statistics
• /
• v.35 no.4
• /
• pp.543-552
• /
• 2022

When frost occurs, crops are directly damaged. When crops come into contact with low temperatures, tissues freeze, which hardens and destroys the cell membranes or chloroplasts, or dry cells to death. In July 2020, a sudden sub-zero weather and frost hit the Minas Gerais state of Brazil, the world's largest coffee producer, damaging about 30% of local coffee trees. As a result, coffee prices have risen significantly due to the damage, and farmers with severe damage can produce coffee only after three years for crops to recover, which is expected to cause long-term damage. In this paper, we tried to predict frost using frost generation data and weather observation data provided by the Korea Meteorological Administration to prevent severe frost. A model was constructed by reflecting weather factors such as wind speed, temperature, humidity, precipitation, and cloudiness. Using XGB(eXtreme Gradient Boosting), SVM(Support Vector Machine), Random Forest, and MLP(Multi Layer perceptron) models, various hyper parameters were applied as training data to select the best model for each model. Finally, the results were evaluated as accuracy(acc) and CSI(Critical Success Index) in test data. XGB was the best model compared to other models with 90.4% ac and 64.4% CSI, followed by SVM with 89.7% ac and 61.2% CSI. Random Forest and MLP showed similar performance with about 89% ac and about 60% CSI.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.23 no.6
• /
• pp.559-575
• /
• 2021

Due to the increase in ground excavation work, the possibility of ground subsidence accidents is increasing. And it is very difficult to prevent these risk fundamentally through institutional reinforcement such as the special law for underground safety management. As for the various cases of urban ground excavation practice, the ground subsidence behavior characteristics which is predicted using various information before excavation showed a considerable difference that could not be ignored compared to the results real construction data. Changes in site conditions such as seasonal differences in design and construction period, changes in construction methods depending on the site conditions and long-term construction suspension due to various reasons could be considered as the main causes. As the countermeasures, the safety management system through various construction information is introduced, but there is still no suitable system which can predict the effect of excavation and risk assessment. In this study, a web-based system was developed in order to predict the degree of impact on the ground subsidence and surrounding structures in advance before ground excavation and evaluate the risk in the design and construction of urban ground excavation projects. A system was built using time series analysis technique that can predict the current and future behavior characteristics such as ground water level and settlement based on past field construction records with field monitoring data. It was presented as a geotechnical data visualization (GDV) technology for risk reduction and disaster management based on web-based system, Using this newly developed web-based assessment system, it is possible to predict ground excavation impact prediction and risk assessment.

• Journal of the Korea Convergence Society
• /
• v.12 no.4
• /
• pp.229-236
• /
• 2021

There has been a lot of research on the real estate market, but a lack of research on the supply and demand of housing supply in each region, reflecting the changes in population growth and supply. It is calculated as the transition probability of the Markov chain model by reflecting the data on the number of houses per 1,000 people in the past 35 years and the forecast data for population change by region, in terms of supply (housing) to demand (population) for factors on the real estate market. According to the calculation results of the real estate market by region, the housing supply to the metropolitan area such as Gyeong-gi, Incheon, and Seoul is expected to be insufficient for a considerable period of time, considering the population changes by region. To stabilize the real estate market, it was confirmed that it was necessary to actively apply the differentiation of housing supply by region. It is meaningful in terms of verifying long term trends in the real estate market by region that reflect the prediction of population change, and it is expected that the methods used in this study will be practical through the analysis results using the historical data.

• Smart Media Journal
• /
• v.11 no.10
• /
• pp.65-75
• /
• 2022

Deep learning is used as a creative tool that could overcome the limitations of existing analysis models and generate various types of results such as text, image, and music. In this paper, we propose a method necessary to preprocess audio data using the Niko's MIDI Pack sound source file as a data set and to generate music using Bi-LSTM. Based on the generated root note, the hidden layers are composed of multi-layers to create a new note suitable for the musical composition, and an attention mechanism is applied to the output gate of the decoder to apply the weight of the factors that affect the data input from the encoder. Setting variables such as loss function and optimization method are applied as parameters for improving the LSTM model. The proposed model is a multi-channel Bi-LSTM with attention that applies notes pitch generated from separating treble clef and bass clef, length of notes, rests, length of rests, and chords to improve the efficiency and prediction of MIDI deep learning process. The results of the learning generate a sound that matches the development of music scale distinct from noise, and we are aiming to contribute to generating a harmonistic stable music.

• Korean Security Journal
• /
• no.30
• /
• pp.139-169
• /
• 2012

The purpose of this study is to contribute to establishing the scientific policing policies through deriving the time series models that can forecast the occurrence of major crimes such as murder, robbery, burglary, rape, violence and identifying the occurrence of major crimes using the models. In order to achieve this purpose, there were performed the statistical methods such as Generation of Time Series Model(C) for identifying the forecasting models of time series, Generation of Time Series Model(C) and Sequential Chart of Time Series(N) for identifying the accuracy of the forecasting models of time series on the monthly incidence of major crimes from 2002 to 2010 using IBM PASW(SPSS) 19.0. The following is the result of the study. First, murder, robbery, rape, theft and violence crime's forecasting models of time series are Simple Season, Winters Multiplicative, ARIMA(0,1,1)(0,1,1), ARIMA(1,1,0 )(0,1,1) and Simple Season. Second, it is possible to forecast the short-term's occurrence of major crimes such as murder, robbery, burglary, rape, violence using the forecasting models of time series. Based on the result of this study, we have to suggest various forecasting models of time series continuously, and have to concern the long-term forecasting models of time series which is based on the quarterly, yearly incidence of major crimes.

• Journal of Radiation Protection and Research
• /
• v.44 no.4
• /
• pp.128-148
• /
• 2019

Massive environmental monitoring has been conducted continuously since the Fukushima Daiichi Nuclear Power accident in March of 2011 by different monitoring methods that have different features together with migration studies of radiocesium in diverse environments. These results have clarified the characteristics of radiological environments and their temporal change around the Fukushima site. At three months after the accident, multiple radionuclides including radiostrontium and plutonium were detected in many locations; and it was confirmed that radiocesium was most important from the viewpoint of long-term exposure. Radiation levels around the Fukushima site have decreased greatly over time. The decreasing trend was found to change variously according to local conditions. The air dose rates in environments related to human living have decreased faster than expected from radioactive decay by a factor of 2-3 on average; those in pure forest have decreased more closely to physical decay. The main causes of air dose rate reduction were judged to be radioactive decay, movement of radiocesium in vertical and horizontal directions, and decontamination. Land-use categories and human activities have significantly affected the reduction tendency. Difference in the air dose rate reduction trends can be explained qualitatively according to the knowledge obtained in radiocesium migration studies; whereas, the quantitative explanation for individual sites is an important future challenge. The ecological half-lives of air dose rates have been evaluated by several researchers, and a short-term half-life within 1 year was commonly observed in the studies. An empirical model for predicting air dose rate distribution was developed based on statistical analysis of an extensive car-borne survey dataset, which enabled the prediction with confidence intervals. Different types of contamination maps were integrated to better quantify the spatial data. The obtained data were used for extended studies such as for identifying the main reactor that caused the contamination of arbitrary regions and developing standard procedures for environmental measurement and sampling. Annual external exposure doses for residents who intended to return to their homes were estimated as within a few millisieverts. Different forms of environmental data and knowledge have been provided for wide spectrum of people. Diverse aspects of lessons learned from the Fukushima accident, including practical ones, must be passed on to future generations.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.1 no.1
• /
• pp.1-11
• /
• 1999

Rice yield and primary productivity (NPP) are dependent upon the variability of climate and soil. The variability and regionality of the rice yield and net primary productivity were evaluated with the meteorological data collected from Korea Meteorology Administration and the actual rice yield data from the Ministration of Agriculture and Forestry, Korea. The estimated NPP using the three models, dependent upon temperature(NPP-T), precipitation(NPP-P) and net radiation(NPP-R), ranged from 10.87 to 17.52 Mg ha$^{-1}$ with average of 14.69 Mg ha$^{-1}$ in the South Korea and was ranged 6.47 to 15.58 Mg ha$^{-1}$ with average of 12.59 Mg ha$^{-1}$ in the North Korea. The primary limiting factor of NPP in Korea was net radiation, and the secondary limiting factor was temperature. Spectral analysis on the long term change in air temperature in July and August showed periodicity. The short periodicity was 3 to 7 years and the long periodicity was 15 to 43 years. The coefficient of variances, CV, of the rice yield from 1989 to 1998 ranged 3.23 percents to 12.37 percents which were lower than past decades. The CV's in Kangwon and Kyeongbuk were high while that in Chonbuk was the lowest. The prediction model based on th e yield index and yield response to temperature obtain ed from the field crop situation showed reasonable results and thus the spatial distributions of rice yield and predicted yield could be expressed in the maps. The predicted yields was well fitted with the actual yield except Kyungbuk. For better prediction, modification should be made considering radiation factor in further development.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.15 no.2
• /
• pp.91-98
• /
• 2009

The long-term water-quality change of Asan Bay by the influx of polluted disposal water was predicted through a simulation with an Eco-hydrodynamic model. Eco-hydrodynamic model is composed of a multi-level hydrodynamic model to simulate the water flow and an ecosystem model to simulate water quality. The water quality simulation revealed that the COD(Chemical Oxygen Demand), dissolved inorganic nitrogen(DIN) and dissolved inorganic phosphorus(DIP) are increased at 5 stations for the subsequent 6 months after the influx of the effluent. COD, DIN and DIP showed gradual decreases in concentration during the period of one to two years after the increase of last 6 months and reached steady state for next three to ten years. Concentration levels of COD, DIN, and DIP showed the increase by the ranges of $11{\sim}67%$, $10{\sim}67%$, and $0.5{\sim}7%$, respectively, which represents that the COD and DIN are the most prevalent pollutants among substances in the effluent through the sewage treatment plant. The current water quality of Asan Bay based on the observed COD, TN and TP concentrations ranks into the class II of the Korean standards for marine water quality but the water quality would deteriorate into class III in case that the disposal water by the sewage plant is discharged into the Bay.

• Journal of Intelligence and Information Systems
• /
• v.26 no.4
• /
• pp.127-148
• /
• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.63-83
• /
• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.