• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.152-159
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• 2023

This study endeavors to enrich investment prospects in cryptocurrency by establishing a rationale for investment decisions. The primary objective involves evaluating the predictability of four prominent cryptocurrencies - Bitcoin, Ethereum, Litecoin, and EOS - and scrutinizing the efficacy of trading strategies developed based on the prediction model. To identify the most effective prediction model for each cryptocurrency annually, we employed three methodologies - AutoRegressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Prophet - representing traditional statistics and artificial intelligence. These methods were applied across diverse periods and time intervals. The result suggested that Prophet trained on the previous 28 days' price history at 15-minute intervals generally yielded the highest performance. The results were validated through a random selection of 100 days (20 target dates per year) spanning from January 1st, 2018, to December 31st, 2022. The trading strategies were formulated based on the optimal-performing prediction model, grounded in the simple principle of assigning greater weight to more predictable assets. When the forecasting model indicates an upward trend, it is recommended to acquire the cryptocurrency with the investment amount determined by its performance. Experimental results consistently demonstrated that the proposed trading strategy yields higher returns compared to an equal portfolio employing a buy-and-hold strategy. The cryptocurrency trading model introduced in this paper carries two significant implications. Firstly, it facilitates the evolution of cryptocurrencies from speculative assets to investment instruments. Secondly, it plays a crucial role in advancing deep learning-based investment strategies by providing sound evidence for portfolio allocation. This addresses the black box issue, a notable weakness in deep learning, offering increased transparency to the model.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.936-949
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• 2023

On August 3, 2023, a brutal incident of unprovoked violence, termed as "Abnormal motivated crime," occurred in a multi-use facility, where retail and transportation facilities converge, near Seohyeon Station. The assailant drove onto the sidewalk, hitting pedestrians, and then entered a department store where a knife rampage ensued, resulting in a total of 14 victims. In the aftermath of this incident, numerous murder threats were posted on social media, causing widespread anxiety among the public. This fear was further exacerbated by the emergence of a "Terrorless.01ab.net" service. Purpose: This research aims to explore necessary institutional improvements for private security personnel who protect customers and employees in multi-use facilities, to enable them to perform their duties more effectively. Method: To assess the risk of Abnormal motivated crime, a time series analysis using the ARIMA model was conducted to analyze the domestic trends of such crimes. Additionally, Result: the study presents suggestions for improvements in the domestic security service law and emergency manuals for multi-use facilities. Conclusion: This is informed by a legal analysis of the indemnity rights for weapon use by private security guards abroad and their operational authority beyond weapon usage.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.391-393
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• 2022

In recent years, technologies forecasting demand based on deep learning and big data have accelerated the smartification of the field of e-commerce, logistics and distribution areas. In particular, ports, which are the center of global transportation networks and modern intelligent logistics, are rapidly responding to changes in the global economy and port environment caused by the 4th industrial revolution. Port traffic forecasting will have an important impact in various fields such as new port construction, port expansion, and terminal operation. Therefore, the purpose of this study is to compare the time series analysis and deep learning analysis, which are often used for port traffic prediction, and to derive a prediction model suitable for the future container prediction of Busan Port. In addition, external variables related to trade volume changes were selected as correlations and applied to the multivariate deep learning prediction model. As a result, it was found that the LSTM error was low in the single-variable prediction model using only Busan Port container freight volume, and the LSTM error was also low in the multivariate prediction model using external variables.

• Journal of Korean Society of Transportation
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• v.5 no.2
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• pp.55-72
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• 1987

The work analyzes the existing situation and defines special problems concerning traffic accidents in the two countries. The report is divided into three parts: 1) Using the global approach of SMEED, the data were evaluated using multiple regression analysis, and homogeneous groups of countries were defined by cluster analysis. In the global approach, the linear model is better than SMEED's non-linear model in explaining the number of fatalities. Among the different groups of countries, the linear approach was found to be better suited for industrialized countries and the non-linear approach better for the developing countries. T도 comparison of traffic fatality data for the Federal Republic the developing countries. The comparison of traffic fatality data for the Federal Republic of Germany and the Republic of Korea showed different regression equations during the same time period. 2) The BOX/JENKINS time series analysis on a monthly basis points out clearly similar seasonal patterns for the two countries over the years studied. The decrease in traffic accidents following the intensification of the safety belt requirement was proved in the ARIMA model. It amounts to 7 to 8 percent fewer personal injury accidents and fatal accidents. The identified increase in safety in the Federal Republic of Germany since the 1970s is mainly due to the reduction of accident severity in residential areas. 3) Speeds and headways on motorways in th3e two countries were also compared. The measurements point out that German road users drive faster, take more risks, and accept shorter time gaps than Korean road users. However, the accident statistics show accident rates for Korea that are several times higher than those in the Federal Republic of Germany.

• Korean Journal of Construction Engineering and Management
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• v.14 no.4
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• pp.24-34
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• 2013

Since 2004, the government decided to gradually introduce Actual Cost Data into cost estimate for improving problems of below-cost tendering and to reflect fair market price through competition and carry contract efficiently. However, there are many concerns that Actual Cost Data has not reflected real market price, even that has contributed to reduce the government's budget. General construction firm's burden for labor cost is imputed to specialty contractors and eventually it becomes construction worker's burden. Therefore, realization of Actual Cost Data is very important factor to settle this system. To understand realization level and make short term forecast, this paper drew construction group of which labor cost constitutes more than 95% of direct cost, and compares their Actual Cost Data with relevant skilled workers's unit wage and predicts using time series analysis. The bid price which is not be reflected market price accelerates work environment changes and leads to directly affect such as late disbursement of wages, bankruptcy to workers. Therefore this paper is expected to be used to the preliminary data for solving the problem and establishing improvement of Actual Cost Data.

• Water for future
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• v.11 no.2
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• pp.54-61
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• 1978

An attemption of synthesizing and forecasting of monthly river flow has been made by employing a linear stochastic difference equation model. As one of the linear stochestic difference equation model, an ARIMA Type is tested to find the suitability of the model to the monthly river flows. On the assumption of the stationary covariacne of differenced monthly river flows the model is identrfield and is evaluated so that the residuale have the minimum variance. Finally a test is performed to finld the residerals beings White noise. Monthly river flows at six stations in Han River Basin are applied for case studies. It was found that the difference operator is a good measure of forecasting the monthly river flow.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.383-388
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• 2024

This paper is a study to derive the predicted value of power generation based on the photovoltaic power generation data measured in Jeollanam-do, South Korea. Multivariate variables such as direct current, alternating current, and environmental data were measured in the inverter to measure the amount of power generation, and pre-processing was performed to ensure the stability and reliability of the measured values. Correlation analysis used only data with high correlation with power generation in time series data for prediction using partial autocorrelation function (PACF). Deep learning models were used to measure the amount of power generation to predict the amount of photovoltaic power generation, and the results of correlation analysis of each multivariate variable were used to increase the prediction accuracy. Learning using refined data was more stable than when existing data were used as it was, and the solar power generation prediction algorithm was improved by using only highly correlated variables among multivariate variables by reflecting the correlation analysis results.

• International Journal of Highway Engineering
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• v.9 no.3
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• pp.1-12
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• 2007

Roads in Korea can be classified into four types according to their responsible authorities. For example, Motorway is constructed, managed, and operated by the Korea Highway Corporation. Ministry of Construction and Transportation is in charge of National Highway, and Province Roads are run by each province government. Urban/county Roads are run by corresponding local government. This study analyses the trends of road accidents for each road type. For this purpose, the numbers of accidents, fatalities, and injuries are compared for each road type for last 15 years. The result shows that Urban/County Roads are the most dangerous, while Motorways are the safest, when we simply compare the numbers of accidents, fatalities, and injuries. However, when we compare these numbers by dividing by total road length, National Highway becomes the most dangerous while Province Roads becomes the safest. In the case of road accidents, fatalities, and injuries per vehicle km, which is known as the most objective comparison measure, it turns out that National Highway is the most dangerous roads again. This study also developed time series models to estimate trends of fatalities for each road type. These models will be useful when we set up or evaluate targets of national road safety.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.10
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• pp.391-398
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• 2021

Since the leased line is a structure that exclusively uses two connected areas for data transmission, a stable quality level and security are ensured, and despite the rapid increase in the number of switched lines, it is a line method that is continuously used a lot in companies. However, because the cost is relatively high, one of the important roles of the network operator in the enterprise is to maintain the optimal state by properly arranging and utilizing the resources of the network leased line. In other words, in order to properly support business service requirements, it is essential to properly manage bandwidth resources of leased lines from the viewpoint of data transmission, and properly predicting and managing leased line usage becomes a key factor. Therefore, in this study, various prediction models were applied and performance was evaluated based on the actual usage rate data of leased lines used in corporate networks. In general, the performance of each prediction was measured and compared by applying the smoothing model and ARIMA model, which are widely used as statistical methods, and the representative models of deep learning based on artificial neural networks, which are being studied a lot these days. In addition, based on the experimental results, we proposed the items to be considered in order for each model to achieve good performance for prediction from the viewpoint of effective operation of leased line resources.

• The Journal of Bigdata
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• v.5 no.2
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• pp.111-120
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• 2020

Despite the recent economic contraction caused by the Corona 19 incident, interest in the residential environment is growing as more people live at home due to the increase in telecommuting, thereby increasing demand for remodeling. In addition, the government's real estate policy is also expected to have a visible impact on the sales of the interior and furniture industries as it shifts from regulatory policy to the expansion of housing supply. Accurate demand forecasting is a problem directly related to inventory management, and a good demand forecast can reduce logistics and inventory costs due to overproduction by eliminating the need to have unnecessary inventory. However, it is a difficult problem to predict accurate demand because external factors such as constantly changing economic trends, market trends, and social issues must be taken into account. In this study, LSTM model and 1D-CNN model were compared and analyzed by artificial intelligence-based time series analysis method to produce reliable results for manufacturers producing furniture components.