• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.3
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• pp.215-222
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• 2022

The purpose of air traffic flow management is to balance demand and capacity in the national airspace, and its performance relies on an accurate capacity prediction of the airport or airspace. This paper developed a regression model that predicts the number of aircraft actually departing and arriving in a terminal maneuvering area. The regression model is based on a boosting ensemble learning algorithm that learns past aircraft operational data such as time, weather, scheduled demand, and unfulfilled demand at a specific airport in the terminal maneuvering area. The developed model was tested using historical departure and arrival flight data at Incheon International Airport, and the coefficient of determination is greater than 0.95. Also, the capacity of the terminal maneuvering area of interest is implicitly predicted by using the model.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Journal of the Korea Convergence Society
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• v.10 no.5
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• pp.125-131
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• 2019

This study tries to grasp the factor that affects the acceptance of beacon as an O2O marketing tool. This study examined whether there is a difference between beacon accepter as a means of marketing communication and non-accepter in terms of related variables. As a result, there were significant differences between beacon perceiver and non-perceiver in 'smartphone usage' and 'brand consciousness'. In order to understand the predictive variables influencing beacon acceptance as a means of marketing communication, this study used 'perceiving beacon as a marketing communication media' as a dependent variable to perform logistic regression analysis. As a result of the analysis, it was found that 'smartphone usage' and 'brand consciousness' were the predictive variables affecting beacon perceiving. This study tried to analysis the results in the viewpoint of perceived usefulness and ease-of-use which were insisted by TAM.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.223-223
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• 2018

취수원에서 정수장과 배수지를 거쳐 수용가에 이르기까지 공급되는 급수량을 결정하는데 있어 각 수용가별 물 사용 패턴은 수요량을 예측하여 취수량을 결정하는데 있어 매우 중요한 지표이다. 생활용수 추정은 용도별(가정용, 상업용, 공업용 등)로 분류하여 경향성이 나타날 수 있도록 과거 사용실적을 바탕으로 장래 용도별 사용량을 추정한다. 이는 경험을 바탕으로 한 것으로 일반적으로 시계열 모형을 이용하는데 수요예측의 실패 가능성이 높으며 효율적인 방법이라 할 수 없다. 이에 본 연구에서는 최근 통신기술의 발달로 양방향 통신이 가능한 AMI(Advanced Metering Infrastructure, 원격검침인프라)센서를 영종도 112블록의 528개의 수용가에 설치하였다. AMI는 스마트 미터에서 측정한 데이터를 원격 검침기를 통해 물 사용량을 자동으로 계측할 수 있다. AMI 데이터를 이용하여 영종도 112블록의 운북동과 운서동의 각 용도별, 요일별, 그리고 도심지와 농가의 실시간 물 사용 패턴을 분석하였다. 분석 결과 운북동과 운서동의 물 사용 패턴은 비슷한 경향을 보이는 것으로 보이나 도시화된 운서동에 비해 운북동의 물사용량이 상대적으로 적고 첨두사용량의 발생시간 또한 빠른 것으로 나타났다. 또한 가정용과 공공용의 경우 시간별 물 사용량이 요일에 따라 일정한 경향이 있으나 상업용과 공업용은 일정한 사용량을 보였다. 향후 112블록의 관망해석에 실시간 물사용 패턴을 적용하여 효율적으로 급수량 결정을 할 수 있을 것으로 사료된다.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.44-50
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• 2021

Recently, enterprise storage systems that require large-capacity storage devices to accommodate big data have used large-capacity flash memory-based storage devices with high density compared to cost and size. This paper proposes a high-efficiency life prediction method with slope descent to maximize the life of flash memory media that directly affects the reliability and usability of large enterprise storage devices. To this end, this paper proposes the structure of a matrix for storing metadata for learning the frequency of defects and proposes a cost model using metadata. It also proposes a life expectancy prediction policy in exceptional situations when defects outside the learned range occur. Lastly, it was verified through simulation that a method proposed by this paper can maximize its life compared to a life prediction method based on the fixed number of times and the life prediction method based on the remaining ratio of spare blocks, which has been used to predict the life of flash memory.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.267-278
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• 2020

ESS is an essential requirement for resolving power shortages and power demand management and promoting renewable energy at a time when the energy paradigm changes. In this paper, we propose a cost-effective ESS Peak-Shaving operation plan through load and solar power generation forecast. For the ESS operation plan, electric load and solar power generation were predicted through RMS, which is a statistical measure, and a target load reduction guideline for one hour was set through the predicted electric load and solar power generation amount. The load and solar power generation amount from May 6th to 10th, 2019 was predicted by simulation of load and photovoltaic power generation using real data of the target customer for one year, and an hourly guideline was set. The average error rate for predicting load was 7.12%, and the average error rate for predicting solar power generation amount was 10.57%. Through the ESS operation plan, it was confirmed that the hourly guide-line suggested in this paper contributed to the peak-shaving maximization of customers.Through the results of this paper, it is expected that future energy problems can be reduced by minimizing environmental problems caused by fossil energy in connection with solar power and utilizing new and renewable energy to the maximum.

• KSBB Journal
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• v.8 no.1
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• pp.83-88
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• 1993

Lipase was separated using reverse mlcelles in a spray column. The 50 mM AOT-Isooctane solution was used as reverse micellar solution for the extraction of lipase (crude containing 25% Protein). Ionic strength was controlled by KCl(0.1M KCl for extraction, 0.5M KCl for back exlractlon). Acetate buffer and phosphate buffer were used for control of pH. The efficiencies of extraction and stripping were 30% and 50%. An increase of circulation did not change the efficiency of extraction in forward extraction. The optimum flow rate was around 0.10ml/sec.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.1295-1298
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• 2004

국내 정보화 서비스 수준의 향상과 멀티미디어 유형의 자료의 증가로 초고속 광 네트워크 기술에 대한 관심이 높아지고 있다. 한편, 국방정보통신망은 ATM 기술을 기반으로 한 백본 망을 구성하여 운영 중이나 최근에는 매년 평균 1.5배 규모로 통화량이 증가하고 있는 추세이다. 이에 따라 수년 내에 현 백본 망에서 수용할 수 있는 통화량의 한계점에 도달할 것으로 예측되어 백본구조의 개선이 필요한 시점이다. 따라서 본 논문에서는 초고속 광 네트워크 기술을 이용하여 국방정보통신망의 백본구조를 개선하는 방안을 제시하고자 한다. 이를 위하여 최신 광 전송 및 네트워크 기술을 살펴보고, 실행 가능한 2가지 대안을 제시한다. 이어서 이들 대안들에 대한 장점, 제한사항 그리고 정량적인 비용분석을 통하여 최적안을 제안한다. 그 결과 국방정보통신망의 5년간 증설 및 운용유지 비용에서 28% 정도를 절감하면서 통화량 수용능력을 최소 1.7배 이상 확대하여 차후 망의 증설에 대비한 유연성을 확보하는 효과가 기대된다.

• Journal of Advanced Navigation Technology
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• v.27 no.5
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• pp.666-673
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• 2023

The runway occupancy time of landing aircraft is an important factor in determining runway capacity. The purpose of this study is to suggest improvement measures for runway occupancy time to improve the operation of existing airports. In order to derive improvement measures, a comparative analysis was conducted on the effectiveness of improvement using aircraft operation status data for specific days at the case airport. The FAA REDIM model was used to analyze the improvement plan, and the improvement application function of the model was used to confirm the effect of improving runway capacity by adding a rapid escape taxiway to an airport without a rapid escape taxiway. This study's approach can be applied to the derivation of runway improvement measures and preliminary prediction of effectiveness, and it presents cases that can be applied to future airport construction projects or airport improvement projects.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.3
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• pp.521-528
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• 2016

The tollbooths of a main motorway toll plaza are usually operated proactively responding to the variations of traffic demands of two-type vehicles, i.e. cars and the other (heavy) vehicles, respectively. In this vein, it is one of key elements to forecast accurate traffic volumes for the two vehicle types in advanced tollgate operation. Unfortunately, it is not easy for existing univariate short-term prediction techniques to simultaneously generate the two-vehicle-type traffic demands in literature. These practical and academic backgrounds make it one of attractive research topics in Intelligent Transportation System (ITS) forecasting area to forecast the future traffic volumes of the two-type vehicles at an acceptable level of accuracy. In order to address the shortcomings of univariate short-term prediction techniques, a Multiple In-and-Out (MIO) forecasting model to simultaneously generate the two-type traffic volumes is introduced in this article. The MIO model based on a non-parametric approach is devised under the on-line access conditions of large-scale historical data. In a feasible test with actual data, the proposed model outperformed Kalman filtering, one of a widely-used univariate models, in terms of prediction accuracy in spite of multivariate prediction scheme.