• Journal of Korean Society for Quality Management
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• v.36 no.4
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• pp.93-101
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• 2008

The rare metals, used for semiconductors, PDP-LCS and other specialized metal areas necessarily, has been playing a key role for the Korean economic development. Rare metals are influenced by exogenous variables, such as production quantity, price and supplied areas. Nowadays the supply base of rare metals is threatened by the sudden increase in price. For the stable supply of rare metals, a rational demand outlook is needed. In this study, focusing on the domestic demand for chromium, the uncertainty and probability materializing from demand and price is analyzed, further, a demand forecast model, which takes into account various exogenous variables, is suggested, differing from the previously static model. Also, through the OOS(out-of-sampling) method, comparing to the preexistence ARIMA model, ARMAX model, multiple regression analysis model and ECM(Error Correction Mode) model, we will verify the superiority of suggested model in this study.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1512-1516
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• 2007

자신의 현재와 과거의 시계열데이터만을 가지고 시계열 모형을 구축하는 단변량 ARIMA모형 분석법과는 달리, 관심의 대상이 되는 출력시계열과 이와 관련있는 입력시계열의 동태적 특성을 나타내는 전이함수모형(Transfer function model)을 사용하여 소양강댐, 충주댐, 화천댐에 대한 월별 수문자료를 이용하여 유입량을 예측해 보고자 한다. 본 연구의 주요 목적은 다변량 추계학적 시스템의 해석을 위한 모형의 추정과 등정을 위한 과정을 개발하는데 있다. 일반적 추계학적 시스템 모형이 표현되며 그것으로부터 수문학적 시스템의 모형을 매우 적절하게 유도하기 위한 다중 입력-단일 출력 TF, TFN모형을 유도하는데 있다. 이 모형은 수문학적 시스템을 위한 경우에 있어 상관된 입력을 설명할 수 있도록 개발된다. 일반적으로 모형을 만드는 전략이 유도되며 실제유역시스템에 적용하여 검토된다. 한강수계 주요 다목적댐인 소양강댐, 충주댐, 화천댐의 수문자료를 가지고 추계학적 모형(TF, TFN)에 의한 결과와 실제유입량을 비교하여 검토하고자 한다.

• Proceedings of the Safety Management and Science Conference
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• 2011.04a
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• pp.177-183
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• 2011

Korea has performed strategies for the third industrial accident prevention in order to minimize industrial accident. However, the occupational fatality rate and industrial accident rate appears to be stagnated for 11 years. Therefore, this paper forecasts the occupational fatality rate and industrial accident rate for 10 years. Also, this paper applies regression method (RA), exponential smoothing method (ESM), double exponential smoothing method (DESM), autoregressive integrated moving average (ARIMA) model and proposed analytical function method (PAFM) for trend of industrial accident. Finally, this paper suggests fundamental strategies for industrial accident prevention by forecasting of industrial accident rate in the long term.

• Proceedings of the Safety Management and Science Conference
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• 2008.04a
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• pp.461-468
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• 2008

<그림 1>과 <그림 2>, <그림 3>은 통계 패키지(Econometric Views)를 사용하여 제조업용 로봇의 수출입을 2007년부터 2008년까지 추정한 값이며, 자료는 2001년도 이후의 관세청 수출입 실적자료를 활용하였다. <그림 1>은 SAENGF는 제조업용 로봇 국내생산의 추정치이며, 점선은 95% 신뢰구간을 의미한다. <그림 2>는 통계 패키지(Econometric Views)를 사용하여 제조업용 로봇의 수출을 2007년부터 2008년까지 추정한 값이며, EXPORTF는 제조업용 로봇 수출의 추정치이며, 점선은 95% 신뢰구간을 의미한다. <그림 3>은 통계 패키지(Econometric Views)를 사용하여 제조업용 로봇의 수입을 2007년부터 2008년까지 추정한 값이며, IMPORTF는 제조업용 로봇 수출의 추정치이며, 점선은 95% 신뢰구간을 의미한다. <표 1>은 국내 제조업용 로봇의 국내생산, 수출과 수입의 추정치이며, ARIMA모형을 사용하였으며, 자료는 2001년도 이후의 데이터로 관세청 수출입 실적자료를 활용하였다.

• Journal of Korean Society of Water and Wastewater
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• v.10 no.2
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• pp.88-103
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• 1996

The objective of this study is to develop stochastic and deterministic models which could be used to synthesize water application time series. Adaptive models using mulitivariate ARIMA(Transfer Function Model) are developed for daily urban water use forecasting. The model considers several variables on which water demands is dependent. The dynamic response of water demands to several factors(e.g. weekday, average temperature, minimum temperature, maximum temperature, humidity, cloudiness, rainfall) are characterized in the model by transfer functions. Daily water use data of Kumi city in 1992 are employed for model parameter estimation. Meteorological data of Seonsan station are utilized to input variables because Kumi has no records about the meteorological factor data.To determine the main factors influencing water use, autocorrelogram and cross correlogram analysis are performed. Through the identification, parameter estimation, and diagnostic checking of tentative model, final transfer function models by each month are established. The simulation output by transfer function models are compared to a historical data and shows the good agreement.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.329-352
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• 2022

It is reported that particulate matter(PM) penetrates the lungs and blood vessels and causes various heart diseases and respiratory diseases such as lung cancer. The subway is a means of transportation used by an average of 10 million people a day, and although it is important to create a clean and comfortable environment, the level of particulate matter pollution is shown to be high. It is because the subways run through an underground tunnel and the particulate matter trapped in the tunnel moves to the underground station due to the train wind. The Ministry of Environment and the Seoul Metropolitan Government are making various efforts to reduce PM concentration by establishing measures to improve air quality at underground stations. The smart air quality management system is a system that manages air quality in advance by collecting air quality data, analyzing and predicting the PM concentration. The prediction model of the PM concentration is an important component of this system. Various studies on time series data prediction are being conducted, but in relation to the PM prediction in subway stations, it is limited to statistical or recurrent neural network-based deep learning model researches. Therefore, in this study, we propose four transformer-based models including spatiotemporal transformers. As a result of performing PM concentration prediction experiments in the waiting rooms of subway stations in Seoul, it was confirmed that the performance of the transformer-based models was superior to that of the existing ARIMA, LSTM, and Seq2Seq models. Among the transformer-based models, the performance of the spatiotemporal transformers was the best. The smart air quality management system operated through data-based prediction becomes more effective and energy efficient as the accuracy of PM prediction improves. The results of this study are expected to contribute to the efficient operation of the smart air quality management system.

• Journal of Korea Port Economic Association
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• v.34 no.1
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• pp.81-98
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• 2018

Port logistics is essential for Korea's economy which heavily rely on international trade. Vast amounts of capital and time are consumed for the operation and development of ports to improve their competitiveness. Therefore, it is important to forecast cargo volume in order to establish the optimum level of construction and development plan. Itemized forecasting is necessary for appropriate port planning, since disaggregate approach is able to provides more realistic solution than aggregate forecasting. We introduce a new time series model which is Two-way Seasonality Multiplied Regressive Model (TSMR) to forecast oil cargo volume, which accounts for a large portion of total cargo volume in Korea. The TSMR model is designed to take into account the characteristics of oil cargo volume which exhibits trends with short and long-term seasonality. To verify the TSMR model, existing forecasting models are also used for a comparison reason. The results shows that the TSMR excels the existing models in terms of forecasting accuracy whereas the TSMR displays weakness in short-term forecasting. In addition, it was shown that the TSMR can be applied to other cargoes that have trends with short- and long-term seasonality through testing applicability of the TSMR.

• 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 the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.625-637
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• 2020

In order to decide the naval ship weapon system acquisition for national policy/market economy activities, the decision makers can determine policy based on current technology level/concentration/utilization. For this, the decision makers apply the major common technology field analysis using patents data. As a method for collecting patent data, we can collect patent data of domestic mobile carriers through the Korea Intellectual Property Rights Information System of Korean Intellectual Property Office. As a result, we collected 14,964 patents/352 International Patent Classification(IPC) types. Based on these data, we performed three analysis processes (SNA, PCA, ARIMA, Text Mining) and got each result from extracting 58 IPC types of SNA and 7 IPC types of PCA. Based on the analysis results, we have confirmed that 7 IPC(B63B, H01M, F03D, B01D, H02K, B23K, H01H) types are the Major Common Technology Distribution of domestic Defense Companies.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.191-209
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• 2022

This paper is a review of studies that focus on the prediction of a won/dollar exchange rate before and after the covid 19 pandemic. The Korea economy has an unprecedent situation starting from 2021 up till 2022 where the won/dollar exchange rate has exceeded 1,400 KRW, a first time since the global financial crisis in 2008. The US Federal Reserve has raised the interest rate up to 2.5% (2022.7) called a 'Big Step' and the Korea central bank has also raised the interested rate up to 2.5% (2022.8) accordingly. In the unpredictable economic situation, the prediction of the won/dollar exchange rate has become more important than ever. The authors separated the period from 2015.Jan to 2022.Aug into three periods and built a best fitted ARIMA/ARDL prediction model using the period 1. Finally using the best the fitted prediction model, we predicted the won/dollar exchange rate for each period. The conclusions of the study were that during Period 3, when the usual relationship between exchange rates and economic factors appears, the ARDL model reflecting the variable relationship is a better predictive model, and in Period 2 of the transitional period, which deviates from the typical pattern of exchange rate and economic factors, the SARIMA model, which reflects only historical exchange rate trends, was validated as a model with a better predictive performance.