• Journal of Korea Port Economic Association
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• v.29 no.1
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• pp.67-76
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• 2013

It has been more than four years since the outbreak of global financial crisis. However, the world economy continues to be challenged with new crisis such as the European debt crisis and the fiscal cliff issue of the U.S. The global economic environment remains fragile and prone to further disappointment, although the balance of risks is now less skewed to the downside than it has been in recent years. It's no wonder that maritime business will be bearish since the global business affects the maritime business directly as well as indirectly. This paper, hence, aims to predict the Baltic Dry Index representing the shipping business using the ARIMA-type models and Hodrick-Prescott filtering technique. The monthly data cover the period January 2000 through January 2013. The out-of-sample forecasting performance is measured by three summary statistics: root mean squared percent error, mean absolute percent error and mean percent error. These forecasting performances are also compared with those of the random walk model. This study shows that the ARIMA models including Intervention-ARIMA have lower rmse than random walk model. This means that it's appropriate to forecast BDI using the ARIMA models. This paper predicts that the shipping market will be more bearish in 2013 than the year 2012. These pessimistic ex-ante forecasts are supported by the Hodrick-Prescott filtering technique.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.723-737
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• 2017

In order to decide the national technical standards policy for national policy/market economy activities, the people in charge commonly make policy decisions based on the current technology level/concentration/utilization by means of major common technology field analysis using patent data. One possible source of such patent data is the domestic mobile carriers through the Korea Intellectual Property Rights Information System (KIPRIS) of the Korean Intellectual Property Office (KIPO). Using this system, we collected 20,294 patents and 152 International Patent Classification (IPC) types and confirmed KTs (9,738 cases / 47.98%), which perform relatively high technology retention activities compared to other mobile carriers through the KIPRIS of KIPO. Based on these data, we performed three analyses (SNA, PCA, ARIMA) and extracted 30 IPC types from the SNA and 4 IPC types from the PCA. Based on the above analysis results, we confirmed that 4 IPC (H04W, H04B, G06Q, H04L) types are the major common technology field of the domestic mobile carriers. Finally, the number of 4 IPC (H04W, H04B, G06Q, H04L) forecast averages of the ARIMA forecast result is lower than the number of existing time series patent data averages.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.991-998
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• 2009

This paper deals with directivity forecasting of time series which is useful for futures trading in stock market. Directivity forecasting of time series is to forecast whether a given time series will rise or fall at next observation time point. For directional forecasting, we consider time regression model and ARIMA model. In particular, we study two statistics, intra-model and extra-model deviation and then show usefulness of intra-model deviation.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.11
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• pp.38-45
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• 1996

In recent years, ultrasonic has been widely applied in measurement and industrial fields and its application range has been expanded as a result of continuous research and development. Wire Bonding Machine, an instrument fabricating semi-conductor, makes use of ultrasonic bonding method. Specially, the method utilizes the longitudinal vibration of ultrasonic transducer composed of piezoelectric vibrator and horn. This work investigates the design conditions affecting the dynamic characteristics through the theretical and experimental analysis. It conducts separately the system identification of piezoelectric vibrator in time domain and the modal analysis of horn in frequency domain. The integrated modeling is conducted via a combbination of dynamic identification of piezoelectric vibrator and theroretical analysis of horn. Then comparison is made for theroretical and experimental results of the dynamic characteristics of the ultrasonic transducer comprised of piezoelectric vibrator and horn. Form the results of the comparison we develop the design technique of ultrasonic transducer using dynamic characteristics analysis and propose the possibility of ultrasonic bonding considering the optimal conditions for the longitudinal vibration of ultrasonic transducer and other conditions.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.571-576
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• 1994

In recent years, ultrasonic has been widely applied in measurement and industrial fields and its application range has been expanded as a result of continuous research and development. Wire Bonding Machine, an instrument fabricating semi-conductor, makes use of ultrasonic bonding method. In order to improve the currently used wire bonding machine using ultrasonic energy, technical accumulation is needed steadily through development of exciting device of ultrasonic composed of piezoelectic vibrator and horn. This study investigates the design conditions affecting the dynamic characteristics through the theoretical and experimental analysis of piezoelectric vibrator and horn, The study conducts separately the system identification of piezoelectric vibrator in time domain and the modal analysis of horn in frequency domain. In theoretical model, the integrated modeling is conducted via a combination of dynamic identification of piezoelectric vibrator and theoretical analysis of horn. Hence comparison is made for theoretical and experimental results of the dynamic characteristics of the ultrasonic transducer composed of piezoelectric vibrator and horn. Form the results of this study we develop the design technique of ultrasonic transducer using dynamic characteristic analysis and propose the possibility of ultrasonic welding considering the optimal condition of the natural frequency and vibration mode of horn.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.6 no.1
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• pp.35-39
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• 2001

Multibeam echosounder data, collected to investigate seabed features and topography, are usually subject to outliers resulting from the ship's irregular movements and insufficient correction for pressure calibration to the positions of beams. We introduce a statistical method which adjusts the outliers using the ARMA (Autoregressive Moving Average) technique. Our method was applied to a set of real data acquired in the East Sea. In our approach, autocorrelation of the data is modeled by an AR (1) model. If an observation is substantially different from that obtained from the estimated AR (1) model, it is declared as an outlier and adjusted using the estimated AR (1) model. This procedure is repeated until no outlier is found. The result of processing shows that outliers that are far greater than signals in amplitude were successfully removed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.2
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• pp.284-294
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• 2018

Statistical methods have been widely used to estimate the remaining battery runtime of mobile smart devices, such as smart phones, smart gears, tablets, and etc. However, existing work available in the literature only considers a particular statistical method. Thus, it is difficult to determine whether statistical methods are applicable to estimating thr remaining battery runtime of mobile devices or not. In this paper, we evaluated the performance of statistical methods applicable to estimating the remaining battery runtime of mobile smart devices. The statistical estimation methods evaluated in this paper are as follows: simple and moving average, linear regression, multivariate adaptive regression splines, auto regressive, polynomial curve fitting, and double and triple exponential smoothing methods. Research results presented in this paper give valuable data of insight to IT engineers who are willing to deploy statistical methods on estimating the remaining battery runtime of mobile smart devices.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.819-821
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• 2005

Since the System Marginal Price (SMP) is a vital factor to the market entities who intend to maximize the their profit, the short-term marginal price forecasting should be performed correctly. In a electricity market, the short-term trading between the market entities can be generally affected a short-term market price. Therefore, the exact forecasting of SMP can influence on the profit of market participants. This paper presents a methodology of day-ahead SMP foretasting using Autoregressive Integrated Moving Average (ARIMA). To show the efficiency and effectiveness of the proposed method, the numerical studies have been performed using historical data of SMP in 2004.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.953-955
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• 2020

공컨테이너(Empty Container)는 적컨테이너(Full Container)와 달리, 화물이 적재되지 않은 비어있는 컨테이너로 공컨테이너 재고는 수출에 비해 수입이 많은 항만에서, 수요는 수입에 비해 수출이 많은 항만에서 발생한다. 그러나 수입과 수출은 기간, 지역에 따라 유동적이기 때문에 수요와 재고량 예측에 어려움이 있는데, 본 연구에서는 자기회귀누적이동평균(ARIMA)과 머신러닝 기법을 활용하여 이를 예측하는 방법을 제시한다. 본 연구에 활용된 데이터와 프로그램 소스코드는 Kaggle 에 공개되어 있다.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.11-20
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• 2017

In this paper, we introduced linear spatial time series (space-time autoregressive and moving average model) and nonlinear spatial time series (space-time bilinear model). Also we estimated the parameters by Kalman Filter method and made comparative studies of power of forecast in the final model. We proposed several weight matrices such as equal proportion allocation, reciprocal proportion between distances, and proportion of population sizes. For applications, we collected Mumps data at Korea Center for Disease Control and Prevention from January 2001 until August 2008. We compared three approaches of weight matrices using the Mumps data. Finally, we also decided the most effective model based on sum of square forecast error.