• Transactions of the Korean Society of Automotive Engineers
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• v.8 no.6
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• pp.156-164
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• 2000

In this study, a discrete time model for a simplified front wheel suspension system which has nonlinear dampling and stiffness property is introduced. The model is estimated from the discrete data which are generated based on the real car parameter. The performance of the proposed method is evaluated through numerical simulation, and the simulation results show that the proposed method can estimate the nonlinear behavior of the suspension system very well.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2014.05a
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• pp.14-15
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• 2014

The modular construction is as yet early stage of market in Korea. So It is have difficulty of market demand forecast of the modular building. Therefore, this study was done analysis for market trends of the modular building using ARIMA(Auto Regressive Integrated Moving Average) model by time series data.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1989.10a
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• pp.46-50
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• 1989

Methods for the estimation of the coefficient matrices in the equation of motion for a linear multi-degree-of-freedom structure arc studied. For this purpose, the equation of motion is transformed into an auto-regressive and moving average with auxiliary input (ARMAX) model. The ARMAX parameters are evaluated using several methods of parameter estimation; such as toe least squares, the instrumental variable, the maximum likelihood and the limited Information maximum likelihood methods. Then the parameters of the equation of motion are recovered therefrom. Numerical example is given for a 3-story building model subjected to an earthquake exitation.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.8
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• pp.1014-1017
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• 2012

In this paper, we introduce a method which enhances the azimuth resolution to obtain the high-resolution SAR image. We used ${\omega}-k$ algorithm to obtain the SAR image and extrapolation using auto-regressive(AR) method to enhance the azimuth resolution in the 2-D frequency domain. The AR method is a linear prediction model-based extrapolation technique. In the result, we showed the performance comparison with respect to the target range and the prediction order of Burg algorithm which is one of AR method.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.29-29
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• 2015

수자원의 지속적인 관리 및 효율적인 활용을 위하여 수문량의 예측과 분석은 필수적인 과정이라 할 수 있으며 이에 따라 다양한 수문 모형이 구축되고 강우, 유량 등 대표적인 수문량의 예측이 수행되어져 왔다. 그 중에서도 수문 시계열 모형은 시간의 흐름에 따라 일정하게 기록되어온 수문 자료를 확률적인 과정을 통하여 모형을 구축하고 이를 바탕으로 미래 수문량을 예측하는 데활용되는 모형으로, 과거에 기록된 수문 패턴이 미래에도 지속된다는 가정 하에 구축된다. 일반적으로 시계열 모형은 하나의 자료계열로 모형을 구축하는 단변량 모형과 원 자료계열 외에 다른 자료계열을 고려하여 모형을 구축하는 다변량 모형이 있으며, 다변량 모형은 원 자료계열에 영향을 미치는 외부변수를 고려함으로써 두 자료계열간의 상관성을 모형에 반영할 수 있는 장점을 가지고 있다. 또한 자료계열의 계절성을 고려하여 시계열 모형을 구축할 경우, 수문 시계열이 가지고 있는 계절적 영향을 잘 반영할 수 있다. 따라서 본 연구에서는 계절성을 고려한 다변량 시계열 모형인 SARIMAX(Seasonal AutoRegressive Integrated Moving Average with eXogenous) 모형을 이용하여 대표적인 수공구조물인 댐의 유입량 예측을 수행하였다. 일반적으로 댐 유입량 예측에는 댐의 유입량과 상관성이 높은 강우가 외부변수로 사용되어져 왔으나, 이 외에도 영향을 미칠 수 있는 지점특성치를 고려하여 모형을 구축한 후 비교하였다.

• Animal Bioscience
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• v.34 no.4
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• pp.770-782
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• 2021

Objective: The aim of the present study was to compare the effectiveness of three approaches (the seasonal auto-regressive integrated moving average [SARIMA] model, the nonlinear autoregressive exogenous [NARX] artificial neural networks and Wood's model) to the prediction of milk yield during lactation. Methods: The dataset comprised monthly test-day records from 965 Polish Holstein-Friesian Black-and-White primiparous cows. The milk yields from cows in their first lactation (from 5 to 305 days in milk) were used. Each lactation was divided into ten lactation stages of approximately 30 days. Two age groups and four calving seasons were distinguished. The records collected between 2009 and 2015 were used for model fitting and those from 2016 for the verification of predictive performance. Results: No significant differences between the predicted and the real values were found. The predictions generated by SARIMA were slightly more accurate, although they did not differ significantly from those produced by the NARX and Wood's models. SARIMA had a slightly better performance, especially in the initial periods, whereas the NARX and Wood's models in the later ones. Conclusion: The use of SARIMA was more time-consuming than that of NARX and Wood's model. The application of the SARIMA, NARX and Wood's models (after their implementation in a user-friendly software) may allow farmers to estimate milk yield of cows that begin production for the first time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.8
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• pp.1551-1559
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• 2015

Recently, the performance of smart devices is almost similar to that of the existing PCs, thus the users of smart devices can perform similar works such as messengers, SNSs(Social Network Services), smart banking, etc. originally performed in PC environment using smart devices. Although the development of smart devices has led to positive impacts, it has caused negative changes such as an increase in security threat aimed at mobile environment. Specifically, the threats of mobile devices, such as leaking private information, generating unfair billing and performing DDoS(Distributed Denial of Service) attacks has continuously increased. Over 80% of the mobile devices use android platform, thus, the number of damage caused by mobile malware in android platform is also increasing. In this paper, we propose android based malware detection mechanism using time-series analysis, which is one of statistical-based detection methods.We use auto-regressive moving-average model which is extracting accurate predictive values based on existing data among time-series model. We also use fast and exact malware detection method by extracting possible malware data through Z-Score. We validate the proposed methods through the experiment results.

• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.71-81
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• 2011

In this paper, we systematically analyzed the variation of KOSPI returns using a GARCH-ARJI(auto regressive jump intensity) model. This model is possibly to capture time varying volatility as well as time varying conditional jump intensity. Thus, we can decompose return volatility into usual variation explained by the GARCH model and unusual variation that resulted from external news or shocks. We found that the jump intensity implied on KOSPI return series clearly shows time varying. We also found that conditional volatility due to jump is generally smaller than that resulted from usual variation. We also analyzed the effect of 9.11 and the 2008 financial crisis on the volatility of KOSPI returns and conclude that there is strong and persistent impact on the KOSPI from the 2008 financial crisis.

• Journal of the Korean Geotechnical Society
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• v.40 no.2
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• pp.65-79
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• 2024

This study employs traditional statistical auto-regressive integrated moving average (ARIMA) and deep learning-based long short-term memory (LSTM) models to predict the deformation of earth retaining walls using inclinometer data from excavation sites. It compares the predictive capabilities of both models. The ARIMA model excels in analyzing linear patterns as time progresses, while the LSTM model is adept at handling complex nonlinear patterns and long-term dependencies in the data. This research includes preprocessing of inclinometer measurement data, performance evaluation across various data lengths and input conditions, and demonstrates that the LSTM model provides statistically significant improvements in prediction accuracy over the ARIMA model. The findings suggest that LSTM models can effectively assess the stability of retaining walls at excavation sites. Additionally, this study is expected to contribute to the development of safety monitoring systems at excavation sites and the advancement of time series prediction models.

• Journal of Advanced Marine Engineering and Technology
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• v.22 no.6
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• pp.928-934
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• 1998

This paper describes a fully digitalized driver for BLDC motors which is realized by a single chip microprocessor. The speed change can be done by using the signal obtained from the position detecting sensor and adjusting the pulse width at the input channel of power module. In order to establish a speed control system a repetitive control method is adopted to track a periodic refer-ence change in the BLDC motor system. The experimental results show accurate reference track-ing performance under the given periodic reference in the repetitive controller design.