• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1756-1761
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• 2021

Although the COVID-19 outbreak that occurred in Wuhan, Hubei around December 2019, seemed to be gradually decreasing, it was gradually increasing as of November 2020 and June 2021, and estimated confirmed cases were 192 million worldwide and approximately 184 thousand in South Korea. The Central Disaster and Safety Countermeasures Headquarters have been taking strong countermeasures by implementing level 4 social distancing. However, as the highly infectious COVID-19 variants, such as Delta mutation, have been on the rise, the number of daily confirmed cases in Korea has increased to 1,800. Therefore, the number of cumulative confirmed COVID-19 cases is predicted using ARIMA algorithms to emphasize the severity of COVID-19. In the process, differences are used to remove trends and seasonality, and p, d, and q values are determined and forecasted in ARIMA using MA, AR, autocorrelation functions, and partial autocorrelation functions. Finally, forecast and actual values are compared to evaluate how well it was forecasted.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.29 no.4
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• pp.180-188
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• 2017

Recently, as the strength of winds and waves increases due to the climate change, abnormal waves such as swells have been also increased, which results in the increase of downtime events of loading/unloading in a harbour. To reduce the downtime events, breakwaters were constructed in a harbour to improve the tranquility. However, it is also important and useful for efficient port operation by predicting accurately and also quickly the downtime events when the harbour operation is in a limiting condition. In this study, numerical simulations were carried out to calculate the wave conditions based on the forecasted wind data in offshore area/outside harbour and also the long-term observation was carried out to obtain the wave data in a harbour. A forecasting method was designed using an auto-regressive (AR) and artificial neural networks (ANN) models in order to establish the relationship between the wave conditions calculated by wave model (SWAN) in offshore area and observed ones in a harbour. To evaluate the applicability of the proposed method, this method was applied to predict wave heights in a harbour and to forecast the downtime events in Pohang New Harbour with highly complex topography were compared. From the verification study, it was observed that the ANN model was more accurate than the AR model.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.4
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• pp.105-110
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• 2012

The CCA (canonical correlation analysis) is a well known analysis tool that measures the linear relationship between two variable sets and it can be used for blind source separation (BSS). In previous works, a blind source separation scheme based on the CCA and auto regression was proposed. Unfortunately, the proposed scheme requires high signal-to-noise ratio for successful source separation. In this paper, we propose an improved BSS scheme based on the CCA and auto regression by eliminating the main diagonal elements of auto covariance matrix. Compared to the previously proposed BSS scheme, the proposed BSS scheme not only offers better source separation performance but also requires low computational complexity.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2002.10b
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• pp.259-260
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• 2002

In this paper, electrical discharge machined (EDM) surfaces machined with various machining parameters are characterized and simulated. Three-dimensional surface topography of EDM surfaces are measured by a stylus instrument. Surface topography is characterized with auto-correlation coefficient and height probability density functions. Then, EDM surfaces are modeled and computer-simulated by using the non-causal 2-D auto-regressive model. Simulation results show that EDM surfaces are characterized well by a few parameters.

• Journal of Advanced Navigation Technology
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• v.11 no.3
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• pp.252-258
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• 2007

There has been a lot of research done in scheme guaranteeing user's mobility and effective resources management to satisfy the requested by users in the wireless/mobile networks. In this paper, we propose a predictive resource allocation scheme based on ARMA(Auto Regressive Moving Average) prediction model to meet QoS requirements(handoff dropping rate) for guaranteeing users' mobility. The proposed scheme predicts the demanded amount of resource in the future time by ARMA time series prediction model, and then reserves it. The ARMA model can be used to take into account the correlation of future handoff resource demands with present and past handoff demands for provision of targeted handoff dropping rate. Simulation results show that the proposed scheme outperforms the existing RCS(Reserved channel scheme) in terms of handoff connection dropping rate and resource utilization.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.22 no.2
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• pp.451-457
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• 1998

This paper describes a modelling method for SCR(selective catalystic reduction) system in refuse incineration plant. We consider the SCR system as a single input and single output system. For modelling the SCR system, an auto regressive exogeneous(ARX) modelling method is used. In this case, we should design the white noise input for modelling and put it on the system as an input$(NH_3)$, and take an outlet NOx as an output. From these two relations, we design the ARX model with 45 second delay time and transform to a discrete system with sampling time of 0.5 second. Using the obtained SCR model, we verify that the outlet NOx is deeply related with stoker`s moving in boiler of refuse incineration plant.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.342-345
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• 2005

This paper describes dynamics analysis of a small training boat and a new type of ship's autopilot not only to keep her course but also to reduce her roll motion. Firstly, statistical analysis through multi-variate auto regressive model is carried out using the real data collected from the sea trial on an actual small training boat Sazanami after the navigational system of the boat was upgraded. It is shown that the roll motion is strongly influenced by the rudder motion and it is suggested that there is a possibility of reducing the roll motion by controlling the rudder order properly. Based on this observation, a new type of ship's autopilot that takes the roll motion into account is designed using the muti-variate modern control theory. Lastly, digital simulations by white noise are carried out in order to evaluate the proposed system and a typical result is demonstrated. As results of simulations, the proposed autopilot had good performance compared with the original data.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.2
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• pp.89-98
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• 2020

Prediction of clock behaviors is necessary to generate very high stable system time which is essential for a satellite navigation system. For the purpose, we applied the Auto-Regressive Integrated Moving Average (ARIMA) model to the prediction of two hydrogen masers' behaviors with respect to the rapid Coordinated Universal Time (UTCr). Using the packaged programming language R, we made an analysis and prediction of time series data of [UTCr - clocks]. The maximum variation width of the residuals which were obtained by the difference between the predicted and measured values, was 6.2 ns for 106 days. This variation width was just one-sixth of [UTCr-UTC (KRIS)] published by the BIPM for the same period. Since the two hydrogen masers were found to be strongly correlated, we applied the Vector Auto-Regressive Moving Average (VARMA) model for more accurate prediction. The result showed that the prediction accuarcy was improved by two times for one hydrogen maser.

• Asian-Australasian Journal of Animal Sciences
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• v.25 no.4
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• pp.462-470
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• 2012

The availability and efficient use of the feed resources in India are the primary drivers to maximize productivity of Indian livestock. Feed security is vital to the livestock management, extent of use, conservation and productivity enhancement. Assessment and forecasting of livestock feed resources are most important for effective planning and policy making. In the present study, 40 years of data on crop production, land use pattern, rainfall, its deviation from normal, area under crop and yield of crop were collected and modeled to forecast the likely production of feed resources for the next 20 years. The higher order auto-regressive (AR) models were used to develop efficient forecasting models. Use of climatic variables (actual rainfall and its deviation from normal) in combination with non-climatic factors like area under each crop, yield of crop, lag period etc., increased the efficiency of forecasting models. From the best fitting models, the current total dry matter (DM) availability in India was estimated to be 510.6 million tonnes (mt) comprising of 47.2 mt from concentrates, 319.6 mt from crop residues and 143.8 mt from greens. The availability of DM from dry fodder, green fodder and concentrates is forecasted at 409.4, 135.6 and 61.2 mt, respectively, for 2030.

• The Journal of the Acoustical Society of Korea
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• v.14 no.6
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• pp.62-67
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• 1995

In this paper, the features extraction of reflected ultrasonic signals from various type of defects existing in Al metal has been studied by digital signal processing. Since the reflected signals from various type of the defects are ambiguous in features distinction from effects of noise, Wiener filtering using AR (auto-regressive) technique and least-absolute-values norm method has been used in features extraction and comparison of signals. In this experiment, three types of the defect in aluminum specimen have been considered: a flat cut, an angular cut, a circular hole. And the reflected signal have been measured by pulse-echo methods. In the result of digital signal processing of the reflected signal, it has been found that the features extraction method have been effective for classification of the reflected signals from various defects.