• Proceedings of the KSME Conference
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• 2001.06b
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• pp.700-705
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• 2001

The paper presents a seam tracking controller of high speed rotating arc sensor developed by microprocessor based system. The seam tracking algorithm is based on the average current value at each interval region of four phase points on one rotating cycle. To remove the noise effect for the measured current, the area during one rotating cycle is separated into four regions of front, rear, left and right. The average values at each region are calculated, using the regional current values and a low pass filter incorporating the moving average and exponential smoothing methods is adopted.

• Journal of Power System Engineering
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• v.6 no.4
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• pp.43-48
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• 2002

This paper presents a seam tracking controller of high speed rotating arc sensor developed by microprocessor based system. The seam tracking algorithm is based on the average current value at each interval region of four phase points on one rotating cycle. To remove the noise effect for the measured current, the area during one rotating cycle is separated into four regions of front, rear, left and right. The average values at each region are calculated, using the regional current values and a low pass filter incorporating the moving average and exponential smoothing methods is adopted. The effectiveness is proven through the experimental results for several kinds of welding condition.

• Journal of the Korean Institute of Gas
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• v.18 no.1
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• pp.8-16
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• 2014

In this study, the number of gas accidents prediction model was suggested by analyzing the gas accidents occurred in Korea. In order to predict the number of gas accidents, simple moving average method (3, 4, 5 period), weighted average method and exponential smoothing method were applied. Study results of the sum of mean-square error acquired by the models of moving average method for 4 periods and weighted moving average method showed the highest value of 44.4 and 43 respectively. By developing the number of gas accidents prediction model, it could be actively utilized for gas accident prevention activities.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.1
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• pp.39-43
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• 2007

In high speed communication networks, the determination of a transmission rate is critical for the stability of a closed-loop network system with the congestion control scheme. In ATM networks, the available bit rate (ABR) service is based on a feedback mechanism, i.e., the network status is transferred to the ABR source by a resource management (RM) cell. RM cells contain the traffic information of the downstream nodes for the traffic rate control. However, the traffic status of the downstream nodes can not be directly transferred to the source node in the IP based networks. In this paper, a new rate-based congestion control scheme using an exponential weighted moving average algorithm is proposed to build an efficient feedback control law for congestion avoidance in high speed communication networks. The proposed congestion control scheme assures the stability of switch buffers and higher link utilization of the network. Moreover, we note that the proposed congestion scheme can flexibly work along with the increasing number of input sources in the network, which results in an improved scalability.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.7
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• pp.852-860
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• 2019

In this paper, we proposed a new trilateration technique based on exponential moving average (EMA) filter with adaptive signal model which enhances accuracy of positioning system even if the RSSI changes randomly due to movement of obstacles or blind node in indoor environment. In the proposed scheme, three fixed transmitters sent out the signal to blind node. The transmitter decides the location of the blind node based on RSSI and it estimates the cause of RSSI fluctuation which is interference of obstacle or movement of blind node. When the path between blind node and transmitter has become NLOS path because of obstacles, the transmitter ignores the measured RSSI in NLOS path and replace estimated RSSI in LOS environment. In the other case, the transmitter updated the new RSSI to represent of movement of blind node. The proposed scheme has been verified on a ZigBee testbed and we proved the improved positioning accuracy compared to the existing indoor position system.

• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.385-393
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• 2022

In this paper, we consider univariate time series models that are well known in the field of forecasting and we study on forecasting performance for their simple combinations. The univariate time series models include exponential smoothing methods and ARIMA (autoregressive integrated moving average) models, their extended models, and non-seasonal and seasonal random walk models, which is frequently used as benchmark models for forecasting. The median and mean are simply used for the combination method, and the data set used for performance evaluation is M3-competition data composed of 3,003 various time series data. As results of evaluating the performance by sMAPE (symmetric mean absolute percentage error) and MASE (mean absolute scaled error), we assure that the simple combinations of the univariate models perform very well in the M3-competition dataset.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.117-124
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• 2009

If time series data with seasonal effect exist, various statistical models like winters for successful forecasts could be used. But if the data are not enough to estimate seasonal effect, not much methods are available. This paper proposes the statistical forecasting method based on cumulative data when the data are not enough to estimate seasonal effect. We apply this method to real cosmetic sales data and show its better performance over moving average method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.8
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• pp.708-715
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• 2014

In this paper, we suggest a method of realtime confidence interval estimation to detect abnormal states of sensor data. For realtime confidence interval estimation, the mean square errors of the exponential smoothing method and moving average method, two of the time series analysis method, where compared, and the moving average method with less errors was applied. When the sensor data passes the bounds of the confidence interval estimation, the administrator is notified through alarming. As the suggested method is for realtime anomaly detection in a ship, an Android terminal was adopted for better communication between the wireless sensor network and users. For safe navigation, an administrator can make decisions promptly and accurately upon emergency situation in a ship by referring to the anomaly detection information through realtime confidence interval estimation.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.203-215
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• 2022

In this paper, we consider the control charts applicable to monitoring the change of the population mean for sequentially observed individual data. The most representative control charts are Shewhart's individual control chart, the exponential weighted moving average (EWMA) control chart, and their combined control chart. We compare their performance based on a simulation study, and also, through real data analysis, we present how to apply control charts in practical application and investigate the problems of each control chart.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4887-4907
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• 2017

Accurate traffic flow forecasting is critical to the development and implementation of city intelligent transportation systems. Therefore, it is one of the most important components in the research of urban traffic scheduling. However, traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during workday peak periods, and a lot of research has shown that traffic flow data reveals a seasonal trend. This paper proposes a new traffic flow forecasting model that combines seasonal relevance vector regression with the hybrid chaotic simulated annealing method (SRVRCSA). Additionally, a numerical example of traffic flow data from The Transportation Data Research Laboratory is used to elucidate the forecasting performance of the proposed SRVRCSA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal auto regressive integrated moving average (SARIMA), the double seasonal Holt-Winters exponential smoothing (DSHWES), and the relevance vector regression with hybrid Chaotic Simulated Annealing method (RVRCSA) models. The forecasting performance of RVRCSA with different kernel functions is also studied.