• The Journal of Korean Society for Radiation Therapy
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• v.31 no.1
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• pp.43-49
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• 2019

Purpose : We apply the Layered Rescanning PBS designed to complement the Pencil Beam Scanning(PBS), which is vulnerable to moving organs with the Moving Phantom, and compare the homogeneity with the single scan PBS. Methods and materials: Matrix X (IBA, Belgium) and Moving Phantom (standard imaging, USA) were used. A dose of 200 cGy was measured in the AP direction on a hypothetical tumor $10{\times}10{\times}5cm$. The plan type was planned as 4 kinds of sinlge scan PBS, rescan number 4, 8, 12 times. Were measured three times for each types. During the measurement, the respiratory cycle of the Moving Phantom was generally set to 4 seconds per cycle, and the movement radius in the S-I direction was set to 2 cm. In addition, beam on time was measured. Results : The mean values of $D_{max}$ in the PTV were $246.47{\pm}18.8cGy$, $223.43{\pm}8.92cGy$, and $222.47{\pm}7.7cGy$, $213.9{\pm}6.11cGy$ and the mean values of $D_{min}$ were $165.53{\pm}4.32cGy$, $173.13{\pm}11.94cGy$, $184.13{\pm}8.04cGy$, $182.67{\pm}4.38cGy$ and the mean values of $D_{mean}$ $192.77{\pm}6.98cGy$, $196.7{\pm}4.01cGy$, $198.17{\pm}4.96cGy$, $195.77{\pm}3.15cGy$ respectively. As the number of rescanning increased, the Homogeneity Index converged to 1. The beam on time was measured as 2:15, 3:15, 4:30, 5:37 on average. In the measurement process, in the low dose layer of the MU, the problem was found that it was not rescanned as many times as the set number of rescan. Conclusions : In the treatment of tumors with long-term movements, the application of layered rescanning PBS showed a more uniform dose distribution than single scan PBS. And as the number of rescan increase, the distribution of homogeneity is uniform. Compared with single scan plan and 12 rescan plan, HI value was improved by 0.32. Further studies are expected to be applicable to patients who can not be treated with respiratory synchronous radiation therapy.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.6
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• pp.153-162
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• 2009

This paper suggests the novel light-weight signal processing algorithm for wireless sensor network applications which needs low computing complexity and power consumption. Exponential average method (EA) is utilized by real time, to process the magnetometer signal which is analyzed to understand the own physical characteristic in time domain. EA provides the robustness about noise, magnetic drift by temperature and interference, furthermore, causes low memory consumption and computing complexity for embedded processor. Hence, optimal parameter of proposal algorithm is extracted by statistical analysis. Using general and precision magnetometer, detection probability over 90% is obtained which restricted by 5% false alarm rate in simulation and using own developed magnetometer H/W, detection probability over 60~70% is obtained under 1~5% false alarm rate in simulation and experiment.

• Journal of Korean Society of Transportation
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• v.28 no.6
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• pp.7-15
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• 2010

Research of this paper focused on developing and demonstrating of algorithm with the figures of difference between historical traffic pattern data and real-time traffic data to decide on what the incident is. The aim of this dissertation is to develop incident detection algorithm which can be understood and modified easier to operate. To establish traffic pattern of this algorithm, weighted moving average method was applied. The basis of this method was traffic volume and speed of the same day and time at the same location based on 30-second raw data. The model was completed by a serious of steps of process-screening process of error data, decision of the traffic condition, comparison with pattern data, decision of incident circumstances, continuity test. A variety of parameter value was applied to select reasonable parameter. Results of application of the algorithm came out with figures of average detection rate 94.7 percent, 0.8 percent rate of misinformation and the average detection time 1.6 minutes. With these following results, the detection rate turned out to be superior compared with result of existing model. Applying the concept of traffic patterns was useful to gain excellent results of this study. Also, this study is significant in terms of making algorithm which theorized the decision process of actual operators.

• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.679-690
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• 2011

Two widely used approaches for improving the quality of the output of a process are statistical process control (SPC) and automatic process control (APC). In recent hybrid processes that combine aspects of the process and parts industries, process variations due to both the inherent wandering and special causes occur commonly, and thus simultaneous application of APC and SPC schemes is needed to effectively keep such processes close to target. The simultaneous implementation of APC and SPC schemes is called integrated process control (IPC). In the IPC procedure, the output variables are monitored during the process where adjustments are repeatedly done by its controller. For monitoring the APC-controlled process, control charts can be generally applied to the output variable. However, as an alternative, some authors suggested that monitoring the input variable may improve the chance of detection. In this paper, we evaluate the performance of several monitoring statistics, such as the output variable, the input variable, and the difference variable, for efficiently monitoring the APC-controlled process when we assume IMA(1,1) noise model with a minimum mean squared error adjustment policy.

• The Korean Journal of Applied Statistics
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• v.9 no.1
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• pp.91-109
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• 1996

Exponentially weighted moving averae(EWMA) control chart has been used widely for process monitoring and process adjustment recently, but there has not been many studies about the selection of the parameters. Design of the control chart can be classified into the statistical design and the economic design. The purpose of the economic design is to minimize the cost function in which all the possible costs occurring during the process are probability given the Type I error probability. In this paper the optimal parameters of the EWMA chart are selected for the economic design as well as for the statistical design. The optimal parameters for the economic design show significantly different from those of the statistical design, and especially the weight is always larger than that used in the statistical design. In the economic design, we divide the model into the single assignable cause model and the multiple assignable causes model caacording to number of which is used as the average context of the multiple assignable causes, it shows that the selection of the parameters may be misleading when the multiple assignable causes exist in practice.

• Database Research
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• v.34 no.3
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• pp.58-68
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• 2018

Distributed computing helps to efficiently store and process large data on a cluster of multiple machines. The performance of distributed computing is greatly influenced depending on the state of the servers constituting the distributed system. In this paper, we propose a self-diagnosis system that collects log data in a distributed system, detects anomalies and visualizes the results in real time. First, we divide the self-diagnosis process into five stages: collecting, delivering, analyzing, storing, and visualizing stages. Next, we design a real-time self-diagnosis system that meets the goals of real-time, scalability, and high availability. The proposed system is based on Apache Flume, Apache Kafka, and Apache Storm, which are representative real-time distributed techniques. In addition, we use simple but effective moving average and 3-sigma based anomaly detection technique to minimize the delay of log data processing during the self-diagnosis process. Through the results of this paper, we can construct a distributed real-time self-diagnosis solution that can diagnose server status in real time in a complicated distributed system.

• 전자공학회논문지 IE
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• v.43 no.1
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• pp.59-66
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• 2006

In this study, we have proposed a motion equation to control synchro drive mobile robot, a path plan to compute and track the best path to given destination and a technique utilizing uniform distribution and cluster management based Monte Carlo localization to have track current position of moving robot. In the localization test which was repeated 73 times resulted as following. The average process time of original Monte Carlo localization was 12.8ms. The proposed cluster management Monte Carlo localization resulted 9.3ms. Also the proposed method resulted correctly in the cases where original method failed.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.3
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• pp.188-195
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• 2004

This paper presents neural load torque observer that is used to deadbeat load torque observer and gain compensation by parameter estimator As a result, the response of the PMSM(permanent magnet synchronous motor) follows that nominal plant. The load torque compensation method is composed of a neural deadbeat observer To reduce the noise effect, the post-filter implemented by MA(moving average) process, is adopted. The parameter compensator with RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller The parameter estimator is combined with a high performance neural load torque observer to resolve the problems. The neural network is trained in on-line phases and it is composed by a feed forward recall and error back-propagation training. During the normal operation, the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against the load torque and the Parameter variation. A stability and usefulness are verified by computer simulation and experiment.

• Journal of Electrical Engineering and Technology
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• v.1 no.1
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• pp.110-113
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• 2006

A high rate deposition sputtering process of magnesium oxide thin film in oxide mode has been developed using a 20 kW unipolar pulsed power supply. The power supply was operated at a maximum constant voltage of 500 V and a constant current of 40 A. The pulse repetition rate and the duty were changed in the ranges of $10\sim50$ kHz and $10\sim60%$, respectively. The deposition rate increased with rising incident power to the target. Maximum incident power to the magnesium target was obtained by the control of frequency, duty and current. The deposition rate of a moving state was 9 nm m/min at the average power of 1.5 kW. This result shows higher deposition rate than any other previous work involving reactive sputtering in oxide mode. The thickness uniformities over the entire substrate area of $982mm{\times}563mm$ were observed at the processing pressure of $2.8\sim9.5$ mTorr. The thickness distribution was improved at lower pressure. This technique is proposed for application to a high through-put sputtering system for plasma display panels.

• Journal of applied mathematics & informatics
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• v.5 no.2
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• pp.373-382
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• 1998

Most of the works in Time Series Analysis are based on the Auto Regressive Integrated Moving Average (ARIMA) models presented by Box and Jeckins(1976). If the data exhibits no ap-parent deviation from stationarity and if it has rapidly decreasing autocorrelation function then a suitable ARIMA(p,q) model is fit to the given data. Selection of the orders of p and q is one of the crucial steps in Time Series Analysis. Most of the methods to determine p and q are based on the autocorrelation function and partial autocor-relation function as suggested by Box and Jenkins (1976). many new techniques have emerged in the literature and it is found that most of them are over very little use in determining the orders of p and q when both of them are non-zero. The Durbin-Levinson algorithm and Innovation algorithm (Brockwell and Davis 1987) are used as recur-sive methods for computing best linear predictors in an ARMA(p,q)model. These algorithms are modified to yield an effective method for ARMA model identification so that the values of order p and q can be determined from them. The new method is developed and its validity and usefulness is illustrated by many theoretical examples. This method can also be applied to an real world data.