• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2005.04a
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• pp.308-311
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• 2005

[ $^{222}Rn\;and\;^{226}Ra$ ] in groundwater were determined simultaneously using a gamma-spectroscopy. A nitrogen flushing equipment has been used for elimination and stabilization of high and unstable background activity due to the radon and its progenies in counting shield and room. The aim of present work was to control the background activity for simultaneous measurement of radium$(^{226}Ra)$ and radon$(^{222}Rn)$ in groundwater using a gamma-spectrometry. Background activity was about 1.0dps and the standard deviation was about 50%, The background activity could be minimized using nitrogen flushing equipment in the range of 0.1 to 0.5 and the RSD was about 5% at the experimental condition. The detection limit of $^{222}Rn\;and\;^{226}Ra$ in groundwater was 0.5dps/L in the background control method. In most groundwater used in the work, radon activity was more than the detection limit. However, radium activity in some groundwater was less than the detection limit. If the low level radium in groundwater must be measured, preconcentration process such as concentration should be performed before measuring the groundwater.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.1
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• pp.69-76
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• 2010

The segmentation of moving object in video sequence is a core technique of intelligent image processing system such as video surveillance, traffic monitoring and human tracking. A typical method to segment a moving region from the background is the background subtraction. The steps of background subtraction involve calculating a reference image, subtracting new frame from reference image and then thresholding the subtracted result. One of famous background modeling is Gaussian mixture model (GMM). Even though the method is known efficient and exact, GMM suffers from a problem that includes false pixels in ROI (region of interest), specifically shadow pixels. These false pixels cause fail of the post-processing tasks such as tracking and object recognition. This paper presents a method for removing false pixels included in ROT. First, we subdivide a ROI by using shape characteristics of detected objects. Then, a method is proposed to classify pixels from using histogram characteristic and comparing difference of energy that converts the color value of pixel into grayscale value, in order to estimate whether the pixels belong to moving object area or shadow area. The method is applied to real video sequence and the performance is verified.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.11
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• pp.989-995
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• 2015

Due to the recent increase of the importance and demand of security services, the importance of a surveillance monitor system that makes an automatic security system possible is increasing. As the market for surveillance monitor systems is growing, price competitiveness is becoming important. As a result of this trend, surveillance monitor systems based on an embedded system are widely used. In this paper, an object detection algorithm based on an embedded system for a surveillance monitor system is introduced. To apply the object detection algorithm to the embedded system, the most important issue is the efficient use of resources, such as memory and processors. Therefore, designing an appropriate algorithm considering the limit of resources is required. The proposed algorithm uses two background models; therefore, the embedded system is designed to have two independent processors. One processor checks the sub-background models for if there are any changes with high update frequency, and another processor makes the main background model, which is used for object detection. In this way, a background model will be made with images that have no objects to detect and improve the object detection performance. The object detection algorithm utilizes one-dimensional histogram distribution, which makes the detection faster. The proposed object detection algorithm works fast and accurately even in a low-priced embedded system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.396-402
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• 2021

As the world is trying to establish smart city, unmanned vehicle control systems are being widely used. This paper writes about an unmanned parking control system that uses an adaptive background image modeling method, suggesting the method of updating the background image, modeled with an adaptive Gaussian mixture model, in both global and local way according to the moving object. Specifically, this paper focuses on suggesting two methods; a method of minimizing the influence of a moving object on a background image and a method of accurately updating the background image by quickly removing afterimages of moving objects within the area of interest to be monitored. In this paper, through the implementation of the unmanned vehicle control system, we proved that the proposed system can quickly and accurately distinguish both moving and static objects such as vehicles from the background image.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.66.6-66
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• 2002

$\textbullet$ Introduction $\textbullet$ Technical Background $\textbullet$ Tension control of a Hot Strip Mill $\textbullet$ Simulation $\textbullet$ Conclusion

• 한국전산유체공학회:학술대회논문집
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• 2007.10a
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• pp.120-125
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• 2007

In this paper a new cell sizing method is proposed. The new method calculates cell size at a point using given size control elements directly without the aid of background grid as other cell sizing algorithms do. The calculation method and related definitions are described in detail, and typical cell sizing results are given.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.42.1-42
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• 2001

This paper presents a fuzzy-based method for classification skin color object in a complex background under varying illumination. Parameters of fuzzy rule base are generated using a genetic algorithm(GA). The color model is used in the YCbCr color space. We propose a unique fuzzy system in order to accommodate varying background color and illumination condition. This fuzzy system approach to skin color classification is discussed along with an overview of YCbCr color space.

• Journal of Families and Better Life
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• v.10 no.2
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• pp.75-85
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• 1992

The purpose of the present study was to examine the relationship between children's locus of control and their parents. In addition, this study investigated socio-demographic variables of locus of control in children and their patents. The subjects of this study were 269 elementary school children in the 5th and 6th grade and their parents. To assess children's locus of control, the inter -external locus of control scale developed by H.K. Lee(1980) was used. The locus of control scale designed by J. W. Jo(1989) was administered to parents. The data of the study were analysed with t-test, one was ANOVA and Scheffe's test. Also, Pearson's correlation coefficient was computed. The results of this study were as follows. 1)There were significant differences in children's locus of control according to the children's sex, parent's educational background and family income. Boys were more internal than girls. Children with parent who had higher educational backgrounds were more internal than others. Also, children in higher family income were more internal those in lower educational background and family income. Also parents with religion were more internal than those without religion. 3)children's locus of control was significantly correlated with their parents . Paternal locus of control was significantly correlated with their daughter's and maternal locus of control with their sons'.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.366-369
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• 2005

Background subtraction is mainly used for the real-time extraction and tracking of moving objects from image sequences. In the outdoor environment, there are many changeable factor such as gradually changing illumination, swaying trees and suddenly moving objects, which are to be considered for the adaptive processing. Normally, GMM(Gaussian Mixture Model) is used to subtract the background adaptively considering the various changes in the scenes, and the adaptive GMMs improving the real-time performance were worked. This paper, for on-line background subtraction, applied the improved adaptive GMM, which uses the small constant for learning rate ${\alpha}$ and is not able to speedily adapt the suddenly movement of objects, So, this paper proposed and evaluated the dynamic control method of ${\alpha}$ using the adaptive selection of the number of component distributions and the global variances of pixel values.

• Journal of Korea Multimedia Society
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• v.12 no.12
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• pp.1728-1735
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• 2009

Human action tracking plays an important place in human-computer-interaction, human action tracking is a challenging task because of the exponentially increased computational complexity in terms of the degrees of freedom of the object and the severe image ambiguities incurred by frequent self-occlusions. In this paper, we will propose a novel method to track human action, in our technique, a dynamic background estimation algorithm will be applied firstly. Based on the estimated background, we then extract the human object from the video sequence, and the skeletonization method and Hough transform method will be used to detect the main structure of human body and each part rotation angle. The calculated rotation angles will be used to control a crane in the port, thus we can just control the container crane by using signalman body. And the experimental results can show that our proposed method can get a preferable result than the conventional methods such as: MIT, JPF or MFMC.