• Progress in Medical Physics
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• v.8 no.1
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• pp.37-45
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• 1997

The present work is determine to the dose distribution reduced by the insertion of a shielded into a vaginal cylinder around a $\^$60/CO source in brachytherapy, and to the source calibration. It was investigated by measuring the relative dose around a 2.5cm diameter shielded vaginal cylinder in a polystyrene phantom by use of a ionization chamber. Measurements were made with the cylinder unshielded and 0.55cm thick 90$^{\circ}C$ lead shields inserted. Also, the dose distribution compared measurement value with calculation value according to the device manufacturer and the multiple-divided dose tables. A reduction in dose was observed on the unshielded side of the cylinder which increased with distance from the source and it does 4.4％ within 1cm from the surface of the cylinder. On the shielded side of the cylinder, the dose at the surface is reduced to about 20.4％ of its value without the shield. The effective attenuation factor entered for the 90$^{\circ}C$ lead shielded cylinder was average 0.2 in a $\^$60/CO moving source. In comparision with the dose calculation mathods, the multiple-divided dose tables are difference less than ${\pm}$4.1％ with measured data in a $\^$60/Co source.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.47 no.4
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• pp.411-418
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• 2011

As a fish way is a structure for fish migrating well toward upper stream due to breaking river flow by a dam or dammed pool, the specific fish's swimming ability is one of the main factors in making a plan and managing it. In addition, it also needs to understand the current field in fish road to evaluate its performance. This study is aimed to analyze the swimming patterns with current velocity changes using a Particle Imaging Velocimetry (PIV) in order to understand the swimming ability of silver fish (Plecoglossus altivelis) that is one of the fishes migrating through the fish way of Nakdong River, and to analyze the 2 dimensional current field near to silver fish at swimming momentum. The results showed that average values of tail beat frequencies for continuous swimming with current velocity were 2.8 Hz at 0.3 m/s, 3.2 Hz at 0.4 m/s, 3.8 Hz at 0.5 m/s, respectively. The wake would be produced by direction turning of fish's tail fin and its magnitude would be verified by the difference of pressure. The pressure turbulent flow produced by its tail beat would be made in both sides, and then, the magnitude of wake should be the source of moving direction. The swimming momentum will help to support the primary factor in making a suitable design for specific fish species migrating toward the district river.

• The KIPS Transactions:PartB
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• v.11B no.3
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• pp.247-256
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• 2004

In this paper, we propose the prediction search algorithm for block matching using the temporal/spatial correlation of the video sequence and the renter-biased property of motion vectors The proposed algorithm determines the location of a better starting point for the search of an exact motion vector using the point of the smallest SAD(Sum of Absolute Difference) value by the predicted motion vector from the same block of the previous frame and the predictor candidate pint in each search region and the predicted motion vector from the neighbour blocks of the current frame. And the searching process after moving the starting point is processed a adaptive search pattern according to the magnitude of motion vector Simulation results show that PSNR(Peak-to-Signal Noise Ratio) values are improved up to the 0.75dB as depend on the video sequences and improved about 0.05∼0.34dB on an average except the FS (Full Search) algorithm.

• Korean Journal of Applied Biomechanics
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• v.22 no.1
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• pp.17-24
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• 2012

This study is for providing fundamental data of sport biomechanics in GRF & plantar pressure of stepping foot of skilled & unskilled players' at the soccer instep shooting moments. Wearing Pedar-x of Novel, the study has drawn the following conclusion after measuring and analyzing the impact on the GRF and plantar pressure of stepping foot at the instep shooting moments. First, maximum vertical GRF showed higher in the skilled group than in the unskilled group. The results showed significantly different. This study reached the conclusion that the players in the skilled group performed faster and stronger stepping foot motions that the ones in the unskilled(p<.01). Second, since the plantar pressure of the skilled group appeared significantly higher than that of the unskilled, it has brought us to the conclusion that the skilled group performed faster and stronger stepping foot motions than the unskilled group (p<.05). Third, at the moment of instep kicking, the skilled group's average maximum plantar foot pressure of stepping foot was higher than the unskilled. Though the difference was not statistically significant, it can be concluded that the skilled group performed faster and stronger stepping foot motions than the unskilled group(p>.05). Fourth, for the COP moving route of stepping foot while instep kicking, the skilled people performed accurate and strong shooting motions directly toward the target direction with stable postures, no matter how it's left, right, front or back.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.3
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• pp.195-200
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• 2008

To compress the moving picture data effectively, it is needed to reduce spatial and temporal redundancy of input image data. While motion estimation! compensation methods is effectively able to reduce temporal redundancy but it is increased computation complexity because of the prediction between frames. So, the study of algorithm for computation reduction and real time processing is needed. This paper is presenting quantizer effectively able to quantize DCT coefficient considering the human visual sensitivity. As quantizer that proposed DCT-based H.263 could make transmit more frame than TMN5 at a same transfer speed, and it could decrease the frame drop effect. And the luminance signal appeared the difference of $-0.3{\sim}+0.65dB$ in the average PSNR for the estimation of objective image quality and the chrominance signal appeared the improvement in about 1.73dB in comparision with TMN5. The proposed method reduces $30{\sim}31%$ compared with NTSS and $20{\sim}21%$ compared to 4SS in comparition of calculation quantity.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.206-211
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• 2009

This paper presents a methodology to 1-day-forecast stock index using the automatic feature extraction method based on the neural network with weighted fuzzy membership functions (NEWFM). The distributed non-overlap area measurement method selects the minimized number of input features by automatically removing the worst input features one by one. CPP$_{n,m}$(Current Price Position of the day n: a percentage of the difference between the price of the day n and the moving average from the day n-1 to the day n-m) and the 2 wavelet transformed coefficients from the recent 32 days of CPP$_{n,m}$ are selected as minimized features using bounded sum of weighted fuzzy membership functions (BSWFMs). For the data sets, from 1989 to 1998, the proposed method shows that the forecast rate is 60.93%.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.5
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• pp.367-377
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• 2019

As an alternative navigation system for the non-GNSS (Global Navigation Satellite System) environment, a new type of DBRN (DataBase Referenced Navigation) which applies both gravity gradient and terrain, and combines filter-based algorithm with profile matching was suggested. To improve the stability of the performance compared to the previous study, both centralized and decentralized EKF (Extended Kalman Filter) were constructed based on gravity gradient and terrain data, and one of filters was selected in a timely manner. Then, the final position of a moving vehicle was determined by combining a position from the filter with the one from a profile matching. In the simulation test, it was found that the overall performance was improved to the 19.957m by combining centralized and decentralized EKF compared to the centralized EKF that of 20.779m. Especially, the divergence of centralized EKF in two trajectories located in the plain area disappeared. In addition, the average horizontal error decreased to the 16.704m by re-determining the final position using both filter-based and profile matching solutions. Of course, not all trajectories generated improved performance but there is not a large difference in terms of their horizontal errors. Among nine trajectories, eights show smaller than 20m and only one has 21.654m error. Thus, it would be concluded that the endemic problem of performance inconsistency in the single geophysical DB or algorithm-based DBRN was resolved because the combination of geophysical data and algorithms determined the position with a consistent level of error.

• Smart Structures and Systems
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• v.18 no.3
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• pp.501-523
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• 2016

Electromechanical impedance (EMI) based structural health monitoring is performed by measuring the variation in the impedance due to the structural local damage. The impedance signals are acquired from the piezoelectric patches that are bonded on the structural surface. The impedance variation, which is directly related to the mechanical properties of the structure, indicates the presence of local structural damage. Two traditional EMI-based damage detection methods are based on calculating the difference between the measured impedance signals in the frequency domain from the baseline and the current structures. In this paper, a new structural damage detection approach by analyzing the time domain impedance responses is proposed. The measured time domain responses from the piezoelectric transducers will be used for analysis. With the use of the Time Frequency Autoregressive Moving Average (TFARMA) model, a damage index based on Singular Value Decomposition (SVD) is defined to identify the existence of the structural local damage. Experimental studies on a space steel truss bridge model in the laboratory are conducted to verify the proposed approach. Four piezoelectric transducers are attached at different locations and excited by a sweep-frequency signal. The impedance responses at different locations are analyzed with TFARMA model to investigate the effectiveness and performance of the proposed approach. The results demonstrate that the proposed approach is very sensitive and robust in detecting the bolt damage in the gusset plates of steel truss bridges.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.69-81
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• 2019

We consider bivariate long range dependent (LRD) time series forecasting using a deep learning method. A long short-term memory (LSTM) network well-suited to time series data is applied to forecast bivariate time series; in addition, we compare the forecasting performance with bivariate fractional autoregressive integrated moving average (FARIMA) models. Out-of-sample forecasting errors are compared with various performance measures for functional MRI (fMRI) data and daily realized volatility data. The results show a subtle difference in the predicted values of the FIVARMA model and VARFIMA model. LSTM is computationally demanding due to hyper-parameter selection, but is more stable and the forecasting performance is competitively good to that of parametric long range dependent time series models.

• Journal of the Korea Society of Computer and Information
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• v.24 no.5
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• pp.149-156
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• 2019

The existing subtract image based intrusion detection system for CCTV digital images has a problem that it can not distinguish intruders from moving backgrounds that exist in the natural environment. In this paper, we tried to solve the problems of existing system by designing real - time intrusion detection system for CCTV digital image by combining subtract image based intrusion detection method and background learning artificial neural network technology. Our proposed system consists of three steps: subtract image based intrusion detection, background artificial neural network learning stage, and background artificial neural network evaluation stage. The final intrusion detection result is a combination of result of the subtract image based intrusion detection and the final intrusion detection result of the background artificial neural network. The step of subtract image based intrusion detection is a step of determining the occurrence of intrusion by obtaining a difference image between the background cumulative average image and the current frame image. In the background artificial neural network learning, the background is learned in a situation in which no intrusion occurs, and it is learned by dividing into a detection window unit set by the user. In the background artificial neural network evaluation, the learned background artificial neural network is used to produce background recognition or intrusion detection in the detection window unit. The proposed background learning intrusion detection system is able to detect intrusion more precisely than existing subtract image based intrusion detection system and adaptively execute machine learning on the background so that it can be operated as highly practical intrusion detection system.