• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.473-479
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• 2023

In this study, the dose of photoneutrons generated during radiotherapy of prostate cancer using high energy was measured using a photo-stimulated luminescence dosimeter. In addition, this study was intended to study the probability of side effects occurring in the abdomen. A medical linear accelerator capable of generating 15 MV energy, True Beam STx (Varian Medical Systems, USA) and a radiation treatment planning system (Eclipse, Varian Medical Systems, USA) were used. A human body phantom was installed on the couch of the linear accelerator, and an Albedo Neutron Optical Stimulation Luminescence Neutron Detector (Landauer Inc., IL, USA) was used to measure the photoneutron dose. The photoneutron dose value in the abdomen of VMAT and 3C-CRT was 52.8 mSv, more than twice as high as VMAT compared to 3D-CRT. During radiotherapy of prostate cancer, the probability of causing side effects in the abdomen due to light neutron dose was calculated to be 3.2 per 1,000 for VMAT and 1.4 for 3D-CRT. By studying the abdomen, which has a major side effect that can occur during radiotherapy of prostate cancer, it is expected that it will be used as a meaningful study to study the quality of life and stochastic effect of prostate cancer patients

• Wind and Structures
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• v.10 no.4
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• pp.367-382
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• 2007

A nonlinear numerical method was developed to assess the stability of suspension bridge catwalks under a wind load. A section model wind tunnel test was used to obtain a catwalk's aerostatic coefficients, from which the displacement-dependent wind loads were subsequently derived. The stability of a suspension bridge catwalk was analyzed on the basis of the geometric nonlinear behavior of the structure. In addition, a full model test was conducted on the catwalk, which spanned 960 m. A comparison of the displacement values between the test and the numerical simulation shows that a numerical method based on a section model test can be used to effectively and accurately evaluate the stability of a catwalk. A case study features the stability of the catwalk of the Runyang Yangtze suspension bridge, the main span of which is 1490 m. Wind can generally attack the structure from any direction. Whenever the wind comes at a yaw angle, there are six wind load components that act on the catwalk. If the yaw angle is equal to zero, the wind is normal to the catwalk (called normal wind) and the six load components are reduced to three components. Three aerostatic coefficients of the catwalk can be obtained through a section model test with traditional test equipment. However, six aerostatic coefficients of the catwalk must be acquired with the aid of special section model test equipment. A nonlinear numerical method was used study the stability of a catwalk under a yaw wind, while taking into account the six components of the displacement-dependent wind load and the geometric nonlinearity of the catwalk. The results show that when wind attacks with a slight yaw angle, the critical velocity that induces static instability of the catwalk may be lower than the critical velocity of normal wind. However, as the yaw angle of the wind becomes larger, the critical velocity increases. In the atmospheric boundary layer, the wind is turbulent and the velocity history is a random time history. The effects of turbulent wind on the stability of a catwalk are also assessed. The wind velocity fields are regarded as stationary Gaussian stochastic processes, which can be simulated by a spectral representation method. A nonlinear finite-element model set forepart and the Newmark integration method was used to calculate the wind-induced buffeting responses. The results confirm that the turbulent character of wind has little influence on the stability of the catwalk.

• The Journal of Engineering Geology
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• v.3 no.2
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• pp.125-148
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• 1993

Groundwater flow in fractured rock masses is controlled by combined effects of fracture networks, state of geostafic stresses and crossflow between fractures and rock matrix. Furthermore the scaie dependent, anisotropic properties of hydraulic parameters results mainly from irregular paftems of fracture system, which can not be evaluated properly with the methods available at present. The basic assumpfion of discrete fracture network model is that groundwater flows only along discrete fractures and the flow paths in rock mass are determined by geometric paftems of interconnected fractures. The characteristics of fracture distribution in space and fracture hydraulic parameters are represented as the probability density functions by stochastic simulation. The discrete fracture network modelling was aftempted to characterize the groundwater flow in the vicinity of existing large cavems located in Wonjeong-ri, Poseung-myon, Pyeungtaek-kun. The fracture data of $1\textrm{km}^2$ area were analysed. The result indicates that the fracture sets evaluated from an equal area projection can be grouped into 6 sets and the fracture sizes are distributed in longnormal. The conductive fracture density of set 1 shows the highest density of 0.37. The groundwater inflow into a carvem was calculated as 29ton/day with the fracture transmissivity of $10^{-8}\textrm{m}^2/s$. When the fracture transmissivity increases in an order, the inflow amount estimated increases dramatically as much as fold, i.e 651 ton/day. One of the great advantages of this model is a forward modelling which can provide a thinking tool for site characterization and allow to handle the quantitative data as well as qualitative data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.1
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• pp.114-123
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• 2018

This paper concerns a design and learning method of softmax function neural networks based on K-means clustering. The partial discharge data Information is preliminarily processed through simulation using an Epoxy Mica Coupling sensor and an internal Phase Resolved Partial Discharge Analysis algorithm. The obtained information is processed according to the characteristics of the pattern using a Motor Insulation Monitoring System program. At this time, the processed data are total 4 types that void discharge, corona discharge, surface discharge and slot discharge. The partial discharge data with high dimensional input variables are secondarily processed by principal component analysis method and reduced with keeping the characteristics of pattern as low dimensional input variables. And therefore, the pattern classifier processing speed exhibits improved effects. In addition, in the process of extracting the partial discharge data through the MIMS program, the magnitude of amplitude is divided into the maximum value and the average value, and two pattern characteristics are set and compared and analyzed. In the first half of the proposed partial discharge pattern classifier, the input and hidden layers are classified by using the K-means clustering method and the output of the hidden layer is obtained. In the latter part, the cross entropy error function is used for parameter learning between the hidden layer and the output layer. The final output layer is output as a normalized probability value between 0 and 1 using the softmax function. The advantage of using the softmax function is that it allows access and application of multiple class problems and stochastic interpretation. First of all, there is an advantage that one output value affects the remaining output value and its accompanying learning is accelerated. Also, to solve the overfitting problem, L2-normalization is applied. To prove the superiority of the proposed pattern classifier, we compare and analyze the classification rate with conventional radial basis function neural networks.

• Analytical Science and Technology
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• v.29 no.2
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• pp.79-84
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• 2016

A small and inexpensive thermal cycler (PCR machine), known as the MiniPCR^TM Mini8 Thermal Cycler (Amplyus, Cambridge, MA, USA), was developed. In this study, the performance of this PCR machine was compared with the GeneAmp^® PCR system 9700 (Applied Biosystems) using four autosomal short tandem repeat (STR) kits, a Y-chromosome STR kit, and a mitochondrial DNA HV1/HV2 sequence analysis. The sensitivity and stochastic effects of the STR multiplex kits and the quality of the DNA sequence analysis were similar between the two PCR machines. The MiniPCR^TM Mini8 Thermal Cycler could be used for analyses at forensic DNA laboratories and crime scenes. The cost of the PCR is so economical that school laboratories and individuals could use the machines.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.1118-1123
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• 2009

Ten hospitals from the Gwangju area were used to examine shallow dose to eyes and thyroid from panoramagraphy. Thermoluminescent dosimeter (TLD) and Photoluminescent dosimeter (PLD) were used as measurement devices at each hospital. ICRP 60 and ICRP 73 set standards for acceptability for eyes at 15mSv and thyroid at 1mSv per year. Left eye measures with TLD and PLD resulted in 0.19mSv and 0.24mSv respectively. Right eye measures with TLD and PLD resulted in 0.23mSv and 0.25mSv respectively. Thyroid measures with TLD and PLD resulted in 0.08mSv and 0.25mSv respectively with both measures not exceeding standards for acceptance. There was a significant difference in comparing the left eye and thyroid for TLD and PLD (p<0.01). There was no significant difference with the right eye (p>0.05). The absorbed dose measurements for eyes and thyroid using TLD and PLD in regards to panorama devices at each hospital were within the ICRP 60 recommendations; however, with the possibility of stochastic effect, all dose levels were taken into consideration.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.15-26
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• 2014

This study focus on a economic value of the Big Data technologies by real options model using big data technology company's stock price to determine the price of the economic value of incremental assessed value. For estimating stochastic process of company's stock price by big data technology to extract the incremental shares, Generalized Moments Method (GMM) are used. Option value for Black-Scholes partial differential equation was derived, in which finite difference numerical methods to obtain the Big Data technology was introduced to estimate the economic value. As a result, a option value of big data technology investment is 38.5 billion under assumption which investment cost is 50 million won and time value is a about 1 million, respectively. Thus, introduction of big data technology to create a substantial effect on corporate profits, is valuable and there are an effects on the additional time value. Sensitivity analysis of lower underlying asset value appear decreased options value and the lower investment cost showed increased options value. A volatility are not sensitive on the option value due to the big data technological characteristics which are low stock volatility and introduction periods.

• Journal of the Korean Society of Radiology
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• v.12 no.2
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• pp.149-157
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• 2018

One of the purposes of radiation protection is to minimize stochastic effects. PCXMC 2.0 is a Monte Carlo Simulation based program and makes it possible to predict effective dose and the probability of cancer development through entrance surface dose. Therefore, it is especially important to measure entrance surface dose through dosimeter. The purpose of this study is to measure entrance surface dose through semiconductor dosimeter, general dosimeter, glass dosimeter, and to compare and analyze the effective dose and probability of disease of critical organs. As an experimental method, the entrance surface dose of skull, chest, abdomen was measured per dosimeter and the effective dose and the probability of cancer development of critical organs per area was evaluated by PCXMC 2.0. As a result, the entrance surface dose per area was different in the order of a general dosimeter, a semiconductor dosimeter, and a glass dosimeter even under the same condition. Base on this analysis, the effective dose and probability of developing cancer of critical organs were also different in the order of a general dosimeter, a semiconductor dosimeter, and a glass dosimeter. In conclusion, it was found that the effective dose and the risk of diseases differ according to the dosimeter used, even under the same conditions, and through this study it was found that it is important to present an accurate entrance surface dose model according to each dosimeter.

• Journal of the Korean association of regional geographers
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• v.17 no.4
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• pp.432-442
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• 2011

Climatic variables such as temperature and precipitation tend to vary both in space and in time simultaneously. Thus, it is necessary to include space-time autocorrelation into conventional spatial interpolation methods for reliable time-series mapping. This paper introduces and applies space-time variogram modeling and space-time kriging to generate time-series temperature maps using hourly Automatic Weather System(AWS) temperature observation data for a one-month period. First, temperature observation data are decomposed into deterministic trend and stochastic residual components. For trend component modeling, elevation data which have reasonable correlation with temperature are used as secondary information to generate trend component with topographic effects. Then, space-time variograms of residual components are estimated and modelled by using a product-sum space-time variogram model to account for not only autocorrelation both in space and in time, but also their interactions. From a case study, space-time kriging outperforms both conventional space only ordinary kriging and regression-kriging, which indicates the importance of using space-time autocorrelation information as well as elevation data. It is expected that space-time kriging would be a useful tool when a space-poor but time-rich dataset is analyzed.

• Journal of the Korea Society for Simulation
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• v.22 no.2
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• pp.101-107
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• 2013

Because forest fires are predicted to increase in severity and frequency under global climate change with important environmental implications, an understanding of fire dynamics is critical for mitigation of these negative effects. For the reason, researchers with different background, such as ecologists, physicists, and mathematical biologists, have developed the simulation models to mimic the forest fire spread patterns. In this study, we suggested a novel model considering the wind effect. Our theoretical forest was comprised of two different tree species with varying probabilities of transferring fire that were randomly distributed in space at densities ranging from 0.0 (low) to 1.0 (high). We then studied the distributional patterns of burnt trees using a two-dimensional stochastic cellular automata model with minimized local rules. We investigated the time, T, that the number of burnt trees reaches 25% of the whole trees for different values of the initial tree density, fire transition probability, and the degree of wind strength. Simulation results showed that the values of T decreased with the increase of tree density, and the wind effect decreased in the case of too high or low tree density. We believe that our model can be a useful tool to explore forest fire spreading patterns.