• Journal of the Korea Society of Computer and Information
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• v.28 no.3
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• pp.25-33
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• 2023

Noise generated during the acquisition and transmission of CT images acts as a factor that degrades image quality. Therefore, noise removal to solve this problem is an important preprocessing process in image processing. In this paper, we remove noise by using a deformable convolutional autoencoder (DeCAE) model in which deformable convolution operation is applied instead of the existing convolution operation in the convolutional autoencoder (CAE) model of deep learning. Here, the deformable convolution operation can extract features of an image in a more flexible area than the conventional convolution operation. The proposed DeCAE model has the same encoder-decoder structure as the existing CAE model, but the encoder is composed of deformable convolutional layers and the decoder is composed of conventional convolutional layers for efficient noise removal. To evaluate the performance of the DeCAE model proposed in this paper, experiments were conducted on CT images corrupted by various noises, that is, Gaussian noise, impulse noise, and Poisson noise. As a result of the performance experiment, the DeCAE model has more qualitative and quantitative measures than the traditional filters, that is, the Mean filter, Median filter, Bilateral filter and NL-means method, as well as the existing CAE models, that is, MAE (Mean Absolute Error), PSNR (Peak Signal-to-Noise Ratio) and SSIM. (Structural Similarity Index Measure) showed excellent results.

• Convergence Security Journal
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• v.22 no.5
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• pp.95-105
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• 2022

The Army, Navy, and Air Force are making various efforts to develop a weapon system that incorporates the 4th industrial revolution technology so that it can be used in multi-domain operations. In order to effectively demonstrate the integrated combat power through the weapon system to which the new technology is applied, it is necessary to establish a network environment in which each weapon system can transmit and receive information smoothly. For this, it is essential to analyze the Information Exchange Requirement(IER) of each weapon system, but many IER analysis studies did not sufficiently reflect the various considerations of the actual tactical network. Therefore, this study closely analyzes the research methods and results of the existing information exchange requirements analysis studies. In IER analysis, the size of the message itself, the size of the network protocol header, the transmission/reception structure of the tactical network, the information distribution process, and the message occurrence frequency. In order to be able to use it for future IER prediction, we present a technique for calculating the information exchange requirement as a probability distribution using the Poisson distribution and the probability generating function. In order to prove the validity of this technique, the results of the probability distribution calculation using the message list and network topology samples are compared with the simulation results using Network Simulator 2.

• Journal of Food Hygiene and Safety
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• v.14 no.4
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• pp.319-326
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• 1999

Recently, it is continuously rising to concern about the health risk being induced by microorganisms in food such as Escherichia coli O157:H7 and Listeria monocytogenes. Various organizations and regulatory agencies including U.S.FPA, U.S.DA and FAO/WHO are preparing the methodology building to apply microbial quantitative risk assessment to risk-based food safety program. Microbial risks are primarily the result of single exposure and its health impacts are immediate and serious. Therefore, the methodology of risk assessment differs from that of chemical risk assessment. Microbial quantitative risk assessment consists of tow steps; hazard identification, exposure assessment, dose-response assessment and risk characterization. Hazard identification is accomplished by observing and defining the types of adverse health effects in humans associated with exposure to foodborne agents. Epidemiological evidence which links the various disease with the particular exposure route is an important component of this identification. Exposure assessment includes the quantification of microbial exposure regarding the dynamics of microbial growth in food processing, transport, packaging and specific time-temperature conditions at various points from animal production to consumption. Dose-response assessment is the process characterizing dose-response correlation between microbial exposure and disease incidence. Unlike chemical carcinogens, the dose-response assessment for microbial pathogens has not focused on animal models for extrapolation to humans. Risk characterization links the exposure assessment and dose-response assessment and involve uncertainty analysis. The methodology of microbial dose-response assessment is classified as nonthreshold and thresh-old approach. The nonthreshold model have assumption that one organism is capable of producing an infection if it arrives at an appropriate site and organism have independence. Recently, the Exponential, Beta-poission, Gompertz, and Gamma-weibull models are using as nonthreshold model. The Log-normal and Log-logistic models are using as threshold model. The threshold has the assumption that a toxicant is produce by interaction of organisms. In this study, it was reviewed detailed process including risk value using model parameter and microbial exposure dose. Also this study suggested model application methodology in field of exposure assessment using assumed food microbial data(NaCl, water activity, temperature, pH, etc.) and the commercially used Food MicroModel. We recognized that human volunteer data to the healthy man are preferred rather than epidemiological data fur obtaining exact dose-response data. But, the foreign agencies are studying the characterization of correlation between human and animal. For the comparison of differences to the population sensitivity: it must be executed domestic study such as the establishment of dose-response data to the Korean volunteer by each microbial and microbial exposure assessment in food.

• The Korean Journal of Nuclear Medicine
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• v.31 no.3
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• pp.330-338
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• 1997

This study investigated the method to adjust acquisition time(a) and injection dose (i) to make the best basal and subtraction images in consecutive SPECT. Image quality was assumed to be mainly affected by signal to noise ratio(S/N). Basal image was subtracted from the second image consecutively acquired at the same position. We calculated S/N ratio in basal SPECT images($S_1/N_1$) and subtraction SPECT images(Ss/Ns) to find a(time) and i(dose) to maximize S/N of both images at the same time. From phantom images, we drew the relation of image counts and a(time) and i(dose) in our system using fanbeam-high-resolution collimated triple head SPECT. Noise by imaging process depended on Poisson distribution. We took maximum tolerable duration of consecutive acquisition as 30 minutes and maximum injectible dose as 1,850MBq(50 mCi)(sum of two injections) per study. Counts of second-acquired image($S_2$), counts($S_s$) and noise($N_s$) of subtraction SPECT were as follows. $C_1$ was the coefficient of measurement with our system. $$S_2=S_1{\cdot}(\frac{30-a}{a})+background{\cdot}(1-\frac{30-a}{a})+C_1{\cdot}(30-a){\cdot}{\epsilon}{\cdot}(50-i)$$ $$Ss=S_2-\{S_1{\cdot}(\frac{30-a}{a})+background{\cdot}(1-\frac{(30-a)}{a})\}$$ $$Ns={\sqrt{N_2^2+N_1^2{\cdot}\frac{(30-a)^2}{a^2}}={\sqrt{S_2+S_1{\cdot}\frac{(30-a)^2}{a^2}}$$ In case of rest/acetazolamide study, effect(${\epsilon}$) of acetazolamide to increase global brain uptake of Tc-99m-HMPAO could be 1.5 or less. Varying ${\epsilon}$ from 1 to 1.5, a(time) and i(dose) pair to maximize both $S_1/N_l$ and Ss/Ns was determined. 15 mCi/17 min and 35mCi/13min was the best a(time) and i(dose) pair for rest/acetazolamide study(when ${\epsilon}$ were 1.2) and came to be used for our clinical routine after this study. We developed simple method to maximize S/N ratios of basal and subtraction SPECT from consecutive acquisition. This method could be applied to ECD/HMPAO and brain activation studies as well as rest/acetazolamide studies.