• The KIPS Transactions:PartB
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• v.15B no.6
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• pp.561-574
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• 2008

The spot detection phase of the 2-DE image analysis program segments a gel image into spot regions by an image segmentation algorithm and fits the spot regions to a spot shape model and quantifies the spot informations for the next phases. Currently the watershed algorithm is generally used as the segmentation algorithm and there are the Gaussian model and the diffusion model for the shape model. The diffusion model is closer to real spot shapes than the Gaussian model however spots have very various shapes and especially an asymmetric formation in x-coordinate and y-coordinate. The reason for asymmetric formation of spots is known that a protein could not be diffused completely because the 2-DE could not be processed under the ideal environment usually. Accordingly we propose an asymmetric diffusion model in this paper. The asymmetric diffusion model assumes that a protein spot is diffused from a disc at initial time of diffusing process, but is diffused asymmetrically for x-axis and y-axis respectively as time goes on. In experiments we processed spot matching for 19 gel images by using three models respectively and evaluated averages of SNR for comparing three models. As averages of SNR we got 14.22dB for the Gaussian model, 20.72dB for the diffusion model and 22.85dB for the asymmetric diffusion model. By experimental results we could confirm the asymmetric diffusion model is more efficient and more adequate for spot matching than the Gaussian model and the diffusion model.

• Journal of Radiation Protection and Research
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• v.23 no.1
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• pp.1-6
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• 1998

A diffusion model of radioactive liquid effluents is developed and applied for YGN NPP's site, based on the Gaussian plume type model. Due to the complexity of oceanic diffusion characteristics of YGN site, a simple and reliable statistical model based on Reg. Guide 1.113 is developed. Also, a computer code package to calculate dilution factors as a function of plant operation conditions and pathway of radioactive materials. A liquid effluents diffusion model is developed by dividing the diffusion range into two categories, i. e, a near field mixing region and a far field mixing region. In the near field, the initial mixing is affected by a buoyance force, a high initial turbulence and momentum which is characterized by a plant operation condition and environmental conditions. The far field mixing is similar to gaseous effluents diffusion. So, beyond the near field region, wellknown Gaussian plume model was adopted. A different area averages of Gaussian plume equation was taken for each radioactive exposure pathway. As a result, we can get different dilution factors for different pathways. Results shows that present dilution factors used for YGN ODCM is too much overestimated compared with dilution factors calculated with the developed model.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2004.06a
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• pp.368-376
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• 2004

The estimation of the diffusion coefficients of the Gaussian plume model and the release rate by assimilation of tracer-gas measurements on Younggwang site was tested. Diffusion coefficients were modified by linear programming of both the measurements and the simulated using the Gaussian plume model. The application of the modified diffusion coefficients improved the prediction ability of the Gaussian plume model on both 3 km and 8 km arc lines. And, the release rate of tracer gas was estimated using least squares method. The optimal source rate was estimated by minimizing the errors between the measured concentrations and the computed ones by the Gaussian plume model. The obtained release rate showed a good agreement with the real release rate of the Younggwang experiment in 24%.

• 한국정보컨버전스학회:학술대회논문집
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• 2008.06a
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• pp.113-116
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• 2008

In this paper we suggest two novel methods for an implementation of the spot detection phase in the 2-DE gel image analysis program. The one is the adaptive thresholding method for eliminating noises and the other is the asymmetric diffusion model for spot matching. Remained noises after the preprocessing phase cause the over-segmentation problem by the next segmentation phase. To identify and exclude the over-segmented background regions, il we use a fixed thresholding method that is choosing an intensity value for the threshold, the spots that are invisible by one's human eyes but mean very small amount proteins which have important role in the biological samples could be eliminated. Accordingly we suggest the adaptive thresholding method which comes from an idea that is got on statistical analysis for the prominences of the peaks. There are the Gaussian model and the diffusion model for the spot shape model. The diffusion model is the closer to the real spot shapes than the Gaussian model, but spots have very various and irregular shapes and especially asymmetric formation in x-coordinate and y-coordinate. The reason for irregularity of spot shape is that spots could not be diffused perfectly across gel medium because of the characteristics of 2-DE process. Accordingly we suggest the asymmetric diffusion model for modeling spot shapes. In this paper we present a brief explanation ol the two methods and experimental results.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.1
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• pp.90-98
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• 1993

A Gaussian is formed by diffusing a spot excitation. In this paper, a diffusion neural network model is derived from the diffusion equation. And it is shown that a difference of two Gaussians(DOG) may have the same shape as a Laplacian of Gaussian(LOG), A neural network model executing a DOG convolution by diffusing an external excitation is proposed. By this model intensity changes of image may be detected. This model may be implemented economically because each neuron has only four fixed-valued synapes.

• Journal of Environmental Science International
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• v.3 no.3
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• pp.241-252
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• 1994

Meteorological parameters In the atmospheric boundary layer and the vertical and horizontal dispersion parameters were determined by analyzing the data obtained by the special upper-air observations of one clear day for each season from October 1991 to August 1992. The concentration of the aklospheric pollutants over Taegu was analyzed by using the application of the Gaussian diffusion model. In the diurnal variation of diffusion of atmospheric pollutants, vertical diffusion due to turbulence is active in daytime while horizontal diffusion due to wind is active in nighttime. The mean concentration of pollutants in the side of downwind is higher during the daytime than the nighttime. Thus, the height of the mixed-layer at the nighttime considered as the most important parameter of the mean concentration of pollutants. In the seasonal variation of diffusion of atmospheric pollutants, vertical diffusion due to strong solar radiation is active in summer case day, and horizontal diffusion due to strong wind is active in winter case day. In winter case day, the mean concentration of pollutants in the side of downwind is maximum in the daytime. However, in summer case day, that is maximum in the nighttime.

• Nuclear Engineering and Technology
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• v.17 no.3
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• pp.216-223
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• 1985

The Gaussian plume model is widely used to calculate the concentrations of gaseous radioactive effluents in the atmosphere. This model assumes that the terrain is flat, so that the dispersion coefficients which are the most important parameters in this model must be compensated in complex terrain such as in Korea. In this study the compensation of vertical dispersion coefficient in two dimensional x-z plane has been accomplished by comparing the Gaussian plume model with numerical model. The results show that the concentractions of radioactive effluents over complex terrain are more dilluted than those expected over flat terrain.

• Journal of Radiation Protection and Research
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• v.9 no.1
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• pp.26-32
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• 1984

The exposure doses by the radioactive gaseous effluents from nuclear power plants are investigated in the two cases of normal operation and hypothetical accident. Gaussian equation is adapted in the normal operation as the diffusion model of effluents for long period, which uses annual average meteorological data. But the real time models have been used in the case of accidents which analyze the changes of wind direction and speed. In this study the annual exposure doses by the normal operation of Kori unit 1 during $1977{\sim}1982$ were calculated on the basis of the atmospheric diffusion factor by the Gaussian straight line model. And the image processing technique was suggested as the effective method through the wind tunnel experiments to get the characteristic value of atmospheric diffusion coefficient required especially in the accidents of nuclear power plants.

• Environmental Sciences Bulletin of The Korean Environmental Sciences Society
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• v.4 no.4
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• pp.263-271
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• 2000

To investigate the distribution of air pollutants dispersion in the horizontal wind fields, a chaff release experiment was carried out by an airplane. The temporal and spatial variations of a chaff plume from an elevated point source using the WSR-88D(NEXRAD) radar. The observed profiles of radar reflectivity were compared with the Gaussian diffusion model at slightly unstable atmospheric condition. The present study shows that the distributions of radar reflectivity from chaffs and their concentration by the model are in general agreement with time variation. The dispersion coefficients in downwind($\sigma$(sub)x) and crosswind($\sigma$(sub)y) spread data exceeded what has generally been found at Pasquill and Brigg\`s estimates. As a result, it was clearly shown that horizontal and vertical diffusion coefficients are more accurately determined as compared with theoretical coefficients. At longer diffusion distances(than 10km), a radar observation provided the determination of maximum range and diffusion height more qualitatively, too.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.1
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• pp.120-126
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• 1995

The diffusion neural network can be cfficiently applied to the Gaussian processing. For example, a difference of two Gaussians(DOG) is performed by this network with ease. In this paper, we model a neural network to perform the function /t(.del.${\Delta}^{2}$G) by using the diffusion neural network. This model is used to detect the edges of moving target in image. By this model not only moving target is separated from stationary background but also their trajectories are obtained using accumulated past information in the diffusion neural network. Furthermore this model needs a small number of connections per cell and the connection weights are fixed-valued. Therefore its hardware can be easily implemented with simple structure.