• Particle and aerosol research
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• v.7 no.2
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• pp.59-68
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• 2011

The objective of the study was to investigate the main factors that contribute the variation of $PM_{10}$ concentration of Seoul and to quantify their effects using generalized additive model (GAM). The analysis was performed with 3 year air pollution data (2004~2006) measured at 27 urban sites and 7 roadside sites in Seoul, a background site in Gangwha and a rural site in Pocheon. The diurnal variation of urban $PM_{10}$ concentrations of Seoul showed a typical bimodal pattern with the same peak times as that of roadside, and the maximum difference of $PM_{10}$ level between urban and roadside was about $14{\mu}g/m^{3}$ at 10 in the morning. The wind direction was found to be a major factor that affects $PM_{10}$ level in all investigated areas. The overall $PM_{10}$ level was reduced when air came from east, but background $PM_{10}$ level in Gangwha was rather higher than the urban $PM_{10}$ level in Seoul, indicating that the $PM_{10}$ level in Gangwha is considerably influenced by that in Seoul metropolitan area. When hourly variations of $PM_{10}$ were analyzed using GAM, wind direction and speed explained about 34% of the variance in the model where the variables were added as a 2-dimensional smoothing function. In addition, other variables, such as diurnal variation, difference of concentrations between roadside and urban area, precipitation, month, and the regression slope of a plot of carbon monooxide versus $PM_{10}$, were found to be major explanatory variables, explaining about 64% of total variance of hourly variations of $PM_{10}$ in Seoul.

• Journal of Korea Multimedia Society
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• v.3 no.3
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• pp.253-262
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• 2000

This paper proposes a new RBF neural network that determines the number and the center of hidden neurons based on the adaptive feature extraction for the input data. The principal component analysis is applied for extracting adaptively the features by reducing the dimension of the given input data. It can simultaneously achieve a superior property of both the principal component analysis by mapping input data into set of statistically independent features and the RBF neural networks. The proposed neural networks has been applied to classify the 200 breast cancer databases by 2-class. The simulation results shows that the proposed neural networks has better performances of the learning time and the classification for test data, in comparison with those using the k-means clustering algorithm. And it is affected less than the k-means clustering algorithm by the initial weight setting and the scope of the smoothing factor.

• Korean Journal of Applied Biomechanics
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• v.16 no.4
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• pp.115-124
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• 2006

This study was conducted to investigate the running motion in the max-velocity phase(150-160m) and the fatigue phase(350-360m) during 400m sprint by performed elementary school athletes. Eighteen elementary school male athletes who achieved at least the 3rd place in the sprint at the Korea Gangwon-Do elementary school track and field meetings during 2004 and 2005 were selected as subjects. The running motions performed by the subjects were recorded using two 8mm high speed cameras at the nominal speed of 100 frames per second. The Direct Linear Transformation technique was adopted from the beginning of filming to the final stage of data extraction. KWON 3D motion analysis package program was used to compute the 3 Dimensional coordinates, smoothing factor in which lowpass filtering method was used and cutoff frequency was 6.0 Hz. The movement patterns during foot touchdown and takeoff for the running stride were related with the biomechanical consideration. Within the limitations of this study it is concluded: In order to increase running velocity, several conditions must be fullfilled at the instant of leg touchdown and takeoff during the fatigue phase(350-360m). First, the body C.O.G(Center of Gravity) height should be raised at the instant of leg touchdown and takeoff during the fatigue phase. Second, the foot contact time should be shortened and the takeoff distance should be increased at the foot takeoff during the fatigue phase. Third, the shank angular velocity with respect to a transverse axis through the center of gravity should be increased during the leg touchdown and takeoff in the fatigue phase. Forth, the active landing style described as clawing the ground with the sole of the foot should be performed during the leg touchdown and takeoff in the fatigue phase) phase. Fifth, In order to increase running velocity in the fatigue phase while taking a slightly greater leg knee angle and body lean angle within the range of the subject's running motion during the fatigue phase would result in greater flight distance.

• Progress in Medical Physics
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• v.16 no.3
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• pp.138-147
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• 2005

The Monte Carlo method cannot have been used for routine treatment planning because of heavy time consumption for the acceptable accuracy. Since calculation time is proportional to particle histories, we can save time by decreasing the number of histories. However, a small number of histories can cause serious uncertainties. In this study, we proposed Monte Carlo dose computation time and uncertainty reduction method using specially designed filters and adaptive denoising process. Proposed algorithm was applied to 6 MV photon and 21 MeV electron dose calculations in homogeneous and heterogeneous phantoms. Filtering time was negligible comparing to Monte Carlo simulation time. The accuracy was improved dramatically in all situations and the simulation of 1 $\%$ to 10$\%$ number of histories of benchmark in photon and electron dose calculation showed the most beneficial result. The empirical reduction of necessary histories was about a factor of ten to fifty from the result.

• The Transactions of the Korea Information Processing Society
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• v.7 no.11
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• pp.3408-3416
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• 2000

This paper proposes an efficient method for improving the performance of a general regression neural network by using the feature to the independent variables as the center for partern-layer neurons. The adaptive principal component analysis is applied for extracting, efficiently the fcarures by reducing the dimension of given independent variables. In can acluevc a supertor property of the principal component analysis that converts input data into set of statistically independent features and the general regression neuralnetwork, espedtively. The proposed general regression neural network has been applied to regress the Solow's economy(2-independent variable set) and the wie elephone(1-independent vanable set). The simulation results show that the proposed meural networks have better performances of the regressionfor the lest data, in comparison with those using the means or the weighted means of independent variables. Also,it is affected less by the number of neurons and the scope of the smoothing factor.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3708-3728
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• 2021

Indian herbal plants are used in agriculture and in the food, cosmetics, and pharmaceutical industries. Laboratory-based tests are routinely used to identify and classify similar herb species by analyzing their internal cell structures. In this paper, we have applied computer vision techniques to do the same. The original leaf image was preprocessed using the Chan-Vese active contour segmentation algorithm to efface the background from the image by setting the contraction bias as (v) -1 and smoothing factor (µ) as 0.5, and bringing the initial contour close to the image boundary. Thereafter the segmented grayscale image was fed to a leaky capacitance fired neuron model (LCFN), which differentiates between similar herbs by combining different groups of pixels in the leaf image. The LFCN's decay constant (f), decay constant (g) and threshold (h) parameters were empirically assigned as 0.7, 0.6 and h=18 to generate the 1D feature vector. The LCFN time sequence identified the internal leaf structure at different iterations. Our proposed framework was tested against newly collected herbal species of natural images, geometrically variant images in terms of size, orientation and position. The 1D sequence and shape features of aloe, betel, Indian borage, bittergourd, grape, insulin herb, guava, mango, nilavembu, nithiyakalyani, sweet basil and pomegranate were fed into the 5-fold Bayesian regularization neural network (BRNN), K-nearest neighbors (KNN), support vector machine (SVM), and ensemble classifier to obtain the highest classification accuracy of 91.19%.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.9 no.1
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• pp.9-23
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• 1997

A numerical study of a two-layer, stratified flow is investigated, using the implicit finite difference method in one dimension. The results of computational method have been tested and, in case of lock exchange flow, compared with the results of experimental data. The results of model experiments with various interfacial, bottom friction coefficients along with various time weighting factor of numerical scheme and dissipative interface are shown and discussed. Two-layer model experiment has been also carried out to investigate the generation and propagation characteristics of internal tidal wave over the steep bottom topography under stratified condition. The internal wave seems to well radiate through the downstream boundary under the experiments adopting radiation conditions both at two layers and only at upper layer, confirming the applicability of radiational boundary condition in stratified flows. It is also shown that the internal wave through the downstream boundary propagates more actively with increasing thickness of lower layer in the downstream. This implies that the potential tidal energy in the interface will depend upon the thickness of lower layer for the constant thickness of upper layer.

• Journal of the Korean association of regional geographers
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• v.22 no.3
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• pp.681-702
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• 2016

Two different modelling approaches to dune dynamics have been established thus far; continuous models that emphasize the precise representation of wind field, and feedback-based models that focus on the interactions between dunes, rather than aerodynamics. Though feedback-based models have proven their capability to capture the essence of dune dynamics, the compatibility issues on these models have less been addressed. This research investigated, mostly from the theoretical point of view, the algorithmic compatibility of three feedback-based dune models: sand slab models, Nishimori model, and de Castro model. Major findings are as follows. First, sand slab models and de Castro model are both compatible in terms of flux perspectives, whereas Nishimori model needs a tuning factor. Second, the algorithm of avalanching can be easily implemented via repetitive spatial smoothing, showing high compatibility between models. Finally, the wind shadow rule might not be a necessary component to reproduce dune patterns unlike the interpretation or assumption of previous studies. The wind shadow rule, rather, might be more important in understanding bedform-level interactions. Overall, three models show high compatibility between them, or seem to require relatively small modification, though more thorough investigation is needed.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.10
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• pp.391-398
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• 2021

Since the leased line is a structure that exclusively uses two connected areas for data transmission, a stable quality level and security are ensured, and despite the rapid increase in the number of switched lines, it is a line method that is continuously used a lot in companies. However, because the cost is relatively high, one of the important roles of the network operator in the enterprise is to maintain the optimal state by properly arranging and utilizing the resources of the network leased line. In other words, in order to properly support business service requirements, it is essential to properly manage bandwidth resources of leased lines from the viewpoint of data transmission, and properly predicting and managing leased line usage becomes a key factor. Therefore, in this study, various prediction models were applied and performance was evaluated based on the actual usage rate data of leased lines used in corporate networks. In general, the performance of each prediction was measured and compared by applying the smoothing model and ARIMA model, which are widely used as statistical methods, and the representative models of deep learning based on artificial neural networks, which are being studied a lot these days. In addition, based on the experimental results, we proposed the items to be considered in order for each model to achieve good performance for prediction from the viewpoint of effective operation of leased line resources.

• Journal of the Korean Child Neurology Society
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• v.26 no.4
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• pp.227-232
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• 2018

Purpose: Febrile seizure (FS) is the most common type of seizure in children between 6 months to 5 years of age. A family history of febrile seizures can increase the risk a child will have a FS. Yet, prevalence of FS regarding external environment has not been clearly proved. This study attempts to determine the association between prevalence of FS and weather. Methods: This study included medical records from the Korea National Health Insurance Review and Assessment Service. Data were collected from 29,240 children, born after 2004, diagnosed with FS who were admitted to one of the hospitals in Seoul, Korea, between January 2009 and December 2013. During the corresponding time period, data from the Korea Meteorological Administration on daily monitoring of four meteorological factors (sea-level pressure, amount of precipitation, humidity and temperature) were collected. The relationships of FS prevalence and each meteorological factor will be designed using Poisson generalized additive model (GAM). Also, the contributory effect of viral infections on FS prevalence and weather will be discussed. Results: The amount of precipitation was divided into two groups for comparison: one with less than 5 mm and the other with equal to or more than 5 mm. As a result of Poisson GAM, higher prevalence of FS showed a correlation with smaller amount of precipitation. Smoothing function was used to classify the relationships between three variables (sea-level pressure, humidity, and temperature) and prevalence of FS. FS prevalence was correlated with lower sea-level pressure and lower humidity. FS prevalence was high in two temperature ranges (-7 to $-1^{\circ}C$ and $18-21^{\circ}C$). Conclusion: Low sea-level pressure, small amount of precipitation, and low relative air humidity may increase FS prevalence risk.