• Journal of Preventive Medicine and Public Health
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• v.43 no.2
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• pp.109-116
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• 2010

Objectives: The pandemic of novel influenza A (H1N1) virus has required decision-makers to act in the face of the substantial uncertainties. In this study, we evaluated the potential impact of the pandemic response strategies in the Republic of Korea using a mathematical model. Methods: We developed a deterministic model of a pandemic (H1N1) 2009 in a structured population using the demographic data from the Korean population and the epidemiological feature of the pandemic (H1N1) 2009. To estimate the parameter values for the deterministic model, we used the available data from the previous studies on pandemic influenza. The pandemic response strategies of the Republic of Korea for novel influenza A (H1N1) virus such as school closure, mass vaccination (70% of population in 30 days), and a policy for anti-viral drug (treatment or prophylaxis) were applied to the deterministic model. Results: The effect of two-week school closure on the attack rate was low regardless of the timing of the intervention. The earlier vaccination showed the effect of greater delays in reaching the peak of outbreaks. When it was no vaccination, vaccination at initiation of outbreak, vaccination 90 days after the initiation of outbreak and vaccination at the epidemic peak point, the total number of clinical cases for 400 days were 20.8 million, 4.4 million, 4.7 million and 12.6 million, respectively. The pandemic response strategies of the Republic of Korea delayed the peak of outbreaks (about 40 days) and decreased the number of cumulative clinical cases (8 million). Conclusions: Rapid vaccination was the most important factor to control the spread of pandemic influenza, and the response strategies of the Republic of Korea were shown to delay the spread of pandemic influenza in this deterministic model.

• Journal of Korean Society of Transportation
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• v.24 no.3 s.89
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• pp.17-27
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• 2006

Car following model is a theory that examines changes of condition and interrelationship of acceleration deceleration. headway, velocity and so on closely based on the hypothesis that the Posterior vehicle always follows the preceding vehicle. Car following mode) which is one of the research fields of microscopic traffic flow was first introduced in 1950s and was in active progress in 1960s. However, due to the limitation of data gathering the research depression was prominent for quite a while and then soon was able to tune back on track with development in global positioning system using satellite and generalization of computer use. Recently, there has been many research studies using reception materials of global Positioning system(GPS). Introducing GPS technology to traffic has made real time tracking of a vehicle position possible. Position information is sequential in terms of time and simultaneous measurement of several vehicles in continuous driving is also practicable. Above research was focused on judging whether it is feasible to overcome the following model research by adopting the GPS reception device that was restrictively proceeded due to the limitation of data gathering. For practical judgment, we measured the accuracy and confidence level of the GPS reception devices material by carrying out a practical experiment. Car following model is also being applied in simulations of traffic flow analysis, but due to the difficulty of estimating parameters the basis of the above result. it is our goal to produce an accurate calibration of car following model's parameters that is suitable in this domestic actuality.

• Journal of Astronomy and Space Sciences
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• v.23 no.4
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• pp.345-354
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• 2006

Korean Peninsula has been postulated to be on the Eurasian plate(EU). On the other hand, recent seismological works and GPS researches suggest that it is on a separate plate called the Amurian plate (AM). However, the GPS results we inconsistent with each other beyond the estimated statistical errors. Moreover, the estimated plate motion parameter, which we obtained from the velocity data of six Korean GPS stations, was not well agreeing with any existing results. Therefore, independent measurements are required to distinguish those results. In near future, we will have 4 VLBI stations in Korea. This compact Korean VLBI array is capable of achieving good determination of the plate motion parameters if it is located on stable sites. We estimated the precision of the AM motion parameters with the Korean VLBT array. The results showed that the Korean VLBI array would verify the existence of the AM, as far as the observation precision of 0.2-0.5mm/yr for station velocities is achieved. Therefore, new Korean geodetic VLBI array can contribute to crustal deformation studies in East Asia.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Herbal Formula Science
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• v.25 no.4
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• pp.483-497
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• 2017

Background : Behavioral stress has been suggested as one of the significant factors that is able to disrupt physiological systems and cause depression as well as changes in various body systems. The stressful events can alter cognition, learning, memory and emotional responses, resulting in mental disorders such as depression and anxiety. Results : We used a restraint stress model to evaluate the alteration of behavior and stress-related blood parameter. The animals were randomly divided into two groups of five animals each group. Furthermore, we assessed the change of body weight to evaluate the locomotor activity as well as status of emotional and anxiety in mice. After 7 days of restraint stress, the body weight had significantly decreased in the restraint stress group compared with the control group. We also observed stress-associated behavioral alterations, as there was a significant decrease in open field and forced swim test, whereas the immobilization time was significantly increased in the stress group compared to the control group. We observed the morphological changes of neuronal death and microglia by immunohistochemistry and western blot. In our study restraint stress did not cause change in neuronal cell density in the frontal cortex and CA1 hippocampus region, but there was a trend for an increased COX-2 and iNOS protein expression and microglia (CD11b) in brain, which is restraint stress. Conclusion : Our study, there were significant alterations observed in the behavioral studies. We found that mice undergoing restraint stress changed behavior, confirming the increased expression of inflammatory factors in the brain.

• The Korean Journal of Nuclear Medicine
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• v.27 no.2
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• pp.261-269
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• 1993

When monoclonal antibodies are used in radioimmunoassay or immunoscintigraphic studies, post-labelling immunoreativity is a critical parameter. $^{125}I$ was incorporated to CEA-79 (anti CEA monoclonal antibody developed in Korea) by chloramine T and iodogen method with variable specific activities from $0.1{\mu}Ci/{\mu}g$ to $100{\mu}Ci/{\mu}g$. We used a new method to evaluate the immunoreactivites of modified antibody relative to the unlabelled native antibody from competitive binding assay. The effect of immunoreactivity and specific activity to the sensitivity of radioimmunometric assay was also evaluated. As a result, chloramine T method was better than iodogen method in radioiodination of CEA-79, because the immunoreactivity of antibody was relatively well reserved and more stable. New competitive binding assay was simple and effective to evaluate the change of immunoreactivity in radiolabelling. Antibody with high immunoreactivity and high specific activity improved the sensitivity of radioimmunometric assay, whereas antibody with high specific activity but low immunoreactivity didn't. The immunoreactivity and specific activity should be optimized according to the clinical un, and competitive binding method is useful in selection of optimal radiolabelling assay.

• Journal of the Korean Society of Groundwater Environment
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• v.2 no.2
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• pp.78-84
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• 1995

In 1983, 83 Wenner vertical electrical sounding(VES)s and 22 pumping tests had been carried out by Korea Agricultural Development Corporation(KADC) in Guam Myun, Yeoju Gun, Kyounggi Province. Also, 10 boreholes had been constructed in the area. Using these data electrical and hydraulic properties of aquifer in the Ganam area are investigated in this study. Assuming that the underground is 1-D, VES data are analyzed. Data analysis shows that the subsurface of study area can be interpreted as 4-layer structure and the 3rd layer which is regarded as aquifer has mean thickness of 10 m and mean resistivity of 506 ohm-m and rests on resistive bedrock. Under the circumstances, as most part of electric current flows parallel to the bedding, longitudinal unit conductance is an important parameter controlling VES curves and very closely correlates with transmissivity of aquifer in the study area. Thus, relation between longitudinal unit conductance and transmissivity is investigated in this study. Since resistivity and thickness of each layer are obtained from interpretation of VES data, the relations between transmissivity and resistivity, and between hydraulic conductivity and resistivity are also studied. Studies of such relations show that longitudinal conductance is proportional to transmissivity, and resistivity is inversely proportional to transmissivity and hydraulic conductivity.

• Journal of Korean Society of Transportation
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• v.34 no.3
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• pp.278-290
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• 2016

Maximum traffic flow rate is an important performance measure of operational status in transport networks, and has been considered as a key parameter for transportation operation since a bottleneck in congestion decreases maximum traffic flow rate. Although previous studies for traffic flow analysis have been widely conducted, a detection method for changes in dynamic traffic flow has been still veiled. This paper explores the dynamic traffic flow detection that can be utilized for various traffic operational strategies. Turning point analysis (TPA), as a statistical method, is applied to detect the changes in traffic flow rate. In TPA, Bayesian approach is employed and vehicle arrival is assumed to follow Poisson distribution. To examine the performance of the TPA method, traffic flow data from Jayuro urban expressway were obtained and applied. We propose a novel methodology to detect turning points of dynamic traffic flow in real time using TPA. The results showed that the turning points identified in real-time detected the changes in traffic flow rate. We expect that the proposed methodology has wide application in traffic operation systems such as ramp-metering and variable lane control.

• Journal of Korean Society of Environmental Engineers
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• v.28 no.4
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• pp.429-437
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• 2006

To establish the efficient treatment technology of chemical cleaning wastewater from power plant, several AOPs($UV/H_2O_2,\;UV/TiO_2/H_2O_2$, Photo-Fenton oxidation) were investigated. Treatment efficiencies and the electrical energy requirements based on the EE/O parameter(the electrical energy, required per order of pollutant removal in $1m^3$ wastewater) were evaluated. TOC removal efficiencies of $UV/H_2O_2,\;UV/TiO_2/H_2O_2$, Photo-Fenton oxidation at the optimum conditions were 95.5%, 92.3%, 91.5%, respectively. The electrical energy requirements of $UV/H_2O_2,\;UV/TiO_2/H_2O_2$, Photo-Fenton oxidation were $11.26kWh/m^3,\;3.85kWh/m^3,\;0.799kWh/m^3$, respectively. From these results, it could be concluded that all of the three oxidation processes were effective for the degradation of citric acid. Considering the treatment efficiency and economical aspect, photo-Fenton oxidation was the most efficient treatment process among the three processes tested.

• 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.