• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6B
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• pp.597-603
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• 2006

The purpose of this study is to improve the short term rainfall forecast skill using neural network model that can deal with the non-linear behavior between satellite data and ground observation, and minimize the flood damage. To overcome the geographical limitation of Korean peninsula and get the long forecast lead time of 3 to 6 hour, the developed rainfall forecast model took satellite imageries and wide range AWS data. The architecture of neural network model is a multi-layer neural network which consists of one input layer, one hidden layer, and one output layer. Neural network is trained using a momentum back propagation algorithm. Flood was estimated using rainfall forecasts. We developed a dynamic flood inundation model which is associated with 1-dimensional flood routing model. Therefore the model can forecast flood aspect in a protected lowland by levee failure of river. In the case of multiple levee breaks at main stream and tributaries, the developed flood inundation model can estimate flood level in a river and inundation level and area in a protected lowland simultaneously.

• Advances in nano research
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• v.16 no.4
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• pp.325-340
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• 2024

There are several novel uses for dispersing many nanoparticles into a conventional fluid, including dynamic sealing, damping, heat dissipation, microfluidics, and more. Therefore, melting heat and mass transfer characteristics of a 3-D MHD Hybrid Nanofluid flow over a rotating disc with presenting dufour and soret effects are assessed numerically in this study. In this instance, we investigated both ferric sulfate and molybdenum disulfide as nanoparticles suspended within base fluid water. The governing partial differential equations are transformed into linked higher-order non-linear ordinary differential equations by the local similarity transformation. The collection of these deduced equations is then resolved using a Chebyshev spectral collocation-based algorithm built into the Mathematica software. To demonstrate how different instances of hybrid/ nanofluid are impacted by changes in temperature, velocity, and the distribution of nanoparticle concentration, examples of graphical and numerical data are given. For many values of the material parameters, the computational findings are shown. Simulations conducted for different physical parameters in the model show that adding hybrid nanoparticle to the fluid mixture increases heat transfer in comparison to simple nanofluids. It has been identified that hybrid nanoparticles, as opposed to single-type nanoparticles, need to be taken into consideration to create an effective thermal system. Furthermore, porosity lowers the velocities of simple and hybrid nanofluids in both cases. Additionally, results show that the drag force from skin friction causes the nanoparticle fluid to travel more slowly than the hybrid nanoparticle fluid. The findings also demonstrate that suction factors like magnetic and porosity parameters, as well as nanoparticles, raise the skin friction coefficient. Furthermore, It indicates that the outcomes from different flow scenarios correlate and are in strong agreement with the findings from the published literature. Bar chart depictions are altered by changes in flow rates. Moreover, the results confirm doctors' views to prescribe hybrid nanoparticle and particle nanoparticle contents for achalasia patients and also those who suffer from esophageal stricture and tumors. The results of this study can also be applied to the energy generated by the melting disc surface, which has a variety of industrial uses. These include, but are not limited to, the preparation of semiconductor materials, the solidification of magma, the melting of permafrost, and the refreezing of frozen land.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.923-932
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• 2024

The purpose of this study is to explore the direction of teaching and learning in the era of science and technology. The conclusion is as follows: From the perspective of new materialism, the direction of teaching and learning first emphasizes the importance of learning experience. Second, a teaching method that emphasizes giving meaning is needed. Third, there is a need to consider various perspectives and backgrounds. The direction of teaching and learning in the era of phenomenology is, first, the meaning and meaning of learning is important. Second, it emphasizes the role of independent learners. Third, diverse experiences must be integrated. Fourth, a flexible approach is needed depending on the environment and situation. Rather than a fixed teaching method, it is necessary to use a variety of teaching strategies according to the situation and needs of the learner. From the perspective of actor network theory, the direction of teaching and learning needs to first embrace a variety of actors. Second, there is a need to understand the role of the mediator actor. Third, there is a need to emphasize the dynamic aspect of the network. Fourth, there is a need to emphasize interaction and giving meaning. Fifth, there is a need to strengthen interaction with technology. Sixth, there is a need to create a non-linear and open learning environment. The direction of society and teaching and learning presented in Trend Korea 2024 considers time as a precious resource in a divided society, and teaching and learning must adapt and develop in a useful direction in a changing environment.

• Korean Journal of Biological Psychiatry
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• v.5 no.1
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• pp.95-101
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• 1998

The changes of electroencephalogram(EEG) in patients with dementia of Alzheimer's type are most commonly studied by analyzing power or magnitude in traditionally defined frequency bands. However because of the absence of an identified metric which quantifies the complex amount of information, there are many limitations in using such a linear method. According to the chaos theory, irregular signals of EEG can be also resulted from low dimensional deterministic chaos. Chaotic nonlinear dynamics in the EEG can be studied by calculating the largest Lyapunov exponent($L_1$). The authors have analyzed EEG epochs from three patients with dementia of Alzheimer's type and three matched control subjects. The largest $L_1$ is calculated from EEG epochs consisting of 16,384 data points per channel in 15 channels. The results showed that patients with dementia of Alzheimer's type had significantly lower $L_1$ than non-demented controls on 8 channels. Topographic analysis showed that the $L_1$ were significantly lower in patients with Alzheimer's disease on all the frontal, temporal, central, and occipital head regions. These results show that brains of patients with dementia of Alzheimer's type have a decreased chaotic quality of electrophysiological behavior. We conclude that the nonlinear analysis such as calculating the $L_1$ can be a promising tool for detecting relative changes in the complexity of brain dynamics.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.5
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• pp.1082-1090
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• 2009

Peak expiratory flow rate(PEF) is a very important diagnostic parameter obtained from the forced vital capacity(FVC) test. The expiratory flow rate increases during the short initial time period and may cause measurement error in PEF particularly due to non-ideal dynamic characteristic of the transducer. The present study evaluated the initial rise slope($S_r$) on the flow rate signal to compensate the transducer output data. The 26 standard signals recommended by the American Thoracic Society(ATS) were generated and flown through the velocity-type respiratory air flow transducer with simultaneously acquiring the transducer output signal. Most PEF and the corresponding output($N_{PEF}$) were well fitted into a quadratic equation with a high enough correlation coefficient of 0.9997. But only two(ATS#2 and 26) signals resulted significant deviation of $N_{PEF}$ with relative errors>10%. The relationship between the relative error in $N_{PEF}$ and $S_r$ was found to be linear, based on which $N_{PEF}$ data were compensated. As a result, the 99% confidence interval of PEF error was turned out to be approximately 2.5%, which was less than a quarter of the upper limit of 10% recommended by ATS. Therefore, the present compensation technique was proved to be very accurate, complying the international standards of ATS, which would be useful to calibrate respiratory air flow transducers.