• Journal of the Korean Chemical Society
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• v.12 no.2
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• pp.61-64
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• 1968

Compressibility and expansibility of activation, ${\Delta}K^{\neq}\;and\;{\Delta}E^{\neq},$ are defined with the use of general principles that an activation parameter is the difference in partial molar quantities of the parameter for the transition and initial state. Two related parameters, (${\partial}{\Delta}H^{\neq}/{\partial}P)_{\gamma}\;and\;{\Delta}W^{\neq}(=P{\Delta}V^{\neq}),$ are also derived. Simpler interpretation of the existing kinetic data are possible with these activation parameters, while other derivations lead to complicated expressions of no practical significance.

• Fashion & Textile Research Journal
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• v.10 no.3
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• pp.394-398
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• 2008

One of barely water soluble ketones, acetophenone (AP) was dissolved in methanol and then was mixed with aqueous solution of C. I. Red Acid 114. In order to find out the role of AP in the dyeing process the rate constants and the activation parameters were calculated. The rate for the dyeing with AP was faster than that without it. Because of the reduced temperature dependence by AP the activation energy ($E_a$) for the dyeing with AP was smaller than that without it. With increasing temperature the activation enthalpy (${\Delta}H^*$), the activation entropy (${\Delta}S^*$), and the activation free energy ($G^*$) decreased, which was more noticeable in dyeing with AP. The rate constants and the activation parameters agreed well with the results from the previous reports that the ability of AP to increase disaggregation of dye molecules, loosening the wool fiber, and wickabilty of dyeing solution made it possible to dye wool fiber at low temperature.

• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.616-625
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• 2021

Deep neural networks are an approximation method that approximates an arbitrary function to a linear model and then repeats additional approximation using a nonlinear active function. In this process, the method of evaluating the performance of approximation uses the loss function. Existing in-depth learning methods implement approximation that takes into account loss functions in the linear approximation process, but non-linear approximation phases that use active functions use non-linear transformation that is not related to reduction of loss functions of loss. This study proposes parametric activation functions that introduce scale parameters that can change the scale of activation functions and location parameters that can change the location of activation functions. By introducing parametric activation functions based on scale and location parameters, the performance of nonlinear approximation using activation functions can be improved. The scale and location parameters in each hidden layer can improve the performance of the deep neural network by determining parameters that minimize the loss function value through the learning process using the primary differential coefficient of the loss function for the parameters in the backpropagation. Through MNIST classification problems and XOR problems, parametric activation functions have been found to have superior performance over existing activation functions.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.9
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• pp.371-380
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• 2022

Convolutional neural networks are widely used to manipulate data arranged in a grid, such as images. A general convolutional neural network consists of a convolutional layers and a fully connected layers, and each layer contains a nonlinear activation functions. This paper proposes a combined parametric activation function to improve the performance of convolutional neural networks. The combined parametric activation function is created by adding the parametric activation functions to which parameters that convert the scale and location of the activation function are applied. Various nonlinear intervals can be created according to parameters that convert multiple scales and locations, and parameters can be learned in the direction of minimizing the loss function calculated by the given input data. As a result of testing the performance of the convolutional neural network using the combined parametric activation function on the MNIST, Fashion MNIST, CIFAR10 and CIFAR100 classification problems, it was confirmed that it had better performance than other activation functions.

• Nuclear Engineering and Technology
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• v.55 no.4
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• pp.1571-1584
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• 2023

In this study, a numerical analysis was performed as part of an international joint research project to reproduce a real cable tray fire that occurred in the heater bay area of the turbine building of a nuclear power plant. A sensitivity analysis was performed on various input parameters to derive results consistent with the sprinkler activation time obtained from the fire event analysis. For all sensitive parameters, the normalized sprinkler activation time correlated well with the power function of the normalized sprinkler height. A correlation equation was developed to identify the sprinkler activation time at any location when determining the slope or fire growth rate under the conditions assuming a linear or t-squared heat release rate (HRR) time curve. Various cable fire growth assumptions were used to determine which assumption was better to provide the prediction coincident with the information given from the fire event analysis in terms of the sprinkler activation time and total energy generated from cables damaged by fire. In the comprehensive analysis of all the sensitive parameters, the standard deviation of the input parameters increased as the sprinkler height decreased. Within the range of the sensitivity parameter values given in this study, when considering all sprinkler heights, the standard deviation of the cable model change was the largest and that of the overhang position change was the smallest.

• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.4226-4230
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• 2022

The Korea Atomic Energy Research Institute (KAERI) has designed a Hybrid-Low Power Research Reactor (H-LPRR) which can be used for critical assembly and conventional research reactor as well. It is an open tank-in-pool type research reactor (Thermal Power: 50 kW_th) of which the most important applications are Neutron Activation Analysis (NAA), Radioisotope (RI) production, education and training. There are eight irradiation holes on the edge of the reactor core: IR (6 holes for RI production) and NA (2 holes for NAA) holes. In order to quantify the elemental concentration in target samples through the Instrumental Neutron Activation Analysis (INAA), it is necessary to measure neutron spectrum parameters such as thermal neutron flux, the deviation from the ideal 1/E epithermal neutron flux distribution (α), and the thermal-to-epithermal neutron flux ratio (f) for the irradiation holes. In this study, the MCNP6.1 code and FORTRAN 90 language are applied to determine the parameters for the two irradiation holes (NA-SW and NA-NW) in H-LPRR, and in particular its α and f parameters are compared to values of other research reactors. The results confirmed that the neutron irradiation holes in H-LPRR are designed to be sufficiently applied to neutron activation analysis, and its performance is comparable to that of foreign research reactors including the TRIGA MARK II.

• The Journal of the Korea Contents Association
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• v.10 no.4
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• pp.10-18
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• 2010

This paper presents a optimization of sigmoid activation function parameter using genetic algorithms and pattern recognition analysis in input space of two spirals benchmark problem. To experiment, cascade correlation learning algorithm is used. In the first experiment, normal sigmoid activation function is used to analyze the pattern classification in input space of the two spirals problem. In the second experiment, sigmoid activation functions using different fixed values of the parameters are composed of 8 pools. In the third experiment, displacement of the sigmoid function to determine the value of the three parameters is obtained using genetic algorithms. The parameter values applied to the sigmoid activation functions for candidate neurons are used. To evaluate the performance of these algorithms, each step of the training input pattern classification shows the shape of the two spirals.

• Bulletin of the Korean Chemical Society
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• v.29 no.3
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• pp.575-579
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• 2008

• Bulletin of the Korean Chemical Society
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• v.12 no.5
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• pp.468-473
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• 1991

Viscoelastic properties of PET filament fibers on stress relaxation were investigated in the solvents of $H_2$O, 0.05% NaOH and 50% DMF using an Instron (UTM4-100 Tensilon) with solvent chamber. The theoretical stress relaxation equation derived by applying the Ree-Eyring's hyperbolic sine law to dashpot of three element non-Newtonian model was applied to the experimental stress relaxation curves, and the model parameters $G_1,G_2$, ${\alpha}$ and ${\beta}$ were obtained. By analyzing temperature dependency of the relaxation time, the values of activation entropy, activation enthalpy and activation free energy for flow in PET filament fiber were evaluated, the activation free energy being about 25.7 kcal/mol. The self diffusion coefficient and hole distance were obtained from parameters ${\alpha}$, ${\beta}$ and crystallite size in order to study the self diffusion and the orientation of crystallites in amorphous region and the effect of solvent.

• Corrosion Science and Technology
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• v.6 no.1
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• pp.7-11
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• 2007

In this study, new evaluation method for the stability and corrosion resistance properties of passive films has been suggested by means of observation of self-activation process in open-circuit state and galvanostatic nanoscale reduction test. The experiments were performed for air-formed oxide film in case of plain carbon steel, and for anodically passivated films formed in aqueous sulfuric acid solutions in case of titanium and 304 stainless steel. From these experimental results, we derived two parameters, $i_{0}$ and $q_{0}$, which characterize the self-activation process and the properties of passive film on a stainless steel surface. The parameter $i_{0}$ was defined as the rate of self-activation, and $q_{0}$, the reduced amount of charge during the self-activation process. In conclusion, it is considered that the stability and corrosion resistance of passive metals and alloys can be evaluated quantitatively by three parameters of $\tau_{0}$, $q_{0}$, and $i_{0}$, which easily obtain by means of observing the self-activation process and galvanostatic nanoscale reduction test.