• 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.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.347-352
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• 2022

The paper presents how to update impedance control parameters for dual-arm manipulators using EMG signals and motions of the operator. Since the hand motions of the dual-arm are modeled to be the mass-spring-damper system in this paper, the impedance parameter update method is an important issue to reflect the operator's force. However, task space inertia to be used as the mass parameter goes to infinity if the manipulator approaches a kinematic singularity. To alleviate this issue, the impedance (stiffness and damping) parameters are divided with a diagonal element of the task space inertia. Also, the stiffness and damping matrices are updated using the normalized EMG signals captured from the operator's forearm. Through this process, the motion of the dual-arm manipulator is more stabilized even though it approaches the kinematic singularity.

• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.54-58
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• 2023

In this study, we compared the detection accuracy of the parameters of the scaffold failure detection model. A detection algorithm based on convolutional neural network was used to construct a failure detection model for scaffold. The parameter properties of the model were changed and the results were quantitatively verified. The detection accuracy of the model for each parameter was compared and the parameter with the highest accuracy was identified. We found that the activation function has a significant impact on the detection accuracy, which is 98% for softmax.

• Carbon letters
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• v.10 no.4
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• pp.293-299
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• 2009

Potassium hydroxide activated carbons were prepared from Egyptian petroleum cokes with different KOH/coke ratios and at different activation temperatures and times. The textural properties were determined by adsorption of nitrogen at $-196^{\circ}C$. The adsorption of iodine and methylene blue was also investigated at $30^{\circ}C$. The surface area and the non-micropore volume increased whereas the micropore volume decreased with the increase of the ratio KOH/coke. Also the surface area and porosity increased with the rise of activation temperature from 500 to $800^{\circ}C$. Textural parameter considerably increased with the increase of activation time from 1 to 3 h. Further increasing of activation time from 3 to 4 h was associated with a less pronounced increase in textural parameters. The adsorption of iodine shows the same trend of surface area and porosity change exhibited by nitrogen adsorption, with KOH/coke ratio and temperature of activation. Adsorption of methylene blue follows pseudo-first-order kinetics and its equilibrium adsorption follows Langmuir and D-R models.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.10
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• pp.407-420
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• 2021

Deep neural networks are widely used to solve various problems. However, the deep neural network with a deep hidden layer frequently has a vanishing gradient or exploding gradient problem, which is a major obstacle to learning the deep neural network. In this paper, we propose a parametric activation function to alleviate the vanishing gradient problem that can be caused by nonlinear activation function. The proposed parametric activation function can be obtained by applying a parameter that can convert the scale and location of the activation function according to the characteristics of the input data, and the loss function can be minimized without limiting the derivative of the activation function through the backpropagation process. Through the XOR problem with 10 hidden layers and the MNIST classification problem with 8 hidden layers, the performance of the original nonlinear and parametric activation functions was compared, and it was confirmed that the proposed parametric activation function has superior performance in alleviating the vanishing gradient.

• Journal of Applied Reliability
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• v.15 no.1
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• pp.52-59
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• 2015

In this Study, we tested electroluminescent film to accelerate life by temperature, humidity, voltage, and frequency. We analyzed brightness data to estimate activation energy and verify it's suitability. All of the tests performed in operating condition. Because electroluminescent film is mostly degraded by fluorescent in operating condition. Two different sample groups were tested and compared to find common parameter.

• Journal of the Korean Ceramic Society
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• v.35 no.8
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• pp.890-893
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• 1998

• ETRI Journal
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• v.15 no.2
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• pp.35-51
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• 1993

This paper presents function approximation based on nonparametric estimation. As an estimation model of function approximation, a three layered network composed of input, hidden and output layers is considered. The input and output layers have linear activation units while the hidden layer has nonlinear activation units or kernel functions which have the characteristics of bounds and locality. Using this type of network, a many-to-one function is synthesized over the domain of the input space by a number of kernel functions. In this network, we have to estimate the necessary number of kernel functions as well as the parameters associated with kernel functions. For this purpose, a new method of parameter estimation in which linear learning rule is applied between hidden and output layers while nonlinear (piecewise-linear) learning rule is applied between input and hidden layers, is considered. The linear learning rule updates the output weights between hidden and output layers based on the Linear Minimization of Mean Square Error (LMMSE) sense in the space of kernel functions while the nonlinear learning rule updates the parameters of kernel functions based on the gradient of the actual output of network with respect to the parameters (especially, the shape) of kernel functions. This approach of parameter adaptation provides near optimal values of the parameters associated with kernel functions in the sense of minimizing mean square error. As a result, the suggested nonparametric estimation provides an efficient way of function approximation from the view point of the number of kernel functions as well as learning speed.

• Advances in materials Research
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• v.3 no.4
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• pp.217-225
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• 2014

In this study, a forging steel alloyed with both Nb and V was used as experimental material and the hot deformation behavior has been studied for this steel by conducting the compressive deformation test at temperature of $900-1150^{\circ}C$ and strain rate of $0.01-0.01s^{-1}$ in a MMS-300 thermo-mechanical simulator. The microstructure evolution, particularly the dynamically recrystallized microstructure, of the experimental steel at elevated temperatures, strain rates and strain levels, was characterized by optical microstructural observation and the constitutive equation in association with the activation energy and Zener-Hollomon parameter. The curves of strain hardening rate versus stress were used to determine the critical strain and peak strain, and their relation was connected with Zener-Hollomon parameter. Under the conditions of processing temperature $900^{\circ}C$ and strain rate $0.01s^{-1}$, the dynamic recrystallization took place and the austenite grain size was refined from $164.5{\mu}m$ to $28.9{\mu}m$.

• Nuclear Engineering and Technology
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• v.6 no.2
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• pp.89-96
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• 1974

The cross-section ratios of the nuclear isomeric pairs $^{80}$ B $r^{m, g}$, sup 81/S $e^{m, g}$, $^{104}$ R $h^{m, g}$, $^{116}$ I $n^{m, g}$ and $^{134}$ C $s^{m, g}$ through the radiative thermal neutron capture process have been studied. The experimental values of these ratios obtained by the activation method have been compared with the calculated ones deduced from the modified Huizenga-Vandenbosch method. Agreement between these values within 30% could be attained by controlling the spin cut-off parameter and gamma-ray multiplicity.