• International journal of advanced smart convergence
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• v.13 no.2
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• pp.80-87
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• 2024

This paper focuses on improving accuracy in constrained computing settings by employing the ReLU (Rectified Linear Unit) activation function. The research conducted involves modifying parameters of the ReLU function and comparing performance in terms of accuracy and computational time. This paper specifically focuses on optimizing ReLU in the context of a Multilayer Perceptron (MLP) by determining the ideal values for features such as the dimensions of the linear layers and the learning rate (Ir). In order to optimize performance, the paper experiments with adjusting parameters like the size dimensions of linear layers and Ir values to induce the best performance outcomes. The experimental results show that using ReLU alone yielded the highest accuracy of 96.7% when the dimension sizes were 30 - 10 and the Ir value was 1. When combining ReLU with the Adam optimizer, the optimal model configuration had dimension sizes of 60 - 40 - 10, and an Ir value of 0.001, which resulted in the highest accuracy of 97.07%.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.125-130
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• 2020

At the end of 1997, the volatility of the exchange rate intensified as the nation's exchange rate system was converted into a free-floating exchange rate system. As a result, managing the exchange rate is becoming a very important task, and the need for forecasting the exchange rate is growing. The exchange rate prediction model using the existing exchange rate prediction method, statistical technique, cannot find a nonlinear pattern of the time series variable, and it is difficult to analyze the time series with the variability cluster phenomenon. And as the number of variables to be analyzed increases, the number of parameters to be estimated increases, and it is not easy to interpret the meaning of the estimated coefficients. Accordingly, the exchange rate prediction model using artificial neural network, rather than statistical technique, is presented. Using DNN, which is the basis of deep learning among artificial neural networks, and LSTM, a recurrent neural network model, the number of hidden layers, neurons, and activation function changes of each model found the optimal exchange rate prediction model. The study found that although there were model differences, LSTM models performed better than DNN models and performed best when the activation function was Tanh.

• Resources Recycling
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• v.12 no.3
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• pp.13-24
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• 2003

The production characteristics of activated carbon from waste walnut shell have been investigated by taking activation temperature, activation time, amount of activating agent, and kind of activating agent as the major influential factors. The adsorption capacity of the activated carbon which was produced using phosphoric acid as the activating agent increased with activation temperature and showed its greatest value at about $550^{\circ}C$. Yield for activated carbon was observed to decrease continuously as the activation temperature was raised. The optimal activation time for the highest adsorption capacity was found to be about 2 hr, and as the activation time increased the yield for activated carbon was showed to decrease continuously. The increase in the amount of activating agent resulted in the increase of the yield for activated carbon, however, excessive amount of activating agent deteriorated its adsorption capacity reversely. The variations of the microstructure of activated carbon observed by SEM with several influential factors, correlated very well with its changes in the adsorbability with the same factors and the kind of activating agent was found to play an important role in the determination of the adsorption capacity of activated carbon. To investigate the adsorption characteristics of the produced activated carbon, the adsorption reactions of $Cu^{2+}$ ion were examined using the produced activated carbon as the adsorbent. In general, the kinetics of the adsorption of $Cu^{2+}$ ion was observed to follow a 2nd-order reaction and the rate constant for adsorption reaction increased as the initial concentration of adsorbate was diminished. The equilibrium adsorption of $Cu^{2+}$ was explained well with Freundlich model and its adsorption reaction was found to be endothermic. The activation energy for adsorption was calculated to be 13.07 kcal/mol, which implied that the adsorption reaction was very irreversible, and several thermodynamic parameters of adsorption reaction were estimated using van't. Hoff equation and thermodynamic relationships.

• Journal of Physiology & Pathology in Korean Medicine
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• v.24 no.5
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• pp.872-876
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• 2010

The present study found that EA pre-treatment effectively attenuated both spinal ERK activation and hyperalgesia against carrageenan-induced inflammation, whereas EA co-treatment with carrageenan injection unexpectedly elevated ERK activation in a synergistic manner and virtually had no analgesic effect. Therefore, we have concluded that the molecular mechanism of EA anagesia may be related to the inhibition of spinal ERK activation. Further experiments are required to find the intermediate candidates which transmits the pain-alleviating signals on the way of inhibiting ERK activation by EA.

• Bulletin of the Korean Chemical Society
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• v.29 no.10
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• pp.1920-1926
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• 2008

We investigated the C-H bond activation mechanism of aldimine by the [RhCl$(PPH_3)_3$] model catalyst using DFT B3LYP//SBKJC/6-31G*/6-31G on GAMESS. Due to their potential utility in organic synthesis, C-H bond activation is one of the most active research fields in organic and organometallic chemistry. C-H bond activation by a transition metal catalyst can be classified into two types of mechanisms: direct C-H bond cleavage by the metal catalyst or a multi-step mechanism via a tetrahedral transition state. There are three structural isomers of [RhCl$(PH_3)_2$] coordinated aldimine that differ in the position of chloride with respect to the molecular plane. By comparing activation energies of the overall reaction pathways that the three isomeric structures follow in each mechanism, we found that the C-H bond activation of aldimine by the [RhCl$(PH_3)_3$] catalyst occurs through the tetrahedral intermediate.

• Journal of Digital Convergence
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• v.9 no.1
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• pp.107-114
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• 2011

In order to have the highest level of a certain society's technology be evaluated as digital contents technology the value concept of such technology's social utilization must be established while active investment on the technology takes place and makes it the subject of social capitalization. This study wishes to discuss the strategies and methods of establishing and managing IP holding company which requires business activation with digital contents technology at its base, research ways of vitalizing IP holding company to expand social utility values of contents technology, suggest necessary systemic improvements and investment activation methods, management structure, and governance structure by investigating ways to stimulate the industrialization of contents technology through the establishment and management of this IP holding company, and finally come up with a realistic measure to establish and manage a IP holding company. Strategies on commercialization of digital contents technology and acceleration of technology development, as well as activation of venture business set-ups will be analyzed and suggested based on such suggestions while IP holding company's digital contents technology investment activation model will be established to produce means to realize discovering superior contents companies and activation of investment, and activating high quality contents production for the global market.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.29 no.1
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• pp.17-22
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• 2016

The activation energy of a material is an important factor that significantly affects the lifetime and can be used to develop a degradation model. In this study, a thermal analysis was carried out to evaluate and collect quantitative data on the degradation of insulation materials like EPR and CSP used for nuclear power plant cables. The activation energy was determined from the relationship between log ${\beta}$ and 1/T based on the Flynn.Wall.Ozawa method, by a TGA test. The activation energy was also derived from the relationship between ln(t) and 1/T based on isothermal analysis, by an OIT test. The activation energy of EPR derived from thermal analysis was used to calculate the accelerated aging time corresponding to the number of years of use, employing the Arrhenius equation, and determine the elongation corresponding to the accelerated aging time.

• Biomolecules & Therapeutics
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• v.30 no.1
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• pp.55-63
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• 2022

Oleanolic acid (OA), a natural pentacyclic triterpenoid, has been reported to exert protective effects against several neurological diseases through its anti-oxidative and anti-inflammatory activities. The goal of the present study was to evaluate the therapeutic potential of OA against acute and chronic brain injuries after ischemic stroke using a mouse model of transient middle cerebral artery occlusion (tMCAO, MCAO/reperfusion). OA administration immediately after reperfusion significantly attenuated acute brain injuries including brain infarction, functional neurological deficits, and neuronal apoptosis. Moreover, delayed administration of OA (at 3 h after reperfusion) attenuated brain infarction and improved functional neurological deficits during the acute phase. Such neuroprotective effects were associated with attenuation of microglial activation and lipid peroxidation in the injured brain after the tMCAO challenge. OA also attenuated NLRP3 inflammasome activation in activated microglia during the acute phase. In addition, daily administration of OA for 7 days starting from either immediately after reperfusion or 1 day after reperfusion significantly improved functional neurological deficits and attenuated brain tissue loss up to 21 days after the tMCAO challenge; these findings supported therapeutic effects of OA against ischemic stroke-induced chronic brain injury. Together, these findings showed that OA exerted neuroprotective effects against both acute and chronic brain injuries after tMCAO challenge, suggesting that OA is a potential therapeutic agent to treat ischemic stroke.

• Proceedings of the Korean Nuclear Society Conference
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• 1996.05b
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• pp.637-642
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• 1996

This paper presents the analysis results for the core degradation processes and the fission product release of the PHEBUS FPT0 experiment using MELCOR1.8.3. The objective of this study is to assess models associated with the core damage and fission product behavior in MELCOR. The calculation results were much improved through sensitivity studies. Thermal/hydraulic behavior in the core and the circuit was well predicted under the intact core geometry. In non-eutectic model case. the UO$_2$ dissolution model in the MELCOR always showed such a tendency that the resulting dissolved UO$_2$ mass was small at the highly oxidized condition due to the model logic. Total H$_2$ generation mass was underpredicted because the stiffner was not modeled and the liner in the shroud was not allowed to be oxidized in MELCOR. Some difficulties were found in modeling the activation product were solved by manipulating the RN input associated with the initial fission product inventory. These problem were occurred because there are no control rod model in MELCOR. Generally the fission product release ratio showed a similar trend compared with the measured data except the activation product. which have no model to simulate in MELCOR.

• Atmosphere
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• v.31 no.1
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• pp.73-83
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• 2021

Numerical weather prediction (NWP) models play an essential role in predicting weather factors, but using them is challenging due to various factors. To overcome the difficulties of NWP models, deep learning models have been deployed in weather forecasting by several recent studies. This study adapts long short-term memory (LSTM), which demonstrates remarkable performance in time-series prediction. The combination of LSTM model input of meteorological features and activation functions have a significant impact on the performance therefore, the results from 5 combinations of input features and 4 activation functions are analyzed in 9 Automated Surface Observing System (ASOS) stations corresponding to cities/islands/mountains. The optimized LSTM model produces better performance within eight forecast hours than Local Data Assimilation and Prediction System (LDAPS) operated by Korean meteorological administration. Therefore, this study illustrates that this LSTM model can be usefully applied to very short-term weather forecasting, and further studies about CNN-LSTM model with 2-D spatial convolution neural network (CNN) coupled in LSTM are required for improvement.