• Journal of The Korean Astronomical Society
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• v.53 no.6
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• pp.139-147
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• 2020

The sunspot area is a critical physical quantity for assessing the solar activity level; forecasts of the sunspot area are of great importance for studies of the solar activity and space weather. We developed an innovative hybrid model prediction method by integrating the complementary ensemble empirical mode decomposition (CEEMD) and extreme learning machine (ELM). The time series is first decomposed into intrinsic mode functions (IMFs) with different frequencies by CEEMD; these IMFs can be divided into three groups, a high-frequency group, a low-frequency group, and a trend group. The ELM forecasting models are established to forecast the three groups separately. The final forecast results are obtained by summing up the forecast values of each group. The proposed hybrid model is applied to the smoothed monthly mean sunspot area archived at NASA's Marshall Space Flight Center (MSFC). We find a mean absolute percentage error (MAPE) and a root mean square error (RMSE) of 1.80% and 9.75, respectively, which indicates that: (1) for the CEEMD-ELM model, the predicted sunspot area is in good agreement with the observed one; (2) the proposed model outperforms previous approaches in terms of prediction accuracy and operational efficiency.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.59-66
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• 2020

In this study, we consider the problem of forecasting the number of inbound foreigners visiting Korea. Forecasting tourism demand is an essential decision to plan related facilities and staffs, thus many studies have been carried out, mainly focusing on the number of inbound or outbound tourists. In order to forecast tourism demand, we use a seasonal ARIMA (SARIMA) model, as well as a SARIMAX model which additionally comprises an exogenous variable affecting the dependent variable, i.e., tourism demand. For constructing the forecasting model, we use a search procedure that can be used to determine the values of the orders of the SARIMA and SARIMAX. For the exogenous variable, we introduce factors that could cause the tourism demand reduction, such as the 9/11 attack, the SARS and MERS epidemic, and the deployment of THAAD. In this study, we propose a procedure, called Measuring Impact on Demand (MID), where the impact of each factor on tourism demand is measured and the value of the exogenous variable corresponding to the factor is determined based on the measurement. To show the performance of the proposed forecasting method, an empirical analysis was conducted where the monthly number of foreign visitors in 2019 were forecasted. It was shown that the proposed method can find more accurate forecasts than other benchmarks in terms of the mean absolute percentage error (MAPE).

• Bulletin of the Korean Chemical Society
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• v.8 no.4
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• pp.242-246
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• 1987

The hydrogen abstraction reactions of $SiH_4, SiH_2Cl_2 \;and\; SiHCl_3$ by ground state chlorine atoms generated photochemically from chlorine molecules have been studied at temperatures between 15 and $100^{\circ}C.$ The absolute rates for the reactions have been obtained by a competition technique using ethane as a competitor. The rate expressions ($in cm^3/mol/s$) are found to conform to an Arrhenius rate law: $k_{SiH_4} = (7.98 {\pm} 0.42) {\times} 10^{13}$ exp $[-(1250 {\pm}20)/T].$ $k_{SiH_2Cl_2} = (2.25 {\pm} 0.12) {\times} 10^{15}$ exp[-(1010 ${\pm}$ 10)/T]. $k_{SiHCl_3} = (9.04 {\pm} 0.28) {\times} 10^{14}\; exp[-(1200 {\pm} 10)/T].$ The activation energies obtained from this study represent the same trend as with the carbon analogues, while this trend was not found with respect to the bond dissociation energies among silicon compound homologues. These anomalous behaviors were interpreted in terms of electronic effects and of the structural differences between these compounds.

• English & American cultural studies
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• v.14 no.1
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• pp.193-218
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• 2014

This essay explores Congreve's last play The way of the World in terms of English new identity of the gentry represented by Mirabell in political, social and historical context of the Bloodless Revolution. Particularly, this essay focuses on behavioral differences between Mirabell and Fainall as characters who manage a certain type of acceptable Englishness through their heir. The acceptable Englishness separates what the differences are between two rakes from the outside of normative principle. The Way of the World reflects Lockean republican ideology in personal and familial relationships. Mirabell as a heroic rake represents new expectations for Englishmen who rejects absolute sovereign contrasted by Fainall's foreign tyrannical ways of domesticity. The Foreignness of Fainall's in the play is displaced by corollary change in the new model of English identity exemplified by Mirabell. Through the play, Congreve tends to satirize repressive morality of Hobbesian extremism and emphasizes the Revolution settlement based on consent sand trust instead. Mirabell's normative will harmonizes individual desire for happiness with social demand. In a sense Congreve's The Way of the World is a play reaching typical Restoration ending of intrigue and conspiracy through two rakes's interaction. Accordingly, this essay tries to show what separates the heroic rake from tyrannical libertine through their way of love, money, compromise and negotiation, which is their way of life.

• Journal of English Language & Literature
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• v.57 no.1
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• pp.51-72
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• 2011

This paper explores the issue of strangers and of hospitality in Mary Shelley's Frankenstein, based on Kant's concept of hospitality as "the right of a stranger" and on Derrida's discussion of hospitality. It first examines the similarities between the domestic relations within the Frankenstein family and Frankenstein's relation to the monster: an effort to create unity out of a multiplicity of elements, and what can be called a "debt economy." Then, reading the animation scene of the monster as a version of the advent of a stranger, it deals with the question of hospitality. More specifically, the arrival of Clerval immediately follows the animation of the monster because it effectively dramatizes the paradox that there is no hospitality without hostility. The opposition and the apposition between hospitality and hostility are also seen in the De Lacey family's welcoming Safie and rejecting the monster. Frankenstein's failure and the De Lacey family's failure to welcome the monster show that hospitality as "right" exemplified by Kantian hospitality does not apply to a stranger like the monster who has neither name nor relation and who is categorized into what Derrida terms "an absolute other." This paper also looks at Safie's problematic subversion against her father, which loses its subversive charge in the context of racial relations between Turkish Mahometans and European Christians. Safie's father looms large in the context of the issue of hospitality because his episode suggests that the category of race causes hospitality to malfunction.

• Steel and Composite Structures
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• v.39 no.1
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• pp.81-93
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• 2021

Splitting tensile strength (STS) is an important mechanical parameter of concrete. This study offers novel methodologies for the early prediction of this parameter. Artificial neural network (ANN), which is a leading predictive method, is synthesized with two metaheuristic algorithms, namely atom search optimization (ASO) and equilibrium optimizer (EO) to achieve an optimal tuning of the weights and biases. The models are applied to data collected from the published literature. The sensitivity of the ASO and EO to the population size is first investigated, and then, proper configurations of the ASO-NN and EO-NN are compared to the conventional ANN. Evaluating the prediction results revealed the excellent efficiency of EO in optimizing the ANN. Accuracy improvements attained by this algorithm were 13.26 and 11.41% in terms of root mean square error and mean absolute error, respectively. Moreover, it raised the correlation from 0.89958 to 0.92722. This is while the results of the conventional ANN were slightly better than ASO-NN. The EO was also a faster optimizer than ASO. Based on these findings, the combination of the ANN and EO can be an efficient non-destructive tool for predicting the STS.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.3027-3033
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• 2022

Background: In this study, various types of deep-learning models for predicting in vitro radiosensitivity from gene-expression profiling were compared. Methods: The clonogenic surviving fractions at 2 Gy from previous publications and microarray gene-expression data from the National Cancer Institute-60 cell lines were used to measure the radiosensitivity. Seven different prediction models including three distinct multi-layered perceptrons (MLP), four different convolutional neural networks (CNN) were compared. Folded cross-validation was applied to train and evaluate model performance. The criteria for correct prediction were absolute error < 0.02 or relative error < 10%. The models were compared in terms of prediction accuracy, training time per epoch, training fluctuations, and required calculation resources. Results: The strength of MLP-based models was their fast initial convergence and short training time per epoch. They represented significantly different prediction accuracy depending on the model configuration. The CNN-based models showed relatively high prediction accuracy, low training fluctuations, and a relatively small increase in the memory requirement as the model deepens. Conclusion: Our findings suggest that a CNN-based model with moderate depth would be appropriate when the prediction accuracy is important, and a shallow MLP-based model can be recommended when either the training resources or time are limited.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.350-358
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• 2021

In modern years, the performance of the students is analysed with lot of difficulties, which is a very important problem in all the academic institutions. The main idea of this paper is to analyze and evaluate the academic performance of the college students with bipolar disorder by applying data mining classification algorithms using Jupiter Notebook, python tool. This tool has been generally used as a decision-making tool in terms of academic performance of the students. The various classifiers could be logistic regression, random forest classifier gini, random forest classifier entropy, decision tree classifier, K-Neighbours classifier, Ada Boost classifier, Extra Tree Classifier, GaussianNB, BernoulliNB are used. The results of such classification model deals with 13 measures like Accuracy, Precision, Recall, F1 Measure, Sensitivity, Specificity, R Squared, Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, TPR, TNR, FPR and FNR. Therefore, conclusion could be reached that the Decision Tree Classifier is better than that of different algorithms.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.89-94
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• 2022

This paper proposes a computationally efficient 2-D direction-of-arrival (DoA) estimation method with a uniform rectangular array (URA). This method is called the direct signal-based method in the sense that it is based directly on the phase relationships among the signals arriving at each antenna of an antenna array rather than their correlation matrix. According to the simulation results, it has be shown that the direct signal-based method, with much less computations than any existing methods, yields the performance comparable to that of the MUSIC (MUltiple SIgnal Classification) method in terms of the root-mean-squared error (RMSE) and the maximum absolute error.

• Computers and Concrete
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• v.32 no.4
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• pp.399-410
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• 2023

This study proposes a novel use of backpropagated Levenberg-Marquardt neural networks based on computational intelligence heuristics to comprehend the examination of hybrid nanoparticles on the flow of dusty liquid via stretched cylinder. A two-phase model is employed in the present work to describe the fluid flow. The use of desulphated nanoparticles of silver and molybdenum suspended in water as base fluid. The mathematical model represented in terms of partial differential equations, Implementing similarity transformationsis model is converted to ordinary differential equations for the analysis . By adjusting the particle mass concentration and curvature parameter, a unique technique is utilized to generate a dataset for the proposed Levenberg-Marquardt neural networks in various nanoparticle circumstances on the flow of dusty liquid via stretched cylinder. The intelligent solver Levenberg-Marquardt neural networks is trained, tested and verified to identify the nanoparticles on the flow of dusty liquid solution for various situations. The Levenberg-Marquardt neural networks approach is applied for the solution of the hybrid nanoparticles on the flow of dusty liquid via stretched cylinder model. It is validated by comparison with the standard solution, regression analysis, histograms, and absolute error analysis. Strong agreement between proposed results and reference solutions as well as accuracy provide an evidence of the framework's validity.