• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.327-340
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• 2022

The Collapse Margin Ratio (CMR) is a notable index used for seismic assessment of the structures. As proposed by FEMA P695, a set of analyses including the Nonlinear Static Analysis (NSA), Incremental Dynamic Analysis (IDA), together with Fragility Analysis, which are typically time-taking and computationally unaffordable, need to be conducted, so that the CMR could be obtained. To address this issue and to achieve a quick and efficient method to estimate the CMR, the Artificial Neural Network (ANN), Response Surface Method (RSM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) will be introduced in the current research. Accordingly, using the NSA results, an attempt was made to find a fast and efficient approach to derive the CMR. To this end, 5016 IDA analyses based on FEMA P695 methodology on 114 various Reinforced Concrete (RC) frames with 1 to 12 stories have been carried out. In this respect, five parameters have been used as the independent and desired inputs of the systems. On the other hand, the CMR is regarded as the output of the systems. Accordingly, a double hidden layer neural network with Levenberg-Marquardt training and learning algorithm was taken into account. Moreover, in the RSM approach, the quadratic system incorporating 20 parameters was implemented. Correspondingly, the Analysis of Variance (ANOVA) has been employed to discuss the results taken from the developed model. Additionally, the essential parameters and interactions are extracted, and input parameters are sorted according to their importance. Moreover, the ANFIS using Takagi-Sugeno fuzzy system was employed. Finally, all methods were compared, and the effective parameters and associated relationships were extracted. In contrast to the other approaches, the ANFIS provided the best efficiency and high accuracy with the minimum desired errors. Comparatively, it was obtained that the ANN method is more effective than the RSM and has a higher regression coefficient and lower statistical errors.

• Steel and Composite Structures
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• v.43 no.5
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• pp.581-601
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• 2022

The main objective of this research work is to investigate the free vibration behavior of annular sandwich plates resting on the Kerr foundation at thermal conditions. This sandwich configuration is composed of two FGM face sheets as coating layer and a porous GPLRC (GPL reinforced composite) core. It is supposed that the GPL nanofillers and the porosity coefficient vary continuously along the core thickness direction. To model closed-cell FG porous material reinforced with GPLs, Halpin-Tsai micromechanical modeling in conjunction with Gaussian-Random field scheme is used, while the Poisson's ratio and density are computed by the rule of mixtures. Besides, the material properties of two FGM face sheets change continuously through the thickness according to the power-law distribution. To capture fundamental frequencies of the annular sandwich plate resting on the Kerr foundation in a thermal environment, the analysis procedure is with the aid of Reddy's shear-deformation plate theory based high-order shear deformation plate theory (HSDT) to derive and solve the equations of motion and boundary conditions. The governing equations together with related boundary conditions are discretized using the generalized differential quadrature (GDQ) method in the spatial domain. Numerical results are compared with those published in the literature to examine the accuracy and validity of the present approach. A parametric solution for temperature variation across the thickness of the sandwich plate is employed taking into account the thermal conductivity, the inhomogeneity parameter, and the sandwich schemes. The numerical results indicate the influence of volume fraction index, GPLs volume fraction, porosity coefficient, three independent coefficients of Kerr elastic foundation, and temperature difference on the free vibration behavior of annular sandwich plate. This study provides essential information to engineers seeking innovative ways to promote composite structures in a practical way.

• Journal of Korea Trade
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• v.25 no.3
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• pp.116-133
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• 2021

Purpose - Intra-industry trade of agricultural products can eliminate the disadvantage of Korea's traditional agriculture and improve its lack of comparative advantage. The main purpose of this paper is to measure the level and index of intra-industry trade of Korean agricultural products and to explore the spatial dependence and spillover effect associated with this type of trade. The main factors influencing intra-agricultural trade are analyzed from two perspectives: the population and the classification of agricultural products. Design/methodology - First, the level of intra-industry trade of Korean agricultural products is measured. Second, to obtain a more accurate estimate of the influence of various factors, and based on two types of weight matrices, a spatial econometric model is constructed from two aspects: population and classification of agricultural products. The status and the factors influencing intra-industry trade are also studied. Findings - It is concluded that there is a positive spatial correlation between Korea's intra-industry trade in agricultural products and that of its trading partners. The spatial spillover effect of this type of trade is verified by using the spatial autoregressive model (SAR). Labor-intensive agricultural products are found to have a positive spillover effect on intra-industry trade, while land-intensive products do not have a significant effect. Originality/value - In this paper, the two types of agricultural products are meticulously distinguished, and the spatial effect of the intra-industry trade of agricultural products as well as the influence of various factors are analyzed. In addition, the accuracy of the estimation of the coefficients of the factors by using the spatial econometric model is higher than that of the ordinary panel data model.

• Advances in nano research
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• v.12 no.5
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• pp.467-482
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• 2022

In the current work, static and free torsional vibration of functionally graded (FG) nanorods are investigated using Fourier sine series. The boundary conditions are described by the two elastic torsional springs at the ends. The distribution of functionally graded material is considered using a power-law rule. The systems of equations of the mechanical response of nanorods subjected to deformable boundary conditions are achieved by using the modified couple stress theory (MCST) and taking the effects of torsional springs into account. The idea of the study is to construct an eigen value problem involving the torsional spring parameters with small scale parameter and functionally graded index. This article investigates the size dependent free torsional vibration based on the MCST of functionally graded nano/micro rods with deformable boundary conditions using a Fourier sine series solution for the first time. The eigen value problem is constructed using the Stokes' transform to deformable boundary conditions and also the convergence and accuracy of the present methodology are discussed in various numerical examples. The small size coefficient influence on the free torsional vibration characteristics is studied from the point of different parameters for both deformable and rigid boundary conditions. It shows that the torsional vibrational response of functionally graded nanorods are effected by geometry, small size effects, boundary conditions and material composition. Furthermore, for all deformable boundary conditions in the event of nano-sized FG nanorods, the incrementing of the small size parameters leads to increas the torsional frequencies.

• Smart Structures and Systems
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• v.30 no.1
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• pp.75-88
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• 2022

Engineering structures in operation essentially belong to time-varying or nonlinear structures and the resultant response signals are usually non-stationary. For such time-varying structures, it is of great importance to extract time-dependent dynamic parameters from non-stationary response signals, which benefits structural health monitoring, safety assessment and vibration control. However, various traditional signal processing methods are unable to extract the embedded meaningful information. As a newly developed technique, variational mode decomposition (VMD) shows its superiority on signal decomposition, however, it still suffers two main problems. The foremost problem is that the number of modal components is required to be defined in advance. Another problem needs to be addressed is that VMD cannot effectively separate non-stationary signals composed of closely spaced or overlapped modes. As such, a new method named generalized adaptive variational modal decomposition (GAVMD) is proposed. In this new method, the number of component signals is adaptively estimated by an index of mean frequency, while the generalized demodulation algorithm is introduced to yield a generalized VMD that can decompose mode overlapped signals successfully. After that, synchrosqueezing wavelet transform (SWT) is applied to extract instantaneous frequencies (IFs) of the decomposed mono-component signals. To verify the validity and accuracy of the proposed method, three numerical examples and a steel cable with time-varying tension force are investigated. The results demonstrate that the proposed GAVMD method can decompose the multi-component signal with overlapped modes well and its combination with SWT enables a successful IF extraction of each individual component.

• The Korea Journal of Herbology
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• v.28 no.3
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• pp.61-68
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• 2013

Objectives : A precise and simple system of nomenclature was required to avoid error, ambiguity or confusion. Although medicinal plants must be produced or distributed based on a pharmacopoeia described origin including scientific name, the Korean Pharmacopoeia tenth edition (KP 10) had many names against the nomenclature. Therefore, this study aimed at searching correct scientific names for 241 plants in KP 10. Methods : Authoritative databases - The Plant List, International Plant Name Index, YList, Tropicos, eFloras, World Checklist of Selected Plant Families, The Global Compositae Checklist, The International Legume Database and Information Service, et al. - and previously performed researches, floras were cross-checked. Results : The arrangement of this list was designed for four cases, errors including illegitimate, nomenclatural synonyms, recommended names and decision reserved names. Consideration about the scientific names produced nine correct names for ten misspellings and illegitimate, and thirty-six correct names for forty-one nomenclatural synonyms. These results should be reflected in the next of KP 10. Separately, ten recommended names were also suggested for taxonomic synonyms which had been used indiscriminately due to diverse taxonomic opinions. In addition to those, decision reserved names were suggested for thirteen species which had been corridor of uncertainty. Then again, there was need to study about authorship, because KP 10 did not keep recommendations for author citations. Conclusions : Correction of scientific names for some medicinal plants which violated the International Code of Nomenclature would be useful to improve the accuracy of a Pharmacopoeia as the criterional materials.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.31-39
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• 2022

Stock prices are influenced by a number of external factors, such as laws and trends, as well as number-based internal factors such as trading volume and closing prices. Since many factors affect stock prices, it is very difficult to accurately predict stock prices using only fragmentary stock data. In particular, since the value of a company is greatly affected by the perception of people who actually trade stocks, emotional information about a specific company is considered an important factor. In this paper, we propose a deep learning-based stock price prediction model using sentiment analysis with news data considering temporal characteristics. Stock and news data, two heterogeneous data with different characteristics, are integrated according to time scale and used as input to the model, and the effect of time scale and sentiment index on stock price prediction is finally compared and analyzed. Also, we verify that the accuracy of the proposed model is improved through comparative experiments with existing models.

• Journal of Applied Biological Chemistry
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• v.65 no.3
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• pp.195-202
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• 2022

Soybean (Glycine max L.) leaves have been researched as functional food stuff actively, but there is no validation method to control quality of soybean leaves (SL). In this study, we annotated seven kaempferol derivatives to confirm camelliaside A as index metabolite in SL using UHPLC-ESI-TOF-MS. HPLC-UVD validation method of camelliaside A in hot-water extract of SL was established according to validation guideline of functional foods from the Ministry of Food and Safety of Korea. The HPLC-UVD method was validated with reliable parameters for examining specificity, accuracy, precision, limit of detection and quantification and linearity. The established method gave the suitable ranges to qunatitate camelliaside A from the hot-water extract of soybean leaves.

• Science of Emotion and Sensibility
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• v.25 no.2
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• pp.3-10
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• 2022

Memorability is an indicator of how well a stimulus can be remembered. Studies on memorability have shown that stimulus memorability cannot be explained by the perceptual and semantic properties of a stimulus, suggesting that memorability is an intrinsic property of a stimulus. Though real-world scenes almost always contain multiple objects, previous studies on memorability have mainly tested memory performance using a single stimulus. In the current study, we investigated how multiple stimuli with different levels of memorability interact with each other. Participants were asked to remember a high or low memorability target presented with a high or low memorability distractor in the encoding block. Participants' memory accuracy was measured by a sensitivity index in the testing block. Results showed that a high memorability target was easier to remember. However, the distractor memorability level did not modulate this target memorability effect. The current results support previous studies that showed a highly memorable stimulus does not automatically induce bottom-up attentional shifts.

• Geomechanics and Engineering
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• v.33 no.1
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• pp.1-9
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• 2023

The unconfined compression strength (UCS) of soils is commonly used either before or during the construction of geo-structures. In the pre-design stage, UCS as a mechanical property is obtained through a laboratory test that requires cumbersome procedures and high costs from in-situ sampling and sample preparation. As an alternative way, the empirical model established from limited testing cases is used to economically estimate the UCS. However, many parameters affecting the 1D soil compression response hinder employing the traditional statistical analysis. In this study, gene expression programming (GEP) is adopted to develop a prediction model of UCS with common affecting soil properties. A total of 79 undisturbed soil samples are collected, of which 54 samples are utilized for the generation of a predictive model and 25 samples are used to validate the proposed model. Experimental studies are conducted to measure the unconfined compression strength and basic soil index properties. A performance assessment of the prediction model is carried out using statistical checks including the correlation coefficient (R), the root mean square error (RMSE), the mean absolute error (MAE), the relatively squared error (RSE), and external criteria checks. The prediction model has achieved excellent accuracy with values of R, RMSE, MAE, and RSE of 0.98, 10.01, 7.94, and 0.03, respectively for the training data and 0.92, 19.82, 14.56, and 0.15, respectively for the testing data. From the sensitivity analysis and parametric study, the liquid limit and fine content are found to be the most sensitive parameters whereas the sand content is the least critical parameter.