• Structural Monitoring and Maintenance
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• v.9 no.3
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• pp.221-235
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• 2022

Among nondestructive damage detection methods, impedance-based methods have been recognized as an effective technique for damage identification in many kinds of structures. This paper proposes a method to detect cracks in metal structures by combining electro-mechanical impedance (EMI) responses and artificial neural networks (ANN). Firstly, the theories of EMI responses and impedance-based damage detection methods are described. Secondly, the reliability of numerical simulations for impedance responses is demonstrated by comparing to pre-published results for an aluminum beam. Thirdly, the proposed method is used to detect cracks in the beam. The RMSD (root mean square deviation) index is used to alarm the occurrence of the cracks, and the multi-layer perceptron (MLP) ANN is employed to identify the location and size of the cracks. The selection of the effective frequency range is also investigated. The analysis results reveal that the proposed method accurately detects the cracks' occurrence, location, and size in metal structures.

• Computers and Concrete
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• v.30 no.1
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• pp.33-42
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• 2022

Experimentally predicting the compressive strength (CS) of concrete (for a mix design) is a time-consuming and laborious process. The present study aims to propose surrogate models based on Support Vector Machine (SVM) and Gaussian Process Regression (GPR) machine learning techniques, which can predict the CS of concrete containing nano-silica. Content of cement, aggregates, nano-silica and its fineness, water-binder ratio, and the days at which strength has to be predicted are the input variables. The efficiency of the models is compared in terms of Correlation Coefficient (CC), Root Mean Square Error (RMSE), Variance Account For (VAF), Nash-Sutcliffe Efficiency (NSE), and RMSE to observation's standard deviation ratio (RSR). It has been observed that the SVM outperforms GPR in predicting the CS of the concrete containing nano-silica.

• Advances in nano research
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• v.15 no.2
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• pp.105-111
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• 2023

Integrin αvβ3 is one of the receptors expressed in cancer cells. RGD peptides have the potential to target integrin αvβ3 (receptor), which can increase drug delivery efficiency. In this study, 55 different RGD dimer motifs were investigated. At first, the binding energy between RGD peptides and the receptor was calculated using molecular docking. Then, three RGD peptides with the strongest binding energy with the receptor were selected, and their dynamic adsorption on the receptor was simulated by molecular dynamics (MD). The obtained results showed that a sequence that has RGD at the beginning and end with tryptophan (TRP) has strong Lennard-Jones (LJ) and electrostatic interactions with Integrin αvβ3 and has changed the conformation of receptor significantly, which analyzed by root mean square deviation (RMSD) and radius of gyration.

• Computers and Concrete
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• v.32 no.6
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• pp.607-613
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• 2023

The objective of this work is to determine the compressive strength of geopolymer concrete utilizing four distinct machine learning approaches. These techniques are known as gradient boosting machine (GBM), generalized linear model (GLM), extremely randomized trees (XRT), and deep learning (DL). Experimentation is performed to collect the data that is then utilized for training the models. Compressive strength is the response variable, whereas curing days, curing temperature, silica fume, and nanosilica concentration are the different input parameters that are taken into consideration. Several kinds of errors, including root mean square error (RMSE), coefficient of correlation (CC), variance account for (VAF), RMSE to observation's standard deviation ratio (RSR), and Nash-Sutcliffe effectiveness (NSE), were computed to determine the effectiveness of each algorithm. It was observed that, among all the models that were investigated, the GBM is the surrogate model that can predict the compressive strength of the geopolymer concrete with the highest degree of precision.

• Geomechanics and Engineering
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• v.28 no.4
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• pp.397-404
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• 2022

Accurate prediction of the undrained shaft resistance is essential for robust design of bored piles in undrained condition. The undrained shaft resistance is calculated using the undrained adhesion factor multiplied by the undrained cohesion of the soil. However, the available correlations to predict the undrained adhesion factor have been developed using simple regression techniques and the accuracy of these correlations has not been thoroughly assessed in previous studies. The lack of the assessment of these correlations made it difficult for geotechnical engineers to select the most accurate correlation in routine designs. Furthermore, limited attempts have been made in previous studies to use advanced data mining techniques to develop simple and accurate correlation to predict the undrained adhesion factor. This research, therefore, has been conducted to fill these gaps in knowledge by developing novel and robust correlation to predict the undrained adhesion factor. The development of the new correlation has been conducted using the multi-objective evolutionary polynomial regression analysis. The new correlation outperformed the available empirical correlations, where the new correlation scored lower mean absolute error, mean square error, root mean square error and standard deviation of measured to predicted adhesion factor, and higher mean, a20-index and coefficient of correlation. The correlation also successfully showed the influence of the undrained cohesion and the effective stress on the adhesion factor. Hence, the new correlation enhances the design accuracy and can be used by practitioner geotechnical engineers to ensure optimized designs of bored piles in undrained conditions.

• Bioinformatics and Biosystems
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• v.1 no.2
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• pp.115-122
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• 2006

A three-dimensional pharmacophore model was developed based on 24 currently available inhibitors, which were rationally selected from 472 compounds with diverse molecular structure and bioactivity, for generating pharmacophore of uPA (Urokinase Plasminogen Activator) inhibitors. The best hypothesis (Hypo1) comprised of five features, namely, one positive ionizable group, one hydrogen-bond acceptor group and three hydrophobic aromatic groups. The correlation coefficient, root mean square deviation and cost difference were 0.973, 0.695, and 94.291 respectively, suggesting that a highly predictive pharmacophore model was successfully obtained. The application of the model showed great success in predicting the activities of 251 known uPA inhibitors (test set) with a correlation coefficient of 0.837, and there was also none of the outcome hypotheses that had similar cost difference and RMS deviation (RMSD) with that of the initial hypothesis generated by Cat-Scramble validation test with 95% confidence level. Accordingly, our model should be reliable in identifying structurally diverse compounds with desired biological activity.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.27 no.11
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• pp.596-602
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• 2015

In this study, experiments using bioelectronic signals and questionnaire surveys were carried out in learning conditions when temperatures changed from low- and high-uncomfortable to comfortable. As a result, the stress factor Photoplethysmography (PPG) decreased, while the Root Mean Square of Standard Deviation (RMSSD) of PPG increased when the indoor temperature was changed from low- or high-uncomfortable to comfortable. Additionally, the absolute power of the ${\alpha}$-wave in the brain increased. According to the analysis of the association between the questionnaire and bioelectronic signals, the standard deviation of the stress factor as measured by pulse was closely related to the result of the thermal sensation questionnaire. In addition, it was found that the concentration on studying improved under comfortable temperatures when compared to uncomfortable temperatures.

• Nuclear Engineering and Technology
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• v.52 no.8
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• pp.1771-1776
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• 2020

Adverse effects in the measured gamma spectrum caused by radioactive statistical fluctuations, gamma ray scattering, and electronic noise can be reduced by energy spectrum denoising. Wavelet threshold denoising can be used to perform multi-scale and multi-resolution analysis on noisy signals with small root mean square errors and high signal-to-noise ratios. However, in traditional wavelet threshold denoising methods, there are signal oscillations in hard threshold denoising and constant deviations in soft threshold denoising. An improved wavelet threshold calculation method and threshold processing function are proposed in this paper. The improved threshold calculation method takes into account the influence of the number of wavelet decomposition layers and reduces the deviation caused by the inaccuracy of the threshold. The improved threshold processing function can be continuously guided, which solves the discontinuity of the traditional hard threshold function, avoids the constant deviation caused by the traditional soft threshold method. The examples show that the proposed method can accurately denoise and preserves the characteristic signals well in the gamma energy spectrum.

• Tunnel and Underground Space
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• v.12 no.4
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• pp.268-276
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• 2002

In this study, a 3D coordinate measurement system equipped with a laser displacement meter for digitizing rock joint surface was established and the digitized data were used to calculate several roughness parameters. The parameters used in this study were micro avenge inclination $angle(i_{ave})$, average slope of joint $asperity(SL_{ ave})$, root mean square of $i-angle(i_{rms})$, standard deviation of height(SDH), standard deviation of $i-angle(SD_i)$, roughness profile $index(R_P)$, and fractal dimension(D). The relationships between the roughness parameters based on the digitzation of the surface profile were analyzed. Since the measured value varied according to the degree of reflection and the variation of colors at the measuring point, rock joint surface was painted in white to minimize the influence of the surface conditions. The comparison of the measured values and roughness parameters before and after painting revealed the better consequence from measurement on the painted surfaces. Also, effect of measuring interval was studied. As measured interval was increased, roughness parameters were exponentially decreased. The incremental sequence of degree of decrease was $SDH\; i_{ave},\; i_{rms},\; SD_i,\;and\; R_ p-1$. As a result of comparison of parameters from pin-type measurement system and laser type measurement system, all value of parameters were higher when laser-type measurement system was used, except SDH.

• Korean Journal of Psychosomatic Medicine
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• v.28 no.1
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• pp.81-88
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• 2020

Objectives : This study aimed to compare autonomic nervous system (ANS) dysregulation and differential relationships with clinical severities between anxiety disorder, depressive disorder, and trauma·stressor related disorder using heart rate variability (HRV) parameters. Methods : We conducted a retrospective chart review of outpatients from 2017 to 2018 in Stress Clinic of National Center for Mental Health. Total 473 patients were included; 166 anxiety disorder; 184 depressive disorder ; 123 trauma·stressor related disorder. Parameters of 5-min analysis of HRV were compared in three groups. Additionally, we investigated the differential association of each parameters with Clinical Global Impression-Severity Scale (CGI-S) across each group. Results : No significant differences were found in all HRV parameters between the three groups. However, significant group interactions by CGI-S were found in standard deviation of all RR intervals (SDNN) and the square root of the mean squared differences of successive normal-to-normal intervals (RMSSD) (SDNN, p=0.017 ; RMSSD, p=0.034). A negative relationship between CGI-S and SDNN, RMSSD has been found in anxiety disorder and depressive disorder. However, a positive relationship between CGI-S and SDNN, RMSSD has been found in trauma·stressor related disorder. Conclusions : Despite of no significant differences of each HRV parameter, our findings suggested the differential associations of HRV parameters with clinical severity among anxiety disorder, depressive disorder and trauma·stressor related disorder. In trauma·stressor related disorder, the clinical severity and degree of ANS dysregulation may differ, so more aggressive treatment is suggested.