• Smart Structures and Systems
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• v.15 no.4
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• pp.997-1018
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• 2015

Structural health monitoring along with damage detection and assessment of its severity level in non-accessible reinforced concrete members using piezoelectric materials becomes essential since engineers often face the problem of detecting hidden damage. In this study, the potential of the detection of flexural damage state in the lower part of the mid-span area of a simply supported reinforced concrete beam using piezoelectric sensors is analytically investigated. Two common severity levels of flexural damage are examined: (i) cracking of concrete that extends from the external lower fiber of concrete up to the steel reinforcement and (ii) yielding of reinforcing bars that occurs for higher levels of bending moment and after the flexural cracking. The purpose of this investigation is to apply finite element modeling using admittance based signature data to analyze its accuracy and to check the potential use of this technique to monitor structural damage in real-time. It has been indicated that damage detection capability greatly depends on the frequency selection rather than on the level of the harmonic excitation loading. This way, the excitation loading sequence can have a level low enough that the technique may be considered as applicable and effective for real structures. Further, it is concluded that the closest applied piezoelectric sensor to the flexural damage demonstrates higher overall sensitivity to structural damage in the entire frequency band for both damage states with respect to the other used sensors. However, the observed sensitivity of the other sensors becomes comparatively high in the peak values of the root mean square deviation index.

• Korean Chemical Engineering Research
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• v.56 no.2
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• pp.222-228
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• 2018

Benzene is one of the most widely used basic materials in the petrochemical industry. Generally, benzene exists as a mixture with alcohols rather than as a pure substance. Further, the alcohols-added mixtures usually exhibit an azeotropic composition. In this context, knowledge of the phase equilibrium behavior of the mixture is essential for its separation and purification. In this study, the vapor-liquid equilibrium data were measured in favor of a recirculating VLE apparatus under constant pressure for the 1 - propanol / benzene system. The measured vapor - liquid equilibrium data were also correlated by using the UNIQUAC and WILSON models and the thermodynamic consistency test based on the Gibbs/Duhem equation was followed. The results of the phase equilibrium experiment revealed RMSEs (Root Mean Square Error) and AADs (Average Absolute Deviation) of less than 0.05 for both models, indicating a good agreement between the experimental value and the calculated value. The results of the thermodynamic consistency test also confirmed through the residual term within ${\pm}0.2$.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.12
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• pp.525-530
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• 2022

Although the iterative closest point (ICP) algorithm has been widely used to align two point clouds, ICP tends to fail when the initial orientation of the two point clouds are significantly different. In this paper, when two triangular meshes A and B have significantly different initial orientations, we present an algorithm to align them. After obtaining weighted centroids for meshes A and B, respectively, vertices that are likely to correspond to each other between meshes are set as feature points using the distance from the centroid to the vertices. After rotating mesh B so that the feature points of A and B to be close each other, RMSD (root mean square deviation) is measured for the vertices of A and B. Aligned meshes are obtained by repeating the same process while changing the feature points until the RMSD is less than the reference value. Through experiments, we show that the proposed algorithm aligns the mesh even when the ICP and Go-ICP algorithms fail.

• Smart Structures and Systems
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• v.19 no.4
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• pp.361-369
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• 2017

Electromechanical impedance method as an efficient tool in Structural Health Monitoring (SHM) utilizes the electromechanical impedance of piezoelectric materials which is directly related to the mechanical impedance of the host structure and will be affected by damages. In this paper, electromechanical impedance of piezoelectric patches attached to simply support rectangular plate is determined theoretically and experimentally in order to detect damage. A pairs of piezoelectric wafer active sensor (PWAS) patches are used on top and bottom of an aluminum plate to generate pure bending. The analytical model and experiments are carried out both for undamaged and damaged plates. To validate theoretical models, the electromechanical impedances of PWAS for undamaged and damaged plate using theoretical models are compared with those obtained experimentally. Both theoretical and experimental results demonstrate that by crack generation and intensifying this crack, natural frequency of structure decreases. Finally, in order to evaluate damage severity, damage metrics such as Root Mean Square Deviation (RMSD), Mean Absolute Percentage Deviation (MAPD), and Correlation Coefficient Deviation (CCD) are used based on experimental results. The results show that generation of crack and crack depth increasing can be detectable by CCD.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.449-451
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• 2006

With the increasing danger of personal information being exposed, a technique to protect personal information by identifying a non-user in case it is exposed. A study to construct a neural network recognizer for developing a economical and effective user protecting system. For this, time variables regarding user typing patterns from a pattern extraction device. With the variations in the standard deviation for the collected time variables, non-user patterns were generated. The recognition performance increased with the increase in the standard deviation and a higher recognition was achieved at 2.5. Also, five types of training data were generated and the recognition performance was examined as a function of the number of non-user patterns. With the increase in non-suer patterns, the recognition error quantified in the root mean square error (RMSE) was reduced. The smallest RMSE was obtained at the type 5 and 90 non-user patterns. In overall, the type 3 model yielded the highest recognition accuracy Particularly, a perfect recognition of 100% was achieved at 45 non-user patterns.

• Annals of Clinical Neurophysiology
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• v.16 no.2
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• pp.49-54
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• 2014

Heart rate variability is significantly associated with cardiovascular complications in various neurological disorders with cardiac impairment. Measures of spontaneous heart rate variability might be different from provocating tests of heart rate variability such as deep breathing and Valsava maneuver. Methods for analysis are divided into time domain methods and frequency domain methods. There are standard deviation of NN interval, standard deviation of average NN interval, root mean square of the successive differences, NN50, and pNN50 in time domain methods. Frequency domain bands can be divided into very low, low, and high frequency. Each variables are influenced by sympathetic and/or parasympathetic activity.

• Current Optics and Photonics
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• v.4 no.6
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• pp.516-523
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• 2020

The normal low-cost manufacturing process for freeform vehicle side mirrors causes deviations from the design curvature. Here, an improved manufacturing process is proposed, combining photorealistic ray-traced visualization of each deviation and subsequent analysis of its deviated reflective scene compared to that of the original design. The proposed mechanism successfully highlights the overlap and mismatch regions of deviated reflected scenes with reference to the desired reflective scenes. We benchmarked the robustness of freeform mirror manufacturing by evaluating the 10, 20, and 30% root-mean-square (RMS) deviated curvature, and concluded that the acceptable deviation needs to be below RMS20% to avoid mismatched regions that could mislead the driver.

• Structural Engineering and Mechanics
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• v.81 no.5
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• pp.565-574
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• 2022

The infrastructures such as offshore, bridges, power plant, oil and gas piping and aircraft operate in a harsh environment during their service life. Structural integrity of engineering components used in these industries is paramount for the reliability and economics of operation. Two regression models based on the concept of Gaussian process regression (GPR) and Minimax probability machine regression (MPMR) were developed to predict stress intensity factor range (𝚫K). Both GPR and MPMR are in the frame work of probability distribution. Models were developed by using the fatigue crack growth data in MATLAB by appropriately modifying the tools. Fatigue crack growth experiments were carried out on Eccentrically-loaded Single Edge notch Tension (ESE(T)) specimens made of API 5L X65 Grade steel in inert and corrosive environments (2.0% and 3.5% NaCl). The experiments were carried out under constant amplitude cyclic loading with a stress ratio of 0.1 and 5.0 Hz frequency (inert environment), 0.5 Hz frequency (corrosive environment). Crack growth rate (da/dN) and stress intensity factor range (𝚫K) values were evaluated at incremental values of loading cycle and crack length. About 70 to 75% of the data has been used for training and the remaining for validation of the models. It is observed that the predicted SIF range is in good agreement with the corresponding experimental observations. Further, the performance of the models was assessed with several statistical parameters, namely, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Efficiency (E), Root Mean Square Error to Observation's Standard Deviation Ratio (RSR), Normalized Mean Bias Error (NMBE), Performance Index (ρ) and Variance Account Factor (VAF).

• Structural Engineering and Mechanics
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• v.53 no.6
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• pp.1127-1141
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• 2015

The optimum design of liquid column dampers in seismic vibration control considering system parameter uncertainty is usually performed by minimizing the unconditional response of a structure without any consideration to the variation of damper performance due to uncertainty. However, the system so designed may be sensitive to the variations of input system parameters due to uncertainty. The present study is concerned with robust design optimization (RDO) of liquid column vibration absorber (LCVA) considering random system parameters characterizing the primary structure and ground motion model. The RDO is obtained by minimizing the weighted sum of the mean value of the root mean square displacement of the primary structure as well as its standard deviation. A numerical study elucidates the importance of the RDO procedure for design of LCVA system by comparing the RDO results with the results obtained by the conventional stochastic structural optimization procedure and the unconditional response based optimization.

• Communications for Statistical Applications and Methods
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• v.16 no.6
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• pp.1037-1046
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• 2009

Copy number variations(CNVs) are known as one of the most important factors in susceptibility to genetic disorders because they affect expression levels of genes. In previous studies, pyrosequencing, mini-sequencing real-time polymerase chain reaction(PCR), invader assays and other techniques have been used to detect CNVs. However, the higher the copy number in a genome, the more difficult it is to resolve the copies, so a more accurate method for measuring CNVs and assigning genotype is needed. PCR followed by a quantitative oligonucleotide ligation assay(qOLA) was developed for quantifying CNVs. The aim of this study was to compare the two methods for detecting and quantifying the CNVs of duplicated gene: the published pyrosequencing assay(pyro_CNV) and the newly developed qOLA_CNV. The accuracy and precision of the assay were evaluated for porcine KIT, which was selected as a model locus. Overall, the root mean squares(RMSs) of bias and standard deviation of qOLA_CNV were 2.09 and 0.45, respectively. These values are less than half of those of pyro CNV.