• Steel and Composite Structures
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• v.24 no.5
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• pp.635-641
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• 2017

Safety service improvement and development of efficient maintenance strategies for corroded steel structures are undeniably essential. Therefore, understanding the influence of damage caused by corrosion on the remaining load-carrying capacities such as tensile strength is required. In this study, artificial neural network (ANN) approach is proposed in order to produce a simple, accurate, and inexpensive method developed by using tensile test results, material properties and finite element method (FEM) results to train the ANN model. Initially in reproducing corroded model process, FEM was used to obtain tensile strength of artificial corroded plates, for which surface is developed by a spatial autocorrelation model. By using the corroded surface data and material properties as input data, with tensile strength as the output data, the ANN model could be trained. The accuracy of the ANN result was then verified by using leave-one-out cross-validation (LOOCV). As a result, it was confirmed that the accuracy of the ANN approach and the final output equation was developed for predicting tensile strength without tensile test results and FEM in further work. Though previous studies have been conducted, the accuracy results are still lower than the proposed ANN approach. Hence, the proposed ANN model now enables us to have a simple, rapid, and inexpensive method to predict residual tensile strength more accurately due to corrosion in steel structures.

• Structural Engineering and Mechanics
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• v.46 no.5
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• pp.713-734
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• 2013

Bridges are vital components of the railroads. High speed of travel, the periodic and oscillatory nature of the loads and the comparable vehicle bridge weight ratio distinguish the railway bridges from the road bridges. The close proximity between estimations by some numerical methods and the measured data for the bridge-vehicle dynamic response under the moving load conditions has boosted the confidence in the numerical analyses. However, there is hardly any report regarding the responses of the railway bridges under the effect of the trains entering from the opposite directions while running at unequal speed and having dissimilar geometries. It is the purpose of this article to present an analytical method for the dynamic analysis of the railway bridges under the influence of two opposing series of moving loads. The bridge structural damping and many modes of vibrations are included. The concept of modal superposition is used to solve for the system motion equations. The method of solution is indeed a computer assisted analytical solution. It solves for the system motion equations and gives output in terms of the bridge deflection. Some case studies are also considered for the validation of the proposed method. Furthermore, the effects of varying some parameters such as the distance between the bogies, and the bogie wheelset distance are studied. Also, the conditions of resonance and cancellation in the dynamic response for a variety of vehicle-bridge specifications are investigated.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.5
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• pp.451-461
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• 2002

Differential evolution(DE) has been preyed to be an efficient method for optimizing real-valued multi-modal objective functions. DE's main assets are its conceptual simplicity and ease of use. However, the convergence properties are deeply dependent on the control parameters of DE. This paper proposes an adaptive differential evolution(ADE) method which combines with a variant of DE and an adaptive mechanism of the control parameters. ADE contributes to the robustness and the easy use of the DE without deteriorating the convergence. 12 optimization problems is considered to test ADE. As an application of ADE the paper presents a supervised clustering method for predicting events, what is called, an evolutionary event clustering(EEC). EEC is tested for 4 cases used widely for the validation of data modeling.

• Archives of Pharmacal Research
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• v.24 no.6
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• pp.601-606
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• 2001

A simple assay method of recombinant human epidermal growth factor (rhEGF) in a pharmaceutical preparation was studied and validated by capillary electrophoresis (CE) using micellar electrokinetic chromatography (MEKC) techniques. Factors affecting the migration behavior and separation performances of the peptide; type of buffers pH, butler concentration, and concentration of sodium dodecyl sulfates (SDS) were investigated to optimize the analytical performance. CE was performed using running buffers 50.0 mM borate (pH 8.5) containing 12.5 mM SDS at 20 $mutextrm{V}$ of the applied voltage. Calibration curves for the rhEGF showed good linearity (r>0.999) over the wide dynamic range from 1.25 to $100{\mu\textrm{g}}/ml$. Sample analysis was performed by using standard addition method to eliminate the matrix effects of dosage vehicle. This method is assumed to be useful for quality control (QC) of various forms of pharmaceutical products of the peptide.

• Smart Structures and Systems
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• v.25 no.4
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• pp.393-399
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• 2020

In this paper, the design method and experimental validation for the two-degree-of-freedom (2DOF) electromagnetic energy harvester are presented. The harvester consists of the rigid body suspended by four tension springs and electromagnetic transducers. Once the two resonant frequencies and the mass properties are specified, both the constant and the positions for the springs can be determined in the closed form. The designed harvester can locate two resonant peaks close to each other and forms the extended frequency bandwidth for power harvesting. Halbach magnet array is also introduced to enhance the output power. In the experiment, two resonant frequencies are measured at 34.9 and 37.6 Hz and the frequency bandwidth improves to 5 Hz at the voltage level of 207.9 mV. The normalized peak power of 4.587 mW/G² is obtained at the optimal load resistor of 367 Ω.

• Archives of Pharmacal Research
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• v.27 no.8
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• pp.820-824
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• 2004

An optical purity test was indirectly performed on (S)-ibuprofen as its diastereomeric (R)-(+)-1-phenylethylamide derivative using achiral gas chromatography (GC). The method for the determination of trace (R)-ibuprofen (optical impurity), within the range 1.0 to 50 ng, from a racemic ibuprofen standard was linear (r＝0.9997) with acceptable precision (％ $RSD{\leq}5.3$) and accuracy (％ RE＝0.7~-3.9). Similar results were obtained with the method validation for the quantification of (S)-ibuprofen within the range 0.1 to 2.0 $\mu\textrm{g}$ using a (S)-ibuprofen stan-dard. When applied to seven different commercial (S)-ibuprofen products, their optical purities (98.7~99.1％) were determined with good precision (％ $RSD{\leq}4.0$).

• Bulletin of the Korean Chemical Society
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• v.34 no.1
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• pp.139-142
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• 2013

A study was carried out to evaluate the solid phase micro-extraction (SPME) for formaldehyde emission analysis of uncoated plywood. In SPME, formaldehyde was on-fiber derivatized through headspace extraction and analyzed by gas chromatography coupled with mass spectrometry (GC/MS). The SPME was compared with desiccators (DC-JAS 233), small-scale chamber (SSC-ASTM D6007) and liquid-liquid extraction (LLE-EPA 556) methods which were performed in accordance with their respective standards. Compared to SSC (RSD 4.3%) and LLE (RSD 5.0%), the SPME method showed better repeatability (RSD 1.8%) and not much difference from DC (RSD 1.4%). The SPME has proven to be highly precise (at 95% confidence level) with better recovery (REC 102%). Validation of the SPME method for formaldehyde quantitative analysis was evidenced. In addition, the SPME by air sampling directly from plywood specimens (SPME-W) correlated best with DC ($r^2$ = 0.983), followed by LLE ($r^2$ = 0.950) and SSC ($r^2$ = 0.935).

• ETRI Journal
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• v.44 no.3
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• pp.512-523
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• 2022

Random delay countermeasures introduce random delays into the execution flow to break the synchronization and increase the complexity of the side channel attack. A novel method for attacking devices with random delay countermeasures has been proposed by using a maximal overlap discrete wavelet transform (MODWT)-based power trace alignment algorithm. Firstly, the random delay in the power traces is sensitized using MODWT to the captured power traces. Secondly, it is detected using the proposed random delay detection algorithm. Thirdly, random delays are removed by circular shifting in the wavelet domain, and finally, the power analysis attack is successfully mounted in the wavelet domain. Experimental validation of the proposed method with the National Institute of Standards and Technology certified Advanced Encryption Standard-128 cryptographic algorithm and the SAKURA-G platform showed a 7.5× reduction in measurements to disclosure and a 3.14× improvement in maximum correlation value when compared with similar works in the literature.

• Journal of the Korean Society of Food Science and Nutrition
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• v.41 no.12
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• pp.1753-1757
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• 2012

A new liquid chromatographic tandem mass spectrometric (LC-MS/MS) assay for the quantification of ginsenoside Rb1 in human plasma was developed and validated. The separation was performed on a Agilent C18 column ($4.6mm{\times}150mm$, particle size 5 ${\mu}m$) with a gradient elution of 0.1% formic acid in water and 0.1% formic acid in methanol and a flow rate of 0.9 mL/min. The analyte was determined using electrospray positive ionization mass spectrometry in the multiple reaction monitoring (MRM) mode (m/z 1131.714${\rightarrow}$365.303). Human plasma samples were extracted with acetone : water (50:50) by the liquid-liquid extraction method. The method was linear over the dynamic range of 10~500 ng/mL with a correlation coefficient of r=0.9995. The intra-and inter-day precision over the concentration range of ginsenoside Rb1 was lower than 5.8% (correlation of variance, CV), and the accuracy was between 96.0~104.6%. This LC-MS/MS assay of ginsenoside Rb1 in human plasma is applicable for quantification in a pharmacokinetic study.

• Smart Structures and Systems
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• v.32 no.2
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• pp.123-133
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• 2023

This paper proposes a thermography-based coating thickness estimation method for steel structures using model-agnostic meta-learning. In the proposed method, a halogen lamp generates heat energy on the coating surface of a steel structure, and the resulting heat responses are measured using an infrared (IR) camera. The measured heat responses are then analyzed using model-agnostic meta-learning to estimate the coating thickness, which is visualized throughout the inspection surface of the steel structure. Current coating thickness estimation methods rely on point measurement and their inspection area is limited to a single point, whereas the proposed method can inspect a larger area with higher accuracy. In contrast to previous ANN-based methods, which require a large amount of data for training and validation, the proposed method can estimate the coating thickness using only 10- pixel points for each material. In addition, the proposed model has broader applicability than previous methods, allowing it to be applied to various materials after meta-training. The performance of the proposed method was validated using laboratory-scale and field tests with different coating materials; the results demonstrated that the error of the proposed method was less than 5% when estimating coating thicknesses ranging from 40 to 500 ㎛.