• Fashion & Textile Research Journal
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• v.6 no.4
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• pp.503-510
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• 2004

The study is one of fundamental researches for the development of future smart clothing and textile products with blocking properties from electromagnetic waves by analyzing human physical symptoms in using electromagnetic products in such an environments. Among various textiles in the experiment, nano silver has shown the best blocking performance from electromagnetic waves, which decreases depending on the distance. The power spectrum distribution and the incidence of electroencephalogram between blocking materials and aural stimuli has shown that, ${\beta}$, wave appeared to be active in all channels except for $T_4$, whereas all waves appeared with processed materials and especially with nano silver silk(NSS), ${\alpha}$, ${\beta}$, ${\theta}$, ${\gamma}$ waves appeared active in all regions. As for the brain mapping of ${\alpha}$ wave according to time, there found a strong activity in $P_3$, $P_4$ of the parietal lobe, with all materials on all time regions. With silk nylon metal(SNM) and NSS, it appeared strong in $F_3$, $F_4$ as well. As for ${\beta}$, wave, the activity appeared strong in frontal lobe before 7min. 30sec, where it tends to diminish abruptly in 7min. 30sec. to 13min. 30sec. region. After 13min., it regained gradually. With NSS, it appeared strong in all areas except for the farthest $T_4$. The appearance of ${\nu}$ wave can be deduced as it can affect human body with its toxic property while the silver particles become nano-sized. Therefore, the study conducted with human participants requires a proper particle size of it which would not penetrate cellular tissues and a proper binder and binding treatment for it, to prevent the physical fatigues and the potential diseases. However, it is highly required for back-up researches to verify various aspects in applying nano silver to textile products.

• Smart Structures and Systems
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• v.25 no.5
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• pp.605-617
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• 2020

The existence of damages in structures causes changes in the physical properties by reducing the modal parameters. In this paper, we develop a two-stages approach based on normalized Modal Strain Energy Damage Indicator (nMSEDI) for quick applications to predict the location of damage. A two-dimensional IsoGeometric Analysis (2D-IGA), Machine Learning Algorithm (MLA) and optimization techniques are combined to create a new tool. In the first stage, we introduce a modified damage identification technique based on frequencies using nMSEDI to locate the potential of damaged elements. In the second stage, after eliminating the healthy elements, the damage index values from nMSEDI are considered as input in the damage quantification algorithm. The hybrid of Teaching-Learning-Based Optimization (TLBO) with Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) are used along with nMSEDI. The objective of TLBO is to estimate the parameters of PSO-ANN to find a good training based on actual damage and estimated damage. The IGA model is updated using experimental results based on stiffness and mass matrix using the difference between calculated and measured frequencies as objective function. The feasibility and efficiency of nMSEDI-PSO-ANN after finding the best parameters by TLBO are demonstrated through the comparison with nMSEDI-IGA for different scenarios. The result of the analyses indicates that the proposed approach can be used to determine correctly the severity of damage in beam structures.

• Fashion & Textile Research Journal
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• v.6 no.6
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• pp.810-814
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• 2004

This study is one of the fundamental researches for the development of future smart clothing and textile products using silver(Ag) nano powder. Our study was focused on the blocking or insulating effects of nano-processed textiles from electromagnetic waves. Also, for the surveying of the actual effect to human body, we measure the variation of electroencephalogram which is an indication of human physical symptoms. Among various textiles in this experiment, nano silver processed case has shown the best blocking performance from the electromagnetic waves, which decreases depending on the distance. As a reference model of working environment, we setup the visual stimuli object on the computer that is a source of electromagnetic wave. The power spectrum distribution and the incidence of electroencephalogram was measured. The analysed data has shown that, with nano-processed textiles, ${\beta}$ wave does not appear very often where ${\beta}$ wave appears only to illustrate the stable states of human's body. However, as for the materials without nano processing, the ratio of ${\gamma}$ waves in the total level of electroencephalogram becomes higher in spite of short exposure to visual stimuli in work environment, which shows that the worker becomes stressed. The ${\beta}$ wave electroencephalogram of all materials is drawn in calcarine fissure of occipital lobe to show the convergent distribution, and stronger with block-processed Nano Silver Silk(NSS). The study based on the potential risks of human diseases such as physical fatigue by electromagnetic waves, and has shown that the application of Nano Silver textile for human uses require a proper particle size of it which would not penetrate cellular tissues, and a proper binder and binding treatment for it. However, it is highly required for back-up researches to verify various aspects in applying nano silver to textile products.

• Smart Structures and Systems
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• v.26 no.4
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• pp.451-468
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• 2020

Although model updating has been widely applied using a specific optimization algorithm with a single objective function using frequencies, mode shapes or frequency response functions, there are few studies that investigate hybrid optimization algorithms for real structures. Many of them did not take into account the sensitivity of the updating parameters to the model outputs. Therefore, in this paper, optimization algorithms and sensitivity analysis are applied for model updating of a real cable-stayed bridge, i.e., the Kien bridge in Vietnam, based on experimental data. First, a global sensitivity analysis using Morris method is employed to find out the most sensitive parameters among twenty surveyed parameters based on the outputs of a Finite Element (FE) model. Then, an objective function related to the differences between frequencies, and mode shapes by means of MAC, COMAC and eCOMAC indices, is introduced. Three metaheuristic algorithms, namely Gravitational Search Algorithm (GSA), Particle Swarm Optimization algorithm (PSO) and hybrid PSOGSA algorithm, are applied to minimize the difference between simulation and experimental results. A laboratory pipe and Kien bridge are used to validate the proposed approach. Efficiency and reliability of the proposed algorithms are investigated by comparing their convergence rate, computational time, errors in frequencies and mode shapes with experimental data. From the results, PSO and PSOGSA show good performance and are suitable for complex and time-consuming analysis such as model updating of a real cable-stayed bridge. Meanwhile, GSA shows a slow convergence for the same number of population and iterations as PSO and PSOGSA.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.37 no.4
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• pp.296-301
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• 2001

Electro-Rheological (ER) fluids belong to a class of colloidal suspensions whose global characteristics can be controlled by the imposition of an appropriate external electrical field upon the fluid domain. The ER fluids for smart hydraulic system are a class of colloidal dispersion which exhibit large reversible changes in their rheological behavior when subjected to external electrical fields. This paper presents experimental results on mechanical properties of an ER fluids subjected to electrical fatigues. As a first step, ER fluid is made of methyl cellulose(MC) ingredient choosing 25% of particle weight-concentration. Following the construction of test for mechanical properties of ER fluid, the shear stress, dynamic yield stress and current density of the ER fluids are experimentally distilled as a function of electric field cycles. The mechanical properties test of operated ER fluids are distilled and compared with those of unused ER fluids.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2006.06a
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• pp.346-347
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• 2006

기계적 품질계수(Qm)를 향상시키기 위해 $Y_2O_3$와 $MnO_2$를 첨가함에 따른 $SrBi_2Nb_2O_9$ 세라믹스의 강유전 특성을 알아보았다. 합성분말의 입도를 분석 한 결과 $SrBi_2Nb_2O_9$의 경우 781.27nm였고, $Y_2O_3$와 $MnO_2$를 첨가한 경우 각 각 830.4nm와 981.1nm로 particle size는 증가하였고, 소결 후 소결밀도는 차이가 거의 없었으며, grain size는 $SrBi_2Nb_2O_9$과 $Y_2O_3$를 첨가했을 경우 $1{\mu}m$이하이며 반면, $MnO_2$를 첨가하였을 때 결정립이 성장하여 $3{\sim}4{\mu}m$로 나타났다. 또한, 모두가 $450^{\circ}C$ 이상의 상전이온도를 갖았다.

• Smart Structures and Systems
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• v.23 no.1
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• pp.81-90
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• 2019

The shear behavior of soil-concrete interface is mainly affected by the surface roughness of the two contact surfaces. The present research emphasizes on investigating the effect of roughness of soil-concrete interface on the interface shear behavior in two-layered laboratory testing samples. In these specially prepared samples, clay silt layer with density of $2027kg/m^3$ was selected to be in contact a concrete layer for simplifying the laboratory testing. The particle size testing and direct shear tests are performed to determine the appropriate particles sizes and their shear strength properties such as cohesion and friction angle. Then, the surface undulations in form of teeth are provided on the surfaces of both concrete and soil layers in different testing carried out on these mixed specimens. The soil-concrete samples are prepared in form of cubes of 10*10*30 cm. in dimension. The undulations (inter-surface roughness) are provided in form of one tooth or two teeth having angles $15^{\circ}$ and $30^{\circ}$, respectively. Several direct shear tests were carried out under four different normal loads of 80, 150, 300 and 500 KPa with a constant displacement rate of 0.02 mm/min. These testing results show that the shear failure mechanism is affected by the tooth number, the roughness angle and the applied normal stress on the sample. The teeth are sheared from the base under low normal load while the oblique cracks may lead to a failure under a higher normal load. As the number of teeth increase the shear strength of the sample also increases. When the tooth roughness angle increases a wider portion of the tooth base will be failed which means the shear strength of the sample is increased.

• Smart Structures and Systems
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• v.23 no.5
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• pp.479-493
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• 2019

In this paper, the interaction between notch and micro pore under uniaxial compression has been performed experimentally and numerically. Firstly calibration of PFC2D was performed using Brazilian tensile strength, uniaxial tensile strength and biaxial tensile strength. Secondly uniaxial compression test consisting internal notch and micro pore was performed experimentally and numerically. 9 models consisting notch and micro pore were built, experimentally and numerically. Dimension of these models are 10 cm*1 cm*5 cm. the length of joint is 2 cm. the angularities of joint are $30^{\circ}$, $45^{\circ}$ and $60^{\circ}$. For each joint angularity, micro pore was situated 2 cm above the lower tip of the joint, 2 cm above the middle of the joint and 2 cm above the upper of the joint, separately. Dimension of numerical models are 5.4 cm*10.8 cm. The size of the cracks was 2 cm and its orientation was $30^{\circ}$, $45^{\circ}$ and $60^{\circ}$. Diameter of pore was 1cm which situated at the upper of the notch i.e., 2 cm above the upper notch tip, 2 cm above the middle of the notch and 2 cm above the lower of the notch tip. The results show that failure pattern was affected by notch orientation and pore position while uniaxial compressive strength is affected by failure pattern.

• Smart Structures and Systems
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• v.31 no.1
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• pp.57-68
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• 2023

Bridge hangers, such as those in suspension and cable-stayed bridges, suffer from cumulative fatigue damage caused by dynamic loads (e.g., cyclic traffic and wind loads) in their service condition. Thus, the identification of damage to hangers is important in preserving the service life of the bridge structure. This study develops a new method for condition assessment of bridge hangers. The tension force of the bridge and the damages in the element level can be identified using the Bayesian optimization method. To improve the number of observed data, the additional mass method is combined the Bayesian optimization method. Numerical studies are presented to verify the accuracy and efficiency of the proposed method. The influence of different acquisition functions, which include expected improvement (EI), probability-of-improvement (PI), lower confidence bound (LCB), and expected improvement per second (EIPC), on the identification of damage to the bridge hanger is studied. Results show that the errors identified by the EI acquisition function are smaller than those identified by the other acquisition functions. The identification of the damage to the bridge hanger with various types of boundary conditions and different levels of measurement noise are also studied. Results show that both the severity of the damage and the tension force can be identified via the proposed method, thereby verifying the robustness of the proposed method. Compared to the genetic algorithm (GA), particle swarm optimization (PSO), and nonlinear least-square method (NLS), the Bayesian optimization (BO) performs best in identifying the structural damage and tension force.

• Smart Structures and Systems
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• v.31 no.3
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• pp.247-257
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• 2023

In structural health monitoring of large-scale structures, optimal sensor placement plays an important role because of the high cost of sensors and their supporting instruments, as well as the burden of data transmission and storage. In this study, a vibration sensor placement algorithm based on deep reinforcement learning (DRL) is proposed, which can effectively solve non-convex, high-dimensional, and discrete combinatorial sensor placement optimization problems. An objective function is constructed to estimate the quality of a specific vibration sensor placement scheme according to the modal assurance criterion (MAC). Using this objective function, a DRL-based algorithm is presented to determine the optimal vibration sensor placement scheme. Subsequently, we transform the sensor optimal placement process into a Markov decision process and employ a DRL-based optimization algorithm to maximize the objective function for optimal sensor placement. To illustrate the applicability of the proposed method, two examples are presented: a 10-story braced frame and a sea-crossing bridge model. A comparison study is also performed with a genetic algorithm and particle swarm algorithm. The proposed DRL-based algorithm can effectively solve the discrete combinatorial optimization problem for vibration sensor placements and can produce superior performance compared with the other two existing methods.