• KCI Concrete Journal
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• v.11 no.3
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• pp.153-164
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• 1999

The objective of this research is to observe the actual response of a low-rise nonseismic moment-resisting masonry-infilled reinforced concrete frame subjected to varied levels of earthquake ground motions. The reduction scale for the model was determined as 1 : 5 considering the capacity of the shaking table to be used. This model was, then, subjected to the shaking table motions simulating Taft N2IE component earthquake ground motion, whose peak ground acceleration(PGA) was modified to 0.12g, 0.2g, 0.3g, and 0.4g. The g1oba1 behavior and failure mode were observed. The lateral accelerations and displacements at each story and local deformations at the critical portions of the structure were measured. Before and after each earthquake simulation test, free vibration tests and white noise tests were performed to find the changes in the natural period of the model. When the results of the masonry-infilled frame are compared with those of the bare frame, it can be recognized that masonry infills contribute to the large increase in the stiffness and strength of the g1oba1 structure whereas it also accompanies the increase of earthquake inertia forces. However, it is judged that masonry infills may be beneficial to the performance of the structure since the rate of increase in strength appears to be greater than that of the induced earthquake inertia forces.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.317-319
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• 1996

In this paper, an active vibration control system using a voice coil type linear oscillating actuator(LOA) is studied to suppress structural vibration. Being compared with a hydraulic actuator, a LOA has simplified structure and requires a few elements in the driving system, so it has lots of merits with respect to economics and maintenance. The general mathematical dynamic model to obtain the algorithm for the realization of vibration active control system is treated. Actually, the performance test of the control system using LOA is carried out on a steel test structure under sinusoidal and white noise excitation. From this test it is conformed that acceleration level of test structure is reduced near the resonance region. In the future research on the application to large structures will be studied.

• Journal of the Earthquake Engineering Society of Korea
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• v.8 no.2
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• pp.45-53
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• 2004

In this paper, the stiffness and damping matrix are experimentally constructed using the structural modal information on frequencies, damping ratio and modal vectors, which are obtained by shaking table tests. Free vibration, harmonic and white noise vibration tests are performed. The acceleration of the shaking table was used as the input signal, and the corresponding accelerations of each floor were measured as output signals. The characteristics and limitations of modal information from each test are compared. The results of this study would be a basic resource of the analytical and experimental studies on the system identification of structures.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.5
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• pp.593-598
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• 2005

The standard Kalman filter has been used to estimate the states of the target, but in the presence of a maneuver, its error is occurred and performance may be seriously degraded. To solve this problem, this paper presents a new intelligent tracking algorithm using the fuzzy Kalman filter. In this algorithm, the unknown acceleration is regarded as an additive process noise by using the fuzzy logic based on genetic algorithm(GA) method. And then, the modified filter is corrected by the new update equation method which is a fuzzy system using the relation between the filter residual and its variation. To shows the feasibility of the suggested method with only one filter, the computer simulations system are provided, this method is compared with multiple model method.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.8
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• pp.8-17
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• 2019

In this study, a model reduction technique was used to develop a precise noise and vibration prediction model for the individual components of a driveline system. The dynamic reduced-order model generated by the Craig-Bampton method was applied to perform dynamic analysis of an electric agricultural power cart. The natural frequency and acceleration response results were analyzed according to the different number of dominant sub-structural modes contained in the reduced-order models. Through the analysis results, it was confirmed that a sufficient number of dominant sub-structures to satisfy the operating conditions should be selected to construct an optimal reduced-order model.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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• pp.90-96
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• 2021

As real-time data of factories can be collected using various sensors, the adaptation of intelligent unmanned processing systems is spreading via the establishment of smart factories. In intelligent unmanned processing systems, data are collected in real time using sensors. The equipment is controlled by predicting future situations using the collected data. Particularly, a technology for the prediction of tool wear and for determining the exact timing of tool replacement is needed to prevent defected or unprocessed products due to tool breakage or tool wear. Directly measuring the tool wear in real time is difficult during the cutting process in milling. Therefore, tool wear should be predicted indirectly by analyzing the cutting load of the main spindle, current, vibration, noise, etc. In this study, data from the current and acceleration sensors; displacement data along the X, Y, and Z axes; tool wear value, and shape change data observed using Newroview were collected from the high-speed, two-edge, flat-end mill machining process of SKD11 steel. The support vector machine technique (machine learning technique) was applied to predict the amount of tool wear using the aforementioned data. Additionally, the prediction accuracies of all kernels were compared.

• Smart Structures and Systems
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• v.23 no.5
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• pp.507-520
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• 2019

In the structural health monitoring field, damage detection has been commonly carried out based on the structural model and the engineering features related to the model. However, the extracted features are often subjected to various errors, which makes the pattern recognition for damage detection still challenging. In this study, an automated damage identification method is presented for hanger cables in a tied-arch bridge using a convolutional neural network (CNN). Raw measurement data for Fourier amplitude spectra (FAS) of acceleration responses are used without a complex data pre-processing for modal identification. A CNN is a kind of deep neural network that typically consists of convolution, pooling, and fully-connected layers. A numerical simulation study was performed for multiple damage detection in the hangers using ambient wind vibration data on the bridge deck. The results show that the current CNN using FAS data performs better under various damage states than the CNN using time-history data and the traditional neural network using FAS. Robustness of the present CNN has been proven under various observational noise levels and wind speeds.

• Investigative Magnetic Resonance Imaging
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• v.25 no.4
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• pp.229-251
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• 2021

Arteriosclerosis is the leading cause of stroke, with a fatality rate surpassing that of ischemic heart disease. High-resolution vessel wall magnetic resonance imaging is generally recognized as a non-invasive and panoramic method for the evaluation of arterial plaque; however, this method requires improved signal-to-noise ratio and scanning speed. Recent advances in high-density head and neck coil arrays are characterized by broad coverage, multiple channels, and closefitting designs. This review analyzes fast magnetic resonance imaging from the perspective of accelerated algorithms for vessel wall imaging and demonstrates the need for effective algorithms for signal acquisition using advanced radiofrequency system. We summarize different phased-array structures under various experimental objectives and equipment conditions, introduce current research results, and propose prospective research studies in the future.

• Composites Research
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• v.34 no.3
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• pp.192-198
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• 2021

Due to enormous market growing of electric vehicles without combustion engine, reducing unwanted BSR (buzz, squeak, and rattle) noise is highly demanded for vehicle quality and performance. Particularly, innerbelt weatherstrips which not only block wind noise, rain, and dust from outside, but also reduce noise and vibration of door glass and vehicle are required to exhibit high damping properties for improved BSR performance of the vehicle. Thermoplastic elastomers (TPEs), which can be recycled and have lighter weight than thermoset elastomers, are receiving much attention for weatherstrip material, but TPEs exhibit low material damping and compression set causing frictional noise and vibration between the door glass and the weatherstrip. In this study, high damping EPDM (ethylene-propylene-diene monomer)/PP (polypropylene) thermoplastic vulcanizates (TPV) were investigated by varying EPDM/PP ratio and ENB (ethylidene norbornene) fraction in EPDM. Viscoelastic properties of TPV materials were characterized by assuming that the material damping is directly related to the viscoelasticity. The optimum material damping factor (tanδ peak 0.611) was achieved with low PP ratio (14 wt%) and high ENB fraction (8.9 wt%), which was increased by 140% compared to the reference (tanδ 0.254). The improved damping is believed due to high fraction of flexible EPDM chains and higher interfacial slippage area of EPDM particles generated by increasing ENB fraction in EPDM. The stick-slip test was conducted to characterize frictional noise and vibration of the TPV weatherstrip. With improved TPV material damping, the acceleration peak of frictional vibration decreased by about 57.9%. This finding can not only improve BSR performance of electric vehicles by designing material damping of weatherstrips but also contribute to various structural applications such as urban air mobility or aircrafts, which require lightweight and high damping properties.

• Korean Journal of Radiology
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• v.21 no.12
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• pp.1334-1344
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• 2020

Objective: Compressed sensing (CS) has gained wide interest since it accelerates MRI acquisition. We aimed to compare the 3D post-contrast T1-weighted volumetric isotropic turbo spin echo acquisition (VISTA) with CS (VISTA-CS) and without CS (VISTA-nonCS) in intracranial vessel wall MRIs (VW-MRI). Materials and Methods: From April 2017 to July 2018, 72 patients who underwent VW-MRI, including both VISTA-CS and VISTA-nonCS, were retrospectively enrolled. Wall and lumen volumes, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured from normal and lesion sites. Two neuroradiologists independently evaluated overall image quality and degree of normal and lesion wall delineation with a four-point scale (scores ≥ 3 defined as acceptable). Results: Scan coverage was increased in VISTA-CS to cover both anterior and posterior circulations with a slightly shorter scan time compared to VISTA-nonCS (approximately 7 minutes vs. 8 minutes). Wall and lumen volumes were not significantly different with VISTA-CS or VISTA-nonCS (interclass correlation coefficient = 0.964-0.997). SNR was or trended towards significantly higher values in VISTA-CS than in VISTA-nonCS. At normal sites, CNR was not significantly different between two sequences (p = 0.907), whereas VISTA-CS provided lower CNR in lesion sites compared with VISTA-nonCS (p = 0.003). Subjective wall delineation was superior with VISTA-nonCS than with VISTA-CS (p = 0.019), although overall image quality did not differ (p = 0.297). The proportions of images with acceptable quality were not significantly different between VISTA-CS (83.3-97.8%) and VISTA-nonCS (75-100%). Conclusion: CS may be useful for intracranial VW-MRI as it allows for larger scan coverage with slightly shorter scan time without compromising image quality.