• Structural Engineering and Mechanics
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• v.75 no.4
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• pp.445-454
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• 2020

Structural damage identification (SDI) is a crucial step in structural health monitoring. However, some of the existing SDI methods cannot provide enough identification accuracy and efficiency in practice. A novel whale optimization algorithm (WOA) based method is proposed for SDI by weighting modal data and flexibility assurance criterion in this study. At first, the SDI problem is mathematically converted into a constrained optimization problem. Unlike traditional objective function defined using frequencies and mode shapes, a new objective function on the SDI problem is formulated by weighting both modal data and flexibility assurance criterion. Then, the WOA method, due to its good performance of fast convergence and global searching ability, is adopted to provide an accurate solution to the SDI problem, different predator mechanisms are formulated and their probability thresholds are selected. Finally, the performance of the proposed method is assessed by numerical simulations on a simply-supported beam and a 31-bar truss structures. For the given multiple structural damage conditions under environmental noises, the WOA-based SDI method can effectively locate structural damages and accurately estimate severities of damages. Compared with other optimization methods, such as particle swarm optimization and dragonfly algorithm, the proposed WOA-based method outperforms in accuracy and efficiency, which can provide a more effective and potential tool for the SDI problem.

• The KIPS Transactions:PartC
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• v.9C no.4
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• pp.459-466
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• 2002

This paper proposes a robust speaker identification algorithm based on the wavelet filter banks and multiple decision-making scheme. Since the proposed speaker identification algorithm has a structure performing the identification algorithm independently for each subband, the noise effect of an subband can be localized. Through this process, we can obtain more robust results for the environmental noises which generally have band limited frequency. In the experiments, the proposed method showed more 15∼60% improvement than the vector quantization method for the various noisy environments.

• 한국지구물리탐사학회:학술대회논문집
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• 2007.12a
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• pp.15-25
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• 2007

Gravity method could be one of the most effective tool for evaluating the soundness of basement which is directly correlated with density and its variations. Moreover, Gravimeter is easy to handle and strong to electromagnetic noises. But, gravity anomaly due to the target structures in engineering and environmemtal applications are too small to detect, comparing to the external changes, such as, elevation, topography, and regional geological variations. Gravity method targeting these kinds of small anomaly sources with high precision usually called microgravity. Microgravimetry with precision and accuracy of few ${\mu}Gal$, can be achieved by the recent high-resolution gravimeter, careful field acquisition, and sophisticated processing, analysis, and interpretation routines. This paper describes the application of the microgravity, such as, density structure of a rock fill dam, detection of abandoned mine-shaft, detection and mapping of karstic cavities in limestone terrains, and time-lapse gravity for grout monitoring. The case studies show how the gravity anomalies detect the location of the targets and reveal the geologic structure by mapping density distributions and their variations.

• Journal of Biomedical Engineering Research
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• v.18 no.4
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• pp.421-428
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• 1997

Magnetic field sensors made from superconducting quantum interference device (SQUID) are the most sensitive low-frequency sensors available, enabling measurements of extremely weak magnetic fields from the brain. Neuromagnetic measurements allow superior spatial resolution, compared with the present electric measurements, and superior temporal resolution, compared with the fMRl and PET, providing useful informations for the functional diagnoses of the brain. We developed a 4-channel SQUID system for neuromagnetic applications. The main features of the system are its simple readout electronics and compact pickup coil structure. A magnetically shielded room has been constructed for the reduction of environmental magnetic noises. The developed SQUID system has noise level lower than the magnetic noise from the brain. Magnetic field signals of the spontaneous r-rhythm activity and auditory evoked magnetic fields have been measured.

• Journal of Korean Academy of Nursing
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• v.35 no.7
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• pp.1401-1409
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• 2005

Purpose: Infants at neonatal intensive care units (NICU) are invariably exposed to various procedural and environmental stimuli. The study was performed to compare the pain responses in three NICU stimulants and to examine the clinical feasibility for NICU infants using CRIES, FLACC and PIPP. Method: In a correlational study, a total of 94 NICU stimulants including angio-catheter insertions, trunk-rubbings and loud noises, was observed for pain responses among 64 infants using CRIES, FLACC and PIPP. Results: A significant difference was identified among the mean scores in CRIES($F_{(2, 91)}$=47.847, p=.000), FLACC($F_{(2, 91)}$=41.249, p=.000) and PIPP($F_{(2. 91)}$=16.272, p=.000) to three stimulants. In a Post-hoc Scheff test, an angio-catheter insertion showed the highest scores in CRIES, FLACC and PIPP compared to the other two stimulations. A strong correlation was identified between CRIES and FLACC in all three stimulations(.817 < r < .945) while inconsistent findings were identified between PIPP and CRIES or FLACC. Conclusions: The results of the study support that CRIES and FLACC are reliable and clinically suitable pain measurements for NICU infants. Further studies are needed in data collection time-point as well as clinical feasibility on PIPP administration to assess pain response in infants, including premature infants.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1732-1739
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• 2018

With the explosion of digital devices, interaction technologies between human and devices are required more than ever. Especially, hand gesture recognition is advantageous in that it can be easily used. It is divided into the two groups: the contact sensor and the non-contact sensor. Compared with non-contact gesture recognition, the advantage of contact gesture recognition is that it is able to classify gestures that disappear from the sensor's sight. Also, since there is direct contacted with the user, relatively accurate information can be acquired. Electromyography (EMG) and force-sensitive resistors (FSRs) are the typical methods used for contact gesture recognition based on muscle activities. The sensors, however, are generally too sensitive to environmental disturbances such as electrical noises, electromagnetic signals and so on. In this paper, we propose a novel contact gesture recognition method based on Flexible Epidermal Tactile Sensor Array (FETSA) that is used to measure electrical signals according to movements of the wrist. To recognize gestures using FETSA, we extracted feature sets, and the gestures were subsequently classified using the support vector machine. The performance of the proposed gesture recognition method is very promising in comparison with two previous non-contact and contact gesture recognition studies.

• Structural Engineering and Mechanics
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• v.73 no.4
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• pp.367-379
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• 2020

In recent railway projects where the railway connects between cities, newly planned tunnels are often located close to, or beneath an existing tunnel. Many claims and petitions have voiced public concern about the vibration and noise resulting from the situation. Vibrations and noises are engineering issues as well as environmental problems, and have become more important as people have become more concerned with their the quality of life. However, it is unlikely that the effects of vibration in situations where trains simultaneously pass a multileveled tunnel crossing have been appropriately considered in the phase of planning and design. This study investigates the superposition characteristic of ground-born vibrations from a multileveled tunnel crossing. The results from model tests and numerical analysis show that the ground-born vibration can be amplified by a maximum of about 30% compared to that resulting from the existing single tunnel. Numerical parametric study has also shown that the vibration amplification effect increases as the ground stiffness, the tunnel depth, and the distance between tunnels decrease.

• Korean Journal of Remote Sensing
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• v.24 no.1
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• pp.79-89
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• 2008

Irregular temporal sampling is a common feature of geophysical and biological time series in remote sensing. This study proposes an on-line system for reconstructing observation image series contaminated by noises resulted from mechanical problems or sensing environmental condition. There is also a high likelihood that during the data acquisition periods the target site corresponding to any given pixel may be covered by fog or cloud, thereby resulting in bad or missing observation. The surface parameters associated with the land are usually dependent on the climate, and many physical processes that are displayed in the image sensed from the land then exhibit temporal variation with seasonal periodicity. A feedback system proposed in this study reconstructs a sequence of images remotely sensed from the land surface having the physical processes with seasonal periodicity. The harmonic model is used to track seasonal variation through time, and a Gibbs random field (GRF) is used to represent the spatial dependency of digital image processes. The experimental results of this simulation study show the potentiality of the proposed system to reconstruct the image series observed by imperfect sensing technology from the environment which are frequently influenced by bad weather. This study provides fundamental information on the elements of the proposed system for right usage in application.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.1
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• pp.48-56
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• 2023

This paper implements many-to-many communication between users and develops a multi-functional smart helmet for worker protection and environmental safety in the shipbuilding and shipping industry. First, the communication situation is recorded in the field to perform signal processing for noise that interferes with communication. Then, it deals with the contents of developing smart helmets, data acquisition, algorithms, and simulations. The simulation results analyzed by applying the adaptive algorithm are shown, and their usefulness is confirmed. In conclusion, looking at the optimization process for the convergence factor of the Least Mean Square and Filtered-x Least Mean Square Adaptation Algorithm was possible. It is thought that it has laid the foundation for implementing many-to-many communication, the function of smart helmets that reduces or removes various noises at the shipyard in the future.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.74.2-75
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• 2021

We demonstrate that a deep learning classifier that only uses to gravitational wave (GW) detectors auxiliary channel data can distinguish various types of non-Gaussian noise transients (glitches) with significant accuracy, i.e., ≳ 80%. The classifier is implemented using the multi-scale neural networks (MSNN) with PyTorch. The glitches appearing in the GW strain data have been one of the main obstacles that degrade the sensitivity of the gravitational detectors, consequently hindering the detection and parameterization of the GW signals. Numerous efforts have been devoted to tracking down their origins and to mitigating them. However, there remain many glitches of which origins are not unveiled. We apply the MSNN classifier to the auxiliary channel data corresponding to publicly available GravitySpy glitch samples of LIGO O1 run without using GW strain data. Investigation of the auxiliary channel data of the segments that coincide to the glitches in the GW strain channel is particularly useful for finding the noise sources, because they record physical and environmental conditions and the status of each part of the detector. By only using the auxiliary channel data, this classifier can provide us with the independent view on the data quality and potentially gives us hints to the origins of the glitches, when using the explainable AI technique such as Layer-wise Relevance Propagation or GradCAM.