• 한국방재학회:학술대회논문집
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• 2010.02a
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• pp.79.1-79.1
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• 2010

본 연구에서는 케이슨식 방파제 구조물에 대하여 기초마운드의 잠재적인 손상 모니터링에 관한 연구를 수행하였다. 이를 위해 첫째, 케이슨식 방파제 기초마운드의 이상 상태 여부를 판단하기 위하여 진동응답 분석기법을 선정하였다. 둘째, 선정된 기법에 의한 손상 예측 가능성의 검증을 위하여 케이슨식 방파제의 구조모형을 제작하였다. 셋째, 모형케이슨에 대한 유한요소 모델 생생하여 기초마운드의 손상에 따른 진동응답을 분석하였다. 마지막으로, 모형케이슨에 대한 진동실험을 통하여 기초마운드의 손상 예측을 수행하였다.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.1
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• pp.57-65
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• 2023

A vibration response-based detection system was used to investigate the adhesive areas of single-lap joints using a nonlinear transformation approach for deep learning. In industry or engineering fields, it is difficult to know the condition of an invisible part within a structure that cannot easily be disassembled and the conditions of adhesive areas of adhesively bonded structures. To address these issues, a detection method was devised that uses nonlinear transformation to determine the adhesive areas of various single-lap-jointed specimens from the vibration response of the reference specimen. In this study, a frequency response function with nonlinear transformation was employed to identify the vibration characteristics, and a virtual spectrogram was used for classification in convolutional neural network based deep learning. Moreover, a vibration experiment, an analytical solution, and a finite-element analysis were performed to verify the developed method with aluminum, carbon fiber composite, and ultra-high-molecular-weight polyethylene specimens.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.845-850
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• 2023

In this paper, we propose a method for diagnosing ball bearing vibration using transfer learning. STFT, which can analyze vibration signals in time-frequency, was used as input to CNN to diagnose failures. In order to rapidly learn CNN-based deep artificial neural networks and improve diagnostic performance, we proposed a transfer learning-based deep learning learning technique. For transfer learning, the feature extractor and classifier were selectively learned using a VGG-based image classification model, the data set for learning was publicly available ball bearing vibration data provided by Case Western Reserve University, and performance was evaluated by comparing the proposed method with the existing CNN model. Experimental results not only prove that transfer learning is useful for condition diagnosis in ball bearing vibration data, but also allow other industries to use transfer learning to improve condition diagnosis.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.662-667
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• 2021

Vibrotactile feedback is a major function of the latest touch displays, which greatly improves the user's operability and immersion when interacting with the interface on the screen. In this study, we propose a vibrotactile actuator suitable for mounting on the back side of a mid- to large-sized display because it can generate a strong vibration output by applying an electrostatic force-based mechanism and can be manufactured in a thin flat panel type. The proposed actuator was developed in a structure capable of amplifying the vibration force by alternating up and down with electrostatic force by the upper and lower electrodes that are spaced apart from the electrically grounded mass suspended from a radial leaf spring. As a result of the performance evaluation, the developed bar-type module with two built-in actuators showed excellent vibration output of up to 3.3 g at 170 Hz, confirming the possibility of providing haptic feedback in medium and large touch displays.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.1
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• pp.19-28
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• 2022

In this study, three types of combined isolator consisting of High Damping Rubber Bearing (HDRB) and wire isolator were developed for Uninterruptible Power Supply system (UPS). The dynamic characteristics of the combined isolator were investigated through one-axis shaking table test. The input acceleration were generated in accordance with ICC-ES AC156 code. Scale factors of the input acceleration were designed to be 0.5-2 times the required response spectrum defined in ICC-ES AC156. Based on the test results, damage and dynamic characteristics of the UPS were investigated: including natural frequency, damping ratio, acceleration time history response, dynamic amplification factor and relative displacement. Based on that, it was found that the combined isolator developed in this study could improve the seismic behavior of the UPS, in particular, the response acceleration.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.112-121
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• 2024

The pipping system is widely used in many industries as equipment for transporting fluids over long distances. In high-pressure pipe, as the speed of the fluid increases, a loud noise is generated. Therefore, various studies have been conducted to reduce pipe noise. In this paper, a pipe noise analysis was developed to predict and quantitatively assess the flow-induced vibration and acoustic-induced vibration due to valve flow in high-temperature and high-pressure. To do this, a high-fidelity fluid analysis technique was developed for predicting internal flow in the pipe with valve. In additional, the contribution of compressible/incompressible pressure by frequency band was evaluated using the wavenumber-frequency analysis. To predict a low/middle frequency pipe noise, the vibroacoustic analysis method was developed based on Finite Element Method (FEM). And the pipe noise prediction method for the middle/high frequency was developed based on Statistical Energy Analysis (SEA).

• ICROS
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• v.20 no.3
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• pp.22-26
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• 2014

본 기술 동향에서는 햅틱 기술과 관련된 최근의 동향에 대해서 살펴보기로 한다. 특히 진동 촉각을 기반으로 한 모바일 기기용 햅틱스는 관련 연구들이 활발하게 진행 중이며 이 중에 몇 가지 연구들과 활용되는 액추에이터 등에 대한 연구들을 바탕으로 앞으로의 촉각 기반 생태계를 예측해 보고, 아울러 의료 휴대폰, 컴퓨터, 재활 분야등에서 필요로 하고 또 활용 가치가 있는 햅틱 기술들에 대해서 고찰해 보기로 한다. 최근에 각광받는 웨어러블 기기등도 내부에 다양한 센서와 액추에이터를 내장하려는 움직임이 있고, 휴대폰 등에서 진동 액추에이터를 활용하여 소리를 대체하듯 촉각 기술이 터치 스크린 기반 시장을 한 단계 발전시켜 차세대 입력 장치로 발전할 수 있는 가능성을 가늠해 볼 수 있을 것이다. 지속적인 연구를 통해 관련 분야에서 필요한 촉각 기술을 개발한다면 햅틱 연구자들이 그토록 염원하는 촉각 기반 시장의 확대 성장도 그리 오랜 시일이 걸리지 않을 것으로 기대한다.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.11 no.9
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• pp.414-421
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• 2001

If a machine has some faults, we can detect them using vibration or noise signals. However some maintenance engineers who don\`t have export knowledge, need the help of vibration experts for diagnosing the machine. In this paper a case based reasoning (CBR) system is developed which is able to manipulate the past experiences of vibration diagnosis experts. In the CBR system, the maintenance engineers can retrieve the information form previous cases which are most similar to new problem s that they can solve new problem using solutions form the previous cases. In this paper, a new case retrieval method of CBR system using Petri net is proposed and also applied to diagnosis for electric motors as a practical problem.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.27-28
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• 2018

본 논문에서는 구동 장치의 다양한 상태를 나타내는 소음과 진동으로부터 특징데이터를 추출하여 이를 학습 한 후 실시간으로 장치의 상태를 진단하는 하였다. 실제 현장에서 발생할 수 있는 예측 외 소음환경에 유연하게 대처하기 위해 CNN모델 사용과 소리, 진동 데이터의 Butterworth filter와 Kalman filter를 적용하여 노이즈 배제처리 하였다. 제안된 시스템의 유용성을 확인하기 위해 제안된 시스템과 기존 CNN기반 시스템을 소음환경에서 비교 실험하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.07a
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• pp.9-10
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• 2017

본 논문에서 소음, 진동을 이용한 딥러닝 기반 기계 고장진단 임베디드 시스템을 제안하였다. 제안된 시스템은 기계로부터 취득된 소리와 진동을 바탕으로 학습한 DNN모델을 통해 실시간으로 기계 고장을 진단한다. 딥러닝 기술을 사용하여 학습에 따라 적용대상이 변경될 수 있도록 함으로써 특정 기계에 종속적이지 않고 가변적으로 다양한 기계에 대해 고장 예지 및 건전성 관리를 제공하도록 설계하였으며, 이를 증명하기 위해 액추에이터를 환풍기로 설정하여 정상상태와 4가지 비정상상태의 5가지상태를 학습하여 실험한 결과 93%의 정확도를 얻었다.