DOI QR코드

DOI QR Code

RGB-Depth Camera for Dynamic Measurement of Liquid Sloshing

RGB-Depth 카메라를 활용한 유체 표면의 거동 계측분석

  • Kim, Junhee (Department of Architectural Engineering, Dankook University) ;
  • Yoo, Sae-Woung (Department of Architectural Engineering, Dankook University) ;
  • Min, Kyung-Won (Department of Architectural Engineering, Dankook University)
  • Received : 2018.10.12
  • Accepted : 2018.11.07
  • Published : 2019.02.28

Abstract

In this paper, a low-cost dynamic measurement system using the RGB-depth camera, Microsoft $Kinect^{(R)}$ v2, is proposed for measuring time-varying free surface motion of liquid dampers used in building vibration mitigation. Various experimental studies are conducted consecutively: performance evaluation and validation of the $Kinect^{(R)}$ v2, real-time monitoring using the $Kinect^{(R)}$ v2 SDK(software development kits), point cloud acquisition of liquid free surface in the 3D space, comparison with the existing video sensing technology. Utilizing the proposed $Kinect^{(R)}$ v2-based measurement system in this study, dynamic behavior of liquid in a laboratory-scaled small tank under a wide frequency range of input excitation is experimentally analyzed.

본 논문에서는 건축물 진동저감장치에 적용되는 액체감쇠기 내 유체 자유표면의 동적 거동 계측을 위해 저가형 RGB-depth 센서인 Microsoft사 $Kinect^{(R)}$ v2의 활용과 계측시스템을 구축하는 방법을 제안하였다. $Kinect^{(R)}$ v2의 성능검토 및 실효성 확인, SDK(software development kit)를 사용한 실시간 모니터링, 3D 공간상에서 유체의 표면 정보 취득, 기존 비디오 센싱기법과의 비교를 통해 본 연구에서 제안한 유체의 동적 거동 계측 시스템의 정확성과 우수성을 검증하였다. 제안된 계측시스템을 활용하여 소형 수조 내 액체에 대한 동적 거동 정밀계측을 수행하였으며, 이를 바탕으로 광범위한 가진입력에 대한 유체 자유표면의 동적 거동 특징을 확인하였다. 본 연구의 결과를 바탕으로 RGB-depth센서의 건축물 진동저감 적용을 통해 정밀한 모니터링 시스템을 구축하고 최적화된 액체감쇠기의 설계 및 운용을 기대할 수 있다.

Keywords

References

  1. Abdelbarr. M., Chen, Y.L, Mohammad, R.J., Masri. S.F., Shen, W.M., Qidwai, U.A (2017) 3D Dynamic Displacement-Field Measurement for Structural Health Monitoring using Inexpensive RGB-D based Sensor, Smart Mater. & Struct., 26, 125016, pp.1-23.
  2. Branko, K. (2013) Calibration of Depth Measurement Model for Kinect-Type 3D Vision Sensors, WSCG 2013 Conference on Computer Graphics, Visualization and Computer Vision. pp.61-64.
  3. Chen, Y.L., Mohamed, A., Mohammad, R.J., Masri, S.F. (2017) Color and Depth Data Fusion using and RGB-D Sensor for Inexpensive and Contactless Dynamic Displacement Field Measurement, WILEY, pp.1-14.
  4. Kim, J., Park, C.S., Min, K.W. (2015) Easy-to-tune Reconfigurable Liquid Column Vibration Absorbers with Multiple Cells, Smart Mater. & Struct., 24, 065041, pp.1-12.
  5. Kim, J., Park, C.S., Min, K.W. (2016) Fast Vision-based Wave Height Measurement for Dynamic Characterization of Tuned Liquid Column Dampers, Measurement, 89, pp.189-196. https://doi.org/10.1016/j.measurement.2016.04.030
  6. Kim, J., Yoo, S.W., Min, K.W. (2018) Microsoft Kinect-based Indoor Building Information Model Acquisition, J. Comput. Struct. Eng. Inst. Korea, 31(4), pp.207-213. https://doi.org/10.7734/COSEIK.2018.31.4.207
  7. Lee, S.K., Lee, H.R., Min, K.W. (2012) Experimental Verification on Nonlinear Dynamic Characteristic of a Tuned Liquid Column Damper Subjected to Various Excitation Amplitudes, Struct. Des. Tall & Spec. Build., 21, pp.374-388. https://doi.org/10.1002/tal.606
  8. Lachat, E., Macher, H., Landes, T., Grussenmeyer, P. (2015) Assessment and Calibration of a RGB-D Camera (Kinect v2 Sensor) Towards a Potential Use for Close-Range 3D Modeling, Remote Sens., 7. pp.13070-13097. https://doi.org/10.3390/rs71013070
  9. Mattias, G. (1998) Motion Control of Open Containers with Slosh Constraints, Lund inst. Tech., pp.69-75.
  10. Qi, X., Lichti, D., El-Badry, M., Chow, J, Ang, K. (2014) Vertical Dynamic Deflection Measurement in Concrete Beams with the Microsoft Kinect, Sens., 14, pp.3293-3307. https://doi.org/10.3390/s140203293
  11. Sawada, K., Ohshina, T., Hizawa, T., Takao, H., Ishida, M. (2005) A Novel Fused Sensor for Photo- and Ion-sensing, Sens. & Actuators B: Chemical, 106, pp.614-618. https://doi.org/10.1016/j.snb.2004.07.029
  12. Smisek, J., Jancosek, M., Paidla, T. (2011) 3D with Kinect, 2011 IEEE Int. Conf. Computer Vision Workshops, Barcelona, Spain, pp.1154-1160.