Volume 18 - Issue 4

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IJERD : Volume 18 - Issue 4

(July August- 2022)


Long Qiao

Wavelet Transformation Analysis for Damage Detection on a Three-Story Building
  • Abstract
  • Keywords
  • Reference
  • Full Article
Civil structures are susceptible to damages over their service lives due to aging, environmental loading, fatigue and excessive response.Such deterioration significantly affects the performance and safety of structure. In this study, the measured structure vibration signals were decomposed by Fast Fourier Transform (FFT), Continuous Wavelet Transform (CWT) and Wavelet Packet Transform (WPT) to extract the sensitive features of the structural response, and to form one-dimensional or two-dimensional feature patterns.Correlation pattern recognition was used to perform pattern-matching for damage detection.To demonstrate the validity and accuracy of the method, experimental study was conducted on a small-scale three-story building. The results showed that the features of the signal for different damage scenarios can be uniquely identified by these transformations, and correlation algorithm can then be used to identify the most probable damage scenario.
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[1]. Adeli, H. and Jiang, X. 2006. Dynamic fussy wavelet neural network model for structural system identification. Journal of Structural Engineering. 132:1,102-111.
[2]. Ding, Y.L., Li, A.Q.and Liu, T. 2008. A study on the WPT-based structural damage alarming of the ASCE benchmark experiments. Advances in Structural Engineering. 11:1,121-127.
[3]. Fang, X.,Luo, H.and Tang, J. 2005. Structural damage detection using neural network with learning rate improvement. Computers and Structures. 83,2150-2161.
[4]. Farrar, C.R., Doebling, S.W.and Nix, D. 2001. Vibration-based structural damage identification. Philosophical Transactions of Royal Society of London Series A: Mathematical, Physical and Engineering Science, London. 359,131-149.
[5]. Fasel, T.R., Sohn, H., Park, G.and Farrar, C.R. 2005. Active sensing using impedance-based ARX models and extreme value statistics for damage detection. Earthquake Engineering and Structural Dynamics. 34,763-785.

Citation
Long Qiao "Wavelet Transformation Analysis for Damage Detection on a Three-Story Building" International Journal of Engineering Research and Development, Volume 18, Issue 04 (July-August 2022)
MID 1804.067X.0001.
Page 01-08      Download Certificate
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M. Balaji, S. Prabhakaran, S. Kolli Balasivarama Reddy

Human-Robot Interaction: Virtual Simulation of Real-Time Gesture Recognition System using Continuous Hand Movement Tracking
  • Abstract
  • Keywords
  • Reference
  • Full Article
Certain occupational environments pose major threats to human safety, potentially keeping their lives in danger and due to which various kinds of robots are designed to perform such complicated tasks. Robots were formerly guided by mechanical instruments, but with advent developments in Human-Computer Interaction, they are presently controlled by hand gestures and speech, which have introduced a new field in robotics, called Collaborative Robots. Among them, gesture recognition is the most sort after technology as it uses hand movements as input to perform the desired task. Thisarticlereports a novel system to detect the gestures in real time with continuous hand movement tracking via microprocessors and position sensors. The system consists of transmitting module and receiving module. The transmitting module is a glove comprising of an Arduino controller, flex sensors, MPU-6050 sensor, Bluetooth module and a power source. The method is virtually validated in MATLAB Simulink by simulation of robot arm through user hand movements via electro-mechanical sensors.Wireless communication and quick response time serves as the main feature of the suggested methodology.
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[1]. Kim D, Kim Y-S, Noh K, Jang M, & Kim S. (2020). Wall-Climbing Robot with Active Sealing for Radiation Safety of Nuclear Power Plants. Nucl. Sci. Eng., 194(12), 1162–1174. https://doi.org/10.1080/00295639.2020.1777023
[2]. Vescovo VL, Jamieson AJ, Lahey P, McCallum R, Stewart HA, & Machado C. (2021). Safety and conservation at the deepest place on Earth: A call for prohibiting the deliberate discarding of nondegradable umbilicals from deep-sea exploration vehicles. Mar. Policy, 128, 104463. https://doi.org/10.1016/j.marpol.2021.104463
[3]. Liu Y, Zhang W, Pan S, Li Y, & Chen Y. (2020). Analyzing the robotic behavior in a smart city with deep enforcement and imitation learning using IoRT. Comput. Commun., 150, 346–356. https://doi.org/10.1016/j.comcom.2019.11.031
[4]. Gundupalli SP, Hait S, & Thakur A. (2017). A review on automated sorting of source-separated municipal solid waste for recycling. Waste Management, 60, 56–74. https://doi.org/10.1016/j.wasman.2016.09.015
[5]. Parvathy P, Subramaniam K, Prasanna Venkatesan GKD, Karthikaikumar P, Varghese J, & Jayasankar T. (2020). Development of hand gesture recognition system using machine learning. J. Ambient Intell. Human Comput., 12(6), 6793–6800. https://doi.org/10.1007/s12652-020-02314-2

Citation
M. Balaji, S. Prabhakaran, S. Kolli Balasivarama Reddy "Human-Robot Interaction: Virtual Simulation of Real-Time Gesture Recognition System using Continuous Hand Movement Tracking" International Journal of Engineering Research and Development, Volume 18, Issue 04 (July-August 2022)
MID 1804.067X.0002. India
Page 09-16      Download Certificate
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