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Vibration-based structural health monitoring using CAE-aided unsupervised deep learning
Tong Guo,Minte Zhang,Ruizhao Zhu,Yueran Zong,Zhihong Pan 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.30 No.6
Vibration-based structural health monitoring (SHM) is crucial for the dynamic maintenance of civil building structures to protect property security and the lives of the public. Analyzing these vibrations with modern artificial intelligence and deep learning (DL) methods is a new trend. This paper proposed an unsupervised deep learning method based on a convolutional autoencoder (CAE), which can overcome the limitations of conventional supervised deep learning. With the convolutional core applied to the DL network, the method can extract features self-adaptively and efficiently. The effectiveness of the method in detecting damage is then tested using a benchmark model. Thereafter, this method is used to detect damage and instant disaster events in a rubber bearing-isolated gymnasium structure. The results indicate that the method enables the CAE network to learn the intact vibrations, so as to distinguish between different damage states of the benchmark model, and the outcome meets the high-dimensional data distribution characteristics visualized by the t-SNE method. Besides, the CAE-based network trained with daily vibrations of the isolating layer in the gymnasium can precisely recover newly collected vibration and detect the occurrence of the ground motion. The proposed method is effective at identifying nonlinear variations in the dynamic responses and has the potential to be used for structural condition assessment and safety warning.
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Simulation of Position based Robotic Visual Servo
Rui Zhao,Maheshwar Pd. Sah,Hyongsuk Kim,FU Zhu-mu 제어로봇시스템학회 2010 제어로봇시스템학회 합동학술대회 논문집 Vol.2010 No.12
This paper presents a complete design methodology for Cartesian position based visual servo control of robots with a single camera mounted on the end-effectors. Generally Position based visual servo control requires explicit calculation of the relative position and orientation of the object with respect to the camera. In Position based visual servo, the image capturing and processing time cannot be neglected and also the process of estimating the object position is greatly affected by noise. In this paper, Kalman filter is implemented on the visual servo for the estimation of object motion. In order to control it’s angle and position, a PD controller adjust PD parameters to minimize the exceedance and respond-time, and results the emulator to approve the PD arithmetic which reduces the influence of noise and time elapse compare to the position based visual servo method. The features of algorithm, is verified by simulation results
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