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Damage detection of 3D structures using nearest neighbor search method

Earthquake Engineering and Engineering Vibration 2021年第3期20卷 705-725页

作者： Ali Abasi Vahid Harsij Ahmad Soraghi Department of Civil and Environmental Engineering Western UniversityLondonONCanada Department of Civil Engineering Amirkabir University of Technology(Tehran Polytechnic)TehranIran Department of Civil Engineering University of AkronASEC 210Sumner StAkronOH 44325USA

An innovative damage identification method using the nearest neighbor search method to assess 3D structures is *** frequency response function was employed as the input parameters to detect the severity and place of damage in 3D spaces since it includes the most dynamic characteristics of the ***-dimensional principal component analysis was utilized to reduce the size of the frequency response function *** nearest neighbor search method was employed to detect the severity and location of damage in different damage *** accuracy of the approach was verified using measured data from an experimental test;moreover,two asymmetric 3D numerical examples were considered as the numerical *** superiority of the method was demonstrated through comparison with the results of damage identification by using artificial neural *** levels of white gaussian noise were used for polluting the frequency response function data to investigate the robustness of the methods against noise-polluted *** results indicate that both methods can efficiently detect the damage properties including its severity and location with high accuracy in the absence of noise,but the nearest neighbor search method is more robust against noisy data than the artificial neural network.

关键词： damage identification damage index frequency response function two-dimensional principal component analysis nearest neighbor search artificial neural network white gaussian noise

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Tracking Performance Under Additive gaussian noise and Control Energy Constraint for Networked Control Systems

Tracking Performance Under Additive Gaussian Noise and Contr...

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第24届中国控制与决策会议

作者： Ming Chi 1 , Zhi-Hong Guan 1 , Li Ding 2 , Fu-Shun Yuan 3 1. Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China2. School of Power and Mechanical Engineering, Wuhan University, Wuhan, 430072, China 3. School of Mathematics and Statistics, Anyang Normal University, Anyang, 455002, China

There is an increasing interest in studying the optimal tracking performance of network control systems. This paper investigates the optimal tracking performance issues of finite-dimensional, continuous-time, linear time-invariant network control systems, in which the forward path is subject to noise disturbance and an additive white gaussian noise existed in the output feedback. The reference input is a Brownian motion random process. The two-parameter controller is employed and an integral square criterion is adopted to measure the tracking error and the input energy. The plant under consideration is assumed to be non-minimum phase and unstable, that is, the plant has zeros in the right half and unstable poles. The explicit expression of the tracking performance has been obtained. The results show that the white gaussian noise, as well as the unstable poles and non-minimum phase zeros, can worsen the tracking performance.

关键词： Tracking performance white gaussian noise control energy constraint two-parameter control unstable poles non-minimum phase zeros.

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EMD-based Speech Signal noise Reduction With Soft Thresholding

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中国科技纵横 2017年第23期 12-13页

作者：王雨亭电子科技大学清水河校区四川成都611731

In our project, a speech signal with white gaussian noise has been denoised by a brand-new method called Empirical Mode Decomposition (EMD). The signal is decomposed into many components called Intrinsic Mode Function (IMF) firstly, and then with Soft Thresholding Method, most part of the noise has been removed efficiently. Finaly, for noise with -5dB, 0dB and 5dB SNR, our denoising result turns out to be 4dB, 7dB and 10dB, respectively. The denoising system based on EMD in our project turn out to have the ability to deal with the signal which are non-stationary and non-linear signal, which have the self-adaptability at the same time.

关键词： speech signal white gaussian noise EMD soft-threshold

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