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The Principles
Published in Hollnagel Erik, FRAM: The Functional Resonance Analysis Method, 2017
Whereas classical resonance has been known and used for several thousand years, stochastic resonance is of a more recent origin. In stochastic resonance there is no forcing function, but rather random noise, which every now and then pushes a subliminal signal over the detection threshold. Stochastic resonance can be defined as the enhanced sensitivity of a device to a weak signal that occurs when random noise is added to the mix. The outcome of stochastic resonance is non-linear, which simply means that the output is not directly proportional to the input. The outcome can also occur - or emerge - instantaneously, unlike classical resonance which must be built-up over time.
Sonar Performance Models
Published in Paul C. Etter, Underwater Acoustic Modeling and Simulation, 2017
Stochastic resonance refers to a phenomenon that is manifested in nonlinear systems whereby weak signals can be amplified in the presence of noise (Gammaitoni et al., 1998). Three components are necessary: a threshold, a periodic signal, and a source of noise. The response of the system undergoes resonance-like behavior as the noise level is varied. Also see the related work reported by Zhang et al. (2008).
What Visual Discrimination of Fractal Textures Can Tell Us about Discrimination of Camouflaged Targets
Published in Dee H. Andrews, Robert P. Herz, Mark B. Wolf, Human Factors Issues in Combat Identification, 2010
Vincent A. Billock, Douglas W. Cunningham, Brian H. Tsou
Based on the performance of the hypoplastic but seemingly normal observer (VB) in Figure 6.3, it might be advisable for sensor operators to be screened for spatial sampling problems (sub-clinical amblyopia) by measuring their contrast sensitivity functions. It is worth noting that humans can become proficient at naming the spectral exponents of images (or equivalently, fractal dimension, which is a linear transform of the exponent; Cutting and Garvin, 1987; Kumar et al., 1993; Pentland, 1988). Aneural ability to estimate the spectral drop-off and exploit it has been speculated on and deserves additional attention (Billock, 2000; Billock, de Guzman, et al., 2001; Campbell et al., 1978; Hammett and Bex, 1996; Rogowitz and Voss, 1990). Taken together with the natural image regularities and perceptual pop-out findings discussed earlier, this suggests that β is a key signature, both for images and for the visual systems that evolved to transduce images. Of particular interest is the finding that, under some conditions (e.g., nonlinear systems near threshold), adding noise can facilitate detection and identification of some signals, including images (Repperger et al., 2001; Simonotto et al., 1997; Yang, 1998)-an example of stochastic resonance as an image enhancement mechanism. Dynamic noise is more effective than static (Simonotto et al, 1997). Because other studies of stochastic resonance show that 1/fβ noise can be more efficient than white noise in inducing stochastic resonance effects (Billock and Tsou, 2007; Hangi, Jung, Zerbe, and Moss, 1993; Nozaki, Collins, Yamamota, 1999), further studies of discrimination in spatiotemporal fractal noise (at various contrast levels) would be warranted and might uncover some practical applications. In particular, it may be possible to break many camouflage schemes by adding filtered noise to the sensor images. This seemingly perverse aspect of stochastic resonance should be exploited if possible. Since stochastic resonance’s effectiveness is often dependent on the Fourier spectral qualities of the noise, fractal camouflage may be particularly vulnerable (because the spectral qualities of simple fractals are easily matched by varying one noise parameter). Multi-fractals may be less vulnerable in this regard. It would be ironic if the beautiful mathematical attributes of fractals (which give it so much utility in describing the natural environment and make it such an elegant solution to the problem of designing camouflage) also prove to be its Achilles’ heel.
Analysis of early fault vibration detection and analysis of offshore wind power transmission based on deep neural network
Published in Connection Science, 2022
Boyu Yang, Anmin Cai, Weirong Lin
It can be seen from Figure 5 that in the second-order stochastic resonance system, no matter how the system parameter a changes, the change trend of the signal-to-noise ratio of the first-order stochastic resonance system with the noise intensity is consistent. The signal-to-noise ratio will first increase with the increase of the noise intensity, that is, the stochastic resonance phenomenon occurs at this time, and then the best stochastic resonance under this set of parameters is reached. Among the large-capacity grid-connected wind turbines that have been put into production, horizontal-axis, three-bladed, upwind, and doubly-fed wind turbines still occupy a dominant position, which is also the mainstream technology of international wind turbines. So the improved system model can theoretically generate stochastic resonance. System parameters will affect the best signal-to-noise ratio of stochastic resonance. When the system parameters reach a certain state of coordination with noise and weak signals, the global best stochastic resonance will occur, and the noise reduction effect at this time is the most ideal. Therefore, when the noise and signal are constant in practical applications, the maximum noise reduction effect can be achieved by adjusting the parameters of the stochastic resonance system. The following simulation compares the detection effects of the first-order stochastic resonance system and the improved second-order stochastic resonance system.
Weak fault diagnosis of rolling bearing based on FRFT and DBN
Published in Systems Science & Control Engineering, 2020
For reducing non-stationary and nonlinear signal noises, empirical mode decomposition (EMD) method (Cheng, Wang, Chen, Zhang, & Huang, 2019; Tao, 2019; Zhang, Zhao, & Deng, 2018) is a common method, but in principle there are problems with modal aliasing and endpoint effects. For noise reduction, singular value decomposition (SVD) method (An, Zeng, Yang, & An, 2017; Cai & Xiao, 2019; Wang et al., 2019) has good performance, but the number of singular values depends on the man-made. If the singular value is too large, it will influence the noise reduction effect. Otherwise, the useful signal will be lost. There is still no valid singular value selection method. Stochastic resonance method (Lei, Qiao, Xu, Lin, & Niu, 2017; Qiao, Lei, Lin, & Jia, 2017) can transfer a part of the noise energy to the signal, while reducing the noise and enhancing the characteristics of the weak signal. However, the traditional adaptive stochastic resonance method has certain limitations because it only has a certain system parameter adjusted and the interactions among parameters are ignored.