Conditions Of Stochastic Resonance Research Articles

Training augmentation using additive sensory noise in a lunar rover navigation task.

The uncertain environments of future space missions means that astronauts will need to acquire new skills rapidly; thus, a non-invasive method to enhance learning of complex tasks is desirable. Stochastic resonance (SR) is a phenomenon where adding noise improves the throughput of a weak signal. SR has been shown to improve perception and cognitive performance in certain individuals. However, the learning of operational tasks and behavioral health effects of repeated noise exposure aimed to elicit SR are unknown. We evaluated the long-term impacts and acceptability of repeated auditory white noise (AWN) and/or noisy galvanic vestibular stimulation (nGVS) on operational learning and behavioral health. Subjects (n = 24) participated in a time longitudinal experiment to access learning and behavioral health. Subjects were assigned to one of our four treatments: sham, AWN (55 dB SPL), nGVS (0.5 mA), and their combination to create a multi-modal SR (MMSR) condition. To assess the effects of additive noise on learning, these treatments were administered continuously during a lunar rover simulation in virtual reality. To assess behavioral health, subjects completed daily, subjective questionnaires related to their mood, sleep, stress, and their perceived acceptance of noise stimulation. We found that subjects learned the lunar rover task over time, as shown by significantly lower power required for the rover to complete traverses (p < 0.005) and increased object identification accuracy in the environment (p = 0.05), but this was not influenced by additive SR noise (p = 0.58). We found no influence of noise on mood or stress following stimulation (p > 0.09). We found marginally significant longitudinal effects of noise on behavioral health (p = 0.06) as measured by strain and sleep. We found slight differences in stimulation acceptability between treatment groups, and notably nGVS was found to be more distracting than sham (p = 0.006). Our results suggest that repeatedly administering sensory noise does not improve long-term operational learning performance or affect behavioral health. We also find that repetitive noise administration is acceptable in this context. While additive noise does not improve performance in this paradigm, if it were used for other contexts, it appears acceptable without negative longitudinal effects.

Stochastic resonance of multi-stable energy harvesting system with high-order stiffness from rotational environment

This paper aims to study the stochastic resonance of multi-stable energy harvesting system and enhance the performance of energy harvesting from random rotational environment. The influences of high-order stiffness terms and multi-stable characteristics are analyzed. The spectral amplification is derived to quantify the stochastic resonance for the energy harvesting system with penta-stable configuration driven by noise and periodic forcing. The non-monotonic dependence of spectral amplification on noise intensity indicates the existence of stochastic resonance. In particular, a critical high-order stiffness coefficient is revealed, for which the stochastic resonance is optimum. Meanwhile, the maximum of spectral amplification can be optimized by choosing appropriate unstable equilibrium positions. In addition, it is found that both the average input energy and the overall efficiency in power conversion can be further enhanced under conditions of stochastic resonance. Finally, the penta-stable energy harvester demonstrates a good performance in output power for different noise intensities and damping coefficients.

Stochastic Resonance with Parameter Estimation for Enhancing Unknown Compound Fault Detection of Bearings.

Although stochastic resonance (SR) has been widely used to enhance weak fault signatures in machinery and has obtained remarkable achievements in engineering application, the parameter optimization of the existing SR-based methods requires the quantification indicators dependent on prior knowledge of the defects to be detected; for example, the widely used signal-to-noise ratio easily results in a false SR and decreases the detection performance of SR further. These indicators dependent on prior knowledge would not be suitable for real-world fault diagnosis of machinery where their structure parameters are unknown or are not able to be obtained. Therefore, it is necessary for us to design a type of SR method with parameter estimation, and such a method can estimate these parameters of SR adaptively by virtue of the signals to be processed or detected in place of the prior knowledge of the machinery. In this method, the triggered SR condition in second-order nonlinear systems and the synergic relationship among weak periodic signals, background noise and nonlinear systems can be considered to decide parameter estimation for enhancing unknown weak fault characteristics of machinery. Bearing fault experiments were performed to demonstrate the feasibility of the proposed method. The experimental results indicate that the proposed method is able to enhance weak fault characteristics and diagnose weak compound faults of bearings at an early stage without prior knowledge and any quantification indicators, and it presents the same detection performance as the SR methods based on prior knowledge. Furthermore, the proposed method is more simple and less time-consuming than other SR methods based on prior knowledge where a large number of parameters need to be optimized. Moreover, the proposed method is superior to the fast kurtogram method for early fault detection of bearings.

On the Breaking of the Milankovitch Cycles Triggered by Temperature Increase: The Stochastic Resonance Response

Recent decades have registered the hottest temperature variation in instrumentally recorded data history. The registered temperature rise is particularly significant in the so-called hot spot or sentinel regions, characterized by higher temperature increases in respect to the planet average value and by more marked connected effects. In this framework, in the present work, following the climate stochastic resonance model, the effects, due to a temperature increase independently from a specific trend, connected to the 105 year Milankovitch cycle were tested. As a result, a breaking scenario induced by global warming is forecasted. More specifically, a wavelet analysis, innovatively performed with different sampling times, allowed us, besides to fully characterize the cycles periodicities, to quantitatively determine the stochastic resonance conditions by optimizing the noise level. Starting from these system resonance conditions, numerical simulations for increasing planet temperatures have been performed. The obtained results show that an increase of the Earth temperature boosts a transition towards a chaotic regime where the Milankovitch cycle effects disappear. These results put into evidence the so-called threshold effect, namely the fact that also a small temperature increase can give rise to great effects above a given threshold, furnish a perspective point of view of a possible future climate scenario, and provide an account of the ongoing registered intensity increase of extreme meteorological events.

On the Effect of the Electrical Load on Vibration Energy Harvesting Under Stochastic Resonance

Abstract Vibration energy harvesting (VEH) is a promising alternative for powering wireless electronics in many practical applications. Ambient vibration energy in the surrounding space of a target application often involves an inescapable randomness in the exciting vibrations, which may lead to deterioration of the expected power gains due to insufficient tuning and limited optimal designs. Stochastic resonance (SR) is a concept that has recently been considered for exploiting this randomness toward improving power generation from vibrating systems, based on the coexistence of near-harmonic vibrations with broadband noise excitations in a variety of practical mechanical systems. This paper is concerned with the optimal conditions for SR in vibration energy harvesters, exploring the frequently neglected effect of realistic architectures of the electrical circuit on the system dynamics and the achievable power output. A parametric study is conducted using a numerical path integration (PI) method to compute the response probability density functions (PDFs) of vibration energy harvesters, focusing on the effect of standard electrical components; namely, a load resistor, a rectifier, and a capacitor. It is found that the conditions for SR exhibit a nonlinear dependence on the weak harmonic excitation amplitude. Moreover, the modified nonlinear dissipation properties introduced by the rectifier and the capacitor lead to a tradeoff between the power output and the nonconducting dynamics that is essential in order to determine optimal harvesting designs.

Collective behavior of a nearest neighbor coupled system in a dichotomous fluctuating potential

In this study, the collective and stochastic resonance (SR) behavior of a stochastic nearest neighbor coupled system in a dichotomous fluctuating potential driven by a periodic force are investigated. The exact conditions of stability, synchronization and SR of the system are given analytically and verified by a numerical algorithm. We find that the final dynamic behaviors of the system are the results of the synergy of the coupled system, noise and external periodic force: the stationary regime criterion is only related to the noise parameters and system potential parameter; the synchronization criterion is related to the number of particles, coupling strength, noise parameters and system potential parameter. Furthermore, for the stationary regime, all particles synchronously move with their mean field and show SR phenomenon. For the non-stationary regime, the power-law distribution is observed when the synchronization criteria is satisfied.

A Physical–Mathematical Approach to Climate Change Effects through Stochastic Resonance

The aim of this work is to study the effects induced by climate changes in the framework of the stochastic resonance approach. First, a wavelet cross-correlation analysis on Earth temperature data concerning the last 5,500,000 years is performed; this analysis confirms a correlation between the planet’s temperature and the 100,000, 41,000, and 23,000-year periods of the Milankovitch orbital cycles. Then, the stochastic resonance model is invoked. Specific attention is given to the study of the impact of the registered global temperature increase within the stochastic model. Further, a numerical simulation has been performed, based on: (1) A double-well potential, (2) an external periodic modulation, corresponding to the orbit eccentricity cycle, and (3) an increased value of the global Earth temperature. The effect of temperature increase represents one of the novelties introduced in the present study and is determined by downshifting the interaction potential used within the stochastic resonance model. The numeric simulation results show that, for simulated increasing values of the global temperature, the double-well system triggers changes, while at higher temperatures (as in the case of the absence of a global temperature increase although with a different threshold) the system goes into a chaotic regime. The wavelet analysis allows characterization of the stochastic resonance condition through the evaluation of the signal-to-noise ratio. On the basis of the obtained findings, we hypothesize that the global temperature increase can suppress, on a large time scale corresponding to glacial cycles, the external periodic modulation effects and, hence, the glacial cycles.

Improved SNR to detect the unknown characteristic frequency by SR

Stochastic resonance (SR) is widely used in signal processing issues. The classic evaluation index of SR must know the characteristic frequency in prior. However, the accuracy frequency which needs to be detected is not known in advance. To solve this problem, the authors propose a new index, which calls improved signal-to-noise ratio (SNR) in adaptive SR. This new index is effective without knowing the accuracy characteristic frequency first. Meanwhile, the general scale transformation and random particle swarm optimisation algorithm are used to satisfy the conditions of SR and help to obtain the optimal system parameters. On the basis of this new index, the simulation and experimental bearing fault signals are both processed perfectly when compared with the classic SNR index. More importantly, it overcomes the drawbacks of the classic SNR index that the accuracy characteristic frequency must be known in advance. Therefore, these results indicate that new index has important practical values in signal processing issues.

A new weak fault diagnosis method based on multi-scale wavelet noise tuning cascaded multi-stable stochastic resonance

For detecting the weak fault diagnosis submerged in heavy noise, a new method called multi-scale cascaded multi-stable stochastic resonance (MCMSR) is studied. The method can effectively extract weak fault diagnosis from noise background using multi-scale wavelet noise tuning stochastic resonance (SR). Firstly, input signal with noise is decomposed by multi-scale wavelets transformation, and each scale signal is adjusted by scaling factor, then the decomposed signal is used as the input of cascaded multi-stable systems to achieve the detection of fault diagnosis. If the input signal is a large parameter signal, to conform to the conditions of SR, the decomposed signal must be processed by twice sampling. The simulation and experimental signals are carried out to test the feasibility of the method. From the signal to noise ratio (SNR) comparison curves of original signal, SR output signal and MCMSR output signal plotted together, we can find that the useful signal can be enhanced by MCMSR method than SR method. The experimental results indicate that the MCMSR can extract fault diagnosis from heavy background noise.

Weak Fault Signal Detection of Rotating Machinery Based on Multistable Stochastic Resonance and VMD‐AMD

For solving detection problems of multifrequency weak signals in noisy background, a novel weak signal detection method based on variational mode decomposition (VMD) and rescaling frequency‐shifted multistable stochastic resonance (RFMSR) with analytical mode decomposition (AMD) is proposed. In this method, different signal frequency bands are processed by rescaling subsampling compression to make each frequency band meet the conditions of stochastic resonance. Before the enhanced signal components are synthesized, they are processed to achieve the enhanced signal by means of AMD, leaving only the enhanced sections of the signal. The processed signal is decomposed into intrinsic mode functions (IMF) by VMD, in order to require the detection of weak multifrequency signals. The experimental analysis of the rolling bearing inner ring fault and gear fault diagnosis demonstrate that the proposed method can not only enhance signal amplitude, reduce false components, and improve the VMD algorithm’s accuracy, but also effectively detect weak multifrequency signals submerged by noise.

Effects of Stochastic Resonance on Sensorimotor Performance during Exercise-Induced Muscle Damage

The aim of this study was to assess the effects of stochastic resonance (SR) stimulation on sensorimotor performance during an episode of exercise-induced muscle damage (EIMD). Thirty four men (age: 21.3 (±2.6) years; height 1.78 (±0.06) m; body mass 72.3 (±7.4) kg (mean (±SD)) gave their informed consent to participate in this study.Sensorimotor performance (error in replicating a target force) of the knee flexors was assessed prior to, and at 0.5 and 48 h after (i) a treatment condition involving a single-leg EIMD conditioning of the non-preferred leg, with concomitant responses to (ii) randomised presentation of SR, no SR and placebo conditions. Results showed a significant ANOVA interaction for sensorimotor performance amongst factors of condition (control period; EIMD), time (pre; post 0.5 h; post 48 h) and stimuli (SR; no SR; placebo) (F[1.5,29.3] = 5.7; p < 0.01). While scores during an antecedent control period had remained relatively constant, the EIMD protocol had elicited increased error in replicating a target force for the knee flexors of the non-preferred leg over time (worsened sensorimotor performance) that had been most prominent at 48 h after exercise, but whose negative effects had been ameliorated under conditions of SR (5.6 ± 3.1% (no SR) versus 3.7 ± 2.3% (SR) (pre) and 10.3 ± 4.2% (no SR) versus 8.1 ± 5.1% (SR) (48 h), respectively; F[1,36] = 6.0; p < 0.01). In conclusion, this study has shown that SR conditioning-related increases in the sensorimotor performance of the hamstring muscle group led to some protection from performance loss following EIMD.

A Stochastic Resonance of Line-spectrum Extraction Method for Underwater Target Recognition

Because of development of noise reduction technology, the complexity of the underwater acoustic channel and increasingly human activities, passive acoustic detection of underwater target signal is facing serious challenges, especially in the low signal to noise ratio. In this paper, the method of combining the empirical mode decomposition with stochastic resonance is applied to detect the weak periodic signal. Firstly, empirical mode decomposition algorithm is used to stationary handling of ship radiated noises. Secondly, the basic mathematical model of stochastic resonance is improved and then sub-band signal after decomposition and smoothing is sent to the improved model of stochastic resonance. By changing internal noise intensity and parameters of the stochastic resonance system, stochastic resonance conditions are met. Finally, the weak periodic signal is enhanced under the synergies of the system, the signal and noise. The simulation results show that line spectrum value of period signal is improved nearly 25 dB relative to the background noise for actual underwater signal.

Ghost stochastic resonance induced by a power-law distributed noise in the FitzHugh–Nagumo neuron model

We numerically investigate the ghost stochastic resonance phenomenon induced by a power-law distributed noise in the neuron FitzHugh–Nagumo model. The input noise considered is produced by a Langevin process including both multiplicative and additive Gaussian noise sources. In this process, the power-law decay exponent of the resulting noise distribution is governed by the off-set of the multiplicative noise, thus allowing us to probe both regimes of Gaussian and strongly non-Gaussian noises. Ghost stochastic resonance, i.e., stochastic resonance in a missing fundamental harmonic, occurs in this model. Deviations from the Gaussianity of the input noise are shown to reduce both the additive noise intensity corresponding to the optimal identification of the missing fundamental as well as the number of firing events at the ghost stochastic resonance condition.

Stochastic resonance of vortices in a washboard pinning potential

In a bistable potential at low temperatures, stochastic resonance can be characterized as a synchronization effect of the hopping mechanism induced by an external periodic stimulus, where synchronization attains a maximum by fine-tuning the forcing frequency close to the relevant switching rate. In this work, we theoretically investigate the nonlinear single-vortex dynamics in a tilted cosine (multistable) washboard pinning potential at nonzero temperature in the presence of dc and ac currents of arbitrary amplitudes and frequency. The conditions for stochastic resonance to appear are derived on the basis of the exact solution of the corresponding Langevin equation for non-interacting vortices in terms of a matrix continued fraction. The nonlinear ac voltage response is analyzed as a function of temperature, dc bias, ac amplitude and frequency, with particular focus on the amplification of the external harmonic signal and its conversion to the third harmonics of the input frequency.

Stochastic resonance in an RF SQUID with shunted ScS junction

Using a point (superconductor–constriction–superconductor, ScS) contact in a single-Josephson-junction superconducting quantum interference device (RF SQUID) provides stochastic resonance conditions at any arbitrary small value of loop inductance and contact critical current, unlike SQUIDs with more traditional tunnel (superconductor–insulator–superconductor, SIS) junctions. This is due to the unusual potential energy of the ScS RF SQUID which always has a barrier between two wells, thus making the device bistable. This paper presents the results of a numerical simulation of the stochastic dynamics of the magnetic flux in an ScS RF SQUID loop affected by band-limited white Gaussian noise and low-frequency sine signals of small and moderate amplitudes. The difference in stochastic amplification of RF SQUID loops incorporating ScS and SIS junctions is discussed.

Stochastic resonance at a subharmonic of a periodic modulation signal in solid-state lasers

The stochastic excitation of a subharmonic of a periodic modulation signal in the intensity spectrum of a solid-state laser is experimentally studied upon modulation of the pump rate by the noise and periodic signal. The stochastic resonance (SR) is observed in the presence of bistability in the laser. The conditions for SR at a subharmonic of the periodic modulation signal are determined.

Delay-aided stochastic multiresonances on scale-free FitzHugh–Nagumo neuronal networks

The stochastic resonance in paced time-delayed scale-free FitzHugh–Nagumo (FHN) neuronal networks is investigated. We show that an intermediate intensity of additive noise is able to optimally assist the pacemaker in imposing its rhythm on the whole ensemble. Furthermore, we reveal that appropriately tuned delays can induce stochastic multiresonances, appearing at every integer multiple of the pacemaker's oscillation period. We conclude that fine-tuned delay lengths and locally acting pacemakers are vital for ensuring optimal conditions for stochastic resonance on complex neuronal networks.

Delay-induced multiple stochastic resonances on scale-free neuronal networks

We study the effects of periodic subthreshold pacemaker activity and time-delayed coupling on stochastic resonance over scale-free neuronal networks. As the two extreme options, we introduce the pacemaker, respectively, to the neuron with the highest degree and to one of the neurons with the lowest degree within the network, but we also consider the case when all neurons are exposed to the periodic forcing. In the absence of delay, we show that an intermediate intensity of noise is able to optimally assist the pacemaker in imposing its rhythm on the whole ensemble, irrespective to its placing, thus providing evidences for stochastic resonance on the scale-free neuronal networks. Interestingly thereby, if the forcing in form of a periodic pulse train is introduced to all neurons forming the network, the stochastic resonance decreases as compared to the case when only a single neuron is paced. Moreover, we show that finite delays in coupling can significantly affect the stochastic resonance on scale-free neuronal networks. In particular, appropriately tuned delays can induce multiple stochastic resonances independently of the placing of the pacemaker, but they can also altogether destroy stochastic resonance. Delay-induced multiple stochastic resonances manifest as well-expressed maxima of the correlation measure, appearing at every multiple of the pacemaker period. We argue that fine-tuned delays and locally active pacemakers are vital for assuring optimal conditions for stochastic resonance on complex neuronal networks.

Stochastic resonance of a periodically driven neuron under non-Gaussian noise

We investigate the first-passage-time statistics of the integrate–fire neuron model driven by a sub-threshold harmonic signal superposed with a non-Gaussian noise. Here, we considered the noise as the result of a random multiplicative process displaced from the origin by an additive term. Such a mechanism generates a power-law distributed noise whose characteristic decay exponent can be finely tuned. We performed numerical simulations to analyze the influence of the noise non-Gaussian character on the stochastic resonance condition. We found that when the noise deviates from Gaussian statistics, the resonance condition occurs at weaker noise intensities, achieving a minimum at a finite value of the distribution function decay exponent. We discuss the possible relevance of this feature to the efficiency of the firing dynamics of biological neurons, as the present result indicates that neurons would require a lower noise level to detect a sub-threshold signal when its statistics departs from Gaussian.

Temperature factor and magnetic noise under conditions of stochastic resonance of magnetosomes

The influence of magnetic noise on the dynamics of magnetic nanoparticles under the conditions of stochastic resonance is considered. The effect of the magnetic noise is shown to be equivalent to the growth of the effective thermostat temperature for the particles at the permanent actual temperature of the medium. This regularity may be used for testing the hypothesis on the involvement of magnetic nanoparticles in the formation of biological effects of weak magnetic fields.