Stochastic Resonance Research Articles

Effects of spike-time-dependent plasticity on stochastic resonance in excitatory-inhibitory neuronal networks

Effects of spike-time-dependent plasticity on stochastic resonance in excitatory-inhibitory neuronal networks

Entropic stochastic resonance of finite-size particles in confined Brownian transport.

We demonstrate the existence of entropic stochastic resonance (ESR) of passive Brownian particles with finite size in a double- or triple-circular confined cavity, and compare the similarities and differences of ESR in the double-circular cavity and triple-circular cavity. When the diffusion of Brownian particles is constrained to the double- or triple-circular cavity, the presence of irregular boundaries leads to entropic barriers. The interplay between the entropic barriers, a periodic input signal, the gravity of particles, and intrinsic thermal noise may give rise to a peak in the spectral amplification factor and therefore to the appearance of the ESR phenomenon. It is shown that ESR can occur in both a double-circular cavity and a triple-circular cavity, and by adjusting some parameters of the system, the response of the system can be optimized. The differences are that the spectral amplification factor in a triple-circular cavity is significantly larger than that in a double-circular cavity, and compared with the ESR in a double-circular cavity, the ESR effect in a triple-circular cavity occurs within a wider range of external force parameters. In addition, the strength of ESR also depends on the particle radius, and smaller particles can induce more obvious ESR, indicating that the size effect cannot be safely neglected. The ESR phenomenon usually occurs in small-scale systems where confinement and noise play an important role. Therefore, the mechanism that is found could be used to manipulate and control nanodevices and biomolecules.

Matched Stochastic Resonance Enhanced Underwater Passive Sonar Detection under Non-Gaussian Impulsive Background Noise.

Remote passive sonar detection with low-frequency band spectral lines has attracted much attention, while complex low-frequency non-Gaussian impulsive noisy environments would strongly affect the detection performance. This is a challenging problem in weak signal detection, especially for the high false alarm rate caused by heavy-tailed impulsive noise. In this paper, a novel matched stochastic resonance (MSR)-based weak signal detection model is established, and two MSR-based detectors named MSR-PED and MSR-PSNR are proposed based on a theoretical analysis of the MSR output response. Comprehensive detection performance analyses in both Gasussian and non-Gaussian impulsive noise conditions are presented, which revealed the superior performance of our proposed detector under non-Gasussian impulsive noise. Numerical analysis and application verification have revealed the superior detection performance with the proposed MSR-PSNR detector compared with energy-based detection methods, which can break through the high false alarm rate problem caused by heavy-tailed impulsive noise. For a typical non-Gasussian impulsive noise assumption with α=1.5, the proposed MSR-PED and MSR-PSNR can achieve approximately 16 dB and 22 dB improvements, respectively, in the detection performance compared to the classical PED method. For stronger, non-Gaussian impulsive noise conditions corresponding to α=1, the improvement in detection performance can be more significant. Our proposed MSR-PSNR methods can overcome the challenging problem of a high false alarm rate caused by heavy-tailed impulsive noise. This work can lay a solid foundation for breaking through the challenges of underwater passive sonar detection under non-Gaussian impulsive background noise, and can provide important guidance for future research work.

Adaptive stochastic resonance under Poisson white noise background and its application for bolt looseness detection

Adaptive stochastic resonance under Poisson white noise background and its application for bolt looseness detection

Multi-Type Stochastic Resonances for Noise-Enhanced Mechanical, Optical, and Acoustic Sensing.

Stochastic resonance (SR) typically manifests in nonlinear systems, wherein the detection of a weak signal is bolstered by the addition of noise. Since its first discovery in a study of ice ages on Earth, various types of SRs have been observed in biological and physical systems and have been implemented in sensors to benefit from noise. However, a universally designed sensor architecture capable of accommodating different types of SRs has not been proposed, and the widespread applications of SRs in daily environments have not yet been demonstrated. Here, we propose a sensor architecture to simultaneously realize multi-type SRs and demonstrate their wide applications in mechanical, optical, and acoustic sensing domains. In particular, we find the coexistence of excitable SR and bistable SR in a sensor architecture composed of wirelessly coupled inductor-capacitor resonators connected to a nonlinearly saturable amplifier. In both types of SRs, adding noise to the system leads to a characteristic noise-enhanced signal-to-noise ratio (SNR). We further validate our findings through mechanical, optical, and acoustic sensing experiments and obtain noise-enhanced SNR by 9 dB, 3 dB, and 7 dB, respectively, compared to the standard methods devoid of SR integration. Our findings provide a general strategy to design various types of SRs and pave the way for the development of a distinctive class of sensors leveraging environmental noise, with potential applications ranging from biomedical devices to ambient sensing.

Overdamped Ising machine with stochastic resonance phenomena in large noise condition

Gain-dissipative Ising machines (GIMs) are dedicated devices that can rapidly solve combinatorial optimization problems. The noise intensity in traditional GIMs should be significantly smaller than its saturated fixed-point amplitude, indicating a lower noise margin. To overcome the existing limit, this work proposes an overdamped bistability-based GIM (OBGIM). Numerical test on uncoupled spin network show that the OBGIM has a different bifurcation dynamics from that of the traditional GIM. Moreover, the domain clustering dynamics on non-frustrated network proves that the overdamped bistability enables the GIM to suppress noise-induced random spin-state switching effectively; thus, it can function normally in an environment with a relatively large noise level. Besides, some prevalent frustrated graphs from the SuiteSparse Matrix Collection were adopted as MAXCUT benchmarks. The results show that the OBGIM can induce stochastic resonance phenomenon when solving difficult benchmarks. Compared with the traditional GIM, this characteristic makes the OBGIM achieve comparable solution accuracy in larger noise environment, thus achieving strong noise robustness.

Theoretical analysis and potential engineering application of an energy harvester

The technique of energy harvesting, which captures the energy from ambient vibrations, has received increasing attention due to its potential to power low-powered wireless sensors. Therefore, it is important to reveal the nonlinear dynamic mechanism to design a high-performance energy harvester. This paper describes an energy harvester with the decoupled bistable potential (DBEH), in which the potential function can be controlled independently by the barrier height or the barrier width. The harmonic balance method (HBM) is used to obtain the theoretical solutions, and the Jacobian matrix is provided to determine their stability. Thirdly, this paper thoroughly discusses the influence of barrier height and width on output responses and the multi-solution range. Finally, numerical simulations observe the stochastic resonance (SR) phenomenon and the inverse SR phenomenon of the DBEH with Gaussian white noise injection. The SR phenomenon of the DBEH is applied to bearing fault diagnosis, effectively enhancing fault characteristics.

Optimizing Work Extraction in the Presence of an Entropic Potential: An Entropic Stochastic Resonance.

We study the nontrivial thermodynamic responses of an overdamped Brownian system driven by an unbiased driving force when the particle is confined inside a bilobal irregular structure. The spatial irregularity of the confinement results in an effective entropic bistable potential along the direction of transport. We calculate the thermodynamic response functions in terms of the averaged work done and the absorbed heat over a cycle of driving. We find that the thermodynamic responses are influenced by the nonlinearity of the effective entropic potential, the frequency of the external periodic driving force, and the random thermal fluctuations in a nontrivial way. In the presence of an optimal amount of thermal noise and a favoring driving frequency, the process exhibits a resonance-like precedent in terms of both output work and absorbed heat. We explore the conditions to get best synchronized work extraction (or absorbed heat), which can be utilized as a potential quantifier of an entropic stochastic resonance phenomenon. Finally, we identify a hallmark of entropy dominance over an analogous energy-driven scenario in terms of output work.

Dynamics in a light-sensitive neuron with two capacitive variables

Involvement of two capacitive variables into neuron models provides better description of the cell membrane property and then the diversity effect of electromagnetic field inner and outer of the cell membrane can be estimated in clear way. Specific electric components can be combined to build equivalent neural circuits for reproducing similar neural activities under some self-adaptive control schemes. A phototube converts external light into electric stimuli and the injected energy is encoded to excite the cell membranes for presenting suitable firing patterns. Two capacitors are connected via a linear resistor for mimicking the energy exchange and changes of membrane potentials. Combination of memristor into an additive branch circuit of the neural circuit can estimate the effect of electromagnetic induction and energy absorption. The energy function H for this light-sensitive and memristive neuron is calculated in theoretical way, and the average energy function 〈H〉 can predict the occurrence of stochastic resonance, which can be confirmed by estimating the distribution of signal to noise ratios. The firing mode is relative to the energy value of the neuron, and a control law is suggested to control the mode transition in neural activities in an adaptive way.

A feature extraction method of rub-impact based on adaptive stochastic resonance and Hjorth parameter.

The characteristic frequency of a rub-impact fault is usually very complex and may contain higher harmonics and subharmonics. Due to the uncertainty of harmonic components and the complexity of signal-to-noise ratio (SNR) operation, the general scale transformation stochastic resonance (GSTSR) has certain limitations in the identification of rub-impact faults. To solve this problem, the paper starts with complexity and proposes a rub-impact fault identification method combining a swarm intelligence optimized algorithm (SIOA) with Hjorth parameters and GSTSR. The complexity of vibration signals will change greatly before and after rub-impact faults. The complexity parameter in Hjorth parameters can effectively embody the complexity of signals and is invulnerable to noise interference. Therefore, the complexity parameter in the Hjorth parameters is taken as the objective function of SIOA and combined with GSTSR. Vibration signals from cases are taken as input to adaptive stochastic resonant (ASR) systems, and the system parameters are adaptively and synchronously adjusted to realize the maximal resonant effect. Finally, the spectrum analysis of signals obtained from ASR is used to extract failure features and recognize faults in the rotor-stator rub-impact. The proposed method is verified by comparing it with other schemes under different SIOAs and different operating conditions. The result of the comparison shows that the complexity parameter of the Hjorth parameters can be taken as the objective function of SIOA to accurately identify the rub-impact fault. Meanwhile, the proposed method, compared with the method of taking SNR as an objective function, has a better effect on reducing time costs and strengthening fault characteristics.

A Novel Noise-Aided Fault Feature Extraction Using Stochastic Resonance in a Nonlinear System and Its Application

A Novel Noise-Aided Fault Feature Extraction Using Stochastic Resonance in a Nonlinear System and Its Application

Noise-induced excitation wave and its size distribution in coupled FitzHugh-Nagumo equationson a square lattice.

There are various research topics such as stochastic resonance, coherent resonance, and neuroavalanche in excitable systems under external noises. We perform numerical simulation of coupled noisy FitzHugh-Nagumo equationson the square lattice. Excitation waves are generated most efficiently at an intermediate noise strength. The cluster size distributions obey a power-law-like distribution at a certain parameter range. However, we consider that this is not a self-organized critical phenomenon, partly because the exponent of the power law is not constant. We have studied the propagation of excitation waves in the coupled noisy FitzHugh-Nagumo equationswith a one-dimensional pacemaker region and found that there is a phase-transition-like phenomenon from the short-range propagation to the whole-system propagation by changing the noise strength T. The power-law distribution is observed most clearly near the phase transition of the propagation of excitation waves in the coupled noisy FitzHugh-Nagumo equationswithout the one-dimensional pacemaker.

Two-stage benefits of internal and external noise to enhance early fault detection of machinery by exciting fractional SR

For early mechanical fault diagnosis, stochastic resonance (SR) breaks the previous perception that “noise is useless” and uses internal noise or external noise to enhance weak fault characteristics. Moreover, the fractional-order derivative can reinforce noise-enhanced weak fault detection. However, in the face of extremely low signal-to-noise ratio conditions, the enhancement effect of fractional SR induced by a single excitation of internal noise or external noise is unsatisfactory. To solve the above drawbacks, this paper investigates two-stage benefits of both internal and external noise to enhance early fault detection of machinery by exciting parallel array of fractional SR, and using the signal-to-noise ratio (SNR) to adjust these parameters of fractional SR. Then, two experiments including rolling element bearings and gearboxes were performed to validate it. Experimental results show that the proposed method takes on obvious detection ability for low SNR signals, where the amplitude at the fault characteristic frequency is amplified by beyond 300 times in bearing and gearbox fault experiments than one-stage those. In addition, it is also found that as the noise intensity and the number of iterations increase, the amplitude at the fault characteristic frequency tends to the peak value and then falls into a saturation value. Finally, compared with empirical mode decomposition (EMD), the proposed method can amplify the amplitude at the fault characteristic frequency beyond 1000 times in bearing and gearbox fault experiments. It was concluded that the proposed method has obvious advantages in extracting weak fault characteristics of machinery submerged by strong background noise.

A novel stochastic resonance based deep residual network for fault diagnosis of rolling bearing system

Rolling bearings constitute one of the most vital components in mechanical equipment, monitoring and diagnosing the condition of rolling bearings is essential to ensure safe operation. In actual production, the collected fault signals typically contain noise and cannot be accurately identified. In the paper, stochastic resonance (SR) is introduced into a spiking neural network (SNN) as a feature enhancement method for fault signals with varying noise intensities, combining deep learning with SR to enhance classification accuracy. The output signal-to-noise ratio(SNR) can be enhanced with the SR effect when the noise-affected fault signal input into neurons. Validation of the method is carried out through experiments on the CWRU dataset, achieving classification accuracy of 99.9%. In high-noise environments, with SNR equal to −8 dB, SRDNs achieve over 92% accuracy, exhibiting better robustness and adaptability.

A two-stage Duffing equation-based oscillator and stochastic resonance for mechanical fault diagnosis

Incipient fault-induced weak impulses will not be detected easily using the existing fault extraction methods. Stochastic resonance can flexibly use noises to enhance the signal-to-noise ratio, but it lacks the ability of anti-interference to obtain robustness and reliability fault detection performance. A two-stage method is presented to diagnose faults in mechanical components. The first is to judge the fault existence according to the characteristics of the phase space trajectory diagram of the Duffing oscillator. The second is to employ stochastic resonance method based on the Duffing equation to improve the signal-to-noise ratio of the raw signal, in which the two barrier parameters in the Duffing equation-based bistable system are carefully selected using the artificial bee colony algorithm. In the present two-stage method, the existence of faults and further the fault types in mechanical components are stepwise detected to make full usages of the advantages of Duffing oscillator and Duffing equation-based stochastic resonance method. Numerical simulations and two experimental cases verify the performance of the proposed method for mechanical components fault detection in a strong noise environment.

Parameter influence analysis of stochastic resonance and stochastic P-bifurcation for the shape-memory alloy laminate

This paper investigates the transverse vibration of an axially moving shape-memory alloy (SMA) fiber hybrid laminations under the combined action of transverse harmonic excitation and stochastic disturbance. Considering the shape memory alloy (SMA) fiber volume fraction random field, the kinetic energy and strain potential energy of SMA laminates are solved, and the axial motion equation of SMA laminates is derived according to Hamilton principle, a non-dimensional differential equation of the transverse vibration of the axially moving SMA laminated plates is obtained by adopting the Galerkin integral method. Considering the volume-fraction stochastic field, the stochastic natural vibration of a SMA laminate is analyzed. Considering the stochastic resonance of the SMA laminate under the action of Gaussian white noise and harmonic excitation, the effects of geometric parameters, physical parameters of materials, and temperature on the stochastic resonance behavior of the SMA laminate is analyzed. Finally, the effect of the stochastic P-bifurcation phenomenon of the parameters on the steady-state response of the SMA laminate is analyzed.

Structural rotor rub-impact diagnosis under intricate noise interferences based on targeted component extraction and stochastic resonance enhancement

Rub-impact is a common nonlinear fault of the rotor system, occurring in rotating machines with radial clearance between the rotor and the stator, which may lead to serious consequences. Since the vibration response of rotor rub-impact is shown as multicomponent with time-varying characteristics of undulatory instantaneous frequency, it is desired to exploit advanced signal processing methods for rub-related feature excavation and failure diagnosis under complex noise interferences, which is of crucial significance to ensure the stable and efficient operation of the whole unit. This paper concerns the processing of acceleration signals and proposes a novel intrawave frequency modulation detection approach for structural rotor rubbing diagnosis based upon targeted component extraction and stochastic resonance enhancement. First, the acquired vibratory acceleration signal is converted into displacement signal via a two-stage integration strategy. Next, to extract the rotating frequency component of high information clarity for further time–frequency analysis from the multicomponent signal, an especially designed improved variational mode decomposition method based on the modified target frequency index is put forward, and the instantaneous frequency of the objective component is estimated. Then, the optimum stochastic resonance is leveraged for intrawave frequency modulation enhancement. Finally, the rotor rub-related symptom can be distinctly revealed and the diagnostic procedure can be performed. The effectiveness and superiority of the proposed rotor rub-impact diagnosis approach are demonstrated through both simulations and experiments, indicating that it is suitable to be implemented in practical applications, with high noise-resistance ability, and can efficiently extract the potential characteristics of rotor rub-impact malfunction from multicomponent signals.

DOA Estimation Approach Based on Parallel Stochastic Resonance System and Signal Correlation Calculation

DOA Estimation Approach Based on Parallel Stochastic Resonance System and Signal Correlation Calculation

Enhancement of weak signals by dynamic stochastic resonance in dark-field microscopy imaging

Abstract Enhancing weak signal acquisition is pivotal for bolstering analysis and detection capabilities. Herein, a dynamic stochastic resonance (DSR) algorithm for weak signal amplification that was identified to significantly improve the imaging visibility of dark-field microscopy nanoparticles under low illumination conditions is presented. When further combined with composite filed microscopy, DSR displays signal amplification much more effectively, showing high promise for detection purposes.

Stochastic resonance in the small-world networks with higher order neural motifs interactions

Stochastic resonance in the small-world networks with higher order neural motifs interactions