Noise Intensity Changes Research Articles

Bifurcation and Stability Analysis of Time-Delayed Wheelset System under White Noise Excitation

Considering the impact of time delay in the lateral stiffness of the primary suspension and stochastic disturbances of equivalent conicity on the wheelset system, a stochastic time-delayed wheelset system is established. The wheelset system is transformed into a one-dimensional Itô stochastic differential equation using central manifold and stochastic averaging methods. The analysis of the system's stochastic stability is conducted through the maximum Lyapunov exponent and singular boundary theory. The combination of the stationary probability density method and numerical simulation is employed to discuss the types and conditions of stochastic P-bifurcation in the wheelset system. The results indicate that changes in speed and time delay induce stochastic P-bifurcations in the wheelset system, while changes in noise intensity do not lead to stochastic P-bifurcations. Both time delay and equivalent conicity affect the critical speed of the wheelset system, and the critical speed gradually increases with the decrease of time delay and equivalent conicity.

Coherence resonance in a memristive map neuron and adaptive energy regulation

Involvement of memristive term and additive physical variables including magnetic flux and charge can enhance the physical description of the biophysical neurons. Neural circuits coupled with memristors can be built and tamed to mimic the intrinsic biophysical characteristics and dynamical properties of biological neurons, and these memristive oscillator models are effective in predicting the mode transition in neural activities and self-organization in collective electric behaviors of neural networks. Any proposal of memristive map neurons requires reliable physical description. For example, the energy definition and self-adaptive working mechanism are crucial to verify the reliability of memristive maps. A capacitive variable is useful to describe the membrane potential, while the complexity of ion channels requires careful evaluation and description by using inductive variables relative to the electromagnetic field. In this work, a charge-controlled memristor is connected to an inductor in series for building a hybrid ion channel, and then a capacitor and a nonlinear resistor are combined to couple the ion channel. As a result, a simple memristive neural circuit is designed to discern the inner effect of electric field and magnetic field synchronously. The energy function is defined and verified with theoretical proof. Furthermore, a linear transformation is applied to convert this memristive neuron into a memristive map with an exact energy description, in which its dynamics and mode transition will be controlled by an adaptive law when its energy is beyond the threshold. Additive noise is imposed to induce coherence resonance, which can be detected by using the statistical analysis and average value for Hamilton energy function during changes in noise intensity. This scheme provides guidance for energy definition in memristive maps and the intrinsic energy regulation mechanism in neural activities is explained from physical and dynamical aspects.

Information dissemination behavior of self-media in emergency: Considering the impact of information synergistic-hedging effect on public utility

In the context of emergency, besides handling the incident itself, controlling public opinion is also crucial. Self-media, as a crucial channel for the public to access information, controlling its dissemination behavior is key to public opinion management. Given the complexity of self-media information dissemination, we propose an evolutionary model for social media information dissemination behavior based on complex network theory and evolutionary game theory. Firstly, aiming at the synergistic and hedging effects of information dissemination, we use the utility function to depict the public's information demand. Next, based on the public's information needs, evolutionary game theory is introduced to analyze the information dissemination behavior between two self-media entities. Lastly, we map the dissemination behavior of self-media onto network space, analyzing the information propagation behavior of self-media within a complex network. The study shows that the probability of self-media disseminating positive information increases with the rise of the positive information synergistic effect and decreases with the increase of the negative information synergistic effect. When positive and negative information coexists, the dissemination behavior of self-media depends on the influence size of positive and negative information. With the improvement of government credibility, the probability of self-media disseminating positive information decreases slightly at first, and then increases rapidly. When the government's penalties increase, the probability of self-media disseminating positive information quickly rises. The changes in noise intensity have an uncertain impact on the positive information dissemination behavior of self-media. Compared with other factors, self-media is more sensitive to government credibility and punishment.

Threshold dynamics of stochastic SIRSW infectious disease model with multiparameter perturbation

<p>In this paper, we investigated a stochastic SIRS epidemic infectious disease model that accounted for environmentally driven infection and incorporated multiparameter perturbations. In addition to establishing the existence and uniqueness of the global positive solution of the model, we derived the threshold conditions for the extinction and persistence of the disease using the comparison theorem and It$ \hat{o} $'s formula of stochastic differential equations. Subsequently, we obtained the asymptotic stability of both the disease-free equilibrium and the endemic equilibrium of the deterministic model corresponding to the stochastic model through stochastic stability theory. The results indicated that high-intensity noise perturbation can inhibit the spread of the disease, and the dynamic behavior of the disease transitioned from persistence to extinction as noise intensity increased. Our study also demonstrated that, compared to perturbations in the indirect infection rate, changes in noise intensity that affect the direct infection rate will have a more significant impact on disease transmission.</p>

Dynamical analysis of a stochastic epidemic HBV model with log-normal Ornstein–Uhlenbeck process and vertical transmission term

Considering the transmission rate perturbed by log-normal Ornstein–Uhlenbeck process, we develop a stochastic HBV model with vertical transmission term. For higher-dimensional deterministic system, the local asymptotic stability of the endemic equilibrium is given by proving the global stability of the corresponding linearized system. For stochastic system, the existence of stationary distribution is obtained by constructing several suitable Lyapunov functions and using the ergodicity of the Ornstein–Uhlenbeck process and the critical value corresponding to the basic reproduction number for determined system is derived, which means the persistence of the disease. And sufficient conditions for disease extinction are given. Furthermore, by solving five-dimensional Fokker–Planck equation, the exact expression of the probability density function near the quasi-equilibrium is provided to reveal the statistical properties. In the end, numerical simulations illustrate our theoretical results and exhibit the trends of the critical values for persistence and extinction of diseases along with the change of noise intensity and reversion speed.

Colored noise in river level oscillations as triggering factor for unstable dynamics in a landslide model with displacement delay

In the present paper we examine the effect of the noise in river level oscillation on the landslide dynamics. The analysis is conducted in several phases. In the first phase, we analyze the multi-annual level oscillation of the Kolubara and the Ibar river (Serbia). Based on the observed dataset, we suggest a deterministic model for the river level oscillation with the additional contribution of the noise part, which we confirm to have the properties of colored noise. In the second phase of the research, we introduce the influence of the river-level oscillation, with the included effect of colored noise in the spring-block delay model of landslide dynamics. Results of the research indicate conditions under which the effect of river noise has both stabilizing and destabilizing effects on the landslide dynamics. The effect of noise intensity D and correlation time ε is systematically analyzed in interaction with delayed interaction, spring stiffness and friction parameters. It is determined that the landslide dynamics is sensitive to the change of noise intensity and that the increase of noise intensity leads to onset of unstable landslide dynamics. On the other hand, results obtained indicate that the examined model of landslide dynamics is rather robust towards the change of correlation time ε. Interaction of this parameter and some of the friction parameters leads to stabilization of landslide dynamics, which confirms the importance of the influence of the noise color in river level oscillations on the landslide dynamics.

Image denoising using adaptive bi-dimensional stochastic resonance system

Using stochastic resonance (SR) mechanism, the output signal can be enhanced by adding noise to the nonlinear system. Therefore, an image denoising algorithm based on adaptive bi-dimensional stochastic resonance (ABSR) is proposed in this paper. Firstly, the image is sampled as a bi-dimensional signal, and an adaptive bi-dimensional dynamic nonlinear system model is constructed. The peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the output image are used as the double evaluation model of the adaptive system, and the optimal parameters of the model are automatically obtained by adjusting the parameters of the dynamic nonlinear system using the reverse positioning method. Compared with the traditional mean filter, median filter and one-dimensional stochastic resonance, the image restoration effect of dynamic adaptive bi-dimensional stochastic resonance is more closer to the original image, and the histogram, PSNR and SSIM of the output image are also significantly better than the other three methods. The results show that dynamic adaptive bi-dimensional stochastic resonance has better denoising effect and better robustness to the change of noise intensity in image processing.

Attention-Based Fault-Tolerant Approach for Multi-Agent Reinforcement Learning Systems.

The aim of multi-agent reinforcement learning systems is to provide interacting agents with the ability to collaboratively learn and adapt to the behavior of other agents. Typically, an agent receives its private observations providing a partial view of the true state of the environment. However, in realistic settings, the harsh environment might cause one or more agents to show arbitrarily faulty or malicious behavior, which may suffice to allow the current coordination mechanisms fail. In this paper, we study a practical scenario of multi-agent reinforcement learning systems considering the security issues in the presence of agents with arbitrarily faulty or malicious behavior. The previous state-of-the-art work that coped with extremely noisy environments was designed on the basis that the noise intensity in the environment was known in advance. However, when the noise intensity changes, the existing method has to adjust the configuration of the model to learn in new environments, which limits the practical applications. To overcome these difficulties, we present an Attention-based Fault-Tolerant (FT-Attn) model, which can select not only correct, but also relevant information for each agent at every time step in noisy environments. The multihead attention mechanism enables the agents to learn effective communication policies through experience concurrent with the action policies. Empirical results showed that FT-Attn beats previous state-of-the-art methods in some extremely noisy environments in both cooperative and competitive scenarios, much closer to the upper-bound performance. Furthermore, FT-Attn maintains a more general fault tolerance ability and does not rely on the prior knowledge about the noise intensity of the environment.

A Fast Method of Visually Lossless Compression of Dental Images

A noniterative approach to the problem of visually lossless compression of dental images is proposed for an image coder based on the discrete cosine transform (DCT) and partition scheme optimization. This approach considers the following peculiarities of the problem. It is necessary to carry out lossy compression of dental images to achieve large compression ratios (CRs). Since dental images are viewed and analyzed by specialists, it is important to preserve useful diagnostic information preventing appearance of any visible artifacts due to lossy compression. At last, dental images may contain noise having complex statistical and spectral properties. In this paper, we have analyzed and utilized dependences of three quality metrics (Peak signal-to-noise ratio, PSNR; eak Signal-to-Noise Ratio using Human Visual System and Masking (PSNR-HVS-M); and feature similarity, FSIM) on the quantization step (QS), which controls a compression ratio for the so-called advanced DCT coder (ADCTC). The threshold values of distortion visibility for these metrics have been considered. Finally, the recent results on detectable changes in noise intensity have been incorporated in the QS setting. A visual comparison of original and compressed images allows to conclude that the introduced distortions are practically undetectable for the proposed approach; meanwhile, the provided CR lies within the interval.

Road Identification Algorithm for Remote Sensing Images Based on Wavelet Transform and Recursive Operator

Road edge detection from remote sensing images, as an important ground object type, plays an important role in people’s life and travel and urban planning and development, and extracting road information from remote sensing images has practical scientific value and practical significance. However, with the development of remote sensing technology, while the resolution of remote sensing images is improved, the information describing ground objects becomes more and more abundant, and the difficulty of identifying and extracting road information is also increased. In the process of acquiring remote sensing images, the actual system is subjected to various kinds of noise interference. Different environmental interference and system defects will introduce noises with completely different distribution and statistical characteristics to remote sensing images. Aiming at the problem that the detection effect of traditional algorithms becomes worse due to the influence of noise on remote sensing images, a wavelet transform denoising method and morphological gradient operator are proposed. By selecting appropriate structural elements of remote sensing images, noise pixels cannot participate in morphological calculation, and the noise intensity changes with the size of quantum superposition state structural elements. Therefore, a morphological gradient operator is established and applied to edge detection of remote sensing images. Finally, the experimental results show that the method proposed in this article is better than other directions in terms of effect through road edge detection and matching. This method can effectively reduce noise. Compared with other algorithms, the method proposed in this article has certain advantages.

Electric activities of time-delay memristive neuron disturbed by Gaussian white noise.

To study the effect of electromagnetic induction on the electric activities of neuron, memristive neuron models have been proposed by coupling membrane potential with magnetic flux. In this paper, on the basis of memristive Hindmarsh-Rose neuron model, time-delay memristive Hindmarsh-Rose neuron model is described and the responses of neuron in electrical activities are detected. The effect of time-delay on the dynamical behaviors of the neuron is discussed and the transition of electrical activities of the neuron is investigated with the change of noise intensity. It is found that, both the time-delay and the noise have effect on the electrical activities of the neuron. Especially, by selecting appropriate parameters, the noise not only can excite neuron from quiescent state to bursting state, but also can suppress the electrical activities in neuron during certain discharge period. Results mean that multiple modes and coherence resonance can be observed by changing the size of time-delay or the noise intensity, which could be associated with memory effect and self-adaption in neurons.

Stochastic bifurcations of generalized Duffing–van der Pol system with fractional derivative under colored noise**Project supported by the National Natural Science Foundation of China (Grant No. 11302157), the Natural Science Basic Research Plan in Shaanxi Province of China (Grant No. 2015JM1028), the Fundamental Research Funds for the Central Universities, China (Grant No. JB160706), and Chinese–Serbian Science and Technology Cooperation for the

The stochastic bifurcation of a generalized Duffing–van der Pol system with fractional derivative under color noise excitation is studied. Firstly, fractional derivative in a form of generalized integral with time-delay is approximated by a set of periodic functions. Based on this work, the stochastic averaging method is applied to obtain the FPK equation and the stationary probability density of the amplitude. After that, the critical parameter conditions of stochastic P-bifurcation are obtained based on the singularity theory. Different types of stationary probability densities of the amplitude are also obtained. The study finds that the change of noise intensity, fractional order, and correlation time will lead to the stochastic bifurcation.

The impact of airport noise as part of a Strategic Environmental Assessment, case study: The Tivat (Montenegro) Airport expansion plan

Strategic Environmental Assessment (SEA) is one of the most important instruments for making relevant decisions on the basis of which spatial planning is aligned with the tenets and principles of sustainable spatial development. Its primary application is in spatial (strategic) and urban planning, as well as in the planning and design of sectoral policies in the areas of energy, water management, waste management, transport, etc. The implementation of SEA allows developers to establish the benefits and implications of the proposed spatial changes, taking into account the capacity of the space to sustain the planned development, and to determine the degree of acceptability of the proposed spatial changes. This paper presents a specific method of assessing the impact of airport noise as part of a particular SEA. The particularity of this method is that it integrates the objectives, indicators and criteria for assessing the impact of airport noise on the population using the method of multi-criteria evaluation, applied in the preparation of the SEA for the Urban development plan regarding the expansion of Tivat Airport, Montenegro. The changes in noise intensity within the planning horizon to 2030 were predicted taking into account the following factors: physical, geographic and demographic characteristics of the space, the projected increase in the number and nature of the flights operated within the planning period, as well as the types of aircraft. On the basis of these data noise dispersion modelling was carried out using the IMMI model, and the results obtained were used in a multi-criteria evaluation as part of the SEA. The results of the research do not indicate any significant increase in noise intensity within the planning period to 2030 and they represent a good basis for making relevant decisions regarding the future development of Tivat Airport.

Optimising threshold levels for information transmission in binary threshold networks: Independent multiplicative noise on each threshold

The problem of optimising the threshold levels in multilevel threshold system subject to multiplicative Gaussian and uniform noise is considered. Similar to previous results for additive noise, we find a bifurcation phenomenon in the optimal threshold values, as the noise intensity changes. This occurs when the number of threshold units is greater than one. We also study the optimal thresholds for combined additive and multiplicative Gaussian noise, and find that all threshold levels need to be identical to optimise the system when the additive noise intensity is a constant. However, this identical value is not equal to the signal mean, unlike the case of additive noise. When the multiplicative noise intensity is instead held constant, the optimal threshold levels are not all identical for small additive noise intensity but are all equal to zero for large additive noise intensity. The model and our results are potentially relevant for sensor network design and understanding neurobiological sensory neurons such as in the peripheral auditory system.

Noise-controlled bistability in an excitable system with positive feedback

We study the interplay between noise and a positive feedback mechanism in an excitable system that generates events. We show that such a system can exhibit a bistability in the dynamics of the event generation (states of low and high activity). The stability of the two states is determined by the strength of the noise such that a change of noise intensity permits complete control over the probabilities with which the two states are occupied. The bistability also has strong implications for the regularity of the event generation. While the irregularity of the interevent interval (short-time variability) and the asymptotic Fano factor of the event count (long-time variability) are limited if the system is only in one of the two states, we show that both measures of variability display giant values if both states are equally likely. The long-time variability is additionally amplified by long-range positive correlations of the interevent intervals.

The Research of Signal Detection Method Based on LFMCW Lidar

In order to resolve the problem of echo signal threshold detection about LFMCW lidar system, a new signal threshold detection method based on the constant false alarm rate(CFAR) detecting technology is put forward. This method constructed the CFAR detection model from the analysis about the noise’s statistics characteristic in frequency domain, and then analyzed the relation between the false alarm rate and threshold, at last contrasted CFAR threshold with constant threshold under the condition that the noise intensity changes by simulating technique. The simulated experiment result indicates, the CFAR detecting technology applies to an echo signal threshold detecting is able to ensure that the false alarm rate size is constant invariable when the threshold value follows the noise intensity changes, thereby this method is not only improved the lidar’s acclimatization, but also enhanced the systematic detecting function of LFMCW lidar effectively.

Rigorous theory of stochastic resonance in overdamped bistable oscillators for weak signals

Abstract The results of the theoretical consideration of stochastic resonance in overdamped bistable oscillators are given. These results are founded not on the model of two states as in [McNamara B, Wiesenfeld K. Theory of stochastic resonance. Phys Rev A 1989;39:4854–69], but on splitting of motion into regular and random and the rigorous solution of the Fokker–Planck equation for the random component. We show that this resonance is caused by a change, under the influence of noise, of the system’s effective stiffness and damping factor contained in the equation for the regular component. For a certain value of the noise intensity the effective stiffness is minimal, and this fact causes non-monotonic change of the output signal amplitude as the noise intensity changes. It is important that the location of the minimum and its value depend essentially on the signal frequency.

Gamma-band activity dissociates between matching and nonmatching stimulus pairs in an auditory delayed matching-to-sample task

Electro- and magnetoencephalography studies have suggested that increased gamma-band activity (GBA) is a correlate of activated neural stimulus representations. In this study, a delayed matching-to-sample paradigm for auditory spatial information was employed to investigate the role of magnetoencephalographic gamma-band activity in the differentiation between matching and nonmatching stimulus pairs. Twelve subjects made same–different judgments about the lateralization angle of pairs of filtered noise stimuli (S1 and S2) presented with 0.8-s delays. One half of the subjects had to respond to matching stimulus pairs, the other half to nonmatching stimulus pairs. Cortical oscillatory activity in the memory task was compared to a control task requiring the detection of background noise intensity changes. Memory-related GBA increases were revealed over midline parietal areas in the middle of the delay phase and during the presentation of S2 and over frontocentral areas at the end of the delay phase. This replicated previous findings. In addition, nonmatching trials were associated with increased GBA over right parietal areas in response to S2. The midline parietal GBA increase during S2 in the memory condition may have reflected the representation of S1 needed for a comparison between S1 and S2. When S1 and S2 were identical, no further representation was required. In contrast, for nonmatching pairs, a second representation was activated over right parietal areas.

Explicit internal signal stochastic resonance and mechanic of noise-resistance in a chemical oscillator

The explicit internal signal stochastic resonance (EISSR) that happened in the Willamowski–Rössler reaction model was investigated. The z-variable's output signal-to-noise ratio (SNR) as a function of the noise intensity shows non-monotonic behavior, indicating the occurrence of EISSR. It is first found that EISSR happened in the periodic-2 oscillations state. A worthy noting point is that the fundamental frequency of the intrinsic period signal of the system is shifted little with the change of noise intensity. EISSR is a cooperative effect of the intrinsic period signal and external noise or fluctuations. Our studies have shown that EISSR can lead to noise-resistance of the chemical oscillator.

Reentrant Transitions Induced by Correlation Between Colored Noises

In the study, a bistable system is driven by additive colored noise and multiplicative colored noise, whose correlation is of an exponential form. When the noises are correlated, it is found that the system undergoes a reentrant transition as the correlation strength between noises or multiplicative noise intensity changes, while there is no such phenomenon as the additive noise intensity or the time of correlation changes. No reentrant transition exists when the noises are uncorrelated.