Fluctuation and Noise Letters

This paper proposes a novel segmented underdamped asymmetric tristable stochastic resonance (SUATSR) system. The research involves deriving the equivalent potential function for the underdamped background and analyzing the system’s output saturation. Results demonstrate that the SUATSR system effectively mitigates output saturation issues, enhancing the output amplitude. The study also employs adiabatic approximation theory to derive theoretical expressions for mean first passage time (MFPT) and spectral amplification (SA). The paper discusses the impact of varying system parameters on these performance indicators. Numerical simulations of the classical tristable stochastic resonance (CTSR), segmented underdamped tristable stochastic resonance (SUTSR) and SUATSR systems validate the theoretical derivations. Furthermore, the three systems are applied to different bearing fault tests and optimized using the adaptive genetic algorithm (AGA). The results demonstrate that the SUATSR system outperforms the other two systems, exhibiting higher SA and signal-to-noise ratio (SNR) values at the fault frequency. This highlights the SUATSR system’s superior noise resistance and its effectiveness in detecting and enhancing fault signals.

This study was conducted to assess noise annoyance following the installation of various types of sound barriers on elevated roads and to forecast the noise reduction effect. The level of annoyance was evaluated using a five-level linguistic rating scale, and noise values were anticipated for each floor of the building using Cadna/A software based on different road service levels. The results showed that for each service level road, the sound barrier’s noise reduction impact was most pronounced at heights of 3.5–5m, and the ratio of the acoustic barrier’s length extension to its distance from the building should not be greater than 4:1. After comparison, it was found that the fully enclosed sound barrier has the best noise reduction effect. Additionally, the noise annoyance level varied significantly at different service levels, with 60.5dB chosen as the noise threshold value. This study could offer an optional path for noise reduction facility design.

This paper proposes a new approach for filtering out impulse noise in digital color images. This work is of critical interest in biomedical imaging, visual tracking, etc. The conventional filtering methods operate by applying a noise reduction scheme, generally the vector median filtering approach and its variants, for the center pixel of a suitably chosen window that iteratively slides along the entire image. These methods consider the window in its entirety in the filtering process. This consideration, however, comprehends the noise within the noisy pixels in the filtering process and could prove detrimental to the overall output. The method proposed in this paper operates by clustering the pixels in the chosen window into two groups, one that corresponds to the pixel intensities that lie in the signal space and the other to those that lie in the noise space. The motivating rationale for this clustering scheme is to marginalize those pixels that lie in the noise space that seemingly do not contribute to the information in the image. The median filter is then applied to the pixels that contribute to the signal, in isolation from the color components, to filter out the impulse noise. Simulation results show that the proposed method outperforms conventional filtering methods in terms of noise reduction and structural similarity and thus validates the proposed approach. The proposed method is applied to analyze CT scans for improved diagnosis of SARS-COV-2 COVID-19 disease. The method is also applied to a visual tracking example as a preprocessor.

Sentiment analysis (SA) is used to identify the person’s opinion from unstructured information. It is broadly applied to predict stock market movement direction to recognize the public opinion towards a company or products. The conventional techniques designed for SA do not provide higher accuracy, which impacts the reliability of stock market prediction. In order to improve the prediction performance, a Gensim Lovins Truncative Morisita–Horn’s Broken-stick Regression-based Recursive deep neural network (GLTMBR-RDNN) is introduced for predicting the future outcomes in the stock market with a lesser error rate and minimal time. The customer reviews are collected from a large database. The GLTMBR-RDNN includes different layers for learning the input reviews. In the GLTMBR-RDNN technique, the first preprocessing of the text is carried out in the first hidden layer by removing stop words, stem words, truncation and so on. First, the Gensim tokenizer is applied in the preprocessing step to partition the text into a number of words. The proposed GLTMBR-RDNN technique uses a Sklearned model for stop word removal. Followed by Lovins truncative stemming process is carried out in the preprocessing step. Finally, the Normalization process is done for transforming the words into a standard form. After preprocessing, Morisita–Horn’s Broken-stick Regression process is performed in the second hidden layer for predicting the future stock market value based on the classification of customer reviews by setting the breakpoint to the similarity score between the reviews. In this way, future stock market values are efficiently identified with enhanced classification accuracy in the output layer. The result of the suggested GLTMBR-RDNN technique is analyzed using metrics such as accuracy, F-measure, precision, recall and prediction time based on various input reviews. The results prove that the introduced GLTMBR-RDNN technique improves the performance of accuracy with less prediction time when compared to existing methods.

In this study, an intelligent computing framework is presented to explore the influence of unpredictable environmental factors on the spread of infections. A stochastic non-linear SIS epidemic model (SISEM) is examined that incorporates a non-linear incidence rate, and impact of random fluctuations on the disease transmission. The knacks of artificial neural networks Levenberg–Marquardt (ANNLMA) technique are exploited to illustrate the behavioral complexities of stochastic non-linear SISEM, which explores the dynamics of disease transmission in a stochastic environment and provides insights into the interactions between susceptible and infectious populations. The Milstein approach of order 1 generates a dataset for the proposed ANNLMA technique by varying integrated parameters, such as the rates of recovery, death, disease transmission, noise standard deviation and the entire population, for various SISEM scenarios. The referenced dataset is randomly divided into test, validation and train sets for the weight learning of the proposed ANNLMA. The viability of the ANNLMA is measured using intensive simulation-based investigations through analysis of mean squared errors, histograms, regression and autocorrelation studies. The investigations highlighted that the ANNLMA results closely match the reference results, illustrating convergence within the 10${-2}^{}$ to 10${}^{-7}$ range, indicating the validity of the proposed methodology.

In this work, we study the rectification and optimization of the transport of Brownian particles in deformable channels, both numerically and analytically. It should be noted that particle transport via channels encompasses a wide range of phenomena, such as osmosis and the passage of ions through ionic channels, particle separation, modeling the movement of dilute mixtures of microscopic particles, and so on. Throughout all of this, the volume of the point particles moving through the channel is determined by the geometry of the latter. To ensure the confinement of particles during their displacement in a deformable channel, we applied the reflection condition. The Remoissenet–Peyrad deformable potential generates a novel appealing and simple deformable channel with a shape parameter r. As such, we may change the geometry of the potential well and the potential barrier, which can be thin or wide, as well as the depth, height, and opening of the channels entrance. Characteristics that affect the systems efficiency. In the absence of an external force, the particles move due to thermal agitation and eventually achieve a condition of equilibrium where they are virtually uniformly distributed in the channels cells. They move faster or less swiftly in the presence of external forces, depending on the strength and direction of the force, as well as their mass and thermal agitation. Moreover, it appears to have a symmetry related to the shape of the channel for nonlinear mobility, effective diffusion coefficient, and efficiency of rectification.

The aim of this paper is to develop a forecasting method with global interval input and output for interval-valued financial time series by combining the VAR$(p)$ process, the volatility information and neural network, namely VAR-NN. To reflect the volatility information, four types of interval-valued data volatility information from both the relative and absolute perspectives are constructed. Furthermore, the neural network is combined to produce the parameters. The developed forecasting model is finally applied to the highest and lowest hourly prices of the Shanghai Composite Index prediction. Numerical study shows the feasibility and validity of the developed improved VAR model.

We introduce a new inaccuracy measure in terms of Fisher information. The proposed information measure is referred to as Fisher-based inaccuracy information (FBII) measure. Next, we examine some properties of this information measure and specifically examine it for escort and equilibrium distributions. Further, we propose Bayes–Fisher-based inaccuracy information (BFBII) measure and examine its connection to Kullback–Leibler and chi-square divergence measures. Moreover, in three different optimization problems, we show that the harmonic-mixture distribution gives optimal information based on BFBII measure. Some examples of FBII measure and escort density related to skew-normal and Student-t distributions are also illustrated, and then are applied to fish condition factor time series.

Reservoir computers have recently become one of the most widely exploited model-free approaches to chaos synchronization due to their fast convergence speed and simple yet effective learning rules. In this study, reservoir computing has been utilized to emulate the dynamics of chaotic Thirring and Gursey systems. In the supervised learning of the reservoir, one-step ahead states of the dynamical systems have been employed as the teaching signals. After the training phase, the reservoir was first run autonomously and then weakly driven by chaotic systems. It has been shown that the trained reservoir computers can exhibit the same characteristics as the attractors of the learned chaotic systems, which enable their use in synchronization tasks of chaotic systems with possible application in cracking chaos-based cryptography. To investigate the effect of noise on the performance of reservoir computers, noise signals with different colors and amplitudes have been included in the transmitted signals. The obtained results indicate that the proposed scheme can provide lower error metrics for both models.

In this paper, we introduce detrended cross-correlation analysis (DCCA) method into visibility graph (VG) algorithm and propose VGDCCA method to reflect the time series irreversibility from a new perspective. The validity and reliability of the proposed VGDCCA method is supported by numerical simulations on synthesized short-term correlated chaotic systems and long-term correlated fractal processes, and by the empirical analysis on stock indices and traffic parameter. The VGDCCA planes suggest that autoregressive fractionally integrated moving average (ARFIMA) and fractional Gaussian noise (FGN) series show time reversible behavior, whatever the long-term correlation of the series is or however strong the persistence or anti-persistence of the series is. Meanwhile, Logistic map with $a=3.3\sim 3.6$ and Hénon map show more time irreversible behaviors than those of ARFIMA, FGN series and other Logistic maps. It can be found that the relationship between cross-correlation of ingoing degree sequence and outgoing degree sequence for the simulated series with different parameters is consistent with the complexity and autocorrelation behavior in the corresponding definition of the time series. For the empirical analysis, VGDCCA method declares the similarity and dissimilarity between stock indices on time series irreversibility and captures the time irreversibility of traffic time series recorded by the detectors in different locations.

In this study, we propose a multiscale permutation Jensen–Shannon distance (MPJSD) to measure irreversibility of complex time series. The new quantifier is based on symbolic permutation pattern and Jensen–Shannon distance. As an alternative, the new method offers the best characterization of the underlying irreversibility on different scales. The ARFIMA process and three dissipative chaotic systems are used to verify the effectiveness of the new method. The numerical results indicate that the MPJSD can unveil subtle and interesting findings on different scales and the permutation Jensen–Shannon distance (PJSD) is scale-dependent. Furthermore, we apply the approach to detect the multiscale irreversibility of ECG and financial data. The underlying irreversible nature of the investigated series is well discriminated. The method here introduced gives a new way to distinguish different degrees of irreversibility.

The common method for estimating nonlinear regression coefficients with Gaussian errors is nonlinear least squares. It is not robust for regression with heavy-tailed errors. The nonlinear least trimmed squares method is an efficient procedure for regression with heavy-tailed errors. We are introducing nonlinear least trimmed squares for the nonlinear regression with non-Gaussian stable errors and estimate the parameters of the stable distributions based on the order statistics. The traditional least trimmed squares regression algorithm is adopted for errors with infinite variance by modifying the trimming procedure. This paper estimated the nonlinear regression coefficients with stable errors through the proposed nonlinear least trimmed squares algorithm based on a new trimming procedure based on the properties of stable order statistics moments. The proposed algorithm was applied to real and simulated datasets and evaluated using a bootstrap standard deviation estimator.

In this paper, a study of noise reduction of an electrochemical sensor is presented. This study is initiated based on the fact that the signal from the electrochemical sensor has quite high noise. The current signal from the sensor is very weak and noisy due to high impedance and complex process on the sensor and measured media such as a solution of dissolved oxygen. The current is usually converted using a transimpedance amplifier with high voltage-to-current ratio by a feedback resistance, which usually also produces large noise due to high feedback resistance value. This will contribute to an additional noise to the natural noise of the sensor and measured media, causing more noisy result. A modified transimpedance amplifier is proposed to reduce the noise from the sensor and converter to increase signal-to-noise ratio. The modification employs a current compensation, so that the current noise is suppressed at the converter input. The noise and signal measurements at the output of the compensated transimpedance amplifier show noise reduction compared to the standard transimpedance amplifier and measurement error reduction of the measured dissolved oxygen down to 0.89%.

This paper provides the first evidence for understanding the information flow and the microstructure of GameFi market by investigating the multifractal characteristics of return and trading volume from 61 GameFi coins. The empirical results mainly show that: (1) There is a significant cross-correlation between return and trading volume in the GameFi market. Both exhibit multifractal behavior across different time scales. (2) The GameFi market is highly unstable and volatile in the short term, but relatively stable in the long term. (3) The multifractal characteristics of the GameFi market evolve dynamically, with noticeable cyclical fluctuations occurring within a one-year period.

Poisson white noise is a more accurate representation than ideal Gaussian white noise for simulating the noise background during early bearing failures. In the context of strong Poisson white noise interference, the failure mode of rolling bearings usually remains undetermined. This paper proposes a new method for diagnosing unknown bearing faults in bearings, diagnosis is proposed, which effectively identifies unknown faults across various components. The theoretical characteristics of Poisson white noise are initially analyzed and discussed. Then, to extract potential fault characteristic information, a false characteristic frequency of bearing is constructed in the response spectrum, and the response results at this false fault characteristic are obtained. Finally, coherent resonance (CR) is introduced, and false frequencies caused by false faults are eliminated by defining a quality factor. To verify the effectiveness of the method, the experimental and simulation results were compared with the decomposition results of the SVMD algorithm. The SNR of the experimental signals for outer and inner ring faults under variable speed conditions increased to 8.62dB and 11.74dB, respectively. Results indicate show that this method not only successfully identifies fault features, but also exhibits a strong noise reduction effect.