Influence Of Noise Research Articles

Mathematical modeling of radiation transparencies in the countable implementation of the dual energy method based on analog amplitude analysis of original signals

A mathematical model of radiation transparency in the computational implementation of the dual energy method based on analog discrimination of the original signals is given. The generalized mathematical model of radiation transparency in the analyzed implementation of the dual energy method is based on the analog separation of the initial electrical signals from the X-ray detector by amplitude into low-energy and high-energy signals with subsequent counting of these signals. Analog separation of the output signals of the X-ray detector by amplitude is carried out using a two-channel amplitude analyzer. The proposed model takes into account the maximum energy of X-ray photons, the energy threshold for separating signals into low-energy and high-energy signals, materials and sizes of radiation-sensitive detector elements, and parameters of control objects. The model can be used to conduct research on the influence of noise caused by the quantum nature of X-ray radiation on the quality of identification of the attenuating material, for example, by effective atomic number, in relation to the considered implementation of the dual energy method, as well as for a reasonable choice of parameters of the corresponding dual-energy digital radiography systems and X-ray computed tomography.

Investigating the influence of multiplicative noise on available resources: a stochastic production–consumption model incorporating a logistic-like weak Allee effect

Investigating the influence of multiplicative noise on available resources: a stochastic production–consumption model incorporating a logistic-like weak Allee effect

An Optimal Spatio-Temporal Hybrid Model Based on Wavelet Transform for Early Fault Detection.

An optimal spatio-temporal hybrid model (STHM) based on wavelet transform (WT) is proposed to improve the sensitivity and accuracy of detecting slowly evolving faults that occur in the early stage and easily submerge with noise in complex industrial production systems. Specifically, a WT is performed to denoise the original data, thus reducing the influence of background noise. Then, a principal component analysis (PCA) and the sliding window algorithm are used to acquire the nearest neighbors in both spatial and time dimensions. Subsequently, the cumulative sum (CUSUM) and the mahalanobis distance (MD) are used to reconstruct the hybrid statistic with spatial and temporal sequences. It helps to enhance the correlation between high-frequency temporal dynamics and space and improves fault detection precision. Moreover, the kernel density estimation (KDE) method is used to estimate the upper threshold of the hybrid statistic so as to optimize the fault detection process. Finally, simulations are conducted by applying the WT-based optimal STHM in the early fault detection of the Tennessee Eastman (TE) process, with the aim of proving that the fault detection method proposed has a high fault detection rate (FDR) and a low false alarm rate (FAR), and it can improve both production safety and product quality.

Gone with the wind: environmental variation influences detection efficiency in a coastal acoustic telemetry array

Range tests play a critical role in designing acoustic telemetry studies, guiding equipment configuration, deployment techniques, and the analysis of animal movement data. These studies often strive to capture the effects of environmental variation on detection efficiency over time but are frequently limited in spatial and temporal scale. This could lead to disparities between test results and the circumstances encountered during animal tracking studies. In this study, we evaluated detection range and efficiency at two distinct spatial and temporal scales in a dynamic intertidal ecosystem. Two range tests were conducted, the first being a small-scale study using 6 receivers deployed over 1 month. Using modern acoustic receivers with built-in transmitters and environmental sensors, we then conducted a large-scale range test with 22 receiver stations over a full year to approximate the area and duration of a typical animal movement study. Differences in detection range between the two studies occurred as a result of environmental variation and tag power output, with midpoint ranges estimated as 123 m (small scale, low power), 149 m (small scale, high power) and 311 m (large scale, very high power). At both scales, wind speed emerged as the most influential factor explaining temporal variation in predicted detection efficiency. However, this effect was modulated by wind direction which varied as a result of land sheltering and fetch between the two study scales. At the small scale, detection efficiency decreased with winds from the south and east, while at the large scale, northern and westerly winds were most detrimental. Water temperature had a positive effect on predicted detection efficiency at both scales, while relative water level was positive at the small scale and negative at the large scale. Additional factors, including precipitation and Topographic Position Index, were found to influence detection efficiency at a large scale. Moreover, sensors associated with receivers in the larger array revealed the significant influences of receiver tilt and ambient noise. These discrepancies in the outcomes of the two studies underscore the critical role of scale in range test design and emphasize the need for long-term, in situ range testing at relevant spatial scales.

Wong–Zakai approximation for a stochastic 2D Cahn–Hilliard–Navier–Stokes model

AbstractIn this paper, we demonstrate the Wong–Zakai approximation results for two dimensional stochastic Cahn–Hilliard–Navier–Stokes model. The model consists of a Navier–Stokes system coupled with convective Cahn–Hilliard equations. It describes the motion of an incompressible isothermal mixture of two (partially) immiscible fluids under the influence of multiplicative noise. Our main result describes the support of the distribution of solutions. As in [2], both inclusions are proved by means of a general Wong–Zakai type result of convergence in probability for nonlinear stochastic PDEs driven by a Hilbert‐valued Brownian motion and some adapted finite‐dimensional approximation of this process. Note that the coupling between the Navier–Stokes system and the Cahn–Hilliard equations makes the analysis more involved.

Identification of switched gated recurrent unit neural networks with a generalized Gaussian distribution

Due to the limitations of the model itself, the performance of switched autoregressive exogenous (SARX) models will face potential threats when modeling nonlinear hybrid dynamic systems. To address this problem, a robust identification approach of the switched gated recurrent unit (SGRU) model is developed in this paper. Firstly, all submodels of the SARX model are replaced by gated recurrent unit neural networks. The obtained SGRU model has stronger nonlinear fitting ability than the SARX model. Secondly, this paper departs from the conventional Gaussian distribution assumption for noise, opting instead for a generalized Gaussian distribution. This enables the proposed model to achieve stable prediction performance under the influence of different noises. Notably, no prior assumptions are imposed on the knowledge of operating modes in the proposed switched model. Therefore, the EM algorithm is used to solve the problem of parameter estimation with hidden variables in this paper. Finally, two simulation experiments are performed. By comparing the nonlinear fitting ability of the SGRU model with the SARX model and the prediction performance of the SGRU model under different noise distributions, the effectiveness of the proposed approach is verified.

Mammal responses to human recreation depend on landscape context.

Rapid growth in outdoor recreation may have important and varied effects on terrestrial mammal communities. Few studies have investigated factors influencing variation in observed responses of multiple mammal species to recreation. We used data from 155 camera traps, in western Alberta (Canada), and a hierarchical Bayesian community modelling framework to document 15 mammal species responses to recreation, test for differential responses between predators and prey, and evaluate the influence of local context. Factors characterizing context were trail designation (i.e., use by motorized vs non-motorized), management type, forest cover, landscape disturbance, and season. We used three measures to characterize variation in recreation pressure: distance to trail, trail density, and an index of recreation intensity derived from the platform Strava. We found limited evidence for strong or consistent effects of recreation on mammal space use. However, mammal space use was better explained by an interaction between recreation and the influencing factors than by either on their own. The strongest interaction was between trail density and management type; mammals were more likely to avoid sites near a higher density of trails in areas with more restrictive management. We found that responses to recreation varied with the trail designation, although there were not clear or consistent differences between responses to trails designated for motorized vs. non-motorized use. Overall, we found that responses were species- and context-dependent. Limiting the density of trails may be important for reducing negative impacts to mammals within conservation areas. We show that using multiple measures of recreation yields more insight into the varied effects of human disturbances on wildlife. We recommend investigating how different characteristics of recreation (noise, speed, and visibility) influence animal behaviors. Multispecies monitoring and modelling across multiple landscapes that vary in recreation pressure can lead to an adaptive management approach to ensuring outdoor recreation coexistence with wildlife.

Multi-ion Frequency Reference Using Dynamical Decoupling.

We present the experimental realization of a continuous dynamical decoupling scheme which suppresses leading frequency shifts in a multi-ion frequency reference based on ^{40}Ca^{+}. By near-resonant magnetic coupling of the ^{2}S_{1/2} and ^{2}D_{5/2} Zeeman sublevels using radio-frequency dressing fields, engineered transitions with reduced sensitivity to magnetic-field fluctuations are obtained. A second stage detuned dressing field reduces the influence of amplitude noise in the first stage driving fields and decreases 2nd-rank tensor shifts, such as the electric quadrupole shift. Suppression of the quadratic dependence of the quadrupole shift to 3(2) mHz/μm^{2} and coherence times of 290(20)ms on the optical transition are demonstrated even within a laboratory environment with significant magnetic field noise. Besides removing inhomogeneous line shifts in multi-ion clocks, the demonstrated dynamical decoupling technique may find applications in quantum computing and simulation with trapped ions by a tailored design of decoherence-free subspaces.

A novel adaptive blind deconvolution algorithm: application to feature extraction of weak faults in RV reducer gears

Abstract Aiming at the problem that the non-stationary and nonlinear weak fault signal of RV (rotate vector) reducers is hard to extract fault features due to the influence of noise and transmission paths, as well as the selection of parameters for maximum correlation kurtosis deconvolution (MCKD) relies heavily on manual experience, this article proposes a fault feature extraction method based on parameter adaptive MCKD for the gear faults of RV reducers. Firstly, the sparrow search algorithm combining sine-cosine and Cauchy mutation (SCSSA) is used to adaptively search for the input parameters of MCKD and obtain the signal after deconvolution with the optimal parameters. Secondly, the deconvoluted signal is subjected to ensemble empirical mode decomposition to obtain modal components on different frequency bands. Finally, calculate the multi-scale fuzzy entropy (MFE) of each component, constructing a MFE feature set vector, and input the feature vector into the support vector machine for fault classification and recognition. The experimental analysis and verification results both indicate that the proposed method can adaptively enhance the weak impact components in the gear signals of the RV reducer, effectively extracting weak fault features disturbed by noise. Compared with minimum entropy deconvolution, multipoint optimal minimum entropy deconvolution adjusted and MCKD, the proposed method has improved identification rate by 17.50%, 10.63% and 15.63%, respectively. In addition, in comparison to multiverse optimization and particle swarm optimizatio algorithms, the SCSSA exhibits superior performance when optimizing MCKD parameters, offering faster convergence speed, higher accuracy, and greater robustness.

CoMix: Confronting with Noisy Label Learning with Co-training Strategies on Textual Mislabeling

The existence of noisy labels is inevitable in real-world large-scale corpora. As deep neural networks are notably vulnerable to overfitting on noisy samples, this highlights the importance of the ability of language models to resist noise for efficient training. However, little attention has been paid to alleviating the influence of label noise in natural language processing. To address this problem, we present CoMix, a robust Noise-Against training strategy taking advantage of Co-training that deals with textual annotation errors in text classification tasks. In our proposed framework, the original training set is first split into labeled and unlabeled subsets according to a sample partition criteria and then applies label refurbishment on the unlabeled subsets. We implement textual interpolation in hidden space between samples on the updated subsets. Meanwhile, we employ peer diverged networks simultaneously leveraging co-training strategies to avoid the accumulation of confirm bias. Experimental results on three popular text classification benchmarks demonstrate the effectiveness of CoMix in bolstering the network’s resistance to label mislabeling under various noise types and ratios, which also outperforms the state-of-the-art methods.

Channel selection method for the CH4 profile retrieval using the Atmospheric Sounder Spectrometer by Infrared Spectral Technology

Methane (CH4) is the second-largest greenhouse gas contributing to global warming, surpassed only by CO2, has a large difference in its vertical concentration distribution, and closely affects the global environment and climate change. The variations in the vertical concentrations of CH4 need to be monitored. Ground-based infrared hyperspectrometers can measure the fine variations of the CH4 concentrations in the vertical distribution within the planetary boundary layer (PBL). However, different detection channels are easily affected by instrumental noise and other environmental factors, leading to differences in the channel spectral characteristics and thereby affecting the accuracy of the CH4 profile retrieval. In this study, an information-weighted channel selection method is proposed for the CH4 profile retrieval using the Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST) to address the differences in the channel characteristics from the different interference factors. This method leverages the information content of CH4 and its environmental interference factors in each channel to derive the weighting factors, and a comprehensive weighting approach is subsequently applied to ascertain the effective information content of CH4. The method then establishes the threshold for the effective CH4 information content, considering the influence of noise, to select the optimal channels. We employ this method in our study, and 22 channels are selected as the optimal channels for the CH4 profile retrieval. We also evaluate the retrieval capability of the CH4 profile and the anti-interference ability of the selected channels using simulated spectra under clear-sky conditions. When retrieving the CH4 profile using the 1200–1390 cm−1 band (394 channels in total), the CH4 profile is mainly affected by temperature, water vapor, aerosol optical depth (AOD) and N2O. In addition, the mean absolute error (MAE) and the root mean square error (RMSE) for the CH4 profile retrieval using the selected channels are substantially reduced.

Moving horizon estimation and control of a binary simulated moving bed chromatographic processes with Langmuir isotherms

Simulated moving bed chromatographic (SMB) chromatographic processes are widely used for separations in pharmaceutical and biotechnological industries. In the present work, the control of such processes is proposed based on a semi-centralized control scheme that utilizes a combination of model predictive control (MPC) and classical PID control. The necessary information from the process, i.e. the internal concentration profiles, for the MPC is obtained by a moving horizon estimator (MHE) in real-time from the available limited measurements (the cycle-averaged concentrations of the extract and raffinate product streams and the dimensionless retention times of the concentration waves in the regeneration zones). As a test case study, the separation of a hypothetical system governed by the Langmuir isotherms in the nonlinear concentration range of the isotherms is considered. First, the stand-alone MHE is validated in open loop mode with no plant-model mismatch under deterministic and stochastic conditions. In the latter case, the true measurements are subject to random normally distributed noise. To evaluate the performance of the proposed control strategy, a reference tracking (change of the requirements for both the purities and the retention times) scenario is simulated. The investigated scenarios are: (i) no plant-model mismatch, (ii) MHE with ideal noiseless measurements and finally (iii) MHE under the influence of measurement noise. Results show that the controller is able to follow the change of the references from reduced purity to complete separation and vice versa closely.

A statistic-based tracking method of equivalent constraint position for monopile supported offshore wind turbine

Bearing capacity of pile foundation is one of the important factors affecting the safe operation of the offshore wind turbines. However, due to the influence of different geological conditions, pile types and other factors, it is difficult for the existing monitoring methods to obtain the change of real constraints of the pile foundation. A statistic-based equivalent constraint position prediction method for offshore wind turbines is proposed. The method is based on the first-order displacement and first-order frequency extracting from the measured acceleration signals obtained by the accelerometers installed above the water surface, and statistical method is adopted to eliminate the influence of noise and reduce the error caused by single prediction. The most achievement of the proposed method is that the constraint of pile foundation related to bearing capacity is obtained and quantified by equivalent constraint position, and more accurate assessment of safety state of the offshore wind turbine can be achieved. To verify the reliability of proposed method, numerical model of a 4-MW monopile supported offshore wind turbine considering the pile–soil interaction is carried out. The results show that the proposed method can accurately obtain the equivalent constraint position by a combination of first-order frequency and first-order displacement. Further, the field measurement of an offshore wind turbine located in Rudong County, Jiangsu Province of China is performed. A stable prediction of equivalent constraint range from -30.86 m to -30.15 m is obtained over 43 days of the test. Therefore, the proposed method can be used to predict the change of pile foundation constraint, and is of great significance to the safety assessment of offshore wind turbines.

Improved method for drone sound event detection system aiming at the impact of background noise and angle deviation

Drones pose a hidden threat to public safety, and effective and accurate detection technology for the drone that exist in the environment is imminent, in which how to weaken the influence of target sound source localization deviation and strong background interference noise on the detection task is the key to improve the accuracy of drone sound event detection. In this paper, a method has been proposed to improve the accuracy of drone acoustic event detection, named as the linear shrinkage-subspace projection-power spectral density filter method (LSP). This method mainly including covariance matrix reconstruction, steering vector recalibration, and filter coefficient redesign method. Firstly, based on Minimum Variance Distortionless Response, the linear shrinkage method is used to suppress the interference and noise components in the signal plus interference covariance matrix, and the sample covariance matrix is reconstructed to eliminate background interference noise. Then, the correlation between the steering vector and the eigenvector is used to eliminate the angle correlation term, and the subspace projection method is combined to recalibrate the steering vector, so as to improve the ability of the beamforming method to resist the angle deviation and realize the correction of the target source positioning deviation. Next, a redesign method for wiener filter coefficients based on the estimated power spectral density is used to further weaken background interference noise. In order to verify the accuracy of the proposed method, a complete drone sound event detection system is constructed by combining the deep learning drone sound event detection classifier, and the evaluation is carried out according to different angular deviations and interference sound distances. In addition, a new evaluation criterion is proposed, named as the Machine-Human Extreme Hearing Distance Rate (MHDR), which analogizes the system's detection ability with the ear's auditory detection ability. The research results of this article indicate that the detection accuracy of the detection system shows satisfactory accuracy when the proposed method is applied to circular microphone array, that improved by 15.12 % compared to existing methods. The proposed method improves the detection accuracy of the drone acoustic event detection task under the influence of sound source position deviation and strong background speech interference, and provides a reference for the development of anti-drone technology.

Highly dispersive eighth-order embedded solitons with cubic–quartic χ(2) and χ(3) nonlinear susceptibilities under the influence of multiplicative white noise using Itô calculus

This research explores the intriguing realm of eighth-order embedded solitons in highly dispersive media with cubic–quartic nonlinear susceptibilities [Formula: see text] and [Formula: see text], all within the dynamic context of multiplicative white noise and the framework of Itô calculus. Two different approaches are used in this study. The new auxiliary equation approach produces the bright soliton and singular soliton solutions, while the addendum Kudryashov’s approach produces the bright soliton, singular soliton and combo bright–singular soliton solutions. The system under investigation and the results documented within this work stand as pioneering and original contributions to the field of nonlinear optics. Furthermore, a collection of 2D, 3D and contour plots is produced to visually represent the spatial distribution and progression of different solutions. This not only contributes to the advancement of nonlinear equations in theory but also offers valuable insights for practical applications.

Timing limits of ultrafast cross-luminescence emission in CsZnCl-based crystals for TOF-CT and TOF-PET

BackgroundGood timing resolution in medical imaging applications such as TOF-CT or TOF-PET can boost image quality or patient comfort significantly by reducing the influence of background noise. However, the timing resolution of state-of-the-art detectors in CT and PET are limited by their light emission process. Core-valence cross-luminescence is an alternative, but well-known compounds (e.g. BaF2) pose several problems for medical imaging applications, such as their emission wavelength in the deep UV. CsZnCl-based materials show promise to solve this issue, as they provide fast decay times of 1–2 ns and an emission wavelength around 300 nm.ResultsIn this work, we investigated two CsZnCl-compounds: Cs2ZnCl4 and Cs3ZnCl5. We validated the previously published decay times on a time-correlated single-photon counting setup with 1.786 ± 0.016 ns for Cs2ZnCl4 and 1.034 ± 0.013 ns for Cs3ZnCl5. The setup’s high resolution enabled the discovery of an additional prompt emission component with a significant abundance of 98 ± 18 (Cs2ZnCl4) and 86 ± 14 (Cs3ZnCl5) photons/MeV energy deposit. In a PET coincidence experiment, we measured the best coincidence time resolution (CTR) of 62 ps (FWHM) for Cs2ZnCL4 coupled to FBK VUV SiPMs with silicon oil. To assess the CTR for lower energies, we filtered the energy along the Compton continuum and found a deteriorated CTR that seems to be mainly influenced by photon statistics. Furthermore, this study gave us a rough estimate of e.g. 150 ps (FWHM) CTR at 100 keV energy for Cs2ZnCL4. From measurements with high activity of 14 MBq to check for pile-up effects we assume that Cs2ZnCl4 is better suited for high-rate time-of-flight applications than lutetium-based oxides. Simulations demonstrated that the stopping power of Cs2ZnCl4 is lower than for LSO:Ce,Ca, meaning that a high amount of material would be needed for TOF-PET applications. However, the stopping power seems acceptable for applications in TOF-CT.ConclusionsThe fast decay time, state-of-the-art CTR in benchtop experiments and high-rate suitability make CsZnCl materials a promising candidate for time-of-flight experiments. We consider especially TOF-CT a suitable application due to its relatively low X-ray energies (~ 100 keV) and the thusly acceptable stopping power of Cs2ZnCl4. Currently, further exploration of the prompt emission and its creation mechanism is planned, as well as investigating the light transport of Cs2ZnCl4 in longer crystals.

Fundamental Limits in Measuring the Anisotropic Rotational Diffusion of Single Molecules.

Many biophysical techniques, such as single-molecule fluorescence correlation spectroscopy, Förster resonance energy transfer, and fluorescence anisotropy, measure the translation and rotation of biomolecules to quantify molecular processes at the nanoscale. These methods often simplify data analysis by assuming isotropic rotational diffusion, e.g., that molecules wobble within a circular cone. This simplification ignores the anisotropy present in many biological contexts that may cause molecules to exhibit different degrees of diffusion in different directions. Here, we loosen this assumption and establish a theoretical framework for describing and measuring anisotropic rotational diffusion using fluorescence imaging. We show that anisotropic wobble is directly quantified by the eigenvalues of a 3-by-3 positive-semidefinite Hermitian matrix M consisting of the second-order moments of a molecule's transition dipole μ. This formalism enables us to model the influence of unavoidable shot noise using a Hermitian perturbation matrix E; the eigenvalues of E directly bound errors in measurements of wobble via Weyl's inequality. Quantifying various perturbations E reveals that anisotropic wobble measurements are generally more sensitive to errors compared to quantifying isotropic wobble. Moreover, severe shot noise can induce negative eigenvalues in estimates of M, thereby causing the anisotropic wobble measurement to fail. Our analysis, using Fisher information, shows that techniques with worse orientation measurement sensitivity experience stronger perturbations E and require larger signal to background ratios to measure anisotropic rotational diffusion accurately. Our work provides deep insights for improving the state of the art in imaging the orientations and anisotropic rotational diffusion of single molecules.

Adaptive mode decomposition method based on fault feature orientation and its application to compound fault diagnosis of planetary gearboxes

Abstract Planetary gearboxes often experience multiple component failures during service, which can accelerate the degradation and failure of industrial equipment. Accurate separation and identification of multiple faults is an important means of ensuring the safe and stable operation of equipment. However, different faults can interact with each other, along with the influence of background noise, making it challenging to accurately extract faults with relatively weak energy among multiple faults. This difficulty leads to the problems of potential misdiagnosis and underdiagnosis. To address this issue, an adaptive mode decomposition method based on fault feature orientation (AMD-FF) is proposed in this paper. Initially, a fault impact indicator (FII) is constructed based on period-weighted kurtosis of envelope spectral and correlated combination negentropy to effectively characterize the impulsiveness and periodicity of fault features. Furthermore, with the objective of maximizing the FII, an adaptive decomposition of the original signal is designed based on blind convolution theory using a finite-impulse response filter group. Subsequently, a variable weight particle swarm optimization is employed to adaptively optimize the key decomposition parameters. Finally, the data of industrial-grade planetary gear transmission test rig are collected to validate the proposed method for compound fault diagnosis of planetary gearboxes. The results indicate that the AFMD-FF can effectively separate and extract compound faults in planetary gearboxes, demonstrating superior fault separation and diagnostic performance compared to the fault mode decomposition (FMD) and adaptive FMD. This method offers a novel approach to diagnosing compound faults in rotating equipment in industrial scenarios.

Noise analysis and optical response of microwave kinetic inductance detectors with an optical stack

We report on the experimental investigation of optical coupling for superconducting microresonators known as microwave kinetic inductance detectors (MKIDs) in the visible and near-infrared bands. MKIDs are photon-counting, time and energy-resolving detectors that still suffer from a poor quantum efficiency. To improve this efficiency, we propose to add a superconducting reflective layer below the absorbing part of the detector separated by a transparent Al2O3 layer with a quarter-wavelength thickness optimized around a single wavelength λ = 405 nm. We have first fabricated samples patterned from stoichiometric TiN ( Tc∼4 K), one with the full optical stack, one without for reference and one with a partial optical stack in order to characterize the noise influence of each layer individually. We observe that the full optical stack geometry has the most impact on the resonator’s noise and quality factors. A second design was fabricated to characterize the optical response to short pulses of the optical stack and we show from both the frequential noise and optical response that a strong signature of TLS is still present in the optical stack sample. We have finally obtained single-photon response with the optical stack using a more sensitive tri-layer TiN/Ti/TiN absorber ( Tc∼1.3 K) for which a maximum energy resolving power of R=E/ΔE∼ 1.3 was achieved using 405 nm laser pulses at 225 mK. The quality factors of both the reference and optical stack samples are similar but the frequency noise is still a tenfold higher for the optical stack sample which degrades the energy-resolving power of the detector.

Complex dynamics of a magnetic microrobot driven by single deformation soft tail in random environment

This study focuses on exploring the complex dynamical behaviors of a magnetic microrobot in a random environment. The purpose is to reveal the mechanism of influence of random disturbance on microrobot dynamics. This paper establishes stochastic dynamic models for the microrobot before and after deformation, considering the influence of Gaussian white noise. The system responses are analyzed via steady-state probability density functions and first deformation time. The effects of different magnetic field strengths and fluid viscosities on the movement speed and angular velocity of the microrobot are studied. The results indicate that random disturbances can cause deformation of microrobots in advance compared to the deterministic case. This work contributes to the design and motion control of microrobots and enhances the theoretical foundation of microrobots.