White Noise Research Articles

Uncertainty quantification via localized gradients for deep learning-based medical image assessments.

Deep learning models that aid in medical image assessment tasks must be both accurate and reliable to be deployed within clinical settings. While deep learning models have been shown to be highly accurate across a variety of tasks, measures that indicate the reliability of these models are less established. Increasingly, uncertainty quantification (UQ) methods are being introduced to inform users on the reliability of model outputs. However, most existing methods cannot be augmented to previously validated models because they are not post hoc, and they change a model's output. In this work, we overcome these limitations by introducing a novel post hoc UQ method, termed Local Gradients UQ, and demonstrate its utility for deep learning-based metastatic disease delineation. This method leverages a trained model's localized gradient space to assess sensitivities to trained model parameters. We compared the Local Gradients UQ method to non-gradient measures defined using model probability outputs. The performance of each uncertainty measure was assessed in four clinically relevant experiments: (1) response to artificially degraded image quality, (2) comparison between matched high- and low-quality clinical images, (3) false positive (FP) filtering, and (4) correspondence with physician-rated disease likelihood. (1) Response to artificially degraded image quality was enhanced by the Local Gradients UQ method, where the median percent difference between matching lesions in non-degraded and most degraded images was consistently higher for the Local Gradients uncertainty measure than the non-gradient uncertainty measures (e.g., 62.35% vs. 2.16% for additive Gaussian noise). (2) The Local Gradients UQ measure responded better to high- and low-quality clinical images (p<0.05 vs p>0.1 for both non-gradient uncertainty measures). (3) FP filtering performance was enhanced by the Local Gradients UQ method when compared to the non-gradient methods, increasing the area under the receiver operating characteristic curve (ROC AUC) by 20.1% and decreasing the false positive rate by 26%. (4) The Local Gradients UQ method also showed more favorable correspondence with physician-rated likelihood for malignant lesions by increasing ROC AUC for correspondence with physician-rated disease likelihood by 16.2%. In summary, this work introduces and validates a novel gradient-based UQ method for deep learning-based medical image assessments to enhance user trust when using deployed clinical models.

Numerical and experimental studies on a 2-module VLFS with hinged connector

The design of Very Large Floating Structures (VLFSs) often involves multiple modules with connectors. Consequently, Fluid Structure Interaction (FSI) has been attracted more and more attention in the design of VLFSs due to their considerable dimensions. This paper presented results of numerical and experimental studies on a 2-module VLFS with hinged connectors. A Flexible Module and Flexible Connector (FMFC) method was established in this paper, which allows for considering the influence of elastic deformation on each module and connectors. Model test was designed and conducted in wave basin, including wet modal tests in still water in, a white noise wave test and regular wave tests. The present method was verified by the model test, and good agreements were observed between the numerical and experimental results. Subsequently, the characteristic of wave loads in different wave directions was investigated by numerical calculation, and the design wave parameters were obtained by short-term analysis. Several useful conclusions were summarized, which can provide a reference for the analysis of dynamic responses, design, and construction of the VLFSs.

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

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

A Bayesian semi-parametric scalar-on-function regression with measurement error using instrumental variables.

Wearable devices such as the ActiGraph are now commonly used in research to monitor or track physical activity. This trend corresponds with the growing need to assess the relationships between physical activity and health outcomes, such as obesity, accurately. Device-based physical activity measures are best treated as functions when assessing their associations with scalar-valued outcomes such as body mass index. Scalar-on-function regression (SoFR) is a suitable regression model in this setting. Most estimation approaches in SoFR assume that the measurement error in functional covariates is white noise. Violating this assumption can lead to underestimating model parameters. There are limited approaches to correcting measurement errors for frequentist methods and none for Bayesian methods in this area. We present a non-parametric Bayesian measurement error-corrected SoFR model that relaxes all the constraining assumptions often involved with these models. Our estimation relies on an instrumental variable allowing a time-varying biasing factor, a significant departure from the current generalized method of moment (GMM) approach. Our proposed method also permits model-based grouping of the functional covariate following measurement error correction. This grouping of the measurement error-corrected functional covariate allows additional ease of interpretation of how the different groups differ. Our method is easy to implement, and we demonstrate its finite sample properties in extensive simulations. Finally, we applied our method to data from the National Health and Examination Survey to assess the relationship between wearable device-based measures of physical activity and body mass index in adults in the United States.

Enhanced Discrete Wavelet Transform–Non-Local Means for Multimode Fiber Optic Vibration Signal

Real-time monitoring of heartbeat signals using multimode fiber optic microvibration sensing technology is crucial for diagnosing cardiovascular diseases, but the heartbeat signals are very weak and susceptible to noise interference, leading to inaccurate diagnostic results. In this paper, a combined enhanced discrete wavelet transform (DWT) and non-local mean estimation (NLM) denoising method is proposed to remove noise from heartbeat signals, which adaptively determines the filtering parameters of the DWT-NLM composite method using objective noise reduction quality assessment metrics by denoising different ECG signals from multiple databases with the addition of additive Gaussian white noise (AGW) with different signal-to-noise ratios (SNRs). The noise reduction results are compared with those of NLM, enhanced DWT, and conventional DWT combined with NLM method. The results show that the output SNR of the proposed method is significantly higher than the other methods compared in the range of −5 to 25 dB input SNR. Further, the proposed method is employed for noise reduction of heartbeat signals measured by fiber optic microvibration sensing. It is worth mentioning that the proposed method does not need to obtain the exact noise level, but only the adaptive filtering parameters based on the autocorrelation nature of the denoised signal. This work greatly improves the signal quality of the multimode fiber microvibration sensing system and helps to improve the diagnostic accuracy.

The filtering-based recursive least squares identification and convergence analysis for nonlinear feedback control systems with coloured noises

The coloured noise is ubiquitous in industrial processes. This paper addresses the identification problem for the nonlinear feedback systems with coloured noise. Firstly, a direct identification scheme based on the least squares principle is developed to estimate the whole parameters of the nonlinear feedback systems and the convergence analysis is carried out through the stochastic stability theory. Secondly, for the purpose of improving the estimation accuracy, a filtering-based identification framework is proposed by constructing a linear filter for filtering the input data, output data and the coloured noise, and the coloured noise is transformed into a white noise. This identification scheme based on the filtering technique can effectively reduce the adverse effects caused by coloured noise and parameter estimation accuracy is enhanced compared with the direct least squares algorithm. Meanwhile, the convergence analysis of the filtering-based identification algorithm is given to provide a theoretical analysis. Finally, the simulation example is carried out by performance test and comparison analysis and simulation results show the effectiveness of the proposed identification methods.

Exploring the efficacy of GRU model in classifying the signal to noise ratio of microgrid model

Microgrids are small-scale energy system that supplies power to homes, businesses, and industries. Microgrids can be considered as a trending technology in energy fields due to their power to supply reliable and sustainable energy. Microgrids have a mode called the island, in this mode, microgrids are disconnected from the major grid and keep providing energy in the situation of an energy outage. Therefore, they help the main grid during peak energy demand times. The microgrids can be connected to the network, which is called networked microgrids. It is possible to have flexible energy resources by using their enhanced energy management systems. However, connection microgrid systems to the communication network introduces various challenges, including increased in systems complicity and noise interference. Integrating network communication into a microgrid system causes the system to be susceptible to noise, potentially disrupting the critical control signals that ensure smooth operation. Therefore, there is a need for predicting noise caused by communication network to ensure the operation stability of microgrids. In addition, there is a need for a simulation model that includes communication network and can generate noise to simulate real scenarios. This paper proposes a classifying model named Noise Classification Simulation Model (NCSM) that exploits the potential of deep learning to predict noise levels by classifying the values of signal-to-noise ratio (SNR) in real-time network traffic of microgrid system. This is accomplished by initially applying Gaussian white noise into the data that is generated by microgrid model. Then, the data has noise and data without noise is transmitted through serial communication to simulate real world scenario. At the end, a Gated Recurrent Unit (GRU) model is implemented to predict SNR values for the network traffic data. Our findings show that the proposed model produced promising results in predicting noise. In addition, the classification performance of the proposed model is compared with well-known machine learning models and according to the experimental results, our proposed model has noticeable performance, which achieved 99.96% classification accuracy.

Influence of LES Inflow Conditions on Simulations of a Piloted Diffusion Flame

This study investigates the influence of different turbulent inflow conditions on the Large Eddy Simulation (LES) of turbulent combustion problems. Four turbulent inflow conditions are studied, including two Synthetic turbulence methods (White Noise and Random Spot), and two Precursor simulation methods (Library and Library-Rescale). The simulation results are compared with experimental measurements. While the White Noise method yields a longer flame with poor agreement against experiments, the Random Spot method has good predictions initially but overpredicts turbulent fluctuations downstream. The results obtained by the Library and Library-Rescale methods are similar, and the discrepancies mainly lie in the region around the centerline. By rescaling the turbulent inlet library to match the experimental profiles, the Library Rescale method improves the prediction of turbulence fluctuations near the nozzle. The present study shows the pronounced sensitivity of LES accuracy in turbulent jet flames to the choice of turbulent inflow conditions.

Minimization of Gaussian Noise in Voice Signals Using Wavelets and Dynamics Transform

Minimization of Gaussian Noise in Voice Signals Using Wavelets and Dynamics Transform

Optimized dispersion Higuchi fractal dimension and its refined composite multi-scale version for signal analysis

Higuchi fractal dimension (HFD), as a classic nonlinear dynamic metric, which is commonly used to detect signal dynamic changes. However, it is difficult for HFD to process signal outliers. To address this issue, dispersion HFD (DHFD) is proposed, which improves the signal complexity representation ability of HFD by introducing the normal cumulative distribution function and round function in dispersion entropy. Nevertheless, the parameter selection of DHFD can affect the complexity value. Therefore, an optimized dispersion HFD (ODHFD) is proposed, which solves the threshold selection problem of DHFD and can more effectively reflect the complexity of the signal. In addition, an optimized refined composite DHFD (ORCMDHFD) has been proposed, which can more comprehensively reflect the complexity information for the signal at multiple scales. The simulation experiment results show that DHFD has a smaller standard deviation than HFD when calculating white noise signal complexity, and DHFD have the least dependence on signal length compared to other metrics, as well as RCMDHFD has the best separability for simulated noise signals. Actual experiments have shown that ODHFD and ORCMDHFD is superior to other entropy and fractal dimension metrics in distinguishing ship radiated noise and mechanical fault signals, and has broad application prospects in the field of signal analysis.

Design of High-Secure Digital/Optical Double Color Image Encryption Assisted by 9D Chaos and DnCNN

With the rapid development of multimedia technology, securing the transfer of images becomes an urgent matter. Therefore, designing a high-speed/secure system for color images is a real challenge. A nine-dimensional (9D) chaotic-based digital/optical encryption schem is proposed for double-color images in this paper. The scheme consists of cascaded digital and optical encryption parts. The nine chaotic sequences are grouped into three sets, where each set is responsible for encryption one of the RGB channels independently. One of them controls the fusion, XOR operation, and scrambling-based digital part. The other two sets are used for controlling the optical part by constructing two independent chaotic phase masks in the optical Fourier transforms domain. A denoising convolution neural network (DnCNN) is designed to enhance the robustness of the decrypted images against the Gaussian noise. The simulation results prove the robustness of the proposed scheme as the entropy factor reaches an average of 7.997 for the encrypted color lena-baboon images with an infinite peak signal-to-noise ratio (PSNR) for the decrypted images. The designed DnCNN operates efficiently with the proposed encryption scheme as it enhances the performance against the Gaussian noise, where the PSNR of the decrypted Lena image is enhanced from 27.01 dB to 32.56 dB after applying the DnCNN.

Head and neck tumor segmentation from [18F]F-FDG PET/CT images based on 3D diffusion model.

Head and neck (H&N) cancers are among the most prevalent types of cancer worldwide, and [18F]F-FDG PET/CT is widely used for H&N cancer management. Recently, the diffusion model has demonstrated remarkable performance in various image-generation tasks. In this work, we proposed a 3D diffusion model to accurately perform H&N tumor segmentation from 3D PET and CT volumes.&#xD;&#xD;Approach. The 3D diffusion model was developed considering the 3D nature of PET and CT images acquired. During the reverse process, the model utilized a 3D U-Net structure and took the concatenation of 3D PET, CT, and Gaussian noise volumes as the network input to generate the tumor mask. Experiments based on the HECKTOR challenge dataset were conducted to evaluate the effectiveness of the proposed diffusion model. Several state-of-the-art techniques based on U-Net and Transformer structures were adopted as the reference methods. Benefits of employing both PET and CT as the network input as well as further extending the diffusion model from 2D to 3D were investigated based on various quantitative metrics and the uncertainty maps generated.&#xD;&#xD;Main results. Results showed that the proposed 3D diffusion model could generate more accurate segmentation results compared with other methods (mean Dice of 0.739 compared to less than 0.726 for other methods). Compared to the diffusion model in 2D format, the proposed 3D model yielded superior results (mean Dice of 0.739 compared to 0.669). Our experiments also highlighted the advantage of utilizing dual-modality PET and CT data over only single-modality data for H&N tumor segmentation (with mean Dice less than 0.570).&#xD;&#xD;Significance. This work demonstrated the effectiveness of the proposed 3D diffusion model in generating more accurate H&N tumor segmentation masks compared to the other reference methods.&#xD.

Forecasting the Unemployment Rate in the Philippines using Box-Jenkins Methodology: Advancing Economic Strategies for Sustainable Development Goals

Unemployment remains a critical macroeconomic issue in Southeast Asia, significantly impacting the Philippines. This study aims to forecast the unemployment rate in the Philippines using ARIMA models to inform policy decisions and economic planning. The study utilized ARMA model for time series forecasting, leveraging past values and error terms to predict future data points. The methodology involved identifying suitable models through stationarity tests and autocorrelation analyses, estimating model parameters, and conducting diagnostic checks to ensure accuracy. Finally, the models were used to forecast future values and compute confidence intervals, providing insights into potential future trends. Monthly unemployment data from 2019 to 2023 was analyzed using the ARIMA (1,1,0) model, selected for its optimal fit based on statistical criteria. Initial findings indicated non-stationary unemployment data, necessitating first differencing to achieve stationarity. The ARIMA (1,1,0) model showed robustness, evidenced by diagnostic checks and Portmanteau test results confirming white noise residuals. Forecasts from 2024 to 2050 suggest fluctuating unemployment rates until 2034, followed by a gradual decline, indicating potential economic growth and improved labor market conditions. These forecasts align with the broader goal of sustainable economic development and highlight the importance of strategic policy interventions to foster job creation and economic stability in the Philippines. The study highlights the efficacy of ARIMA models in capturing unemployment trends and aiding policymakers in mitigating the socio-economic impacts of unemployment.

Random Vibration Analysis of Double Deck Rocking Self-Centering Pier Under Seismic Excitation

This paper investigates nonlinear random vibration of double deck rocking self-centering pier structure subjected to seismic excitation. First of all, a novel type of double deck rocking self-centering pier that offers better resilience is designed. The stochastic dynamical model of the system is then established, in which the seismic excitation is viewed as Kanai–Tajimi filtered white noise and the self-centering restoring force is characterized by classical flag-shaped hysteretic model. Subsequently, the self-centering restoring force is decomposed into equivalent quasi-linear elastic and damping forces by adopting the harmonic balance (HB) scheme. Following this, the stochastic averaging (SA) technique is implemented to derive the averaged Itô equation. The steady-state response probability density with respect to the amplitudes is solved from the averaged Fokker–Planck–Kolmogorov (FPK) equation. Finally, the effects of system parameters on the stochastic response of the double deck rocking self-centering pier structure are performed, and validated by the Monte Carlo simulation (MCS). In addition, with the help of the Laplace transform, the transition function as well as conditional power spectral density are achieved, which are combined with the steady-state probability density to obtain the power spectral density response. This research will contribute to the optimal seismic design of double-deck rocking self-centering piers.

Parameter Sharing Fault Data Generation Method Based on Diffusion Model Under Imbalance Data

Abstract Rotating machinery will inevitably fail under long-term heavy load working conditions. Obtaining enough data to train the deep learning model can enable managers to detect and deal with related failures in time, which greatly improves the safety of equipment operation. Mechanical fault samples are often much smaller than healthy samples. Traditional data enhancement methods mostly change the original data, but cannot improve the diversity of its features, so that the number of fault samples becomes larger, but the features remain unchanged. Aiming at the above problems, a diffusion model based on parameter sharing and inverted bottleneck residual structure (DDPM) is proposed. Firstly, the diffusion process gradually covers the original data with Gaussian noise, to learn the corresponding fault characteristics of the original data. In the diffusion process, the parameter sharing attention mechanism is embedded in the learning process of the diffusion process. Then, the feature extraction module is constructed by using the inverted bottleneck residual structure to enhance the learning ability of the network. After obtaining the fault characteristics of the original data, the reverse process of the results restores the Gaussian noise to data with different fault characteristics through the same steps as the diffusion process. By comparing the results of various generation models and analysing the characteristics of the generated data, the feasibility and universality of the proposed method in data augmentation tasks are verified.

Influence of Image Factors on the Performance of Ophthalmic Ultrasound Deep Learning Model

ObjectiveThis study aims to evaluate the impact of image factors on the performance of deep learning models used for ophthalmic ultrasound image diagnosis. MethodsA total of 3,373 ophthalmic ultrasound images are used to deeply evaluate the influence of image factors on the performance of deep learning classification models. Inceptionv3, Xception, and the fusion model Inceptionv3-Xception are used to explore how brightness, contrast, gain, noise, size, format, pseudo-color seven image-related factors affect the classification performance of the model. ResultsInceptionv3-Xception has advantages in the recognition accuracy of various image factors. When the image brightness changes, the model's performance shows a downward trend (0.5 vs. 1 vs. 1.8, ACC 95.73 vs. 97.06 vs. 93.54, P < 0.05). When the image contrast changes, the model's performance is comparable (0.5 vs. 1 vs. 1.2, ACC 96.23 vs. 96.95 vs. 97.45, P > 0.05). When the image gain drops to 50 dB, the model's accuracy decreases significantly (50 dB vs. 105 dB, ACC 96.49 vs. 97.57, P < 0.05). When Gaussian noise is added to the image, the model's performance gradually decreases (0.02 vs. 0, ACC 89.48vs97.06, P < 0.05). When the image size drops to 25% of the original image, the model's performance decreases significantly (25% vs. 100%, ACC 93.18 vs. 97.06, P < 0.01). When the image format changes, the model's recognition accuracy is similar (JPG vs. BMP vs. PNG, ACC 96.98 vs. 97.06 vs. 97.06, P > 0.05). The accuracy of the model in recognizing pseudo-color images decreases significantly compared to grayscale images (grayscale vs. pseudo-color, ACC 35.96 vs. 97.06). ConclusionThese results indicate that image quality greatly influences the model training process, and acquiring high-quality images is an important prerequisite for high recognition performance of the model. This study offers valuable insights for the improvement of other robust deep learning models for ophthalmic ultrasound image recognition.

ReF-DDPM: A novel DDPM-based data augmentation method for imbalanced rolling bearing fault diagnosis

Effective bearing fault diagnosis is crucial to ensure the safety and reliability of mechanical systems. Due to the complex and harsh working environment, mechanical data often comes from imbalanced datasets, which is a pressing problem in diagnosis applications. However, currently proposed data augmentation methods mainly based on generative adversarial networks, remain challenging in balancing the quality and diversity of the generation samples. To solve it, this paper proposes a new data enhancement method called the reparameterized residual denoising diffusion probability model (ReF-DDPM) and applies it to fault diagnosis. The proposed architecture includes a forward diffusion process and a reverse denoising process, where Gaussian noise and original samples are transformed by Markov chains. To improve the quality of generation samples, the noise prediction network is modified for better feature representation by enhancing intra-level and inter-level features. Furthermore, signal labels are added to the model as conditional information to direct the generation of relevant category samples during the sampling process. The study provides a new data augmentation method for bearing imbalanced data, and generation data can be further used for fault diagnosis tasks. Verification experiments demonstrate the effectiveness and good generalization of the method, and improve the accuracy of imbalance fault diagnosis.

Modeling of Granulation in Red Supergiants in the Magellanic Clouds with the Gaussian Process Regressions

The granulation of red supergiants (RSGs) in the Magellanic Clouds is systematically investigated by combining the latest RSG samples and light curves from the Optical Gravitational Lensing Experiment and the All-Sky Automated Survey for Supernovae. The present RSG samples are first examined for foreground stars and possible misidentified sources, and the light curves are sequentially checked to remove the outliers by white noise and photometric quality. The Gaussian process (GP) regression is used to model the granulation, and the Markov Chain Monte Carlo is applied to derive the granulation amplitude σ and the period of the undamped oscillator ρ, as well as the damping timescale τ. The dimensionless quality factor Q is then calculated through Q = π τ/ρ. RSGs around Q=1/2 are considered to have significant granulation signals and are used for further analysis. Combining granulation parameters with stellar parameters, robust scaling relations for the timescale ρ are established, while the scaling relations for amplitude σ are represented by a piecewise function, possibly related to the tendency of amplitudes in faint RSGs to converge toward a certain value. Comparing results between the Small Magellanic Cloud and the Large Magellanic Cloud confirms that amplitudes and timescales become larger with metallicity. In examining the scaling relations between the two galaxies, it is found that ρ is nearly independent of metallicity, whereas σ is more significantly affected by metallicity. The GP method is compared with the periodogram fitting of the granulations, and the advantages of either are discussed.

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 the 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.

Compressed intracellular motility via non-uniform temporal sampling in dynamic optical coherence tomography.

Optical coherence tomography has great utility for capturing dynamic processes, but such applications are particularly data-intensive. Samples such as biological tissues exhibit temporal features at varying time scales, which makes data reduction challenging. We propose a method for capturing short- and long-term correlations of a sample in a compressed way using non-uniform temporal sampling to reduce scan time and memory overhead. The proposed method separates the relative contributions of white noise, fluctuating features, and stationary features. The method is demonstrated on mammary epithelial cell spheroids in three-dimensional culture for capturing intracellular motility without loss of signal integrity. Results show that the spatial patterns of motility are preserved and that hypothesis tests of spheroids treated with blebbistatin, a motor protein inhibitor, are unchanged with up to eightfold compression. The ability to measure short- and long-term correlations compressively will enable new applications in (3+1)D imaging and high-throughput screening.