White Noise Research Articles

A linearly implicit finite element full-discretization scheme for SPDEs with nonglobally Lipschitz coefficients

Abstract The present article deals with strong approximations of additive noise driven stochastic partial differential equations (SPDEs) with nonglobally Lipschitz nonlinearity in a bounded domain $ \mathcal{D} \in{\mathbb{R}}^{d}$, $ d \leq 3$. As the first contribution, we establish the well-posedness and regularity of the considered SPDEs in space dimension $d \le 3$, under more relaxed assumptions on the stochastic convolution. This improves relevant results in the literature and covers both the space-time white noise ($d=1$) and the trace-class noises ($\text{Tr} (Q) < \infty $) in multiple dimensions $d=2,3$. Such an improvement is achieved based on a key perturbation estimate for a perturbed PDE, with the aid of which we prove the convergence and uniform regularity of a spectral approximation of the SPDEs and thus get the improved regularity results. The second contribution of the paper is to propose and analyze a spatio-temporal discretization of the SPDEs, by incorporating a standard finite element method in space and a linearly implicit nonlinearity-tamed Euler method for the temporal discretization. The proposed time-stepping scheme is linearly implicit and does not suffer from solving nonlinear algebra equations as the backward Euler scheme does. Based on the improved regularity results, we recover the expected strong convergence rates of the fully discrete scheme and reveal how the convergence rates rely on the regularity of the noise process. In particular, a classical convergence rate of order $O(h^{2} +\tau )$ can be obtained even in high dimension $d=3$, as the driven noise is of trace class and satisfies certain regularity assumptions. The optimal error estimates turn out to be challenging and face some essential difficulties when the tamed time-stepping scheme meets the finite element spatial discretization, particularly in the context of low regularity and multiple dimensions $d \le 3$. Some highly nontrivial arguments are introduced to overcome the difficulties. Finally, numerical examples corroborate the claimed strong orders of convergence.

Decision support system for policy-making: Quantifying skill and chance in daily fantasy sports

We explore the question of skill versus chance dominance in Daily Fantasy Sports (DFS), which has been the subject of numerous legal disputes around the world. Our study examines whether a contestant's winnability in DFS is influenced by factors reflecting skills using cricket-based daily fantasy contest data and a true fixed effects stochastic frontier model. We find that skill contributes significantly towards winnability in five ways. First, contestants performing well in the past do better in the present. Second, gaining more game experiences improves performance. Third, contestants who participated recently, tend to exhibit higher winnability. Fourth, selecting an appropriate contest type enhances winnability. Fifth, the large estimated signal-to-noise ratio indicates that the unobserved skill measured by a non-negative error has a much greater impact on winnability than the regular two-sided random shocks. These results are robust to varying specifications and subsets of data. Decision makers and regulators can use the model presented in the study to distinguish skill-dominant DFS from chance-dominant DFS.

Improved Underwater Single-Vector Acoustic DOA Estimation via Vector Convolution Preprocessing

Remote passive sonar detection with underwater acoustic vector sensor (UAVS) has attracted increasing attention due to its merit in measuring the full sound field information. However, the accurate estimation of the direction-of-arrival (DOA) remains a challenging problem, especially under low signal-to-noise ratio (SNR) conditions. In this paper, a novel convolution (COV)-based single-vector acoustic preprocessing method is proposed on the basis of the single-vector acoustic preprocessing model. In view of the theoretical analysis of the classical single-vector acoustic DOA estimation method, the principle of preprocessing can be described as “to achieve an improved denoising performance in the constraint of equivalent amplitude gain and phase response.” This can be naturally guaranteed by our proposed COV method. In addition, the upper bound with matched filtering (MF) preprocessing is provided in the consideration of the optimal linear signal processing for weak signal detection under Gaussian noise. Numerical analyses demonstrate the effectiveness of our proposed preprocessing method with both vector array signal processing-based and intensity-based methods. Experimental verification conducted in South China Sea further verifies the effectiveness of our approach for practical applications. This work can lay a solid foundation in improving underwater remote vector acoustic DOA estimation under low SNR, and can provide important guidance for future research work.

A Hierarchical Fault Diagnosis Model for Planetary Gearbox with Shift-invariant Dictionary and OMPAN

Abstract Planetary gearbox has been widely applied in the mechanical transmission system, and the failure types of planetary gearbox are more and more diversified. The conventional fault diagnosis methods focus on identifying the faults in the fault library, but ignored the faults outside the fault library. However, it is impossible to build a fault library for all failure types. Targeting the problem of identifying the faults outside the fault library, a hierarchical fault diagnosis method for planetary gearbox with shift-invariant dictionary and orthogonal matching pursuit with adaptive noise (OMPAN) is proposed in this paper. By k-means singular value decomposition (K-SVD) dictionary learning method and shift-invariant strategy, a shift-invariant dictionary is constructed so that the normal modulation components of signals can be completed decomposed. OMPAN algorithm is proposed, which uses the white Gaussian noise to improve the solution method of the orthogonal matching pursuit (OMP) algorithm so that it can separate the modulation components in the signal more accurately. The fault feature extraction is developed via shift-invariant dictionary and OMPAN. A hierarchical classifier is proposed with 3 sub-classifiers so that both the faults in the fault library and the faults outside the fault library are identified. The effectiveness of the proposed hierarchical fault diagnosis method is validated by a planetary gearbox experiment. Result show that the proposed shift-invariant dictionary and OMPAN method has achieved a superior performance in highlighting fault features compared with other two sparse decomposition methods. The proposed hierarchical fault diagnosis approach has achieved a good performance both in classification of the faults in the fault library and identification of the faults outside the fault library.

Estimation of Multiple Contact Conductances in a Silicon-Indium-Silicon Stack

Estimation of Multiple Contact Conductances in a Silicon-Indium-Silicon Stack

Impact of sound-insulated joints in the dynamic behavior of Cross-Laminated Timber structures

Cross-Laminated Timber (CLT) has gained an increasing success and widespread diffusion among wood products. Due to the novelty of the components and the dry-assembling techniques, certain aspects of its behavior and performance remain unexplored; among these, the use of sound-insulated joints and brackets, along with their possible drawbacks on mechanical performance.In this paper the authors discuss the results of dynamic identification (ID) tests carried out on twin full-size CLT mockups (two-story), one of which was furnished with sound-insulation details based on an elastomeric interlayer. Ambient (white noise and random excitations) and forced vibration (mass shaker) tests were carried out to identify modal parameters.The paper mainly aims at: i) assessing potential differences in the overall dynamic behavior of the two mockups, likely due to the introduction of acoustic insulation, ii) detecting possible drawbacks of insulation layers on mechanical performances, iii) increasing the knowledge about dynamics of CLT structures.The results of Operational Modal Analysis (OMA) are presented and the variations in the dynamics between the mockups are discussed. The construction of finite element (FE) models and their calibration – with joint properties variations – are then presented. The outcomes revealed a drop of modal frequencies values (at least 20 %) upon the introduction of elastomeric insulation, whilst no significant alteration was observed in the mode shapes. Model updating based on the FE models demonstrated that the dynamic response and the frequency variations were mostly associated with wall-to-wall and wall-to-diaphragms joints, and hold-down connections.

ED-SS based Cognitive Radio (CR) over Various Fading Channels for Modern Wireless Communications

Cognitive Radio (CR) has already been proved to provide the better solution for the problem of spectrum insufficiency in which every wireless system occupies the limited bandwidth spectrum (LBS) allotting to the licensed users. Sometimes, this LBS is empty in a certain place at a certain time that is called as spectrum holes estimated by spectrum sensing (SS). The spectrum sensing is the main technique used in cognitive radio and various spectrum sensing techniques are available in the literature such as: energy detection (ED), matched filter detection (MFD) and cyclo-stationary feature detection (CFD). In the present work, energy detection-spectrum sensing (ED-SS) based cognitive radio system has been implemented which can analyze the energy of the spectrum. The various fading channels such as additive white gaussian noise (AWGN), Rayleigh, and Nakagami have been incorporated in the present work over which the performance of cognitive radio has been analyzed and compared using the ED-SS technique. The experimental results show thatt the detection of the presence of primary user signal using proposed ED-SS technique is easier with low computational complexities in Cognitive Radio (CR) networks. The present work also demonstrates when the performance of proposed ED-SS technique over aforesaid three fading channels are compared, Nakagami-m fading channels exhibits superior improvement in Pd compared to Rayleigh and AWGN fading channels

Robustness analysis and experimental validation of a deep neural network for acoustic source imaging

Deep Neural Network (DNN) models offer an attractive alternative to existing acoustic source imaging techniques, such as acoustic beamforming, due to their ever-growing potential with increasing computational power. Source resolution of acoustic beamforming methods is limited at lower frequencies and their source maps may possess sidelobes at higher frequencies. However, acoustic beamforming methods are typically robust over a wide range of simulation and experimental conditions, such as (i) the number of sources present, (ii) source frequency and (ii) extraneous noise sources. The performance of DNN models, when these conditions are varied from their specific design criteria, is yet to be investigated and much work is needed in this area before DNN models can be utilized in experiments, such as wind tunnel tests. Furthermore, few studies have been conducted on experimental validation of DNN models, predominately due to the difficulty of large sets of experimentally obtained data needed for DNN model training and the sensitivity of DNN model performance when any of the aforementioned experimental conditions are varied. In this paper, a series of studies on the robustness of DNN models based on numerical data and experimental data are presented. Numerical DNN (NDNN) models are trained using in-phase and random-phase pressure data generated from six sources over design frequencies from 500 Hz to 20,000 Hz. The robustness of the NDNN models is tested via (1) inclusion of extraneous Gaussian white noise, (2) inclusion of extraneous tonal noise near the design frequency, (3) using source frequencies that slightly differ from the design frequencies and (4) using a number of sources that differs from the design source number. DNN model performance metrics are introduced that present a promising framework for future DNN model studies and bridging the gap between NDNN and experimentally trained DNN models. A preliminary experimental validation was conducted using a single speaker that was systematically placed over a speaker grid to generate training data via acoustic superposition, from which an experimentally trained DNN (EDNN) model was produced. The EDNN model yields exceptional noise source localization capability of the DNN model, revealing a promising start for a more sophisticated EDNN model.

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

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

A new self-adaptive mission aborting policy for systems operating in uncertain random shock environment

For systems operating in random shock environment, mission aborting policies based on the number of shocks experienced during a certain time interval are common and have received intensive attention. However, the number of shocks-based aborting policy (NSAP) is not effective when the shock rate during the primary mission is uncertain because it cannot adapt itself to the shock rate. This paper puts forward a new, self-adaptive inter-shock interval-based aborting policy (ISIAP), which determines the inter-shock interval within which the primary mission should be aborted upon the next shock based on the previous inter-shock interval. A probabilistic approach is suggested for assessing the mission success probability (MSP) and the system survival probability (SSP) under a given ISIAP. The optimal ISIAP problem is formulated and solved with the objective to maximize the expected MSP while meeting certain requirement on the SSP. A detailed case study of an unmanned aerial vehicle performing a payload delivery mission is provided to demonstrate the proposed mission aborting model and compare the effectiveness of the proposed ISIAP and the conventional NSAP. Impacts of the SSP requirement and the shock rate parameter on the mission performance metrics and optimal solutions are also examined in the case study.

Gloss-driven Conditional Diffusion Models for Sign Language Production

Sign Language Production (SLP) aims to convert text or audio sentences into sign language videos corresponding to their semantics, which is challenging due to the diversity and complexity of sign languages, and cross-modal semantic mapping issues. In this work, we propose a Gloss-driven Conditional Diffusion Model (GCDM) for SLP. The core of the GCDM is a diffusion model architecture, in which the sign gloss sequence is encoded by a Transformer-based encoder and input into the diffusion model as a semantic prior condition. In the process of sign pose generation, the textual semantic priors carried in the encoded gloss features are integrated into the embedded Gaussian noise via cross-attention. Subsequently, the model converts the fused features into sign language pose sequences through T-round denoising steps. During the training process, the model uses the ground-truth labels of sign poses as the starting point, generates Gaussian noise through T rounds of noise, and then performs T rounds of denoising to approximate the real sign language gestures. The entire process is constrained by the MAE loss function to ensure that the generated sign language gestures are as close as possible to the real labels. In the inference phase, the model directly randomly samples a set of Gaussian noise, generates multiple sign language gesture sequence hypotheses under the guidance of the gloss sequence, and outputs a high-confidence sign language gesture video by averaging multiple hypotheses. Experimental results on the Phoenix2014T dataset show that the proposed GCDM method achieves competitiveness in both quantitative performance and qualitative visualization.

Using an Improved Visual Geometry Group Neural Network for Skin Cancer Detection

Over time, deep learning has become more accurate and efficient at dealing with more complex risks. However, training deep neural networks remains a daunting problem and challenges remain, possibly due to several issues, including relevance, generalizability, and training time. Producing specific processors to manipulate artificial networks and reduce task-specific network learning time is natural, but the challenge of fit and generalizability remains undisputed. These networks have been used to detect and diagnose skin cancer, one of the most dangerous types of cancer caused by DNA damage. Automatic recognition of skin cancer is important to help doctors detect the disease in its early stages. In this research, the proposed vgg16 method was used to classify benign and malignant medical images. Image processing was used, where Gaussian noise and salt and pepper noise were added to increase the data, and filters for the arithmetic mean and median were added for each noise. The network was implemented using the Keras interface. Appling suggest model shown that the height accuracy 0.86 for train and accuracy 0.92 for test

Surface quasi-geostrophic equation perturbed by derivatives of space-time white noise

Surface quasi-geostrophic equation perturbed by derivatives of space-time white noise

Analysis of a positivity-preserving splitting scheme for some semilinear stochastic heat equations

We construct a positivity-preserving Lie--Trotter splitting scheme with finite difference discretization in space for approximating the solutions to a class of semilinear stochastic heat equations with multiplicative space-time white noise. We prove that this explicit numerical scheme converges in the mean-square sense, with rate $1/4$ in time and rate $1/2$ in space, under appropriate CFL conditions. Numerical experiments illustrate the superiority of the proposed numerical scheme compared with standard numerical methods which do not preserve positivity. p, li { white-space: pre-wrap; }

Morphosyntactic agreement in English: does it help the listener in noise?

English morphosyntactic agreement, such as determiner–noun agreement in These cabs broke down and noun–verb agreement in The cabs break down, has a few interesting properties that enable us to investigate whether agreement has a psycholinguistic function, that is, whether it helps the listener process linguistic information expressed by a speaker. The present project relies on these properties in a perception experiment, examines the two aforementioned types of English agreement, and aims at analyzing whether and how native English listeners benefit from agreement. The two types of agreement were contrasted with cases without any overtly agreeing elements (e.g. The cabs broke down). Native speakers of English with normal hearing heard short English sentences in quiet and in more or less intense white noise and were requested to indicate whether the second word of the sentence (e.g. These cabs broke down) was a singular or plural noun. Accuracy was entered as the response variable in the binomial logistic regression model. Results showed that overt determiner–noun agreement clearly increased response accuracy, while noun–verb agreement had at best marginal effects. The findings are interpreted against the background of functional aspects of linguistic structures in English, in the context of unfavorable listening conditions in particular.

Method for the P-wave arrival pickup of rock fracture acoustic emission signals under strong noise

This research aimed to investigate the accuracy of picking of P-wave arrival times in rock fracture acoustic emission signals. In order to simulate the mining scenario, Gaussian white noise and pulse noise were added to the data collected in the laboratory. Complete ensemble empirical mode decomposition with adaptive noise + Wavelet (CEEMDAN + Wavelet) was improved in this paper, where the Spearman rank correlation coefficient was adopted to effectively select intrinsic mode functions for denoising which retained the inherent characteristics of the rock fracture signal. The absolute amplitude and energy change rate of the envelope signal, calculated based on the Hilbert transform, were used as the input of the short term average/long term average (STA/LTA) normalization algorithm to pickup the P-wave arrival time. The reliability of this method was tested on 30 groups of recorded rock fracture laboratory data and 60 groups of added noise data. Taking the manual pickup results as the standard, the errors of CEEMDAN + Wavelet + STA/LTA + AIC (Akaike information criterion) method with the absolute amplitude of the signal as the input are all within 10 ms, and 86.67% of the results are within 5 ms. The method proposed in this paper effectively addressing the issue of false pickup caused by the sensitivity of AIC and traditional STA/LTA method for strong noise, and achieving relatively high accuracy and stability in processing low signal-to-noise ratio signals. This work contributes to monitor microscopic changes in rock bodies and is of great significance for the prediction and monitoring of geological disasters.

Optimization of the Weight Processing Algorithm in Multichannel Doppler Filtering

Introduction. The use of non-equidistant pulse sequences as probing radar signals makes it possible to eliminate blind spots in speed and range. However, the implementation of multi-channel Doppler filtering (MDF) based on the classical fast Fourier transform (FFT) algorithm of non-equidistant signal samples in the signal detection problem is associated with energy losses. The use of modified FFT algorithms increases the efficiency of MDF against the background of white Gaussian noise, while reducing the efficiency of signal accumulation in the part of signal processing channels blocked by the narrow-band clutter. To eliminate this drawback, the authors previously proposed using combined classical and modified FFT algorithms. However, the use of the combined method does not lead to an optimal solution in terms of MDF efficiency.Aim. Optimization of weight processing of non-equidistant signals to improve the efficiency of MDF.Materials and methods. An MDF synthesis was carried out using optimization procedures, and the effectiveness of the algorithms was assessed using computer calculations.Results. The results show that the Kaiser Bessel window with a window parameter of 4.42 provides the highest signal-(clutter+noise) ratio improvement coefficient averaged over frequency channels equal to 30.06 dB and the highest probability of correct signal detection averaged over MDF channels equal to 0.5 at processing of non-equidistant pulse sequences. Optimization of the weight processing of MDF under the specified conditions increased the average efficiency characteristics used of up to 53.18 dB and 0.92, respectively.Conclusion. Separate optimization of weighting processing for each frequency channel can significantly improve the average efficiency characteristics of a multichannel Doppler filter and eliminate all the shortcomings of the classical and modified FFT algorithms when processing non-equidistant pulse sequences. However, these advantages are achieved at the cost of not using the FFT, i.e., implemented within the framework of the discrete Fourier transform (DFT) algorithm.

A Pilot Study of Observation Poisoning on Selective Reincarnation in Multi-Agent Reinforcement Learning

This research explores the vulnerability of selective reincarnation, a concept in Multi-Agent Reinforcement Learning (MARL), in response to observation poisoning attacks. Observation poisoning is an adversarial strategy that subtly manipulates an agent’s observation space, potentially leading to a misdirection in its learning process. The primary aim of this paper is to systematically evaluate the robustness of selective reincarnation in MARL systems against the subtle yet potentially debilitating effects of observation poisoning attacks. Through assessing how manipulated observation data influences MARL agents, we seek to highlight potential vulnerabilities and inform the development of more resilient MARL systems. Our experimental testbed was the widely used HalfCheetah environment, utilizing the Independent Deep Deterministic Policy Gradient algorithm within a cooperative MARL setting. We introduced a series of triggers, namely Gaussian noise addition, observation reversal, random shuffling, and scaling, into the teacher dataset of the MARL system provided to the reincarnating agents of HalfCheetah. Here, the “teacher dataset” refers to the stored experiences from previous training sessions used to accelerate the learning of reincarnating agents in MARL. This approach enabled the observation of these triggers’ significant impact on reincarnation decisions. Specifically, the reversal technique showed the most pronounced negative effect for maximum returns, with an average decrease of 38.08% in Kendall’s tau values across all the agent combinations. With random shuffling, Kendall’s tau values decreased by 17.66%. On the other hand, noise addition and scaling aligned with the original ranking by only 21.42% and 32.66%, respectively. The results, quantified by Kendall’s tau metric, indicate the fragility of the selective reincarnation process under adversarial observation poisoning. Our findings also reveal that vulnerability to observation poisoning varies significantly among different agent combinations, with some exhibiting markedly higher susceptibility than others. This investigation elucidates our understanding of selective reincarnation’s robustness against observation poisoning attacks, which is crucial for developing more secure MARL systems and also for making informed decisions about agent reincarnation.

The effect of facilitated tucking and white noise on stress and sleep of newborns receiving nasal continuous positive airway pressure

ObjectiveTo investigate the effects of facilitated tucking and white noise on stress and sleep in neonates receiving nasal continuous positive airway pressure (CPAP). MethodThis study was conducted as a randomised controlled experimental study of neonates receiving nasal CPAP in neonatal intensive care units. The study sample consisted of 108 newborns (facilitated tucking (n = 36), white noise (n = 36) and control (n = 36)) receiving nasal CPAP support in the NICU. The neonates' sleep parameters were recorded by actigraphy for 24 h. Data were collected using the Neonatal Descriptive Information Form, the Sleep Tracking Form and the Neonatal Stress Scale. Percentage, mean, chi-squared and one-way ANOVA were used for data analysis. ResultsIt was found that 50.9% of the newborns were female, their mean gestational age was 33.54 ± 3.38, their mean height was 43.56 ± 5.12, and their mean weight was 2139.23 ± 827.82. The total sleep time of the neonates in the facilitated tucking and white noise group increased by 3 h, their sleep efficiency increased by 20% and their mean stress scores decreased (p < 0.05). ConclusionFacilitated tucking and white noise each showed a similar improvement in sleep duration and sleep efficiency and a reduction in stress scores in neonates receiving nasal CPAP. Close monitoring of sleep in this population and supportive care practices are recommended. Practice implicationsThe findings of this study may help to reduce sleep problems and stress levels in the clinical care of neonates in the NICU through developmental nursing practices.

Identification of full-field wind loads on buildings using a mechanism-inspired recursive convolutional neural network with partial structural responses

Indirect identification approaches through structural responses have proven effective for wind load estimation in real-world engineering. Currently, methods for identifying wind loads mainly rely on theoretical inverse identification, with rare research based on the mapping relationship between structural responses and wind loads through machine learning. In this paper, a scheme for identifying full-field wind loads using a recursive convolutional neural network (CNN) inspired by physical mechanisms is proposed. The recursive form of the network, as well as the inspiration for its inputs and outputs, is inspired by the spatial correlation and the mapping relationship between wind loads and structural responses. Thus, the network inputs comprise a fusion of structural acceleration and inter-story displacement responses, while the network outputs represent the independent wind loads on structures. Notably, mismatch test is employed by the network, wherein the training and testing datasets originate from entirely different sources. Specifically, during training, Gaussian white noises that simulate wind loads are utilized, while real wind load data are used for testing. The generalization of the proposed scheme is demonstrated through the identification of full-field wind loads generated by different stationary or non-stationary wind spectra of the 76-story wind-excited benchmark building. Furthermore, the proposed scheme is validated by identifying the full-field wind loads of a 67-story shear wall structure with wind tunnel test data.