Noise Operation Research Articles

An exponential stochastic Runge–Kutta type method of order up to 1.5 for SPDEs of Nemytskii-type

Abstract For the approximation of solutions for stochastic partial differential equations, numerical methods that obtain a high order of convergence and at the same time involve reasonable computational cost are of particular interest. We therefore propose a new numerical method of exponential stochastic Runge–Kutta type that allows for convergence with a temporal order of up to $\frac{3}/{2}$ and that can be combined with several spatial discretizations. The developed family of derivative-free schemes is tailored to stochastic partial differential equations of Nemytskii-type, i.e., with pointwise multiplicative noise operators. We prove the strong convergence of these schemes in the root mean-square sense and present some numerical examples that reveal the theoretical results.

Measuring drone noise for environmental reviews

In the United States, federal actions are covered by the National Environmental Policy Act which requires a review process to assess a proposed action's potential impact on the environment. One of the environmental impact categories that must be considered is noise. In order to assess the noise generated during a drone's typical operations, noise measurements of each portion of the drone's flight profile are needed. Because measurements required for type certification of aircraft are focused on source noise, they might not be sufficient to determine the noise resulting from a drone's entire operation from takeoff to landing. This paper discusses a range of feasible noise measurement approaches for assessing a drone operation's noise characteristics along with their associated fidelity. The paper presents a suggested practical measurement protocol that a drone manufacturer or operator could perform.

Wavelet denoising analysis on vacuum-process monitoring signals of aerospace vacuum vessel structures

Abstract The monitoring of micro-defects or external actions induced vacuum degradation in aerospace vacuum vessels is an important challenge. A vacuum-process monitoring method based on quasi-distributed fiber Bragg grating (FBG) sensing technology is proposed. Due to the influence of environmental noise and vacuum pump operation noise, the raw signals measured by FBG sensors contain a large amount of noises, which affects the measurement accuracy and data analysis. Therefore, the wavelet threshold (WT) denoising method is proposed to analyze the influence of noises on the monitoring signals measured by two different kinds of FBG sensors. The evaluation of the monitoring signals after denoising indicates that the proposed method can effectively remove the noise and significantly improve signal quality. The highest signal-to-noise ratio of the processed signals can reach 37.61 dB and the mean square error is 3.68 × 10−7, while retaining the key features of the original signal. The proposed WT denoising method demonstrates better performance and feasibility compared with moving average filtering and Kalman filtering methods. The study provides critical supports for improving the performance and reliability of the vacuum vessel monitoring system.

Rolling bearing fault diagnosis method based on multi-scale pooling residual convolutional neural network under noisy environment

To address the issues of unstable performance and poor generalization ability of bearing fault diagnosis model caused by strong noise and variable operating conditions in actual production，this paper proposes a rolling bearing fault diagnosis method based on MSPRCNN model. By converting vibration signals to frequency domain with FTand utilizing wide convolution kernels for feature extraction, the approach aims to enhance fault detection. A MSPFE module captures information at different scales to simplify complexity, while an UPA module establishes correlations between frequency domain positions. To reduce the impact of noise and address the vanishing gradient problem, the MSPRCNN model employs GC instead of standard convolution, and utilizes the IReLU activation function to improve model feature representation. Experimental results on two datasets show that the fault recognition accuracy is 98.71% under variable loads and 98.2% under variable speeds. The MSPRCNN model outperforms other methods in fault recognition and generalization in noisy environments.

Leveraging Machine Learning for Advanced Nanoscale X-ray Analysis: Unmixing Multicomponent Signals and Enhancing Chemical Quantification.

Energy dispersive X-ray (EDX) spectroscopy in the transmission electron microscope is a key tool for nanomaterials analysis, providing a direct link between spatial and chemical information. However, using it for precisely determining chemical compositions presents challenges of noisy data from low X-ray yields and mixed signals from phases that overlap along the electron beam trajectory. Here, we introduce a novel method, non-negative matrix factorization based pan-sharpening (PSNMF), to address these limitations. Leveraging the Poisson nature of EDX spectral noise and binning operations, PSNMF retrieves high-quality phase spectral and spatial signatures via consecutive factorizations. After validating PSNMF with synthetic data sets of different noise levels, we illustrate its effectiveness on two distinct experimental cases: a nanomineralogical lamella, and supported catalytic nanoparticles. Not only does PSNMF obtain accurate phase signatures, but data sets reconstructed from the outputs have demonstrably lower noise and better fidelity than from the benchmark denoising method of principle component analysis.

Quantum continuity equations and quantum evolution systems for white noise operators

Quantum continuity equations and quantum evolution systems for white noise operators

Low noise operation of an all polarization-maintaining figure-9 Er:fiber laser with near-zero cavity dispersion

We demonstrate the low noise operation of a figure-9 1.5-μm Er:fiber laser constructed by all polarization-maintaining (PM) fiber and fiber components. Starting from the original rate equations and nonlinear Schrödinger equation, the cavity roundtrip evolution toward stable mode locking state is present. The radio frequency spectrum shows a 94.6-MHz fundamental repetition rate with up to 100 dB high signal-to-noise ratio. The net dispersion is tailored to near zero by incorporating and optimizing the length of PM dispersion-compensating fiber in the cavity. As a result, an integrated root-mean-square relative intensity noise of 0.0026 % [1 Hz, 1 MHz] and 10.8-fs timing jitter [100 Hz, 1 MHz] at the fundamental repetition rate are measured. We also lock the fundamental repetition frequency to a stable radio frequency reference and an in-loop relative stability of 2.1 × 10−12 at 1-s gate time is obtained.

A Semi-supervised Four-Chamber Echocardiographic Video Segmentation Algorithm Based on Multilevel Edge Perception and Calibration Fusion

ObjectiveEchocardiographic videos are commonly used for automatic semantic segmentation of endocardium, which is crucial in evaluating cardiac function and assisting doctors to make accurate diagnoses of heart disease. However, this task faces two distinct challenges: one is the edge blurring, which is caused by the presence of speckle noise or excessive de-noising operation, and the other is the lack of an effective feature fusion approach for multilevel features for obtaining accurate endocardium. MethodsIn this study, a deep learning model, based on multilevel edge perception and calibration fusion is proposed to improve the segmentation performance. First, a multilevel edge perception module is proposed to comprehensively extract edge features through both a detail branch and a semantic branch to alleviate the adverse impact of noise. Second, a calibration fusion module is proposed that calibrates and integrates various features, including semantic and detailed information, to maximize segmentation performance. Furthermore, the features obtained from the calibration fusion module are stored by using a memory architecture to achieve semi-supervised segmentation through both labeled and unlabeled data. ResultsOur method is evaluated on two public echocardiography video data sets, achieving average Dice coefficients of 93.05% and 93.93%, respectively. Additionally, we validated our method on a local hospital clinical data set, achieving a Pearson correlation of 0.765 for predicting left ventricular ejection fraction. ConclusionThe proposed model effectively solves the challenges encountered in echocardiography by using semi-supervised networks, thereby improving the segmentation accuracy of the ventricles. This indicates that the proposed model can assist cardiologists in obtaining accurate and effective research and diagnostic results.

Quantitative analysis of the characteristics and influencing factors of hearing loss among workers exposed to noise in Tianjin

Objective: To analyze the characteristics of high-frequency average hearing loss in both ears of noise exposed workers in Tianjin in 2020, and quantitatively analyze the influencing factors of high-frequency hearing loss in both ears of workers. Methods: In March 2023, Collect and organize basic information about noise-hazardous enterprises and personal information of workers exposed to noise. Data from the Tianjin Occupational Disease and Health Hazard Factors Information Monitoring System from January 2020 to December 2020, and analyze the impact of basic information of employees, enterprise size, regional distribution, industry category, and economic type on the high-frequency average hearing loss of workers during work. Apply logistic regression to quantitatively analyze the influencing factors of abnormal high-frequency average hearing threshold of noise exposed workers. Results: The size, economic type, industry category, and regional distribution of enterprises, as well as the gender, age, length of service of workers, have an impact on the abnormal high-frequency average hearing threshold of noise exposed workers (χ(2)=733.56、3 497、27、1352.84、1197.62、2570.59、22.30、506.60, P<0.001) . Quantitative analysis using a logistic regression model showed that in the basic information of workers, noise exposed workers were male (OR=2.500, P<0.001) and aged 30-39, 40-49, and 50-59 years (OR=1.33, P<0.001; OR=1.68, P<0.001; OR=1.52, P< 0.001) , with a length of service of 4 to<10 years and≥10 years (OR=1.08, P<0.001; OR=1.615, P<0.001) being the influencing factors for high-frequency hearing loss in both ears of noise exposed workers; In terms of enterprise characteristics, medium-sized, small and micro enterprises (OR=1.12, P<0.001; OR=1.75, P<0.001; OR=2.09, P<0.001) , enterprises located in the fourth district around the city (OR=1.268, P<0.001) , and enterprises with economic types of collective economy, other economy, private economy, Hong Kong, Macao and Taiwan investment, shareholding system, and other industry economies (OR are all >1, P<0.001) are all factors affecting high-frequency hearing loss in noise exposed personnel. Conclusion: Noise is a common occupational hazard factor in Tianjin's enterprises, especially for workers in micro enterprises who face a high risk of hearing abnormalities. Therefore, enterprises need to strengthen the management and intervention of noise operations to prevent the occurrence of hearing loss in workers.

Building footprint extraction from aerial images using an edge-aware YOLO-v8 network

Abstract. Building footprint extraction is a critical indicator for assessing urban infrastructure, and extracting building footprints from remote sensing imagery can have significant practical applications. However, achieving rapid and accurate extraction of building footprints remains highly challenging, especially in scenarios with complex scenes, dense building distributions, and small targets. The instance segmentation models of the YOLO series offer strong real-time performance, reducing considerable time and effort in practical applications. Therefore, we propose building footprint extraction based on an enhance YOLO-v8 network. This study focuses on three enhancements to the YOLO-v8 network to improve extraction accuracy. Building upon the YOLO-v8 framework, we have incorporated the Feature Pyramid Network (FPN) module into feature maps at all scales to efficiently propagate high-level semantic information. Additionally, we introduce the Triple Feature Encoder (TFE) module, which integrates spatial detail information from feature maps at three different scales to enhance the network's ability to extract multi-scale information. Finally, we explore the integration of the Prewitt model, a conventional edge detection operator, to assist in extracting edge features in target regions of feature maps. This integration aims to reduce the jagged edges frequently seen in the outcomes of the original YOLO-v8. Furthermore, the Prewitt operator's noise suppression capability helps mitigate the influence of non-target areas in the feature maps. The proposed framework achieves an instance segmentation accuracy of mAP50 is 84.6% and mAP50:95 is 51.4% on public datasets, outperforming the original YOLO-v8 network.

Quantum analogues of white noise delta functions

In this paper, we construct a quantum analogue of a white noise delta function. For our main purpose, we first discuss several white noise delta functions, specially, an infinite-dimensional analogue of Donsker’s delta function. Then as a general setting, we construct white noise delta functions on white noise test functionals and we derive a Cauchy problem whose solution is given by the white noise delta function. Also, we prove that the white noise delta function is an eigenvector of the exotic Laplacian. By applying the canonical topological isomorphisms between the spaces of white noise operators and the spaces of white noise functionals, we formulate a quantum analogue of the white noise delta function which satisfies a quantum white noise differential equation associated with the quantum Volterra Laplacian. Finally, we prove that quantum analogue of the white noise delta function is an eigenoperator of the quantum exotic Laplacian.

A lifetime prediction model based on two-path convolution with attention mechanism and bidirectional long short-term memory network

With the continuous advancement of technology, modern industrial equipment is becoming increasingly complex, integrated, and automated. The complexity of industrial processes often involves multiple variables, strong coupling, nonlinearity, variable operating conditions, and significant noise, making the establishment of accurate remaining useful life (RUL) prediction models a challenging research direction. This paper proposes a lifetime prediction model based on two-path convolution with attention mechanisms and a bidirectional long short-term memory (BiLSTM) network. The model’s front end employs two-path convolution scales and attention modules to extract key fault information from bearings, enhancing the model’s noise resistance. It utilizes adaptive batch normalization and Meta-Aconc activation functions to adaptively adjust the neurons of the model, thereby enhancing its generalization capabilities. The model’s back end uses a BiLSTM network to remember and process the degradation information of bearings, achieving the prediction of bearing RUL. Furthermore, the model’s accuracy is evaluated using root mean square error and a scoring function assessment system. Comparative experiments demonstrate the model’s higher predictive accuracy. Finally, robustness and generalization experiments have proven the model to adapt well in scenarios with noise interference and working condition transitions. This model provides a reference for the prediction of the life of rotating machinery in practical scenarios with strong noise and variable operating conditions.

Simulation and Experimental Study on Corona Noise Distribution of UHV Transmission Lines

Introduction: The rise of ultra-high voltage AC grids creates corona noise, which is crucial for assessing transmission line environments. Method: This paper analyzes corona noise distribution around AC transmission lines using a 3D model for quantitative assessment. We employ the Finite Element Method, and factors like electric field intensity, conductor splitting, and sag are considered. Corona noise is calculated using CEPRI prediction equations and validated with field data. Result: We found strong corona noise concentrating within a 20m lateral and 240m vertical radius. Within 50m, lateral attenuation is 3.6%, and longitudinal attenuation is 3.3%. Conclusion: This research provides insights for monitoring and controlling corona noise in transmission line design and operation.

High-precision hemoglobin detection based on hyperspectral reconstruction of RGB images

Hemoglobin concentration is an important indicator for evaluating human health. The concentration value is an important evidence for early diagnosis of anemia and other blood diseases. However, existing optical detection methods usually suffer from low precision, strong noise and tedious operation, makes it difficult to obtain an accurate hemoglobin concentration evaluation. In this work, we propose and demonstrate a method for hemoglobin detection with the hyperspectral reconstruction of RGB images. To be specific, this overall model consists of a SHR-Net (Skin Hyperspectral Reflectance Network) to reconstruct hyperspectral images from RGB images of fingers, a feature selection process to capture full-band reflectance spectra based on the maximum blood perfusion region and an SVR (Support Vector Regression) algorithm to predict the hemoglobin concentration. The experimental results show that the coefficient of determination between the predicted and true values is 0.945. The mean square error of cross-validation is 0.49 g/dL. Therefore, this method provides a new solution for the accurate and noninvasive measurement of hemoglobin.

Novel Investigation of Stochastic Fractional Differential Equations Measles Model via the White Noise and Global Derivative Operator Depending on Mittag-Leffler Kernel

Novel Investigation of Stochastic Fractional Differential Equations Measles Model via the White Noise and Global Derivative Operator Depending on Mittag-Leffler Kernel

Smoothing of Binary Codes, Uniform Distributions, and Applications.

The action of a noise operator on a code transforms it into a distribution on the respective space. Some common examples from information theory include Bernoulli noise acting on a code in the Hamming space and Gaussian noise acting on a lattice in the Euclidean space. We aim to characterize the cases when the output distribution is close to the uniform distribution on the space, as measured by the Rényi divergence of order α∈(1,∞]. A version of this question is known as the channel resolvability problem in information theory, and it has implications for security guarantees in wiretap channels, error correction, discrepancy, worst-to-average case complexity reductions, and many other problems. Our work quantifies the requirements for asymptotic uniformity (perfect smoothing) and identifies explicit code families that achieve it under the action of the Bernoulli and ball noise operators on the code. We derive expressions for the minimum rate of codes required to attain asymptotically perfect smoothing. In proving our results, we leverage recent results from harmonic analysis of functions on the Hamming space. Another result pertains to the use of code families in Wyner's transmission scheme on the binary wiretap channel. We identify explicit families that guarantee strong secrecy when applied in this scheme, showing that nested Reed-Muller codes can transmit messages reliably and securely over a binary symmetric wiretap channel with a positive rate. Finally, we establish a connection between smoothing and error correction in the binary symmetric channel.

Interferometry-Integrated Noise-Immune Quantum Memory.

A quantum memory with the performances of low noise, high efficiency, and high bandwidth is of crucial importance for developing practical quantum information technologies. However, the excess noises generated during the highly efficient processing of quantum information inevitably destroy quantum state. Here, we present a quantum memory with built-in excess-noise eraser by integrating a photon-correlated quantum interferometry in quantum memory, where the memory efficiency can be enhanced and the excess noises can be suppressed to the vacuum level via destructive interference. This quantum memory is demonstrated in a rubidium vapor cell with a 10-ns-long photonics signal. We observe ∼80% noise suppression, the write-in efficiency enhancement from 87% to 96.2% without and with interferometry, and the corresponding memory efficiency excluding the noises from 70% to 77%. The fidelity is 93.7% at the single-photon level, significantly exceeding the no-cloning limit. Such interferometry-integrated quantum memory, the first expansion of quantum interference techniques to quantum information processing, simultaneously enables low noise, high bandwidth, high efficiency, and easy operation.

Managing noise from port operations via the use of automated noise monitoring systems

Ports are key pieces of infrastructure that enable the transportation of goods and passengers. Bays Port, Sydney NSW is located in close proximity to communities and balances the increased demand for Port facilities and services with community expectations, environmental requirements and regulatory approvals. For noise generated from Port activities at Bays Port, historically, individual port users have been required to monitor and evaluate noise under different environmental requirements and planning approvals, which can lead to inconsistent reporting, noise limits and regulation between operators and a lack of clarity for the community. Port Authority of NSW has implemented a first of its kind policy in Australia for the management of Port noise from both individual vessel and landside noise sources. To achieve these objectives, an automated directional noise monitoring system has been implemented to provide real-time measurement and analysis of noise contributions from vessel and landside activities. The system utilizes live data of individual vessel tracking locations, meteorology and an array of 6 direction microphones located around the port infrastructure. This information feeds into a live platform to facilitate management of port noise operations. This paper provides an overview of the Port Noise Policy and the use of unattended directional noise monitoring systems to meet the policy’s aims. Case studies are also provided to illustrate the benefits of real time monitoring systems in pro-actively managing port noise.

Universal Scaling Bounds on a Quantum Heat Current.

In this Letter, we derive new bounds on a heat current flowing into a quantum L-particle system coupled with a Markovian environment. By assuming that a system Hamiltonian and a system-environment interaction Hamiltonian are extensive in L, we prove that the absolute value of the heat current scales at most as Θ(L^{3}) in a limit of large L. Furthermore, we present an example of noninteracting particles globally coupled with a thermal bath, for which this bound is saturated in terms of scaling. However, the construction of such a system requires many-body interactions induced by the environment, which may be difficult to realize with the existing technology. To consider more feasible cases, we consider a class of the system where any nondiagonal elements of the noise operator (derived from the system-environment interaction Hamiltonian) become zero in the system energy basis, if the energy difference exceeds a certain value ΔE. Then, for ΔE=Θ(L^{0}), we derive another scaling bound Θ(L^{2}) on the absolute value of the heat current, and the so-called superradiance belongs to a class for which this bound is saturated. Our results are useful for evaluating the best achievable performance of quantum-enhanced thermodynamic devices, including far-reaching applications such as quantum heat engines, quantum refrigerators, and quantum batteries.

A novel fault location method based on traveling wave for multi-branch distribution network

In this paper, a novel fault location method is proposed for multi-branch distribution network based on traveling wave. The improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and Teager energy operator (TEO) is used to detect the arrival time of traveling waves. Firstly, according to the signal processing ability of the ICEEMDAN method, the ICEEMDAN method is used to decompose the sampled signals when the fault occurs, and extract the high-frequency component of the sampled signals. Then, the TEO is used to extract the abrupt characteristics of high-frequency modal components. And the first maximum mutation point of the signal is regarded as the initial fault traveling wave signal. In addition, considering the transmission characteristic of fault traveling wave signal in multi-branch lines of distribution network, the difference value characteristic of branches is defined. And the recognition principle and the fault location method of multi-branch lines are established to realize the accurate location of distribution network line faults. Simulation results show that the proposed ICEEMDAN-TEO method can effectively detect the fault traveling wave signals, and has a better detection performance than CEEMDAN-TEO and HHT methods. The proposed fault location method can effectively locate fault positions and reduce location errors.© 2017 Elsevier Inc. All rights reserved.