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Machine learning (ML) methods applied in scientific research often deal with interrelated features in high‐dimensional data. Reducing data noise and redundancy is needed to increase prediction accuracy and efficiency especially when dealing with data from field sensors. We explored an unsupervised learning method, the denoising autoencoder (DAE), to extract the underlying data structure from noisy raw data in the context of predicting hydrologic quantities from multiple field sensors. These sensors have intrinsic instrumental noise and occasional malfunctions that cause missing values. Our DAE neural network reconstructed meteorological sensor data containing noise and missing values to predict evapotranspiration in a mountainous watershed. The DAE reconstructed the sensor variables with a mean coefficient of determination r2 $\\left({r}^{2}\\right)$ value of 0.77 across 15 dimensions representing individual sensors. It reduced variance and bias uncertainties compared to a classical autoencoder model. The reconstruction quality varied across dimensions depending on their cross‐correlation and alignment with the underlying data structure. Uncertainties arising from the model structure were overall higher than those resulting from data corruption. We attached the DAE structure to a downstream ET‐prediction neural network in three formats and achieved reasonably accurate ET predictions r2≃0.7 $\\left({r}^{2}\\simeq 0.7\\right)$. The use of the DAE notably reduced variance uncertainty in ET prediction. However, excessive variance reduction may be accompanied by an increase in bias due to the intrinsic bias‐variance tradeoff. Our method of evaluating and reducing uncertainties in aggregated data from different sources can be used to improve predictive models, process understanding, and uncertainty quantification for better water resource management.

One of the main drawbacks of Digital Holography (DH) is the coherent nature of the light source, which severely corrupts the quality of holographic reconstructions. Although numerous techniques to reduce noise in DH have provided good results, holographic noise suppression remains a challenging task. We propose a novel framework that combines the concepts of encoding multiple uncorrelated digital holograms, block grouping and collaborative filtering to achieve quasi noise-free DH reconstructions. The optimized joint action of these different image-denoising methods permits the removal of up to 98% of the noise while preserving the image contrast. The resulting quality of the hologram reconstructions is comparable to the quality achievable with non-coherent techniques and far beyond the current state of art in DH. Experimental validation is provided for both single-wavelength and multi-wavelength DH, and a comparison with the most used holographic denoising methods is performed. Digital holography: noise practically eliminated By combining smart recording with numerical processing methods, a research team has realized digital holography that is virtually noise free. Noise originating from the coherent nature of laser light is the scourge of digital holography, always causing holographic images to be of lower quality than conventional photographs. Now, Pasquale Memmolo of the Institute of Applied Sciences and Intelligent Systems in Italy and co-workers have devised a way to practically eliminate this noise. They achieved this by using a two-stage algorithm: one stage is based on the encoding of multiple holograms, providing the enhanced grouping and the next one performs sparsity enhancement filtering. The output obtained exhibited both qualitative and quantitative improvement over recently developed de-noising techniques. In particular, the algorithm reduced noise in background regions by 98% and in signal regions by 92%.

With the growing severity of road traffic noise pollution, poroelastic road surfaces (PERSs) have garnered significant attention due to their excellent noise reduction performance. However, the unique structure of PERSs results in insufficient durability, which limits their practical application. This study aims to develop a novel type of composite-modified asphalt that addresses the technical challenge of simultaneously improving low-temperature performance and durability, thereby providing a solid material foundation for boosting the engineering application of PERS pavements. Firstly, polyurethane (PU) and styrene-butadiene-styrene (SBS) are utilized to modify 90# matrix asphalt, resulting in the fabrication of a composite material. The basic properties of the composite-modified asphalt material are examined through penetration, ductility, softening point, segregation, and aging tests. Further, dynamic shear rheometer (DSR) and bending beam rheometer (BBR) tests are employed to assess its rheological behavior at high and low temperatures. Next, fluorescence microscopy and atomic force microscopy are used to analyze the micro-modification mechanism of the multiphase system. The results reveal that the PU-SBS composite-modified asphalt exhibits significantly improved low-temperature ductility compared with the matrix asphalt, while also maintaining excellent high-temperature performance and fatigue resistance at medium temperatures. Microscopic characterization suggests that a chemical reaction between PU-2 and SBS-modified asphalt leads to the formation of a stable system. This modification process not only enhances the performance of the composite asphalt at both high and low temperatures but also ensures excellent storage stability. Overall, the proposed PU-SBS composite-modified asphalt effectively resolves the conflict between strength and flexibility in poroelastic pavements. The synergistic mechanism of strong interfacial interactions and physical filling at the microscopic level provides a theoretical basis for the design of modified asphalt. These research findings offer a comprehensive technical solution for the widespread adoption of new environmentally friendly pavements.

Nowadays, ultra-wide cross section highway is a hotspot in construction and brings some unique noise distribution characteristics. In this work, we further investigate noise distribution characteristics of diverse building layouts along ultra-wide cross section highway in Guangdong Province with multiple noise mitigation measures. By the aid of vehicle noise emission model and noise mapping, the influence of high-rise building layouts and shielding in the urban planning on noise mitigation is also considered. Some key findings are summarized as follows: (1) Under the same distance, the noise level of non-frontage building facades is higher than frontage building facades. After taking noise reduction measures, the noise reduction effect of non-street-facing building facades, buildings facing the road, and buildings at a long distance to the road is greater than street-facing building facades, buildings sideways to the road, and buildings at a short distance; (2) the distribution trend of insertion loss (IL) of non-frontage buildings is influenced by the height of the frontage buildings. Specifically, the trend of insertion loss first increases and then decreases as the floor rises when the height of non-frontage buildings is higher than frontage buildings. Comparatively, the trend of insertion loss decreases as the floor rises when the height of non-frontage buildings is equal to frontage buildings; (3) when double noise reduction measures are implemented, the noise distribution trend in buildings is similar to that observed with individual noise reduction measure, where the difference between both is only 0.6 dB(A). Thanks to the high representativeness of the case area, this work can provide some design guidance for the urban planning and the selection of noise reduction measures along the ultra-wide cross section highway.

Cemented carbide circular saw blades are widely used for wood cutting, but they often suffer from vibration and noise issues. This study presents a multi-objective optimization method that integrates ANSYS and MATLAB to optimize the design of noise reduction slots in circular saw blades. A mathematical model was developed to correlate the emitted sound power with the overall vibration intensity. A multi-objective optimization model was then formulated to map the slot shape parameters to the deformation, equivalent stress, and vibration intensity during sawing. The ABAQUS thermal–mechanical coupling analysis was used to determine the sawing force and temperature field. The NSGA-II algorithm was applied on the ANSYS–MATLAB platform to iteratively compute slot shape parameters and conduct optimization searches for a globally optimal solution. Circular saw blades were fabricated based on the optimization results, and experimental results showed a significant reduction in sawing noise by 2.4 dB to 3.0 dB on average. The noise reduction effect within the specified frequency range closely agreed with the simulation results, validating the method’s efficiency. This study provides a feasible and cost-effective solution to the multi-objective optimization design problem of noise reduction slots for circular saw blades.

Due to the fault vibration signal of the rolling bearing is greatly interfered by the background noise, the fault features are easily submerged and result in a low fault diagnosis accuracy. A novel fault diagnosis method of rolling bearing is proposed based on improved VMD-adaptive wavelet threshold combined with noise reduction in this paper. Firstly, the modal components are obtained based on VMD decomposition; Secondly, the dual determination criteria of sample entropy and correlation coefficient are constructed to filter the components; Subsequently, an adaptive wavelet thresholding function is proposed, and quadratic noise reduction is applied to mixed IMFs, which in turn reconstructs each component to achieve joint noise reduction. Finally, based on traditional machine learning and deep learning diagnosis methods, the features of noise reduction signals are extracted to realize fault diagnosis. By verifying and analyzing the simulated signal with the measured signal, noise components, the expression of fault characteristics, and the accuracy of fault diagnosis are eliminated, enhanced, and improved.

A novel microstructurally controlled graded micro-porous material was developed and experimentally validated for noise reduction through a normal incidence impedance test. Extensive parametric studies were conducted to understand the influence of test specimen size, particle size, porosity, pore size, and its distribution on acoustic absorption and transmission loss. Based on previous research, this study evaluates the application of graded microporous material as an acoustic liner technology for aircraft turbomachine engines. The liner was fabricated in eight 45° segments, assembled in an aluminum test rig, and tested on NASA Glenn Research Center’s Advanced Noise Control Fan (ANCF) low-speed test bed for tonal and broadband noise. The study demonstrates that microstructurally controlled graded microporous material is very effective in dissipating sound energy with reductions in tonal sound pressure level (SPL) of 2 to 13 dB at blade passing frequencies and reductions in broadband SPL of about 2 to 3 dB for the shaft order greater than 40. While the proposed two-layer graded liner model successfully validated the concept, additional design optimization is needed to enhance performance further. This work highlights the potential of graded microporous material as next-generation acoustic liners, offering lightweight, efficient, and scalable aircraft engine noise reduction solutions.

Mixed Gaussian and Random-valued impulse noise (RVIN) removal is still a big challenge in the field of image denoising. Existing denoising algorithms have defects in denoising performance and computational complexity. Based on the improved “detecting then filtering” strategy and the idea of inpainting, this paper proposes an efficient method to remove mixed Gaussian and RVIN. The proposed algorithm contains two phases: noise classification and noise removal. The noise classifier is based on Adaptive center-weighted median filter (ACWMF), three-sigma rule and extreme value processing. Different from the traditional “detecting then filtering” strategy, a preliminary RVIN removal step is added to the noise removal phase, which leads to three steps in this phase: preliminary RVIN removal, Gaussian noise removal and final RVIN removal. Firstly, RVIN is processed to obtain a noisy image approximately corrupted by Gaussian noise only. Subsequently, Gaussian noise is re-estimated and then denoised by Block Matching and 3D filtering method (BM3D). At last, the idea of inpainting is introduced to further remove RVIN. Extensive experimental results demonstrate that the proposed method outperforms quantitatively and visually to the state-of-the-art mixed Gaussian and RVIN removal methods. In addition, it greatly shortens the computation time.

The mine wind speed sensor is an important intelligent sensing equipment in the mine intelligent ventilation system that can provide accurate and key wind speed parameters for the intelligent ventilation system. The turbulent pulsation characteristics of the airflow in the underground tunnel are a major factor for the inaccurate measurement of mine wind speed. Therefore, according to the random non-stationary characteristics of a turbulent pulsation signal, a denoising method based on adaptive complete ensemble empirical mode decomposition (CEEMDAN) combined with the wavelet threshold is proposed for suppressing the turbulent pulsation noise in the wind speed signal. First, the CEEMDAN algorithm is used for decomposing the wind speed signal into a series of IMF components. Second, the continuous mean square error criterion is used for determining the high-frequency IMF components with more noise. The wavelet threshold denoising method is used for denoising the high-frequency IMF components with more noise. Finally, the denoised IMF components and remaining low-frequency IMF components are reconstructed for obtaining the denoised signal. The results of the denoising analysis of measured turbulent pulsation signals, comparative analysis of denoising of simulated turbulent pulsation signals by different joint denoising methods, and denoising analysis of actual mine wind speed sensor data indicate that the joint denoising method proposed in this study has a higher signal-to-noise ratio and lower root mean square error of the wind speed signal after denoising. Compared with the EMD-wavelet threshold and EEMD-wavelet threshold denoising methods, the denoising method proposed in this study is better and has higher denoising accuracy, which provides a new method for processing actual mine wind speed sensor data.

Establishing road shelter forests is a key method to reduce traffic noise pollution. However, the characteristics of various types of road shelter forests and their effectiveness in reducing traffic noise remain extensively unexplored. This study focused on five types of pure road shelter forests (PFs) and one type of mixed coniferous broad-leaved forest (MCBLF). By conducting field noise monitoring and spectrum simulations, we analyzed average mass density, additional noise reduction and economic benefits. With a forest belt width of 60 m, the MCBLF reduced additional noise by 6.6 dB(A). Additionally, Forest height, crown shape, average mass density and noise frequency were all positively linked to noise reduction. The width of shelter forests was the main factor affecting noise reduction. Linear regression analysis results showed that cumulative mass surface density was a significant factor in noise reduction (p < 0.01, R2 = 0.93). Furthermore, the type and composition of the shelter forest had indirect effects on noise reduction. The MCBLF had better noise-reducing effects compared to both broad-leaved PFs and needle-leaved PFs due to its more complex structure. Interestingly, as the forest belt became wider, the noise reduction benefits per unit area decreased, implying that a 10 m wide forest belt offered higher economic returns. Considering that a 10 m wide shelter forest belt did not meet noise reduction requirements. This study suggested that the 20 m wide MCBLF was an optimal choice as an urban road shelter forest, providing both effective noise reduction and maximized economic benefits. Our findings provide a basis for the construction and sustainable development of road shelter forests with noise reduction functions.