Extraneous Noise Research Articles

Bioacoustic and Ecoacoustic Data in Audiovisual Core

Audiovisual Core (Audiovisual Core Maintenance Group 2023), is the TDWG standard for metadata related to biodiversity multimedia. The Audiovisual Core Maintenance Group has been working to expand the standard to provide the terms necessary for handling sound recordings. Audiovisual Core can now handle acoustic metadata related to biodiversity from single species (bioacoustics) to the ecosystem scale (ecoacoustics). Bioacoustics The Natural History Museum, London has a significant collection of recorded insect sounds (Ragge and Reynolds 1998) that are often directly linked to museum specimens (Fig. 1). The sound collection has previously been digitised and made available electronically through the BioAcoustica project (Baker et al. 2015). The BioAcoustica platform allows for annotation of audio files with tags including "Call" for deliberate sound made by an organism, "Voice Introduction" for metadata, and "Extraneous Noise." These boundaries are defined by their start and end times (in seconds) relative to the start of the file (Fig. 2). Ecoacoustics Ecoacoustics deals with the sounds present within an entire soundscape or ecosystem. The calls of individual species form the biological part of the soundscape (biophony) alongside sounds produced by non-living natural sources (geophony) and humans (anthropophony). Individual components are often defined by date and time boundaries, and sometimes by upper and lower frequency limits (Fig. 3). Regions of Interest The recently added concept of a "Region of Interest" (ROI) allows for the annotation of sound files, identifying multiple regions within a single recording with time and/or frequency bounds. However, the vocabulary of ROI*1 is not just intended for sounds. Equivalent terms also allow for regions to be specified with images and videos. The use of well-defined annotations has the potential to generate large amounts of training data for machine learning models and provide a standard for generating observation records from these models (e.g., BirdNet, see Kahl et al. 2021), which can be verified by linking them to audio segments within a much larger recording. The development of a metadata standard for regions of interest has several interesting possibilities, including linking multiple observation records to a single soundscape recording (the recording acts similarly to a voucher specimen) and aggregating regions across multiple datasets to create larger corpora for training machine learning models.

Non-Destructive Analysis for Machine-Picked Tea Leaf Composition Using Near-Infrared Spectroscopy Combined Chemometric Methods

This paper aimed to predict the mechanical composition of machine-picked fresh tea leaves (MPFTLs) using near-infrared spectroscopy (NIRS) rapidly and non-destructively. Samples of MPFTL with different mechanical composition ratios were collected and subjected to NIRS analysis. Subsequently, various preprocessing methods were employed to eliminate extraneous noise information. Next, characteristic spectral information was extracted using the backward interval partial least squares (biPLS) method, which was subsequently subjected to principal component analysis (PCA). Finally, a predictive model was constructed by applying the back propagation artificial neural network (BP-ANN) method, which was tested by external samples to assess its predictive efficacy, and the results were expressed as root mean square error and determination coefficient of prediction (Rp2). The optimal spectral pretreatment method was the following: (standard normal variate (SNV) + second derivative (SD)). Four characteristic spectral subintervals of ([2, 3, 7, 10]) were screened out, and the cumulative contribution rate of 95.20%, attributable to the first three principal components, was determined. When the tanh transfer function was applied to construct the BP-ANN-NIRS model, the results demonstrated optimal performance, exhibiting a root mean square error and a determination coefficient of prediction (Rp2) of 0.976 and 0.027, respectively. The absolute values of prediction deviation for all prediction set samples were found to be less than 0.04. The results of the best BP-ANN model for external samples were found to be in close agreement with those of the prediction set model. NIRS technology has successfully achieved the forecasting of the mechanical composition of machine-picked fresh tea leaves rapidly and accurately, providing a fair and convenient new method for purchasing fresh tea raw materials by machines, according to their quality, and promoting the sustainable high-quality and healthy development of the tea industry.

A novel intelligent fault diagnosis method for gearbox based on multi-dimensional attention denoising convolution.

In the field of intelligent fault diagnosis, particularly concerning rotating machinery, convolutional neural networks (CNNs) face significant challenges when applied to real industrial vibration data. These data are not only contaminated by various types of noise but also exhibit fault features that vary across different scales. Consequently, the effective suppression of extraneous noise and accurate extraction of multi-scale fault features are crucial issues. To address these challenges, this study proposes a novel deep neural network framework, termed the Multidimensional Fusion Residual Attention Network (MFRANet), for gearbox fault diagnosis. The MFRANet employs a multi-scale deep separable convolution module to thoroughly investigate the fundamental characteristics of the original vibration signals in both the time and time-frequency domains. To enhance the detailed analysis of diagnostic data and mitigate the risks of overfitting and noise interference, an efficient residual channel attention module is incorporated to weight and denoise the feature maps. Additionally, an external attention module is introduced to create implicit connections between the denoised multi-scale feature maps and to highlight potential correlations within the sample data, thereby improving the accuracy of fault diagnosis. Experimental evaluations on a gearbox fault dataset demonstrate that the proposed method surpasses several benchmark and state-of-the-art techniques in terms of diagnostic performance, exhibiting robust noise resilience across various noise levels. This indicates enhanced reliability and accuracy in gearbox fault diagnosis, providing an innovative and efficient solution for fault diagnosis in rotating machinery. The study underscores the contributions of artificial intelligence through the innovative structure of the method and the integration of advanced deep learning modules, while its engineering application is evidenced by addressing practical challenges in rotating machinery fault diagnosis. This work meets the urgent need for reliable diagnostic methods in industrial environments.

Creating a specialized dataset for enhanced noise compliance in live concert environments

Regulatory guidelines mandate strict noise levels during concerts, specially for night leisure soundscapes, yet accurate compliance assessment is hindered by the presence of extraneous noises. This paper presents a methodology focused on creating a specialized dataset tailored to address this challenge. We have captured several live concerts, ensuring the dataset's relevance and applicability in noise compliance assessments. This paper exposes the methodology followed since the deployment of the sensors until the labeling of the most relevant parts of it by using the LAeq levels to select the most prominent sound sources. The dataset has been used to use a PANN and incorporate a classification mechanism into a noise monitoring platform for live concerts. By leveraging LAeq levels to identify prominent sound sources, our methodology ensures the inclusion of key acoustic events in the dataset, facilitating subsequent analysis and classification tasks. The integration of a PANN (Pretrained Audio Neural Network) enables the development of a robust classification mechanism tailored to the intricacies of live concert environments. This work underscores the importance of specialized datasets in addressing the challenges of noise compliance in concert settings, offering a modest resource for advancing research and practical applications in this field.

Homodyne quadrature laser interferometry for the characterization of low-frequency residual vibrational noise in cryogenic trapped-ion systems.

Cryogenic trapped-ion systems (CTISs) have emerged as indispensable platforms for the advancement of quantum computation and precision measurement techniques. However, the sensitivity of these systems to vibrational noise, especially during the compression and expansion cycles of the cold head in a Gifford-McMahon cycle refrigerator (GMCR), poses a significant challenge. To mitigate this, we have crafted an innovative methodology for characterizing low-frequency residual vibrational noise in closed-cycle cryogenic trapped-ion systems. Our methodology is underpinned by a compact homodyne quadrature laser interferometer (HQLI) vibrometer system that boasts nanometer-scale accuracy. This state-of-the-art system leverages elliptic curve fitting to rectify nonlinear noise artifacts and applies an inverse tangent function to demodulation phase techniques, enabling accurate vibrational displacement measurements. Unlike the conventional approach, our scheme circumvents the introduction of extraneous vibrational noise associated with piezoelectric ceramic mirrors, which are conventionally employed to track target vibrations for locking the interference signal intensity in the reference arm. This innovation not only improves the overall CTIS performance but is also significantly applied to characterize the practical realization of quantum computation and precision measurement.

Singular value decomposition: A useful technique for image denoising

A key function of image processing is picture denoising, which improves the quality of images by eliminating extraneous noise while keeping crucial information in tact. Singular Value Decomposition (SVD) is a linear algebraic technique that reduces the original datas complexity and scale by breaking down the matrices and extracting the important information. With the power of decomposition which utilizes the non-local self-similarity property of an image to achieve satisfactory denoising performance, SVD denoising has become a potent tool in image processing. In this paper, SVD is outlined and its working, applications, and challenges as a denoising technique in image denoising are discussed. The author discovered that Singular Value Decomposition can be a significant factor in image denoising by applying it to the image. As a result, Singular Value Decomposition could be thought as a helpful image denoising approach in the image processing sequence that will raise the images Peak Signal-to-Noise Ration (PSNR) and improve the quality of the image.

Coherent Erbium Spin Defects in Colloidal Nanocrystal Hosts.

We demonstrate nearly a microsecond of spin coherence in Er3+ ions doped in cerium dioxide nanocrystal hosts, despite a large gyromagnetic ratio and nanometric proximity of the spin defect to the nanocrystal surface. The long spin coherence is enabled by reducing the dopant density below the instantaneous diffusion limit in a nuclear spin-free host material, reaching the limit of a single erbium spin defect per nanocrystal. We observe a large Orbach energy in a highly symmetric cubic site, further protecting the coherence in a qubit that would otherwise rapidly decohere. Spatially correlated electron spectroscopy measurements reveal the presence of Ce3+ at the nanocrystal surface, which likely acts as extraneous paramagnetic spin noise. Even with these factors, defect-embedded nanocrystal hosts show tremendous promise for quantum sensing and quantum communication applications, with multiple avenues, including core-shell fabrication, redox tuning of oxygen vacancies, and organic surfactant modification, available to further enhance their spin coherence and functionality in the future.

Fuzzy Classification of the Maturity of the Orange (Citrus × sinensis) Using the Citrus Color Index (CCI)

The orange (Citrus sinensis) is a fruit of the Citrus genus, which is part of the Rutaceae family. The orange has gained considerable importance due to its extensive range of applications, including the production of juices, jams, sweets, and extracts. The consumption of oranges confers several nutritional benefits, including flavonoids, vitamin C, potassium, beta-carotene, and dietary fiber. It is crucial to acknowledge that the primary quality criterion employed by consumers and producers is maturity, which is correlated with the visual quality associated with the color of the epicarp. This study proposes the implementation of a computer vision system that estimates the degree of ripeness of oranges Valencia using fuzzy logic (FL); the soluble solids content was determined by refractometry, while the firmness of the fruit was evaluated through the fruit firmness test. The proposed method was divided into five distinct steps. The initial stage involved the acquisition of RGB images. The second stage presents the segmentation of the fruit, which entails the removal of extraneous noise and backgrounds. The third and fourth steps involve determining the centroid of the fruit, and five regions of interest were obtained in the centroid of the fruit of the Citrus Color Index (CII), ranging from 3 × 3 to 11 × 11 pixels. Finally, in the fifth step, a model was created to estimate maturity, °Brix, and firmness using Matlab 2024 and the Fuzzy Logic Designer and Neuro-Fuzzy Designer applications. Consequently, a statistically significant correlation was established between maturity, degree Brix, and firmness, with a value greater than 0.9, using the Citrus Color Index (CII), which reflects the physical–chemical changes that occur in the orange.

ANALYSIS OF TUBERCULOSIS SEGMENTATION ON CHEST X-RAYS USING THE METHOD OF CONVOLUTIONAL NEURAL NETWORK “ATTENTION”

This study presents an experimental study using an advanced Attention U-Net deep learning model to improve tuberculosis (TB) segmentation in chest X-ray images. The goal of this study is to use convolutional neural networks (CNNs) with integrated attentionmechanisms improve the accuracy of medical image analysis. In the experiment, two different datasets were combined and augmented, resulting in a complete set of 800 images. These images were used to thoroughly train and validate the model's performance. The methodical approach allowed the Attention U-Net model to effectively highlight meaningful features while suppressing extraneous background noise. This ability is important for achieving accuracy in medical diagnosis. The empirical results were convincing: the model demonstrated outstanding segmentation abilities, as evidenced by high metrics: accuracy reached 0.9824, Dice score was 0.9767, and IOU was 0.9778. The integration of attention mechanisms into the CNN architecture has improved accuracy, leading to better detection and diagnosis of lung diseases. This study illustrates how the application of advanced machine learning techniques can help radiologists in image analysis.

MODIFICATION OF A QUANTUM-INSPIRED GENETIC ALGORITHM FOR NUMERICAL OPTIMIZATION USING QUDIT UNDER CONDITIONS OF SIMULATING QUANTUM DECOHERENCE

The genetic algorithm for numerical optimization (GA) of the metaheuristic class is a method for finding optimal solutions based on the biological principles of natural selection and variability. GA is characterized by high operating speed, resistance to noise in the data, low probability of hitting the local extremum of the multimodal objective function, as well as the simultaneous application of probabilistic and deterministic rules for generating search space points. An alternative to the classical GA is the quantum-inspired genetic algorithm for numerical optimization (QIGA), which has advantages that are unattainable for GA by using the concepts and principles of quantum computing. The article proposes a new approach to the implementation of a quantum-inspired genetic numerical optimization algorithm for searching for the global maximum of the objective function, based on modeling the functioning of the GA by simulating the execution of quantum calculations based on qudit in the conditions of the existence of quantum decoherence in the era of noisy medium-scale quantum algorithms. For this purpose, to carry out quantum operations of rotating the states of multilevel quantum systems, the paper presents a density matrix based on Heisenberg-Weyl operators as an analogue of the Bloch sphere for qudits. The simulation of quantum decoherence is interpreted from the point of view of the influence of extraneous noise emanating from the environment on the qudit and is presented as the use of a normal random variable with zero mathematical expectation and unit variance in quantum gates. At the same time, the work presents detailed pseudocodes of the functioning of both the most modified quantum-inspired genetic algorithm for numerical optimization and its individual operations. Testing is carried out by conducting computational experiments with the implementation of a modified algorithm on two-dimensional and multidimensional functions of test optimization problems, as well as when solving an applied optimization problem of planning hybrid flow production in the manufacturing industry based on financial costs and solving the problem of increasing forecasting accuracy based on compact extreme learning machines. The experimental results demonstrate the superiority of the new algorithm over QIGA and classical optimization algorithms in the accuracy of the solution, the speed of convergence with the target value of the global maximum and the execution time of the algorithm.

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.

Predicting leak aperture in the pipeline of ultra-long coal mine working faces under strong noise interference based on joint denoising and random forest

Ultra-long coal mine working faces are characterized by harsh conditions, making water supply pipelines susceptible to leakage incidents, posing risks to both personnel and equipment safety. However, detecting leaks using infrasound signals is challenging due to complex environmental noise interference. Additionally, spatial constraints make inspections difficult for maintenance personnel. Hence, it is vital to enhance the clarity of leakage data and accurately assess leak severity for effective pipeline maintenance. This study combines Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Sparse Representation (SR) techniques to create a signal processing model. This model serves two purposes: reducing extraneous noise interference and enhancing leak-related signals. Subsequently, we analyse leak signal characteristics across various operational scenarios and formulate a leak severity assessment model using the Random Forest (RF) algorithm. Empirical experiments confirm its effectiveness, with a root mean square error (RMSE) of 0.1135 millimeters between predicted and actual leak severity values. This result validates the method’s efficacy in estimating leak severity in water supply pipelines, offering a dependable approach to enhance pipeline maintenance and operations.

Choosing Optimal Cutoff Frequencies for Filtering Linear Acceleration and Angular Velocity Signals Associated with Head Impacts Measured by Instrumented Mouthguards

Head impact sensors worn in the mouth are popular because they couple directly to the teeth and provide six-degree-of-freedom head measurements. Mouthpiece signal filters have conventionally used cutoff frequencies lower than recommended practices (Society of Automotive Engineers, SAE J211-1) to eliminate extraneous noise when measuring with live subjects. However, there is little information about the effects of filter choice on the accuracy of signals measured by instrumented mouthpieces. Lack of standardization in head impact measurement device post-processing techniques can result in data that are not comparable across studies or device brands. This study sought optimal filter cutoff frequencies for six-degree-of-freedom measurements made at the teeth using instrumented mouthguards. We collected linear acceleration and angular velocity signals at the head center of gravity (CG) using laboratory-grade instrumentation. We also collected and filtered similar six-degree-of-freedom measurements from an instrumented mouthguard using 24 cutoff frequencies, from 25 to 600 Hz. We transformed the measurements to linear acceleration at the center of gravity of the head (CG) using all kinematic variables at the teeth, optimizing linear and angular mouthguard cutoff frequencies with one equation. We calculated the percent error in transformed peak resultant linear acceleration and minimized the mean and standard deviation in error. The optimal cutoff frequencies were 175 Hz for linear acceleration and 250 Hz for angular velocity. Rigid impacts (3–5 ms duration) had higher optimal cutoff frequencies (175 Hz linear acceleration, 275 Hz angular velocity) than padded impacts (10–12 ms duration; 100 Hz linear acceleration, 175 Hz angular velocity), and all impacts together (3–12 ms duration; 175 Hz linear acceleration, 250 Hz angular velocity). Instrumented mouthpiece manufacturers and researchers using these devices should consider these optimal filter cutoff frequencies to minimize measurement error. Sport-specific filter criteria for teeth-based sensors may be warranted to account for the difference in optimal cutoff frequency combination by impact duration.

Automatic bioacoustics noise reduction method based on a deep feature loss network

Acoustic sensors that collect acoustic data over extended periods and broad ranges are widely used in bioacoustics monitoring. However, in open environments, acoustic data collected using acoustic sensors can be subject to interference from various real-world noises, thereby influencing the subsequent analysis and processing of bioacoustic data. Existing bioacoustic noise reduction methods are limited in their application because of their low efficiency, unsuitability for non-stationary noise, generally unimproved signal-to-noise ratio (SNR) efficacy, and considerable amounts of residual noise. These limitations hinder the effective processing of recorded signals for which extraneous noise overlaps with bird vocalizations. In this study, we propose a bioacoustic noise reduction method based on a deep feature loss network for bird sounds. The method has a rapid denoising speed and can more effectively remove background noise from field recording signals without distorting the bird acoustic spectrum. The denoising effects of the proposed method were compared with those of a speech enhancement generative adversarial network, web real-time communications denoising, and other noise reduction methods. The denoising ability of these methods for different noises was evaluated using spectrograms and objective evaluation measures such as the SNR and perceptual evaluation of speech quality (PESQ). The experimental results revealed that our proposed noise reduction method can obtain higher SNRs and PESQ scores than other noise reduction methods, with the SNR increasing by up to 35.83 dB following denoising.

Sleep duration and perceptions of sleep quality in British Army recruits during basic training - an observational analysis.

Sleep is critical to the health, wellbeing and performance of military personnel during basic training. This two-part study evaluated sleep-wake patterns and sleep disturbances in junior soldiers (JS) and infantry recruits in Autumn 2021 (study 1), and non-infantry recruits in spring 2022 (study 2). During studies 1 and 2, validated wearable technology combined with a sleep diary was used to quantify sleep-wake indices, sleep disturbances and perceptions of sleep quality. Sleep diary data was analysed descriptively. A series of repeated-measures ANOVAs examined differences in objective sleep-wake indices. Correlation analysis determined associations between time in bed (TIB) and total sleep time (TST). Significant (p < 0.05) differences in most sleep-wake indices were observed between weeks of basic training for all cohorts. Strong positive correlations between TIB and TST were observed for each cohort across basic training (r = 0.681 - 0.970, p < 0.001), with longer TST associated with greater TIB. The mean±SD sleep duration (hours and mins [hm]) for JS (06:22 ± 00:27hm), non-infantry (05:41 ± 00:47hm) and infantry (05:46 ± 00:34hm) recruits across basic training was consistently below national recommendations. The mean±SD bed and wake times for JS (bedtime: 23:01 ± 00:32hm; awake: 05:34 ± 00:10hm), non-infantry (bedtime: 23:38 ± 01:09hm; awake: 04:47 ± 00:58hm), and infantry (bedtime: 23:13 ± 00:29hm; awake: 05:38 ± 00:26hm) recruits varied across weeks of basic training, with over 80% reporting "fairly bad" or "very bad" sleep quality and frequent periods of "dozing off" during daytime activity. The most commonly reported sleep disturbing factors identified during basic training involved: late-night military admin (e.g., ironing, boot cleaning, kit set up etc), early morning wake times, extraneous noise, light and hot room temperatures within the primary sleeping environment, bed/mattress discomfort, muscle soreness and feelings of stress and anxiety. Our findings contribute to the existing evidence that long-term sleep loss is pervasive during initial military training programmes. The average sleep durations indicate chronic and unrecoverable sleep loss which would be expected to significantly impair physical and cognitive military performance, and increase the risk of injury, illness and attrition rates during basic training. Changes in the design and scheduling of basic training programmes to enable, at the least, minimum sleep recommendations to be met, and to improve sleep hygiene in the primary sleeping environment are warranted.

An efficient heuristic power analysis framework based on hill-climbing algorithm

Traditional nonprofiling side-channel analysis frequently adopts divide-and-conquer strategy to recover the secret key of a cryptographic algorithm. Only a single key byte is used, whereas the remaining bytes are considered extraneous noise. If the cryptographic algorithm is implemented in hardware with parallel processing, the strategy will lead to the inefficient use of information. The combination of intelligent algorithms and side-channel analysis offers a different idea for nonprofiling methods for parallel hardware implementations. It transforms the problem of key recovery into key optimization. In this paper, we adopt this idea and propose an efficient heuristic framework to assist correlation power analysis. The multipoint hill-climbing algorithm will be used to find the optimal key candidates with higher scores, and the correct key will be found in the optimal key candidates by key enumeration. Multiple bytes of the secret key are used to take full advantage of the helpful information. Besides, two search strategies are introduced to overcome the efficiency problem, and two key enumeration strategies are introduced to solve the key recovery problem when the traces are insufficient. Experimental results on the public DPA Contest V1 dataset show that the heuristic framework performs better than other classical methods, verifying its effectiveness.

Research on motor rotation anomaly detection based on improved VMD algorithm

PurposeThe electromechanical brake system is leading the latest development trend in railway braking technology. The tolerance stack-up generated during the assembly and production process catalyzes the slight geometric dimensioning and tolerancing between the motor stator and rotor inside the electromechanical cylinder. The tolerance leads to imprecise brake control, so it is necessary to diagnose the fault of the motor in the fully assembled electromechanical brake system. This paper aims to present improved variational mode decomposition (VMD) algorithm, which endeavors to elucidate and push the boundaries of mechanical synchronicity problems within the realm of the electromechanical brake system.Design/methodology/approachThe VMD algorithm plays a pivotal role in the preliminary phase, employing mode decomposition techniques to decompose the motor speed signals. Afterward, the error energy algorithm precision is utilized to extract abnormal features, leveraging the practical intrinsic mode functions, eliminating extraneous noise and enhancing the signal’s fidelity. This refined signal then becomes the basis for fault analysis. In the analytical step, the cepstrum is employed to calculate the formant and envelope of the reconstructed signal. By scrutinizing the formant and envelope, the fault point within the electromechanical brake system is precisely identified, contributing to a sophisticated and accurate fault diagnosis.FindingsThis paper innovatively uses the VMD algorithm for the modal decomposition of electromechanical brake (EMB) motor speed signals and combines it with the error energy algorithm to achieve abnormal feature extraction. The signal is reconstructed according to the effective intrinsic mode functions (IMFS) component of removing noise, and the formant and envelope are calculated by cepstrum to locate the fault point. Experiments show that the empirical mode decomposition (EMD) algorithm can effectively decompose the original speed signal. After feature extraction, signal enhancement and fault identification, the motor mechanical fault point can be accurately located. This fault diagnosis method is an effective fault diagnosis algorithm suitable for EMB systems.Originality/valueBy using this improved VMD algorithm, the electromechanical brake system can precisely identify the rotational anomaly of the motor. This method can offer an online diagnosis analysis function during operation and contribute to an automated factory inspection strategy while parts are assembled. Compared with the conventional motor diagnosis method, this improved VMD algorithm can eliminate the need for additional acceleration sensors and save hardware costs. Moreover, the accumulation of online detection functions helps improve the reliability of train electromechanical braking systems.

Efficient Classification of Marine Debris using SVM with Noise Removal and Feature Extraction Techniques with Improved Performances

Marine debris is a significant environmental issue, necessitating the development of precise and efficient technologies for classifying and reducing its impact. This research tries to address the issues of image noise in ocean trash classification using four alternative filters the Gaussian, Bilateral, Mean, and Alpha-Trimmed Mean (ATM) filters for noise reduction and SVM for classification. The accuracy of categorization algorithms can be considerably impacted by the presence of noise in photos of maritime debris. In this study, we suggest a two-pronged strategy: To effectively decrease noise and improve the quality of the input photos, we first apply filters. These filters were chosen with care to protect significant details while eliminating extraneous noise. Second, we classify the debris into various categories based on its visual attributes using SVM, a powerful ML technique. The ATM filter significantly reduces noise and improves the clarity of photographs of ocean trash, according to the results of our experiments. This work proposes a novel approach for classifying marine debris using advanced machine learning algorithms. For improved classification accuracy, we suggest combining Support Vector Machines (SVM) with Adaptive Thresholding Mean (ATM) filtering and Histogram of Oriented Gradients (HOG) feature extraction. According to this research, the ATM filter is a promising option for noise reduction in ocean trash imaging, potentially increasing the precision of subsequent classification algorithms and assisting in efficient environmental monitoring and marine ecosystem conservation efforts. The language used for execution is Python.

Robust multi-task learning network for complex LiDAR point cloud data preprocessing

The utilization of 3D point clouds acquired via Light Detection and Ranging (LiDAR) is widespread in the fields of autonomous driving, satellite remote sensing, and spatial mapping. However, due to hardware limitations of the laser launch system and environmental interferences, the quality of point cloud data obtained through various types of LiDAR is often poor in real-world scenarios, containing extraneous noise and irrelevant data points. This poses a challenge for subsequent point cloud downstream tasks that (e.g., point cloud detection, recognition and tracking) require high-quality data. We propose a robust multi-task learning network for pre-processing LiDAR data. Our approach utilizes a shared PointNet encoder and three branching networks that perform denoising, single-object segmentation, and completion. The denoising branch network incorporates the traditional model based on geometric projection, leveraging the dual-driven approach of data and model for better capturing the characteristics of the point cloud. Regarding the segmentation branch network, we integrate an attention mechanism module suitable for single-object segmentation, enabling the network to better extract the point cloud features of complex objects. For the completion branch network, we employ a folded network structure to achieve a coarse-to-fine completion effect of the point cloud. We discuss the training methods, that is, end-to-end and step-by-step methods, which can enhance flexibility during the training and usage phase. Our proposed network outperforms prior state-of-the-art approaches in all three tasks on both ShapeNet and simulated point cloud data of the sea face scene while demonstrating superior robustness.

Multiresolution Quality Inspection of Layerwise Builds for Metal 3D Printer and Scanner

Abstract Automated optical inspection (AOI) is increasingly advocated for in situ quality monitoring of additive manufacturing (AM) processes. The availability of layerwise imaging data improves the information visibility during fabrication processes and is thus conducive to performing online certification. However, few, if any, have investigated the high-speed contact image sensors (CIS) (i.e., originally developed for document scanners and multifunction printers) for AM quality monitoring. In addition, layerwise images show complex patterns and often contain hidden information that cannot be revealed in a single scale. A new and alternative approach will be to analyze these intrinsic patterns with multiscale lenses. Therefore, the objective of this article is to design and develop an AOI system with contact image sensors for multiresolution quality inspection of layerwise builds in additive manufacturing. First, we retrofit the AOI system with contact image sensors in industrially relevant 95 mm/s scanning speed to a laser-powder-bed-fusion (LPBF) machines. Then, we design the experiments to fabricate nine parts under a variety of factor levels (e.g., gas flow blockage, re-coater damage, laser power changes). In each layer, the AOI system collects imaging data of both recoating powder beds before the laser fusion and surface finishes after the laser fusion. Second, layerwise images are pre-preprocessed for alignment, registration, and identification of regions of interests (ROIs) of these nine parts. Then, we leverage the wavelet transformation to analyze ROI images in multiple scales and further extract salient features that are sensitive to process variations, instead of extraneous noises. Third, we perform the paired comparison analysis to investigate how different levels of factors influence the distribution of wavelet features. Finally, these features are shown to be effective in predicting the extent of defects in the computed tomography (CT) data of layerwise AM builds. The proposed framework of multiresolution quality inspection is evaluated and validated using real-world AM imaging data. Experimental results demonstrated the effectiveness of the proposed AOI system with contact image sensors for online quality inspection of layerwise builds in AM processes.