Influence Of Noise Research Articles

A comprehensive study on impact of noise exposure on roadside school childrens’ cognitive performance

An uncontrolled vehicular population escalates distraction and affects mental wellness due to ambient noise, profoundly affecting the academic landscape. This study investigates the significant influence of ambient noise on schoolchildren’s learning, attention, and cognition, underscoring the critical challenges and potential limitations it poses to their academic progress. Cognitive assessments were conducted under prevailing noise and controlled conditions. Noise levels (LAeq, L90, L10) were measured using a sound level metre (Kimo dB300) in classrooms of ten roadside schools. Findings reveal that noise levels exceed WHO thresholds for classroom environments. Statistical analyses (z-test, paired t-test, ANOVA, regression analysis) indicate significant differences in noise levels and cognitive test scores between both noise exposure conditions. Schools S8 and S10 showed a remarkable 15% improvement in cognitive test scores when transitioning from noisy to silent conditions. Noise disproportionately affected students aged 11–13, particularly boys. Effective noise control measures were suggested to create conducive learning environments.

Thermal imaging-based identification of facial features in noisy environment

<p class="Abstract">Face identification is amongst the most efficacious and extensive applications in biometrics involving extraction and locating facial features. With identification being monotonous task attributable to reliance on parameters like varied cameras, fluctuating backgrounds, and exposure to the environment in which an individual is present. Thermal imaging is endeavoring to resolve the accuracy issue of apparent imaging, such as lighting and brightness intensity, among all biometric variables. This paper presents a study of thermal imaging and effective methods involved in the feature extraction process for facial features with thermal imaging under the influence of varied noise. A novel face dataset is created TID comprising 27 thermal images and its corresponding visual band image using Fluke 480 Ti Pro camera. The study analyses detection efficiency of six feature extraction techniques in visible and thermal bands in facial features identification. Also, the influence of noise in the thermal band within the region of interest using feature points FIN, FOUT has been estimated. Throughout TID dataset, ORB extraction technique has been able to identify strongest inlier features FIN to a maximum extent with detection around the nose, eyes, and mouth. Further, results indicate feature detection in thermal images being invariant to effect of noise for detecting facial features.</p>

End-to-end deep learning method to reconstruct full-field stress distribution for ship hull structure with stress concentrations

An image-based deep learning framework is developed to reconstruct full-field stress distribution of ship hull structure from discrete measurements in this paper. The work is motivated by the lack of data to assess the strength state of vessel. The proposed deep learning framework is based on a conditional generative adversarial network (cGAN) which contains a generator and a discriminator. They are mutually trained in a supervised manner based on an adversarial mechanism. The hull structure of interest is selected to be an opening deck of a bulk carrier. A novel image encoding algorithm is also designed to fuse global stress distribution and high gradient stresses at hatch corner regions into an image. A total of 3400 stress fields are generated and simulated using finite element method (FEM) to provide a sufficiently large dataset for training and testing the network. It is shown that the proposed deep learning approach can efficiently reconstruct the full-field stress distribution of hull structure. Furtherly, the influence of measurement noise on reconstruction accuracy is discussed. The results demonstrate that the well-trained network can directly generate robust solutions to full-field stress distribution under given discrete measurements.

Deterministic Gradient-Descent Learning of Linear Regressions: Adaptive Algorithms, Convergence Analysis and Noise Compensation.

Weight learning forms a basis for the machine learning and numerous algorithms have been adopted up to date. Most of the algorithms were either developed in the stochastic framework or aimed at minimization of loss or regret functions. Asymptotic convergence of weight learning, vital for good output prediction, was seldom guaranteed for online applications. Since linear regression is the most fundamental component in machine learning, we focus on this model in this paper. Aiming at online applications, a deterministic analysis method is developed based on LaSalle's invariance principle. Convergence conditions are derived for both the first-order and the second-order learning algorithms, without resorting to any stochastic argument. Moreover, the deterministic approach makes it easy to analyze the noise influence. Specifically, adaptive hyperparameters are derived in this framework and their tuning rules disclosed for the compensation of measurement noise. Comparison with four most popular algorithms validates that this approach has a higher learning capability and is quite promising in enhancing the weight learning performance.

Exploration of novel solitary waves in presence of higher order polynomial nonlinearity and spatio-temporal dispersion via itô calculus

This study investigates highly stochastic solitary waves inside the structure of nonlinear Schrödinger equation of eighth-order, characterized by heightened polynomial nonlinearity and influenced by multiplicative white noise modeled in the Itô sense. To discern the influence of white noise on the governing model, the bright and dark soliton profiles are generated by using the Sardar sub-equation and G′/(bG′+G+a)-expansion methods. Our approach yields diverse solutions, encompassing singular solitary waves, breather, periodic, and dark–bright solitons. Visual representations through 3D and 2D graphs with relevant parameter values enhance the comprehension of results. The distinctive nature of our methods, untested in this context, results in novel soliton solutions, underscoring their efficacy. This research not only deepens understanding in soliton wave theory but also demonstrates its practical relevance for solving nonlinear complexities in engineering and scientific fields.

Intuitionistic Fuzzy SVM based on Kernel Gray Relational Analysis

Fuzzy Support Vector Machine (FSVM) is a machine learning algorithm that combines fuzzy logic with Support Vector Machine (SVM) to deal with the uncertainty and fuzziness in classification and regression problems. This algorithm improves the performance of traditional SVM by introducing fuzzy membership degrees, making it more robust when handling datasets with noise or uncertainty. Although the existing FSVM algorithms can overcome the influence of noise to a certain extent, they cannot effectively distinguish outliers or abnormal values from boundary support vectors. To solve this problem, this study proposes an Intuitionistic Fuzzy Support Vector Machine algorithm (KGRA-IFSVM) based on Kernel Grey Relational Analysis (KGRA). This approach utilizes gray relational analysis in the kernel space to calculate the gray relational degree between each sample and its K isomorphic neighboring points, and takes the average value as the membership degree of the sample. Then, the same approach is used to compute the gray relational degree between each sample and its K heterogeneous neighboring points, and the average value is taken as the non-membership degree of the sample. Finally, each sample is assigned with an appropriate fuzzy value based on intuitionistic fuzzy sets using a specific scoring function. Test results on UCI datasets show that KGRA-IFSVM has better classification performance and stronger noise resistance.

Wave-induced motion prediction of a deepwater floating offshore wind turbine platform based on Bi-LSTM

The prediction of platform motions induced by irregular waves is of paramount importance for the safe monitoring and anticipatory control of a floating offshore wind turbine (FOWT) installed in deep sea areas. Within this study, a neural network method utilizing the Bi-directional Long Short Term Memory (Bi-LSTM) model is introduced to predict three representative motions of the OO-Star semi-submersible platform, designed for 10 MW wind turbines, with a focus on model hyperparameter optimization and the influence of noise. The neural network considers the high characteristic correlation between the wave conditions as input and platform motions as output to construct the Bi-LSTM model. Through the comparative analysis, the neurons, layers and optimizers were optimized to confirm the best configuration for the Bi-LSTM model structure. To verify the feasibility of the Bi-LSTM model, the platform motions under the wave configurations that are different from those used in training and validation are tested. With the help of the neural network, reliable dynamic responses up to 10 s can be predicted with a high determination coefficient of over 90% based on 30s-long historical motion data and the future wave kinematics that can be obtained by ocean data measurement devices. Furthermore, the model still maintains a reasonable accuracy even with the high noisy disturbance, indicating that the proposed model's ability to filter uncertainty factors. This research is anticipated to support to the development of structural condition monitoring and anticipatory control of floating offshore wind turbines.

Generalized reassigning transform: Algorithm and applications

Time-frequency analysis (TFA) has attracted the attention of many scholars and engineers for analyzing nonstationary signals in the field of condition monitoring of rotating machinery. For complex mechanical equipment, measured signals always contain both harmonic and impulsive components, which presents a challenge for current TFA methods. To concurrently characterize harmonic and impulsive components, a novel TFA algorithm, called generalized reassigning transform (GRT) is developed in this paper. First, the time-frequency fusion extraction criterion (TFFEC), which includes time-frequency data fusion (TFDF) and time-frequency data extraction (TFDE), is designed to calculate time-frequency representation (TFR) from short-time Fourier transform (STFT) results under different window sizes, which improves time-frequency resolution and eliminates noise influence. Furthermore, the chirp rate (CR)-based postprocessing strategy is constructed to characterize nonstationary signals with both harmonic-like and impulsive-like components. Specifically, the CR discrimination criterion is introduced to classify the TFFEC result into two distinct types: harmonic-like component and impulsive-like component, and then, the TFR with high energy concentration and strong readability is obtained by advanced postprocessing TFA including the synchro-reassigning transform (SRT) and horizontal reassigning transform (HRT). The effectiveness of the GRT in condition monitoring and fault diagnosis is validated through numerical and experiment verification.

Exploration of the normalized intradopant transition energy of GaAs quantum dot under the influence of noise

In this work the intradopant transition energy (IDTE) and the normalized intradopant transition energy (NIDTE) of GaAs quantum dot (QD) have been minutely explored. The QD contains Gaussian impurity as dopant. Gaussian white noise, applied via two different routes (additive and multiplicative), also becomes part of the QD confinement potential. Energy values are calculated using linear variational theory. The IDTE and NIDTE exhibit (depending on presence/absence of noise, mode of entrance of noise and the given physical parameter undergoing change) steady growth, steady fall, maximization, minimization and saturation. Throughout the entire study, the NIDTE plots efficiently highlight some weak features which IDTE plots fail to do and clearly announce their superiority over the IDTE plots in realizing the influences of various physical parameters. The findings of the study are expected to be quite relevant in elucidating the optical properties of low-dimensional nanostructures containing impurity and under the sway of noise.

Pressure Control of Multi-Mode Variable Structure Electro-Hydraulic Load Simulation System.

During the loading process, significant external position disturbances occur in the electro-hydraulic load simulation system. To address these position disturbances and effectively mitigate the impact of uncertainty on system performance, this paper first treats model parameter uncertainty and external disturbances as lumped disturbances. The various states of the servo valve and the pressures within the hydraulic cylinder chambers are then examined. Building on this foundation, the paper proposes a nonlinear multi-mode variable structure independent load port electro-hydraulic load simulation system that is tailored for specific loading conditions. Secondly, in light of the significant motion disturbances present, this paper proposes an integral sliding mode active disturbance rejection composite control strategy that is based on fixed-time convergence. Based on the structure of the active disturbance rejection control framework, the fixed-time integral sliding mode and active disturbance rejection control algorithms are integrated. An extended state observer is designed to accurately estimate the lumped disturbance, effectively compensating for it to achieve precise loading of the independent load port electro-hydraulic load simulation system. The stability of the designed controller is also demonstrated. The results of the simulation research indicate that when the command input is a step signal, the pressure control accuracy under the composite control strategy is 99.94%, 99.86%, and 99.76% for disturbance frequencies of 1 Hz, 3 Hz, and 5 Hz, respectively. Conversely, when the command input is a sinusoidal signal, the pressure control accuracy remains high, measuring 99.94%, 99.8%, and 99.6% under the same disturbance frequencies. Furthermore, the simulation demonstrates that the influence of sensor random noise on the system remains within acceptable limits, highlighting the effective filtering capabilities of the extended state observer. This research establishes a solid foundation for the collaborative control of load ports and the engineering application of the independent load port electro-hydraulic load simulation system.

Sub-Hz fundamental, sub-kHz integral linewidth self-injection locked 780 nm hybrid integrated laser

Today’s precision experiments for timekeeping, inertial sensing, and fundamental science place strict requirements on the spectral distribution of laser frequency noise. Rubidium-based experiments utilize table-top 780 nm laser systems for high-performance clocks, gravity sensors, and quantum gates. Wafer-scale integration of these lasers is critical for enabling systems-on-chip. Despite progress towards chip-scale 780 nm ultra-narrow linewidth lasers, achieving sub-Hz fundamental linewidth and sub-kHz integral linewidth has remained elusive. Here we report a hybrid integrated 780 nm self-injection locked laser with 0.74 Hz fundamental and 864 Hz integral linewidths and thermorefractive-noise-limited 100 Hz2/Hz at 10 kHz. These linewidths are over an order of magnitude lower than previous photonic-integrated 780 nm implementations. The laser consists of a Fabry-Pérot diode edge-coupled to an on-chip splitter and a tunable 90 million Q resonator realized in the CMOS foundry-compatible silicon nitride platform. We achieve 2 mW output power, 36 dB side mode suppression ratio, and a 2.5 GHz mode-hop-free tuning range. To demonstrate the potential for quantum atomic applications, we analyze the laser noise influence on sensitivity limits for atomic clocks, quantum gates, and atom interferometer gravimeters. This technology can be translated to other atomic wavelengths, enabling compact, ultra-low noise lasers for quantum sensing, computing, and metrology.

Sound-Based Unsupervised Fault Diagnosis of Industrial Equipment Considering Environmental Noise.

The influence of environmental noise is generally excluded during research on machine fault diagnosis using acoustic signals. This study proposes a fault diagnosis method using a variational autoencoder (VAE) and domain adaptation neural network (DANN), both of which are based on unsupervised learning, to address this problem. The proposed method minimizes the impact of environmental noise and maintains the fault diagnosis performance in altered environments. The fault diagnosis algorithm was implemented using acoustic signals containing noise, present in the malfunctioning industrial machine investigation and inspection open dataset, and the fault prediction performance in noisy environments was examined based on forklift acoustic data using the VAE and DANN. The VAE primarily learns from normal state acoustic data and determines the occurrence of faults based on reconstruction error. To achieve this, statistical features of Mel frequency cepstral coefficients were extracted, generating features applicable regardless of signal length. Additionally, features were enhanced by applying noise reduction techniques via magnitude spectral subtraction and feature optimization, reflecting the characteristics of rotating equipment. Furthermore, data were augmented using generative adversarial networks to prevent overfitting. Given that the forklift acoustic data possess time-series characteristics, the exponentially weighted moving average was determined to quantitatively track time-series changes and identify early signs of faults. The VAE defined the reconstruction error as the fault index, diagnosing the fault states and demonstrating excellent performance using time-series data. However, the fault diagnosis performance of the VAE tended to decrease in noisy environments. Moreover, applying DANN for fault diagnosis significantly improved diagnostic performance in noisy environments by overcoming environmental differences between the source and target domains. In particular, by adapting the model learned in the source domain to the target domain and considering the domain differences based on signal-to-noise ratio, high diagnostic accuracy was maintained regardless of the noise levels. The DANN evaluated interdomain similarity using cosine similarity, enabling the accurate classification of fault states in the target domain. Ultimately, the combination of the VAE and DANN techniques enabled effective fault diagnosis even in noisy environments.

Multi-layer controlled remote implementation of partially unknown single-qudit operations

Our concern is to investigate controlled remote implementation of partially unknown operations with multiple layers. We first propose a scheme to realize the remote implementation of single-qubit operations belonging to the restricted sets. Then, the proposed scheme is extended to the case of single-qudit operations. As long as the controller and the higher-layer senders consent, the receiver can restore the desired state remotely operated by the sender. It is worth mentioning that the recovery operation is deduced by general formulas which clearly reveal the relationship with the measurement outcomes. For the sake of clarity, two specific examples with two levels are given respectively. In addition, we discuss the influence of amplitude-damping noise and utilize weak measurement and measurement reversal to effectively resist noise.

Research and Evaluation of a New Structural Damage Identification Method Based on a Refined Genetic Algorithm

In order to solve the problem of structural damage location and degree identification, the weighted mean of vectors algorithm (INFO), a high-performance optimization algorithm, was first introduced to identify structural damage. By comparison with the refined genetic algorithm (RGA), the accuracy and advantages of INFO are analyzed and evaluated. An objective function is constructed by combining the dynamic response transfer ratio without modal analysis. The INFO and RGA algorithms are used to optimize the objective function for damage identification. The simulation results verify the effectiveness of the three damage identification methods. The results show that the identification effect of the INFO algorithm can reach nearly 100% without noise influence, and the anti-noise ability is the strongest. Among the three algorithms, the damage identification accuracy of the INFO algorithm is the highest, followed by the RGA algorithm and the GA algorithm.

Linear prediction coefficients correction method for digital speech processing systems with data compression based on the autoregressive model of a voice signal

The problem of distortion of the autoregressive model of the voice signal under the influence of additive background noise in digital speech processing systems with data compression based on linear prediction is considered. In the frequency domain, these distortions are observed in the weakening of the main formants responsible for the intelligibility of the speaker’s speech. To compensate for formant attenuation, it is proposed to modify the parameters of the autoregressive model (linear prediction coefficients) using the impulse response of a recursive shaping filter. Along with the amplitude amplification of the formants, their frequencies remain unchanged to make the speaker’s voice recognizable. The effectiveness of the method was studied experimentally using specially developed software. Based on the experimental results, conclusions were drawn about a significant increase in the relative level of formants in the power spectrum of the corrected voice signal.

Sensitivity improvement of the closed-loop resonant fiber-optic gyroscope interrogated with a broadband source

The resonant fiber-optic gyroscope (RFOG), interrogated with a broadband light source, represents an intriguing optical gyroscope that has been proposed as an extension of the traditional RFOG, which is driven by a narrow linewidth laser source. The implementation of a closed-loop detection system enhances the linearity and dynamic range of the gyroscope output. This paper is devoted to improving the sensitivity of the closed-loop broadband source-driven RFOG. First, a model for analysis and optimization of the feedback loop gain of a closed-loop broadband source-driven resonant fiber-optic gyroscope (RFOG) is developed, and the feedback loop gain is optimized. The experimental results indicate that the influence of the feedback loop noise on the gyro performance can be neglected. Subsequently, the noise contributions, including the shot noise, detector noise, and relative intensity noise, in the broadband source-driven RFOG system are investigated. A theoretical derivation and experimental verification of an expression for the reduction of the relative intensity noise by a fiber-optic ring resonator are presented. Finally, a packaged broadband source-driven RFOG prototype using a 175 m long fiber coil with an average diameter of 5.7 cm was constructed and evaluated. The experimental results demonstrate that the prototype achieves navigation-grade precision with an angular random walk of 0.003∘/h1/2 and a bias instability of 0.005°/h.

Event-Driven Maximum Correntropy Filter Based on Cauchy Kernel for Spatial Orientation Using Gyros/Star Sensor Integration.

Gyros/star sensor integration provides a potential method to obtain high-accuracy spatial orientation for turntable structures. However, it is subjected to the problem of accuracy loss when the measurement noises become non-Gaussian due to the complex spatial environment. This paper presents an event-driven maximum correntropy filter based on Cauchy kernel to handle the above problem. In this method, a direct installation mode of gyros/star sensor integration is established and the associated mathematical model is derived to improve the turntable's control stability. Based on this, a Cauchy kernel-based maximum correntropy filter is developed to curb the influence of non-Gaussian measurement noise for enhancing the gyros/star sensor integration's robustness. Subsequently, an event-driven mechanism is constructed based on the filter's innovation information for further reducing the unnecessary computational cost to optimize the real-time performance. The effectiveness of the proposed method has been validated by simulations of the gyros/star sensor integration for spatial orientation. This shows that the proposed filtering methodology not only has strong robustness to deal with the influence of non-Gaussian measurement noise but can also achieve superior real-time spatial applications with a small computational cost, leading to enhanced performance for the turntable's spatial orientation using gyros/star sensor integration.

Enhanced Machine Learning Framework for Autonomous Depression Detection Using Modwave Cepstral Fusion and Stochastic Embedding

Depression is a prevalent mental illness that requires autonomous detection systems due to its complexity. Existing machine learning techniques face challenges such as background noise sensitivity, slow adaptation speed, and imbalanced data. To address these limitations, this study proposes a novel ModWave Cepstral Fusion and Stochastic Embedding Framework for depression prediction. Then, the Gain Modulated Wavelet Technique removes background noise and normalises audio signals. Difficulties with generalisation, which results in a lack of interpretability, hinder extracting relevant characteristics from speech. To address these issues, an Auto Cepstral Fusion extracts relevant features from speech, capturing temporal and spectral characteristics caused by background voice. Feature selection becomes imperative when choosing relevant features for classification. Selecting irrelevant features can result in overfitting, the curse of dimensionality, and less robustness to noise. Hence, the Principal Stochastic Embedding technique handles high-dimensional data, minimising noise influence and dimensionality. Furthermore, the XGBoost classifier differentiates between depressed and non-depressed individuals. As a result, the proposed method uses the DAIC-WOZ dataset from USC for detecting depressions, achieving an accuracy of 97.02%, precision of 97.02%, recall of 97.02%, F1-score of 97.02%, RMSE of 2.00, and MAE of 0.9, making it a promising tool for autonomous depression detection.

Variational Type Graph Autoencoder For Denoising On Event Recommendation

Recommendations for events play a pivotal role in facilitating the discovery of upcoming intriguing events within Event-Based Social Networks (EBSNs). Previous research has established the crucial significance of mining contextual features and implicit relationships to enhance recommendation performance and alleviate data sparsity issues. However, the noise inherent in contextual features exacerbates data sparsity and hampers the ability of previous methods to explore implicit relationships for mitigating data sparsity. To address this challenge, we propose a variational type graph autoencoder model that attenuates the influence of noise in different types of context features by introducing type-specific latent variables. Firstly, we introduce a heterogeneous denoising convolution module composed of two components: 1) Denoising attention aggregation is proposed to mitigate the influence of noisy structures and uncover implicit relationships. 2) A heterogeneous normalization module leverages context features within the same type to alleviate the effects of noise in context features and data sparsity. Furthermore, we propose a learnable heterogeneous mixture prior that assists in assigning different priors to distinct types of latent variables, effectively modeling different types of contextual features. Through comprehensive experiments conducted on real-world datasets, we demonstrate the compelling performance of our model compared to state-of-the-art competitive approaches.

Π-shaped pixel pattern for shift multiplexing surface holograms in holographic data storage

In this study, a method for shift multiplexing surface holograms was proposed for data storage systems. Shift multiplexing of surface holograms is inhibited by noise specific to Raman–Nath diffraction. We proposed a model to analyze the influence of noise. Subsequently, we proposed a method for noise suppression to arrange signal and reference pixels in a shape similar to the Greek letter “Π.” The proposed method is generally applicable to thin holograms in which Raman–Nath diffraction occurs. The effectiveness of noise suppression using the proposed method was experimentally demonstrated. We theoretically estimated that a shift multiplexing surface holographic data storage system using the proposed method could achieve a significantly higher data transfer rate than conventional optical discs while maintaining a comparable storage density.