Impact Of Environmental Noise Research Articles

Time delay reservoir computing with a silicon microring resonator and a fiber-based optical feedback loop

Silicon microring resonators serve as critical components in integrated photonic neural network implementations, owing to their compact footprint, compatibility with CMOS technology, and passive nonlinear dynamics. Recent advancements have leveraged their filtering properties as weighting functions, and their nonlinear dynamics as activation functions with spiking capabilities. In this work, we investigate experimentally the linear and nonlinear dynamics of microring resonators for time delay reservoir computing, by introducing an external optical feedback loop. After effectively mitigating the impact of environmental noise on the fiber-based feedback phase dependencies, we evaluate the computational capacity of this system by assessing its performance across various benchmark tasks at a bit rate of few Mbps. We show that the additional memory provided by the optical feedback is necessary to achieve error-free operation in delayed-boolean tasks that require up to 3 bits of memory. In this case the microring was operated in the linear regime and the photodetection was the nonlinear activation function. We also show that the Santa Fe and Mackey Glass prediction tasks are solved when the microring nonlinearities are activated. Notably, our study reveals competitive outcomes even when employing only 7 virtual nodes within our photonic reservoir. Our findings illustrate the silicon microring’s versatile performance in the presence of optical feedback, highlighting its ability to be tailored for various computing applications.

Denoised single-pixel imaging in a Fourier acquisition mode.

The degradation of imaging quality caused by environmental noise during Fourier single pixel imaging (FSPI) is a big problem. In this paper, we propose simple and efficient denoised single-pixel imaging schemes by using linear filters to reduce the impact. Two filters, such as an average filter and Gaussian filter, are employed, and their corresponding schemes are named SCH-A and SCH-G, respectively. The experimental and simulation results show that both schemes can effectively reduce the impact of environmental noise and have greater robustness in comparison with those using the deringing SPI and conventional SPI. Compared with SCH-A, the reconstructed image by SCH-G keeps more details and edges. With the increase of template size, SCH-A and SCH-G have better filtering effects with the same variance. Meanwhile, the larger the template size is, the better filtering effect SCH-A has. This conclusion is also applicable to SCH-G under the same variance.

Risk-Aware Continuous Control with Neural Contextual Bandits

Recent advances in learning techniques have garnered attention for their applicability to a diverse range of real-world sequential decision-making problems. Yet, many practical applications have critical constraints for operation in real environments. Most learning solutions often neglect the risk of failing to meet these constraints, hindering their implementation in real-world contexts. In this paper, we propose a risk-aware decision-making framework for contextual bandit problems, accommodating constraints and continuous action spaces. Our approach employs an actor multi-critic architecture, with each critic characterizing the distribution of performance and constraint metrics. Our framework is designed to cater to various risk levels, effectively balancing constraint satisfaction against performance. To demonstrate the effectiveness of our approach, we first compare it against state-of-the-art baseline methods in a synthetic environment, highlighting the impact of intrinsic environmental noise across different risk configurations. Finally, we evaluate our framework in a real-world use case involving a 5G mobile network where only our approach satisfies consistently the system constraint (a signal processing reliability target) with a small performance toll (8.5% increase in power consumption).

Investigating the Relationship between Noise Exposure and Human Cognitive Performance: Attention, Stress, and Mental Workload Based on EEG Signals Using Power Spectrum Density

A pervasive environmental stressor is one that damages mental and physical health as well as cognitive abilities by producing noise at a specific frequency and level. Current noise pollution levels pose a significant threat to public health, potentially leading to impaired cognitive function, increased stress, and other negative health consequences. This study aims to investigate the relationship between noise exposure and human cognitive abilities using a comprehensive analysis of power spectrum density (PSD) derived from EEG signals. Twenty-four participants completed the experiment to identify the effect of exposure to different noise levels (55 dB, 65 dB, 70 dB, 75 dB, 80 dB, and 85 dB) and two types of continuous and intermittent noise. The Stroop Color–Word Test and the Emotive Epoch EEG are cognitive task instruments used during experiments. Behavioral performance (accuracy and response time) and power spectrum electroencephalographic density were collected and analyzed. The methodology involved collecting EEG data from participants exposed to controlled noise stimuli and a subsequent PSD analysis to uncover frequency-specific patterns associated with cognitive processes. Attention levels were measured by examining beta wave activity, while stress responses were evaluated through an alpha wave analysis. Additionally, mental workload was assessed by considering the overall distribution of PSD through the theta-to-alpha ratio. The results revealed a significant relationship between the exposure to noise types and levels and human cognitive ability. The analysis of the power spectrum density on the cognitive aspects of attention and stress yielded results indicating that participants were in the best attention condition and in a relaxed or unstressed state when exposed to noise levels of 65 dB in both continuous and intermittent noise types. For the mental workload aspect, participants exposed to both continuous and intermittent noise types at a noise level of 70 dB began to indicate the presence of mental workload. These findings supported the importance of considering the impact of environmental noise on human cognitive well-being and demonstrated the potential of EEG monitoring as an objective tool for assessing the impact of noise on cognitive performance.

Application of voice enhancement based on virtual sensor network in enterprise financial management

Based on virtual sensor network, this paper explores the application of voice enhancement technology in enterprise financial management. Virtual sensor network is a low cost, easy to expand and flexible network system based on simulation and simulation technology. In this network system, the virtual voice sensor node can collect and process the voice information inside the enterprise. Speech enhancement technology uses signal processing and machine learning algorithms to reduce noise, enhance and clarify speech signals, thereby improving the accuracy and reliability of speech data. By constructing virtual voice sensor nodes in virtual sensor networks and applying voice enhancement technology, the research results show that enterprises can collect and process voice data more accurately in the process of financial management. Voice enhancement technology can reduce the impact of environmental noise on financial data and improve the clarity of voice signals, so as to effectively improve the quality of data. Through the voice enhancement technology based on virtual sensor network, high-quality voice data acquisition and processing can be realized in the financial management of enterprises, improve the accuracy and effect of financial decision-making, and reduce the cost and complexity of enterprises.

The impact of environmental noise on drivers' cognitive abilities: A case study on in-vehicle voice interaction interfaces

To investigate the impact of environmental noise on the cognitive abilities of drivers, this study, using in-vehicle voice interaction as an example, conducted laboratory experiments to assess the effects of road traffic noise, entertainment noise, and white noise stimuli on drivers' attention and short-term memory. The noise levels simulated to mimic acoustic conditions during car driving ranged from 35 dB(A) to 65 dB(A). The conclusions drawn were as follows: (1) Noise levels directly influenced subjective annoyance levels, with annoyance linearly increasing as noise levels escalated; (2) Both attention and short-term memory task reaction times of drivers were significantly influenced by noise types. Compared to traffic noise and white noise, drivers' cognitive efficiency was lower under entertainment noise. (3) Performance in complex cognitive tasks was more susceptible to noise levels compared to simple cognitive tasks; (4) Experimentally, it was found that drivers exhibited the highest cognitive efficiency in cognitive tasks when the environmental noise level was 55 dB(A), as opposed to noise levels of 35 dB(A), 45 dB(A), and 65 dB(A).

Low intensity beam current measurement of the Associated Proton Beam Line at CSNS

The Associated Proton beam Experiment Platform (APEP) beamline is the first proton irradiation facility to use naturally-stripped protons which come from H-beams interacting with the residual gas in the linac beampipe at CSNS. The stripped beam current, which is in the order of 0.1% of the original H-beam and approximately 10 μA, should be measured precisely to provide the proton number for irradiation experiments. Therefore, a low-intensity beam current measurement system was developed with considerations to eliminate the external interferences. An anti-interference design is adopted in this system with an elaboration of probes, cables and electronic low-noise technology to minimize the impact of environmental noise and interferences. This improves the signal-to-noise ratio and enables a more precise measurement of the microampere level pulsed beam current. The system was installed and tested during the summer maintenance in 2021 and 2022. It shows a good agreement with the measurement of the Faraday cup.

Stochastic analysis of survival and sensitivity in a competition model influenced by toxins under a fluctuating environment

<abstract><p>This paper proposed a stochastic toxin-dependent competition model to investigate the impact of environmental noise on species interaction dynamics. First, a survival analysis was conducted to establish the sufficient conditions for population extinction and persistence. Second, we proved the existence of a unique ergodic stationary distribution. Finally, the spatial arrangement of random states near the deterministic attractor was investigated using the stochastic sensitivity functions technique. This analytical approach facilitates constructing confidence ellipses and estimating critical noise intensity corresponding to the onset of transition. Both theoretical and numerical findings demonstrated that significant levels of noise experienced by one species lead to its extinction while promoting persistence in its competitor; conversely, negligible levels of noise did not alter the original competition outcomes in the deterministic model. However, when both species encounter moderate levels of noise, various modifications can occur in competition outcomes. These findings have significant implications for preserving ecosystem diversity.</p></abstract>

Reduction of Environmental Noise Impact on Preterm Babies in NICU by Using Ear Plugs

Objective − To determine if using ear plugs reduce the impact of noise on preterm babies admitted to neonatal intensive care units in terms of changes in their vital parameters and their discomfort level, assessed using the Neonatal Infant Pain Scale (NIPS) score.Materials and Methods − This is an interventional study conducted in a tertiary level neonatal intensive care unit (NICU) (Bangalore, India) over a period of 3 months, ranging from June 2022 to September 2022. This study included 20 preterm babies with different gestational ages and birth weights who were admitted to the NICU during that period. The babies’ response to noise, in terms of changes in heart rate, respiratory rate, blood pressure, oxygen saturation and discomfort level, were assessed with ear plugs and without ear plugs on alternate days and the results were recorded. Descriptive statistics were used for statistical analysis. Results − On days 1 and 3, mean heart rate and systolic blood pressure were lower in babies wearing ear plugs (141.55±2.42 SD and 60.1±11.58, respectively) than in the same babies without ear plugs (157.55±2.52 SD and 61.85±1.6 SD, respectively) on days 2 and 4. The mean oxygen saturation was higher in babies wearing ear plugs (96.7%±0.80 SD) than in the same babies without the intervention (93.75%±1.29 SD). The mean NIPS score was lower with the intervention (2.05±0.22) than without ear plugs (5.05±0.22 SD).Conclusion – Ear plugs were an effective alternative in situations where noise in the NICU cannot be consistently maintained at a lower level than the standard recommendation by the AAP of 45 decibels.

Cascaded detection method for surface defects of lead frame based on high-resolution detection images

In the field of semiconductor production and manufacturing, the detection of defects on lead frame surfaces is a vital process. This process plays a key role in ensuring the quality of the final product. Using high-resolution detection images to detect multi-scale tiny surface defects is necessary, but this amplifies the impact of environmental noise. Therefore, suppressing both the false negative rate and false positive rate in practical detection scenarios is a challenge that needs to be overcome. Current research on lead frame surface defect detection is mostly concentrated on the downloaded standard original images, which limits its application in actual production lines. This paper presents a cascaded detection method for surface defects of lead frame based on high-resolution detection images. Firstly, this study presents the unit cell extraction module to convert the detection object from high-resolution image to hundreds of unit cells. The proposed module can handle real-time detection images in the production pipeline, especially addressing situations such as lighting imbalances and tilted detection images. Subsequently, this study proposes a lead frame surface defect detection network (LDD-net), which takes unit cells as inputs and can effectively detect multi-scale defects. Compared to other models, LDD-net can effectively capture the features of subtle defects. Additionally, this paper introduces the deviation in the central width direction into the CIoU localization loss, enhancing the accuracy of defect localization in LDD-net. The data set is constructed using the machine vision detection system and conducts training and testing. Specifically, experiments of LDD-net on the data set obtained 85.01% mean average precision (mAP) and 37 ms of inference time, respectively. The detection accuracy exceeds 95%, and the false negative rate can be controlled below 6%. This approach will assist manual monitoring personnel in evaluating product quality.

Multi-Channel Time-Domain Boring-Vibration-Enhancement Method Using RNN Networks.

The larvae of certain wood-boring beetles typically inhabit the interior of trees and feed on the wood, leaving almost no external traces during the early stages of infestation. Acoustic techniques are commonly employed to detect the vibrations produced by these larvae while they feed on wood, significantly increasing detection efficiency compared to traditional methods. However, this method's accuracy is greatly affected by environmental noise interference. To address the impact of environmental noise, this paper introduces a signal separation system based on a multi-channel attention mechanism. The system utilizes multiple sensors to receive wood-boring vibration signals and employs the attention mechanism to adjust the weights of relevant channels. By utilizing beamforming techniques, the system successfully removes noise from the wood-boring vibration signals and separates the clean wood-boring vibration signals from the noisy ones. The data used in this study were collected from both field and laboratory environments, ensuring the authenticity of the dataset. Experimental results demonstrate that this system can efficiently separate the wood-boring vibration signals from the mixed noisy signals.

MITDCNN: A multi-modal input Transformer-based deep convolutional neural network for misfire signal detection in high-noise diesel engines

The accurate detection of faults in diesel engines is crucial for extending their operational lifespan, ensuring safety, and yielding significant economic and societal benefits. However, existing detection methodologies encounter difficulties in the presence of substantial environmental noise and lack generalizability across various operating conditions. Traditional fault diagnosis models also predominantly rely on single data detection schemes, such as one-dimensional data input for anomaly analysis, which may not account for the inherent correlations and distribution discrepancies within the same data source presented in different formats. To tackle these challenges, we proposed a novel multi-modal input Transformer-based convolutional neural network (MITDCNN) for misfire detection in diesel engines under significant environmental noise and diverse working conditions. We collected vibration signals from engine cylinder heads at different speeds and extracted both one-dimensional amplitude vector features and two-dimensional image properties. These features were input into the multi-modal feature extraction network and subsequently processed and identified by the cross-channel feature fusion detection network. Our fusion module incorporated a dual-dimension spatial and channel attention mechanism to suppress vibration noise interference effectively, so that the network is able to counteract the impact of environmental noise and varying operating conditions on the final detection outcomes. We validated the efficacy of the proposed approach through a curated dataset and benchmark comparisons with the established algorithms. Our results demonstrate that MITDCNN's accuracy and noise robustness are notably superior to other available methods. Across four datasets generated under distinct working conditions, our approach achieves a minimum accuracy of 99.008%, even with a signal-to-noise ratio of −4dB, greatly surpassing other methods.

INVESTIGATING THE ENVIRONMENTAL NOISE IMPACT OF MASS RAPID TRANSIT ON NEARBY COMMUNITIES AT BATU 11 CHERAS, MALAYSIA

Mass Rapid Transit (MRT) in Malaysia is designed to provide commuters with a comfortable journey that is highly functional and safe. Besides, it allows commuters to explore the city with ease and convenience. Due to the extension of the network throughout the years, the noise generated from MRT had increased and this environmental noise tends to annoy the adjacent community. This research aims to evaluate the environmental noise impact generated by MRT on the surrounding community at Batu 11 Cheras Station. A total of 4 monitoring points were selected for environmental noise impact assessment. After that, a sound level meter was set up at these monitoring points to measure the sound level and some parameters such as LAeq, Lmax, Lmin, L10, L50, and L90. For each monitoring point, the noise level was measured for 6 hours continuously from 7:00 am to 1:00 pm. Based on the results, the noise levels at critical monitoring points exceeded 2.71% of the permissible sound level in Batu 11 Cheras Station. While for the maximum sound level, all four-monitoring points A, B, C and D had exceeded 2.75%, 10.13%, 4.38%, and 7.13%, respectively. For community annoyance impact, little levels of environmental impact and subjectively perceived noticeable differences were found at all the monitoring points.

Evaluating the efficiency of primary health care institutions in China: an improved three-stage data envelopment analysis approach

BackgroundPrimary health care (PHC) institutions are key to realizing the main functions of the health care system. Since the new health care reform in 2009, the Chinese government has invested heavily in PHC institutions and launched favorable initiatives to improve the efficiency of such institutions. This study is designed to gauge the efficiency of PHC institutions by using 2012–2020 panel data covering 31 provinces in China.MethodsThis study applied an improved three-stage data envelopment analysis (DEA) model to evaluate the efficiency of PHC institutions in China. Unlike the traditional three-stage DEA model, the input-oriented global super-efficiency slack-based measurement (SBM) DEA model is used to calculate the efficiency in the first and third stages of the improved three-stage DEA model, which not only allows the effects of environmental factors and random noise to be taken into account but also deal with the problem of slack, super-efficiency and the comparability of interperiod efficiency values throughout the efficiency measurement.ResultsThe results show that the efficiency of PHC institutions has been overestimated due to the impact of external environmental factors and random noise. From 2012 to 2020, the efficiency of PHC institutions displayed a downward trend. Moreover, there are significant differences in the efficiency of PHC institutions between regions, with the lowest efficiency being found in the northeast region. The efficiency of PHC institutions is significantly affected by residents’ annual average income, per capita GDP, population density, the percentage of the population aged 0–14, the percentage of the population aged 65 and older, the number of people with a college education and above per 100,000 residents, and the proportion of the urban population.ConclusionsSubstantial investment in PHC institutions has not led to the expected efficiency gains. Therefore, more effective measures should be taken to improve the efficiency of PHC institutions in China based on local conditions. This study provides a new analytical approach to calculating the efficiency of PHC institutions, and this approach can be applied to efficiency evaluation either in other fields or in other countries.

A multi-sensor fault diagnosis method for rotating machinery based on improved fuzzy support fusion and self-normalized spatio-temporal network

Recently, the fault diagnosis of rotating machinery based on deep learning has achieved increasingly widespread applications. However, it is often difficult to achieve the expected results by relying on a single sensor due to the limited information obtained by the single sensor and the susceptibility to the influence of the additive noise. To address the above problems, this paper proposes a multi-sensor fusion fault diagnosis method for rotating machinery based on improved fuzzy support fusion and self-normalized spatio-temporal network to enhance feature learning while achieving multi-sensor data fusion. This method includes a data pre-processing module, a fusion module and a fault recognition module. In the first module, a complete ensemble empirical mode decomposition with adaptive noise algorithm is introduced to decompose and reconstruct the multi-source sensor signals, thereby reducing the impact of environmental noise on data quality. In the fusion module, a data fusion algorithm based on improved fuzzy support is designed to achieve the data-level fusion of multi-source sensors. By introducing the self-normalized properties into the convolutional structure with bi-directional gated recurrent unit, a self-normalized spatio-temporal network is designed in the fault recognition module to perform the fault diagnosis of rotating machinery. The experimental results show that the proposed method can achieve high quality data-level fusion and outperforms the state-of-the-art fault diagnosis methods in terms of fault classification.

ResNet Based on Multi-Feature Attention Mechanism for Sound Classification in Noisy Environments

Environmental noise affects people’s lives and poses challenges for urban sound classification. Traditional algorithms such as Mel frequency cepstral coefficients (MFCCs) struggle due to audio signal complexity. This study applied an attention mechanism to a deep residual network (ResNet) deep learning network to overcome the structural impact of urban noise on audio signals and improve classification accuracy. We propose a three-feature fusion ResNet + attention method (Net50_SE) to maximize information representation in environmental sound signals. This method uses residual structured convolutional neural networks (CNNs) for feature extraction in sound classification tasks. Additionally, an attention module is added to suppress environmental noise impact and focus on different feature map channels. The experimental results demonstrate the effectiveness of our method, achieving 93.2% accuracy compared with 82.87% with CNN and 84.77% with long short-term memory (LSTM). Our model provides higher accuracy and confidence in urban sound classification.

Advanced Noise Indicator Mapping Relying on a City Microphone Network.

In this work, a methodology is presented for city-wide road traffic noise indicator mapping. The need for direct access to traffic data is bypassed by relying on street categorization and a city microphone network. The starting point for the deterministic modeling is a previously developed but simplified dynamic traffic model, the latter necessary to predict statistical and dynamic noise indicators and to estimate the number of noise events. The sound propagation module combines aspects of the CNOSSOS and QSIDE models. In the next step, a machine learning technique-an artificial neural network in this work-is used to weigh the outcomes of the deterministic predictions of various traffic parameter scenarios (linked to street categories) to approach the measured indicators from the microphone network. Application to the city of Barcelona showed that the differences between predictions and measurements typically lie within 2-3 dB, which should be positioned relative to the 3 dB variation in street-side measurements when microphone positioning relative to the façade is not fixed. The number of events is predicted with 30% accuracy. Indicators can be predicted as averages over day, evening and night periods, but also at an hourly scale; shorter time periods do not seem to negatively affect modeling accuracy. The current methodology opens the way to include a broad set of noise indicators in city-wide environmental noise impact assessment.

Research on Transformer Voiceprint Feature Extraction Oriented to Complex Noise Environment

Transformer fault diagnosis based on acoustic characteristics is a new non-contact and non-destructive monitoring method. It has the advantages that the acoustic signal detection is not disturbed by electric and magnetic fields, and the monitoring process does not affect the normal operation of the transformer. Aiming at the difficulty of extracting transformer voiceprint features in complex noise environment, a transformer voiceprint feature extraction method based on Variable Mode Extraction (VME) is proposed. In this method, the center frequency of the Intrinsic Mode Function(IMF) is set according to the generation mechanism of transformer radiated noise, thus the uncertainty of decomposition results caused by random distribution and other frequency search methods is eliminated; Then, taking the frequency-domain energy aggregation of IMF and the minimum center frequency energy of residual signal as the optimization objectives, the cyclic iterative decomposition is used to identify and extract the transformer voiceprint features, so as to reduce the impact of environmental noise and other equipment noise. The analysis results of simulation signals and field signals show that this method can effectively reduce the impact of environmental noise and extract more clear and accurate transformer voiceprint features.

Advances in the Measurement and Human Response to Noise of Unmanned Aircraft Systems

The sound produced by Unmanned Aerial Systems (known as UAS or Drones) is often considered to be one of the main barriers (alongside privacy and safety concerns) preventing the widespread use of these vehicles in environments where they may be in close proximity to the general public. To better understand the potential environmental noise impact of commercial UAS operations, work undertaken by the University of Salford has focused on two key areas. Firstly, how to characterise and measure the sound produced by UAS during outdoor flight conditions and secondly, better understanding of the dose response of UAS noise when the listener is in either an indoor or outdoor environment. The paper describes a field measurement campaign undertaken to measure several UAS performing flyovers at different speeds and take-off weights. The methodology of the measurement campaign was strongly influenced by emerging guidance and has been used to calculate the directivity of sound propagation which may be of significant benefit when modelling environmental noise impacts. This paper also presents details of a listening experiment designed to investigate the subjective response to a number of UAS operations when the listener is simulated to be either in an indoor or outdoor position. The results of the listening experiment have been analysed using linear regression analysis to understand which 'loudness' metric either conventional (LAeq, LASmax or LAE) or more specialised loudness metrics such as Loudness (N5), Perceived Noise Level (PNL) or Effective Perceived Noise Level (EPNL) are most appropriate for estimating perceived 'loudness' and 'annoyance'. The results of this experiment indicate that both LAeq, LASmax were equally good at predicting the perceived loudness and annoyance with an Adjusted R Squared value of 0.90 and 0.93 for loudness and annoyance respectively. Loudness metric performed marginally better with adjusted R Squared values of 0.96 and 0.90 for annoyance and loudness respectively.

Construction and Simulation Analysis of A New Transformer Noise Prediction Model

In recent years, more and more attention has been paid to the environmental impact of substation operation noise. Therefore, a suitable noise prediction model is needed to evaluate the noise impact. The noise in the substation mainly comes from the main transformer. The predictions of the main transformer noise model in the existing technical specifications often do not correspond to the actual situation. Therefore, based on the theory of planar Nearfield Acoustical Holography (NAH) equivalent source approach, a new transformer noise prediction model called the main transformer noise model of the directional dot-matrix equivalent source is established. Using this model, a typical 500 kV substation is simulated in Cadna/A, and the results are compared with the simulation values and measured data of the existing standard main trans-former noise model. The comparison shows that the new prediction model can accurately predict the noise value of the substation, and the error is less than 2 dB(A).