Noise Problems Research Articles

Multi-position industrial defect inspection using self-training siamese networks with mix strategies

Structural defects account for a large proportion of defects, and acquiring large batches of high-quality labels is labor-intensive and time-consuming for industrial visual defect inspection tasks. This paper addresses the above problem by exploiting sufficient unlabeled samples, and aims to achieve superior model performance with some labeled data by using self-training methods that incorporate positional information. Specifically, this paper proposes a novel self-training architecture, MixSiam, which uses a Multi-Position-based Mix strategy (MPMix) and Siamese network structure for defect classification. Furthermore, considering the prediction noise problem in unlabeled data during training, we propose a progressive MPMix (MPMix+) strategy to reduce the negative impacts of noise on model training. Finally, we validate the effectiveness of our architecture on industrial datasets. For example, our method achieves 71.40% and 87.01% accuracy on the SMT (Surface Mounting Technology) dataset and MBH (Motor Brush Holder) dataset with only 100 labeled samples, which are 2.40% and 5.86% higher than the state-of-the-art FixMatch method, respectively. Compared with the supervised algorithm with 3,600 labels, our method achieves comparable accuracy on the SMT and MBH datasets, respectively, while saving 2/3 the amount of labeled data. In conclusion, MixSiam effectively utilizes unlabeled industrial data and improves model accuracy with fewer labeled samples, thus reducing the burden of data annotation in industrial production.

Robust multi-frequency band joint dictionary learning with low-rank representation

Emotional state recognition is an important part of emotional research. Compared to non-physiological signals, the electroencephalogram (EEG) signals can truly and objectively reflect a person’s emotional state. To explore the multi-frequency band emotional information and address the noise problem of EEG signals, this paper proposes a robust multi-frequency band joint dictionary learning with low-rank representation (RMBDLL). Based on the dictionary learning, the technologies of sparse and low-rank representation are jointly integrated to reveal the intrinsic connections and discriminative information of EEG multi-frequency band. RMBDLL consists of robust dictionary learning and intra-class/inter-class local constraint learning. In robust dictionary learning part, RMBDLL separates complex noise in EEG signals and establishes clean sub-dictionaries on each frequency band to improve the robustness of the model. In this case, different frequency data obtains the same encoding coefficients according to the consistency of emotional state recognition. In intra-class/inter-class local constraint learning part, RMBDLL introduces a regularization term composed of intra-class and inter-class local constraints, which are constructed from the local structural information of dictionary atoms, resulting in intra-class similarity and inter-class difference of EEG multi-frequency bands. The effectiveness of RMBDLL is verified on the SEED dataset with different noises. The experimental results show that the RMBDLL algorithm can maintain the discriminative local structure in the training samples and achieve good recognition performance on noisy EEG emotion datasets.

High fidelity Not gate on superconduting phase qubit

NOT gate is one of the basic units in quantum computing. While it suffers from the noise during the complex operation. In this paper, we simulate the NOT gate on the superconducting phase qubit and solve the noise problem by maximizing the impact of noise cancellation. We first extract the noise term from the Hamiltonian and reset it to zero. Then we give two kinds of pulses based on the non-noise term for the quantum gate. By using these pulses, the fidelity would be improved and benefit quantum computing.

Comparative Analysis of Transmission Housings to Evaluate Whining Noise Performance

A quite cabin is always a preferred choice of the customer. Powertrain acts as a source for many noise and vibration problems in a passenger car. Transmission plays a major role in modulating the power from engine to the drive train and is exposed to a high vibration and noise environments. Hence design of transmission for low vibration & noise is need of the hour. This paper attempts to perform a comparative study of 6 housings of different design of a two-wheel drive passenger car to evaluate the vibration performance and Noise radiation characteristics by using FEM (Finite Element Method) & BEM (Boundary Element Method) & finite element analysis techniques. The authors also try to put forward a layout comparison study for the housings and conclude what design is best suited for better NVH whine performance in view of vibrations & sound radiation. These indicative results from this paper can be referred by the design & analysis engineers during early product development of the transmission.
 Keywords: Transmission, Housings, Whining Noise, Bionic Design, NVH Analysis, Durability Analysis, Study of Housings, Housings NVH Analysis

Acoustic and thermal properties of panels made of fruit stones waste with coconut fibre

Using natural porous and fibrous materials for acoustic and thermal conditioning in the construction sector is a solution to achieve a comfort level in buildings. The novelty of this research work lies in employing agricultural and agro-industry waste to solve noise and thermal insulation problems. This study was conducted to investigate a multilayered panel made of fruit stones and coconut fibre from agro-industry waste. Acoustic absorption, and acoustic and thermal transmission, tests were run on four different fruit stone types, olive, cherry, apricot and peach, plus a coconut fibre layer. Although they are not structural elements, bending tests were run to ensure panels’ structural integrity. Acoustic tests complied with the recommendations of Standards ISO 10534–2 and ASTM E 2611–09, based on the standing wave tube to obtain the acoustic absorption coefficient and sound transmission loss, respectively. Standard EN 12667 was considered for the thermal test to determine the thermal conductivity coefficient based on the hot plate method. The results show an average sound absorption coefficient above 0.5 and sound insulation over 25 dB within certain frequency ranges. The cherry panels had absorption values of around 0.7 and apricots had sound insulation values of up to 30 dB. Samples’ thermal transmittance coefficient values were between 3.0 and 3.4 (W/m2K), which indicate panels’ ability to provide thermal insulation. The cherry and olive panels obtained the lowest thermal conductivity (around 0.14 W/mK). The backing effect of the coconut fibre layer improves all the panels’ acoustic absorption and thermal properties.

Research on the model of staggered tooth phase to reduce vibration of single-stage gear transmission system: Theoretical analysis and experiments.

Aiming at the problems of high vibration and high noise in gear transmission systems, a model of gear with staggered tooth phase structure(GSTPS) for reducing vibration is proposed. Without changing the overall structure of the gear transmission system, the purpose of reducing mesh stiffness fluctuations is achieved by staggering adjacent gears at a certain angle along the axis, thereby the vibration of the gear transmission system could be reduced. The characterization method of time-varying mesh stiffness of the GSTPS is studied. Then, the impact of different staggered tooth phases(STP) on reducing vibration of the transmission system are researched, and the basis for selecting the optimal STP are obtained. The experimental platform for reducing vibration with STP is established. And some experimental studies were conducted to validate the theoretical model.

LED-based temporal variant noise model for Fourier ptychographic microscopy

Fourier ptychographic microscopy (FPM) is a technique to reconstruct a high-resolution image from a set of low-resolution images captured with different illumination angles, which is susceptible to ambient noise, system noise, and weak currents when acquiring large-angle images, especially dark field images. To effectively address the noise problem, we propose an adaptive denoising algorithm based on a LED-based temporal variant noise model. Taking the results of blank slide samples as the reference value of noise, and analyzing the distribution of noise, we establish a statistical model for temporal variant noise, describing the relationship between temporal noise and LED spatial location. Based on this model, Gaussian denoising parameters are selected to adaptively denoise the images with different locations, with which high-resolution images can be reconstructed. Compared with other methods, the experimental results show that the proposed method effectively suppresses the noise, recovers more image details, increases the image contrast, and obtains better visual effects. Meanwhile, better objective evaluation also mirrors the advantages of the proposed algorithms.

An unsupervised low-light image enhancement method for improving V-SLAM localization in uneven low-light construction sites

Construction robots have been increasingly adopted in construction projects to improve productivity, reduce risk, and speed up work cycles. Visual Simultaneous Localization and Mapping (V-SLAM) technology is widely used in construction robots due to its lightweight weight, cost-effectiveness, and provision of semantic information. On real construction sites, lighting conditions are often challenging due to factors such as uneven lighting intensity, inadequate exposure, or robots in backlit positions (e.g. roughcast houses without artificial lighting, underground car parks), which cause images captured under these conditions to exhibit low signal-to-noise ratio, uneven lighting, color distortion, noise, and other problems. These challenges make the extraction and matching of feature points in V-SLAM difficult, resulting in significant positioning errors or the inability to generate positioning trajectories for construction robotics. Although existing methods for enhancing low-light images can certainly improve image brightness, they still cannot effectively address the localization challenges brought about by low-light construction scenes with uneven illumination and noise. To address the interference of uneven and changing lighting conditions on V-SLAM localization in construction sites, this paper proposes the Unsupervised Reflectance Retinex and Noise model (URRN-Net) to enhance low-light construction images. By using a CNN model based on the Retinex theory to decompose the illumination characteristics of images, we achieve effective brightness restoration of uneven low-light images by utilizing unsupervised loss functions. URRE-Net can reduce the root mean square localization error by >65% compared to low-light images in the ORB-SLAM3 method, and the maximum error is reduced by >71% in on-site experiments. The proposed URRE-Net can be integrated with existing V-SLAM algorithms to provide more robust localization services for low-light construction site applications such as building operations (e.g., interior wall spraying robotic) or construction management tasks (e.g., automatic tunnel inspection).

Drag reduction of locomotive and practical scene

As an important tool for peoples long-distance travel and transportation, the design accuracy of the high-speed railway must be strictly controlled. In addition, the train design needs to conform to aerodynamics, such as improving the linearity of the train flow to ensure that the gas flows smoothly through every part of the train, or reducing the front pressure to reduce the wind resistance, so as to reduce the problems of train instability, high noise and high energy consumption. This paper will analyse the design concept, flow linearity and how to reduce the resistance of locomotives under high-speed motion of Shinkansen trains in Japan. Because the Shinkansen trains developed in the early and later generations are mainly flat-headed, the study of Shinkansen can understand the significance of flat-headed trains. The practicability and manoeuvrability of ' flat head and 'bullet head' locomotives and the application of locomotives in real life are analysed in detail. Finally, it is concluded that the bullet train has a large turning angle and needs to open up a special road to run smoothly, but the bullet train is faster than the flat head. The flat-head train is highly practical and suitable for driving in the city, and another advantage of the flat-head train is that it can still maintain high stability and safety when carrying many passengers.

The effect of layout placement on acoustic conditions in call centers: Evaluations within the scope of an example

This article aimed to provide auditory comfort conditions in call centers where the noise problem is very high and its use is increasing day by day. Since there are no partition walls in such places, noises from conversations, telephones and other office equipment, footsteps, air conditioning and lighting, office equipment (fax machines, printers, etc.), and noise from outside can reduce working efficiency and sometimes cause serious health problems. To assess this situation, the current noise levels situation in a call center were revealed with the help of observation, survey, measurement, and simulation. The number of personnel in the volume, different layout models, surface absorption, and obstacle heights were analyzed through the simulation program and appropriate call center models were obtained in terms of acoustic comfort. By taking the models as a reference, noise level maps have been prepared according to different frequency spectrums. When the noise maps are evaluated, while the call center noise level is 60 dBA in the current situation, it has decreased to 45–53 dBA noise levels as a result of the settlement proposal where the employees are facing in opposite directions in pairs. There has been a reduction in noise level of approximately 16%. As a result of the study, the measures to be taken to improve the auditory environment were determined within the scope of architectural and acoustic design, and results were obtained to guide the designers.

Acoustic radiation from multilayered materials using the reciprocity principle.

Application of the reciprocity principle to evaluate the acoustic radiation from arbitrary multilayered fluid and solid materials is described. To include the effect of shear motion in surrounding media, including viscosity in a fluid, equations for the acoustic radiation from such materials under point force excitation are developed in terms of reflection and transmission coefficients for longitudinal and shear waves. Calculations for forcing on either side of the layered material and in arbitrary directions, and for any asymmetric layer arrangements, are conducted. The frequency range of the calculations is not restricted by thin-plate or thick-plate theory. The test case for the radiation from plates embedded in a viscous and attenuating fluid has been investigated in detail. The effect of viscosity and attenuation has been quantified and shown to be significant at high frequencies. Application to the problem of flow noise is also briefly discussed.

Fly Ash and Fiber Waste As Principal Geopolymer Mortar Material: A Review

Geopolymer mortar is an innovative mortar using fly ash waste as an alternative to cement. The development of advanced fiber geopolymer mortar materials is an alternative solution to the weaknesses and shortcomings of conventional materials so that they have the ability to reduce and even absorb sound intensity and prevent noise propagation in overcoming noise problems in the field of building construction. Many factors can affect sound absorption, including the type of aggregate, modification of material composition that has a density level in fiber geopolymer mortar. The development of fiber geopolymer mortar can be developed in the construction field to reduce sound frequency and improve sound insulation. There are also different measurement mechanisms to determine the performance of the material in improving sound quality. This paper aims to review the performance of sound-absorbing fiber geopolymer mortar. This review is expected to provide insight into the development of fiber geopolymer mortar in building construction to improve comfort and reduce noise pollution.

Multipole transfer matrix model-based sparse Bayesian learning approach for sound source identification

Accurate source identification is key to solving complex flow-induced noise problems, and sources often have non-uniform radiation patterns. At the same time, traditional methods have many application limitations, including the need for prior knowledge of the sound field, as well as inability to apply to coherent sources or coexisting multipoles. A multipole transfer matrix model-based sparse Bayesian learning approach for sound source identification (MT-SBL) is proposed. In this algorithm, the transfer relationship for the multipole radiation patterns of sound sources is established. Then, the signal sparse reconstruction is transformed into an iterative updating problem of the feature parameters under a sparse Bayesian learning (SBL) framework, which leads to the accurate identification of multipole sources. Numerical simulations and experiments on electric speakers in an anechoic environment validate the proposed algorithm. Compared with the existing methods, the algorithm is more advantageous in terms of accuracy, resolution, and computational efficiency. Further experiments were conducted on the identification of supersonic jet noise sources, verifying the effectiveness of the method for identifying strongly directional sound sources.

Research on Transmission Efficiency Prediction of Heavy-Duty Tractors HMCVT Based on VMD and PSO–BP

Transmission efficiency is a key characteristic of Hydro-mechanical Continuously Variable Transmission (HMCVT), which is related to the performance of heavy-duty tractors. Predicting the HMCVT transmission efficiency is beneficial for the real-time adjustment of transmission ratio during heavy-duty tractor operations, so as to obtain better performance. Aiming at the problems of accurate method, low accuracy, and high noise in the prediction of HMCVT transmission efficiency, this paper proposes a method based on Variational Mode Decomposition (VMD), Particle Swarm Optimization (PSO), and Back Propagation (BP) neural networks to improve the quality of transmission efficiency prediction. Firstly, a simple theoretical model was established to obtain the influencing factors of transmission efficiency. Then, based on these factors, the transmission efficiency was tested on the bench under multiple conditions and the influence degree of each factor on transmission efficiency was divided using Partial Least Squares (PLS) method. Finally, the VMD method was used to denoise the test data, and a BP model, which was improved using the PSO method, was established to predict the processed data. The results showed that transmission efficiency of HMCVT is most affected by output speed, followed by power, and least by input speed. The VMD method can accurately extract effective signals and noise signals from the original data, and reconstruct signals, reducing the noise proportion. Using three conditions, the prediction regression accuracy of the PSO–BP model is 7.02%, 7.88%, and 9.26% higher than that of the BP model, respectively. In the three prediction experiments, the maximum differences in the MAE, the MAPE, and the RMSE of the PSO–BP model are 0.002, 0.463%, and 0.004, respectively, which are 0.006, 0.796%, and 0.003 lower than those of the BP model.

A novel multi-source navigation algorithm based on factor graph in complex underwater environments of polar regions

Autonomous underwater vehicle (AUV) is an unmanned, cableless, self-propelled, autonomous navigation and control vehicle that maneuvers under unmanned or minimally monitored and intervened conditions. To solve the problem of increasing navigation accuracy error caused by time-varying measurement noise of AUV auxiliary sensors in complex underwater environments of polar regions, a multi-source navigation algorithm for AUV based on improved factor graph is proposed. The algorithm first adds a confidence function to the standard factor graph algorithm, which is used to adjust the covariance matrix of the navigation sensor to suppress the problem of time-varying measurement noise. Then, a sliding window is added to the factor graph algorithm and only nodes within the sliding window are optimized to ensure real-time performance. Finally, considering that inertial navigation has high accuracy in a short period of time, inertial navigation iterations are performed on nodes that are about to be marginalized within the sliding window to improve navigation accuracy. The analyses of the simulation experiment results show that the algorithm can effectively cope with the time-varying problem of measurement noise and ensure the accuracy and robustness of the AUV navigation system in the complex marine environment.

Fixed‐time Synchronization of Stochastic Kuramoto‐Oscillator Networks With Switching Topology Under Dynamic Event‐triggered Scheme

AbstractThis paper proposes a dynamic event‐triggered fixed‐time control scheme for the phase synchronization of Kuramoto networks with switching topology and stochastic coupling noise. To deal with the problems of switching topology and stochastic coupling noise, a multilayered control structure with a specially designed noise reduction layer is developed. The effectiveness is proved based on stochastic differential theory. To save communication resources and reduce control frequency, a dynamic event‐triggered mechanism is proposed. By introducing a dynamic variable into the triggering condition, the triggered instants are further reduced compared to the traditional static event‐triggered method. The synchronizing criteria and parameter conditions are established with stochastic Lyapunov stability theory. The analysis of Zeno behavior is also given. Finally, numerical simulation experiments are conducted to demonstrate the effectiveness of the proposed dynamic event‐triggered control scheme.

Scale Attentive Aggregation Network for Crowd Counting and Localization in Smart City

Recent years have witnessed a remarkable proliferation of applications in smart cities. Crowd analysis is a crucial subject, and it incorporates two subtasks in smart city systems, i.e. , crowd counting and crowd localization. Nevertheless, the presence of adverse intrinsic factors, i.e. , scale variation and background noise severely degrades the performance of counting and localization. Although great efforts have been made on separate research on counting and localization, few works are capable of performing both tasks at the same time. To this aim, the scale attentive aggregation network (SA 2 Net) is proposed to solve the problems of scale variation and background noise in crowd counting and localization tasks synchronously. Specifically, the SA 2 Net has two vital modules, namely multiscale feature aggregator (MFA) module and background noise suppressor (BNS) module. The MFA module is designed in a four-pathway structure, and it aggregates the multiscale feature so as to facilitate the correlation between different scales. The BNS module utilizes the contextual information between the input keys matrix and self-attention matrix to suppress the background noise. Furthermore, a global consistency loss combined with the Euclidean loss is utilized to optimize the network in counting and localization tasks. Extensive experimental results prove that the SA 2 Net outperforms the state-of-the-art competitors both subjectively and objectively.

Bearing Fault Diagnosis Based on VMD-WPT-ViT for Wind Turbine

As an important part of wind turbine, the fault diagnosis of rolling bearings is helpful to maintain the stable operation of the fan. However, most of the existing fault diagnosis methods focus on one-dimensional time series data, which is not only difficult to extract multi-level feature information, but also fails to consider the noise problem in the signal. Therefore, to solve the above problems, a noise reduction algorithm combining variational mode decomposition (VMD) and wavelet packet threshold noise reduction (WPT) is proposed, and Vision-Transformer (ViT) model is combined to realize the fault diagnosis of rolling bearings of wind turbines. VMD is used to decompose the original signal and find out the noisy component. WPT is used to de-noise the component, and the de-noised component and other components are reconstructed to form a pure signal and converted into a two-dimensional image. Finally, a diagnostic model is built based on Vision-transformer. The experimental results show that the proposed method can effectively improve the diagnostic accuracy by extracting the advanced global features of the signal.

A geometric extension of the Itô-Wentzell and Kunita’s formulas

We extend the Itô-Wentzell formula for the evolution along a continuous semimartingale of a time-dependent stochastic field driven by a continuous semimartingale to tensor field-valued stochastic processes on manifolds. More concretely, we investigate how the pull-back (respectively, the push-forward) by a stochastic flow of diffeomorphisms of a time-dependent stochastic tensor field driven by a continuous semimartingale evolves with time, deriving it under suitable regularity conditions. We call this result the Kunita-Itô-Wentzell (KIW) formula for the advection of tensor-valued stochastic processes. Equations of this nature bear significance in stochastic fluid dynamics and well-posedness by noise problems, facilitating the development of certain geometric extensions within existing theories.

Impact of cotton dust, endotoxin exposure, and other occupational health risk due to indoor pollutants on textile industry workers in low and middle- income countries

The textile industry consists of several units that engage different processes namely ginning, spinning, weaving, dyeing, printing, and several other processes which required for converting raw cotton fiber into finished fabrics. Exposure to cotton dust, endotoxin, chemicals, noise, and musculoskeletal disorders causes several health-related hazards to textile workers. This review article aims to study the health issues due to various risk factors associated with the working environment in detail and its impacts on workers' health. This review article also reports dust and endotoxin concentrations in indoor environments of textile industries, as well as discusses the association between workplace exposure to cotton dust, endotoxin, and the prevalence of respiratory disorders. In this review, the focus is also given to the prevalence of indoor pollutants like Particulate Matter (PM), Carbon dioxide (CO2), Carbon monoxide (CO), and Volatile Organic Compounds (VOCs), Formaldehyde (HCHO) in the workplace and its effects on human health. The study expands to other aspects that influence human comfort and health during working hours like occupational noise, musculoskeletal disorders, and eyesight problems. In addition, the advanced technologies for monitoring indoor air quality for control and reduction of pollutants indoor environment the current regulatory limits of cotton dust and indoor pollutants, and suggestions to enhance the occupational safety and health conditions in textile sectors are also pointed out. In conclusion, the current study stresses the need to regulate and apply international standards in the textile sector to prevent short- and long-term occupational illness.