Noise Issues Research Articles

Influence of Distributor Structure on Through-Sea Valve Vibration Characteristics and Improvement Design

To address the issue of excessive transient noise during the opening and closing of a sea valve, a method for reducing pressure fluctuations during the opening of the electromagnetic hydraulic distributor has been proposed by analyzing the structure and working principle of the distributor. Based on theoretical calculation and simulation analysis, the size and shape of the buffer slot of the flow hole are determined under the condition that the stable working flow rate remains unchanged. An improved electromagnetic hydraulic distributor is developed and tested. The results indicate that this method can effectively control the opening and closing transient noise of the sea valve.

Generating high-quality phase-only holograms of binary images using global loss and stochastic homogenization training strategy

Deep learning presents a highly effective for computer-generated holography (CGH). However, existing learning-based CGH algorithms may encounter significant obstacles in generating high-quality holograms for binary images. This study proposes a novel diffraction model-driven neural network, termed load sharing yielding holography (LSY-Holo), which leverages a dual-branch architecture and a global structural similarity index measure (Global_SSIM) loss function to generate phase-only holograms (POHs). LSY-Holo framework facilitates capturing global information, enhancing the network’s performance to address the ill-posed inverse problem. To further optimize the model for binary images, the research introduces a custom Binary dataset alongside a stochastic homogenization preprocessing method (SHPM), which mitigates the prevalent issue of excessive noise in reconstructed images. Additionally, this deep learning approach is integrated with an iterative algorithm, which employs holograms rapidly generated by LSY-Holo as the initial phase of the AdamW Holography (AH) algorithm. This strategy significantly accelerates the convergence of the iterative process. LSY-Holo is further extended to 3D scenes, enabling dynamic defocusing and focusing effects at varying depths. Results from numerical simulations and full-color optical reconstruction experiments demonstrate that the proposed method can enhance image quality and effectively reduce the artifacts in reconstructed images.

Common-mode intensity noise suppression scheme in phase demodulation of 3×3 coupler based on ellipse fitting algorithm

Addressing the issue of light source intensity noise in the phase demodulation scheme of the 3×3 coupler, this paper proposes a common mode intensity noise suppression scheme for differential signals based on the Ellipse Fitting Algorithm (EFA). This scheme employs the EFA to correct the direct current (DC) components of the three interference signals output by the 3×3 coupler. One of the interference signals is selected as a self-reference signal, and the other two interference signals are then subtracted the self-reference signal to obtain differential signals. This effectively eliminates the common-mode intensity noise in the DC components of the interference signals before demodulation, preventing noise from being aliased into the demodulation results. Then, the EFA and ATAN are applied to the two differential signals for signal demodulation. Experimental results show that, compared with the EFA demodulation scheme, the noise floor of the system is reduced by an average of 18 dB, with a maximum reduction of 26 dB to 1.58 μrad/√Hz at 50 kHz, and the total harmonic distortion (THD) is less than 1%.

Hybrid deep learning model with VMD-BiLSTM-GRU networks for short-term traffic flow prediction

Accelerating urbanization and the rapid development of intelligent transportation systems have rendered short-term traffic flow prediction an important research field. Accurate prediction of traffic flow is beneficial for the optimization of traffic planning, improvement of road utilization, reduction of traffic congestion, and reduction in the incidence of traffic accidents. However, data pertaining to traffic flow are typically influenced by a multitude of factors, resulting in data that exhibit a considerable degree of nonlinearity and complexity. To address the issue of noise in raw traffic flow data, this study proposes a hybrid model that combines variational modal decomposition (VMD), a bidirectional long short-term memory network (BiLSTM), and a gated recurrent unit (GRU) for short-term traffic flow prediction. To validate the effectiveness of the model, an experimental validation was conducted based on traffic flow data from UK highways, and the performance of the model was compared with common benchmark models. The experimental results demonstrate that the proposed method yields superior prediction results in terms of mean absolute error, coefficient of determination, and root-mean-square error compared to existing prediction techniques, thereby substantiating its efficacy in short-term traffic flow prediction.

Consistent positive correlation sample distribution: Alleviating the negative sample noise issue in contrastive adaptation

Consistent positive correlation sample distribution: Alleviating the negative sample noise issue in contrastive adaptation

A genetic programming Rician noise reduction and explainable deep learning model for Alzheimer's diseases severity prediction.

Degradation of magnetic resonance imaging (MRI) remains a challenging issue, with noise being a key damaging component introduced due to a variety of environmental and mechanical factors. The aim of this research work is to addresses the issue of noise reduction and to predict Alzheimer's disease detection efficiently. First, we present a genetic programming (GP) technique for reducing Rician noise in MRI images to pre-process the dataset. To effectively reduce Rician noise, this GP approach combines a Feature Extraction component, GP Optimal Expression, and an Optimum Removal Estimation component. In the second phase, we design and develop an explainable Deep Learning framework. This framework uses a local data-driven interpretation technique based on SHAP values to investigate the relationship between the neural network's estimated AD diagnosis and the input MRI images. In addition, we handle class distribution by combining an oversampling strategy with a minority approach. Several assessment metrics are used to analyze the performance of our proposed model. The proposed method is tested on a variety of medical samples, and the results are compared to those obtained using other comparable approaches. We also test and compare our model to three cutting-edge models: DenseNet169, VGGNet15, and Inceptionv3. The empirical results show that our proposed model outperforms others, particularly in handling basic structures with limited spectral features, lower computational complexity, and less overfitting. This research worked addressed Rician noise issue in MRI images and predict AD severity prediction using explainable deep learning framework.

Improvement of multiscale decomposition for space-based gravitational wave signal processing technology.

During the process of detecting gravitational waves in space, addressing noise issues caused by terrestrial vibrations, natural environmental changes, and the factors intrinsic to the detectors, this paper proposes a multiscale variational mode adaptive denoising algorithm based on momentum gradient descent. This algorithm integrates momentum factors and multiscale concepts into the variational mode algorithm to resolve the issue of multiple local optima encountered during operation, reduce oscillations in regions with large or unstable gradient changes, and improve convergence speed. Additionally, the algorithm combines the least mean squares algorithm to automatically adjust weights, thereby mitigating the impact of noise, addressing the issue of noise from multiple and random sources, effectively suppressing noise in the gravitational wave signal, and enhancing the quality and reliability of the gravitational wave signal. Experimental results demonstrate that this algorithm performs better than other algorithms in noise suppression, effectively reducing noise in gravitational wave signals and meeting the noise suppression requirements for space-based gravitational wave detection.

High-Resolution SAR-to-Multispectral Image Translation Based on S2MS-GAN

Synthetic aperture radar (SAR) has been extensively applied in remote sensing applications. Nevertheless, it is a challenge to process and interpret SAR images. The key to interpreting SAR images lies in transforming them into other forms of remote sensing images to extract valuable hidden remote sensing information. Currently, the conversion of SAR images to optical images produces low-quality results and incomplete spectral information. To address these problems, an end-to-end network model, S2MS-GAN, is proposed for converting SAR images into multispectral images. In this process, to tackle the issues of noise and image generation quality, a TV-BM3D module is introduced into the generator model. Through TV regularization, block-matching, and 3D filtering, these two modules can preserve the edges and reduce the speckle noise in SAR images. In addition, spectral attention is added to improve the spectral features of the generated MS images. Furthermore, we construct a very high-resolution SAR-to-MS image dataset, S2MS-HR, with a spatial resolution of 0.3 m, which is currently the most comprehensive dataset available for high-resolution SAR-to-MS image interpretation. Finally, a series of experiments are conducted on the relevant dataset. Both quantitative and qualitative evaluations demonstrate that our method outperforms several state-of-the-art models in translation performance. The solution effectively facilitates high-quality transitions of SAR images across different types.

AVENUE: A novel deep fake detection method based on temporal convolution network & rPPG information

In Deep Learning (DL), an adversary creates Deepfakes by manipulating facial features to fool someone. The Deepfakes pose a security threat to anyone's privacy and a primary concern for our society. It can be detected by utilizing the texture and physiological properties of the face, like eye and lip movements; however, such methods are incompetent when Deepfakes are created using recent generative adversarial networks (GAN). Alternatively, remote Photoplethysmography (rPPG) information can be used for Deepfake detection because GANs neglect human physiological information for Deepfake generation. Such detection can be inaccurate when rPPG signals are affected by the noises induced by facial deformation and illumination variations. Furthermore, the exiting Deepfake detections are usually performed using sequential models, and such models fail to process the long sequence of temporal information. These issues are mitigated by our proposed method AVENUE , that is, \(A\) no \(V\) el d \(E\) epfake detectio \(N\) method based on temporal convol \(U\) tion n \(E\) twork & rPPG information. For mitigating the noise issues in the rPPG signals, the proposed method detects and employs relatively stable clips of the input video for Deepfake detection. The stable clips are those clips that are least affected by facial deformations. Also, we use a modified Temporal convolution network to model the long sequence of Deepfake information rather than the sequential architectures. We performed the experimental result on publically available datasets of Deepfake videos. It demonstrates that our proposed method performs better than the existing rPPG-based Deepfake detection methods.

Intelligent cell images segmentation system: based on SDN and moving transformer

Diagnosing diseases heavily relies on cell pathology images, but the extensive data in each manual identification of relevant cells labor-intensive, especially in regions with a scarcity of qualified healthcare professionals. This study aims to develop an intelligent system to enhance the diagnostic accuracy of cytopathology images by addressing image noise and segmentation issues, thereby improving the efficiency of medical professionals in disease diagnosis. We introduced an innovative system combining a self-supervised algorithm, SDN, for image denoising with data enhancement and image segmentation using the UPerMVit model. The UPerMVit model’s novel attention mechanisms and modular architecture provide higher accuracy and lower computational complexity than traditional methods. The proposed system effectively reduces image noise and accurately segments annotated images, highlighting cellular structures relevant to medical staff. This enhances diagnostic accuracy and aids in the accurate identification of pathological cells. Our intelligent system offers a reliable tool for medical professionals, improving diagnostic efficiency and accuracy in cytopathologic image analysis. It provides significant technical support in regions lacking adequate medical expertise.

Method and Application of Spillway Radial Gate Vibration Signal Denoising on Multiverse Optimization Algorithm-Optimized Variational Mode Decomposition Combined with Wavelet Threshold Denoising

To address the noise issue in the measured vibration signals of spillway radial gate discharge, this paper utilizes the Multiverse Optimization Algorithm (MVO) to optimize the number of decomposition modes (K) and the penalty factor (α) in Variational Mode Decomposition (VMD). This approach ensures improved efficiency of VMD decomposition while maintaining accuracy. Subsequently, the obtained Intrinsic Mode Functions (IMFs) from VMD decomposition are classified based on Multi-scale Permutation Entropy (MPE). IMFs are divided into pure components and noisy components; the noisy components are processed with Wavelet Threshold Denoising (WTD), while the pure components are overlaid and reconstructed to obtain the denoised vibration signal of the gate. Comprehensive comparisons involving artificial signal simulations, gate flow-induced vibration model tests, and numerical simulations lead to the following conclusions: compared to other algorithms, the proposed combined denoising method (MVO-VMD-MPE-WTD) achieves the highest signal-to-noise ratio (SNR) in both the frequency and time domains for artificial signals, while yielding the lowest mean square error (MSE). In the gate flow-induced vibration model tests, the method significantly reduces noise in the vibration signals and effectively preserves characteristic information. The error in preserving characteristic information across model tests and numerical simulations is kept below 1%. Furthermore, compared to other optimization algorithms, the MVO demonstrates higher computational efficiency. The parameter-optimized combined denoising method proposed in this study provides insights into denoising measured vibration signals of hydraulic spillway radial gates and other drainage structures, and it opens possibilities for exploring more efficient optimization algorithms for achieving online monitoring in the future.

A Software Defect Prediction Method That Simultaneously Addresses Class Overlap and Noise Issues after Oversampling

A Software Defect Prediction Method That Simultaneously Addresses Class Overlap and Noise Issues after Oversampling

Feasibility of loop-gain tuning for general measurement systems inspired by quantum locking for DECIGO

Abstract A series of quantum locking theories have been proposed to enhance the quantum-noise-limited target sensitivity of the DECi-hertz Interferometer Gravitational wave Observatory. The quantum locking that uses a square completion optimizes the sensitivity across all frequencies. However, a substantial amount of data-series must be post-processed since the square completion is a form of signal processing technique. This paper approaches the optimal sensitivity across all frequencies from an alternative perspective: by optimizing the frequency dependence of a servo gain in a feedback loop. The optimal servo gain is formulated by comparing the alternative method with the square completion method for the same optical setup. This will be shown in general noise issues extending the framework of the quantum locking. We find that the optimal servo gain forms a non-feasible filter but has certain characteristics. We also find that the noise of the measurement signal deteriorates proportionally to the noise measured in the feedback loop when the servo gain is slightly imperfect.

Slovenian policies and initiatives addressing noise and acoustics issues in kindergartens and schools

The focus of this paper is on Slovenian policies and initiatives addressing acoustics and noise issues in kindergartens and schools. Despite crucial legislation for noise protection in buildings, technical guidelines are inconsistently followed, resulting in reverberation issues in various educational premises. Surveys gathered teachers' opinions on noise disturbance in three categories: kindergartens, primary schools, and a broader survey on dietary guidelines, which included questions on noise in dining areas. Preliminary findings reveal that primary schools, more than kindergartens, view noise as a significant concern, particularly acoustics over external sources. A National Institute of Public Health study, covering 653 education institutions, found that 107 primary schools considered noise disturbing. Poor acoustics was identified as the primary cause of disturbance in 171 of all institutions. Additional reasons included loud children, overcrowded places, and insufficient caregiver supervision. Information from a specialized noise and acoustics company highlighted nearly 180 interventions in Slovenian educational institutions over 15 years, primarily in primary schools (104), followed by kindergartens (11), and music schools (26). The widespread need for improvements underscores the importance of awareness and recommendations for schools, kindergartens, and municipalities to enhance the educational sound environment. This work was partly supported by the EU H2020 Equal-Life project.

Noise sensitivity model- a qualitative investigation

Noise sensitivity is a concept used to explain the different effects of noise on people's health. Numerous definitions of noise sensitivity have been proposed, though none are universally accepted. Hence, a "system model of noise sensitivity" was developed, suggesting that noise sensitivity may contain a set of factors. Based on this, we conducted semi-structured interviews to investigate noise sensitivity and its factors and used qualitative research methods to analyse the data. More factors of noise sensitivity were discovered, and a revised system model was developed. This expanded model provides a more complete definition of noise sensitivity. We hope the improved understanding could lead to identifying ways of coping with noise issues. In the future, more diverse and individualized approaches could be developed to address the many different aspects of noise sensitivity.

Blocked force characterization and transposition at secondary connections of a compressor

Applications in sustainable energy systems, like heat pumps, are encountering emerging challenges, among which acoustic comfort stands out as a critical factor in shaping the end-user experience. Early integration of vibroacoustic into each industrial design phase ensures the prevention and prediction of potential noise issues, thus avoid costly design modification loops. BDR Thermea and CETIM, in a collaborative project, have implemented new methodologies for designing acoustically optimized products, highlighting the importance of considering vibroacoustic design at each design stage of new products developments. In this study, structure-borne noise characterization methods are applied to a heat pump compressor using the ISO 21955 and ISO 20270 standards. The forces transmitted at its suction and discharge ports were studied through component based TPA for the intrinsic characterization of the compressor. Once obtained, these forces were transposed to a new receiving structure. The results allowed a comparison of the outcomes from both approaches and highlighted the importance of considering the influence of the stiffness of the connections at the compressor ports.

Study on territory-wide application of low noise road surface in Hong Kong

Hong Kong is one of the metropolitan cities in the world, with highly concentrated road network system and densely populated developments. At times, it is inevitable to build residential buildings near noisy roads, and traffic noise is one of the environmental problems affecting the locals. The Government strives to pre-empt traffic noise issue through planning new road projects or new residential developments. Concerning traffic noise from existing roads, the Government has implemented noise mitigation measures, for instance, to resurface existing roads with Low Noise Road Surface (LNRS) materials, retrofitting of noise barrier or enclosure, to alleviate traffic noise impact. Moreover, the Government is continuously developing and trialing different LNRS materials such as Polymer Modified Stone Mastic Asphalt (PMSMA6) on roads in Hong Kong, which aims for wider application of LNRS. To explore the feasibility and effectiveness of the wider application of LNRS on local roads, the Government has recently conducted a territory-wide study on the application of LNRS on local roads in Hong Kong. This paper attempts to share the common local road characteristics of applying LNRS material and the latest technical findings, such as the effect on population exposure, of the territory-wide study in Hong Kong.

Artificial intelligent-driven decision-making for automating root fracture detection in periapical radiographs

BackgroundThe detection and early diagnosis of root fractures can be challenging; this difficulty applies particularly to newly qualified dentists. Aside from clinical examination, diagnosis often requires radiographic assessment. Nonetheless, human fallibility can introduce errors due to a lack of experience.AimThe proposed system aimed to assist in detecting root fractures through the integration of artificial intelligence techniques into the diagnosis process as a step for automating dental diagnosis and decision-making processes.Materials and methodA total of 400 radiographic images of fractured and unfractured teeth were obtained for the present research. Data handling techniques were implemented to balance the distribution of the samples. The AI-based system used the voting technique for five different pretrained models namely, VGG16, VGG19, ResNet50. DenseNet121, and DenseNet169 to perform the analysis. The parameters used for the analysis of the models are loss and accuracy curves.ResultsVGG16 exhibited notable success with low training and validation losses (0.09% and 0.18%, respectively), high specificity, sensitivity, and positive predictive value (PPV). VGG19 showed potential overfitting concerns, while ResNet50 displayed progress in minimizing loss but exhibited bias toward unfractured cases. DenseNet121 effectively addressed overfitting and noise issues, achieving balanced metrics and impressive PPVs for both fractured and unfractured cases (0.933 and 0.898 respectively). With increased depth, DenseNet169 demonstrated enhanced generalization capability.ConclusionThe proposed AI- based system demonstrated high precision and sensitivity for detecting root fractures in endodontically treated teeth by utilizing the voting method.

Secondary Skin Facade for Urban Noise Reduction: A Case Study of Bandung Creative Hub Co-working Space

Abstract In recent years, the proliferation of co-working spaces has offered a collaborative environment for entrepreneurs, freelancers, and small businesses in urban areas. However, the issue of environmental noise within these spaces presents a challenge to comfort and productivity. This study focuses on the Bandung Creative Hub (BCH), a prominent co-working space in Bandung, Indonesia, and investigates the efficacy of facade engineering solutions, particularly the installation of secondary skin, in mitigating urban noise pollution. Through a comprehensive analysis utilizing both empirical measurements and computational simulations, the study aims to understand the intricacies of noise dynamics within the BCH co-working space. Empirical measurements using the Noise Tool software capture ambient noise levels, while computational simulations with IBANA provide predictive modeling of acoustic conditions. The results reveal a significant reduction in noise levels, with the sound level around the workspace corridor decreasing by approximately 23.3%, from 62 dB to 47 dB, indicating the effectiveness of facade engineering interventions. The limitations of prior research, which concentrated solely on barrier intervention methods and spacing ratio. Nevertheless, this study introduces a fresh perspective by examining the efficacy of secondary skin facade engineering in achieving noise standards and enhancing the quality of urban environments.

Occupational Health and Safety Risks Associated with Grass Trimmers in One of the Universities in Zimbabwe

Objectives: This research intends to investigate occupational health and safety risks associated with grass-trimming activities at a local university in Zimbabwe and recommend on ways to reduce the risks. Methods: A mixed-method approach was adopted and the research was based on a case study design. Data was collected using questionnaires, observations, and interviews, with a total of 27 participants involved in the study. Data was analysed using thematic analysis and ranking and scoring using a ranking matrix. Results: The findings revealed that the most prevalent risks were attributed to factors such as physical hazards which include noise, dust, solar radiation, and ergonomic issues. Among the identified causes of these risks were a lack of training, and improper use of personal protective equipment. Conclusion: The research concluded that grass-trimming operations introduce various health and safety risks that need to be effectively managed to ensure the well-being of workers and the general public. The researchers recommended the implementation of comprehensive training programs for employees, proper use of protective clothing and equipment to avoid noise and dust such as the use of earplugs, masks and regular maintenance of the grass trimming equipment and machines.