System Noise Research Articles

The vertical accuracy improvement method considering gravitational anomaly for SINS/GNSS

Vertical information (vertical velocity and altitude) is important in three-dimensional navigation. In order to improve the vertical accuracy of strapdown inertial navigation system/global navigation satellite system integrated navigation (SINS/GNSS) while reducing costs, firstly, the propagation laws of vertical error sources in two types of SINS/GNSS (vertical velocity set to 0 and not set to 0) are systematically analyzed. Furthermore, a vertical accuracy improvement method considering the gravitational anomaly is proposed. In this method, the gravitational anomaly is considered as one of the vertical error sources. Then, the processing method of error sources in integrated navigation is referenced, and two processing modes of gravitational anomaly are designed. The first method is to represent the gravitational anomaly as one of the system states of SINS/GNSS. The number of dimensions for the system is expanded from 15 to 16. The corresponding mathematical model is derived to ‘absorb’ the vertical errors caused by the gravitational anomaly. The second is to represent the gravitational anomaly as one of the vertical system noises. Thereby, the Kalman filter is adjusted in real time using the above adaptive method to improve the accuracy of the estimated state. The corresponding errors are then suppressed. Field experiments show that both modes of the proposed method can effectively improve the vertical accuracy.

Retraction Note: Study on optimization of vehicle exhaust system noise and hook position based on multi-source experimental data analysis

Retraction Note: Study on optimization of vehicle exhaust system noise and hook position based on multi-source experimental data analysis

Research on sound quality of roller chain transmission system based on multi-source transfer learning

To establish the sound quality evaluation model of roller chain transmission system, we collect the running noise under different working conditions. After the noise samples are preprocessed, a group of experienced testers are organized to evaluate them subjectively. Mel frequency cepstral coefficient (MFCC) of each noise sample is calculated, and the MFCC feature map is used as an objective evaluation. Combining with the subjective and objective evaluation results of the roller chain system noise, we can get the original dataset of its sound quality research. However, the number of high-quality noise samples is relatively small. Based on the sound quality research of various chain transmission systems, a novel method called multi-source transfer learning convolutional neural network (MSTL-CNN) is proposed. By transferring knowledge from multiple source tasks to target task, the difficulty of small sample sound quality prediction is solved. Compared with the problem that single source task transfer learning has too much error on some samples, MSTL-CNN can give full play to the advantages of all transfer learning models. The results also show that the MSTL-CNN proposed in this paper is significantly better than the traditional sound quality evaluation methods.

Threshold-impeded stochastic production: how noise interacts with disruptive thresholds to affect the production output in fluctuating environments

Introduction: Production systems are bound to operate in stochastic conditions. Prominent sources of performance-reducing uncertainty are constituted by machine failures, decision errors, and fluctuating supplies. This article offers a novel approach to uncertainty through modelling and simulation of nonlinear production systems. In particular, the authors consider production systems where the output is drastically reduced when a resource of fluctuating input values reaches an upper threshold.Methods: The article introduces minimal models of such hreshold-impeded stochastic production (TISP) systems and the system performance (i.e., the output) is analyzed as a function of system parameters (e.g., the type of nonlinearity) and noise input features (e.g., the distribution width or time correlations). Applications to steel manufacturing via continuous casting and power generation through wind turbines are discussed in detail.Results and Discussion: The simulation experiments illustrate that especially the extent of the input fluctuations affects the output performance which is why the authors recommend that TISP system operators counterbalance such fluctuations if possible.

Device and Fitting Protocol for a Transitional Intervention for Debilitating Hyperacusis.

This report describes a hearing device and corresponding fitting protocol designed for use in a transitional intervention for debilitating loudness-based hyperacusis. The intervention goal is to transition patients with hyperacusis from their typical counterproductive sound avoidance behaviors (i.e., sound attenuation and limited exposure to healthy low-level sounds) into beneficial sound therapy treatment that can expand their dynamic range to the point where they can tolerate everyday sounds and experience an improved quality of life. This requires a combination of counseling and sound therapy, the latter of which is provided via the hearing device technology, signal processing, and precision fitting approach described in this report. The device combines a miniature behind-the-ear sound processor and a custom earpiece designed to maximize the attenuation of external sounds. Output-limiting loudness suppression is used to restrict exposure to offending high-level sounds while unity gain amplification maximizes exposure to healthy and tolerable lower level sounds. The fitting process includes measurement of the real-ear unaided response, the real-ear measurement (REM) system noise floor, the real-ear occluded response, real-ear insertion gain, and the output limit. With these measurements, the device can achieve the prescribed unity gain needed to provide transparent access to comfortable sound levels. It also supports individualized configuration of the therapeutic noise from an on-board sound generator and adaptive output limiting based on treatment-induced increases in dynamic range. The utility of this device and fitting protocol, in combination with structured counseling, is highlighted in the outcomes of a successful 6-month trial of the transitional intervention described in a companion report in this issue.

Experimental study on the characteristics of R1150/R600 mixed refrigerant Joule-Thomson low-temperature freezer

In this paper, a low-temperature freezer based on Joule-Thomson system with a variable frequency fan was developed by using the natural binary mixture of R1150/R600. The experiment results demonstrated the feasibility of using this mixture to obtain the low freezing temperature below −70 °C. It could be found that when the concentration of R1150 was 27.5 %, it had the lowest daily energy consumption of 5.94 kWh/24 h. Furthermore, the influences of different fan speeds and active fan speed control on the cool-down characteristics of the system were also studied. The results indicated that lower fan speed in the early stage could effectively improve the cool-down performance. It was 51 min faster than that of the fixed operating fan speed under the optimal fan speed control at the target freezing temperature. Finally, the noise test of the freezer was carried out, and the influences of on–off temperature and fan speed on the system noise were also analyzed. The noise level of the system is between 46.71 dB and 51.62 dB, which is acceptable for the users. Moreover, the influence of fan speed on system noise was greater than that of different on–off temperatures. Therefore, improving the heat exchange efficiency of the condenser to reduce the fan speed is the possible direction of noise reduction for low-temperature freezers in the future.

FPGA Implementation of a Pipelined Kalman Filter for Object Tracking in Two Dimensions

In a location tracking system for a moving object, not only accuracy but also real-time processing are important factors. The Kalman filter, as a recursive function, stands out as one of the prominent algorithms for object tracking. It continuously compares measured data with predicted values based on the system characteristics in real time, and then corrects the error of the predicted values while considering the noise of both system and measured data. This paper focuses on designing a hardware-based Kalman filter for object tracking in two dimensions. Following an analysis of the Kalman filter algorithm, the blocks capable of parallel processing are identified and configured to be processed in parallel, effectively reducing data processing time. The clock speed is enhanced by using the pipeline technique. In addition, the time-sharing technique is applied to increase the utilization of hardware resources and reduce the area. Data was processed at 32-bit floating points to uphold accuracy comparable to software-implemented Kalman filters. The proposed Kalman filter architecture is designed using verilog HDL and then simulated in Synopsys VCS/Verdi. And the accuracy is verified by comparing results with a software-based Kalman filter designed using MATLAB. It was implemented using Zynq ZYNQ-7 ZC702 Programmable logic via Xilnix Vivado, and can operate at 33MHz. It takes a total of 44 clocks, or 1.32 us, to process one data. Therefore, it was confirmed that the designed Kalman filter hardware is suitable for real-time processing.

Quantum Measurement Classification Using Statistical Learning

Interpreting the results of a quantum computer can pose a significant challenge due to inherent noise in these mesoscopic quantum systems. Quantum measurement, a critical component of quantum computing, involves determining the probabilities linked with quantum states post-multiple circuit computations based on quantum readout values provided by hardware. While there are promising classification-based solutions, they can either misclassify or necessitate excessive measurements, thereby proving to be costly. This article puts forth an efficient method to discern the quantum state by analyzing the probability distributions of data post-measurement. Specifically, we employ cumulative distribution functions to juxtapose the measured distribution of a sample against the distributions of basis states. The efficacy of our approach is demonstrated through experimental results on a superconducting transmon qubit architecture, which shows a substantial decrease (88%) in single qubit readout error compared to state-of-the-art measurement techniques. Moreover, we report additional error reduction (12%) compared to state-of-the-art measurement techniques when our technique is applied to enhance existing multi-qubit classification techniques. We also demonstrate the applicability of our proposed method for higher dimensional quantum systems, including classification of single qutrits as well as multiple qutrits.

Analysis and measurement of magnetic noise in multilayer magnetic shielding system combined with mu-metal and ferrite for atomic sensors

The multilayer magnetic shielding system is widely used in atomic sensors. However, for miniaturized atomic sensors with volume limitations, such as spin-exchange relaxation-free (SERF) gyroscopes, the remaining environmental magnetic interference and mu-metal magnetic noise will affect the sensitivity of atomic sensors. In this study, we establish a comprehensive magnetic noise calculation model for the magnetic shielding system of a SERF gyroscope, which considers the influence of the environmental magnetic noise on the magnetic shielding and the coupling effect between different layers. For suppressing magnetic noise of the magnetic shielding system, the property of different ferrites was measured and compared, then the particle swarm optimization is applied to design the magnetic shielding structure. Finally, the experimental device is established for measuring performances of the magnetic shielding system per and post optimization. By comparison, radial and axial magnetic noise post optimization are decreased by 37% and 22%, respectively.

The fabrication and characterization of direct conversion flat panel X-ray imager with TlBr film

The novel direct conversion flat panel detectors (FPDs) utilizing thallium bromide (TlBr) films are developed and characterized in this study. Thin TlBr films, deposited using an evaporation technique, achieved thicknesses of 10 μm and 50 μm. A pixel sensor array, with a pixel size of 235 × 235 μm2 and a sensitive area of 29.61 × 39.48 mm2, was developed using low-temperature polysilicon thin-film transistor (LTPS TFT) technology as the initial study to explore performance characteristics of developed imaging system. The TlBr film was characterized based on the current-voltage (I–V) profile, showing a resistivity of 2.78 × 1010 Ω·cm. The LTPS TFT showed a leakage current of about 10−13 A, while the noise of TlBr FPD system was around 104 e− rms, primarily limited by the readout system. The detector system was characterized using radiation from an X-ray tube with a voltage in the range of 30 kV–100 kV and a current of 2 mA, involving an X-ray test chart as the object with the processing time of 18.15–18.30 ms per frame. Based on the contrast and contrast-to-noise ratio (CNR) values, the 10 μm and 50 μm TlBr FPD clearly captured the image in operation between 30 kV and 100 kV. The 50 μm TlBr FPD demonstrated improved sensitivity, showing pixel values with 10 times higher intensity and enhanced line pair detail at 30 kV X-ray operation. The 10 μm TlBr achieved spatial resolution of 267 μm full width at half maximum (FWHM), while the 50 μm TlBr achieved 250 μm FWHM. In the X-ray imaging demonstration, the shape and structure of anchovy was clearly distinguished. The TlBr film FPD presents promising potential for a room temperature X-ray imager.

Distributed moving horizon fusion estimation for linear constrained uncertain systems

AbstractIn this paper, the distributed moving horizon fusion estimation of uncertain systems with constraints of system noise and state variables is studied. Firstly, relying on the basic idea of consensus algorithm, the cost function in the performance index is reconstructed by weighted fusion of the state prediction values. Secondly, considering the performance index with uncertain parameters, the min‐max optimization problem of the algorithm is transformed into the least squares optimization problem based on 2‐norm regularization method. Thirdly, the scalar‐weighted linear minimum variance fusion estimation strategy is used to realize the weighted fusion of local state estimation values. Then, on the premise of minimum network connectivity and collective observability, the stability of the proposed algorithm is studied, and the sufficient conditions for the expected convergence of the fused estimation error norm square are given. Finally, the effectiveness of the algorithm is verified by numerical simulation.

A noise audit of human-labeled benchmarks for machine commonsense reasoning

With the advent of large language models, evaluating and benchmarking these systems on important AI problems has taken on newfound importance. Such benchmarking typically involves comparing the predictions of a system against human labels (or a single ‘ground-truth’). However, much recent work in psychology has suggested that most tasks involving significant human judgment can have non-trivial degrees of noise. In his book, Kahneman suggests that noise may be a much more significant component of inaccuracy compared to bias, which has been studied more extensively in the AI community. This article proposes a detailed noise audit of human-labeled benchmarks in machine commonsense reasoning, an important current area of AI research. We conduct noise audits under two important experimental conditions: one in a smaller-scale but higher-quality labeling setting, and another in a larger-scale, more realistic online crowdsourced setting. Using Kahneman’s framework of noise, our results consistently show non-trivial amounts of level, pattern, and system noise, even in the higher-quality setting, with comparable results in the crowdsourced setting. We find that noise can significantly influence the performance estimates that we obtain of commonsense reasoning systems, even if the ‘system’ is a human; in some cases, by almost 10 percent. Labeling noise also affects performance estimates of systems like ChatGPT by more than 4 percent. Our results suggest that the default practice in the AI community of assuming and using a ‘single’ ground-truth, even on problems requiring seemingly straightforward human judgment, may warrant empirical and methodological re-visiting.

The evaluation for plasma noise in arbitrary time-delay interferometry combinations

The laser interferometer space antenna (LISA) uses laser interferometry to measure gravitational wave-induced distance changes between freely falling test masses on separate spacecraft. In practice, the space-borne gravitational wave detector operates in a plasma medium, and subsequently, the variations in electron density affect the refractive index and add displacement noise to measurements. Geometric time-delay interferometry (TDI) is the TDI combinations searched by geometric method, which is employed to mitigate overwhelming laser phase noise. This study explores all forty-five second-generation geometric TDI combinations up to sixteen links, analyzing plasma noise effects via power spectral density and cross-spectral density calculations for different optical links. Our findings reveal that plasma noise can exceed the noise floor, which comprises optical metrology system and test mass acceleration noise, in certain low-frequency regions due to different noise transfer behaviors. Further, we establish electron density requirements for various TDI combinations, showing that densities below 100cm−3/Hz @ 10mHz satisfy LISA’s criteria across all forty-five combinations. This allows for optimized TDI selection based on specific plasma densities.

Impact of astrophysical effects on the dark matter mass constraint with 21 cm intensity mapping

ABSTRACT We present an innovative approach to constraining the non-cold dark matter model using a convolutional neural network (CNN). We perform a suite of hydrodynamic simulations with varying dark matter particle masses and generate mock 21 cm radio intensity maps to trace the dark matter distribution at z = 3 in the postreionization epoch. Our proposed method complements the traditional power-spectrum analysis. We compare the results of the CNN classification between the mock maps with different dark matter masses with those from the two-dimensional power spectrum of the differential brightness temperature map of 21 cm radiation. We find that the CNN outperforms the power spectrum. Moreover, we investigate the impact of baryonic physics on the dark matter model constraint, including star formation, self-shielding of H i gas, and ultraviolet background model. We find that these effects may introduce some contamination in the dark matter constraint, but they are insignificant compared to the system noise of the SKA instruments.

Application of Sports Video Image Analysis Based on Fuzzy Support Vector Machine

Sports video image has always been a hot topic in sports video processing. The theoretical and experimental analysis of digital image noise reduction technology is a challenging topic. In this paper, a sports video denoising algorithm is designed by combining the excellent characteristics of curvilinear transformation theory and fuzzy support vector machine. Firstly, the image with noise is curvilinear, and the conversion coefficient is obtained. Then, according to the distribution characteristics of the system noise, the system parameters are divided into space, and the system learning features are constructed. The fuzzy classification of high-frequency curves is realized using the adaptive threshold denoising method. Then, the noise reduction coefficient is reconstructed by the curve-wave method to obtain the processed image. The simulation results show that this method can overcome the pseudo-Gibbs effect effectively and suppress the noise well. This algorithm has a good application prospect in sports video image processing.

Brownian processes in human motor control support descending neural velocity commands

The motor neuroscience literature suggests that the central nervous system may encode some motor commands in terms of velocity. In this work, we tackle the question: what consequences would velocity commands produce at the behavioral level? Considering the ubiquitous presence of noise in the neuromusculoskeletal system, we predict that velocity commands affected by stationary noise would produce “random walks”, also known as Brownian processes, in position. Brownian motions are distinctively characterized by a linearly growing variance and a power spectral density that declines in inverse proportion to frequency. This work first shows that these Brownian processes are indeed observed in unbounded motion tasks e.g., rotating a crank. We further predict that such growing variance would still be present, but bounded, in tasks requiring a constant posture e.g., maintaining a static hand position or quietly standing. This hypothesis was also confirmed by experimental observations. A series of descriptive models are investigated to justify the observed behavior. Interestingly, one of the models capable of accounting for all the experimental results must feature forward-path velocity commands corrupted by stationary noise. The results of this work provide behavioral support for the hypothesis that humans plan the motion components of their actions in terms of velocity.

A Transportable Atomic Gravimeter with Constraint-Structured Active Vibration Isolation.

Many efforts have been taken in recent years to push atomic gravimeters toward practical applications. We demonstrate an atomic gravimeter named NIM-AGRb2 that is transportable and suitable for high-precision gravity measurements. Constraint-structured active vibration isolation (CS-AVI) is used to reduce the ground vibration noise. The constraint structure in CS-AVI ensures that the isolation platform only has vertical translation, with all other degrees of freedom (DoFs) being constrained. Therefore, the stability of active vibration isolation is enhanced. With the implementation of CS-AVI, the sensitivity of NIM-AGRb2 reached as low as 20.5 μGal/Hz1/2. The short-term sensitivity could be further reduced to 10.8 μGal/Hz1/2 in a seismologic observation station. Moreover, we evaluated the system noise of the gravimeter, and the results were consistent with our observations.

Instrumentation for Sub-Ampere Lightning Current Measurement on a Tall Meteorological Tower in Complex Electromagnetic Environment

Measurement of lightning current plays a critical role in the field of atmospheric electricity. Traditionally, Rogowski coils or low-resistance shunts were employed for measuring lightning currents in the range from several amperes up to several hundreds of kilo-amperes, and high-value resistors were utilized for measuring corona discharge currents at sub-ampere levels. However, these approaches were not suitable for continuously recording the vast range of lightning currents. For this sake, we have developed a lightning current measurement system equipped with a shock-tolerant low-noise amplifier module. With the system installed on a tall tower, sub-ampere level currents just before the lightning initiation were observed for the first time. To confirm the authenticity of the recorded currents, the background noise of the measurement system and surrounding environment were identified, and a digital multi-frequency notch filter was proposed for de-noising. Results show that the system can achieve a current identification level of 50 mA even in complex electromagnetic environments, while having a measurement capability of 220 kA.

Active control method for fluid borne noise in aerospace fluid systems of variable operation statuses

In the fluid system of aerospace vehicles, the fluid-borne noise (FBN) may induce vibration and damage to components. The FBN may limit the performance of the entire onboard system. To achieve a stable active elimination for the FBN is not easy, for many variable operation statuses exist in practical systems, such as varying-frequency and varying-load status. In this work, an improved feed-forward active control method based on narrowband adaptive notch filters, with an online identification module, is proposed. The algorithm includes three adaptive filters: an adaptive notch filter for producing cancelling signal by using filtered-X least mean square (FXLMS) algorithm, one for online identification to model the real-time secondary path, and an error prediction filter for extracting the precise error for online identification. In the aircraft hydraulic system of high pressure, a so-called relief scheme, also known as the bypass structure, is used for the FNB, controlling an active control valve in the branched line to overflow the main fluid line. The system is modelled with software AMESim for the fluid part and Matlab for the control algorithm. Simulation results show that the control method can performance well in the situations of varying frequency and load. The test rig of a hydraulic system is established, which could change pump speed and fluid pressure. The test results show that more than 90% reduction for a certain frequency can be achieved for stable pressure statuses. And more than 50% reduction is achieved for varying frequency with step changes. As for the load-varying status, the reduction of 82.7% and 65.8% are obtained before and after pressure change. The method proposed is validated to be effective with both numerical and experimental tests.

Enhanced Design of Sunroof System through Parametric Study Considering Vibration Phenomenon during Vehicle Operation

Recently, performance development related to noise, vibration, and harshness in sunroof systems has attracted significant research attention. However, research thus far has been limited to analytical and experimental studies relating to structural improvement of the individual parts, rather than considering vehicle driving conditions. This study compared the experimental data from actual driving tests with simulation results to examine sunroof vibration characteristics under realistic conditions. Firstly, the characteristics of sunroof vibrations were investigated theoretically in order to derive equations of motion and the natural frequencies of the sunroof. Sunroof vibrations occurring during driving were analyzed through experimental modal analysis and operational deflection shape. A parametric study was conducted adapting design parameters such as the Young’s modulus, glass thickness, and bracket location. The vibration characteristics of the sunroof glass could be improved by changing the support points of the front and rear brackets, which represent the design elements that can achieve the greatest efficiency with minimal design changes.