Sensor Errors Research Articles

Time domain virtual sensing method based on a rigid-sphere transfer function for active noise control headrests

Active noise control (ANC) is expected to be used in public transportation such as airplanes and trains. While using these applications, passengers prefer not to wear earphones or ear microphones, if possible, to reduce ear strain. However, conventional ANC systems require a physical microphone, which acts as an error sensor, at the ear position. To address this issue, a virtual sensing technique has been studied that uses a microphone mounted on a seat’s headrest to estimate the sound pressure at the ear position. In this study, we propose a virtual sensing technique that interpolates the sound pressure at the ear position using a spherical microphone array surrounding the head. This method improves robustness to variations in noise arrival direction using spherical harmonic coefficients without directional dependence. In general, the interpolation formula using spherical harmonic expansion is obtained in the frequency domain. By contrast, the proposed method designs the interpolation process as an IIR filter by considering the minimum phase and estimates the noise signal directly in the time domain, achieving the real-time processing required for ANC. The effectiveness of the proposed method in reducing noise was investigated for various noise arrival directions.

Исследование амплитудной и фазовой ошибок датчика-опоры при ультразвуковой сварке пластмасс

The paper presents a technique making it possible to evaluate amplitude and phase errors of the magnetoelastic support sensor used in the plastics ultrasonic welding. Measurement of the support sensor vibration amplitude allows monitoring alterations in the basic physical properties of the polymer being welded, i.e. its temperature and thickness. The support sensor operation dynamic mode is described. It is established that evaluating measurement errors with the proposed method in manufacture of any support sensor of different design makes it possible to accurately determine its performance with parameters of the polymer material ultrasonic welding process. The problem of evaluating accuracy of the magnetoelastic support sensor operation used in the welding process was considered. The support sensor amplitude and phase errors during ultrasonic welding were studied. A magnetoelastic sensor was designed and developed ensuring improved accuracy in measuring the support vibrations amplitude during the polymer material ultrasonic welding. The experiments carried out confirmed clear relationship between the support vibrations amplitude and heating of the welded samples, their thickness and properties during the ultrasonic welding.

An Improved In-Motion Alignment Method for SINS/GPS With the Sliding Windows Integration

In this paper, an improved in-motion alignment method for SINS/GPS integration is proposed. The alignment errors, which are caused by the maneuvering of the vehicle and the accumulated errors of inertial sensor errors, are suppressed. Firstly, the traditional in-motion alignment method for SINS/GPS integration is reviewed, and the existing problems, which are accumulated errors in the observed vectors, are investigated. To address these problems, a sliding windows operation for vector observation optimization is proposed. The derivation for the calculation of the new vector observations is given in detail. Using the new vector observations, the accumulated errors, which are existing in the observed vectors, are weakened. Thus, the alignment results are more stable whenever any maneuvering of the vehicle. Lastly, the simulation and field tests are designed for verification of the proposed method. Compared with the currently popular methods, the alignment errors are converged to a small value quickly.

Library Visitor Noise Monitoring System Design With Warning Using LED and Telegram

Abstract. The library supports education and is used as a place to learn and gain knowledge. To maximize its use, visitors need comfort by not making noise or upheaval that can disturb other visitors. According to the decision of the Ministry of Environment in 1996, the maximum standard for noise levels in libraries is 55 dB. This research was conducted to design a tool that can monitor the noise that occurs in the library by providing information on room conditions usingLED (Light Emitting Diode) lights, LCD (Liystal Displayquid Cr), and giving notifications to librarians in the form of messages on the Telegram application if the noise exceeds the limit. maximum. The tool is designed using ESP32 as a control, a GY-MAX4466 sensor to detect sound, and the output system uses red, yellow, and greenLED (Light Emitting Diode), LCD (Liquid Crystal Display) 16x2, and the Telegram application. The results of this study concluded that the noise monitoring system was running well, with a GY-MAX4466 sensor error rate of 0.4%, and the best R Square value was obtained at a distance of 1 meter from the sound source with an R Square value of 0,7202.

Impact of Atmospheric Correction on Classification and Quantification of Seagrass Density from WorldView-2 Imagery.

Mapping the seagrass distribution and density in the underwater landscape can improve global Blue Carbon estimates. However, atmospheric absorption and scattering introduce errors in space-based sensors' retrieval of sea surface reflectance, affecting seagrass presence, density, and above-ground carbon () estimates. This study assessed atmospheric correction's impact on mapping seagrass using WorldView-2 satellite imagery from Saint Joseph Bay, Saint George Sound, and Keaton Beach in Florida, USA. Coincident in situ measurements of water-leaving radiance (), optical properties, and seagrass leaf area index (LAI) were collected. Seagrass classification and the retrieval of LAI were compared after empirical line height (ELH) and dark-object subtraction (DOS) methods were used for atmospheric correction. DOS left residual brightness in the blue and green bands but had minimal impact on the seagrass classification accuracy. However, the brighter reflectance values reduced LAI retrievals by up to 50% compared to ELH-corrected images and ground-based observations. This study offers a potential correction for LAI underestimation due to incomplete atmospheric correction, enhancing the retrieval of seagrass density and above-ground Blue Carbon from WorldView-2 imagery without in situ observations for accurate atmospheric interference correction.

Improved noise bias compensation-equivalent circuit modeling strategy for battery state of charge estimation adaptive to strong electromagnetic interference

Strong electromagnetic interference, which has a significant impact on the performance and safety of the lithium-ion battery, usually affects the accurate state of charge (SOC). Different optimization strategies are used to estimate the model parameters and the SOC due to the unknown nonlinear characteristics caused by noise. However, the impact of sensor and model errors is treated separately. To express the sensor and model uncertainties, a noise bias compensation-equivalent circuit model (NBC-ECM) is proposed, in which sensor noise and model error voltages are employed in the model structure and the SOC estimation process of the lithium-ion battery. For parameter identification, a singular value decomposition-bias compensation recursive least squares (SVD-BCRLS) algorithm is proposed to identify the characteristic micro-parameters of the battery. Then, a moving window adaptive extended Kalman filtering (MWAEKF) algorithm based on window functions is proposed for accurate SOC estimation of lithium-ion batteries. The stability of the model parameters and the reliability of the proposed algorithm in estimating the SOC are evaluated using different noise factors: current and voltage sensor noises of 10 and 50 mA. Using the proposed SVD-BCRLS-MWAEKF algorithm, the maximum SOC error is 1.3 %, the root mean square error (RMSE) is 0.3972 %, and the mean absolute error (MAE) is 0.2316 % using the noise of 0.05 V/A under the hybrid power pulse characterization (HPPC) operating condition. With the same noise value under the Beijing bus dynamic stress test (BBDST) operating condition, the proposed algorithm SOC has a maximum SOC error of 1.57 %, an RMSE of 0.5638 %, and an MAE of 0.4475 %. Under noise interference conditions, estimation is more accurate compared to static conditions, proving that the proposed algorithm can overcome the uncertainties encountered by lithium-ion batteries for real-time BMS applications.

A novel unsupervised framework for time series data anomaly detection via spectrum decomposition

Time series is a common type of data that widely exists in various real-world scenarios, such as traffic flow data, network KPI, financial data, which can be regarded as time series. Anomaly detection in time series is an interesting research topic with a wide range of real-world applications, such as network intrusion detection, traffic situation monitoring and sensor error detection. In the real-world scenario, the frequency of anomalies is very low and little anomalous sample is available for analysis. Therefore, unsupervised methods are usually used for anomaly detection. In this paper, based on spectrum analysis and time series decomposition, an unsupervised deep framework for anomaly detection in time series data is designed. First, we decompose the original time series into trend series, seasonal series and residual series based on spectrum analysis. Then, prediction models based on long short-term memory (LSTM) networks and convolutional neural networks (CNNs) are designed to predict the trend series and seasonal series, respectively, and the residual series is reconstructed based on a Gaussian distribution. Next, the time series data are reconstructed by superimposing the predicted trend series and seasonal series and reconstructed residual series. Finally, we compare the original time series with the reconstructed time series and detect anomalies according to a certain threshold. The method proposed in this paper integrates prediction-based and reconstruction-based methods, and the experimental results on four datasets demonstrate the excellent performance of our method.

A fast strapdown gyrocompassing algorithm based on INS differential errors

This paper presents an enhanced algorithm for inertial gyrocompassing using strapdown sensors, which performs faster than the other available ones. The proposed algorithm is based on differential errors in an inertial navigation system and requires only the output of the inertial measurement unit while extracting and compensating for the inertial sensor errors. After eliminating the error of the inertial sensors, which is accomplished swiftly, the coarse alignment algorithm performs with error-compensated sensors, and the true north is extracted accurately. The number of non-observable parameters of the algorithm is equal to that of the fine alignment algorithm; therefore, its accuracy is the same as that of a well-tuned fine alignment. Numerical simulations and lab experiments demonstrate that the proposed method performs heading estimation in the time required to perform the coarse alignment, which is faster than the existing fine alignment algorithms.

Sensor error calibration and optimal geometry analysis of calibrators

The target localization performance is not only affected by the measurement noise, but also related with the sensor error such as sensor position error and measurement biases. If not properly estimated and calibrated, sensor error may cause large localization error or ghost tracks. This paper investigates the sensor error calibration problem with imperfect calibrators whose positions are not accurately known when both the sensor position error and measurement biases exist. The performance loss without considering the sensor error is analyzed by evaluating the mean square error (MSE). A closed-form solution based on weighted least squares (WLS) is proposed to estimate the sensor measurement bias and update the sensor position by using the biased range and angle measurement about calibrators. Moreover, the optimal geometry for calibration objects is analyzed and the optimum criterions for updating sensor position are proposed by minimizing the trace of the CRLB. The optimal calibrator-sensor geometries in the presence of measurement bias are generally different from the optimal calibrator-sensor geometries in the absence of measurement bias. Finally the performance of the proposed solution is verified and the optimal calibrator-sensor geometry results for 2 or 3 sensors are solved efficiently by using Nelder–Mead simplex method in simulation.

Error suppression method of SINS/CNS integrated navigation based on diametrical stargazing

The strap-down inertial navigation system (SINS)/celestial navigation system (CNS) integrated navigation system has been extensively employed in the aerospace field owing to its unique advantages. The internal parameter errors of the star sensor constitute one of the primary factors that affect the navigation accuracy. In this paper, an error suppression method based on diametrical stargazing has been designed to suppress the navigation errors caused by the internal parameter errors of the star sensor. First, a SINS/CNS integrated navigation model based on Kalman filter has been established. Then the influence of the principal point error, image-plane tilt error, and distortions of the optical system on the measurement accuracy of the star sensor have been analyzed. Further, the process of introducing the errors into the measurement equation has been obtained. Finally, for multiple errors, two stargazing methods have been designed to suppress the impact of these errors on the navigation accuracy, whereas the constant errors of the inertial device can be modulated. The simulation results demonstrate that the proposed method can improve the positioning accuracy of the system, and verify the effectiveness of the algorithm.

Wireless positioning method for transmission pipe gallery inspection robot based on the TDOA algorithm

A wireless sensor network for positioning an inspection robot is developed for a transmission pipe gallery environment. The time difference of the arrival algorithm optimized by the least squares method is utilized to realize the wireless positioning function of the robot in the inspection process. The developed system adopts a spiral configuration to install the sensor nodes at equal intervals on the sidewall of the transmission pipe gallery, and only 10 sensor nodes closest to the wireless signal transmission source are activated for each measurement, which avoids the poor long-distance measurement effect of the sensor, ensures sufficient accuracy, and reduces calculation costs. A wireless positioning simulation test was conducted in an experimental transmission corridor environment. The error of the simulation results is equivalent to the measurement error of a single sensor, and the error is not accumulated in the calculation of the algorithm, indicating that the algorithm has achieved good results.

Distance Transform Pooling Neural Network for LiDAR Depth Completion.

Recovering dense depth maps from sparse depth sensors, such as LiDAR, is a recently proposed task with many computer vision and robotics applications. Previous works have identified input sparsity as the key challenge of this task. To solve the sparsity challenge, we propose a recurrent distance transform pooling (DTP) module that aggregates multi-level nearby information prior to the backbone neural network. The intuition of this module is originated from the observation that most pixels within the receptive field of the network are zero. This indicates a deep and heavy network structure has to be used to enlarge the receptive field aiming at capturing enough useful information as most processed signals are uninformative zeros. Our recurrent DTP module can fill in empty pixels with the nearest value in a local patch and recurrently transform distance to reach farther nearest points. The output of the proposed DTP module is a collection of multi-level semi-dense depth maps from original sparse to almost full. Processing this collection of semi-dense depth maps alleviates the network from the input sparsity, which helps a lightweight simplified ResNet-18 with 1M parameters achieve state-of-the-art performance on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) depth completion benchmark with LiDAR only. Besides the sparsity, the input LiDAR map also contains some incorrect values due to the sensor error. Thus, we further enhance the DTP with an error correction (EC) module to avoid the spreading of the incorrect input values. At last, we discuss the benefit of only using LiDAR for nighttime driving and the potential extension of the proposed method for sensor fusion and the indoor scenario. The code has been released online at https://github.com/placeforyiming/DistanceTransform-DepthCompletion.

A Convolutional Fuzzy Neural Network Active Noise Cancellation Approach without Error Sensors for Autonomous Rail Rapid Transit

Autonomous rail rapid transit (ART) is a new type of multiunit, articulated, rubber-wheeled urban transport system. The noise sources of ART have significant time-varying characteristics. It is unsuitable to track the error signal by installing too many error sensors, which poses a significant challenge in the active noise control of ART. Thus, this paper proposes a convolutional fuzzy neural network-based active noise cancellation approach without error sensors to solve this problem. The proposed approach utilizes convolutional neural network (CNN) to extract the noise signal characteristics of ART and trains multiple noise source signals using a CNN to estimate the virtual error signal in the target area. In addition, the proposed approach adopts fuzzy neural network (FNN) for adaptive adjustment of filter weight coefficients to achieve real-time noise tracking control with fast convergence and small error in the convergence process. The experimental results demonstrate that the proposed approach can effectively reduce ART low-frequency noise without error sensors, and the average sound pressure level in the target area is reduced more compared with conventional approaches.

PRESSURE SENSORS WITH METROLOGICAL SELF-CONTROL FUNCTION

The paper considers the advantages of using smart pressure sensors in systems for measuring and controlling technological objects. Modern intelligent sensors that, in addition to the measurement process, convert the measured signals into typical analog and digital values, perform self-diagnosis of their work, remotely adjust the measurement range, pre-process measurement information, and sometimes a number of fairly simple, typical control and management algorithms. They have interfaces to standard / typical field digital networks, which makes them compatible with almost any modern automation equipment, and allows you to communicate with these tools and receive power from the power supplies of these tools. The technical features of the use of modern intelligent pressure sensors are considered. The functions implemented by intelligent sensors, their structural features and characteristics of the main data transfer protocols used in automation systems where these sensors are used are analyzed. The correction of temperature errors of the pressure sensor, which works according to the scheme of the tensor-resistive bridge, with the use of the capabilities of the LabVIEW software environment, is considered. Keywords: Smart pressure sensors, data transfer protocols, sensing element, LabVIEW software.

Insights into the Fusion Correction Algorithm for On-Board NOx Sensor Measurement Results from Heavy-Duty Diesel Vehicles

Over the last decade, Nitrogen Oxide (NOx) emissions have garnered significantly greater attention due to the worldwide emphasis on sustainable development strategies. In response to the issues of dynamic measurement delay and low measurement accuracy in the NOx sensors of heavy-duty diesel vehicles, a novel Multilayer Perceptron (MLP)–Random Forest Regression (RFR) fusion algorithm was proposed and explored in this research. The algorithm could help perform post-correction processing on the measurement results of diesel vehicle NOx sensors, thereby improving the reliability of the measurement results. The results show that the measurement errors of the On-board Nitrogen oxide Sensors (OBNS) were reduced significantly after the MLP-RFR fusion algorithm was corrected. Within the concentration range of 0–90 ppm, the absolute measurement error of the sensor was reduced to ±4 ppm, representing a decrease of 73.3%. Within the 91–1000 ppm concentration range, the relative measurement error was optimised from 35% to 17%, providing a reliable solution to improve the accuracy of the OBNS. The findings of this research make a substantial contribution towards enhancing the efficacy of the remote monitoring of emissions from heavy-duty diesel vehicles.

Edge Detection of Motion-Blurred Images Aided by Inertial Sensors.

Edge detection serves as the foundation for advanced image processing tasks. The accuracy of edge detection is significantly reduced when applied to motion-blurred images. In this paper, we propose an effective deblurring method adapted to the edge detection task, utilizing inertial sensors to aid in the deblurring process. To account for measurement errors of the inertial sensors, we transform them into blur kernel errors and apply a total-least-squares (TLS) based iterative optimization scheme to handle the image deblurring problem involving blur kernel errors, whose relating priors are learned by neural networks. We apply the Canny edge detection algorithm to each intermediate output of the iterative process and use all the edge detection results to calculate the network's total loss function, enabling a closer coupling between the edge detection task and the deblurring iterative process. Based on the BSDS500 edge detection dataset and an independent inertial sensor dataset, we have constructed a synthetic dataset for training and evaluating the network. Results on the synthetic dataset indicate that, compared to existing representative deblurring methods, our proposed approach demonstrates higher accuracy and robustness in edge detection of motion-blurred images.

AUV planning and calibration method considering concealment in uncertain environments

IntroductionAutonomous underwater vehicles (AUVs) are required to thoroughly scan designated areas during underwater missions. They typically follow a zig-zag trajectory to achieve full coverage. However, effective coverage can be challenging in complex environments due to the accumulation and drift of navigation errors. Possible solutions include surfacing for satellite positioning or underwater acoustic positioning using transponders on other vehicles. Nevertheless, surfacing or active acoustics can compromise stealth during reconnaissance missions in hostile areas by revealing the vehicle’s location.MethodsWe propose calibration and planning strategies based on error models and acoustic positioning to address this challenge. Acoustic markers are deployed via surface ships to minimize navigation errors while maintaining stealth. And a new path planning method using a traceless Kalman filter and acoustic localization is proposed to achieve full-area coverage of AUVs. By analyzing the statistics of accumulated sensor errors, we optimize the positions of acoustic markers to communicate with AUVs and achieve better coverage. AUV trajectory concealment is achieved during detection by randomizing the USV navigation trajectory and irregularizing the locations of acoustic marker.ResultsThe proposed method enables the cumulative determination of the absolute position of a target with low localization error in a side-scan sonar-based search task. Simulations based on large-scale maps demonstrate the effectiveness and robustness of the proposed algorithm.DiscussionSolving the problem of accumulating underwater localization errors based on inertial navigation by error modeling and acoustic calibration is a typical way. In this paper, we have implemented a method to solve the localization error in a search scenario where stealth is considered.

Exploring Effective Approaches To Minimize Downtime In Final Assembly Line Of Braking Systems

The global automotive manufacturing industry's growth in downtime reductio is substantial, valued at $3272.6 billion USD with a 3.01% growth rate.This growth in downtime reduction underscores the industry's commitment to enhancing efficiency, quality, and overall productivity across its diverse range of operations. Downtime in the braking system assembly line can lead to utilization loss or technical availability loss. In this context, many proactive maintenance strategies are explored but there's limited focus on addressing error-prone machines and utilizing Machine Learning for predicting downtime. The objective is to prioritize downtime reduction through error analysis, critical machine identification, and implementing ML-based solutions.
 This comprehensive research delved into the significance of minimizing downtime in the braking system final assembly line through meticulous data analysis, visualization techniquesand targeted interventions and then identified key issues and achieved tangible improvements in operational efficiency. The analysis revealed significant findings and then employed the Pareto Principle to identify top downtime machines, illustrating their distribution through a Pareto chart of machine defects. Furthermore, Exceptions and Problem Areas were identified utilizing statistical process controls, offering insights into critical error contributors. Notably, a comprehensive exploration of the most prominent downtime machine was undertaken, evidenced by LCL and UCL charts and a Fishbone Diagram detailing causal relationships. The research leverages real-world data involving dates, machine names, and downtime durations to develop a predictive model that aids in proactively managing production disruptions.
 The application of RPN calculations before and after error correction demonstrated a substantial reduction from of 432 to 75, validating the efficacy of the corrective actions. The real-time data was used to build a model that can predict when production machines downtime might happen. This helps us be prepared and manage any possible disruptions in production.The outcome of the project highlights the intrinsic link between downtime reduction and assembly line efficiency, emphasizing the importance of data-driven interventions. This culminated in the resolution of key issues, illustrated by the mitigation of the PCBA Pressin machine and LVDT sensor errors, yielding tangible reductions in downtime and notable productivity improvements. In this direction, exploring AI-driven predictive maintenance holds immense potential for advancing downtime reduction strategies. Leveraging AI algorithms to analyse live data streams from machinery and sensors can enable the detection of patterns indicating imminent failures and can pre-emptively prevent downtime.

Research on artificial neural networks to accurately predict element concentrations in nutrient solutions

Calcium, potassium, nitrogen, magnesium, and phosphorus, the main elements of the nutrient solution, are absorbed by plants and play an important role in plants. By measuring Ca2+, K+, Mg2+, NH4 +, NO3 −, HPO4 2−, the artificial neural networks (ANNs) were used in this study to accurately calculate the concentrations of these elements. Firstly, the error sources of the calculating element concentration were analyzed based on the data of six-ion measurement experiments. Subsequently, various optimization algorithms were compared to optimize back propagation and radial basis function ANNs. Finally, the results of mean relative errors (MREs) and recovery values show that ANNs can effectively reduce the measurement error of ion sensors. From the perspective of recovery values, the prediction error of all elements can be controlled within 15%. From the perspective of MRE, except for magnesium and phosphorus elements, the improved model prediction errors of other elements were also less than 10%.

Analysis of errors of the artificial horizon sensor based on vision system

The article discusses the features of the practical implementation of the artificial horizon sensor based on the onboard vision system. Proposed, developed on the basis of well-known applications, a variant of the algorithm for the operation of an unmanned aerial vehicle orientation video system. The problems of automatic detection and determination of the position of the horizon line on changing digital images that make up the video stream from the onboard digital camera are shown. The analysis of the factors influencing the accuracy of estimation of carrier orientation angles using the proposed system is carried out. The results of a practical study are presented, characterizing the degree of influence of the considered factors on the total error. A discrete stochastic mathematical model of an unmanned aerial vehicle orientation system based on an onboard vision system has been developed. The possibility of providing an acceptable level of accuracy of the orientation video system due to certain technical and algorithmic solutions is shown. The conclusion is made about the expediency of using this system in autonomous multisensor navigation systems for unmanned aerial vehicles.