Sensor Errors Research Articles

A high-accuracy system model and accuracy evaluation method for transfer alignment

With the increasing demand for high-accuracy navigation, distributed payloads put forward higher requirements for the initial alignment accuracy of their equipped slave inertial navigation systems (INSs). However, the existing transfer alignment models have poor compensation effect on flexible deformation, resulting in low alignment accuracy. Moreover, these models cannot accurately evaluate the accuracy of transfer alignment due to ignoring the errors of the master INS. To address these two problems, a new high-accuracy transfer alignment system model is established by comprehensively considering errors of the master and slave INSs. Further, the propagation characteristics of the master and slave inertial sensor errors in transfer alignment are analyzed, and the relationship expression between the master and slave inertial sensor accuracy and the transfer alignment accuracy is derived. Thus, the accuracy of transfer alignment can be pre-evaluated according to the master and slave inertial sensor accuracy. The experiment results show that the alignment accuracy of the proposed method is improved by more than 71.56% compared with the traditional methods, and the relative errors between the proposed accuracy evaluation method and the Monte Carlo simulations are less than 9.14%. Thus, the correctness, effectiveness and superiority of the proposed method are verified.

Real-time Fishpond Water Temperature Monitoring with Internet of Things (IoT) Technology

The quality of pond water is an important factor in ensuring the health and sustainability of the organisms within it. Internet of Things (IoT) technology has enabled the development of sophisticated and effective monitoring systems to monitor water quality in real-time. One important parameter that needs to be monitored is the temperature of the pond water, as unstable temperatures can harm aquatic organisms. To obtain good quality and disease-free fish at normal times, the optimal temperature for catfish fry is around 28˚C - 30˚C. Manual monitoring or using thermometer measuring instruments is still very traditional and limited to the ability of supervisors to carry out continuous monitoring and may cause delays in detecting significant temperature changes. This research aims to analyze the monitoring system of pool water quality based on temperature by utilizing IoT technology. The proposed system uses a DS18B20 temperature sensor that is wirelessly connected to the IoT network. The temperature data collected in real time will be uploaded to Firebase for storage and further analysis. Through the development of this IoT-based pond water quality monitoring system, it is expected to increase efficiency and reliability in monitoring pond water quality. From the monitoring conducted for approximately 24 hours, the temperature in the fishpond is 28°-30°. After analyzing with direct monitoring, the good temperature is at night at 18.00-00.00 WIB. In conjunction with a standard thermometer, resulted in an average temperature sensor error rate of less than 5%, highlighting the reliability of the IoT technology in real-time environmental monitoring.

Imprecise evidence in social learning

AbstractSocial learning is a collective approach to decentralised decision-making and is comprised of two processes; evidence updating and belief fusion. In this paper we propose a social learning model in which agents’ beliefs are represented by a set of possible states, and where the evidence collected can vary in its level of imprecision. We investigate this model using multi-agent and multi-robot simulations and demonstrate that it is robust to imprecise evidence. Our results also show that certain kinds of imprecise evidence can enhance the efficacy of the learning process in the presence of sensor errors.

Improved ellipsoid fitting aided geomagnetic sensor calibration algorithm

Aiming at the problems that the traditional single ellipsoid fitting fails to eliminate the errors of the geomagnetic sensor and does not consider the geomagnetic anomaly information, etc., this paper proposes a robust least squares combined with the Levenberg–Marquardt (LM-RLS) algorithm to fit an accurate ellipsoid to achieve accurate data calibration. According to the geomagnetic sensor error model, the total amount of geomagnetism is used as the constraint, the fitted ellipsoid coefficients of RLS are used as the initial values, and the LM algorithm is used to calculate the magnetic field dispersion points to the nearest point of the fitted ellipsoid surface. Then, the initial calibration value is solved by the criterion of the minimum weighted sum of squares of the closest point distance. Finally, the accurate ellipsoid is obtained to realize data calibration. The results of the dynamic experiment show that after the ellipsoid is precisely fitted by the LM-RLS algorithm, the sum of the absolute values of the distance from the magnetic field discrete data to the ellipsoid surface is reduced by 48.33% (RLS) and 43.10% (RWTLS). The relative errors of the magnetic field in the X, Y, and Z axes are reduced from 0.48%, 0.78%, 1.48% (RLS) and 0.30%, 0.58%, 1.14% (RWTLS) to 0.10%, 0.04% 0.16 %, respectively. The accuracy of geomagnetic calibration is improved by 68.06% (RLS) and 61.02% (RWTLS) in the dynamic experiment. The algorithm improves the accuracy of geomagnetic measurement, which provides a new idea for the calibration of three-axis geomagnetic sensors.

Modified interactive multiple model particle filter for terrain referenced navigation with classification error minimisation strategy

AbstractThe authors propose an innovative solution to address challenges in terrain‐referenced navigation (TRN). The suggested solution is the interactive multiple‐model particle filter with a classification error minimisation strategy (IMM‐CPF) based on decision theory. TRN is a technique that estimates position by comparing measured terrain altitude to the digital elevation model and critically depends on obtaining accurate altitude measurements. However, these measurements can be easily contaminated to not only from sensor errors but also from vegetation effects. The TRN measurement noise model is characterised as a multi‐modal density, and it reveals an overlap between two density functions, with the mixture weight parameter varying based on surface environmental conditions. This variability can potentially degrade estimation accuracy. The proposed approach integrates truncated likelihoods into the mode estimation process to enhance mode estimation capability using a classification error minimisation strategy. The proposed strategy is based on decision theory and has been modified to be suited in the IMMPF form. The effectiveness of the proposed IMM‐CPF method is verified through simulations conducted under diverse surface conditions, demonstrating significant improvements in estimation accuracy compared to conventional algorithms. Furthermore, the significance of this method is presented in terms of computational cost and robustness.

TEMPERATURE ERROR OF THE MEANS OF MEASURING THE GEOMETRIC CHARACTERISTICS OF BORES OF FIREARMS: MATHEMATICAL MODEL AND QUANTITATIVE EVALUATION

The work is devoted to the construction of mathematical models and quantitative assessment of the components of the temperature error of a laser triangulation sensor for measuring the geometric characteristics of bores of firearms.
 The existence of problematic issues in the field of evaluating the metrological characteristics of laser triangulation means for technical diagnostics of firearms barrel channels is highlighted. An analysis of publications
 on the evaluation of the error of a laser triangulation sensor caused by the influence of ambient temperature is carried out, and known approaches to their mathematical modeling are considered. The need to develop mathematical models of the components of the temperature error of a laser triangulation sensor and to quantify this error is substantiated.
 The parameters of the optical scheme of the laser triangulation sensor, whose deviation from their nominal (calculated) values will affect the conversion function, are determined. It is shown that the presence of a temperature error during the processing of measurement information will be perceived as an apparent change in the measured value with a constant input signal.
 On the basis of algebraic transformations of expressions reflecting the relationship between the output value of a laser triangulation sensor and deviations of the optical circuit parameters from their nominal values, mathematical models of the temperature error components are obtained and presented in the form of error component limits at the input of the measuring instrument. The expressions for determining the confidence limits of the total temperature error, taking into account the correlation between its components, are substantiated.
 A quantitative assessment of the temperature error for the characteristic combinations of weapon parameters (for four ranges of barrel calibers) in the range of ambient temperature variations inherent in the region where measurements are made is carried out. The results of the quantitative evaluation are presented in the form of graphs of the dependence of the limits of relative and absolute temperature errors on temperature and on the increase in the bore radius in the measurement range. The data obtained were analyzed, the peculiarities of the dependencies were determined, and conclusions were drawn about the compliance of the temperature error characteristics of the measuring instrument with the requirements for its accuracy.

Finite-Time Adaptive Fuzzy Backstepping Control for Quadrotor UAV With Stochastic Disturbance

This paper proposes an adaptive fuzzy output feedback control approach for quadrotor unmanned aerial vehicles (QUAV) with stochastic disturbances, besides which we consider the unmeasurable states and unknown nonlinear functions. The QUAV system contains stochastic terms which are not bounded and not differentiable. By combining the It <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\hat{\rm \textbf{o}}$</tex-math> </inline-formula> differential equation with finite-time theory, a novel stochastic finite-time stability controller for QUAV is raised for the first time. The fuzzy logic system (FLS) is utilized to approximate the unknown nonlinear functions in the model. Dynamic surface control technology is introduced to reduce the complexity of differential. Furthermore, an observer with FLS is designed through the adaptive backstepping technique to estimate the immeasurable states. It is proved that this control approach can ensure that all states of the closed-loop QUAV system are semi-global and finite-time stable in probability. Meanwhile, the errors of system outputs and observer converge to a small neighborhood of origin. The simulation results show the effectiveness of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In practical applications, many systems are often affected by internal and external disturbances, and not all of them can be described by mathematical models, so they are called stochastic disturbances. Taking the QUAV as an example, when the system is disturbed by internal parameters, external environment, system input, sensor error, and other stochastic factors, the deterministic system can no longer accurately describe the controlled object. In this paper, the QUAV is regarded as a stochastic system for the first time, and a novel controller is designed to solve the above problems. In addition, the QUAV needs fast action in some applications, such as emergency collision avoidance, which has high requirements for the convergence performance of the controller. Therefore, studying the finite-time control for QUAV systems with stochastic disturbances is necessary. At the same time, this paper also considers the problem that the state is not measurable and the system contains unknown nonlinear equations.

Sistem Monitoring Kualitas Air dan Pakan Otomatis Pada Akuarium Ikan Mas Koki Terintegrasi IoT

Water is a living medium for goldfish, which greatly influences their lives. Water quality as a living medium for fish greatly influences the growth and development of goldfish; therefore, water quality must meet the needs of goldfish. Several water quality parameters that greatly influence fish life are temperature, degree of acidity (pH), and solid substances dissolved in water (TDS). The aim of this research is to design a monitoring system capable of measuring temperature, degree of acidity (pH), and solid substances dissolved in water based on the Internet of Things. This tool is used for an Internet of Things-based water quality monitoring system in goldfish aquariums, where the results obtained will be sent via smartphone to the Blynk application. And it can also provide food automatically and provide notifications if the water becomes cloudy. This tool is designed using an Arduino Uno, a Wemos D1 R2 controller, and sensors used to measure each parameter, namely the DS18B20 sensor, pH sensor, and TDS sensor. Then the feeder uses a servo to open the feed and an RTC as a timer. The results of this research conclude that the calibration results for the DS18B20 sensor have an average accuracy rate of 99% with a sensor error percentage of 1%, the pH sensor has an accuracy rate of 98.7% with an error percentage of 1.3%, and the TDS sensor has an accuracy rate of 94.59% with an error percentage of 5.41%.

Wavelet Dynamic Selection Network for Inertial Sensor Signal Enhancement

As attitude and motion sensing components, inertial sensors are widely used in various portable devices, covering consumer electronics, sports health, aerospace, etc. But the severe intrinsic errors of inertial sensors heavily restrain their function implementation, especially the advanced functionality, including motion trajectory recovery and motion semantic recognition, which attracts considerable attention. As a mainstream signal processing method, wavelet is hailed as the mathematical microscope of signal due to the plentiful and diverse wavelet basis functions. However, complicated noise types and application scenarios of inertial sensors make selecting wavelet basis perplexing. To this end, we propose a wavelet dynamic selection network (WDSNet), which intelligently selects the appropriate wavelet basis for variable inertial signals. In addition, existing deep learning architectures excel at extracting features from input data but neglect to learn the characteristics of target categories, which is essential to enhance the category awareness capability, thereby improving the selection of wavelet basis. Therefore, we propose a category representation mechanism (CRM), which enables the network to extract and represent category features without increasing trainable parameters. Furthermore, CRM transforms the common fully connected network into category representations, which provide closer supervision to the feature extractor than the far and trivial one-hot classification labels. We call this process of imposing interpretability on a network and using it to supervise the feature extractor the feature supervision mechanism, and its effectiveness is demonstrated experimentally and theoretically in this paper. The enhanced inertial signal can perform impracticable tasks with regard to the original signal, such as trajectory reconstruction. Both quantitative and visual results show that WDSNet outperforms the existing methods. Remarkably, WDSNet, as a weakly-supervised method, achieves the state-of-the-art performance of all the compared fully-supervised methods.

Error Analysis of Normal Surface Measurements Based on Multiple Laser Displacement Sensors.

The robotic drilling of assembly holes is a crucial process in aerospace manufacturing, in which measuring the normal of the workpiece surface is a key step to guide the robot to the correct pose and guarantee the perpendicularity of the hole axis. Multiple laser displacement sensors can be used to satisfy the portable and in-site measurement requirements, but there is still a lack of accurate analysis and layout design. In this paper, a simplified parametric method is proposed for multi-sensor normal measurement devices with a symmetrical layout, using three parameters: the sensor number, the laser beam slant angle, and the laser spot distribution radius. A normal measurement error distribution simulation method considering the random sensor errors is proposed. The measurement error distribution laws at different sensor numbers, the laser beam slant angle, and the laser spot distribution radius are revealed as a pyramid-like region. The influential factors on normal measurement accuracy, such as sensor accuracy, quantity and installation position, are analyzed by a simulation and verified experimentally on a five-axis precision machine tool. The results show that increasing the laser beam slant angle and laser spot distribution radius significantly reduces the normal measurement errors. With the laser beam slant angle ≥15° and the laser spot distribution radius ≥19 mm, the normal measurement error falls below 0.05°, ensuring normal accuracy in robotic drilling.

Model-based compensation of a concave nonlinear hysteresis in the displacement electro-hydraulic servo system

Abstract Nonlinearity exerts a profound influence on the displacement performance of actuators within electro-hydraulic servo systems, particularly in the context of hysteresis. This article analyses a specific hysteresis responsible for control instability, a consequence of the concave-hysteresis loop, giving rise to a notable peak phenomenon in displacement. The behavior of the concave-hysteresis loop is studied in the step load experiment of a triaxial apparatus. Subsequently, an accurately formulated mathematical model utilizing a mathematical function is derived. Notably, the article introduces an efficacious model-based compensation algorithm that integrates nonlinear iteration and fuzzy control. This strategic approach effectively addresses the issue associated with calculating the inverse model when the sensor is located in a nonlinear, hook-shaped domain. Simulation and experimentation verified the concave-hysteresis loop model and the compensation algorithm. The experiment demonstrated the elimination of peak phenomena and a decrease in sensor error from 3.7 mm to below 0.5 mm.

Harnessing digital twin and IoT for real-time monitoring, diagnostics, and error correction in domestic solar energy storage

This study introduces a cloud-based platform designed for real-time monitoring and comprehensive analysis of lithium-ion battery performance, incorporating a digital twin Battery Management System (BMS). This system overcomes the limitations of traditional local BMS, especially in historical data analysis. It employs advanced data processing and analytics to improve battery performance and enhance prediction accuracy. Key components include a energy measurement correction method, an coulombic efficiency (CE) estimation technique, and SOC estimation using an optimizer algorithm. These strategies are crafted to address sensor errors and dynamically adjust estimations to minimize inaccuracies. The use of sophisticated algorithms to optimize the objective function has led to significant experimental outcomes, notably in reducing the Mean Square Error (MSE) in estimations. The paper also introduces various novel methods for estimating irregular battery data, using the Central Limit Theorem for improved precision. Experimentally, the system identified a battery CE of 0.978 for a specific battery, demonstrating its capability in monitoring battery health. These advancements offer substantial scholarly insights and pave the way for broader application of advanced digital twin BMS technologies in residential battery storage and other areas. The synergy of this system with other smart grid technologies envisions a future where energy storage and management are not only more efficient and reliable but also finely optimized, enhancing the tracking and management of battery life cycles.

Distributed optical fiber magnetic field sensor based on polarization-sensitive OFDR.

A distributed optical fiber magnetic field sensor based on a polarization-sensitive optical frequency domain reflectometer (POFDR) is proposed. It extracts the accumulated Faraday rotation by combining the Stokes vectors and the backward Mueller matrices from the measured states of polarization (SOPs) and obtains the magnetic field component. This method avoids adjusting the input polarization during the magnetic field sensing process. It overcomes the drawback of the conventional POFDR scheme, which requires at least two sets of different input SOPs for each sensing. Finally, the aforementioned effectiveness has been experimentally verified by using a single-mode sensing fiber. The results show that the sensor has good repeatability and linearity. The measurement error of the magnetic field sensor is 19.4 mT. The measured magnetic field variations agree with the applied ones with similarities higher than 0.98.

A comprehensive quality assurance protocol for electromagnetic tracking in brachytherapy.

Electromagnetic tracking (EMT) systems have proven to be a valuable source of information regarding the location and geometry of applicators in patients undergoing brachytherapy (BT). As an important element of an enhanced and individualized pre-treatment verification, EMT can play a pivotal role in detecting treatment errors and uncertainties to increase patient safety. The purpose of this study is two-fold: to design, develop and test a dedicated measurement protocol for the use of EMT-enabled afterloaders in BT and to collect and compare the data acquired from three different radiation oncology centers in different clinical environments. A novel quality assurance (QA) phantom composed of a scaffold with supports to fix the field generator, different BT applicators, and reference sensors (sensor verification tools) was used to assess the precision (jitter error) and accuracy (relative distance errors and target registration error) of the EMT sensor integrated into an afterloader prototype. Measurements were repeated in different environments where EMT measurements are likely to be performed, namely an electromagnetically clean laboratory, a BT suite, an operating room, and, if available, a CT suite and an MRI suite dedicated to BT. The mean positional jitter was consistently under 0.1mm across all measurement points, with a slight trend of increased jitter at greater distances from the field generator. The mean variability of sensor positioning in the tested tandem and ring gynecological applicator was also below 0.1mm. The tracking accuracy close to the center of the measurement volume was higher than at its edges. The relative distance error at the center was 0.2-0.3mm with maximum values reaching 1.2-1.8mm, but up to 5.5mm for measurement points close to the edges. In general, similar accuracy results were obtained in the clinical environments and in all investigated institutions (median distance error 0.1-0.4mm, maximum error 1.0-2.0mm), however, errors were found to be larger in the CT suite (median distance error up to 1.0mm, maximum error up to 3.6mm). The presented quality assessment protocol for EMT systems in BT has demonstrated that EMT offers a high-accuracy determination of the applicator/implant geometry even in clinical environments. In addition to that, it has provided valuable insights into the performance of EMT-enabled afterloaders across different radiation oncology centers.

A fault-tolerant acceleration control strategy for turbofan engine based on multi-layer perceptron with exponential Gumbel loss

The acceleration performance of turbofan engine is mainly limited by the surge boundary of high-pressure compressor (HPC). A certain amount of surge margin (SM) is reserved between the boundary and the acceleration schedule (AS) of acceleration control to avoid the risk caused by intake distortion, etc. However, the performance improvement by low SM design is the requirement for advanced engines. Hence it must execute a fault-tolerant strategy for the impact of sensor error, fuel metering error, etc. This paper proposes a method by adopting four ASs for weighted average to replace the single AS. The weights are altered by the multi-layer perceptron with reference to the real-time fuel results from four ASs, intake condition and rotor speed. For safety and accuracy, an exponential Gumbel loss function is introduced into the model training. The simulation results demonstrate that the method can significantly tolerate the impact of a single fault and the normal deviations of other factors without causing SM of HPC to be less than 3.6%. These also indicate that the acceleration time on the ground does not exceed acceptable 5 s, with a median that is 0.34 s less than the minimum method of two ASs within the flight envelope.

The Far Ultraviolet Light Curve of 78 Vir (A2p SrCrEu)

All IUE spectra obtained shortwards of 2000 Å with the SWP camera are used to construct a far-ultraviolet (FUV) light curve at 1500 Å for 78 Vir over its rotational period. Setting an arbitrary phase 0.00 for the first spectrum recorded on 1987 May 10, the minimum and maximum occur respectively near phases 0.20–0.25 and 0.60–0.70. The Fine Error Sensor counts, which measure the brightness near the V band, vary in phase with the FUV flux, which is the opposite situation to that observed in another cool Ap star, 21 Com.

Online Identification of Aerodynamic Parameters of Experimental Rockets Based on Unscented Kalman Filtering

Online identification of aerodynamic parameters of experimental rockets was completed based on unscented Kalman filtering (UKF). Numerical simulation, hardware-in-the-loop (HIL) simulation, and flight tests were conducted. The identification error of aerodynamic force in numerical simulation and HIL simulation is within 2%. For flight test data, trajectory reconstruction was performed using the identified aerodynamic forces, and the results showed that the identification results were more accurate than the interpolation table calculation results. The flight test identification results show that the identification method can complete parameter online identification under the conditions of limited performance of onboard computers, real sensor errors, and servo response. The approximate linear correlation between α and δe and the reason for their formation from the moment balance were analyzed. It was pointed out that when the recognition sampling period is long, this phenomenon will affect the identification of parameters, and a solution is proposed.

Practical Finite-Time Event-Triggered Control of Underactuated Surface Vessels in Presence of False Data Injection Attacks

Abstract The results of studies on a trajectory-tracking problem affected by false data injection attacks (FDIAs) and internal and external uncertainties are presented in this paper. In view of the FDIAs experienced by the system, we compensate for the serious navigation deviation caused by malicious attacks by designing an online approximator. Next, we study the internal and external uncertainties introduced by environmental factors, system parameter fluctuations, or sensor errors, and we design adaptive laws for these uncertainties to approximate their upper bounds. To further enhance the response velocity and stability of the system, we introduce finite-time technology to ensure that the unmanned underactuated surface vessels (USVs) reach the predetermined trajectory-tracking target within finite time. To further reduce the update frequency of the controller, we introduced event-triggered control (ETC) technology. This saves the system’s communication resources and optimizes the system. Through Lyapunov stability theory, a strict and complete stability analysis is provided for the control scheme. Finally, the effectiveness of the control scheme is verified using two sets of simulations.

Measurement Errors When Measuring Temperature in the Sun.

In the validation of microclimate simulation software, the comparison of simulation results with on-site measurements is a common practice. To ensure reliable validation, it is crucial to utilize high-quality temperature sensors with a deviation smaller than the average absolute error of the simulation software. However, previous validation campaigns have identified significant absolute errors, particularly during periods of high solar radiation, possibly attributed to the use of non-ventilated radiation shields. This study addresses the issue by introducing a ventilated radiation shield created through 3D printing, aiming to enhance the accuracy of measurements on cloudless summer days with intense solar radiation. The investigation employs two pairs of sensors, each comprising one sensor with a ventilated and one with a non-ventilated radiation shield, placed on a south-oriented facade with two distinct albedos. Results from the measurement campaign indicate that the air temperature measured by the non-ventilated sensor is elevated by up to 2.8 °C at high albedo and up to 1.9 °C at a low albedo facade, compared to measurements with the ventilated radiation shield. An in-depth analysis of means, standard deviations, and 95% fractiles highlights the strong dependency of the non-ventilated sensor error on wind velocity. This research underscores the importance of employing ventilated radiation shields for accurate microclimate measurements, particularly in scenarios involving high solar radiation, contributing valuable insights for researchers and practitioners engaged in microclimate simulation validation processes.

Implementation of Internet of Things (IoT) In Heart Rate Measurement Tool

The heart is one of the vital organs that plays an important role. Checking the condition of the heart is done by measuring the heart rate to find out whether it is beating normally or not as a first step to prevent heart disease. Therefore, this research aims to create an IoT-based heart rate monitor that is easy to use at any time. This research consists of making hardware which includes ESP32-C3, MAX30102 heart rate sensor, Beitian Bn-220 gps, 96''oled display and buzzer and software which includes Arduino IDE as system coding and Thinger.io platform as a medium for receiving and storing sensor data to facilitate a monitoring process. After testing, it was found that the average error of the MAX30102 heart rate sensor was 1.02%, and the difference between the distance of the coordinate points obtained by the Beitian gps with google maps was 3.79 metres indoors and 3.17 metres outdoors. Heart rate data and location points can also be stored in real time on Thinger.io and alerts can be received within 2 to 5 seconds. The results show that the system is working in line with the desired objectives.