Sensor Errors Research Articles

Temperature compensation study of pressure sensors after leadless packaging

Purpose The purpose of this paper is to reduce the problem of temperature drift causing output errors in such sensors, three hardware compensation schemes are proposed in this paper, and three compensation schemes are designed and implemented. Design/methodology/approach In response to the problem of temperature drift causing output errors in this type of sensor, this paper proposes three hardware compensation schemes and carries out the design and implementation of the three compensation schemes. Finally, the advantages and disadvantages of the three compensation schemes are discussed through the analysis of the experimental results. The three hardware compensation methods are series-parallel resistance network compensation, digital signal processor (DSP) compensation and the joint compensation of resistance network and DSP. Series parallel resistance network compensation is to connect the low-temperature drift resistance and the sensor in series and parallel; DSP compensation is based on the combination of cubic spline interpolation and linear fitting algorithm, which uses DSP to process the data. Joint compensation is a new compensation method composed of the above two compensation methods. Findings The experimental results show that the relative error of the output is reduced to a certain extent after the three compensation methods, and the relative error of the output after the joint compensation is reduced to about 0.2%, which proves that the three compensation methods are feasible. Originality/value This paper presents three novel hardware compensation methods to reduce temperature drift in silicon on insulator (SOI) high-temperature pressure sensors. The joint compensation method, combining resistance network and DSP compensation, is particularly innovative and significantly improves output accuracy, reducing relative error to about 0.2%.

Gravity-aided navigation using Viterbi map matching algorithm

Abstract In Global Navigation Satellite Systems (GNSS)-denied environments, aiding a vehicle's inertial navigation system (INS) is crucial to reducing the accumulated navigation drift caused by sensor errors (e.g. bias and noise). One potential solution is to use measurements of gravity as an aiding source. The measurements are matched to a geo-referenced map of Earth's gravity to estimate the vehicle's position. In this paper, we propose a novel formulation of the map matching problem using a hidden Markov model (HMM). Specifically, we treat the spatial cells of the map as the hidden states of the HMM and present a Viterbi style algorithm to estimate the most likely sequence of states, i.e. most likely sequence of vehicle positions, that results in the sequence of observed gravity measurements. Using a realistic gravity map, we demonstrate the accuracy of our Viterbi map matching algorithm in a navigation scenario and illustrate its robustness compared with existing methods.

All-fiber magnetic field sensor based on narrow-bandwidth laser internal cavity modulation with coated magnetic fluid

In this paper we present a magnetic probe structure based on magneto-fluidic and laser internal cavity modulation. We used a single-mode-no-core-single-mode fiber structure coated by a magnetic fluid as a sensing element, and inserted the sensing element in the inner cavity of a laser. Highly sensitive, high signal-to-noise ratio (SNR), narrow half-height width (FWHM) sensing signals have been obtained using intra-laser cavity modulation. The magnetic detection sensitivity of the sensor was improved, and a sensing system with a magnetic detection sensitivity of 1.76 × 10−3 mw/Oe and a linearity of 99.709% was achieved. The temperature sensitivity is 0.035 μW/°C. The limit error of the magnetic field sensor is ±0.006035%. Meanwhile, output signal index of the sensing system is FWHM<40pm.

Enhancing surface water mapping and monthly dynamics monitoring with a stepwise gap-filling method

ABSTRACT Optical satellite imaging for surface water mapping often encounters significant challenges owing to persistent spatial data gaps caused by clouds, shadows, and sensor errors. This study presents a novel Stepwise Gap-Filling (SGF) method, designed to enhance the monthly surface water mapping and monitoring. The SGF method leverages temporal similarities and spatial correlations to reconstruct gap pixels originally classified as invalid observations. We validated this approach against historical high-resolution Google Earth images from 2887 sample points in the Siling Co Basin of the Tibetan Plateau. The results demonstrated substantial improvements in mapping accuracy, achieving an overall accuracy of 98.93%, a producer’ accuracy of 98.59%, and a user’ accuracy of 99.11%, markedly reducing the uncertainties in the original dataset. Importantly, the SGF method offers detailed insights into monthly surface water dynamics, which are closely aligned with annual trends. This study highlights the effectiveness of the SGF method for filling data gaps and its potential for widespread application in the monitoring and management of global water resources.

TO THE MATHEMATICAL ESTIMATION OF THE STRAIN SENSORS ERROR

The work is devoted to the mathematical analysis of the error of the theory of strain sensors. In the theory of deformation sensors, it is assumed that the sensor, due to its geometric and mechanical characteristics, does not make changes (or makes minor changes) to the stress-strain state (SSS) of the tested structure. To numerically estimate the sensor error, an exact solution of the dynamic contact problem of the sensor and the elastic half-space is obtained. For the analytical solution of the problem, the method used by Lamb to determine the dynamic stress-strain state of an elastic half-space under the action of concentrated forces applied to its surface is generalized. A numerical solution of this problem has been performed for sensors and elastic half-space made of various materials. Practical recommendations are formulated. A numerical solution of this problem has been performed.

A Robust Method for Validating Orientation Sensors Using a Robot Arm as a High-Precision Reference.

This paper presents a robust and efficient method for validating the accuracy of orientation sensors commonly used in practical applications, leveraging measurements from a commercial robotic manipulator as a high-precision reference. The key concept lies in determining the rotational transformations between the robot's base frame and the sensor's reference, as well as between the TCP (Tool Center Point) frame and the sensor frame, without requiring precise alignment. Key advantages of the proposed method include its independence from the exact measurement of rotations between the reference instrumentation and the sensor, systematic testing capabilities, and the ability to produce repeatable excitation patterns under controlled conditions. This approach enables automated, high-precision, and comparative evaluation of various orientation sensing devices in a reproducible manner. Moreover, it facilitates efficient calibration and analysis of sensor errors, such as drift, noise, and response delays under various motion conditions. The method's effectiveness is demonstrated through experimental validation of an Inertial Navigation System module and the SLAM-IMU fusion capabilities of the HTC VIVE VR headset, highlighting its versatility and reliability in addressing the challenges associated with orientation sensor validation.

Innovative Modeling of IMU Arrays Under the Generic Multi-Sensor Integration Strategy.

This research proposes a novel modeling method for integrating IMU arrays into multi-sensor kinematic positioning/navigation systems. This method characterizes sensor errors (biases/scale factor errors) for each IMU in an IMU array, leveraging the novel Generic Multisensor Integration Strategy (GMIS) and the framework for comprehensive error analysis in Discrete Kalman filtering developed through the authors' previous research. This work enables the time-varying estimation of all individual sensor errors for an IMU array, as well as rigorous fault detection and exclusion for outlying measurements from all constituent sensors. This research explores the feasibility of applying Variance Component Estimation (VCE) to IMU array data, using separate variance components to characterize the performance of each IMU's gyroscopes and accelerometers. This analysis is only made possible by directly modeling IMU inertial measurements under the GMIS. A real land-vehicle kinematic dataset was used to demonstrate the proposed technique. The a posteriori positioning/attitude standard deviations were compared between multi-IMU and single IMU solutions, with the multi-IMU solution providing an average accuracy improvement of ca. 14-16% in the estimated position, 30% in the estimated roll and pitch, and 40% in the estimated heading. The results of this research demonstrate that IMUs in an array do not generally exhibit homogeneous behavior, even when using the same model of tactical-grade MEMS IMU. Furthermore, VCE was used to compare the performance of three IMU sensors, which is not possible under other IMU array data fusion techniques. This research lays the groundwork for the future evaluation of IMU array sensor configurations.

Comparative Trend Analysis of Precipitation Indices in Several Towns of the Sirba River Catchment (Burkina Faso) from CHIRPS and TAMSAT Rainfall Estimates

The increasingly frequent pluvial flood of West African urban settlements indicates the need to investigate the drivers of local rainfall changes. However, meteorological stations are few, unevenly distributed, and work irregularly. Daily satellite rainfall datasets can be used. Nevertheless, these products often need to be more accurate due to sensor errors and limitations in retrieval algorithms. The problem is, therefore, how to characterize rainfall where there is a need for ground-based rainfall records or incomplete series. This study aims to characterize urban rainfall using two satellite datasets. The analysis was carried out in the Sirba river catchment, Burkina Faso, using the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and the Tropical Applications of Meteorology using SATellite and ground-based data (TAMSAT) datasets. Ten indices from the Expert Team on Climate Change Detection and Indices (ETCCDI) of precipitation were calculated, and their statistical trends were evaluated from 1983 to 2023. The study introduces two key innovations: a comparative analysis of precipitation trends using two satellite datasets and applying this analysis to towns within a previously understudied 39,138 km2 catchment area that is frequently flooded. Both datasets agree on the increase of (i) annual cumulative rainfall over all towns, (ii) five-day maximum rainfall over the town of Manni, (iii) rainfall due to very wet days in Gayéri, (iv) days of heavy rainfall in Bogandé, Manni and Yalgho, and (v) days of very heavy rainfall in Yalgho. These findings suggest the need for targeted pluvial flood prevention measures in towns with increasing trends in heavy rainfall.

A robust error sensing strategy for global active noise control in car cabins.

Global active noise control (ANC) systems reduce noise over the entire car cabin with robust performance even as the human head moves; however, they have not been implemented in real-world applications. A robust error sensing strategy is proposed in this paper that is based on which a feasible global ANC system is realized in an electric car, and real-time ANC experiments demonstrate its effectiveness. Simulations based on measured road noise show that using evenly distributed error sensors is a robust error sensing strategy for different car speeds and the upper limit frequency of 3 dB global control is inversely proportional to the equivalent distance between error sensors. Real-time experiments with a 5-seat A-class sedan demonstrate that an average noise reduction of 3.8 dB was achieved between 50 and 250 Hz with 12 evenly distributed error sensors using the four standard car audio loudspeakers as secondary sources. Virtual sensing techniques can be integrated to constitute a more practical system without obstructing the movement of human heads. The findings in this study establish a benchmark for global noise control in car cabins and can be a starting point for future optimization of the system and implementation of adaptive algorithms.

A Comprehensive IoT Cloud-based Wind Station Ready for Real-time Measurements and Artificial Intelligence Integration

Accurate and efficient wind measurement and monitoring are crucial for various applications, including renewable energy, meteorology, public safety, and environmental research. This paper presents an integrated approach for a cloud-computing Internet of Things-based Automated Weather Station, with edge devices, designed to provide real-time wind measurements in diverse environments. The presented system comprises a dedicated station frame, a self-sufficient solar power setup, an ultrasonic wind sensor, a data transmission node, cloud computing capabilities, and an end-user visualization application. Calibration procedures reduced the maximum mean error of the ultrasonic wind sensors from 0.7 m/s to 0.2 m/s, achieving a 71% improvement in measurement accuracy. Using this method, all station data are processed every 3 seconds and stored ready for Artificial Intelligence usage with an approximate latency of 150–300 milliseconds, representing up to an 85% reduction in processing delay compared to similar solutions with over 1-second latencies. The lightweight and resistant frame design facilitates easy deployment, while the power setup with sub-watt efficiency ensures a reliable energy source. The ultrasonic wind sensor was subjected to calibration procedures and design improvements to increase its performance under specific rain conditions. Internet of Things communication via Hypertext Transfer Protocol enables efficient data transmission between the Automated Weather Station and the cloud server, which processes and stores the data for future analysis. The responsive and auto-adaptive web application allows user-friendly data visualization, making the wind data easily accessible and interpretable. The proposed cloud-computing Internet of Things-based Automated Weather Station framework demonstrates significant potential for accurate and efficient wind measurement and monitoring, paving the way for future advancements in high temporal resolution wind monitoring systems capable of producing big data prepared for subsequent machine learning model approaches.

Cross-Shaped Peg-in-Hole Autonomous Assembly System via BP Neural Network Based on Force/Moment and Visual Information

Currently, research on peg-in-hole (PiH) compliant assembly is predominantly limited to circular pegs and holes, with insufficient exploration of various complex-shaped PiH tasks. Furthermore, the degree of freedom for rotation about the axis of the circular peg cannot be constrained after assembly, and few studies have covered the complete process from autonomous hole-searching to insertion. Based on the above problems, a novel cross-shaped peg and hole design has been devised. The center coordinates of the cross-hole are obtained during the hole-searching process using the three-dimensional reconstruction theory of a binocular stereo vision camera. During the insertion process, 26 contact states of the cross-peg and the cross-hole were classified, and the mapping relationship between the force-moment sensor and relative errors was established based on a backpropagation (BP) neural network, thus completing the task of autonomous PiH assembly. This system avoids hand-guiding, completely realizes the autonomous assembly task from hole-searching to insertion, and can be replaced by other structures of pegs and holes for repeated assembly after obtaining the accurate relative pose between two assembly platforms, which provides a brand-new and unified solution for complex-shaped PiH assembly.

Coordinating variable refrigerant flow system for effective demand response in commercial buildings

Model Predictive Control (MPC) is extensively utilized for demand response (DR) in commercial buildings. When deploying MPC in real buildings, the model uncertainty is unavoidable, which could come from multiple sources: incorrectly identified values of model parameters, unmeasured disturbances, sensor errors, etc. However, there is a lack of research on the effects of model uncertainty on the performance of MPC for DR events. This study addresses this gap by analyzing the impact of model uncertainty on MPC in coordinating multiple VRF systems in commercial buildings. A virtual testbed was developed to benchmark and evaluate different DR control algorithms. We compared rule-based control (RBC), priority stack-based control (PSBC), and MPC with model uncertainties. This involved assuming normal distributions with various means for biased and unbiased uncertainties and different standard deviations. Our results show that MPC is robust to model uncertainty. For costs saving tasks, when the model uncertainty is unbiased, MPC outperforms the RBC controller with fewer discomfort hours and lower energy costs when the uncertainty of cooling load is below 40 % and 90 %, respectively; when the model uncertainty is biased and increases gradually, MPC can only outperform RBC under small uncertainties of cooling load. For load tracking tasks, when the model uncertainty is unbiased, MPC outperforms PSBC in terms of fewer discomfort hours and lower power tracking error, even with a 100 % uncertainty of cooling load; when the model uncertainty is biased, it exhibits a similar pattern to the unbiased uncertainty. Last, this study compared the computation time and control performance of formulating MPC as integer versus linear programming, demonstrating that linear programming is more suitable for coordinating thermally controllable loads (TCLs) at large-scales due to its faster computation time and good tracking performance. This study provides valuable insights into the effects of model uncertainty on MPC performance in demand response and supports its practical application in commercial buildings.

Inverse heat transfer for real-time thermal evaluation of aircraft thermal protection structure with embedded FBG sensors

Thermal Protection Structures (TPSs) are indispensable parts of aircraft. In the context of high-speed aviation vehicles, surface thermal information of the TPS is vital to ensuring the safe operation of the aircraft. However, the real-time measurement of surface thermal information becomes a great challenge due to the complex and volatile flight environment. In this paper, we propose a method for real-time monitoring of the TPS’ surface temperature by using embedded temperature sensors processed with inverse heat conduction model. In the investigation, tube packaged FBG (TP-FBG) sensors that were insensitive to strain and vibration were employed as embedded sensors in thermal protection composites. In the signal processing, thermal hysteresis correction model based on the Fourier heat conduction theory was established. After that, the data processing method of Auto-Regression with eXtra inputs was used to process the data of the sensors. The measurement error and response delay of bare FBG sensor, thermocouple and TP-FBG sensors are compared by a transient thermal shock experiment. The experiment results show that thermal hysteresis effect of the TP-FBG sensors is significant. However, the correction model can effectively correct the measurement errors of TP-FBG caused by thermal hysteresis. Building upon the model, two inverse heat conduction methods with adaptive boundaries were proposed. These methods can retrieve surface thermal information by processing the measured temperatures from embedded sensors. Numerical simulations demonstrate that while the instantaneous heating moment may exhibit significant relative errors, the average relative error remains below 5.54%. Experimental validation using a quartz lamp to heat real TPS materials further confirms the effectiveness of the inversion method. The method successfully mitigates the influence of process deviations during sensor embedding and material preparation, maintaining an average relative error of less than 8.53%. These findings suggest the potential for the proposed correction model and inversion methods in practical engineering applications. Particularly, their applicability to dynamic high temperature monitoring of thermal protection composites.

A Novel Approach for Kalman Filter Tuning for Direct and Indirect Inertial Navigation System/Global Navigation Satellite System Integration.

This work presents an innovative approach for tuning the Kalman filter in INS/GNSS integration, combining states from the inertial navigation system (INS) and data from the Global Navigation Satellite System (GNSS) to enhance navigation accuracy. The INS uses measurements from accelerometers and gyroscopes, which are subject to uncertainties in scale factor, misalignment, non-orthogonality, and bias, as well as temporal, thermal, and vibration variations. The GNSS receiver faces challenges such as multipath, temporary signal loss, and susceptibility to high-frequency noise. The novel approach for Kalman filter tuning involves previously performing Monte Carlo simulations using ideal data from a predetermined trajectory, applying the inertial sensor error model. For the indirect filter, errors from inertial sensors are used, while, for the direct filter, navigation errors in position, velocity, and attitude are also considered to obtain the process noise covariance matrix Q. This methodology is tested and validated with real data from Castro Leite Consultoria's commercial platforms, PINA-F and PINA-M. The results demonstrate the efficiency and consistency of the estimation technique, highlighting its applicability in real scenarios.

The critical impact of remote pilot modelling in evaluation of detect-and-avoid systems explained for ACAS Xu

Detect-and-avoid (DAA) systems for remotely piloted aircraft systems (RPAS) can provide remain well clear (RWC) guidance as well as shorter term resolution advisories (RAs) for collision avoidance, which are both provided in the vertical and horizontal planes. Simulation-based studies for large sets of encounter scenarios are used in the development and evaluation of DAA systems, which encompass safety and operational acceptability of the DAA supported operations. Given the key role of the remote pilot (RP) in responding to RWC guidance and RAs, a RP model is an essential element in such simulations. This paper describes the development of a RP model for evaluation of encounter scenarios involving the ACAS Xu DAA system. The model describes RP situation awareness (perception, comprehension, projection) as basis for decision-making, modes for responding to RAs and/or RWC guidance, response delays, response strengths, and the flight control actions. The RP model includes deterministic and stochastic settings. It is integrated in a simulation environment for encounters of manned and/or unmanned aircraft, the involved DAA and airborne collision avoidance systems, the surveillance and communication systems, and the human operators. Simulation results are provided for a set of encounters between pairs of RPAS both having ACAS Xu for various configurations of the RP model, and for cases with and without sensor errors. The results show that there can be large differences between the results of deterministic and Monte Carlo simulations, indicating that limited sensor errors can have a large impact on the nonlinear system dynamics. Furthermore it is shown that deadlock conditions can exist where the RPAS show oscillatory behaviour and do not manage to effectively pass each other, dependent on the encounter geometry and RP model settings. It is advised to perform a broad sensitively study for RP performance and to study extending the scope of DAA systems to include guidance for efficiently returning to mission without triggering new conflicts.

ROV localization in the nuclear reactor pressure vessel using LSTM and an improved adaptive Kalman Filter

In this paper, a remotely operated vehicle (ROV) localization algorithm for nuclear reactor pressure vessels is introduced. Traditional machine vision localization suffers from visual blind zones. To address this issue, a long short-term memory (LSTM) network was employed to obtain the ROV’s position. To further enhance the accuracy, an improved Sage-Husa adaptive Kalman Filter (SHAKF) was applied to eliminate cumulative errors from LSTM. Finally, upper-diagonal decomposition and confidence checks were used to enhance the traditional SHAKF, addressing issues associated with the non-positive definite error covariance matrix and sensor limitations. Simulated and experimental results demonstrated that the LSTM network significantly reduced the cumulative error from trajectory projection localization. The root mean square error of the localization results was reduced from 51.776 to 12.808 mm due to the improvements made to the traditional SHAKF. Additionally, the improved SHAKF effectively minimized the cumulative LSTM localization error.

Observer-Based Event-Triggered Fuzzy Control for Switched Multi-Agent Systems with State Constraints and Sensor Faults

In this paper, we consider the output feedback event-triggered tracking control problem for a class of switched nonlinear multi-agent systems (MASs) with sensor faults and state constraints. For unknown nonlinear functions in the system, fuzzy logic systems are used to approximate them. To address sensor faults, sensor error compensation coefficients are designed to compensate for the errors caused by sensor faults. Meanwhile, state observer is designed to estimate the unmeasurable states in the system, providing necessary information for control design. Based on directed switching networks containing a directed spanning tree, an event-triggered output feedback controller is designed to enable the MAS to track the leader agent. By constructing a suitable barrier Lyapunov function, the stability of the system is analyzed, and it is proved that the consensus tracking can be achieved without violating the state constraints, the uniform boundedness of all the closed-loop system signals is ensured and the Zeno behavior can be eliminated. Finally, the effectiveness of the proposed algorithm is verified through a simulation example.

Modified TRIAD Method for Solving the Problem of a Moving Object Orientation

Currently, it is relevant to create moving objects orientation systems based on the integration of various types of primary information sensors. One way to solve the orientation problem is to use the TRIAD (Tri-Axial Orientation Determination) method which allows determining the matrix of direction cosines between two coordinates. The traditional TRIAD force method is based on the use of two reference vectors – of gravitational and geomagnetic fields, measured by accelerometers and magnetometers, respectively. The disadvantage of this method is – appearance of additional deviations during the accelerated movement of the object and influence of primary information sensors’ random errors. Modified TRIAD method which is based on measurements of three triads of sensors: magnetometers, accelerometers and gyroscopes was proposed in the article. Estimates of acceleration vectors of gravitational and geomagnetic fields were calculated taking into account gyroscope measurements. Then these estimates were combined with the accelerometers’ and magnetometers’ data. The complex gravitational and geomagnetic fields’ accelerations were used to form the direction cosine matrix by the TRIAD method. The suggested modified method can be used to implement free-form moving objects’ orientation systems, since it is 6–8 times more accurate compared to the classic TRIAD method. Attenuation of random sensor errors and disturbances due to object acceleration can be adjusted by use of weigh factors.

A probabilistic assessment of ship blackout incident with Fault Tree Analysis into (FTA) Bayesian Network (BN)

Blackouts in maritime activities can cause propulsion loss and dangerous maritime conditions. Bayesian risk analysis is applied to ship blackout incidents in this study to improve understanding and reduce risks. Using Fault Tree Analysis (FTA), a Bayesian Network (BN) model incorporates fuel quality, lubricating oil quality, sensor error, injector error, and mechanical defects to estimate blackout probability. The model analyses how hazards and their interactions affect this situation using probabilistic inference. Sensitivity analysis identifies variables that affect blackout probabilities and prioritises risk mitigation solutions. Based on prior and posterior probabilities, ‘Automatic Voltage Regulator Failure’ (0.03 prior, 0.17 posterior), ‘Rotor Mechanical Fault’ (0.03 prior, 0.15 posterior), and ‘High Cooling Water Temperature’ (0.03 prior, 0.13 posterior) are the top three blackout causes. Other significant variables include ‘Switchboard Line Failure,’ ‘Faulty Fuel Pump,’ ‘Rotor Open Circuit,’ and ‘Temperature Sensor Failure’ in relative amounts. Bayesian risk analysis can identify and minimise marine blackout concerns, giving decision-makers a comprehensive framework for informed decision-making and proactive risk management. This research emphasises blackout accidents’ importance, improving maritime transportation safety and reliability.

Home Door Security System with Face Recognition Using ESP 32 Cam

The development of science and technology, especially in the fields of Electronics and Informatics, is expected to enhance room or home security systems, particularly home door security systems. Many door security technologies have been developed to prevent and ensure the comfort of homeowners when they leave their homes. Some commonly used door security systems include CCTV cameras, fingerprint sensors, voice sensors, and RFID. However, with technological advancements, these security systems have shown vulnerabilities, such as frequent errors in fingerprint and voice sensors. Based on this, to create a more secure and innovative home door security system, the authors developed a security system with face detection and a remote warning notification system using Telegram. The researchers created a door security system using the ESP 32-CAM for face detection and as a controller for the Internet of Things (IoT) system. Additionally, a manual system using a keypad for password input was also created. The results of this study include the accuracy and speed of face recognition and the speed of sending security system warning information through the Telegram application. Keywords— Door Lock System, Face Recognition, ESP32 Camera, Microcontroller, Internet of Things.