Sensor Calibration Research Articles

A novel in-situ sensor calibration method for building thermal systems based on virtual samples and autoencoder

Sensor networks are playing an increasingly important role in modern buildings. With the growing size of building sensor networks and the increasing use of low-cost sensors, the accuracy and reliability of these sensor networks face challenges. Therefore, in-situ calibration of sensor networks is crucial to maintain data quality. Various state-of-the-art methods typically require meeting stringent conditions, such as reference sensors or co-located sensors, accurate physical models, and a large amount of operational data, limiting their applicability in some scenarios. This paper addresses a common issue in sensor calibration: the non-differential calibration issue in uncontrolled environments. We propose an in-situ calibration method based on virtual samples and Autoencoder. Virtual samples are generated through Monte Carlo sampling to ensure the completeness of sample information. Autoencoder autonomously establishes relationships within the sensor network, integrating sensor fault detection and calibration into one step. Offline experiments optimize methods, and online experiments are utilized for verification and analysis. The online experiments demonstrated that the proposed method achieved a calibration accuracy above 98.9 % for single and multiple sensor faults, with a calibration error below 3 %. Compared to the SoT methods, our approach consistently delivered superior performance, confirming its outstanding efficacy.

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.

Deep operator and SSA collaboration for space sustainability

The space data association (SDA) has been providing reliable flight safety products for approximately 30 spacecraft operators for 14 years now. The service provides conjunction warnings and operator points of contact for around 700 spacecraft occupying all orbital regimes. The SDA's Space Data Center (SDC), built by AGI and maintained and operated by COMSPOC Corporation, has a proven track record of providing high availability space traffic coordination (STC) products since becoming operational on 15 July 2010.Earlier this year, the SDA, and its chief technical consultant, COMSPOC Corporation, supported the U.S. Department of Commerce (DOC) Geosynchronous Earth Orbit (GEO) and Middle Earth Orbit (MEO) Pilot project by providing comprehensively fused orbit solutions, ten-day orbit ephemeris and covariance predictions, and smoothed reference ephemerides for 100 active spacecraft. Most of these spacecraft are operated by SDA members and participants, allowing the operators to collaboratively contribute their maneuver plans, GPS NavSol measurements, active ranging, and passive RF observations, and authoritative spacecraft dimensions to the DOC Pilot project. For its part, COMSPOC employed its Space Situational Awareness (SSA) Software Suite (SSS)to comprehensively fuse this diverse set of spacecraft operator observations with COMSPOC's own observations from its global network of optical sensors.The first phase of this collaborative data fusion required the establishment of accounts, data connectivity, file transfer methods, and sensor calibration. This required about one month of technical interchange, provision of operator sensor locations and specifics, and COMSPOC SSS operator calibration of those sensors for each spacecraft.But once the data flows, sensor calibrations, and maneuver readers were completed, the second phase drew upon a nearly continuous stream of fused observations and maneuvers to yield accurate and timely predictive ephemerides and covariance time histories. These data products are well-suited to the collision avoidance problem.

Calibration technique for airborne vibration sensors based on actuators

Addressing the challenge of in-situ calibration for onboard vibration sensors in aircraft installations, we propose an online calibration method based on actuators. Leveraging the inverse piezoelectric effect, we employ piezoelectric actuators mounted on the vibration sensors to calibrate vibration signals. Experimental results demonstrate that the influence of the actuator on the energy transfer of the measured sensor’s vibration signal during the calibration process can be negligible, meeting the calibration accuracy requirements of onboard vibration sensors. Through validation experiments, we determine the excitation coefficients of the actuators at different frequencies, subsequently obtaining the sensitivity of the vibration sensors equipped with actuators. The results reveal that within the frequency range, the relative error of sensitivity remains within ±3%, satisfying the relevant calibration requirements. Therefore, the actuator-based online calibration technique offers an efficient means of calibrating onboard vibration sensors, carrying significant practical value.

Expanded Signal to Noise Ratio Estimates for Validating Next-Generation Satellite Sensors in Oceanic, Coastal, and Inland Waters

The launch of the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) and the Surface Biology and Geology (SBG) satellite sensors will provide increased spectral resolution compared to existing platforms. These new sensors will require robust calibration and validation datasets, but existing field-based instrumentation is limited in its availability and potential for geographic coverage, particularly for coastal and inland waters, where optical complexity is substantially greater than in the open ocean. The minimum signal-to-noise ratio (SNR) is an important metric for assessing the reliability of derived biogeochemical products and their subsequent use as proxies, such as for biomass, in aquatic systems. The SNR can provide insight into whether legacy sensors can be used for algorithm development as well as calibration and validation activities for next-generation platforms. We extend our previous evaluation of SNR and associated uncertainties for representative coastal and inland targets to include the imaging sensors PRISM and AVIRIS-NG, the airborne-deployed C-AIR radiometers, and the shipboard HydroRad and HyperSAS radiometers, which were not included in the original analysis. Nearly all the assessed hyperspectral sensors fail to meet proposed criteria for SNR or uncertainty in remote sensing reflectance (Rrs) for some part of the spectrum, with the most common failures (>20% uncertainty) below 400 nm, but all the sensors were below the proposed 17.5% uncertainty for derived chlorophyll-a. Instrument suites for both in-water and airborne platforms that are capable of exceeding all the proposed thresholds for SNR and Rrs uncertainty are commercially available. Thus, there is a straightforward path to obtaining calibration and validation data for current and next-generation sensors, but the availability of suitable high spectral resolution sensors is limited.

Shape Sensing for Continuum Robotics Using Optoelectronic Sensors with Convex Reflectors

Three-dimensional shape sensing in soft and continuum robotics is a crucial aspect for stable actuation and control in fields such as minimally invasive surgery, engine repairs and search and rescue operations, as the estimation of complex curvatures while using continuum robotic tools is required to manipulate through fragile paths. This challenge has been addressed using a range of different sensing techniques, for example, Fibre Bragg grating (FBG) technology, inertial measurement unit (IMU) sensor networks, or stretch sensors. Previously, an optics-based method using optoelectronic sensors was explored, offering a simple and cost-effective solution for shape sensing in a flexible tendon-actuated manipulator in two orientations. This was based on proximity-modulated angle estimation and has been the basis for the shape sensing method addressed in this paper. The improved and miniaturised technique demonstrated in this paper is based on the use of a convex shaped reflector with optoelectronic sensors integrated into a tendon-actuated robotic manipulator. Upgraded sensing capability is achieved using optimisation of the convex reflector shape in terms of sensor range and resolution, and improved calibration is achieved through the integration of spherical bearings for friction-free motion. Shape estimation is achieved in two orientations upon calibration of sensors, with a maximum Root-Mean-Square Error (RMS) of 3.37°.

HypeAIR: A novel framework for real-time low-cost sensor calibration for air quality monitoring in smart cities

While less reliable than authorized air quality stations, low-cost sensors help monitor air quality in areas overlooked by traditional devices. A calibration process in the same environment as the sensor is crucial to enhance their accuracy. Furthermore, low-cost sensors deteriorate over time, necessitating repeated calibration for sustained performance. HypeAIR is a novel open-source framework for the management of sensor calibration in real-time. It incorporates two calibration methodologies: a combination of machine learning models (Voting Regressor and Support Vector Regression) and the Long Short-Term Memory deep learning model. To evaluate the framework, three extensive experiments were conducted over a 2-year period in the city of Modena, Italy, to monitor NO, NO2, and O3 gases. Both calibration methodologies outperform the manufacturer calibration and our baseline (i.e., a variation of the Random Forest algorithm) and maintain efficiency over time. The availability of the source code facilitates customization for monitoring additional pollutants, while shared air quality datasets ensure reproducibility.

Research on Calibration Method of Six Dimensional Force Sensor at the End of Robot

A six-axis force sensor calibration method for industrial robots is proposed to meet the demand for precise force measurement of end effectors in industrial robots. Firstly, establish a sensor data relationship calculation model, and based on this, focus on considering the interference caused by sensor "zero drift"; Then, the least squares method is used to determine the inclination angle between the robot's end effector and the installation of the six-axis force sensor, the gravity of the end effector, and the coordinate parameters of the center of gravity. Finally, a CR5 robotic arm experimental platform was built to verify the feasibility of the algorithm. The experiment showed that the calibrated sensor maintained a stable force value of 0N without external force, verifying the accuracy of the calibration algorithm.

Exploring the potential of smartphone MEMS sensors for cost-effective rotating machinery speed estimation

PurposeThe purpose of this study is to explore the use of smartphone-embedded microelectro-mechanical sensors (MEMS) for accurately estimating rotating machinery speed, crucial for various condition monitoring tasks. Rotating machinery (RM) serves a crucial role in diverse applications, necessitating accurate speed estimation essential for condition monitoring (CM) tasks such as vibration analysis, efficiency evaluation and predictive assessment.Design/methodology/approachThis research explores the utilization of MEMS embedded in smartphones to economically estimate RM speed. A series of experiments were conducted across three test setups, comparing smartphone-based speed estimation to traditional methods. Rigorous testing spanned various dimensions, including scenarios of limited data availability, diverse speed applications and different smartphone placements on RM surfaces.FindingsThe methodology demonstrated exceptional performance across low and high-speed contexts. Smartphones-MEMS accurately estimated speed regardless of their placement on surfaces like metal and fiber, presenting promising outcomes with a mere 6 RPM maximum error. Statistical analysis, using a two-sample t-test, compared smartphone-derived speed outcomes with those from a tachometer and high-quality (HQ) data acquisition system.Research limitations/implicationsThe research limitations include the need for further investigation into smartphone sensor calibration and accuracy in extremely high-speed scenarios. Future research could focus on refining these aspects.Social implicationsThe societal impact is substantial, offering cost-effective CM across various industries and encouraging further exploration of MEMS-based vibration monitoring.Originality/valueThis research showcases an innovative approach using smartphone-embedded MEMS for RM speed estimation. The study’s multidimensional testing highlights its originality in addressing scenarios with limited data and varied speed applications.

The Difference in the Assessment of Knee Extension/Flexion Angles during Gait between Two Calibration Methods for Wearable Goniometer Sensors.

Frontal and axial knee motion can affect the accuracy of the knee extension/flexion motion measurement using a wearable goniometer. The purpose of this study was to test the hypothesis that calibrating the goniometer on an individual's body would reduce errors in knee flexion angle during gait, compared to bench calibration. Ten young adults (23.2 ± 1.3 years) were enrolled. Knee flexion angles during gait were simultaneously assessed using a wearable goniometer sensor and an optical three-dimensional motion analysis system, and the absolute error (AE) between the two methods was calculated. The mean AE across a gait cycle was 2.4° (0.5°) for the on-body calibration, and the AE was acceptable (<5°) throughout a gait cycle (range: 1.5-3.8°). The mean AE for the on-bench calibration was 4.9° (3.4°) (range: 1.9-13.6°). Statistical parametric mapping (SPM) analysis revealed that the AE of the on-body calibration was significantly smaller than that of the on-bench calibration during 67-82% of the gait cycle. The results indicated that the on-body calibration of a goniometer sensor had acceptable and better validity compared to the on-bench calibration, especially for the swing phase of gait.

Evaluation of Humidity Sensor Calibration Uncertainty by Monte Carlo Method

Evaluation of Humidity Sensor Calibration Uncertainty by Monte Carlo Method

A GEO-GEO Stereo Observation of Diurnal Cloud Variations over the Eastern Pacific

Fast atmospheric processes such as deep convection and severe storms are challenging to observe and understand without adequate spatiotemporal sampling. Geostationary (GEO) imaging has the advantage of tracking these fast processes continuously at a cadence of the 10 min global and 1 min mesoscale from thermal infrared (TIR) channels. More importantly, the newly-available GEO-GEO stereo observations from our 3D-Wind algorithm provide more accurate height assignment for atmospheric motion vectors (AMVs) than those from conventional TIR methods. Unlike the radiometric methods, the stereo height is insensitive to radiometric TIR calibration of satellite sensors and can assign the feature height correctly under complex situation (e.g., multi-layer clouds and atmospheric inversion). This paper shows a case study from continuous GEO-GEO stereo observations over the Eastern Pacific during 1–5 February 2023, to highlight diurnal variations of clouds and dynamics in the planetary boundary layer (PBL), altocumulus/congestus, convective outflow and tropical tropopause layer (TTL). Because of their good vertical resolution, the stereo observations often show a wind shear in these cloud layers. As an example, the stereo winds reveal the classic Ekman spiral in marine PBL dynamics with a clockwise (counterclockwise) wind direction change with height in the Northern (Southern) Hemisphere subtropics. Over the Southeastern Pacific, the stereo cloud observations show a clear diurnal variation in the closed-to-open cell transition in the PBL and evidence of precipitation at a lower level from broken stratocumulus clouds.

A Global Seawater Density Distribution Model Using a Convolutional Neural Network.

Seawater density is an important physical property in oceanography that affects the accuracy of calculations such as gravity fields and tidal potentials and the calibration of acoustic and optical oceanographic sensors. In related studies, constant density values are frequently used, which can introduce significant errors. Therefore, this study employs a basic convolutional neural network model to construct a comprehensive model showing the seawater density distribution across the globe. The model takes into account depth, latitude, longitude, and month as inputs. Numerous real seawater datasets were used to train the model, and it has been shown that the model has an absolute mean error and root mean square error of less than 1 kg/m3 in 99% of the test set samples. The model effectively demonstrates the influence of input parameters on the distribution of seawater density. In this paper, we present a newly developed global model for distributing seawater density which is both comprehensive and accurate, surpassing previous models. The utilization of the model presented in this paper for estimating seawater density can minimize errors in theoretical ocean models and serve as a foundation for designing and analyzing ocean exploration systems.

Developing and Validating an Intelligent Mouth-Opening Training Device: A New Solution for Restricted Mouth Opening.

Restricted mouth opening (trismus) is one of the most common complications following head and neck cancer treatment. Early initiation of mouth-opening exercises is crucial for preventing or minimizing trismus. Current methods for these exercises predominantly involve finger exercises and traditional mouth-opening training devices. Our research group successfully designed an intelligent mouth-opening training device (IMOTD) that addresses the limitations of traditional home training methods, including the inability to quantify mouth-opening exercises, a lack of guided training resulting in temporomandibular joint injuries, and poor training continuity leading to poor training effect. For this device, an interactive remote guidance mode is introduced to address these concerns. The device was designed with a focus on the safety and effectiveness of medical devices. The accuracy of the training data was verified through piezoelectric sensor calibration. Through mechanical analysis, the stress points of the structure were identified, and finite element analysis of the connecting rod and the occlusal plate connection structure was conducted to ensure the safety of the device. The findings support the effectiveness of the intelligent device in rehabilitation through preclinical experiments when compared with conventional mouth-opening training methods. This intelligent device facilitates the quantification and visualization of mouth-opening training indicators, ensuring both the comfort and safety of the training process. Additionally, it enables remote supervision and guidance for patient training, thereby enhancing patient compliance and ultimately ensuring the effectiveness of mouth-opening exercises.

Field calibration of low-cost particulate matter sensors using artificial neural networks and affine response correction

Due to detrimental effects of atmospheric particulate matter (PM), its accurate monitoring is of paramount importance, especially in densely populated urban areas. However, precise measurement of PM levels requires expensive and sophisticated equipment. Although low-cost alternatives are gaining popularity, their reliability is questionable, attributed to sensitivity to environmental conditions, inherent instability, and manufacturing imperfections. The objectives of this paper include (i) introduction of an innovative approach to field calibration for low-cost PM sensors using artificial intelligence methods, (ii) implementation of the calibration procedure involving optimized artificial neural network (ANN) and combined multiplicative and additive correction of the low-cost sensor readings, (iii) demonstrating the efficacy of the presented technique using a custom-designed portable PM monitoring platform and reference data acquired from public measurement stations. The results obtained through comprehensive experiments conducted using the aforementioned low-cost sensor and reference data demonstrate remarkable accuracy for the calibrated sensor, with correlation coefficients of 0.86 for PM1 and PM2.5, and 0.76 PM10 (particles categorized as having diameter equal to or less than 1 μm, 2.5 μm, and 10 μm, respectively), along with low RMSE values of only 3.1, 4.1, and 4.9 µg/m3.

LoRa Based IoT Enabled Sensor Networks for Plantations

The integration of Wireless Sensor Network (WSN) technology with the Internet of Things (IoT) in agriculture plays a pivotal role in addressing a range of challenges and constraints faced by the sector, encompassing issues such as labour shortages, suboptimal farm management practises, and unpredictable weather conditions. In response to these pressing concerns, this study focuses on the development of WSN with IoT for agriculture, employing a spread spectrum modulation technique named the Long Range (LoRa) module. By leveraging LoRa devices and wireless radio frequencies, this technology serves as a versatile platform for delivering wireless, long-range, and energy-efficient communication to support small and medium-sized agricultural operations. These operations often lack adequate technological assistance due to factors such as limited expertise and the high costs associated with cutting-edge agricultural technology. The research undertakes a comprehensive exploration, involving LoRa parameter testing, Line-of-Sight evaluations, Non-Line-Of-Sight (NLOS) simulations, and sensor calibration, to assess the efficacy of LoRa-based IoT-enabled sensor networks within plantation environments. The overarching objective of this research endeavour is to provide valuable insights that contribute to the optimisation of agricultural practises through streamlined IoT solutions. By implementing practical and cost-effective strategies, the local agricultural sector stands poised to achieve seamless strides in both sustainable and efficient food production.

Toward Wide Area Remote Sensor Calibrations: Applications and Approaches

Toward Wide Area Remote Sensor Calibrations: Applications and Approaches

Variability between Different Hand-Held Dynamometers for Measuring Muscle Strength.

Muscle strength is routinely measured in patients with neuromuscular disorders by hand-held dynamometry incorporating a wireless load cell to evaluate disease severity and therapeutic efficacy, with magnitude of effect often based on normative reference values. While several hand-held dynamometers exist, their interchangeability is unknown which limits the utility of normative data. We investigated the variability between six commercially available dynamometers for measuring the isometric muscle strength of four muscle groups in thirty healthy individuals. Following electro-mechanical sensor calibration against knowns loads, Citec, Nicholas, MicroFET2, and Commander dynamometers were used to assess the strength of ankle dorsiflexors, hip internal rotators, and shoulder external rotators. Citec, Jamar Plus, and Baseline Hydraulic dynamometers were used to capture hand grip strength. Variability between dynamometers was represented as percent differences and statistical significance was calculated with one-way repeated measures ANOVA. Percent differences between dynamometers ranged from 0.2% to 16%. No significant differences were recorded between the Citec, Nicholas, and MicroFET2 dynamometers (p > 0.05). Citec grip strength measures differed to the Jamar Plus and Baseline Hydraulic dynamometers (p < 0.01). However, when controlling for grip circumference, they were comparable (p > 0.05). Several hand-held dynamometers can be used interchangeably to measure upper and lower limb strength, thereby maximising the use of normative reference values.

Hand-eye calibration of line structured-light sensor by scanning and reconstruction of a free-placed standard cylindrical target

This paper proposed a hand-eye calibration method for a line structured-light (LSL) scanner mounted on industrial robot. Unlike widely used spherical targets, the target used in our work is a cylinder, which is inexpensive and easy to be manufactured in larger size to cover a wider calibration space. In order to address the profile self-occlusion and drastically shape changing of conic section during calibration, an unconstrained dimensionality reduction method is proposed to fit the scanned profiles. Based on fitted ellipse centers under various robot poses, the hand-eye calibration model is developed based on the difference between cylinder radius and the distance from the profile point to the cylinder axis. Simulation and actual experiments have shown that the proposed ellipse fitting method is more accurate and robust, and the calibration method achieves higher reconstruction accuracy under the same robot positioning error, need less robot operations to obtain more accurate hand-eye parameters.

Temperature-dependent calibration and temperature compensation of elastic shape-memory alloy strain sensors for fiber-reinforced composite applications

Abstract Strain sensors for fiber-reinforced plastics require higher elasticity and fatigue resistance than conventional strain gauges. Elastic strain sensors made of shape-memory alloys (SMA) meet this requirement. Due to greater elasticity, other procedures are required for their calibration than those recommended in standards. This paper presents a calibration method for shape-memory strain sensors as a function of ambient temperature and methods for temperature compensation are investigated. SMA strain sensors are manufactured as sensor patches from wire and layers of glass fiber fleece infiltrated with epoxy resin. The patches are bonded to bending specimens made of glass-fiber plastic composites. The calibration of the SMA sensors is implemented by means of a 4-point bending test using a self-built test stand. This is designed for operation in a climate chamber. The results show successful proof of feasibility of temperature compensation. The variation of the sensor signal in the unloaded state in the temperature response is less than 0.4 mV/V. The gauge factor depends on the temperature and is compensated by means of a regression with temperature sensor data. In combination with a temperature sensor, an almost complete compensation of the temperature-dependent behaviour is possible. A procedure is realised for calibrating SMA sensors at larger strains.