High-precision Sensor Research Articles

Proton radiation damage and temperature response mechanism of CMOS image sensor for space high-precision metering

The exoplanet transit telescope uses high-precision CMOS image sensor for planet searching. This device operates at 233 K to reduce dark current and noise interference on the effective signal. This paper examines the effects of space proton radiation on the performance of high-precision CMOS image sensor through proton irradiation and post-irradiation testing at various temperatures. The results indicate that proton irradiation degrades parameters like dark current, dark signal, and fixed pattern noise, with dark current being the most sensitive. As the operating temperature increases, radiation-sensitive parameters degrade rapidly, significantly impairing sensor performance. This indicates that lower temperatures provide better performance. Notably, Dark signal distribution fluctuates with temperature. Compared to before irradiation, the Gaussian mean decreases at 233 K and increases between 243 and 273 K. At low temperature, the ionization damage caused by proton radiation mainly results in the decrease of Gaussian mean, while the displacement damage mainly results in the increase of this. The study concludes that proton-induced displacement damage, which leads to bulk defects, is the primary cause of performance degradation. This provides essential data and foundational support for radiation damage assessment and on-orbit applications.

Application of fiber-optic strain sensing technology in high-precision load prediction of aircraft landing gear

In this paper, a compact and lightweight high-precision airborne fiber-optic strain sensor is designed, and a strain-load prediction model based on convolutional neural network and long short-term memory network (ConvLSTM) is proposed to validate the collected landing gear strain data. Firstly, based on the fiber Bragg grating (FBG) sensing principle and simulation validation, small and lightweight strain and temperature sensors were fabricated, and key performance parameters such as sensitivity and linearity were comprehensively investigated, and they were mounted on the left landing gear of the aircraft to conduct strain monitoring by loading a three-way load, and the ConvLSTM model was used to train and test the strain-load mapping with the maximum relative error, average relative error and variance as indicators to evaluate its prediction accuracy and stability. The experimental results show that the strain measurement accuracy is maintained within 2.5 % and the strain sensitivity is as high as 1.3 pm/με within the ± 5000 με measurement range of the strain sensor; the maximum relative errors of the X, Y, and Z load predictions are 6.03 %, 3.75 %, and 4.12 %, respectively, and the overall average relative errors are 2.38 %, 0.27 %, and 0.76 %, with variances of 0.23 N, 0.61 N, and 0.61 N, respectively. 0.23 N, 0.61 N and 0.12 N, indicating that the model predictions are stable and highly accurate, demonstrating higher prediction accuracy when compared with traditional multiple linear regression methods. The results of this research have important application value in the field of aircraft structural health monitoring.

Monitoring Perishable Commodities Using Cellular IoT: An Intelligent Real-Time Conditions Tracker Design

Perishable product losses can occur throughout postharvest handling. Proper monitoring of key environmental conditions during this period is essential for predicting quality losses throughout their shelf life. This paper presents the design and testing of a portable and compact datalogger for the real-time monitoring of environmental conditions throughout the food supply chain. The device developed incorporates high-precision sensors to measure temperature, relative humidity, CO2 concentration, luminosity and vibrations, as well as wireless communication capabilities for data transmission, simplifying real-time monitoring over existing multi-component systems while keeping costs affordable. Strategies to optimize power consumption allow a month of battery life, being able to cover entire periods of transport and storage, according to the results of the autonomy test performed on the device. The datalogger uses NB-IoT and relies on other wireless communication protocols if not available to send sensor data to a cloud platform. Comparative testing with commercial dataloggers has been carried out to verify correct device measurements, and field testing has validated successful real-time data transmission along an entire refrigerated transport route. The functionality and autonomy of the proposed device meet the needs of live remote monitoring to help reduce food losses.

Dynamic Analysis of Soil-Structure Interaction in Earthquake-Prone Areas

This study used a thorough experimental method to examine the dynamic interaction between soil and structures in earthquake-prone locations. The study challenge concentrated on how different soil types and configurations influence the diversity of structural reactions under seismic loading conditions. The research utilized a mixed methods approach, which involved quantitatively analyzing soil parameters and assessing structure dynamics. The methods employed included the creation of scaled replicas depicting common architectural structures situated on various soil types, including sandy, clayey, and mixed compositions. We used high-precision sensors to record ground motion characteristics such as Acceleration, velocity, and Displacement. The data was then evaluated using statistical methods such as ANOVA and regression analysis. The results revealed substantial differences in the structural reaction based on the type of soil and the parameters of the structure. Structures built on sandy soils saw greater peak accelerations (up to 0.170 g) but smaller displacements. On the other hand, structures on clayey soils had moderate accelerations (up to 0.140 g) but had bigger inter-story drifts. The varied soil layers, ranging from 1.500 Hz to 1.780 Hz, influenced the natural frequencies of the buildings. The damping ratios ranged from 5.000% to 7.800%, indicating that structural damping effectively reduces seismic forces. The results emphasized the critical importance of the interaction between soil and structures in seismic design and the necessity for customized engineering solutions based on the individual soil conditions at the site. Suggested measures include improving methods for soil characterization, optimizing structural dynamics using cutting-edge dampening technologies, and upgrading seismic design codes to enhance the ability of structures to withstand earthquakes in places prone to seismic activity.

High-Precision Two-Dimensional Angular Sensor Based on Talbot Effect.

The precision of two-dimensional angular sensing is crucial for applications such as navigation, robotics, and optical alignment. Conventional methods often struggle to balance precision, dynamic range, and affordability. We introduce a novel method leveraging the Talbot effect, enhanced by 3D printing technology, to fabricate a grating prototype for high-precision angular measurements. The method detects amplitude grating displacement at the self-imaging position and employs a frequency filtering algorithm for improved accuracy. Rigorous validation through simulations and physical experiments demonstrates that our method achieves a detection resolution of 0.4 arcseconds and a dynamic range exceeding 1400 arcseconds. This research presents a cost-effective, high-precision angular detection solution with potential for broad application in precision mechanical assembly, optical alignment, and other relevant domains.

Robustly packaged lithium niobate resonator-based temperature sensor

Abstract Whispering gallery mode (WGM) resonators with high optical quality factor (Q) are highly sensitive to environment changes and are used in various sensing applications. We have developed a high-precision temperature sensor using a compactly packaged lithium niobate (LN) WGM resonator. This sensor offers a sensitivity of 34.38pm/℃ and a high resolution of up to 5.6×10-4 ℃. It is portable with high Q, making it suitable for using outside the laboratory. The device is also resilient to changes in humidity, enhancing its practicality.

High-precision ethanol concentration microsensor with global spectra aided by the multi-layer perceptron

Whispering gallery mode (WGM) resonators can be used for precision measurement thanks to their high sensitivity, small size, and fast response time. Nevertheless, the design of such sensors is usually achieved by selecting a typical single-mode tracking method, which leads to low utilization of a great deal of information in the resonance spectrum and affects the precision. Here, we use the multi-layer perceptron (MLP) deep learning algorithm to train the global spectra and realize the high-precision measurement of ethanol concentration. Firstly, a large number of transmission spectra of different ethanol concentrations are collected and directly used as the original data sets. Secondly, the MLP algorithm is used for training and testing. Finally, the local feature dimension is extracted from the global features of the spectrum for prediction. The results show that the prediction accuracy of the global spectra sensing is 99.81%, which is 13.02% higher than that of extracting 10 local features. In addition, the prediction accuracy of the MLP is compared with four other commonly used machine learning (ML) algorithms, and the results show that the MLP algorithm has the highest prediction accuracy. Therefore, the high-precision ethanol concentration sensor proposed in this paper opens a new way for intelligent optical micro-resonator sensing.

Visual and Radar Data Fusion: Complementary or Alternative Sensors in Intelligent Driving Systems

This article explores the role and advantages and disadvantages of visual and radar data fusion in intelligent driving systems. With the rapid development of intelligent driving technology, the comprehensive perception of the environment by car perception systems has become crucial, usually requiring the combination of low, medium, and high-precision sensors. Visual sensors perform excellently in acquiring high-resolution images and recognizing detailed information, while radar sensors have higher stability and penetration in harsh weather conditions. Due to the limitations of a single sensor in complex driving environments, data fusion has become a key strategy for improving the performance of perception systems. By comparing different perception schemes and application scenarios, this article analyzes the actual effects and potential advantages of visual and radar data fusion, revealing its important role in intelligent driving technology. Fusion technology can compensate for the shortcomings of various sensors, and improve the accuracy and reliability of environmental perception, but still faces challenges in data synchronization, algorithm selection, and real-time performance. This article aims to provide valuable insights for the development of future intelligent driving technology.

Multi-objective optimisation of frequency offset and quality factor in semi-conical microwave resonant cavity for syrup brix measurement

Accurate measurement of syrup brix is essential for automated sucrose production because it significantly influences the quality and efficiency of the production process. The microwave resonant cavity method, an effective approach for syrup brix measurement, imposes special performance requirements on the cavity structure design, potentially necessitating unique shapes to capture the properties of the target substance. Traditional empirical design methods for these sensors have significant limitations with nonconventional shapes, often requiring further tuning and analysis of the parameters to adapt to complex geometries and achieve optimal performance. Previous studies have indicated that syrup brix variation can be predicted by the electromagnetic field characteristics of the resonant cavity, specifically the resonant frequency and quality factor, thereby allowing this relationship to be leveraged for sensor optimisation. Therefore, this study presents a systematic multi-objective optimisation approach using electromagnetic simulation software (ANSYS Electronic HFSS) and an Adaptive Reference-based Non-Dominated Sorting Genetic Algorithm (ARNSGA-II), which optimises the frequency offset and quality factor to enhance the sensor sensitivity and accuracy. Experimental comparisons between the optimised and conventional cavity designs demonstrate substantial improvements, with a 60.8% increase in the frequency offset and a 67.2% enhancement in the quality factor. Furthermore, applying the optimised design to a predictive model reduced the mean absolute error to 1.43°Bx, representing a 38.4% improvement compared to the empirical design-based resonant cavity. Results demonstrated that this study provides a robust framework for optimising complex cavity structures and offers valuable insights for the development of high-precision microwave sensors tailored for syrup brix measurements.

Development of a High-Precision and Large-Range FBG-Based Sensor Inspired by a Crank-Slider Mechanism for Wearable Measurement of Human Knee Joint Angles

Development of a High-Precision and Large-Range FBG-Based Sensor Inspired by a Crank-Slider Mechanism for Wearable Measurement of Human Knee Joint Angles

Inertial Measurement Unit Self-Calibration by Quantization-Aware and Memory-Parsimonious Neural Networks

This paper introduces a methodology to compensate inertial Micro-Electro-Mechanical System (IMU-MEMS) time-varying calibration loss, induced by stress and aging. The approach relies on a periodic assessment of the sensor through specific stimuli, producing outputs which are compared with the response of a high-precision sensor, used as ground truth. At any re-calibration iteration, differences with respect to the ground truth are approximated by quantization-aware trained tiny neural networks, allowing calibration-loss compensations. Due to the unavailability of aging IMU-MEMS datasets, a synthetic dataset has been produced, taking into account aging effects with both linear and nonlinear calibration loss. Also, field-collected data in conditions of thermal stress have been used. A model relying on Dense and 1D Convolution layers was devised and compensated for an average of 1.97 g and a variance of 1.07 g2, with only 903 represented with 16 bit parameters. The proposed model can be executed on an intelligent signal processing inertial sensor in 126.4 ms. This work represents a step forward toward in-sensor machine learning computing through integrating the computing capabilities into the sensor package that hosts the accelerometer and gyroscope sensing elements.

Inclination information-aided circumferential deformation reconstruction error correction method for CFRP belt integrated with optical fiber sensor

In view of the problem that the distributed optical fiber-based deformation reconstruction method can produce serious accumulated errors, this paper developed a smart fiber optic sensor belt that employs a carbon-fiber composite material as the matrix and strain-sensing fiber optics as the sensing element and is based on the key-point dip angle information acquired by a high-precision tilt sensor. The method of in situ correction for the cumulative error of circumferential deformation was investigated, and the particle swarm optimization algorithm was adopted to perform in situ correction for the reconstructed circumferential deformation field of the sensor belt to reduce the cumulative measurement error of the circumferential deformation field. Simulations and indoor tests were systematically performed. After the correction, the errors of the simulation and indoor tests were reduced from 210.83 and 235.82 mm to 2.17 and 4.04 mm, respectively, which verified the effectiveness and accuracy of the aforementioned method.

A double-helix plane winding sensor with wire using transient plane source method

The transient plane source (TPS) method is widely used to measure thermophysical properties of various materials, and the high-precision double-helix sensor is a key technology. A novel sensor using an efficient, simple, and low-cost lacquered micron-wire-wound process is proposed to replace traditional complex micro- and nano-etching processes. The TPS test platform was built based on the Wheatstone Bridge theory. A new sensor sample of diameter 5.377 mm was wound with an enameled copper wire of diameter 0.063 mm. Owing to the characteristics of the process, a 1-mm round hole was bored inside the sensor. An analytical solution for the concentric ring heat transfer model of the sensor with a central circular hole structure was derived, and a two-dimensional concentric circle simulation model of the sensor was established. As the number of turns decreased, the average error between the analytical solution of the concentric ring model with a large center hole (LHAS) and that of the traditional concentric circle model (TAS), as well as LHAS and the simulation model, gradually increased. The maximum relative errors between LHAS and TAS, as well as between LHAS and the simulation model, were 23.69 % and 0.1 %, respectively, and the average errors of the absolute maximum were 11.86 % and −0.06 %, respectively. The LHAS exhibited excellent accuracy because of the structural characteristics of the novel sensor. The accuracy of LHAS has been validated by simulation and hot wire method. Temperature rise data of high-borosilicate glass and polytetrafluoroethylene samples at different powers were obtained. The thermal properties of the samples were determined by fitting LHAS and TAS data. The thermal conductivity errors measured by LHAS were smaller than those measured by TAS, the thermal conductivity and thermal diffusivity errors were less than 3.5 % at different powers, and the measured repeatability errors were less than 1 %, indicating the good application prospect of the proposed model.

Special Issue on Robotics and Mechatronics Technology for Aerial Robots

Aerial robot technologies, such as drones and unmanned aerial vehicles, are rapidly advancing in various domains. These robots now possess enhanced autonomous flying abilities and use sensor technology, Global Navigation Satellite System (GNSS), Light Detection and Ranging (LiDAR) system, and cameras to avoid obstacles, automatically land, and track flights. Evolving technologies in airspace management and route optimization ensure that flights are efficient and safe. Aerial robots have a wide range of applications, such as agriculture, disaster response, environmental monitoring, and inspection. These devices excel at precise data collection thanks to their high-resolution cameras and high-precision sensors. Swarm robotics, which allows collaboration between multiple aerial and ground robots, is also progressing. This enables quick coverage of large areas, which benefits disaster relief and land management. In addition, research and development of flying robots modeled after flying animals is underway. The evolution of aerial robots results in their efficient use in a variety of fields and new applications. This special issue includes the latest research papers and development reports on aerial robotic technology from the aforementioned perspectives. We hope that this special issue will pique researchers’ and engineers’ interest in aerial robots, thereby encouraging additional research and development. Finally, we would like to express our gratitude to all authors, all reviewers, the editorial board of the Journal of Robotics and Mechatronics, and Fuji Technology Press Ltd.

Research on the simulation method of a BP neural network PID control for stellar spectrum.

This study investigated the multiple correlations among spectral simulation units based on digital micromirror device (DMD) spectral simulation, which leads to the problem that conventional spectral simulation methods such as PID control exhibit a low fitting accuracy or long fitting time in the spectral simulation of various targets. In this paper, a method of stellar spectrum simulation based on back propagation neural network-based PID (BP-PID) control is proposed to achieve high efficiency and high precision simulation of various spectral targets. The topology of the BP neural network was constructed based on the spectral modulation model of a DMD stellar spectrum simulation system, and the algorithm of the BP-PID control was designed. Finally, an experimental platform was built to verify the performance and spectral simulation accuracy of the BP-PID control algorithm. The results show that the overshoot and response time of the BP-PID control algorithm decreased by 79.01% and 30%, respectively compared with those of the PID control algorithm. The maximum spectral simulation accuracies of 2000K, 7000K, and 12000K color temperature increased by a factor of 2.311, 1.871, and 2.254, respectively, and the standard deviations of the spectral simulation error decreased by 56%, 41%, and 54%, respectively. In the range of 2000-12000K color temperature, the spectral simulation error of the BP-PID control algorithm is better than ±3.495%, and the standard deviation of the spectral simulation error is between 1.8255 and 2.2358. The proposed method can improve the spectral simulation accuracy and simulation efficiency of a star simulator, reduce the magnitude and spectrum calibration errors caused by the differential response, improve the star feature recognition accuracy of the orbiting star sensor, and hence, provide a theoretical and technical basis for the development of high-precision star sensors.

Analysis of orientation errors in triaxial fluxgate sensors and research on their calibration methods

Abstract. Three-axis magnetic flux gate sensors are widely used in Chinese geomagnetic observatories, but due to their directional errors it is necessary to study error correction methods to improve measurement accuracy. Firstly, the mechanism of directional errors produced by three-axis magnetic flux gate sensors is analyzed, followed by the development of measurement tools for conducting directional error measurement experiments on the high-precision three-axis magnetic flux gate sensors of the Chinese FGM-01 series. Experimental results show that correcting the Z-axis and D-axis directional errors is essential. The observation data after error correction, whether in terms of the standard deviation of its all-day baseline values or the relative difference magnitude with the reference instrument, significantly decrease, demonstrating the clear correction effect and proving the effectiveness of this correction method.

Study on fracture behaviour of pipeline girth welds based on curved wide plate specimens: Experimental and numerical analysis

Understanding the fracture characteristics and strain capacity of pipelines with girth welds under external loads is crucial for evaluating pipeline safety. The use of curved wide plate (CWP) specimens closely resembling full-scale (FS) pipelines in geometry provides a more realistic representation of crack tip constraint states compared to small-size fracture toughness test specimens in laboratory settings. This study aims to investigate the ductile fracture characteristics of center cracks on the outer surface of girth welds in high-grade steel pipelines under external loads through large-scale CWP tensile tests and advanced numerical simulations. Tensile tests were conducted on CWP specimens using a kiloton tensile testing machine, with double crack opening displacement (COD) gauge monitoring crack mouth opening displacement (CMOD), and high-precision displacement sensors and strain gauges tracking deformation and strains at various positions. The study yielded significant experimental results, and a finite element (FE) model of the CWP was developed using the nonlinear finite element method (FEM) to validate the experimental data against simulation results. The FE model was then used to investigate the influence of material properties on crack propagation resistance and driving force curves. A method for determining the ultimate tensile strain capacity (UTSC) of pipelines based on actual material fracture toughness was proposed. This research contributes valuable experimental data for further exploration of fracture behaviour in large-scale pipeline girth welds and offers insights for safety evaluations of such welds.

Implementation of Wearable Wireless Chest Patch Monitoring Terminal

A wearable wireless chest patch monitoring terminal is designed to realize the acquisition, processing, and wireless transmission of ECG, respiration, and body temperature signals. The analog front-end ADS1292R, which integrates respiratory impedance and ECG front-end, is utilized to collect human ECG and respiratory signals. The body temperature is collected using a low-power, high-precision digital temperature sensor MAX30208. A filter algorithm for signal processing and wireless transmission is designed through a low-power nRF52840 Bluetooth SoC with an Arm Cortex-M4F kernel. The experimental results show that the designed monitoring terminal can monitor the ECG, respiration, and body temperature parameters of the human body in real-time and send the monitoring results via Bluetooth, with a continuous working time of more than 13 hours. The wearable wireless chest patch monitoring terminal features good portability, long standby time, and high measurement accuracy, and it has promising application prospects in the fields of family health monitoring, mobile medical treatment, and smart healthcare.

Ultrasensitive integrated circuit sensors based on high-order non-Hermitian topological physics.

High-precision sensors are of fundamental importance in modern society and technology. Although numerous sensors have been developed, obtaining sensors with higher levels of sensitivity and stronger robustness has always been expected. Here, we propose theoretically and demonstrate experimentally an alternative class of sensors with superior performances based on exotic properties of high-order non-Hermitian topological physics. The frequency shift induced by perturbations for these sensors can show an exponential growth with respect to the size of the device, which can grow well beyond the limitations of conventional sensors. The fully integrated circuit chips have been designed and fabricated in a standard 65-nanometer complementary metal-oxide semiconductor process technology. Not only has the sensitivity of systems less than 10-3 femtofarad been experimentally verified, but these systems are also robust against disorders. Our proposed ultrasensitive integrated circuit sensors can have a wide range of applications in various fields and show an exciting prospect for next-generation sensing technologies.

Curing deformation compensation in bonding assembly for high-precision linear displacement encoder sensors

Abstract Linear displacement encoder sensors extensively appear in high-precision instruments, in which the core scale is usually bonded with the scale base by adhesives. However, the curing shrinkage of the adhesives often causes a warping deformation of the moving ruler, which in turn leads to a decrease in measurement accuracy. To improve the accuracy of the bonding assembly, this paper proposes a novel deformation compensation method. A force control fixture is first designed, and a tensile force is applied to tense the scale base. The scale is then bonded to the scale base, and the tensile force is maintained until the adhesive is cured. The optimal tensile force is determined to minimize the warping deformation by combining a finite-element simulation and an artificial fish swarm algorithm. The optimal result is verified experimentally, where the warping deformation is measured using a homemade three-dimensional digital image correlation system. The experimental results demonstrate that the warping deformation of the moving ruler is reduced by 89.2% using the proposed method.