Maximum Full-scale Error Research Articles

Temperature compensation for piezoresistive pressure sensor based on deep learning on graphs

With the escalating demand for precision in piezoresistive pressure sensors to cater to a wide spectrum of applications, a significant challenge emerges due to the material properties of these sensors, which induce a substantial temperature coefficient. This limitation restricts the operational temperature range and is further exacerbated by ambient temperature variations, resulting in pronounced nonlinearity in sensor responses. To address these challenges, A new method using Graph Neural Networks (GNNs) is introduced to address nonlinearity and temperature drift in piezoresistive pressure sensors. GNNs improve the sensors’ accuracy and reliability across different temperatures, expanding their applicability. Test results show a notable accuracy enhancement, with a maximum full-scale error below 0.05% and even lower in specific ranges (under 0.04%). This precision makes the method ideal for industrial pressure sensor applications and production, offering significant benefits.

A comprehensive compensation method for piezoresistive pressure sensor based on surface fitting and improved grey wolf algorithm

The optimized surface fitting method base on improved gray wolf algorithm (IGWO) is proposed to overcome the nonlinearity and temperature drift of piezoresistive pressure sensor. To improve the convergence rate and search ability of gray wolf optimization algorithm (GWO), the adjustable nonlinearity convergence factor is established. The weight vector and weight adjustment coefficient are integrated into the residual function of the least square method. Employing the maximum full range error as the fitness function, the least square method is continuously optimized by IGWO to establish the high-performance comprehensive compensation mathematical formula. The simulation and practical test results indicate that the maximum full-scale error in the full range is better than 0.03 %, and the maximum full-scale error in the local range is less than 0.02 % especially. The proposed method proposed method is very suitable for the application and production of industrial pressure sensor with many advantages.

Research on Temperature Compensation of Multi-Channel Pressure Scanner Based on an Improved Cuckoo Search Optimizing a BP Neural Network.

A multi-channel pressure scanner is an essential tool for measuring and acquiring various pressure parameters in aerospace applications. It is important to note, however, that the pressure sensor of each of these channels will drift significantly with the increase in the temperature range of the pressure measurement, and the output voltage of each of these channels will show nonlinear characteristics, which will constrain the improvements in the accuracy of the measurement. In the regression fitting process, it is difficult to fit nonlinear data with the traditional least-squares method, which leaves pressure measurement accuracy unsatisfactory. A temperature compensation method based on an improved cuckoo search optimizing a BP neural network for a multi-channel pressure scanner is proposed in this paper to improve pressure measurement accuracy in a wide temperature range. Using the chaotic simplex algorithm, we first improved the cuckoo search algorithm, then optimized the connection weights and thresholds of the BP neural network, and finally constructed an experimental calibration system to investigate the temperature compensation of the multi-channel pressure scanning valves in the −40 °C to 60 °C temperature range. The compensation test results show that the algorithm has a better compensation effect and is more suitable for the temperature compensation of multi-channel pressure scanners than the traditional least-squares method and the standard RBF and BP neural networks. The maximum full-scale error of all 32 channels is 0.02% FS (full-scale error) and below, which realizes its high-accuracy multi-point pressure measurement in a wide temperature range.

High-precision and wide-wavelength range FBG demodulation method based on spectrum correction and data fusion.

Optical fiber sensing technology plays an important role in the application of the sensing layer of the Internet of Things. The core of this technology is the demodulation of the fiber Bragg grating (FBG) sensing system. Since the FBG sensor utilizes the wavelength change to respond to the measured size, it is of great significance to improve the accuracy of the FBG wavelength demodulation. However, the demodulation performance of the current FBG wavelength demodulation method still has much room for improvement in terms of accuracy and stability. To this end, we propose a composite gas cell demodulation scheme based on spectrum correction and data fusion by using differential photodetectors, fitting extrapolation, data fusion methods, etc. The issue of low demodulation accuracy arising due to noise, temperature drift, spectral distortion, etc., was addressed to improve the demodulation performance of the FBG. In the experiment, four FBGs with different center wavelengths were used to verify their demodulation accuracy in the range of 1510-1590 nm. The maximum repeatability error of the FBG wavelength was measured to be 2.51 pm, and the linearity was as high as 99.9% or more; under the working environment of -20 °C to 60 °C, the maximum full-scale error did not exceed ±1.71 pm, which is improved by 54.3% compared with the traditional method.

An Impedimetric Cu-Polymer Sensor-Based Conductivity Meter for Precision Agriculture and Aquaculture Applications

This paper presents a two-electrode type conductivity sensor, using copper as electrodes, coated with a polyimide layer called DQN-60. This sensor is specially designed to be robust yet inexpensive for applications like precision farming and fishery. A new conductivity sensing principle is proposed which is based on impedimetric measurement (both magnitude and phase) at a frequency of 200 kHz. This sensing principle is developed by analyzing the electrical equivalent model of the proposed sensor and is aimed at eliminating the effect of the interface impedance and the cell capacitance thus making the measurement linear and accurate. A prototype meter is developed with 3.75% maximum full-scale error in the range of 0.5 μS/cm to 20 mS/cm. The temperature compensation, calibration, and detailed experimental results are also discussed.

Design of a Low-Cost Thermal Dispersion Mass Air Flow (MAF) Sensor

There is a need for a low-cost sensor to be used in many practical applications, such as the control of the air–fuel ratio in combustion burners, which measures the mass flow rate of fluid. This paper focuses on the design, calibration, and testing of a mass flow sensor operating on the principle of thermal dispersion. The developed sensor implements a digital proportional-integral controller which regulates the body temperature of a heated element, recognized as a thermistor, located in the stream of the fluid flow to a constant difference with respect to the ambient air temperature. The power dissipated by this heated element was referenced to known mass flow rates of air to determine the relationship between the dissipated power and ambient temperature to the measured mass flow rate. The inclusion of air flow conditioners, which filtered unwanted debris and delivered a more laminar air flow, was imperative to the success of the design. The designed sensor was proven to measure the incoming mass air flow through a duct, in the presence of moderate disturbances in the intake air pipe and for a wide range of ambient air temperatures, with a maximum full-scale error of 5.5% and a range up to 80 kg/h.

Temperature compensation of micromachined silicon hot wire sensor using ANN technique

Temperature is one of the most important factors influencing accurate silicon sensor devices and is one of the largest sources of error in measurement. In this paper, a model based on Neural Networks (NN), has been implemented to generate fluid velocity data, knowing fluid temperature measurements. The proposed model based on neural networks can provide the calibrated response characteristics irrespective of change in the sensor characteristics due to change in ambient temperature. The NN-based sensor model automatically calibrates and compensates with high accuracy for the nonlinear response characteristics and nonlinear dependency of the sensor characteristics on the environmental parameters. Through extensive simulated experiments, we have shown that the NN-based silicon hot wire sensor model can provide flow speed readout with a maximum full-scale error of only 1.5% over a temperature range from 0 to 40°C for nonlinear dependencies.

Development of Laguerre Neural-Network-Based Intelligent Sensors for Wireless Sensor Networks

The node of a wireless sensor network (WSN), which contains a sensor module with one or more physical sensors, may be exposed to widely varying environmental conditions, e.g., temperature, pressure, humidity, etc. Most of the sensor response characteristics are nonlinear, and in addition to that, other environmental parameters influence the sensor output nonlinearly. Therefore, to obtain accurate information from the sensors, it is important to linearize the sensor response and compensate for the undesirable environmental influences. In this paper, we present an intelligent technique using a novel computationally efficient Laguerre neural network (LaNN) to compensate for the inherent sensor nonlinearity and the environmental influences. Using the example of a capacitive pressure sensor, we have shown through extensive computer simulations that the proposed LaNN-based sensor can provide highly linearized output, such that the maximum full-scale error remains within <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\pm$</tex> </formula> 1.0% over a wide temperature range from <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$-$</tex></formula> 50 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$^{\circ}\hbox{C}$</tex></formula> to 200 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$^{ \circ}\hbox{C}$</tex></formula> for three different types of nonlinear dependences. We have carried out its performance comparison with a multilayer-perceptron-based sensor model. We have also proposed a reduced-complexity run-time implementation scheme for the LaNN-based sensor model, which can save about 50% of the hardware and reduce the execution time by four times, thus making it suitable for the energy-constrained WSN applications.

Tendon sheath analysis for estimation of distal end force and elongation for sensorless distal end

SUMMARYTendon sheath actuation is found in many applications, particularly in robotic hands and surgical robots. Due to the friction between the tendon and sheath, many undesirable characteristic such as backlash, hysteresis and non-linearity are present. It is desirable to know the end-effector force and elongation of the tendon to control the system effectively, but it is not always feasible to fix sensors at the end effector. A method to estimate the end-effector parameters using only a force and position sensor at the proximal site is given. An analytical study is presented and experiments are reported to support the result, showing a maximum full-scale error of approximately 7%. This result is achieved if the shape of the sheath remains the same and buckling is negligible. The results presented in this study could contribute towards haptic development in robotics surgery.

Neural-Network-Based Robust Linearization and Compensation Technique for Sensors Under Nonlinear Environmental Influences

A novel artificial neural network (NN)-based technique is proposed for enabling smart sensors to operate in harsh environments. The NN-based sensor model automatically linearizes and compensates for the adverse effects arising due to nonlinear response characteristics and nonlinear dependency of the sensor characteristics on the environmental variables. To show the potential of the proposed NN-based technique, we have provided results of a smart capacitive pressure sensor (CPS) operating under a wide range of temperature variation. A multilayer perceptron is utilized to transfer the nonlinear CPS characteristics at any operating temperature to a linearized response characteristics. Through extensive simulated experiments, we have shown that the NN-based CPS model can provide pressure readout with a maximum full-scale error of only 1.5% over a temperature range of 50 to 200 with excellent linearized response for all the three forms of nonlinear dependencies considered. Performance of the proposed technique is compared with a recently proposed computationally efficient NN-based extreme learning machine. The proposed multilayer perceptron based model is tested by using experimentally measured real sensor data, and found to have satisfactory performance.

Computational study of the tangential type turbine flowmeter

The rotor-blade driving torque and the meter factor of a tangential type turbine flowmeter are both deduced in theory. Based on computational fluid dynamics (CFD) and a turbulence model, a numerical investigation of the turbulent flow field of a tangential type turbine flowmeter is conducted to investigate the mechanism of the tangential type turbine flowmeter. The analysis method of the rotor-torque-balance in direction Z , which predicts the rotational speed θ of a rotor precisely, is presented subsequently. The key information of the inner meter is obtained through analyzing the pressure flow field and velocity flow field. Comparing with the data of physical experiments, the maximum full-scale error of meter factor is just 7.51%. The results of the comparison show that the simulation model and the analysis method of a tangential type turbine flowmeter is feasible and effective.

Auto-calibration and -compensation of a capacitive pressure sensor using multilayer perceptrons

Using multilayer perceptrons (MLPs), a smart model for a capacitive pressure sensor (CPS) is proposed. When the ambient temperature changes, the nonlinear response characteristics of a CPS may vary widely. Under such conditions, calibration of the sensor and compensation of the nonlinear sensor characteristics to obtain correct readout becomes a difficult task. The proposed MLP model can provide automatic nonlinear compensation and calibration of the CPS characteristics. A microcontroller unit (MCU)-based implementation scheme for this model is also considered. Simulation results show that this model can estimate the pressure with a maximum full-scale error of ±1% over a variation of temperature from −50 to 150°C.