Artificial neural network-based nonlinearity estimation of pressuresensors

A new approach to pressure sensor modeling based on a simple functional link artificial neural network (FLANN) is proposed. The response of the sensor is expressed in terms of its input by a power series. In the direct modeling, using a FLANN trained by a simple neural algorithm, the unknown coefficients of the power series are estimated accurately. The FLANN-based inverse model of the sensor can estimate the applied pressure accurately. The maximum error between the measured and estimated values is found to be only ±2%. The existing techniques utilize ROM or nonlinear schemes for linearization of the sensor response. However, the proposed inverse model approach automatically compensates the effect of the associated nonlinearity to estimate the applied pressure. Frequent modification of the ROM or nonlinear coding data is required for correct readout during changing environmental conditions. Under such conditions, in the proposed technique, for correct readout, the FLANN is to be retrained and a new set of coefficients is entered into the plug-in module. Thus this modeling technique provides greater flexibility and accuracy in a changing environment     

A high accuracy, low power, reproducible temperature telemetrysystem

A low-power temperature telemetry system incorporating a miniature micropower temperature sensor/modulator and hand-held decoder with direct digital readout is described. The system is designed to avoid the need to calibrate and characterize individual units while guaranteeing an accuracy of ±0.25°C and a resolution of 0.1°C. The combined sensor and transmitter is constructed using thick-film technology and weighs less than 2.5 g without batteries. Developed principally for wild life tracking and monitoring, the system also has other applications where remote environmental monitoring is required     

Temperature correction to chemoresistive sensors in an e-NOSE-ANN system

The influence of the temperature coefficient of resistance in the chemoresistive response of inherently conductive polymer (ICP) sensors in the performance of an artificial neural network (ANN) e-natural olfactory sensor emulator (e-NOSE) system is evaluated. Temperature was found to strongly influence the response of the chemoresistors, even over modest ranges (ca. 2/spl deg/C). An e-NOSE array of eight ICP sensor elements, a relative humidity (RH/spl plusmn/0.1%) sensor, and a resistance temperature device (RTD/spl plusmn/0.1/spl deg/C) was tested at five different RH levels while the temperature was allowed to vary with the ambient. A temperature correction algorithm based on the temperature coefficient of resistance /spl beta/ for each material was independently and empirically determined then applied to the raw sensor data prior to input to the ANN. Conversely, uncorrected data was also passed to the ANN. The performance of the ANN was evaluated by determining the error found between the actual humidity versus the calculated humidity. The error obtained using raw input sensor data was found to be 10.5% and using temperature corrected data, 9.3%. This negligible difference demonstrates that the ANN was capable of adequately addressing the temperature dependence of the chemoresistive sensors once temperature was inclusively passed to the ANN.     

Temperature measurement by multifiber optical sensor

The paper deals with a new multifiber temperature sensor, based on the relationship between temperature and the refractive index of a suitable reference liquid, which replaces a part of the fiber cladding. The sensing element, the microcontroller-based hardware, and the artificial neural network (ANN)-based measurement software are described. The results of the tests carried out in the range 40°C-110°C showed good prototype accuracy and repeatability (both contained within ±0.5°C)     

GaAs-based fiber-optic pressure sensor

A new sensor developed for measurement of hydrostatic pressure up to at least 100 MPa at a standard range of ambient temperatures is described. The sensor exploits the displacement of the optical absorption edge occurring in semiconductors under the influence of hydrostatic stress as a result of pressure-induced energy shifting of conduction band extrema. The sensing element is composed of an intrinsically pure GaAs single crystal configured in the form of a microprism located at the sensor tip, and attached to two multimode (50/125 μm) optical fibers designed to deliver input light to the sensor and to output a pressure-modulated light signal to the outside of a pressure region. Characterization of the sensor has been performed for pressures up to 100 MPa and for temperatures ranging from 5 to 50°C. A procedure has been proposed involving the use of two sensors (active and compensating) to minimize temperature drift through appropriate signal processing     

液氧煤油发动机地面试验低温压力传感器校准系统设计

为获得不同环境温度下低温压力传感器的校准系数,提高压力测量的精度,着重介绍低温压力传感器校准系统设计,该校准系统具备温度自适应调整功能,为低温压力传感器校准提供温区连续、大范围温度可控的温度环境以及稳定可调的压力源,在一定程度上消除温度环境因素对传感器灵敏度的影响。通过低温压力传感器现场校准测试,获得压力传感器在不同温度环境下的工作特性并掌握其随温度变化的规律。     

Surface-acoustic-wave (SAW) flow sensor

The use of a surface-acoustic-wave (SAW) device to measure the rate of gas flow is described. A SAW oscillator heated to a suitable temperature above ambient is placed in the path of a flowing gas. Convective cooling caused by the gas flow results in a change in the oscillator frequency. A 73-MHz oscillator fabricated on 128 degrees rotated Y-cut lithium niobate substrate and heated to 55 degrees C above ambient shows a frequency variation greater than 142 kHz for flow-rate variation from 0 to 1000 cm(3)/min. The output of the sensor can be calibrated to provide a measurement of volume flow rate, pressure differential across channel ports, or mass flow rate. High sensitivity, wide dynamic range, and direct digital output are among the attractive features of this sensor. Theoretical expressions for the sensitivity and response time of the sensor are derived. It is shown that by using ultrasonic Lamb waves, propagating in thin membranes, a flow sensor with faster response than a SAW sensor can be realized.     

Study on error calibration of fiber optic gyroscope under intense ambient temperature variation

A novel adaptive forward linear prediction (FLP) denoising algorithm and a temperature drift modeling and compensation concept based on ambient temperature change rate for fiber-optic gyroscope (FOG) are presented to calibrate the errors caused by intense ambient temperature variation. The intense ambient temperature variation will bring large temperature errors, which will degrade the performance of FOG. To analyze the temperature variation, characteristics of FOG temperature experiments are developed at first. Then the adaptive FLP denoising algorithm is employed to eliminate the noise aiming at reducing noise interference. After that, a simple modeling concept of building the compensation model between temperature drift and ambient temperature change rate is first to be given (we have not found a report of better results in any literature). The semiphysical simulation results show that the proposed method significantly reduces the noise and drift caused by intense ambient temperature variation.     

Hybrid artificial neural network based on BP-PLSR and its application in development of soft sensors

A novel hybrid artificial neural network (HANN) integrating error back propagation algorithm (BP) with partial least square regression (PLSR) was proposed to overcome two main flaws of artificial neural network (ANN), i.e. tendency to overfitting and difficulty to determine the optimal number of the hidden nodes. Firstly, single-hidden-layer network consisting of an input layer, a single hidden layer and an output layer is selected by HANN. The number of the hidden-layer neurons is determined according to the number of the modeling samples and the number of the neural network parameters. Secondly, BP is employed to train ANN, and then the hidden layer is applied to carry out the nonlinear transformation for independent variables. Thirdly, the inverse function of the output-layer node activation function is applied to calculate the expectation of the output-layer node input, and PLSR is employed to identify PLS components from the nonlinear transformed variables, remove the correlation among the nonlinear transformed variables and obtain the optimal relationship model of the nonlinear transformed variables with the expectation of the output-layer node input. Thus, the HANN model is developed. Further, HANN was employed to develop naphtha dry point soft sensor and the most important intermediate product concentration (i.e. 4-carboxybenzaldehyde concentration) soft sensor in p-xylene (PX) oxidation reaction due to the fact that there exist many factors having nonlinear effect on them and significant correlation among their factors. The results of two HANN applications show that HANN overcomes overfitting and has the robust character. And, the predicted squared relative errors of two optimal HANN models are all lower than those of two optimal ANN models and the mean predicted squared relative errors of HANN are lower than those of ANN in two applications.     

"Residence times difference" fluxgate magnetometers

We present analytical and experimental results on fluxgate magnetometers that make use of a readout technique based on residence times. This approach allows for enhancing sensitivity to weak target signals in particular when the reduction of the sensor dimensions are considered. Our approach, exploiting the inherent nonlinear character of the bistable core dynamics, is based on the time domain characterization of the transitions between the two saturation states of the hysteresis loop that is inherent in the ferromagnetic core dynamics. This readout technique can be implemented with bias signals having lower amplitude and frequency than those used in conventional fluxgate processing schemes, thus reducing the device power requirements. The efficacy of this strategy is shown through an analytical approach and via experimental results which suggests guidelines for optimal device design and realization. The experiments have been carried out on a miniaturized laboratory fluxgate prototype; this device shows numerous desirable characteristics, including very good sensitivity and resolution, as well as ease of operation and a very low cost.     

Removal of Temperature and Earth's Field Effects of a Magnetoelastic pH Sensor

Magnetoelastic sensors are widely used for chemical and biological monitoring including measurement of pH, glucose, carbon dioxide, and Escherichia coli by applying a mass- or elasticity-changing coating that shifts the sensor's resonant frequency in response to the target analytes. However, the sensor's resonant frequency also varies with the ambient temperature and earth's magnetic fields, reducing the accuracy and reliability of the measurements. This paper presents a technique to eliminate the effects of temperature and earth's magnetic field on the magnetoelastic sensor by detecting the change in its higher order harmonic magnetic fields, which are generated by the sensor when excited by a low frequency magnetic field. The higher order harmonic response of the magnetoelastic sensor is a function of temperature and DC field but remains unaffected by the mass/elasticity change from the chemical or biological responsive coating, thus allowing the calibration of both interfering quantities. This paper illustrates the application of this technique on a magnetoelastic pH sensor, where the results show the calibrated measurements are independent from the ambient temperature and DC magnetic fields such as the earth's field.     

基于炭黑填充导电橡胶的压力/温度传感器非线性特性

通过研究电阻率和电阻几何系数对"负电阻-压力系数"(NPCR)和"正电阻-温度系数"(PTCR)的影响,分析了炭黑填充导电橡胶(导电炭黑/橡胶)的压力/温度传感器的非线性特性。结果表明: 导电炭黑/橡胶的NPCR和PTCR效应产生非线性的主要原因为电阻率的非线性变化; 当炭黑体积分数接近渗流体积分数时,其电阻率对体积的变化敏感程度高,此时,导电炭黑/橡胶的NPCR和PTCR效应的非线性特性较强; 由于导电炭黑/橡胶的体压缩系数大于其热膨胀系数,且导电炭黑/橡胶在压力场和温度场下的形变过程不同,导电炭黑/橡胶NPCR效应的非线性强于PTCR效应的非线性。     

An interface circuit for measuring capacitance changes based upon capacitance-to-duty cycle (CDC) converter

We present a direct-to-digital capacitive sensor readout circuit that converts capacitance changes of a sensor element to changes of the duty cycle of a square-wave oscillator, which, in turn, is converted to a digital output by a counter. The readout circuit resembles a single-slope analog-to-digital converter structure. There are several advantages of this readout scheme. First, due to its simplicity and low number of components, the power consumption of the circuit is expected to be significantly smaller than in similar digital readout designs. Furthermore, linearization of the output may be achieved using an EEPROM lookup table. Another advantage is the possibility of performing adaptive measurements where the sensor resolution and bandwidth may be changed via the readout circuit software. Finally, we present a theory of the adaptive measurement and an analysis of the design tradeoffs. The capacitance-to-duty cycle readout circuit may achieve large bandwidth and high resolution in a modern low-voltage, low-power CMOS implementation. The performance of a prototype readout circuit built from discrete components is 13-bit effective resolution with a 1-kHz bandwidth.     

Application of thin-film optical filters to the temperaturecompensation of optical fiber grating-based devices

A major problem currently affecting the implementation of in-fiber refractive-index grating-based optical fiber devices is the drift in wavelength modulation due to the change in the ambient temperature. For accurate and reliable long-term operation of these devices, suitable temperature compensation techniques are a necessity. This paper presents a novel temperature compensation technique for in-fiber refractive index grating-based devices and components. The proposed technique is based on the temperature-dependent spectral characteristics of a dielectric multilayer thin-film interference filter fabricated on the endface of refractive-index grating impressed optical fiber. Temperature compensation is achieved by comparing the reflected intensities at the grating-reflected and the interference filter-reflected wavelengths. The proposed scheme also compensates for light source fluctuations and lead-in fiber bending losses     

Miniature Accelerometer and Multichannel Signal Processor for Fiberoptic Fabry–Pérot Sensing

A miniature accelerometer based on silicon microelectromechanical systems (MEMS) fabrication technology has been developed. Using a beam-suspended proof mass and a Fabry-Peacuterot sensing gap, this accelerometer is fiber coupled to a miniature, multichannel, optical readout system which was developed for application in compact optical sensor systems. The approximately 4 mmtimes7 mmtimes2 mm accelerometer can be tailored to cover milli-g to kilo-g acceleration ranges. The miniature readout system is enclosed in approximately a 2 cmtimes8 cmtimes1 cm package, one of the smallest ever reported, and implements the complete optical path for a three-channel embodiment of a multichannel, highly sensitive and accurate, in-phase and quadrature (IQ) optical measurement system for Fabry-Peacuterot sensors. A variety of fiber-based sensors (temperature, strain, pressure, etc.) are commercially available using this Fabry-Peacuterot technique. The complete measurement system with the accelerometer was tested using a shaker table. Sample results are presented for 100 Hz, 10-g peak-peak acceleration     

Single-arm double-mode double-order planar waveguide interferometric sensor

A sensor is described for which interference measurements of the phase delay between two propagating modes of different orders in a slab thin-film waveguide are used as the sensing technique. The basic building block of the sensor is a polymer film doped with an indicator dye such as Bromocresol Purple. The modes of two orders such as TM(0) and TM(1) are simultaneously excited in the light-guiding film with a focusing optics and a prism coupler. The modes are decoupled from the film and recombined to produce an interference pattern in the face of an output optical fiber. The sensitivity of the sensor to the ambient temperature change is 1.5 degrees C, and the sensitivity to NH(3) is 200 parts in 10(6) for one full oscillation of the signal.     

用人工神经网络对PZT陶瓷进行性能分析与优化

选取了几种常用的金属氧化物掺杂剂,在均匀实验结构的基础上用人工神经网络方法对掺杂PZT陶瓷的性能进行分析和优化.实验结果表明,掺杂PZT体系的人工神经网络模型要比多重非线形回归模型准确得多,而且以人工神经网络模型为指导对材料进行优化后的性能预测也比较准确,说明人工神经网络在陶瓷这种多组分固溶体材料的性能分析中具有良好的使用前景.     

Intelligent sensors using computationally efficient Chebyshev neural networks

Intelligent signal processing techniques are required for auto-calibration of sensors, and to take care of nonlinearity compensation and mitigation of the undesirable effects of environmental parameters on sensor output. This is required for accurate and reliable readout of the measurand, especially when the sensor is operating in harsh operating conditions. A novel computationally efficient Chebyshev neural network (CNN) model that effectively compensates for such non-idealities, linearises and calibrates automatically is proposed. By taking an example of a capacitive pressure sensor, through extensive simulation studies it is shown that performance of the CNN-based sensor model is similar to that of a multilayer perceptron-based model, but the former has much lower computational requirement. The CNN model is capable of producing pressure readout with a full-scale error of only plusmn1.0% over a wide operating range of -50 to 200degC.     

A fully integrated temperature compensation technique forpiezoresistive pressure sensors

A fully integrated temperature compensation technique for piezoresistive pressure sensors is presented. The technique is suitable for batch fabricated sensors operable over a temperature range of -40°C-130°C and a pressure range of 0-310 kPa. The implementing hardware for the technique is developed and verified through PSpice and VHDL simulations. The technique is very effective for pressure values below 240 kPa and provides reasonable results for higher pressures. The technique is structurally simple and uses standard IC fabrication technologies     

Colour sensitive devices based on double p-i-n-i-p stacked photodiodes

In this work, we report on an amorphous silicon based image sensor with a bias voltage controllable spectral response characteristics. This multilayered device is composed by two stacked p-i-n-i-p structures produced by plasma enhanced chemical vapour deposition on a glass substrate and sandwiched between two transparent conductive oxide electrodes with a patterned molybdenum layer between the sensing and switching structures. Optical readout technique is used for image readout. Device performances have been evaluated from the current-voltage characteristics and spectral response measurements performed for the p-i-n-i-p test structures and stacked device. It is demonstrated that the device is sensitive to blue-green or red light depending on polarity of the bias voltage enabling the detection of colour images. Device design is discussed and supported by a numerical simulation.