An artificial neural network-based smart capacitive pressure sensor

A smart capacitive pressure sensor (CPS) using a multi-layer artificial neural network is proposed in this paper. A switched capacitor circuit (SCC) converts change in capacitance of the CPS due to applied pressure into a proportional voltage. The nonlinear characteristics of the CPS make the SCC output nonlinear. Further, due to dependence of the CPS characteristics on ambient temperature, the SCC output becomes quite complex for obtaining correct digital output of the applied pressure, especially when the ambient temperature varies with time and/or place.

To circumvent this difficulty, an ANN is employed to model the sensor. By training the ANN model suitably, the digital readout of the applied pressure can be obtained which is independent of ambient temperature. A new idea for collecting temperature information from the sensor characteristics themselves, and automatic feeding of this information into the ANN-based CPS model is proposed. From the simulation results it is verified that the ANN model can give correct readout of the applied pressure within ±1% error (FS) over a wide range of temperature variation starting from −20°C to 70°C. This modeling technique of the CPS provides greater flexibility and accuracy in a changing environment.     

Auto-compensation of nonlinear influence of environmental parameters on the sensor characteristics using neural networks

Usually the environmental parameters influence the sensor characteristics in a nonlinear manner. Therefore obtaining correct readout from a sensor under varying environmental conditions is a complex problem. In this paper we propose a neural network (NN)-based interface framework to automatically compensate for the nonlinear influence of the environmental temperature and the nonlinear-response characteristics of a capacitive pressure sensor (CPS) to provide correct readout. With extensive simulation studies we have shown that the NN-based inverse model of the CPS can estimate the applied pressure with a maximum error of +/- 1.0% for a wide temperature variation from 0 to 250 degrees C. A microcontroller unit-based implementation scheme is also proposed.     

ANN-based intelligent pressure sensor in noisy environment

A novel artificial neural network (ANN)-based intelligent capacitive pressure sensor (CPS) in noisy environment is proposed in this paper. A switched capacitor circuit (SCC) is used to convert the change in capacitance of the CPS due to applied pressure into a proportional voltage which is then applied to the ANN model to estimate the pressure. Because of the nonlinear response characteristics of the CPS and its temperature dependence, complex signal processing of the SCC output is required to estimate the applied pressure accurately, especially when the room temperature changes with time, place, or both. The situation becomes further complicated when the CPS encounters random noise, as is the case in many practical situations.

To alleviate these difficulties in estimation of unknown applied pressure in a CPS, a multilayer perceptron (MLP) has been utilized to model the CPS characteristics over a wide temperature range with noise. By training the MLP model suitably, a direct digital readout of the applied pressure can be obtained. From the simulation studies it was verified that the performance of this model is quite satisfactory for a wide variation of temperature, starting from −20°C to 70°C, and for a signal-to-noise ratio (SNR) of 40 dB and above. This modeling technique provides greater flexibility and accuracy in a changing and noisy environment.     

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 degrees C.     

Modeling of an intelligent pressure sensor using functional link artificial neural networks

A capacitor pressure sensor (CPS) is modeled for accurate readout of applied pressure using a novel artificial neural network (ANN). The proposed functional link ANN (FLANN) is a computationally efficient nonlinear network and is capable of complex nonlinear mapping between its input and output pattern space. The nonlinearity is introduced into the FLANN by passing the input pattern through a functional expansion unit. Three different polynomials such as, Chebyschev, Legendre and power series have been employed in the FLANN. The FLANN offers computational advantage over a multilayer perceptron (MLP) for similar performance in modeling of the CPS. The prime aim of the present paper is to develop an intelligent model of the CPS involving less computational complexity, so that its implementation can be economical and robust. It is shown that, over a wide temperature variation ranging from -50 to 150 degrees C, the maximum error of estimation of pressure remains within +/- 3%. With the help of computer simulation, the performance of the three types of FLANN models has been compared to that of an MLP based model.     

Sensor calibration and compensation using artificial neural network

Artificial neural network (ANN) based inverse modeling technique is used for sensor response linearization. The choice of the order of the model and the number of the calibration points are important design parameters in this technique. An intensive study of the effect of the order of the model and number of calibration points on the lowest asymptotic root-mean-square (RMS) error has been reported in this paper. Starting from the initial value of the nonlinearity in the characteristics of a sensor and required RMS error, it is possible to quickly fix the order of the model and the number of calibration points required using results of this paper. The number of epochs needed to calibrate the sensor, and thereafter the epochs needed to recalibrate in event of sensitivity or offset drifts, are also presented to bring out the convergence time of the technique. More importantly, the advantages of the ANN technique over traditional regression based modeling are also discussed from the point of view of its advantage in hardware simplicity in microcontroller based implementation. Results presented in this paper would be of interest to instrumentation design engineers.     

可穿戴式电子织物仿生皮肤设计与应用研究

为满足电子仿生皮肤的穿戴舒适度,提出了一种电容式柔性织物压力传感器,采用丝网印刷工艺,以织物为柔性基体,炭黑填充型复合弹性电介质和有机硅导电银胶制备柔性压敏触觉单元。介绍了压敏单元的结构设计与感知机理,研究了炭黑含量与温度对织物触觉单元性能的影响,改善了该电子织物的输出线性度与重复性,实现了0~700 k Pa量程范围迟滞误差为5.6%,动态响应时间为89 ms,灵敏度为0.025 36%/KPa,同时,引入温度补偿以提升触觉感知准确性。通过将织物压敏单元应用于足底压力信息的时空分布研究和机械手的软抓取实验,其足底压力分布及触觉感知实验结果表明,该电容式柔性织物压力传感器具有良好的工作稳定性与触觉感知功能,为可穿戴式人工皮肤的研究提供了一种设计方案。     

电容压力传感器的FLANN建模方法

旨在开发一种计算简单的电容压力传感器的模型，以便经济、可靠地应用。分析表明采用新型函数链接型神经网络建立的电容压力传感器模型能够精确读出应用压力，它是一种能实现输入到输出的高度非线性映射并且运算高效的非线性网络，在建立传感器模型的类似性能上比多层感知器具有更高的运算优势。     

Performance assessment of decision-making units using an adaptive neural network algorithm: one period case

This study proposes a nonparametric method based on adaptive neural network (ANN) technique for measuring efficiency of decision making units (DMUs) in one period case as a complementary tool for the ANN-based efficiency methods in the previous studies. In previous studies, there are needed to have large volume of data, and so the proposed method in this study is more applicable because it can be used for the cases which have no historical data. In fact, a limitative weakness of the ANN-based efficiency methods about applying them for these cases is removed. So, it can be a competitive method to the other common tools for measuring efficiency. By noting the importance of flexible manufacturing system, this study presents a decision-making model for optimization of operators’ allocation in cellular manufacturing system by computer simulation. The methodology is illustrated through its application on a previously reported dataset. It was found out that ANN provides more robust results and identifies more efficient units than the conventional methods since better performance patterns are explored.     

基于无线传感器网络大棚监测中湿度节点设计

蔬菜大棚监测系统中。空气湿度的测量是一项非常必要的工作。本文选用电容式湿度传感器HS1101,设计了基于JN5121微处理器的测量湿度的方案,详细介绍了系统的软硬件实现。     

Evaluating the effects of process parameters on maximum extrusion pressure using a new artificial neural network-based (ANN-based) partial-modeling technique

Metal extrusion process accounts for the production of the majority of industrial and domestic aluminum sections. A major limitation to the success of any extrusion operation is the capability of the particular extrusion press to meet the maximum pressure requirements for that operation. In the present work, the effects of industrial extrusion process parameters and their interactions on the resulting maximum extrusion pressure, of an industrially extruded aluminum alloy, have been studied using a newly devised ANN-based partial modeling technique. Two operating parameters (initial billet temperature and ram speed) and three geometrical parameters (extrusion ratio, profile average thickness, and number of die cavities) were investigated. The main objective for developing this modeling technique is to overcome the limitations of presently available statistical modeling tools, as foreseen by the modeling needs for a complex thermo-mechanical process such as extrusion. The main present limitations are accounting for non-linearity in the process behavior, incorporating interaction effects and a meaningful determination of the highly significant process parameters and/or interactions. These three features have been, collectively, incorporated into the present model by means of combining statistical analysis of variance into ANN and by using a partial sum of squares analysis, which we propose to call the “present factor analysis.” Normal linear regression has been also employed for comparison purposes. According to the present model, maximum extrusion pressure has shown various degree of non-linearity in behavior with respect to the different process parameters and their significant interactions. It has been found that variations in the maximum extrusion pressure are mainly a function of initial billet temperature and its interactions with other process parameters, especially the ram speed. The present ANN-based model has shown superior prediction capabilities compared to the linear model with a marginal overall prediction error value of ±2.5 %.     

小型综合气象系统的研究与开发

基于C8051F340单片机,集成了温湿度、气压传感器、实验室自主研发的风速传感器,设计了一种小型智能气象系统,具有温湿压、风速风向数据采集、存储、上传等功能.介绍了系统硬件以及软件基本原理,并给出参数补偿方案和实测数据.     

基于2.4GHz模块的轮胎内温度压力测试系统设计

提出一种新的轮胎内温度压力测试的解决方案,即基于2.4GHz无线信息传输模块构成轮胎内温度压力测试装置.文中给出了其工作原理与硬件选型,完成DS18B20温度传感器与微处理器的接口、压力信号采集的前置放大电路、RFW102与微处理器的接口3个主要硬件模块的设计.在软件设计方面,完成温度压力信号采集软件的实现,制定了无线信息传输协议,并完成了传输软件的设计.经测试,所研究基于2.4 GHz模块的轮胎内压测试系统实验运行正常.     

全数字式容栅位移传感器

介绍了基于周期比较测量法的全数字式容栅位移传感器的工作原理和系统构成，分析了该传感器的性能参数和测量精度，设计了容栅传感器的三层极板结构形式和正余弦电极形式，针对容栅传感器的边缘效应给系统带来的非线性误差，提出了结构级的线性化方案，利用单片机和通用器件实现该传感器系统，并给出了实际测量结果，验证了系统方案的可行性。     

An integrated sensor for pressure, temperature, and relative humidity based on MEMS technology

This paper presents an integrated multifunctional sensor based on MEMS technology, which can be used or embedded in mobile devices for environmental monitoring. An absolute pressure sensor, a temperature sensor and a humidity sensor are integrated in one silicon chip of which the size is 5 mmX 5 mm. The pressure sensor uses a bulk-micromachined diaphragm structure with the piezoresistors. For temperature sensing, a silicon temperature sensor based on the spreading-resistance principle is designed and fabricated. The humidity sensor is a capacitive humidity sensor which has the polyimide film and interdigitated capacitance electrodes. The different piezoresistive orientation is used for the pressure and temperature sensor to avoid the interference between sensors. Each sensor shows good sensor characteristics except for the humidity sensor. However, the linearity and hysteresis of the humidity sensor can be improved by selecting the proper polymer materials and structures.     

A humidity generator with a test chamber system

For the calibration of relative humidity hygrometers and dew-point hygrometers with an open sensor, a test chamber system was constructed and connected to a dew-point generator. The system includes several small chambers connected in series. By reversing the flow direction, humidity and temperature gradients in the test chamber system are monitored during calibration measurements. This paper reports the construction of the test chamber system and the improvements in the construction and the analysis of the dew-point generator. A full uncertainty analysis is presented. The expanded uncertainty of the dew-point temperature and the relative humidity with the coverage factor k=2 are from ±0.05°C to ±0.08°C and from ±0.1%rh to ±1.0%rh, respectively. Although the dew-point temperature range is from −40°C to +77°C, the limits for the temperature range of the test chamber system are −20°C and +60°C. The system was not tested with relative humidity values lower than 10% or higher than 95%.     

Cryogenic tilt table

We describe the design and development of a cryogenic tilt table that will be used to test the flight hardware for NASA’s Satellite Test of the Equivalence Principle (STEP). Our table can tilt the hardware around two axes and is part of a test bed that has 6-degree-of-freedom controllability. The goal was to build a tilt table with a resolution better than ∼5 μrad (1 arcsec). Our table consists of three aluminum plates. The outermost plate is attached to the cryogenic probe and is fixed. The middle and inner plates rest on the outer and middle plates, respectively, using knife edges and knife edge holders made of silicon nitride that are glued to the aluminum plates. A cryogenic tilt sensor was also developed and integrated with the table. The sensor consists of an electrically grounded copper cube hanging from a support, and is placed between two pairs of capacitive electrodes. Any motion of the cube caused by tilting is measured differentially using a Wheatstone bridge circuit. The table is connected to the bottom of a cryogenic probe. A voice coil actuator, located on top of the probe at room temperature, is used to create the necessary tilt. A thin fiber is used to connect the actuator and the table. The system is controlled using a dSPACE control card. A test of the table at cryogenic temperatures (4.5 K) and low pressure (1 μTorr) showed a noise level of ∼0.7 μrad (150 marcsec), which is nearly an order of magnitude better than the required resolution.     

采用环形岛棱结构的电容式压力传感器

改变传感器可动极板的结构是解决MEMS电容式压力传感器信号输出与压力输入呈非线性关系问题的方法之一。设计了一种新型结构电容式压力传感器,其可动极板是LPCVD氮化硅构成的一个方形薄膜,薄膜中心到边缘之间由PECVD氧化硅构筑一个环形岛棱,使可动极板在压力负载下的变形趋于平整,减小了边缘杂散电容带来的非线性,从而改善了该传感器的输出特性。利用有限元方法分析了不同尺寸的动极板在不同外加压力作用下压力与形变的关系,以及相应的应力分布情况,并采用最小二乘拟合直线法计算了压力传感器的线性度。分析结果表明,与常规的双极板微型电容式压力传感器相比,新型结构压力传感器的线性度改善了48.38%,达到了1.6％。     

基于TinyOS的无线传感器网络节点

针对无线传感器网络节点能量有限的缺点,详细介绍了无线传感器网络节点的软硬件实现,设计了以TIMSP430F1611单片机为基础的低功耗硬件平台,并基于TinyOS实现了将外界环境中采集到的温、湿度及光照数据传送至网关节点以进行处理的软件平台。组网测试结果表明,该网络能正确组网,并且满足无线传感器网络低功耗的要求,具有一定的应用价值。     

电容式传感器灌充管材料替代工艺研究

在化工行业生产中,自动化控制系统发挥着重要作用,其压力、差压变送器得到广泛应用。电容式传感器是压力、差压变送器的核心部分,而传感器中的灌充管既可用来灌油,又可做为输出的电极。以往用铂金灌充管,每根价格120元,严重制约了产品的市场发展。本文根据多种试验方式,给出了解决电容式传感器中灌充管材料的替代方案,使电容式传感器的成本大大降低。