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.     

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.     

Modeling and development of an ANN-based smart pressure sensor in a dynamic environment

In many engineering applications, a capacitive pressure sensor (CPS) is placed in a dynamic environment in which the temperature variation is quite large. Since the response characteristics of a CPS are highly nonlinear and temperature dependent, in such situations, complex signal processing techniques are needed to obtain correct readout of the applied pressure. We have proposed an artificial neural network (ANN)-based smart capacitive pressure sensor, whose response characteristics can be estimated within an accuracy of ±1% error over a wide variation of temperature starting from −50°C to 150°C. This modeling scheme automatically takes care of all the nonidealities, such as, nonlinearity, offset, gain and temperature dependence, of the sensor. A novel idea of automatic collection of temperature information and its feeding into the ANN model is also proposed. In the practical implementation of this scheme, the hardware complexity poses a serious impairment. Since the tanh() functions are needed for implementation in the ANN-based model, to reduce the hardware requirement, we provide a simple scheme for computation of tanh(). Sensitivity analysis of the model with respect to the finite word-length constraint on the final stored weight values, and number of terms used in the implementation of tanh() function, have been carried out. A microcontroller-based implementation scheme for the ANN-based model is also suggested.     

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.     

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.     

A novel approach based on artificial neural network for calibration of multi-hole pressure probes

Imperfections in the manufacturing process of flow measuring probes affect their measuring behavior. Nevertheless, in order to provide the highest possible accuracy, each individual multi-hole pressure probe has to be calibrated before using them in turbomachinery. This paper presents a novel method based on artificial neural networks (ANN) to predict the flow parameters of multi-hole pressure probes. A two-stage ANN approach using multilayer perceptron (MLP) is proposed in this study. The two-stage prediction approach involves two MLP networks, which represent the calibration data and the prediction error. For a given set of inputs, outputs from both networks are combined to estimate the measured value. The calibration data of a 5-hole probe at RWTH Aachen was used to develop and validate the proposed ANN models and two-stage prediction approach. The results showed that the ANN can predict the flow parameters with high accuracy. Using the two-stage approach, the prediction accuracy was further improved compared to polynomial functions, i.e. a commonly used method in probe calibration. Furthermore, the proposed approach offers high interpolation capabilities while preventing overfitting (i.e. failure to fit new data). Unlike polynomials, it is shown that the ANN based method can provide accurate predictions at intermediate points without large oscillations.     

A hybrid neuro-fuzzy simulation approach for improvement of natural gas price forecasting in industrial sectors with vague indicators

This study presents a hybrid neural network fuzzy mathematical programming approach for improvement of natural gas price estimation in industrial sector. It is composed of artificial neural network (ANN), fuzzy linear regression (FLR), and conventional regression (CR). The preferred FLR, ANN, and CR models are selected via mean absolute percentage of error. The intelligent approach of this study is then applied to estimate natural gas price in industrial sector. Domestic sector is also used to further show the flexibility and applicability of the hybrid approach. The economic indicators used in this paper are consumer price index, population, gross domestic and annual natural gas consumption. The stated indicators could be contaminated with noise and vagueness. Moreover, there is a need to develop a hybrid approach to deal with both noise and vagueness. The input data were divided into train and test datasets. A complete sensitivity analysis has been performed by changing train and test data to show the superiority of the proposed approach. The superiority of ANN for the domestic sector and FLR for the industrial sector was proved by error analysis. The results showed that different models may be selected as preferred model, in different cases and situations. The proposed approach of this study would help policy makers to effectively manage natural gas price in vague, noisy, and complex manufacturing sectors. This is the first study that presents a hybrid approach for estimating the natural gas price in industrial sector with possible noise, non-linearity, and uncertainty.     

Some thoughts on neural network modelling of microabrasion–corrosion processes

There is increasing interest in the interactions of microabrasion, involving small particles of less than 10 μm in size, with corrosion. This is because such interactions occur in many environments ranging from the offshore to health care sectors. In particular, microabrasion–corrosion can occur in oral processing, where the abrasive components of food interacting with the acidic environment, can lead to degradation of the surface dentine of teeth.

Artificial neural networks (ANNs) are computing mechanisms based on the biological brain. They are very effective in various areas such as modelling, classification and pattern recognition. They have been successfully applied in almost all areas of engineering and many practical industrial applications.

Hence, in this paper an attempt has been made to model the data obtained in microabrasion–corrosion experiments on polymer/steel couple and a ceramic/lasercarb coating couple using ANN. A multilayer perceptron (MLP) neural network is applied and the results obtained from modelling the tribocorrosion processes will be compared with those obtained from a relatively new class of neural networks namely resource allocation network.     

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。     

Regime independent flow rate prediction in a gas-liquid two-phase facility based on gamma ray technique and one detector using multi-feature extraction

One of the key challenges in petroleum related industries is how to precisely measure the flow rates of individual phases in a pipeline. To address this challenge, in the present study, an automated two-phase test loop capable of generating different flow patterns in horizontal pathway is used to perform experiments on the flow rates. The measuring package set-up consists of a Cs-137 radiation source with photon energy of 662 keV and one NaI (Tl) scintillation detector to register transmission counts. Multi-layer perceptron (MLP) is the selected processing element. Distinguished property of this paper is considering a feature vector with diverse-nature elements as input and the flow rate values of water and air as the target elements. Several combinations of features were investigated to determine the feature vector which shows the best quality to predict the flow rates. Moreover, two structures of MLP with different scenarios of hidden layers were utilized for examining every feature vector set. Numerous experiments were performed to collect adequate data to train and test the ANN in a wide range of air and water flow rates. The results indicate that the proposed model achieved MAE of less than 1 and 5.9 L/min and MRE% of 1.09% and 1.45% to predict water and air flow rates, respectively. The results show that the presented ANN model outperforms existing methods on multiphase flow rates measurements. Therefore, the proposed feature extraction method is applicable to estimate phase flow rates in a two-phase flow for industrial goals.     

Analysis of hydraulic characteristics for hollow semi-circular weirs using artificial neural networks

Weirs are small overflow dams used to alter and raise water flow upstream and regulate or spill water downstream watercourses and rivers. This paper presents the application of artificial neural network (ANN) to determine the discharge coefficient (Cd) for a hollow semi-circular crested weirs. Eighty five experiments were performed in a horizontal rectangular channel of 10 m length, 0.3 m width and 0.45 m depth for a wide range of discharge. The results of examination for discharge coefficient were yielded by using multiple regression equation based on dimensional analysis. Then, the results obtained were also compared using ANN techniques. A multilayer perceptron MLP algorithm FFBP network was developed. The optimal configuration of ANN was [2,10,1] which gave mean square error (MSE) and correlation coefficient (R) of 0.0011 and 0.91, respectively. Performances of ANN model reveal that the Cd could be better estimated by the ANN technique in comparison with Cd obtained using statistical approach.     

On-Line Monitoring of PVC Foam Density Using Ultrasound Waves and Artificial Neural Networks

An artificial neural network (ANN) model was developed to monitor the density of rigid foam PVC on-line during the extrusion process using an ultrasound sensor mounted on the extruder die. Acoustic properties of the polymer melt, measured by multiple ultrasound echoes propagating through the polymer melt, were used to train a multilayer perceptron (MLP) artificial neural network to estimate the density of the extruded foam. The foam density was varied by varying the processing conditions, i.e. heating zones and screw speed, during the extrusion process. A high correlation was found to exist between the acoustic properties of the polymer melt and the foam density using a three-layer multilayer perceptron artificial neural network. .     

Prediction of wear behaviour of A356/SiCp MMCs using neural networks

In the present investigation, artificial neural network (ANN) approach was used to predict the wear behaviour of A356/SiC metal matrix composites (MMCs) prepared using rheocasting route. The ANN model was obtained to aid in prediction and optimization of the wear rates of the composites. The effect of the SiC particles size, SiC weight percent, applied pressure and test temperature on the wear resistance was evaluated using the ANN model. The results have shown that ANN is an effective tool in the prediction of the properties of MMCs, and quite useful instead of time-consuming experimental processes.     

基于SVM构造的FLANN数据融合方法在CPS修正中的应用

在对常规函数链接型神经网络（FLANN）构造方法的认识基础上，讨论了一种基于支持向量机（SVM）技术的FLANN构造新方法，并利用该方法对实际的电容压力传感器（CPS）系统进行非线性修正及温度补偿。先将SVM的拓扑结构与常规FLANN结构进行比较，确定两者的等价性。因此，可通过SVM求解二次规划问题来实现FLANN结构的唯一优化。用常规FLANN方法在同样条件下进行对比实验，实验结果表明用该方法构造的FLANN具有结果唯一、结构简单、全局优化等特点，特别是在实验数据较少的小样本条件下仍然具有更高的鲁棒性和修正精度。     

Precise volume fraction prediction in oil–water–gas multiphase flows by means of gamma-ray attenuation and artificial neural networks using one detector

Artificial neural network (ANN) is an appropriate method used to handle the modeling, prediction and classification problems. In this study, based on nuclear technique in annular multiphase regime using only one detector and a dual energy gamma-ray source, a proposed ANN architecture is used to predict the oil, water and air percentage, precisely. A multi-layer perceptron (MLP) neural network is used to develop the ANN model in MATLAB 7.0.4 software. In this work, number of detectors and ANN input features were reduced to one and two, respectively. The input parameters of ANN are first and second full energy peaks of the detector output signal, and the outputs are oil and water percentage. The obtained results show that the proposed ANN model has achieved good agreement with the simulation data with a negligible error between the estimated and simulated values. Defined MAE% error was obtained less than 1%.     

降低传感器调理电路噪声的研究

本文通过对高精度表面轮廓传感器调理电路的研究,总结出降低传感器调理电路噪声的分析方法和具体措施。选用同步积分器代替ＲＣ有源带通滤器,并配以同步解调器构成窄频带测量系统,有效地抑制了电路的噪声和干扰。提出电路参数选取原则和前置放大器设计的一般方法。在传感器与放大器之间采用输入变压器作为阻抗匹配,获得前置放大器的最佳源电阻,实现电路的最佳噪声匹配,所研究的调理电路与自制表面轮廓传感器配合,成功地检测出１ｎｍ的微小位移。本文给出的分析方法与措施,对其它测量系统有借鉴意义。     

Prediction of solid circulation rate in an internal circulating fluidized bed: An empirical and ANN approach

Internal Circulating Fluidized beds (ICFBs) are interested in various industries because of their higher thermal efficiency and high reaction rates. However, understanding the complex flow hydrodynamics inside an ICFB is challenging. Also, the experiments needed are expensive and time-consuming. Therefore this study aims to predict the solids circulation rate in an in-house ICFB (0.3 m internal diameter x 3.0 m height) using an empirical model and an Artificial Neural Network (ANN) technique. The solid circulation rates measured at different operating and design conditions using a high-speed video camera are utilized to develop the models mentioned earlier. A dimensionless approach and nonlinear regression models are adopted to derive the empirical model. The Analysis of Variance (ANOVA) technique calculates F-number and their corresponding probabilities (P-values). The ANN model is developed with four input variables: particle size, static bed height, gap height, and gas superficial velocity. Multi-layer Perception model (MLP) with the Feedforward Back Propagation learning rule is employed to build the ANN model. A single hidden layer with nine neurons predicted a close solid circulation rate with minimal error over the empirical model compared to experimental data. Further, the empirical and ANN model's predicting capability is tested against literature data.     

A novel segmentation approach for noisy medical images using Intuitionistic fuzzy divergence with neighbourhood‐based membership function

Medical image segmentation demands higher segmentation accuracy especially when the images are affected by noise. This paper proposes a novel technique to segment medical images efficiently using an intuitionistic fuzzy divergence–based thresholding. A neighbourhood‐based membership function is defined here. The intuitionistic fuzzy divergence–based image thresholding technique using the neighbourhood‐based membership functions yield lesser degradation of segmentation performance in noisy environment. Its ability in handling noisy images has been validated. The algorithm is independent of any parameter selection. Moreover, it provides robustness to both additive and multiplicative noise. The proposed scheme has been applied on three types of medical image datasets in order to establish its novelty and generality. The performance of the proposed algorithm has been compared with other standard algorithms viz. Otsu's method, fuzzy C‐means clustering, and fuzzy divergence–based thresholding with respect to (1) noise‐free images and (2) ground truth images labelled by experts/clinicians. Experiments show that the proposed methodology is effective, more accurate and efficient for segmenting noisy images.