基于RBF的LVDT输出标定研究

线性可变差分变压器(LVDT)式位移传感器是电感式位移传感器常见的一种结构形式,在设计研发LVDT的过程中需要对其进行输出标定.针对这一问题,以普通型交流非标准线性输出回弹式LVDT为研究对象,通过信号调理电路和单片机测量实验数据,利用径向基神经网络(RBF)为标定算法,对LVDT测量的输入与输出数据进行建模,然后将训练好的模型在Matlab中进行离线仿真,仿真结果表明,仿真数据和实测数据的误差在允许范围之内,本模型可以用在LVDT标定中.     

基于四余度设计的驾驶员操纵传感器校准方法

飞行控制系统中,驾驶员操纵传感器作为飞机人工操作的信号源,具有非常重要的作用.目前,驾驶员操纵传感器LVDT采用检定的方式完成计量,忽略了该类传感器的使用要求.以驾驶员传感器技术要求为基础,分析四余度LVDT传感器技术指标特点,提出对其中通道跟踪误差和通道干扰的校准需求和校准方法,并通过Matlab实现通道跟踪误差计算.该方法的建立能够为传感器基于应用需求的校准方案设计提供思路,在今后具有复杂应用环境、特殊应用要求传感器的校准设计中打下基础.     

一种用于位移测量装置校准的LVDT模拟器

为了实现位移测量装置的自动化校准,根据线性可调差动变压器(LVDT)的工作原理,提出了一种基于D/A转换器的LVDT模拟器的设计思想。该模拟器以LVDT主线圈交流激励信号作为参考电压,利用参考电压和数字给定值的乘积关系,通过改变D/A的数字量得到幅值可变的LVDT副线圈输出电压,从而模拟铁心位置改变时LVDT副边信号的变化关系。试验表明:该模拟器具有0.024%的稳态精度和200Hz的动态带宽,可以高度逼真地模拟LVDT电气行为,完全满足LVDT类位移测量装置的维护和校准需要。相对于采用实际LVDT,利用该模拟器进行校准可省去高精度的运动机构,可以提供更大的灵活性,并可以实现校准过程自动化。     

LVDT数字智能变送器设计与实现

针对差动变压器式位移传感器(LVDT)应用中存在的线性度不够好、智能化程度不够高的问题,从软硬件设计层面,考虑了系统的可靠性、稳定性和灵活性。从应用层面,评估了测量数据的精度、线性度、可溯源性和长期稳定性的需求,设计了一种采用集成化LVDT模拟前端的软硬件一体的数字智能变送器。创新性的设计保证了EEPROM存储的多组标定数据和拟合系数,可对应不同量程或品牌的LVDT,且更换LVDT不影响变送器输出正确的位移值。系统能够在不重新标定的情况下,根据精度需求重新计算拟合系数。实际应用表明,该变送器适用于高精度、高可靠性要求的工业应用场合。     

Dynamic output feedback control synthesis for stochastic time-delay systems

This article deals with the dynamic output feedback control synthesis problem for Itô-type stochastic time-delay systems. Our aim is to design a full order dynamic output feedback controller to achieve the desired control objectives. We will formulate the controller design problem as an H _∞ optimisation problem in the mean-square sense. The main contributions of this article are as follows: (i) for stochastic systems, the design of a controller with multiple objectives can be addressed without employing a unique Lyapunov function; (ii) using an inequality technique and Finsler Lemma, we provide convex controller synthesis conditions described by linear matrix inequalities (LMIs). Some examples are presented to show the effectiveness of the developed theoretical results.     

差动电感式位移传感器调理电路设计

介绍了线性可变差变压器(LVDT)的组成和测量原理。通过对比和分析现有LVDT信号调理电路的特点,设计了一种基于AD698芯片的单芯片解决方案的调理电路。该电路采用比例输出,可有效提高调理电路的准确度和抗干扰能力。其输出采用电压隔离芯片ISO124,可实现隔离度达1500 V有效值电压的隔离,减少了不同系统间的传输干扰。设计了变送器输出模块,可通过选择电流输出方式提高长距离传输的可靠性。通过对电路的测试和分析,证明其满足使用单通道LVDT高精度测量的需求。该电路设计方便、准确度高、易于实现,具有很好的应用前景。     

基于ADμC845的LVDT位移传感器非线性补偿

针对LVDT位移传感器测量电路输出电压值和位移量之间存在非线性特征,设计和制作线性度为0.05%高精度传感器比较困难的现状,在分析产生非线性误差主要原因和传统校正方法的基础上,利用单片机软件算法进行非线性校正以提高传感器设计精度。以传感器标定数据为样本,用曲线拟合法求出非线性校正环节的特性曲线,并给出在MATLAB环境下拟合多项式系数的最小二乘求解方法,编程实现位移量和电压输出。仿真分析和实验结果表明,其测量位移的线性度达到了设计要求,非线性校正效果明显,具有良好的应用价值。     

基于AD698的线性差动式位移传感器解码电路设计

线性差动式位移传感器(LVDT)由于其灵敏度高、线性度好、分辨率高、寿命长、可靠性高等优点,已广泛应用于机载测试系统中;为了设计出精度高,稳定性好,能够满足机载测试需求的LVDT传感器解码电路,分析了LVDT传感器磁芯位移与输出电压信号的关系,研究了AD698的内部解调原理,设计出了基于AD698的信号解码电路;该电路通过外围元器件产生传感器所需的激励信号,并对激励信号和传感器输出信号进行解调得到与传感器磁芯位移成正比的直流电压;最后通过实验验证该电路具有结构简单、稳定性高、精度高的优点,能够满足机载测试的要求,且该电路已经过高低温和振动试验,并成功应用于机载测试采集系统中。     

Affective Temperature Control in Food SMEs using Artificial Neural Network

This paper highlights modeling affective temperature control in food small and medium-sized enterprises (SMEs). Modeling defined that workstation temperature set point could be controlled based on worker heart rate and workstation environment using Artificial Neural Network (ANN). The research objectives were: 1) to propose modeling affective temperature control in food SMEs based on heart rate and workstation environment; and 2) to develop an ANN model for predicting workstation temperature set point. Training and validation data were collected from six food SMEs in Yogyakarta Special Region, Indonesia. The data of temperature set points were verified using a simulated confined room. The inputs of the ANN model were worker heart rate, workstation temperature, relative humidity distribution and light intensity. The output was temperature set point. Research results concluded satisfactory performance of ANN. The model could be used to provide environmental ergonomics in food SMEs.     

基于AD698的半桥式电感位移传感器高灵敏度测量电路设计

针对主动磁轴承系统中电感位移传感器的高灵敏度设计要求,在对AD698芯片的输入输出特性进行了实验测量的基础上,对AD698的典型应用电路加以改进,提出了一种基于AD698的半桥式电感位移传感器高灵敏度测量电路设计方案。实验证明,相对于AD698的典型应用电路,这种方案能够显著地提高电路的灵敏度,并且输出电压的噪声没有明显增大。     

In search of an optimization technique for Artificial Neural Network to classify abnormal heart sounds

Artificial Neural Network (ANN) finds use in classification of heart sounds for its discriminative training ability and easy implementation. The selection of number of nodes for an ANN remains an important issue as an overparameterized ANN gets trained along with the redundant information that results in poor validation. Also a larger network means more computational expense, resulting more hardware and time related cost. Therefore, a compact and optimum design of neural network is needed towards real-time detection of pathological patterns, if any from heart sound signals. This work attempts to (i) design a compact form of output layer with less number of nodes than output classes, (ii) select a set of input features that are effective for identification of heart sound signals using Singular Value Decomposition (SVD), QR factorization with column pivoting (QRcp) and Fisher's F-ratio, (iii) make certain optimum selection of nodes in the hidden layer for a more effective ANN structure using SVD and (iv) select and prune weights based on the concept of local relative sensitivity index (LRSI) for empirically chosen overparameterized ANN structure for phonocardiogram (PCG) classification. It is observed that the proposed techniques perform better in terms of reduction of model residues and time complexity for classifying 12 different pathological cases and normal heart sound.     

Optimum model for bearing capacity of concrete-steel columns with AI technology via incorporating the algorithms of IWO and ABC

In a composite column, the performance of both the concrete and steel has a considerable effect on the structural behaviour under different loading conditions. This study applies several artificial intelligence (AI) techniques to optimise the bearing capacity of concrete-filled steel tube (CFST) columns. First, the bearing capacity values of the CFST columns are estimated by an artificial neural network (ANN) technique. Using 303 datasets, the outer diameter, concrete compressive strength, tensile yield stress of the steel column, thickness of the steel cover, and length of the applied samples are considered as the model inputs. Following a series of analyses, several ANN models are developed. The ANN model with 8 neurons and 250 iterations is determined as the best model to predict the bearing capacity of the CFST columns. Subsequently, the invasive weed optimisation (IWO) technique, which is considered the most current optimisation algorithm, is developed to maximise the results of the bearing capacity by considering the selected ANN model. To highlight the ability of IWO, the artificial bee colony (ABC) algorithm is also applied. Consequently, it is found that both optimisation algorithms can design input parameters such that the maximum value of the bearing capacity can be obtained. The bearing capacity of the CFST columns from the ABC and IWO techniques indicates that IWO has a better capability of maximising the bearing. Thus, IWO can optimise similar problems with a high rate of performance.

     

Application of integrated soft computing techniques for optimisation of hybrid CO2 laser–MIG welding process

In this paper, artificial neural networks (ANNs), genetic algorithm (GA), simulated annealing (SA) and Quasi Newton line search techniques have been combined to develop three integrated soft computing based models such as ANN–GA, ANN–SA and ANN–Quasi Newton for prediction modelling and optimisation of welding strength for hybrid CO₂ laser–MIG welded joints of aluminium alloy. Experimental dataset employed for the purpose has been generated through full factorial experimental design. Laser power, welding speeds and wires feed rate are considered as controllable input parameters. These soft computing models employ a trained ANN for calculation of objective function value and thereby eliminate the need of closed form objective function. Among 11 tested networks, the ANN with best prediction performance produces maximum percentage error of only 3.21%. During optimisation ANN–GA is found to show best performance with absolute percentage error of only 0.09% during experimental validation. Low value of percentage error indicates efficacy of models. Welding speed has been found as most influencing factor for welding strength.     

A NEURO-FUZZY MODEL APPLIED TO FULL RANGE SIGNAL VALIDATION OF PWR NUCLEAR POWER PLANT DATA

The surveillance and control of any industrial plant is based on the readings of a set of sensors. Their reliable operation is essential since the output of the sensors provide the only objective information about the state of the process. The signal validation task is intended to confirm whether the sensors are functioning properly

Real-time process signal validation is an application field where the use of fuzzy logic and artificial neural networks (ANNs) can improve the diagnosis of faulty sensors or drift in sensor readings in a robust and reliable way

The present work describes the transient and steady state on-line validation method of plant process signals using ANN and fuzzy logic pattern recognition. This method has been developed at the OECD Halden Reactor Project and tested on simulated scenarios covering the whole range of Pressurized Water Reactors (PWR) operational conditions provided by Electricite' De France (EDF) and the Centre D'Etudes De Cadarache (CEA)in France.     

Printed dipole antenna design using artificial neural network modeling for RFID application

An approach to printed dipole antenna design using the artificial neural network (ANN) modeling technique is presented in this article. Three important antenna‐layout dimensions are used to capture critical input/output relationships in the ANN model. Once fully developed, the ANN model has been shown to be as accurate as an EM simulator and much more efficient computationally in antenna design optimization. © 2006 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2006.     

Application of a new multi-agent Hybrid Co-evolution based Particle Swarm Optimisation methodology in ship design

In this paper, a multiple objective ‘Hybrid Co-evolution based Particle Swarm Optimisation’ methodology (HCPSO) is proposed. This methodology is able to handle multiple objective optimisation problems in the area of ship design, where the simultaneous optimisation of several conflicting objectives is considered. The proposed method is a hybrid technique that merges the features of co-evolution and Nash equilibrium with a ε-disturbance technique to eliminate the stagnation. The method also offers a way to identify an efficient set of Pareto (conflicting) designs and to select a preferred solution amongst these designs. The combination of co-evolution approach and Nash-optima contributes to HCPSO by utilising faster search and evolution characteristics. The design search is performed within a multi-agent design framework to facilitate distributed synchronous cooperation. The most widely used test functions from the formal literature of multiple objectives optimisation are utilised to test the HCPSO. In addition, a real case study, the internal subdivision problem of a ROPAX vessel, is provided to exemplify the applicability of the developed method.     

Development of a neural network based integrated control system of 120 ton/h capacity boiler

An integrated control system based on artificial neural network (ANN) is presented in this paper to control a 120 ton/h capacity boiler of the Zia Fertilizer Company Limited (ZFCL), Ashuganj, Bangladesh. The process inverse dynamic modelling technique is applied to design the proposed controller. A multilayer feed-forward neural network is trained to identify the unknown inverse dynamic model of the boiler plant by a well known learning algorithm called backpropagation. The training data were collected from the history file of ZFCL. A new software controller is then developed for integrated control system of the ZFCL boiler using the weights of the trained network. Both the training mode and running mode of the developed controller are presented in this paper. The controller output is also converted into electrical signal using pulse width control technique. The generated signal is used for on-line regulation of the control valve through the parallel port of the computer. The developed controller is tested by using the boiler input–output data that are not used during the training. The output response and performance of the developed controller is compared with those of the existing PID controller of the plant.     

Analysis of a variable speed vapor compression system using artificial neural networks

An artificial neural network (ANN) is a mathematical model that is inspired by the operation of biological neural networks. However, this is typically considered a computational model. An ANN can easily adapt to multiple situations and extract information that is apparently hidden in a system.An ANN can be used in three basic configurations: mapping, auto-association and classification. An ANN in a mapping configuration can be used to model a two port system with inputs and outputs. Therefore, a vapor compression system can be modeled using an ANN in a mapping configuration. That is, some parameters from the compression system can be used for input while other parameters can be used as output. The simulation experiments include the design, training and validation of a set of ANNs to find the best architecture while minimizing over-fitting.This paper presents a new method to model a variable speed vapor compression system. This method accurately estimates the number of neurons in the hidden layer of an ANN. The analysis and the experimental results provide new insights to understand the dependency between the input and output parameters in a vapor compression system, concluding that the model can predict the energetic performance of a variable speed vapor compression system. Additionally, the simulation results indicate that an ANN can extract, from the data sets, information that is implicit in the configuration of the vapor compression system.     

Prediction of target range of intact parathyroid hormone in hemodialysis patients with artificial neural network

The application of artificial neural network (ANN) to predict outcome and explore potential relationships among clinical data is increasing being used in many clinical scenarios. The aim of this study was to validate whether an ANN is a useful tool for predicting the target range of plasma intact parathyroid hormone (iPTH) concentration in hemodialysis patients. An ANN was constructed with input variables collected retrospectively from an internal validation group (n = 129) of hemodialysis patients. Plasma iPTH was the dichotomous outcome variable, either target group (150 ng/L300 ng/L). After internal validation, the ANN was prospectively tested in an external validation group (n = 32) of hemodialysis patients. The final ANN was a multilayer perceptron network with six predictors including age, diabetes, hypertension, and blood biochemistries (hemoglobin, albumin, calcium). The externally validated ANN provided excellent discrimination as appraised by area under the receiver operating characteristic curve (0.83 +/- 0.11, p = 0.003). The Hosmer-Lemeshow statistic was 5.02 (p= 0.08 > 0.05) which represented a good-fit calibration. These results suggest that an ANN, which is based on limited clinical data, is able to accurately forecast the target range of plasma iPTH concentration in hemodialysis patients.     

Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks

This paper presents a novel multi-objective genetic algorithm (MOGA) based on the NSGA-II algorithm, which uses metamodels to determine optimal sampling locations for installing pressure loggers in a water distribution system (WDS) when parameter uncertainty is considered. The new algorithm combines the multi-objective genetic algorithm with adaptive neural networks (MOGA–ANN) to locate pressure loggers. The purpose of pressure logger installation is to collect data for hydraulic model calibration. Sampling design is formulated as a two-objective optimization problem in this study. The objectives are to maximize the calibrated model accuracy and to minimize the number of sampling devices as a surrogate of sampling design cost. Calibrated model accuracy is defined as the average of normalized traces of model prediction covariance matrices, each of which is constructed from a randomly generated sampling set of calibration parameter values. This method of calculating model accuracy is called the ‘full’ fitness model. Within the genetic algorithm search process, the full fitness model is progressively replaced with the periodically (re)trained adaptive neural network metamodel where (re)training is done using the data collected by calling the full model. The methodology was first tested on a hypothetical (benchmark) problem to configure the setting requirement. Then the model was applied to a real case study. The results show that significant computational savings can be achieved by using the MOGA–ANN when compared to the approach where MOGA is linked to the full fitness model. When applied to the real case study, optimal solutions identified by MOGA–ANN are obtained 25 times faster than those identified by the full model without significant decrease in the accuracy of the final solution.