This paper presents the development of an artificial neural network (ANN)-based linearization technique for the voltage controlled oscillator (VCO) thermistor circuit. VCO thermistor circuit exhibits a linear analog temperature-frequency relation over a temperature range of 0 °C-100 °C with good response and reasonable error. An ANN-based direct modeling technique is used for a nonlinearity estimation of VCO thermistor circuit to further improve the linearity. The performance of the developed technique has been experimentally verified. A linearity of approximately ±0.2% and sensitivity of 5 kHz/°C are achieved over a temperature range of 0 °C-100 °C, with a high thermal stability. A notable feature of the developed technique is linearity and sensitivity of the temperature measurement, which are quite high. The efficacy of the technique is established through simulation studies and its practicality is demonstrated on a prototype unit. © 2001-2012 IEEE.

Vaegae Naveen Kumar

Department of Communication Engineering

School of Electronics Engineering

Vellore Campus

Venkata Lakshmi Narayana K

Department of Instrumentation

School of Electrical Engineering

Kumar V.N

Bhujangarao A

Sankar S.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Development of an ANN-Based Linearization Technique for the VCO Thermistor Circuit

IEEE Sensors Journal

Vaegae Naveen Kumar, Member, IEEE, Venkata Lakshmi Narayana Komanapalli, Member, IEEE, Annepu Bhujangarao, and Samickannu Sankar reply on the sensitivity value for the three thermistors under study which is mistakenly presented as 5 kHz/°C. After intense review of experimental data of the technique, the values of sensitivity are obtained as 0.128 kHz/°C, 0.161 kHz/°C and 0.719 kHz/°C for the thermistor-1, thermistor-2 and thermistor-3 respectively in the temperature range 0-100/°C. The sensitivity value for the thermistor-2 is 0.162 kHz//°C in the range 15-85 /°C and for thermistor-3 is 0.714 kHz//°C in the range 20-70/°C. The theoretical and experimental results must be close to each other when experimental components are used with their designed specifications. The authors suggest amendment in The sensitivity of approximately 0.336 kHz//°C is achieved over a temperature range of 0 /°C-100/°C using the ANN-based linearization technique.

Authors’ Reply

This paper presents the implementation of an intelligent pressure transmitter to measure pressure using a bellow sensor. In industrial applications, the deflection of the bellow due to applied pressure must be translated into an efficient electrical readout for monitoring, transmission and control. An inductive pick-up is used to convert the deflection of the bellow into the change in self-inductance of a coil. The signal conditioning circuit designed for the bellow sensor with the inductive coil is an inductance to voltage conversion circuit (ITVCC). The stray inductances, component tolerances and ambient factors introduce errors in the output of the ITVCC. The voltage-pressure relation exhibits a considerable nonlinearity and limits the measurement to local operations. In this aspect, we propose an artificial neural network (ANN) using a radial basis function to estimate and compensate the nonlinearity of the ITVCC. The intelligence of ANN modeling is incorporated into an embedded plug-in-module (EPIM). The output voltage of the EPIM is converted into a 4-20 mA current signal for further processing. The performance of the proposed technique is experimentally verified. The nonlinearity expressed as maximum deviation from the desired response is within ±0.6% of full scale reading. The design aspects, simulation analysis and experimental results of the technique are reported. © 2015 Elsevier B.V. All rights reserved.

Sensors and Actuators A: Physical

Development of an intelligent pressure measuring technique for bellows using radial basis function neural network

This paper presents the development of an artificial neural network (ANN)-based improved inductive signal conditioning circuit for pressure transducer with bellow as sensor. A bellow is an elastic-type mechanical pressure sensor. The deflection of the bellow due to applied pressure must be translated into an efficient electrical signal for continuous monitoring, wireless transmission, and digital readout. A ferromagnetic wire attached to the bellow as a part of an inductive coil arrangement, gets deflected due to applied pressure, thereby changing the self inductance of the coil. An op-amp inductive signal conditioning circuit (OISCC) is designed to produce voltage proportional to changes in self-inductance, but the OISCC voltage versus applied pressure exhibits a considerable nonlinearity error due to stray inductances and component drifts. The ANN modeling estimates and compensates the nonlinearity of OISCC. An embedded unit is used for implementation of ANN learning process. The pressure transducer with significant stability has exhibited high linearity and sensitivity of ±0.35% and 10 mH/psig, respectively, in the measuring range of 0-70 psig. The design and experimental aspects of the technique are reported. © 2016 IEEE.

Development of an ANN-Based Pressure Transducer

Linearization of NTC Thermistor Characteristic Using Op-Amp Based Inverting Amplifier

To measure temperature using a thermistor as the sensing element, linearization to compensate for the inverse exponential nature of the resistancetemperature characteristic of the thermistor is required. A linearizing dual-slope digital converter (LDSDC) that accepts a thermistor sensor as input and provides a digital output that is directly proportional to the temperature being sensed is presented here. A logarithmic amplifier at the input of the LDSDC compensates for the exponential characteristics. The conversion logic of the underlying dual-slope converter is suitably modified to implement the required inversion and offset correction and thus obtain linearization over a wide range of input temperature. The efficacy of the proposed LDSDC is established through simulation studies and its practicality demonstrated with experimental results obtained on a prototype unit built and tested. Analysis of the proffered method to identify possible sources of errors is also presented. © 2006 IEEE.

Journal	Data powered by TypesetIEEE Sensors Journal
Publisher	Data powered by TypesetInstitute of Electrical and Electronics Engineers (IEEE)
ISSN	1530-437X
Open Access	0