Design and Application of High-Precision Temperature Measuring Instrument for Ice Cover Profile of River Based on the Resistance Residual Compensation Method

Deng, Xiao; Cheng, Yuanguo; Li, Lingao; Feng, Junqiao; Cui, Liqin; Du, Chao; Zhang, Li; Lin, Zhang; Qin, Jianmin

doi:10.1109/access.2020.2964144

Cited by 4 publications

(6 citation statements)

References 24 publications

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“…In both cases, the following polynomial parameters (rounded to four significant digits of precision) were used: As expected, there was a significant difference in the algorithm execution time between polynomial notations (13) and (18). With the first one, with only one temperature, the calculation time was already 7.29 µs.…”

Section: • Approximation Of a Tabular Data With Polynomialsmentioning

confidence: 89%

“…Namely, the control algorithm for the converter itself needed around 18 µs. With the Horner notation (18), calculation times were significantly lower. Namely, the calculation time for all six temperatures was 2.28 µs (on average, each NTC temperature measurement required 0.38 µs), which speaks in favour of Horner's polynomial notation.…”

Section: • Approximation Of a Tabular Data With Polynomialsmentioning

confidence: 99%

“…Their usage is not limited to electronic devices only. In [17], they were used to measure the temperature profile of the ground top layer to determine the effect of weather and rainfall on the quality of the soil; moreover, in [18], they were used to study the quality of ice cover in rivers.…”

Section: Introductionmentioning

confidence: 99%

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Algorithm Execution Time and Accuracy of NTC Thermistor-Based Temperature Measurements in Time-Critical Applications

2021

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This paper addresses the challenges of selecting a suitable method for negative temperature coefficient (NTC) thermistor-based temperature measurement in electronic devices. Although measurement accuracy is of great importance, the temperature calculation time represents an even greater challenge since it is inherently constrained by the control algorithm executed in the microcontroller (MCU). Firstly, a simple signal conditioning circuit with the NTC thermistor is introduced, resulting in a temperature-dependent voltage UT being connected to the MCU’s analog input. Next, a simulation-based approximation of the actual temperature vs. voltage curve is derived, resulting in four temperature notations: for a look-up table principle, polynomial approximation, B equation and Steinhart–Hart equation. Within the simulation results, the expected temperature error of individual methods is calculated, whereas in the experimental part, performed on a DC/DC converter prototype, required prework and available MCU resources are evaluated. In terms of expected accuracy, the look-up table and the Steinhart–Hart equation offer superior results over the polynomial approximation and B equation, especially in the nominal temperature range of the NTC thermistor. However, in terms of required prework, the look-up table is inferior compared to the Steinhart–Hart equation, despite the latter having far more complex mathematical functions, affecting the overall MCU algorithm execution time significantly.

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Section: • Approximation Of a Tabular Data With Polynomialsmentioning

confidence: 89%

Section: • Approximation Of a Tabular Data With Polynomialsmentioning

confidence: 99%

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Algorithm Execution Time and Accuracy of NTC Thermistor-Based Temperature Measurements in Time-Critical Applications

2021

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“…More than half of rivers and reservoirs in the northern hemisphere can form ice above moving water (Akyurt et al 25 2002;Deng et al 2020). The freezing period can be even more than half year.…”

Section: Introductionmentioning

confidence: 99%

Variations of ice thickness in a reservoir along Irtysh River: field measurement and regression analysis

Han

Kang

Zhao

et al. 2023

Preprint

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Abstract. This paper presents the analysis results of temperatures collected at three monitoring stations which were used to study ice freezing and thawing processes on a reservoir along Irtysh River. The measured temperatures were comprehensively analyzed and correlated with air temperature measured at a meteorological station. The results showed that air temperature was closely related to temperature at the ice surface, e.g., T40, T20 and T0, and temperatures in ice almost increased linearly with depth. In addition, ice thicknesses were calculated based on measured temperatures along arrays of temperature and compared with that calculated using a simplified Stefan’s model. The results indicated that ice thickness varied spatially and temporally, and Stefan’s model overestimated the ice thicknesses with a maximum discrepancy of 12 cm. Moreover, the calculated ice thickness was correlated with temperatures, variations of temperature and accumulated freezing degree days (AFDD) based on Pearson correlation analysis, showing that ice thickness was proportional to AFDD with a coefficient of 0.89, and negatively correlated with T0, T−3, and Tice(c) with coefficients of −0.69, −0.73 and −0.62, respectively. Therefore, linear and non-linear models were proposed, which were validated using datasets from three stations in Russia and Finland, demonstrating that the linear model incorporating AFDD and T0 can capture local ice freezing and thawing processes with a relatively minor discrepancy, and the results were consistent at different stations. The paper provides an approach to comprehensively study the ice formation process and a practical model to calculate local ice thickness.

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“…The performance of the microcalorimeter, transfer standard, and measuring system 5 allowed Centro Nacional de Metrología to achieve efficiency measurement and uncertainties analogous to or even less than reported by other national metrology institutes. Analysis of changes in the thickness, growth rate, and temperature profile of ice was performed 6 through data collected using an NTC thermistor.…”

Section: Introductionmentioning

confidence: 99%

An adaptive calibration technique for thermistor with varying temperature coefficient and reference resistance

2022

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Background: A thermistor is a nonlinear sensor requiring a precise calibration technique to achieve accurate temperature measurements. This paper attempts to design a calibration technique employing artificial neural network (ANN) algorithms. The present work fulfills the following objectives: (i) to cover 100% input range in the linearity range measurement; (ii) to make the measurement technique adaptive to variations in reference resistance and thermistor temperature coefficient using a calibration technique. Methods: An ANN-based calibration circuit is cascaded to the data conversion circuit. Optimized ANN is trained with linear data independent of reference resistance and temperature coefficient effects on thermistor output. ANN optimization is performed by comparing various schemes, algorithms, and numbers of hidden layers to achieve a minimum mean square error and a regression close to 1. Results: The proposed technique provides a linear relationship for the system over the entire input range and avoids the requirement of repeated calibrations each time the thermistor is replaced. Practical data are used to validate the proposed measurement technique. Conclusions: The objectives and proposed technique have been demonstrated by results with a root mean square percentage error of 1.8%.

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Design and Application of High-Precision Temperature Measuring Instrument for Ice Cover Profile of River Based on the Resistance Residual Compensation Method

Cited by 4 publications

References 24 publications

Algorithm Execution Time and Accuracy of NTC Thermistor-Based Temperature Measurements in Time-Critical Applications

Algorithm Execution Time and Accuracy of NTC Thermistor-Based Temperature Measurements in Time-Critical Applications

Variations of ice thickness in a reservoir along Irtysh River: field measurement and regression analysis

An adaptive calibration technique for thermistor with varying temperature coefficient and reference resistance

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