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Nearly all circadian clocks maintain a period that is insensitive to temperature changes, a phenomenon known as temperature compensation (TC). Yet, it is unclear whether there is any common feature among different systems that exhibit TC. From a general timescale invariance, we show that TC relies on the existence of certain period-lengthening reactions wherein the period of the system increases strongly with the rates in these reactions. By studying several generic oscillator models, we show that this counterintuitive dependence is nonetheless a common feature of oscillators in the nonlinear (far-from-onset) regime where the oscillation can be separated into fast and slow phases. The increase of the period with the period-lengthening reaction rates occurs when the amplitude of the slow phase in the oscillation increases with these rates while the progression speed in the slow phase is controlled by other rates of the system. The positive dependence of the period on the period-lengthening rates balances its inverse dependence on other kinetic rates in the system, which gives rise to robust TC in a wide range of parameters. We demonstrate the existence of such period-lengthening reactions and their relevance for TC in all four model systems we considered. Theoretical results for a model of the Kai system are supported by experimental data. A study of the energy dissipation also shows that better TC performance requires higher energy consumption. Our study unveils a general mechanism by which a biochemical oscillator achieves TC by operating in parameter regimes far from the onset where period-lengthening reactions exist.

Phosphorus is a key indicator for water quality management due to its role in eutrophication. The variety of phosphorus-containing substances necessitates highly sensitive detection of total phosphorus, particularly through automated methods, to ensure water safety. This study involved the independent development of a sensor featuring an automated in situ detection technique. Utilizing potassium persulfate high-temperature oxidation and phosphorus molybdenum blue spectrophotometry, total phosphorus was monitored in situ via sequential injection technology. Additionally, the detection process and reaction conditions of the sensor were optimized, and a temperature compensation algorithm and turbidity correction were applied to mitigate environmental factors. Under optimal conditions, the sensor demonstrated a detection limit of 1.9 µg/L with a range of 6.5–1000 µg/L in seawater, and 1.2 µg/L with a range of 4.1–2000 µg/L in freshwater. The digestion efficiency for five representative phosphorus-containing substances was found to range from 87.3% ± 1.7% to 103.1% ± 0.6%. Notably, the sensor was deployed for in situ operation at a marine experimental station and online at a river monitoring station. With its integration, low power consumption, and high precision, the sensor enabled long-term unattended monitoring, delivering accurate, stable, and reliable results. [ABSTRACT FROM AUTHOR]

Circadian rhythms synchronize the internal physiology of animals allowing them to anticipate daily changes in their environment. Arctic habitats may diminish the selective advantages of circadian rhythmicity by relaxing daily rhythmic environmental constraints, presenting a valuable opportunity to study the evolution of circadian rhythms. In reindeer, circadian control of locomotor activity and melatonin release is weak or absent, and the molecular clockwork is reportedly non-functional. Here we present new evidence that the circadian clock in cultured reindeer fibroblasts is rhythmic and temperature-compensated. Compared with mouse fibroblasts, however, reindeer fibroblasts have a short free-running period, and temperature cycles have an atypical impact on clock gene regulation. In reindeer cells, Per2 and Bmal1 reporters show rapid responses to temperature cycles, with a disintegration of their normal antiphasic relationship. The antiphasic Per2-Bmal1 relationship re-emerges immediately after release from temperature cycles, but without complete temperature entrainment and with a marked decline in circadian amplitude. Experiments using Bmal1 promoter reporters with mutated RORE sites showed that a reindeer-like response to temperature cycles can be mimicked in mouse or human cell lines by decoupling Bmal1 reporter activity from ROR/REV-ERB-dependent transcriptional regulation. We suggest that weak coupling between core and secondary circadian feedback loops accounts for the observed behavior of reindeer fibroblasts in vitro. Our findings highlight diversity in how the thermal environment affects the temporal organization of mammals living under different thermoenergetic constraints. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper proposes a nanowatt voltage reference (VR) with temperature coefficient compensation. All transistors work in the subthreshold region. The complementary‐to‐absolute‐temperature (CTAT) voltage and proportional‐to‐absolute‐temperature (PTAT) voltage are mainly generated by two NMOS transistors with different threshold voltages. At the same time, a simple temperature compensation circuit is designed to optimize the temperature coefficient at high temperatures, so that the proposed VR circuit can generate a reference voltage with a wider temperature range and a lower temperature coefficient. The proposed VR circuit is implemented using a standard 0.18‐μm CMOS process with an active area of only 0.022 mm2. The postlayout simulation results show that the proposed VR circuit generates a reference voltage of 308.21 mV at room temperature. Its temperature coefficient is 6.4 ppm/°C over a temperature range of −40°C°C–140°C, with a power consumption of 7.4 nW. The voltage line sensitivity (LS) is 0.098%/V, and the power supply ripple rejection (PSRR) is −72 dB @DC and −42 dB @10 MHz. [ABSTRACT FROM AUTHOR]

Gas cloud imaging with uncooled infrared spectroscopy is influenced by ambient temperature, complicating the quantitative detection of gas concentrations in open environments. To solve the aforementioned challenges, the paper analyzes the main factors influencing detection errors in uncooled infrared spectroscopy gas cloud imaging and proposes a temperature correction method to address them. Firstly, to mitigate the environmental effects on the radiative temperature output of uncooled infrared detectors, a snapshot-based, multi-band infrared temperature compensation algorithm incorporating environmental awareness was developed. This algorithm enables precise infrared radiation prediction across a wide operating temperature range. Validation tests conducted over the full temperature range of 0 °C to 80 °C demonstrated that the prediction error was maintained within ±0.96 °C. Subsequently, temperature compensation techniques were integrated, resulting in the development of a comprehensive uncooled infrared spectroscopy gas cloud imaging detection method. Ultimately, the detection limits for SF6, ethylene, cyclohexane, and ammonia were enhanced by 50%, 33%, 25%, and 67%, respectively. [ABSTRACT FROM AUTHOR]

Faced with a complex working environment, pipelines are prone to nonuniform temperature variations, which cause nonuniform phase changes in the guided wave signals during structural health monitoring, thereby increasing the difficulty of monitoring. To address this, a simulation model is established in this paper to analyze the effects of temperature on material parameters and the variation patterns of guided wave signals. A nonuniform temperature compensation method based on local phase matching is proposed. The algorithm first uses cosine similarity to find the locally best-matched signal segments between the monitoring signal and the baseline signal. Then, an indicator is introduced to quantify the differences between these best-matched signal segments, with the maximum difference considered to be the damage index. Three heating experiments on pipelines with nonuniform temperature fields ranging from 24 °C to 80 °C demonstrate that the proposed method can effectively overcome the resulting phase deviations while achieving high detection accuracy and a reduction false positives. Additionally, the method shows high resolution in detecting defects in both temperature-varying and non-temperature-varying regions. [ABSTRACT FROM AUTHOR]

The study investigates methods to enhance the reliability of NO2 monitoring using low-cost electrochemical sensors to measure gaseous pollutants in air by addressing the impacts of temperature and relative humidity. The temperature within a plastic container was controlled using an internal mica heater, an external hot air blower, or cooling packs, while relative humidity was adjusted using glycerine solutions. Findings indicated that the auxiliary electrode signal is susceptible to temperature and moderately affected by relative humidity. In contrast, the working electrode signal is less affected by temperature and relative humidity; however, adjustments are still required to determine gas concentrations accurately. Tests involving on/off cycles showed that the auxiliary electrode signal experiences exponential decay before stabilizing, requiring the exclusion of initial readings during monitoring activities. Additionally, calibration experiments in zero air allowed the determination of the compensation factor nT across different temperatures and humidity levels. These results highlight the importance of compensating for temperature and humidity effects to improve the accuracy and reliability of NO2 measurements using low-cost electrochemical sensors. This refinement makes the calibration applicable across a broader range of environmental conditions. However, the experiments also show a lack of repeatability in the zero air calibration. [ABSTRACT FROM AUTHOR]

This paper presents a temperature compensation model for the Multi-Frame Vibration MEMS Gyroscope (DMFVMG) based on Grey Wolf Optimization Variational Mode Decomposition (GWO-VMD) for denoising and a combination of the Temporal Convolutional Network (TCN) and the Long Short-Term Memory (LSTM) network for temperature drift prediction. Initially, the gyroscope output signal was denoised using GWO-VMD, retaining the useful signal components and eliminating noise. Subsequently, the denoised signal was utilized to predict temperature drift using the TCN-LSTM model. The experimental results demonstrate that the compensation model significantly enhanced the gyroscope's performance across various temperatures, reducing the rate random wander from 102.929°/h/√Hz to 17.6903°/h/√Hz and the bias instability from 63.70°/h to 1.38°/h, with reductions of 82.81% and 97.83%, respectively. This study validates the effectiveness and superiority of the proposed temperature compensation model. [ABSTRACT FROM AUTHOR]

To realize the real-time monitoring and evaluation of the bearing state such as force and deformation, and to meet the intelligent development needs of "self-sensing and self-evaluation" of engineering structures, this paper carried out a finite element simulation of the mechanical properties of spherical bearings and developed a new type of spherical force-measuring bearing based on the button type microsensors through the integration of structural monitoring technology. The simulation results show that the von Mises stress of the spherical bearing is mainly concentrated in the annular region where the edge of the spherical crown is located, and the magnitude of the force value decreases from the edge to the center gradually. The performance characteristics of spherical force-measuring bearings based on the button-type microsensor were studied by combining finite element simulation and experiments. The research results show that the sensitivity of this type of sensor is stable, and the growth trend of the measurement data is ideal with small dispersion, which is in line with the distribution law of the bearing internal force. The temperature loading test was carried out to study the effect of temperature on the bearing measurement accuracy. The fixed force value obtains the relationship between strain and temperature, and the minimum value method is proposed to calculate the temperature compensation coefficient and temperature compensation equation by fitting the slope of a straight line to improve the measurement accuracy of the sensor. Therefore, the research results have good value for engineering applications. [ABSTRACT FROM AUTHOR]

Power conversion devices in railway vehicles are of paramount importance for efficient operation because malfunctions can significantly impact their performance. In this study, we propose an indirect capacitance estimation method that considers the temperature of capacitors of railway vehicle auxiliary power supplies (APSs), which have a high proportion of failures. A data sensing module was designed to estimate the capacitance of the metalized polypropylene film (MPPF) capacitor at the DC-Link stage of the APS, and data such as voltage, current, and temperature were collected. Additionally, to increase the reliability of capacitance estimation, data from the train control and monitoring system were collected and used for data processing. The relationship between the capacitance of the MPPF capacitor and temperature was derived from approximately 3 months of data collected, while the train was in operation, and temperature compensation was applied to minimize the estimated variation in capacitance. Using a regression analysis, the capacitance of the capacitor showed a tendency to decrease, and it is predicted that the capacitance of the MPPF will decrease by 2 [%] after about 49.6 months after the date of operation. [ABSTRACT FROM AUTHOR]

This paper presents a passive Envelope Detector (ED) to be used for reception of OOK-modulated signals, such as in Wake-Up Receivers employed within Wireless Sensor Networks, widely used in the IoT. The main goal is implementing a temperature compensation mechanism in order to keep the passive ED input resistance roughly constant over temperature, making it a constant load for the preceding matching network and ultimately keeping the overall receiving chain sensitivity constant over temperature. The proposed ED was designed using STMicroelectronics 90 nm CMOS technology to receive 1 kbps OOK-modulated packets with a 433 MHz carrier frequency and a 0.6 V supply. The use of a block featuring a Proportional-to-Absolute Temperature (PTAT) current yields a 5 dB reduction in sensitivity temperature variation across the −40 °C to 120 °C range. Moreover, two different implementations were compared, one targeting minimal mismatch and the other one targeting minimal area. The minimal area version appears to be better in terms of estimated overall chain sensitivity at all temperatures despite a higher sensitivity spread. [ABSTRACT FROM AUTHOR]

The iron ore industry faces increasing electricity demand due to industrialization, making effective management of electricity demand crucial. This study proposes a temperature compensation model using Support Vector Regression (SVR), aiming to enhance the accuracy of sensors in monitoring electricity demand. An experiment is conducted to assess the impact of temperature on sensor measurements, and a modified Whale Optimization Algorithm is employed to correct the sensor outputs. The proposed model is compared with both PSOSVR and unimproved WOA-SVR. Results show that the proposed model significantly improves accuracy, achieving a determination coefficient of 0.7882 and a relative standard deviation of the error square sum of 4.6412%. The results of this study not only enhance power demand management in iron mining but also hold potential applications across various industries. [ABSTRACT FROM AUTHOR]

The sensitivity of Tunnel Magnetoresistance (TMR) sensors is characterized by significant temperature drift and poor sensitivity drift repeatability, which severely impairs measurement accuracy. Conventional temperature compensation techniques are often hindered by low compensation precision, inadequate real-time performance, and an inability to effectively address the issue of poor repeatability in temperature drift characteristics. To overcome these challenges, this paper introduces a novel method for suppressing temperature drift in TMR sensors. In this method, an alternating reference magnetic field is applied to TMR sensors, and the output amplitude at the frequency of the reference magnetic field is calculated to compensate the sensitivity temperature drift in real time. Temperature characteristic tests were conducted in a non-magnetic temperature test chamber, and the results revealed that the proposed method significantly reduced the TMR sensitivity drift coefficient from 985.39 ppm/°C to 59.08 ppm/°C. Additionally, the repeatability of sensitivity temperature characteristic curves was enhanced, with a reduction in root mean square error from 0.84 to 0.21. This approach effectively mitigates temperature-induced sensitivity drift without necessitating the use of a temperature sensor, and has the advantages of real-time performance and repeatability, providing a new approach for the high-precision temperature drift suppression of TMR. [ABSTRACT FROM AUTHOR]

This paper presents a compact V-band low-noise amplifier (LNA) featuring temperature compensation, implemented in a 130 nm SiGe BiCMOS process. A negative temperature coefficient bias circuit generates an adaptive current for temperature compensation, enhancing the LNA's temperature robustness. A T-type inductive network is employed to establish two dominant poles at different frequencies, significantly broadening the amplifier's bandwidth. Over the wide temperature range of −55 °C to 85 °C, the LNA prototype exhibits a gain variation of less than 1.5 dB at test frequencies from 40 GHz to 65 GHz, corresponding to a temperature coefficient of 0.01 dB/°C. At −55 °C, 25 °C, and 85 °C, the measured peak gains are 25.5 dB, 25 dB, and 24.4 dB, respectively, with minimum noise figures (NF) of 3.0 dB, 3.5 dB, and 4.2 dB, and DC power consumptions of 22.3 mW, 27.6 mW, and 34.4 mW. Moreover, the total silicon area of the LNA chip is 0.37 mm2, including all test pads, while the core area is only 0.09 mm2. [ABSTRACT FROM AUTHOR]

Copyright of Lubrication Engineering (0254-0150) is the property of Editorial Office of LUBRICATION ENGINEERING and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract During the operation of load cell, heat is generated by the strain gauge and the electronics on the PCB board, which leads to temperature gradients within the sensor itself. These temperature gradients are unstable at different ambient temperatures. Compensation inaccuracies can also occur when compensating for sensor measurements at different temperatures This paper proposes a method to change the position of temperature compensation resistors to address errors caused by the temperature field effect of the strain gauge sensor itself. Without affecting the sensor’s strain measurement, the correctness of the proposed method is demonstrated through steady-state thermal simulation results in ANSYS and experimental results, effectively addressing errors caused by unstable temperature gradients during the operation of strain gauge sensors.

Spectral analysis is an important technical means for the quantitative analysis of coal mine gas. However, the portable infrared gas sensor widely used at present is greatly affected by the ambient temperature in the field test process. In order to improve the analysis accuracy and applicability of the gas analyzer, we carry out theoretical analysis and experimental research around the necessity of constant temperature heating of infrared gas analyzers, reasonable heating temperature range, and optimization model of test data error. Firstly, an experimental platform of infrared gas analyzer covering pre-temperature compensation, constant temperature heating test, post-temperature compensation and different test environment temperature conditions is constructed, and the experimental steps as well as the corresponding analytical methods are proposed; secondly, in the ambient temperature of 25-45 ℃ interval, the tested CO gas volume fraction is 800×10−6, through a lot of tests, it is found that the accuracy of CO gas analyzer is best when the heating temperature is 45 ℃, the test value is the closest to the true value of gas volume fraction. At the same time, CO (10×10−6-500×10−6) standard gas is selected for the accuracy test, and the absolute error of the calculated CO gas is only 0-9×10−6, which is better than the error range specified in the standard; then, setting the variable temperature environment of −15- 45 ℃, the temperature compensation verification of the heated CO gas sensor is carried out, the test results show that the lower the ambient temperature, the higher the volume fraction of the gas to be measured, the greater the sensor detection error, the maximum error can reach 110×10−6; based on this, the error analysis models of BP and GA-BP neural network are established, and the ambient temperature, test volume fraction and standard gas volume fraction are fused with the optimized model. It is found that MAE value obtained by GA-BP neural network model is reduced by 12.982 3×10−6 compared with BP neural network. The MSE and RMSE calculation results also verify the stability and feasibility of GA-BP model in gas analysis.

This letter presents a 0.01–50‐GHz resistive matching power detector implemented in a commercial 0.15‐μm $\mathrm{\mu m}$ GaAs pseudomorphic high electron mobility transistor technology. An analytical expression is derived for the voltage responsivity of the detector as a function of temperature. To compensate for the temperature dependence of the detector, bias diode topology and mesa resistor load are employed. For an input power of −20 dBm at 1 GHz, the maximum variation of the detector output voltage is less than 0.5 dB over the temperature from −55° $-55^\circ $C to 85° $85^\circ $C. The detector's S11 is less than −8 dB, the dynamic range (DR) is 55 dB, and the maximum voltage responsivity is 700 V/W. An on‐chip wideband capacitor with a bent‐strip shape is designed for direct current blocking. The detector can be used for wideband power monitoring and power amplifier control loop for its high DR and temperature stability. [ABSTRACT FROM AUTHOR]

Soil electrical conductivity has an important influence on the growth and development of plants. The existing real-time soil electrical conductivity detection device is affected by temperature, inconvenient to use, expensive, etc.; therefore, based on the classical four-terminal method of soil electrical conductivity detection principle, in this study, we aim to improve the limitations of the constant current source, selecting the high-performance integrated chip AD5941, optimizing the detection circuit and probe structure, improving the achievability of the detection circuit, and designing a type of in situ on-line real-time access to a soil electrical conductivity detection device, and improve the detection accuracy by temperature compensation. In this paper, dynamic performance, steady state performance, radial sensitivity range, and calibration test are carried out for the soil electrical conductivity detection prototype. The test results show that the dynamic response speed of the prototype is less than 50 ms, the steady state error is not more than ±2%, and the radial measurement sensitivity range is 8~10 cm. A comparison with the commercial sensor shows that the linear fit of the two measurements reaches 0.9995, and the absolute error ranges from −61.40 µS/cm to 23.90 µS/cm, with a relative error range of −1.94~1.86%. It shows that the performance of the two sensors is comparable, but the quality/price ratio of the prototype is much higher than that of the commercialized product. In this study, it is demonstrated that a high-precision, low-cost, and easy-to-use in situ online soil electrical conductivity detection device can be provided for agricultural and forestry production. [ABSTRACT FROM AUTHOR]

Piezoresistive pressure sensors have broad applications but often face accuracy challenges due to temperature-induced drift. Traditional compensation methods based on discrete data, such as polynomial interpolation, support vector machine (SVM), and artificial neural network (ANN), overlook the thermal hysteresis, resulting in lower accuracy. Considering the sequence-dependent nature of temperature drift, we propose the RF-IWOA-GRU temperature compensation model. Random forest (RF) is used to interpolate missing values in continuous data. A combination of gated recurrent unit (GRU) networks and an improved whale optimization algorithm (IWOA) is employed for temperature compensation. This model leverages the memory capability of GRU and the optimization efficiency of the IWOA to enhance the accuracy and stability of the pressure sensors. To validate the compensation method, experiments were designed under continuous variations in temperature and actual pressure. The experimental results show that the compensation capability of the proposed RF-IWOA-GRU model significantly outperforms that of traditional methods. After compensation, the standard deviation of pressure decreased from 10.18 kPa to 1.14 kPa, and the mean absolute error and root mean squared error were reduced by 75.10% and 76.15%, respectively. [ABSTRACT FROM AUTHOR]

Ultrasonic thickness measurement of mechanical structures is one of the most popular and commonly used nondestructive methods for various kinds of process control and corrosion monitoring. With ultrasonic propagation speed being temperature-dependent, the thickness measurement can be performed reliably only when the thermal profile is completely known. Most conventional techniques assume the temperature of the test structure is uniform and at room temperature across its thickness. Such assumptions may lead to large errors in the thickness measurement, especially when there are significant temperature variations across the thickness. State-of-the-art techniques use external temperature measurements or implement iterative methods to compensate for the unknown thermal profiles. However, such techniques produce unsatisfactory results when the heat distribution is complex or varies rapidly with time. In this work, we propose a two-sensors technique, using both compressive and shear excitations, with a non-iterative rapid data processing method for accurate thickness measurement under arbitrary time-variant thermal profile. The independent behavior of shear and compressive waves is used to formulate a real-time thickness estimation technique. The developed technique is experimentally validated on a steel plate with fixed acoustic sensors. Test results show that the error in thickness estimation can be reduced by up to 98% compared to conventional thickness gauging methods. [ABSTRACT FROM AUTHOR]

Due to their high accuracy, excellent stability, minor size, and low cost, silicon piezoresistive pressure sensors are used to monitor downhole pressure under high-temperature, high-pressure conditions. However, due to silicon's temperature sensitivity, high and very varied downhole temperatures cause a significant bias in pressure measurement by the pressure sensor. The temperature coefficients differ from manufacturer to manufacturer and even vary from batch to batch within the same manufacturer. To ensure high accuracy and long-term stability for downhole pressure monitoring at high temperatures, this study proposes a temperature compensation method based on bilinear interpolation for piezoresistive pressure sensors under downhole high-temperature and high-pressure environments. A number of calibrations were performed with high-temperature co-calibration equipment to obtain the individual temperature characteristics of each sensor. Through the calibration, it was found that the output of the tested pressure measurement system is positively linear with pressure at the same temperatures and nearly negatively linear with temperature at the same pressures, which serves as the bias correction for the subsequent bilinear interpolation temperature compensation method. Based on this result, after least squares fitting and interpolating, a bilinear interpolation approach was introduced to compensate for temperature-induced pressure bias, which is easier to implement in a microcontroller (MCU). The test results show that the proposed method significantly improves the overall measurement accuracy of the tested sensor from 21.2% F.S. to 0.1% F.S. In addition, it reduces the MCU computational complexity of the compensation model, meeting the high accuracy demand for downhole pressure monitoring at high temperatures and pressures. [ABSTRACT FROM AUTHOR]

Summary Nanopore ion channels are a promising solution for certain molecular structure analyses. Large arrays of nanopore channels and their associated readout circuits are used in many molecular studies such as DNA sequencing. Readout circuits must meet challenging performance criteria such as low noise operation, low power consumption, in‐channel digitization capability, and high linearity. Previously, sigma–delta modulators have been presented to address these criteria; however, their specifications show drifts with temperature. In this paper, an approach is presented to keep modulator performance constant with temperature variations. For this purpose, the sigma–delta modulator's feedforward and feedback branches are modified so that their gain coefficient remains constant over a certain temperature range. With large sensors arrays, solutions employing high bias currents in the feedback paths are not suitable due to power consumption limitations. Here, the design gives the possibility of switching low current levels in the feedback paths without affecting the ENOB. The proposed temperature compensation solution shows good performance when temperature is swept from 27°C to 100°C. Over the mentioned temperature range, the gain and bandwidth of the modulator show a change of less than 0.4%. It is further shown that for a 10 kHz input current signal with an amplitude of 600 pA, the ENOB and power consumption are 12.9 and 4.6 mW, respectively. [ABSTRACT FROM AUTHOR]

AbstractAs a high-precision instrument, electronic pressure scanners have a crucial role in multi-point pressure measurements. However, the internal piezoresistive components drift with temperature, and this phenomenon has become a key factor restricting the accuracy. In this study, a hybrid algorithm based on back propagation neural network (BPNN) with particle swarm optimization (PSO) and gravity search algorithm (GSA) is reported for multi-channel temperature compensation to solve this problem Fundamentally, the global optimization search capability of GSA and the fast local convergence advantage of PSO are utilized so that the model parameters of the BPNN are guaranteed to be reliable and the efficiency of its execution is further improved. To this end, the temperature data between −40 °C and 70 °C obtained through the calibration experimental system are analyzed for compensation of the electronic pressure scanner from the 0 to 700 kPa adiabatic range. The results show that, compared with BP neural network, radial basis function (RBF) neural network, and PSO-BP method, the new PSOGSA-BP approach has higher accuracy in pressure sensor temperature compensation, in which the absolute error is only 0.2301 kPa and the full-scale error is 0.03% full scale. In addition, the method can be applied to electronic pressure scanners with stronger generalization and demonstrated to be suitable for temperature compensation. [ABSTRACT FROM AUTHOR]

To reduce the influence of temperature on difluoromethane (R32) gas sensors using non-dispersive infrared (NDIR), a temperature compensation method based on improved whale optimization algorithm (IWOA) and back propagation neural network (BPNN) is proposed. The IWOA that uses tent chaotic mapping to optimize the initial population of the whale optimization algorithm (WOA) and incorporates adaptive weights in the population iterations is used to optimize the weights and thresholds between nodes in each layer of the BPNN. The optimized BPNN is used to train the temperature compensation model. The experimental results show that the back propagation neural network optimized with improved whale optimization algorithm (IWOA-BPNN) reduces the maximum relative error to 1.33%, compared to 12.46 and 11.66% for the traditional BPNN and the back propagation neural network optimized with whale optimization algorithm (WOA-BPNN), respectively. IWOA-BPNN is also compared with previously reported algorithms, and the results show that the algorithm improves the accuracy of the sensor. [ABSTRACT FROM AUTHOR]

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This study proposes a fusion algorithm based on forward linear prediction (FLP) and particle swarm optimization–back propagation (PSO-BP) to compensate for the temperature drift. Firstly, the accelerometer signal is broken down into several intrinsic mode functions (IMFs) using variational modal decomposition (VMD); then, according to the FE algorithm, the IMF signal is separated into mixed components, temperature drift, and pure noise. After that, the mixed noise is denoised by FLP, and PSO-BP is employed to create a model for temperature adjustment. Finally, the processed mixed noise and the processed IMFs are rebuilt to obtain the enhanced output signal. To confirm that the suggested strategy works, temperature experiments are conducted. After the output signal is processed by the VMD-FE-FLP-PSO-BP algorithm, the acceleration random walk has been improved by 23%, the zero deviation has been enhanced by 24%, and the temperature coefficient has been enhanced by 92%, compared with the original signal. [ABSTRACT FROM AUTHOR]

Phosphorus is a key indicator for water quality management due to its role in eutrophication. The variety of phosphorus-containing substances necessitates highly sensitive detection of total phosphorus, particularly through automated methods, to ensure water safety. This study involved the independent development of a sensor featuring an automated in situ detection technique. Utilizing potassium persulfate high-temperature oxidation and phosphorus molybdenum blue spectrophotometry, total phosphorus was monitored in situ via sequential injection technology. Additionally, the detection process and reaction conditions of the sensor were optimized, and a temperature compensation algorithm and turbidity correction were applied to mitigate environmental factors. Under optimal conditions, the sensor demonstrated a detection limit of 1.9 µg/L with a range of 6.5–1000 µg/L in seawater, and 1.2 µg/L with a range of 4.1–2000 µg/L in freshwater. The digestion efficiency for five representative phosphorus-containing substances was found to range from 87.3% ± 1.7% to 103.1% ± 0.6%. Notably, the sensor was deployed for in situ operation at a marine experimental station and online at a river monitoring station. With its integration, low power consumption, and high precision, the sensor enabled long-term unattended monitoring, delivering accurate, stable, and reliable results.

A hydraulic support height measueement sensor based on Pascal's law is designed to address the difficulties in measuring the height, reliability, and stability of hydraulic supports in fully mechanized working faces. The height measurement sensor is equipped with a slender liquid tube, which is filled with methyl silicone oil. Pressure sensors are installed at both ends of the height measuremenr sensor. By measuring the pressure at both ends of the sealed liquid tube, the height difference between the two ends of the height measuremenr sensor is obtained. Methyl silicone oil has obvious thermal expansion and contraction characteristics when the ambient temperature changes, which can cause a sharp change in pressure in a closed space and affect measurement precision. A compensation method is proposed. The method stores a portion of methyl silicone oil in a corrugated tube, and uses the elasticity of the corrugated tube itself to compensate for the volume change caused by thermal expansion and contraction of methyl silicone oil. The method calibrates the density change caused by the volume change of methyl silicone oil through algorithms in the software to ensure the measurement precision of the height measurement sensor. The test results show that the height measuremenr sensor can operate in an environment of 0-40 ℃, with a measurement error of less than 4 cm. The on-site application results of the hydraulic support in the fully mechanized working face of thin and medium thick coal seams show that compared with manual measurement results, the error of the height measurement sensor is within 5 cm, indicating high reliability of the sensor.

Fiber Bragg Grating (FBG) pressure sensor has the advantages of underwater passive, corrosion resistance, light weight, low cost and anti-electromagnetic interference, which has been widely used. This paper first briefly introduces the basic principle of FBG pressure sensing. It then summarizes the development status of FBG writing technology, pressure sensing and sensitizing technology, and temperature compensation technology. Next, it summarizes the application progress of FBG pressure sensor in the fields of ocean depth, lake level measurement, oil and gas pipeline pressure measurement, and rock and soil pressure monitoring in recent years. Finally, the FBG pressure sensing technology is prospected.

Copyright of Study on Optical Communications / Guangtongxin Yanjiu is the property of Study on Optical Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Micro‐LED is a new display technology that offers advantages such as high brightness, high contrast, and low power consumption. However, the performance of Micro‐LED is greatly affected by temperature, leading to color shift easily. To address this issue, this paper proposes a machine learning‐based approach for achieving global compensation, which exhibits advantages including high compensation accuracy, good smoothness, and strong generalization performance. Experimental validation using both measured and simulated data demonstrates that the proposed method achieves a single‐pixel single‐color(R) brightness prediction accuracy of 1% and the brightness prediction of multi‐pixel single‐color(R) also exhibits high accuracy. Consequently, the proposed method enables global brightness compensation and color shift compensation in Micro‐LED. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Temperature compensation is the main measure to solve the problem that the detection accuracy of non-dispersive infrared CO2 gas sensor is affected by temperature. As the measurement accuracy of the non-dispersive infrared CO2 gas sensor is easily affected by the ambient temperature, this article analyzes the reasons why the sensor is affected by temperature, and proposes a temperature compensation method that integrates the Whale Algorithm (WOA) and BP neural network. The whale algorithm is used to optimize the weights and thresholds of the BP neural network to build a temperature compensation model for the non-dispersive infrared CO2 gas sensor and compare the superiority with the traditional BP neural network model and particle swarm optimization (PSO) BP neural network model. The experimental results show that the temperature compensation model error of WOA-BP algorithm is lower than 30 ppm, and the average absolute error percentage is 3.86%, which is far better than BP neural network and PSO-BP neural network, and effectively reduces the influence of temperature on the accuracy of the sensor. [ABSTRACT FROM AUTHOR]

Nearly all circadian clocks maintain a period that is insensitive to temperature changes, a phenomenon known as temperature compensation (TC). Yet, it is unclear whether there is any common feature among different systems that exhibit TC. From a general timescale invariance, we show that TC relies on the existence of certain periodlengthening reactions wherein the period of the system increases strongly with the rates in these reactions. By studying several generic oscillator models, we show that this counterintuitive dependence is nonetheless a common feature of oscillators in the nonlinear (far-from-onset) regime where the oscillation can be separated into fast and slow phases. The increase of the period with the period-lengthening reaction rates occurs when the amplitude of the slow phase in the oscillation increases with these rates while the progression speed in the slow phase is controlled by other rates of the system. The positive dependence of the period on the period-lengthening rates balances its inverse dependence on other kinetic rates in the system, which gives rise to robust TC in a wide range of parameters. We demonstrate the existence of such period-lengthening reactions and their relevance for TC in all four model systems we considered. Theoretical results for a model of the Kai system are supported by experimental data. A study of the energy dissipation also shows that better TC performance requires higher energy consumption. Our study unveils a general mechanism by which a biochemical oscillator achieves TC by operating in parameter regimes far from the onset where period-lengthening reactions exist. [ABSTRACT FROM AUTHOR]

The main purpose of the paper is to show how a magnetoresistive (MR) element can work as a current sensor instead of using a Wheatstone bridge composed by four MR elements, defining the concept of a magnetoresistive shunt (MR-shunt). This concept is reached by considering that once the MR element is biased at a constant current, the voltage drop between its terminals offers information, by the MR effect, of the current to be measured, as happens in a conventional shunt resistor. However, an MR-shunt has the advantage of being a non-dissipative shunt since the current of interest does not circulate through the material, preventing its self-heating. Moreover, it provides galvanic isolation. First, we propose an electronic circuitry enabling the utilization of the available MR sensors integrated into a Wheatstone bridge as sensing elements (MR-shunt). This circuitry allows independent characterization of each of the four elements of the bridge. An independently implemented MR element is also analyzed. Secondly, we propose an electronic conditioning circuit for the MR-shunt, which allows both the bridge-integrated element and the single element to function as current sensors in a similar way to the sensing bridge. Third, the thermal variation in the sensitivity of the MR-shunt, and its temperature coefficient, are obtained. An electronic interface is proposed and analyzed for thermal drift compensation of the MR-shunt current sensitivity. With this hardware compensation, temperature coefficients are experimentally reduced from 0.348%/°C without compensation to −0.008%/°C with compensation for an element integrated in a sensor bridge and from 0.474%/°C to −0.0007%/°C for the single element. [ABSTRACT FROM AUTHOR]

Copyright of Electronic Components & Materials is the property of Electronic Components & Materials and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Herein, we investigate the temperature compensation for a dual-mass MEMS gyroscope. After introducing and simulating the dual-mass MEMS gyroscope's working modes, we propose a hybrid algorithm for temperature compensation relying on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sample entropy, time–frequency peak filtering, non-dominated sorting genetic algorithm-II (NSGA II) and extreme learning machine. Firstly, we use ICEEMDAN to decompose the gyroscope's output signal, and then we use sample entropy to classify the decomposed signals. For noise segments and mixed segments with different levels of noise, we use time–frequency peak filtering with different window lengths to achieve a trade-off between noise removal and signal retention. For the feature segment with temperature drift, we build a compensation model using extreme learning machine. To improve the compensation accuracy, NSGA II is used to optimize extreme learning machine, with the prediction error and the 2-norm of the output-layer connection weight as the optimization objectives. Enormous simulation experiments prove the excellent performance of our proposed scheme, which can achieve trade-offs in signal decomposition, classification, denoising and compensation. The improvement in the compensated gyroscope's output signal is analyzed based on Allen variance; its angle random walk is decreased from 0.531076°/h/√Hz to 6.65894 × 10−3°/h/√Hz and its bias stability is decreased from 32.7364°/h to 0.259247°/h. [ABSTRACT FROM AUTHOR]

This paper reports a self-temperature compensation barometer based on a quartz resonant pressure sensor. A novel sensor chip that contains a double-ended tuning fork (DETF) resonator and a single-ended tuning fork (SETF) resonator is designed and fabricated. The two resonators are designed on the same diaphragm. The DETF resonator works as a pressure sensor. To reduce the influence of the temperature drift, the SETF resonator works as a temperature compensation sensor, which senses the instantaneous temperature of the DETF resonator. The temperature compensation method based on polynomial fitting is studied. The experimental results show that the accuracy is 0.019% F.S. in a pressure range of 200~1200 hPa over a temperature range of −20 °C~+60 °C. The absolute errors of the barometer are within ±23 Pa. To verify its actual performance, a drone flight test was conducted. The test results are consistent with the actual flight trajectory. [ABSTRACT FROM AUTHOR]

To ensure the long-term stable and uninterrupted service of satellite navigation systems, the robustness and reliability of time–frequency systems are crucial. Integrity monitoring is an effective method to enhance the robustness and reliability of time–frequency systems. Time–frequency signals are fundamental for integrity monitoring, with their time differences and frequency biases serving as essential indicators. These indicators are influenced by the inherent characteristics of the time–frequency signals, as well as the links and equipment they traverse. Meanwhile, existing research primarily focuses on only monitoring the integrity of the time–frequency signals' output by the atomic clock group, neglecting the integrity monitoring of the time–frequency signals generated and distributed by the time–frequency signal generation and distribution subsystem. This paper introduces a time–frequency signal integrity monitoring algorithm based on the temperature compensation frequency bias combination model. By analyzing the characteristics of time difference measurements, constructing the temperature compensation frequency bias combination model, and extracting and monitoring noise and frequency bias features from the time difference measurements, the algorithm achieves comprehensive time–frequency signal integrity monitoring. Experimental results demonstrate that the algorithm can effectively detect, identify, and alert users to time–frequency signal faults. Additionally, the model and the integrity monitoring parameters developed in this paper exhibit high adaptability, making them directly applicable to the integrity monitoring of time–frequency signals across various links. Compared with traditional monitoring algorithms, the algorithm proposed in this paper greatly improves the effectiveness, adaptability, and real-time performance of time–frequency signal integrity monitoring. [ABSTRACT FROM AUTHOR]

This paper presents a bandgap reference (BGR) with high-order temperature-segmented compensation. The compensation signal is generated using the voltage difference between two bipolar junction transistor (BJT) emitter bases, each of which individually loads a proportional-to-absolute temperature (PTAT) current and a zero-to-absolute temperature (ZTAT) current. The proposed BGR achieves a low-temperature coefficient (TC) over a wide temperature range. Simulations using the 0.18 μm Bipolar-CMOS-DMOS process show a typical TC of 2.25 ppm/°C from −40 °C to 125 °C. With an active area of 0.07986 mm2, it consumes 36 μW power under an operating voltage of 1 V. The integrated output noise from 0.1 Hz to 10 Hz is 81.1 μV. [ABSTRACT FROM AUTHOR]

This paper presents a novel method to improve drill pressure measurement accuracy in slim-hole drilling within the petroleum industry, a sector often plagued by extreme conditions that compromise data integrity. We introduce a temperature compensation model based on a Chaotic-Initiated Adaptive Whale Optimization Algorithm (C-I-WOA) for optimizing Convolutional Neural Networks (CNNs), dubbed the C-I-WOA-CNN model. This approach enhances the Whale Optimization Algorithm (WOA) initialization through chaotic mapping, boosts the population diversity, and features an adaptive weight recalibration mechanism for an improved global search and local optimization. Our results reveal that the C-I-WOA-CNN model significantly outperforms traditional CNNs in its convergence speed, global searching, and local exploitation capabilities, reducing the average absolute percentage error in pressure parameter predictions from 1.9089% to 0.86504%, thereby providing a dependable solution for correcting temperature-induced measurement errors in downhole settings. [ABSTRACT FROM AUTHOR]

Aiming at the problem of temperature drift of Hall effect force sensor, a new temperature compensation model of chaotic adaptive whale optimized BP neural network (CIWOA-BP) was proposed. This model uses Cubic mapping as the initial whale population generation method to improve the quality and distribution uniformity of the population. The adaptive weight was introduced to adjust the shrinking and bounding mechanism of the whale to improve the global search ability and convergence of the algorithm. The CIWOA algorithm is used to optimize the initial weights and thresholds of the back propagation (BP) neural network, so that the model has better measurement accuracy and stability. Research results indicate that after temperature compensation, the temperature coefficient of sensitivity for the Hall effect force sensor decreases from 5.08x10-3 / °C to 9. 8x10-5 / °C, reducing by two order of magnitude. The temperature-induced relative error decreases from 19.82% before compensation to 0.38%, which is reduced by over 52 times, effectively mitigating the influence of temperature on measurement results. [ABSTRACT FROM AUTHOR]