Calibration Interval Research Articles

Comparative Analysis and Optimal Selection of Calibration Functions in Pure Rotational Raman Lidar Technique

The pure rotational Raman (PRR) lidar technique relies on calibration functions (CFs) to extract temperature information from raw detection data. The choice of CF significantly impacts the accuracy of the retrieved temperature. In this study, we propose a method that combines multiple Monte Carlo simulation experiments with a statistical analysis, and we first conduct simulated comparisons of the calibration effects of different CFs while considering the impact of noise. We categorized ten common CFs into four groups based on their functional form and the number of calibration coefficients. Based on functional form, specifically, we defined 1/T = f(lnQ) as a forward calibration function (FCF) and lnQ = g(1/T) as a backward calibration function (BCF). Here, T denotes temperature, and Q denotes the signal intensity ratio. Their performance within and outside the calibration interval is compared across different integration times, smoothing methods, and reference temperature ranges. The results indicate that CFs of the same category exhibit similar calibration effects, while those of different categories exhibit notable differences. Within the calibration interval, the FCF performs better, especially with more coefficients. However, outside the calibration interval, the linear calibration function (which can be considered a two-coefficient FCF) has an obvious advantage. Conclusions based on the simulation results are validated with actual data, and the factors influencing calibration errors are discussed. Utilizing these findings to guide CF selection can enhance the accuracy and stability of PRR lidar detection.

Measurements of the Limit of Detection for Electrochemical Gas Sensors

Abstract Electrochemical amperometric gas cells are becoming the sensor of choice when measuring polluting gases using low-cost air quality networks. A number of technical issues remain to be resolved to deliver fit-for-purpose monitoring systems: humidity corrections are needed but not well understood, interfering gases such as ozone can have variable cross-sensitivity and calibration intervals, and procedures are still being investigated. Another unanswered question is the limit of detection (LOD) for electrochemical gas sensors. Estimates range from hundreds of equivalent parts per billion (ppbv) to single-digit ppbv concentrations. We discuss the LOD for nitrogen dioxide (NO2), an important gas when monitoring air quality. Multiple NO2 sensor systems were tested in an environmental chamber to determine, among other parameters, the LOD for NO2 electrochemical gas sensors. Low-noise electronics and battery powering further reduced electronic noise, allowing the intrinsic LOD of the electrochemical cell to be determined. Noise, quantified as the standard deviation in zero air in a very stable temperature and relative humidity–controlled chamber was <500 pA, which translated into 1.6 ppbv, so the LOD, 3 × standard deviation, was 4.8 ppb. Interestingly, the LOD calculated with 300 ppbv NO2 test gas was the same (±0.1 ppbv). Further tests with a higher resolution analog-to-digital converter resulted in the same LOD, further leading to the conclusion that for the Alphasense NO2-A43F NO2 sensor, the limiting value for LOD is 4.8 ppbv.

Predictive algorithm of instrument health condition based on linear regression and radial basis function neural network

Abstract Enterprises and institutions engaged in social production activities need to apply for mandatory verification of general instruments and meters on production equipment according to law, and its verification period is determined by national or local verification and calibration procedures, but the health condition in the calibration intervals is difficult to know. To improve equipment reliability, this paper relies on the linear regression model to research the health condition of general instruments and meters, uses MATLAB tools to analyze the data samples of typical equipment pressure gauge, and combines radial basis function neural network to form a complete algorithm, and finally provides a complete implementation scheme for the health condition prediction of general instruments and meters in service.

Development and Validation of a Near Infra-Red (NIR) Hand-held Spectrophotometric Method Using PCA Approaches and Chemometric Tools: Application for Qualitative and Quantitative Determination of Tadalafil Marketed in Kinshasa—D.R. Congo

A hand-held NIR spectrophotometric method was developed, validated, and applied for the determination of tadalafil in tablets. The aim of our work was to develop analytical methods based on vibrational techniques using low-cost portable equipment. Based on different chemometric modeling, we attempted to validate the method, which gave encouraging results from the principal component analysis (PCA), DD-SIMCA, and PLS modeling. Following this, we optimized the method using an appropriate experiment plan. For validation, we used the total error approach with acceptance limits set at ±10% with a risk level of 5%. The method showed that it was possible to perform both qualitative and quantitative analysis of pharmaceutical products using low-cost portable NIR systems with chemometric tools. The developed approach enabled the completion of the first step in implementing an NIR method for quality control of tadalafil-based drugs in the DRC. Validation difficulties of the PLS method resulted from the lack of information about inter-day serial variations of spectral responses. It would be interesting to extend the study to a larger calibration interval in order to correct uncertainties that may result from the variability observed under different conditions and to verify robustness. These are the limitations of this work, but the results are nevertheless very encouraging.

Wave-Function Network Description and Kolmogorov Complexity of Quantum Many-Body Systems

Programmable quantum devices are now able to probe wave functions at unprecedented levels. This is based on the ability to project the many-body state of atom and qubit arrays onto a measurement basis which produces snapshots of the system wave function. Extracting and processing information from such observations remains, however, an open quest. One often resorts to analyzing low-order correlation functions—that is, discarding most of the available information content. Here, we introduce wave-function networks—a mathematical framework to describe wave-function snapshots based on network theory. For many-body systems, these networks can become scale-free—a mathematical structure that has found tremendous success and applications in a broad set of fields, ranging from biology to epidemics to Internet science. We demonstrate the potential of applying these techniques to quantum science by introducing protocols to extract the Kolmogorov complexity corresponding to the output of a quantum simulator and implementing tools for fully scalable cross-platform certification based on similarity tests between networks. We demonstrate the emergence of scale-free networks analyzing experimental data obtained with a Rydberg quantum simulator manipulating up to 100 atoms. Our approach illustrates how, upon crossing a phase transition, the simulator complexity decreases while correlation length increases—a direct signature of buildup of universal behavior in data space. Comparing experiments with numerical simulations, we achieve cross-certification at the wave-function level up to timescales of 4 μs with a confidence level of 90% and determine experimental calibration intervals with unprecedented accuracy. Our framework is generically applicable to the output of quantum computers and simulators with access to the system wave function and requires probing accuracy and repetition rates accessible to most currently available platforms. Published by the American Physical Society 2024

Determination of the Calibration Interval of Measuring Instruments: Which Method Should I Use?

Determination of the Calibration Interval of Measuring Instruments: Which Method Should I Use?

Calibration method of particulate matter sensor based on density peaks clustering combined with stacking algorithm

More and more low-cost sensors (LCS) were used to obtain monitoring data with higher temporal-spatial resolution, as air quality has become more concerned in recent decades. However, due to its working principle, relatively simple internal structure, and complex external environmental conditions, the accuracy of LCS’s measurement data has always been questioned. Therefore, it is necessary to develop calibration method for LCS to improve the reliability of the data. This study proposed a calibration method for particulate matter LCS using a K-nearest Neighbor Fuzzy Density Peaks Clustering combined with Stacking Ensemble Learning (KFDPC-Stacking). Experiments were conducted using the LCS network in Zhengzhou, China to verify the effectiveness of the developed calibration model. The results show that the developed model had higher accuracy in data calibration compared to other calibration models based on Machine Learning techniques. The transferability of the model had been validated in multiple areas of Zhengzhou, and the results indicated that the calibration model could be applied directly in comparable environments. Additionally, the data of LCS in Zhengzhou City within one year after calibration were analyzed, and the suggested calibration interval for such sensors was determined, which could provide information and supports for the subsequent LCS research and calibration.

A decoupling calculation method of separating temperature component from the nano-scale metal film thickness measurement output by the eddy current sensor

The eddy current method has been widely applied in the field of nano-scale metal film thickness detection. The output signal of an eddy current sensor is generally tiny in practice, and it is easily affected by the ambient temperature variation, which results in a decrease in the measurement accuracy. How to separate the temperature effect on film thickness measurement for achieving a high precision is a major problem. Therefore, a coupling model of an eddy current sensor with an electromagnetic field and a temperature field is established in this study, and the influence of the film thickness and temperature on coil impedance is calculated. It is found that the inductance and resistance of the coil vary monotonically as thickness and temperature with a measure of linearity in a certain range, as well as the real part and imaginary part of the output voltage by using an AC bridge. Then a film thickness-temperature decoupling calculation method is proposed, and a group of linear calibration intervals are further divided considering the linearity and measurement accuracy. According to the calculation results, it is confirmed that the method can decouple the two, and accomplish a higher accuracy. In addition, the method is verified by a series of experiments, and the variation trend of real and imaginary parts of output voltage with thickness and temperature is consistent with simulation results. At the same time, it is feasible to realize a synchronous detection of metal film thickness and ambient temperature in a certain range.

Разработка программного комплекса для испытания цифровых измерительных трансформаторов на вибростенде

The presented work examines a software package for monitoring the condition of a hydraulic servo drive with hydrostatic guides as part of a diagnostic vibration stand for digital transformers. The service life of digital instrument transformers is 25 years, and the calibration interval is 8 years. To achieve such high performance characteristics, it is necessary to conduct a large number of tests for such products. One such test is the vibration test. The uniqueness of the hydraulic drive used in the diagnostic vibration stand lies in its long service life under operational loads (up to 100 million cycles). This reliability is achieved with special hydrostatic guides in the hydraulic drive design and requires special software. The architecture of the software complex for the vibration stand has been created. To test the functionality of the package, simulation modeling of the operation of the components of the software package was performed. Aim. Based on the analysis of the functioning of technological equipment and options for implementing control software, develop the architecture of a software package for implementing a system for condition monitoring of a servo hydraulic drive with hydrostatic guides as part of a diagnostic vibration stand for digital transformers. Materials and methods. The stated scientific problems were solved using methods of system analysis, computer, mathematical and simulation modeling of the interaction of the software complex with the components of the servo hydraulic drive with hydrostatic guides. Results. The main practical result of the research is the development of the architecture of a software package for testing digital instrument transformers on a vibration stand. It is proposed to use cross-platform solutions when implementing the complex software and rely on modern experience in using freely distributed software. Conclusion. The developed architecture of the software package is focused on ensuring the automated operation of servo hydraulic drives, allows you to configure all the necessary equipment parameters, monitor the condition of the equipment using digital data transfer protocols, centrally save and replicate hydraulic drive settings for specific operating conditions on the enterprises. Analysis of implementation options showed that the use of a SCADA system simplifies the process of developing standard projects for dispatch control and data collection and condition monitoring, but at the same time, it has increased complexity at the stage of deployment and operation. The functionality of none of the widespread and reliable SCADA systems covers all the requirements for this project. This particularly applies to USB connectivity and high frequency signal processing required by design requirements. In addition, when using universal SCADA systems, the end user must purchase a license for the RunTime SCADA component.

Improved Hypertension Risk Assessment with Photoplethysmographic Recordings Combining Deep Learning and Calibration.

Hypertension, a primary risk factor for various cardiovascular diseases, is a global health concern. Early identification and effective management of hypertensive individuals are vital for reducing associated health risks. This study explores the potential of deep learning (DL) techniques, specifically GoogLeNet, ResNet-18, and ResNet-50, for discriminating between normotensive (NTS) and hypertensive (HTS) individuals using photoplethysmographic (PPG) recordings. The research assesses the impact of calibration at different time intervals between measurements, considering intervals less than 1 h, 1-6 h, 6-24 h, and over 24 h. Results indicate that calibration is most effective when measurements are closely spaced, with an accuracy exceeding 90% in all the DL strategies tested. For calibration intervals below 1 h, ResNet-18 achieved the highest accuracy (93.32%), sensitivity (84.09%), specificity (97.30%), and F1-score (88.36%). As the time interval between calibration and test measurements increased, classification performance gradually declined. For intervals exceeding 6 h, accuracy dropped below 81% but with all models maintaining accuracy above 71% even for intervals above 24 h. This study provides valuable insights into the feasibility of using DL for hypertension risk assessment, particularly through PPG recordings. It demonstrates that closely spaced calibration measurements can lead to highly accurate classification, emphasizing the potential for real-time applications. These findings may pave the way for advanced, non-invasive, and continuous blood pressure monitoring methods that are both efficient and reliable.

Optimization of calibration interval based on equipment metrological history.

The purpose of this paper is to describe a method for optimizing the calibration period of a dosimetry measurement system with a focus on the personal dose equivalents Hp(d) and individual monitoring services. The systematic and analytical method developed provides a justification to the calibration interval based on experimental data and measurement uncertainties. Its main input is the metrological history of the equipment that is then used to predict the drift of the measurement system. The method is used to make the calibration operation more efficient over time in terms of cost and metrology. After a description of the method itself, the example of the OSL dosemeter reading process will be discussed and results will be shown. The method developed may be applied to other measurement processes no matter the technology used.

A method to extend the calibration beyond the defined periodicity

The assignment of calibration intervals shall be a formal process based on the results of previous calibrations. With the coronavirus pandemic, one means to avoid a non-conformity instrument and ensure an updated calibration program is to extend the calibration interval of laboratories equipment. The ILAC-G24 has published guidelines for that purpose. What can be done if an extension on the calibration validity is further than previously defined? This article presents a way to increase instrument uncertainty and extend the calibration period based on ISO Guide 35, applying a trend analysis and stability criteria of the reference material properties to the case of a measurement system.

Calibration interval scenario approach in spatial modeling of land cover change in East Kalimantan from 2016 to 2036

Spatial modeling can be used to predict future land cover changes based on past and present conditions. However, it is not yet known to what extent this model can be used to predict the future with reliable accuracy. Therefore, by using multi-temporal land cover data, this study aims to build an optimal model based on the calibration interval scenario. The optimal model is then used to predict and analyze changes in land cover in East Kalimantan in 2016–2036. 11 classified multi-temporal land cover maps from the Landsat Time Series using Random Forest in Google Earth Engine are used to model 14 calibration interval scenarios. A land Change Modeler is used to model and predict land cover change with 14 driving variables. The results of the classification of multi-temporal land cover maps show a good level of accuracy, with an Overall Accuracy value of 71.43–85.14% and a Kappa value of 0.667–0.827. Then 2016–2021 is one of the best scenarios with 5-year intervals where the accuracy of future predictions can still be relied upon for up to three prediction iterations. The calibration interval scenario approach in spatial modeling in East Kalimantan can be relied upon to show a decrease in forest cover from 2016 to 2021, with a deforestation rate of 651 km2/year. The prediction of land cover in 2036 estimates that the remaining forest cover area in East Kalimantan is 69.203 km2. It is believed that topography is the most influential variable driving land cover change in East Kalimantan.

Statistical calibration for infinite many future values in linear regression: simultaneous or pointwise tolerance intervals or what else?

AbstractStatistical calibration using regression is a useful statistical tool with many applications. For confidence sets for x-values associated with infinitely many future y-values, there is a consensus in the statistical literature that the confidence sets constructed should guarantee a key property. While it is well known that the confidence sets based on the simultaneous tolerance intervals (STIs) guarantee this key property conservatively, it is desirable to construct confidence sets that satisfy this property exactly. Also, there is a misconception that the confidence sets based on the pointwise tolerance intervals (PTIs) also guarantee this property. This paper constructs the weighted simultaneous tolerance intervals (WSTIs) so that the confidence sets based on the WSTIs satisfy this property exactly if the future observations have the x-values distributed according to a known specific distribution F(⋅). Through the lens of the WSTIs, convincing counter examples are also provided to demonstrate that the confidence sets based on the PTIs do not guarantee the key property in general and so should not be used. The WSTIs have been applied to real data examples to show that the WSTIs can produce more accurate calibration intervals than STIs and PTIs.

A density model for high-pressure carbonate-rich melts applied to carbonatitic magmatism in the upper mantle

Carbonatitic melts (<15 wt% SiO2) are widespread in the Earth's upper mantle and major conveyors of trace and volatile elements. Their migration through the mantle thus shapes the deep volatiles cycle and modifies its geochemical and geophysical signatures. Quantitative modelling of these processes has long been limited by the lack of continuous, predictive models for the physical properties of carbonate-rich melts, namely the density and viscosity, over a broad pressure-temperature-composition (P-T-X) range. Here we present a continuous model for the density of carbonate-rich melts in the system MgO-CaO-Na2O-K2O-Li2O-H2O-CO2 based on data compiled from the literature, including recent high pressure experimental and theoretical results. The model is calibrated on an extensive database of more than 880 data points over a temperature range of 800–2300 K and pressures from 10−4 GPa to 30 GPa, i.e. spanning most conditions of interest for the crust, upper mantle and transition zone. We have adopted a simple, volume-explicit Murnaghan EOS to describe the volumetric properties of each oxide component using 5 adjustable parameters optimized over the entire database and assume linear mixing in volume between the different oxide components in the calibration interval. The density model reproduces the calibration dataset with an average residual of ±0.05 g cm−3 and predicts densities with an average relative error of ±2%. Used in conjunction with reasonable constraints on the volumetric properties of SiO2 and FeO, the model permits density and seismic P-wave velocity VP predictions for complex carbonatitic melts experimentally equilibrated with various mantle lithologies (e.g. peridotite, eclogite, pelite). Our calculations demonstrate the first-order role of pressure by increasing both density and VP, with only a secondary role ascribed to temperature and composition. The role of H2O is contrasted and concealed by the interplay between the P-T-X conditions for the few experimental data available. Our predictions also show that both density and VP contrasts between carbonatitic melts and the host solid mantle diminish with depth, which implies larger effect of carbonate-rich melts on the seismic velocity signature of the Earth's upper mantle at shallower depths. Lastly, we pinpoint that carbonate melts are seismically faster than silicate melts, which together with their higher electrical conductivity, are promising diagnostic features to identify molten phases at depth and to discriminate between carbonate and silicate melts in the Earth's upper mantle using geophysical observations.

Model of operation of computer measuring system

The implementation of the concepts of a smart home - smart city implies a high level of automation using control and measuring devices and equipment, as well as computer and informational technologies. One of the effective trends of automation is the widespread use of computer measuring systems Article presents the model of operation of a computer measuring system with digital data processing is proposed. The presented model is based on the semi-Markov model of operation of an analog-to-digital converter, on the semi-Markov model of an embedded personal computer, on the analytical formula for calculating the availability factor of computer measuring system. The analytical expression for the availability factor of a computer measuring system includes both the technical and operational characteristics of the computer, as well as the metrological and technical characteristics of the analog-to-digital converter. The specified expression allows to find the optimal value of calibration and verification interval. Determination of the optimal calibration interval required to control the metrological characteristics during long-term operation of the computer measuring system. The developed model can be used to set requirements for the metrological support of instruments and de-vices used in the framework of the automation trend in the sphere of housing and communal services.

Features of the production and calibration of reference hardness test blocks

This article provides information on the current production in the Karaganda branch of the RSE «KazStandard» of reference hardness blocks, brief information on the state of production of hardness blocks in the world and research conducted at this institute while improving manufacturing technology. Hardness test blocks are designed to reproduce the hardness of metals according to standardized hardness scales, for example, [1], [2], [3], [4]. Reference hardness test blocks are made in accordance with [5] from carbon and alloy steels, have a rectangular or round shape. Reference hardness test blocks in accordance with the law «On Ensuring the Uniformity of Measurements in the Republic of Kazakhstan» are calibrated. However, in some cases, if necessary, the user verifies them. Primary and periodic verification of measures is carried out by metrological service bodies accredited for the right to verify hardness measuring instruments. The calibration interval is 2 years [6].

A machine learning approach for calibrating seismic interval velocity in 3D velocity model

Velocity model technique is routinely used to convert data from the time-to-depth domain to support prospect evaluation, reservoir modelling, well engineering, and further drilling operation. In Vietnam, the conventional velocity model building workflow oversimplifies the interval velocities as only well interval velocities are populated into 2D grids for depth conversion or oversimplified calibration interval velocities by applying a single scaling factor function. This study explores the 3D velocity model workflow to obtain accurate and high-resolution interval velocities using a machine learning approach for both fields A and B in Cuu Long basin, offshore Vietnam.&#x0D; To design an effective approach to depth conversion, the anisotropy factor analysis was performed to understand the differences between the seismic and well interval velocities in geological layer in the 3D structural model. The seismic interval velocity was multiplied by the anisotropy factor to achieve the scaling seismic interval velocity. The scaling seismic interval velocity, elastic attributes, geometric attributes, structural and stratigraphic attributes were used as training features (variables) for predicting interval velocity using the supervised learning algorithm in the machine learning model. Supervised learning offers an opportunity to develop an expert-knowledge-based automated system, which incorporates both domain knowledge and quantitative data mining [1]. The random forest regression algorithms were selected for predicting interval velocity after evaluating several machine learning algorithms. To provide insight into the uncertainty of final interval velocity, a depth uncertainty analysis was conducted using a blind well test for 24 wells and 7 horizons.&#x0D; The comprehensive 3D velocity model using machine learning approach was built for the first time in Cuu Long basin, offshore Vietnam. The result showed the machine learning algorithm can address the disadvantages of conventional velocity calibration to create highly accurate depth representations of the subsurface including a measure of the uncertainty.

Thermal airflow sensor design and temperature compensation research based on the thermostatic method

PurposeThis paper aims to solve the typical thermal airflow sensor's high power consumption and integration difficulties, based on the FS5 thermal element and constant temperature measurement method, a flow sensor is developed with high measurement accuracy, low power consumption, small size, low cost and easy system integration.Design/methodology/approachA small wind tunnel was used to test and assess the sensor's measurement range, reaction time, stability, repeatability, measurement accuracy and multi-temperature calibration was performed in the temperature range of −10°C to 30°C. The effect of ambient temperature on the sensor's measurement data is investigated, and the coefficient correction method of power function was investigated to implement the sensor's software temperature compensation function.FindingsThe results show that the sensor is stable and repeatable, the output voltage has a power function relationship with the airflow rate, the flow rate measurement range is 0–18 m/s, the response time is less than 3 s, the measurement accuracy at high flow rates is within 0.4 m/s and the temperature-corrected airflow rate measurement error is less than 5%. Setting the temperature calibration interval to 2°C and 5°C has the same temperature compensation effect, reducing the sensor's calibration effort significantly.Originality/valueThis paper demonstrates that a thermostatic method is used to construct a thermal wind speed sensor that delivers accurate measurements in the wind speed measuring range of 0–18 m/s under test conditions. In addition, the sensor's performance is evaluated, and calibration tests for a wide range of temperatures are done. Finally, based on the power function correction method, a temperature compensation algorithm is proposed.

Pixel Image Analysis and Its Application with an Alcohol-Based Liquid Scintillator for Particle Therapy.

We synthesized an alcohol-based liquid scintillator (AbLS), and we implemented an auxiliary monitoring system with short calibration intervals using AbLS for particle therapy. The commercial liquid scintillator used in previous studies did not allow the user to control the chemical ratio and its composition. In our study, the chemical ratio of AbLS was freely controlled by simultaneously mixing water and alcohol. To make an equivalent substance to the human body, 2-ethoxyethanol was used. There was no significant difference between AbLS and water in areal density. As an application of AbLS, the range was measured with AbLS using an electron beam in an image analysis that combined AbLS and a digital phone camera. Given a range–energy relationship for the electron expressed as areal density, the electron beam range (cm) in water can be easily estimated. To date, no literature report for the direct comparison of a pixel image analysis and Monte Carlo (MC) simulation has been published. Furthermore, optical tomography of the inverse problem was performed with AbLS and a mobile phone camera. Analyses of optical tomography images provide deeper insight into Radon transformation. In addition, the human phantom, which is difficult to compose with semiconductor diodes, was easily implemented as an image acquisition and analysis system.