Optimal Calibration Research Articles

Cellulose paper-based SERS sensor for sensitive detection of 2,4-D residue levels in tea coupled uninformative variable elimination-partial least squares

Food safety is a growing concern in recent years. This work presents the design of a simple and sensitive method for predicting 2,4-D (2,4-dichlorophenoxyacetic acid) residue levels in green tea extract employing surface-enhanced Raman spectroscopy (SERS) coupled uninformative variable elimination-partial least squares (UVE-PLS). Herein, SERS active citrate functionalized silver nanoparticles (AgNPs) with enhancement factor 1.51 × 108 was used to prepare cellulose paper (common office) templated SERS sensor for acquiring SERS spectra of 2,4-D. The principle of the work was based on the interaction between 2,4-D and citrate group of AgNPs via chlorine atoms in the concentration range 1.0 × 10−4 to 1.0 × 103 µg/g. Three different wavenumber selection chemometric algorithms were studied comparatively to build an optimum calibration model, among them UVE-PLS showed enhanced performance as evident from the RPD value of 6.01 and Rp = 0.9864. Under optimized experimental condition proposed paper-based SERS sensor exhibited detection limit and RSD of 1.0 × 10−4 µg/g and <5%, respectively. In addition, the validation results by HPLC method were satisfactory (p > 0.05).

Platelets as a prognostic marker for sepsis: A cohort study from the MIMIC-III database.

During sepsis, platelets dysfunction contributes to organ dysfunction. Studies on platelets dysfunction in the long-term prognosis of sepsis are lacking. The aim of this study was to assess the role of platelets in the long-term prognosis of sepsis patients.A total of 4576 sepsis patients were extracted from MIMIC III Database. Survival was analyzed by the Kaplan-Meier method. Univariate and multivariate cox analyses were performed to identify prognostic factors. Significant prognostic factors were combined to build a nomogram to predict 1 year overall survival (OS). The discriminative ability and predictive accuracy of the nomogram were evaluated using the receiver operating characteristic curve (ROC) analysis and calibration curves used for sepsis.The more abnormal the platelet level, the worse prognosis of patients. After final regression analysis, age, blood urea nitrogen, platelets, international normalized ratio, partial thromboplastin time, potassium, hemoglobin, white blood cell count, organ failures were found to be independent predictors of 1 year OS of sepsis patient and were entered into a nomogram. The nomogram showed a robust discrimination, with an area under the receiver operating characteristic curve of 0.752. The calibration curves for the probability of the prognosis of sepsis patients showed optimal agreement between the probability as predicted by the nomogram and the actual probability.Platelet was an independent prognostic predictor of 1 year OS for patients with sepsis. Platelet-related nomogram that can predict the 1 year OS of sepsis patients. It revealed optimal discrimination and calibration, indicating that the nomogram may have clinical utility.

Optimal calibration of the rotational inertia double tuned mass damper (RIDTMD) for rotating wind turbine blades

This paper presents the closed-form formulas for optimal calibration (tuning) of the rotational inertia double tuned mass damper (RIDTMD) for damping edgewise vibrations in rotating wind turbine blades. A 3-DOF model is first established for the rotating blade-RIDTMD system, based on which the optimal calibration formulas are derived by means of pole-placement method. Equal modal damping criterion is applied together with the utilization of the triple-root bifurcation point. It is seen from the derived closed-form formulas that the optimal damper parameters depend explicitly on the rotor rotational speed. Comparison of the optimal RIDTMD and the optimal TMD is performed using the NREL 5 MW wind turbine as the numerical example. Results indicate superior performance of the RIDTMD in damping blade vibrations comparing with the TMD, at the cost of slightly increased damper stroke.

A prognostic nomogram for event-free survival in patients with atrial fibrillation before cardiac resynchronization therapy

Abstract Background Atrial fibrillation (AF), one of the most common comorbidities with heart failure (HF), is associated with worse prognosis in HF patients receiving cardiac resynchronization therapy (CRT). However, there is still no convenient tool to evaluate and identify patients with high risk of mortality and hospitalization due to heart failure in CRT candidates with AF. Methods We included 152 consecutive patients with AF for CRT in our hospital from January 2009 to July 2019. Multivariate Cox regression was applied to derive a nomogram, using multiple imputation for missing values and backward stepwise regression for variable selection. Results Five predictors were incorporated in the nomogram, including N-terminal pro brain natriuretic protein (NTproBNP) &amp;gt;1745pg/mL, history of syncope, previous pulmonary hypertension (PHP), moderate or severe tricuspid regurgitation (TR), thyroid stimulating hormone (TSH) &amp;gt;4mIU/L. Concordance index (0.70, 95% CI 0.62–0.77), corrected concordance index (0.67, 95% CI 0.59–0.74) and calibration curve showed optimal discrimination and calibration of the established nomogram. Significant difference of overall event-free survival was recognized by the nomogram-derived scores in patients with high risk (&amp;gt;50 points), intermediate risk (21–50 points) and low risk (0–20 points) before CRT. Conclusion Our nomogram may be an applicable tool for early risk stratification among CRT candidates with AF. Nomogram and risk stratification Funding Acknowledgement Type of funding source: None

Application of Non-Destructive Rapid Determination of Piperine in Piper nigrum L. (Black Pepper) Using NIR and Multivariate Statistical Analysis: A Promising Quality Control Tool.

Piperine is a bioactive alkaloid compound which provides a unique spicy flavor derived from plants of the Piper nigrum L. Black pepper (n = 160) collected from Vietnam was studied using non-destructive near infrared spectroscopy (NIRS). The spectral acquisition ranged from 1100 to 2500 nm, and a chemometrics analysis program was performed to quantify the piperine contents. High performance liquid chromatography (HPLC) analysis was carried out to develop a chemometric model based on reference values. The black pepper samples were divided into two groups used for calibration (n = 120) and prediction (n = 40) sets. The optimum calibration model was developed by pretreatment of the spectra. The analyses results based on the prediction samples included a coefficient of determination (R2) of 0.914, a root mean square error of prediction (RMSEP) and a standard error of prediction (SEP) of about 0.220 g/100 g, and a ratio performance to deviation (RPD) value of 3.378 regarding the partial least square (PLS) regression model, and an R2 of 0.921, an RMSEP and SEP of 0.210 g/100 g, and an RPD of 3.571, with respect to the principal components (PC) regression model. These results indicate that NIRS can be applicable as a control, or as an alternative rapid and effective method to quantify piperine in P. nigrum L.

Online Optimal Control Strategy Methodology for Power-Split Hybrid Electric Bus Based on Historical Data

An online optimal control strategy methodology on the basis of historical data for a power-split hybrid electric bus (HEB) is proposed in this study. This approach aims to fully utilize the fuel-saving capability of power-split HEB under real operating cycles and provide an effective way for solving the optimal calibration problem in the application promotion. Firstly, a procedure for synthesizing real-world driving cycles based on cluster analysis and Markov chain method is constructed. Subsequently, dynamic programming (DP) control algorithm is performed to explore the fuel economy potential. Moreover, a DP-based rule control strategy with an automated implementation foundation is introduced to achieve online approximate optimal effect. Finally, offline simulation and hardware-in-the-loop test are conducted. Simulation results validate that the proposed online optimal control strategy methodology has similar fuel-saving performance to DP optimal results and good real-time application conditions.

A Novel MEMS Gyroscope In-Self Calibration Approach.

This paper presents a novel approach for hand-held low-cost MEMS (micro-electro-mechanical system) gyroscope in-self calibration. This method does not need the support of external high-precision equipment compared with traditional calibration scheme and can be accomplished by user hand rotation. In this approach, Kalman filter is designed to perform the calibration procedure and estimate gyroscope bias error, scale factor error and non-orthogonal error. The system observability is analyzed and the dynamic rotating conditions under which the sensor errors become observable are derived. The design principles of optimal calibration procedure are provided as well. Both simulated and practical experiments are carried out to test the validation of the proposed calibration algorithm. The achieved results demonstrate that the introduced approach can provide promising calibration scheme for the low-cost MEMS gyroscope.

Holistic Engine and EAT Development of Low NO &lt;sub&gt;X&lt;/sub&gt; and CO &lt;sub&gt;2&lt;/sub&gt; Concepts for HD Diesel Engine Applications

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;The latest legislative tendencies for on-highway heavy duty vehicles in the United States such as the feasibility assessment of low NO&lt;sub&gt;X&lt;/sub&gt; standards of CARB or EPA’s memorandum forecast further tightening of the NO&lt;sub&gt;X&lt;/sub&gt; emissions limits. In addition, the GHG Phase 2 legislation and also phased-in regulations in the EU enforce a continuous reduction in CO&lt;sub&gt;2&lt;/sub&gt; emissions resp. fuel consumption. In order to meet such low NO&lt;sub&gt;X&lt;/sub&gt; emission limits, a rapid heat-up of the exhaust after-treatment system (EATS) is inevitable. However, the required thermal management results in increased fuel consumption, i.e. CO&lt;sub&gt;2&lt;/sub&gt; emissions as shown in numerous previous works also by the authors. A NO&lt;sub&gt;X&lt;/sub&gt;-CO&lt;sub&gt;2&lt;/sub&gt; trade-off for cumulative cycle emissions can be observed, which can be optimized by using more advance technologies on the engine and/or on the EATS side.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;In the present study a systematic investigation is carried out by means of model-based holistic approach targeting the definition of a high efficiency engine layout and optimal thermal management calibration. First, the potentials of combustion process optimization (compression ratio, peak firing pressure) and of turbocharger efficiency increase are quantified aiming engine efficiency increase. Afterwards, using holistic engine and EAT concept development approach, conventional and advanced EATS layouts are tested. The advanced EATS layout consist of a close-coupled dual-stage SCR system. In order to explore the benefits of each layouts, the engine heat-up calibrations are varied and the resulting, cumulative NO&lt;sub&gt;X&lt;/sub&gt;-CO&lt;sub&gt;2&lt;/sub&gt; emissions of the investigated cycle are compared and evaluated. Multiple improvement measures for both engine and EAT system are discussed and an outlook of future powertrain concepts is given.&lt;/div&gt;&lt;/div&gt;

Hyperspectral imaging technology for monitoring of moisture contents of dried persimmons during drying process.

The moisture content of persimmons during drying was monitored by hyperspectral imaging technology. All persimmons were dried using a hot-air dryer at 40°C and divided into seven groups according to drying time: semi-dried persimmons (Cont), 1day (DP-1), 2days (DP-2), 3days (DP-3), 4days (DP-4), 5days (DP-5), and 6days (DP-6). Shortwave infrared hyperspectral spectra and moisture content of all persimmons were analyzed to develop a prediction model using partial least squares regression. There were obvious absorption bands: two at approximately 971nm and 1452nm were due to water absorption related to O-H stretching of the second and first overtones, respectively. The R-squared value of the optimal calibration model was 0.9673, and the accuracy of the moisture content measurement was 95%. These results indicate that hyperspectral imaging technology can be used to predict and monitor the moisture content of dried persimmons during drying.

A Risk Score for Predicting the Incidence of Hemorrhage in Critically Ill Neonates: Development and Validation Study

The aim of the study was to develop and validate a prediction model for hemorrhage in critically ill neonates which combines rotational thromboelastometry (ROTEM) parameters and clinical variables. This cohort study included 332 consecutive full-term and preterm critically ill neonates. We performed ROTEM and used the neonatal bleeding assessment tool (NeoBAT) to record bleeding events. We fitted double selection least absolute shrinkage and selection operator logit regression to build our prediction model. Bleeding within 24 hours of the ROTEM testing was the outcome variable, while patient characteristics, biochemical, hematological, and thromboelastometry parameters were the candidate predictors of bleeding. We used both cross-validation and bootstrap as internal validation techniques. Then, we built a prognostic index of bleeding by converting the coefficients from the final multivariable model of relevant prognostic variables into a risk score. A receiver operating characteristic analysis was used to calculate the area under curve (AUC) of our prediction index. EXTEM A10 and LI60, platelet counts, and creatinine levels were identified as the most robust predictors of bleeding and included them into a Neonatal Bleeding Risk (NeoBRis) index. The NeoBRis index demonstrated excellent model performance with an AUC of 0.908 (95% confidence interval [CI]: 0.870-0.946). Calibration plot displayed optimal calibration and discrimination of the index, while bootstrap resampling ensured internal validity by showing an AUC of 0.907 (95% CI: 0.868-0.947). We developed and internally validated an easy-to-apply prediction model of hemorrhage in critically ill neonates. After external validation, this model will enable clinicians to quantify the 24-hour bleeding risk.

Age estimation of red snapper (Lutjanus campechanus) using FT-NIR spectroscopy: feasibility of application to production ageing for management

Abstract Recent application of Fourier transform near infra-red spectroscopy (FT-NIRS) to predict age in fish otoliths has gained attention among fisheries managers as a potential alternative to costly production ageing of managed species. We assessed the age prediction capability of FT-NIRS scans in whole otoliths from red snapper, Lutjanus campechanus, collected from the US Gulf of Mexico and US Atlantic Ocean (South Atlantic). Otoliths were scanned with an FT-NIR spectrometer and resulting spectral signatures were regressed with traditionally estimated ages via partial least squares regression to produce calibration models, which were validated for predictive capability against test sets of otoliths. Calibration models successfully predicted age with R2 ranging 0.94–0.95, mean squared error ≤1.8 years, and bias &amp;lt;0.02 years. Percent agreement between FT-NIRS and traditional ages was lower than within-reader agreement for traditional estimates, but average percent error was similar and Kolmogorov–Smirnov tests were not significantly different (p ≥ 0.06) between traditional and FT-NIRS predicted ages for optimal calibration models. Ages &amp;gt;31 years were not well predicted, possibly due to light attenuation in the thickest otoliths. Our results suggest that FT-NIRS can improve efficiency in production ageing for fisheries management while maintaining data quality standards.

Optimal Calibration Strategy of a Hybrid Electric Vehicle Equipped with an Ultra-Lean Pre-Chamber SI Engine for the Minimization of CO2 and Pollutant Emissions

The complexity of modern hybrid powertrains poses new challenges for the optimal control concerning, on one hand, the thermal engine to maximize its efficiency, and, on the other hand, the vehicle to minimize the noxious emissions and CO2. In this context, the engine calibration has to be conducted by considering simultaneously the powertrain management, the vehicle characteristics, and the driving mission. In this work, a calibration methodology for a two-stage boosted ultra-lean pre-chamber spark ignition (SI) engine is proposed, aiming at minimizing its CO2 and pollutant emissions. The engine features a flexible variable valve timing (VVT) control of the valves and an E-compressor, coupled in series to a turbocharger, to guarantee an adequate boost level needed for ultra-lean operation. The engine is simulated in a refined 1D model. A simplified methodology, based on a network of proportional integral derivative (PID) controllers, is presented for the calibration over the whole operating domain. Two calibration variants are proposed and compared, characterized by different fuel and electric consumptions: the first one aims to exclusively maximize the brake thermal efficiency, and the second one additionally considers the electric energy absorbed by the E-compressor and drained from the battery. After a verification against the outcomes of an automatic optimizer, the calibration strategies are assessed based on pollutant and CO2 emissions along representative driving cycles by vehicle simulations. The results highlight slightly lower CO2 emissions with the calibration approach that minimizes the E-compressor consumption, thus revealing the importance of considering the engine calibration phase, the powertrain management, the vehicle characteristics, and its mission.

Calibration method and photon flux influences tiled flat-panel photon counting detector image uniformity in computed tomography

The aim of this study is to compare how different calibration methods influence the image quality of photon-counting detector computed tomography (PCD-CT) at high and low photon fluxes. We investigate the performance of flat-field correction, signal-to-equivalent thickness calibration (STC), and polynomial correction (PC) methods using polymethyl methacrylate (PMMA) and iron as calibration materials. Two different cylindrical imaging phantoms containing contrast targets were scanned: an agar phantom and a phantom consisting of titanium hip implant embedded in agar. The scans were acquired using 120 kVp, and the energy thresholds of the PCD were set at 10 keV and 60 keV to obtain low energy (10–60 keV), high energy (60–120 keV) and total energy images (10–120 keV). Additionally, virtual monochromatic images (VMIs) with energies between 60–180 keV with 20 keV increments were generated from PC data. The reconstructions were made using filtered back projection, and image quality was assessed by evaluating image noise, contrast-to-noise ratio (CNR), and image uniformity. Overall, STC with PMMA as calibration material yielded the best image quality in terms of CNR and uniformity. Flat-field correction produced uniform reconstruction at low photon flux, but the performance degraded substantially at high flux. STC with iron as calibration material did not improve the reconstructions of the titanium hip implant. The beam hardening effects arising from metal were reduced when the VMI energy was increased while the CNR evaluated from agar phantom decreased with increasing energy of the VMI. Over the methods investigated, STC with PMMA was the most optimal calibration method for PCD-CT, yielding excellent image uniformity with both photon flux conditions.

Simple, compact, high-resolution monochromatic x-ray source for characterization of x-ray calorimeter arrays.

X-ray calorimeters routinely achieve very high spectral resolution, typically a few eV full width at half maximum (FWHM). Measurements of calorimeter line shapes are usually dominated by the natural linewidth of most laboratory calibration sources. This compounds the data acquisition time necessary to statistically sample the instrumental line broadening and can add systematic uncertainty if the intrinsic line shape of the source is not well known. To address these issues, we have built a simple, compact monochromatic x-ray source using channel cut crystals. A commercial x-ray tube illuminates a pair of channel cut crystals that are aligned in a dispersive configuration to select the Kα1 line of the x-ray tube anode material. The entire device, including the x-ray tube, can be easily hand-carried by one person and may be positioned manually or using a mechanical translation stage. The output monochromatic beam provides a collimated image of the anode spot with magnification of unity in the dispersion direction (typically 100 μm-200 μm for the x-ray tubes used here) and is unfocused in the cross-dispersion direction so that the source image in the detector plane appears as a line. We measured output count rates as high as 10 count/s/pixel for the Hitomi soft x-ray spectrometer, which had 819 μm square pixels. We implemented different monochromator designs for energies of 5.4 keV (one design) and 8.0 keV (two designs), which have effective theoretical FWHM energy resolution of 0.125 eV, 0.197 eV, and 0.086 eV, respectively; these are well-suited for optimal calibration measurements of state-of-the art x-ray calorimeters. We measured an upper limit for the energy resolution of our Cr Kα1 monochromator of 0.7 eV FWHM at 5.4 keV, consistent with the theoretical prediction of 0.125 eV.

Rapid identification and quantitative pit mud by near infrared Spectroscopy with chemometrics

The properties of pit mud determine the quality of Chinese liquor. However, traditional methods for detection total carbon (TC), total nitrogen (TN) and total phosphorus (TN) in pit mud, such as Kjeldahl method, are time consuming and expensive. In this research, a time-saving and cost-effective method for identification and quantification of pit mud has been developed, which is based on near infrared spectroscopy (NIRS) combining with chemometrics. A total of 82 samples of different ages were collected from liquor-making factories and analyzed with NIRS (4000-10 000 cm-1). Four chemical pattern recognition methods including hierarchical cluster analysis (HCA), partial least squares-discriminant analysis (PLS-DA), artificial neural networks (ANN) and support vector machine (SVM) were used to build identification models of data set with aged and new pit mud. Four multivariate calibration methods, i.e., support vector regression (SVR), partial least squares regression (PLSR), ANN and extreme learning machine (ELM) were used to build quantitative model and compared for each data set, separately. Finally, in order to further improve the prediction accuracy, SG smoothing, derivatives, continuous wavelet transform (CWT), standard normal variate (SNV), and multiplicative scatter correction (MSC) were investigated. Results show that PLS-DA and SVM can achieve 100% classification accuracy for identification. PLSR and SVR were found to be optimal calibration methods for quantitative analysis of TC, TN and TP prediction in pit mud. Furthermore, the predictive ability can be improved by pretreating the spectra with optimal preprocessing method. The correlation coefficient (R) and root mean square error of prediction (RMSEP) of the independent validation reached to 0.9717 and 0.3491 for TC, 0.9862 and 0.0675 for TN, 0.9603 and 0.2162 for TP.

Study on calibration model of soil water content based on actively heated fiber-optic FBG method in the in-situ test

• The calibration model for water content measurement is studied by AHFO-FBG method. • Two semi-empirical calibration models for calculating water content are deduced. • The modified calibration model considering dry density is proposed. • The feasibility of the modified calibration model is verified and analyzed. • AHFO-FBG method has unique advantages for laboratory tests accurately. In the water content measurement technology based on actively heated fiber-optic (AHFO) method, how to establish the calibration model between the temperature characteristic value and the water content is very important. Given shortcomings of the existing calibration models, the optimal calibration model was established. On this basis, a modified calibration model considering the change of dry density was proposed by using the actively heated fiber-optic fiber Bragg grating (AHFO-FBG) method, and the feasibility of the model was verified and analyzed. The results show that the Côté and Konrad model is the best model for water content calculation; in the in-situ water content measurement, if the dry density of the soil is small (≤1.5 g/cm 3 ), the modified Côté and Konrad calibration model should be used. The research results are of great significance to guide the application of the AHFO-FBG method in the in-situ soil water content monitoring.

The optimization problem of quantile and poverty measures estimation based on calibration

New calibrated estimators of quantiles and poverty measures are proposed. These estimators combine the incorporation of auxiliary information provided by auxiliary variables related to the variable of interest by calibration techniques with the selection of optimal calibration points under simple random sampling without replacement. The problem of selecting calibration points that minimize the asymptotic variance of the quantile estimator is addressed. Once the problem is solved, the definition of the new quantile estimator requires that the optimal estimator of the distribution function on which it is based verifies the properties of the distribution function. Through a theorem, the nondecreasing monotony property for the optimal estimator of the distribution function is established and the corresponding optimal estimator can be defined. This optimal quantile estimator is also used to define new estimators for poverty measures. Simulation studies with real data from the Spanish living conditions survey compares the performance of the new estimators against various methods proposed previously, where some resampling techniques are used for the variance estimation. Based on the results of the simulation study, the proposed estimators show a good performance and are a reasonable alternative to other estimators.

Variable smoothing of optimal diesel engine calibration for improved performance and drivability during transient operation

The model-based method to define the optimal calibration maps for important diesel engine parameters may involve three major steps. First, the engine speed and load domain – in which the engine is operated – are identified. Then, a global engine model is created, which can be used for offline simulations to estimate engine performance. Finally, optimal calibration maps are obtained by formulating and solving an optimisation problem, with the goal of minimising fuel consumption while meeting constraints on pollutant emissions. This last step in the calibration process usually involves smoothing of the maps in order to improve drivability. This article presents a method to trade off map smoothness, brake-specific fuel consumption and nitrogen oxide emissions. After calculating the optimal but potentially non-smooth calibration maps, a variation-based smoothing method is employed to obtain different levels of smoothness by adapting a single tuning parameter. The method was experimentally validated on a heavy-duty diesel engine, and the non-road transient cycle was used as a case study. The error between the reference and actual engine torque was used as a metric for drivability, and the error was found to decrease with increasing map smoothness. After having obtained this trade-off for various fixed levels of smoothness, a time-varying smoothness calibration was generated and tested. Experimental results showed that, with a time-varying smoothness strategy, nitrogen oxide emissions could be reduced by 4%, while achieving the same drivability and fuel consumption as in the case of a fixed smoothing strategy.

Unequal limit cuckoo optimization algorithm applied for optimal design of nonlinear field calibration problem of a triaxial accelerometer

Abstract A triaxial microelectromechanical system (MEMS) accelerometer is a low-cost sensor for measuring the acceleration. However, the measured values by the sensor are generally noisy and inaccurate. Therefore, calibration algorithms need to be used for the calibration of MEMS accelerometers, such as the field calibration. In the case of an accelerometer, using the magnitude of the gravity vector as a stable reference leads to a nonlinear optimization problem. In this paper, a modified version of the Cuckoo Optimization Algorithm (COA), namely Unequal Limit COA (ULCOA), is introduced to achieve the optimal calibration parameters. Then, its performance is evaluated via a set of nonlinear benchmark functions indicating outperformance of the ULCOA in comparison with the particle swarm optimization and the genetic algorithms in terms of accuracy and robustness. Afterward, the ULCOA-based field calibration for the triaxial MEMS accelerometer is discussed. Finally, experimental results are provided and compared with other calibration methods.

A novel calibration method for digital templating of total hip arthroplasty: a prospective clinical study of dual scale type single marker calibration in supine radiographs.

External calibration markers (ECM) are the standard of care for digital templating in joint replacements. Spherical mono markers are known to be unreliable. Fixed calibration factors (FCF) can reduce the overall error by placement of markers, but do not address individual patient anatomy. Dual scale calibration marker (DSCM) methods were developed to improve precision of calibration but require two markers. This prospective observational study is the first to analyse the application of a dual scale type single marker (DSSM) method in supine radiographs. One hundred patients with unilateral total hip arthroplasty (THA) underwent anterior-posterior pelvis X-ray with ECM and DSSM. The femoral THA head components were used as internal reference (ICM). An empirical fixed calibration factor (FCF; 120%) was used as comparator. Absolute differences of ICM and both ECM and DSSM were calculated. Absolute relative deviations (ARD) were calculated. Mean absolute calibration factor differences of ECM, FCF and DSSM from ICM were 0.105 (0.002-0.182), 0.016 (0.082-0.013) and 0.011 (0.056-0.009), respectively. ARD differences between ECM and DSSM as well as FCF and DSSM were significant (p < 0.001). The DSSM method showed superior results over ECM and FCF to predict the optimal calibration factor in supine radiographs. The novel DSSM method could be used to improve digital templating in supine radiographs without need for additional markers.