Final Calibration Research Articles

Flight test evaluation of trim tab stabilisation system on a PZL-130 orlik aircraft

Purpose The paper presents the results of the final phase of the project, namely flight tests, aimed at developing a stabilisation system utilizing trim tabs for the PZL-130 Orlik turboprop military trainer aircraft. Design/methodology/approach The proposed flight stabilisation system was developed using modern techniques of model-based design, automatic code generation and software and hardware-in-the-loop testing. The project progressed to the flight testing stage, enabling the assessment of the control system’s quality and its final calibration. Findings Analysis of the results obtained during flight tests confirmed the high quality of the stabilisation system. Specifically, the anticipated accuracy of both longitudinal and lateral channel stabilisation was achieved. Originality/value The proposed flight stabilisation system, utilizing trim tabs, offers several advantages over classic automatic flight systems in terms of weight, energy consumption, structural simplicity and obviates the need for primary control modifications on the aircraft. It was developed using modern techniques of model-based design, automatic code generation and hardware-in-the-loop simulations.

Full-reference calibration-free image quality assessment

Objective Image Quality Assessment (IQA) methods often lack of linearity of their quality estimates with respect to scores expressed by human subjects and therefore IQA metrics undergo a calibration process based on subjective quality examples. However, example-based training presents a challenge in terms of generalization hampering result comparison across different applications and operative conditions. In this paper, new Full Reference (FR) techniques, providing estimates linearly correlated with human scores without using calibration are introduced. We show that on natural images, application of estimation theory and psychophysical principles to images degraded by Gaussian blur leads to a so-called canonical IQA method, whose estimates are linearly correlated to both the subjective scores and the viewing distance. Then, we show that any mainstream IQA methods can be reconducted to the canonical method by converting its metric based on a unique specimen image. The proposed scheme is extended to wide classes of degraded images, e.g. noisy and compressed images. The resulting calibration-free FR IQA methods allows for comparability and interoperability across different imaging systems and on different viewing distances. A comparison of their statistical performance with respect to state-of-the-art calibration prone methods is finally provided, showing that the presented model is a valid alternative to the final 5-parameter calibration step of IQA methods, and the two parameters of the model have a clear operational meaning and are simply determined in practical applications. The enhanced performance are achieved across multiple viewing distance databases by independently realigning the blur values associated with each distance.

Enhanced deep leaning model for detection and grading of lumbar disc herniation from MRI.

Lumbar disc herniation is one of the most prevalent orthopedic issues in clinical practice. The lumbar spine is a crucial joint for movement and weight-bearing, so back pain can significantly impact the everyday lives of patients and is prone to recurring. The pathogenesis of lumbar disc herniation is complex and diverse, making it difficult to identify and assess after it has occurred. Magnetic resonance imaging (MRI) is the most effective method for detecting injuries, requiring continuous examination by medical experts to determine the extent of the injury. However, the continuous examination process is time-consuming and susceptible to errors. This study proposes an enhanced model, BE-YOLOv5, for hierarchical detection of lumbar disc herniation from MRI images. To tailor the training of the model to the job requirements, a specialized dataset was created. The data was cleaned and improved before the final calibration. A final training set of 2083 data points and a test set of 100 data points were obtained. The YOLOv5 model was enhanced by integrating the attention mechanism module, ECAnet, with a 3 × 3 convolutional kernel size, substituting its feature extraction network with a BiFPN, and implementing structural system pruning. The model achieved an 89.7% mean average precision (mAP) and 48.7 frames per second (FPS) on the test set. In comparison to Faster R-CNN, original YOLOv5, and the latest YOLOv8, this model performs better in terms of both accuracy and speed for the detection and grading of lumbar disc herniation from MRI, validating the effectiveness of multiple enhancement methods. The proposed model is expected to be used for diagnosing lumbar disc herniation from MRI images and to demonstrate efficient and high-precision performance.

A UK MW catalogue derived from coda envelopes

Summary The United Kingdom (UK) experiences low-to-moderate levels of seismicity; only 12 onshore earthquakes with local magnitude (ML) ≥ 4.0 have been recorded in the past 20 years. It is therefore difficult to estimate moment magnitude (MW) using conventional moment tensor inversion for the majority of UK seismicity, resulting in limited reliable estimates of MW. To address this, we calibrated coda envelopes at 16 broadband seismic stations distributed across the UK, to produce a MW catalogue for 100 events with MW≥2.13 that occurred since 2006. This was achieved using the open-source Coda Calibration Tool, which requires independent source parameter estimates for calibration. For 13 UK events between 2006 and 2022, we used spectral modelling to estimate apparent stress (0.32 to 1.74 MPa), and moment tensor inversion to estimate MW (3.35 to 4.52). These independent source parameters formed a subset of the inputs into the final calibration, which used seismic data from 33 events with coda-derived values of 2.57$\le $Mw$\le $4.49. The resultant coda calibration parameters were applied to 67 further events (MW≥2.13). The coda envelopes exhibit slow seismic coda decay across the UK, with significant energy up to 20 Hz, consistent with other regions of low tectonic activity. This MW catalogue, and the application of the calibration to future UK seismic events, will be useful for both assessing seismic hazard and event characterisation.

Flavonoid Oxidation Potentials and Antioxidant Activities-Theoretical Models Based on Oxidation Mechanisms and Related Changes in Electronic Structure.

Herein, I will review our efforts to develop a comprehensive and robust model for the estimation of the first oxidation potential, Ep1, and antioxidant activity, AA, of flavonoids that would, besides enabling fast and cheap prediction of Ep1 and AA for a flavonoid of interest, help us explain the relationship between Ep1, AA and electronic structure. The model development went forward with enlarging the set of flavonoids and, that way, we had to learn how to deal with the structural peculiarities of some of the 35 flavonoids from the final calibration set, for which the Ep1 measurements were all made in our laboratory. The developed models were simple quadratic models based either on atomic spin densities or differences in the atomic charges of the species involved in any of the three main oxidation mechanisms. The best model takes into account all three mechanisms of oxidation, single electron transfer-proton transfer (SET-PT), sequential proton loss electron transfer (SPLET) and hydrogen atom transfer (HAT), yielding excellent statistics (R2 = 0.970, S.E. = 0.043).

Greenhouse gas reduction in a medium-duty compression ignition engine with optimization for B20

Soy-based biodiesel can reduce well-to-wheels greenhouse gas (GHG) emissions per unit energy (i.e., gCO2e/MJ) by 66%–72% as compared to the petroleum-based diesel fuel with currently adopted agricultural and industrial practices. Biodiesel can reduce particulate matter and carbon monoxide emissions with a manageable degree of increase in NOx emissions. From the perspective of GHG emissions reduction per unit travelling distance (i.e., gCO2e/mile), the application of B20 in compression ignition engines without the adjustment in engine control unit (ECU) settings will not extract the best carbon emissions reduction that B20 could achieve. Optimizing the engine control settings permits re-calibration to achieve the maximum brake fuel conversion efficiency (BFE) based on comprehensive understanding on the impact of both “fuel” and “ECU calibration” on BFE and other criteria pollutant emissions. The maximum GHG emissions reduction with B20 application is experimentally measured with the optimized ECU calibration, thus providing the understanding of the combined impact of biodiesel fuel and calibrations on engine performance and emissions. Six steady operating modes were considered, that can be combined to estimate the US federal test procedure BFE and emissions over the Federal Test Protocol (FTP) 75 cycle. Combined with the weight factors to simulate the EPA FTP 75 cycle from these 6 “mini-map” test points, 0.53% improvement in the energy requirement per unit traveling distance (i.e., MJ/mile) is achieved for B20 with the final ECU calibration, in addition to the degree of GHG emissions reduction on a “gCO2e/MJ” basis from the use of B20 blend of soy biodiesel of ∼12.5% reduction in gCO2e/MJ, for a total GHG emissions reduction of 13%.

Camera calibration based on lightweight fan-shaped target detection and fitness-distance-balance chaotic marine predators algorithm

Camera calibration represents a critical stage in visual measurement, directly impacting the measurement precision. Addressing the limitations of checkerboard and circular targets, resulting in low calibration accuracy and eccentricity errors, this study introduces a camera calibration method that relies on lightweight fan-shaped target detection and Fitness-Distance-Balance Chaotic Marine Predators Algorithm(FDBCMPA). Starting with the improvement of camera calibration targets by refining and designing fan-shaped targets, this paper aims to tackle eccentricity errors through hardware-based solutions. In the process of extracting target feature points, a lightweight object detection algorithm is devised to capture individual fan-shaped pattern positions from images, transitioning from global multi-shape feature detection to localized single-pattern feature extraction. This approach mitigates the distortions inherent in traditional checkerboard targets during global linear feature imaging and partially alleviates interference from background environments and similar features. Following the initial camera calibration, the FDBCMPA is employed to further optimize the calibration outcomes, thereby boosting the calibration method's performance. Experimental findings highlight that the enhanced object detection method in this study attains detection accuracy comparable to mainstream methods, with distinct advantages in detection speed, parameters, and FLOPs, rendering it more suitable for use in camera calibration. Additionally, the FDBCMPA demonstrates improvements in convergence results compared to MPA, PSO, TACPSO, Chimp, and other methods. The final camera calibration results demonstrate an impressive reprojection error of 0.0738 pixels through the utilization of the proposed camera calibration method introduced in this paper, and substantiate the practicality and effectiveness of the calibration method proposed in this study.

Experimental and numerical analysis of residual stresses induced by the manufacturing process of longitudinal welded tubes

In the present work, the process chain in tube production by roll forming of the steel 34MnB5 was examined in more detail. The process chain consisting of the steps (i) roll forming, (ii) HFI welding, and then (iii) straightening and final calibration was mapped using the finite element method. In addition to the pipe geometry, the residual stress distribution was considered as an essential target value for the assessment of the performance of the tubes under service conditions. The objective of the project is to describe the manufacturing process and thus the processing induced residual stress distributions as realistically as possible. For a more detailed characterization of the process and above all for the validation of the numerical simulations, experimental residual stress analyses were carried out for the final state of the tubes using complementary analysis methods. The contour method was used to determine the 2D-distribution of residual stresses across the transverse and longitudinal sections of the manufactured pipes. These measurements were supplemented by local residual stress analyses using the incremental hole drilling method and X-ray residual stress analyses. The work thus pursues both a methodical approach to the analysis of the internal stresses induced by the process using complementary methods and also a manufacturing approach to the analysis and evaluation of the production chain. In particular for the axial components of the residual stresses, it can be shown that experimentally determined residual stresses correlate well with the numerically calculated values. The contour method is excellently suited to monitor the uneven distribution of the internal stresses over the pipe cross-section. The results of the simulation show that the overall residual stress distribution that is determined using the contour method mainly results from the plastic strains introduced by the roll forming process. X-ray and hole drilling analyses are shown to be more suitable for measuring locally present residual stresses at defined positions on the outer tube surface. In this way it can be shown that the simulation approach described provides an accurate model of the process.

Euramet RMO supplementary comparison low pressure air flow between 25m3/h and 400 m3/h

Main textThe purpose of this Supplementary Comparison (SC) for "Low Pressure Air Flow" measurement is to support the Calibration and Measurement Capabilities (CMC) of the participating National Metrology Institutes and Designated Institutes according to CIPM MRA-G-13. The outcome of this comparison should lead to 'a clear and unequivocal' comparison of measurement results between participants.The comparison was performed over the span of one year which started in March 2021 with the determination of the volume flow rate error of the test artifact by VSL. The test artifact was shipped to each participant where they also determined the volume flow rate error in turn. At the completion by all participants, the artifact was shipped back to VSL for a final calibration to show the stability of the artifact.To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/.The final report has been peer-reviewed and approved for publication by the CCM, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).

Calibration trial of an innovative medical device (NEVVA© ) for the evaluation of pain in non-communicating patients in the intensive care unit.

Pain management is an essential and complex issue for non-communicative patients undergoing sedation in the intensive care unit (ICU). The Behavioral Pain Scale (BPS), although not perfect for assessing behavioral pain, is the gold standard based partly on clinical facial expression. NEVVA© , an automatic pain assessment tool based on facial expressions in critically ill patients, is a much-needed innovative medical device. In this prospective pilot study, we recorded the facial expressions of critically ill patients in the medical ICU of Caen University Hospital using the iPhone and Smart Motion Tracking System (SMTS) software with the Facial Action Coding System (FACS) to measure human facial expressions metrically during sedation weaning. Analyses were recorded continuously, and BPS scores were collected hourly over two 8 h periods per day for 3 consecutive days. For this first stage, calibration of the innovative NEVVA© medical device algorithm was obtained by comparison with the reference pain scale (BPS). Thirty participants were enrolled between March and July 2022. To assess the acute severity of illness, the Sequential Organ Failure Assessment (SOFA) and the Simplified Acute Physiology Score (SAPS II) were recorded on ICU admission and were 9 and 47, respectively. All participants had deep sedation, assessed by a Richmond Agitation and Sedation scale (RASS) score of less than or equal to -4 at the time of inclusion. One thousand and six BPS recordings were obtained, and 130 recordings were retained for final calibration: 108 BPS recordings corresponding to the absence of pain and 22 BPS recordings corresponding to the presence of pain. Due to the small size of the dataset, a leave-one-subject-out cross-validation (LOSO-CV) strategy was performed, and the training results obtained the receiver operating characteristic (ROC) curve with an area under the curve (AUC) of 0.792. This model has a sensitivity of 81.8% and a specificity of 72.2%. This pilot study calibrated the NEVVA© medical device and showed the feasibility of continuous facial expression analysis for pain monitoring in ICU patients. The next step will be to correlate this device with the BPS scale.

P16 and p53 can Serve as Prognostic Markers for Head and Neck Squamous Cell Carcinoma

ObjectiveThe present study aimed to explore the expression and clinical significance of human papilloma virus-related pathogenic factors (p16, cyclin D1, p53) in patients with head and neck squamous cell carcinoma (HNSCC) and construct a predictive model. MethodsThe Cancer Genome Atlas was used to obtain clinical data for 112 patients with HNSCC. Expression of p16, p53, and cyclin D1 was quantified. We used the survival package of the R program to set the cut-off value. Values above the cut-off were considered positive, while values below the cut-off were negative. Kaplan–Meier analysis and univariate and multivariate Cox regression analyses were performed to investigate prognostic clinicopathological indicators and the expression of p16, p53, and cyclin D1. A predictive model was constructed based on the results of multifactor Cox regression analysis, and the accuracy of the predictive model was verified through final calibration analysis. Follow-up of patients with HNSCC at the Affiliated Hospital of Binzhou Medical University was conducted from 2015 to 2017, and reliability of the predictive model was validated based on follow-up data and molecular expression levels. ResultsAccording to the results, expression of p16 and p53 was significantly associated with prognosis (P < .05). The predictive model constructed based on the expression levels of p16 and p53 was useful for evaluating the prognosis of patients with HNSCC. The predictive model was validated using follow-up data obtained from the hospital, and the trend of the follow-up results was consistent with the predictive model. Conclusionp16 and p53 can be used as key indicators to predict the prognosis of HNSCC patients and as critical immunohistochemical indicators in clinical practice. The survival model constructed based on p16 and p53 expression levels reliably predicts patient prognosis.

Variability in Estimating Crop Model Genotypic Parameters: The Impact of Different Sampling Methods and Sizes

Generic parameter calibration for crop growth models is a very important step in model use. However, studies of the effect of sample size and sampling methods on the calibration and validation of genotypic parameters have seldom been conducted. Scientists commonly apply the hold-out (HO) method, by default, to deal with samples for calibration and validation in the practice of model use. In this paper, we applied the hold-out, cross-validation (CA), and bootstrapping (BS) methods with different sample sizes to analyze the influence of sampling methods and sample size on the final calibration results of genotypic parameters. The results showed that, (1) overall, CA and BS performed better than HO at most observation stations. However, there was great variability in the calibration and validation results obtained from the three methods. (2) Because of data quality differences, we could not conclude that the more samples there were, the greater the validation accuracy of the three methods. (3) The CV of the genotypic parameter values for the three methods and sample sizes varied greatly. Thus, when genotypic parameter calibration is performed, both sampling methods and sample size should be considered.

Ground flat-field calibration of a space astronomical telescope using a spatial time-sharing calibration method.

Preflight ground flat-field calibration is significant to the development phase of space astronomical telescopes. The uniformity of the flat-field illumination reference source seriously decreases with the increasing aperture and the telescope's field of view, directly affecting the final calibration accuracy. To overcome this problem, a flat-field calibration method that can complete calibration without a traditional flat-field illumination reference source is proposed on the basis of the spatial time-sharing calibration principle. First, the characteristics of the flat field in the spatial domain taken by the space astronomical telescope are analyzed, and the flat field is divided into large-scale flat (L-flat) and pixel-to-pixel flat (P-flat). They are then obtained via different calibration experiments and finally combined with the data fusion process. L-flat is obtained through star field observations and the corresponding L-flat extraction algorithm, which can obtain the best estimation of L-flat based on numerous photometry samples, thereby effectively improving calibration accuracy. The simulation model of flat-field calibration used for accuracy analysis is established. In particular, the error sources or experimental parameters that affect the accuracy of L-flat calibration are discussed in detail. Results of the accuracy analysis show that the combined uncertainty of the proposed calibration method can reach 0.78%. Meanwhile, experiments on an optic system with a Φ142mm aperture are performed to verify the calibration method. Results demonstrate that the RMS values of the residual map are 0.720%, 0.565%, and 0.558% at the large-, middle-, and small-scale, respectively. The combined calibration uncertainty is 0.88%, which is generally consistent with the results of the accuracy analysis.

An Extended Calibration of the Olivine–Spinel Aluminum Exchange Thermometer: Application to the Melting Conditions and Mantle Lithologies of Large Igneous Provinces

Abstract The application of the olivine–spinel aluminum exchange thermometer to natural samples is limited by the restricted experimental data set on which it was calibrated. Here, we present a new data set of 46 high-temperature crystallization experiments and 21 reanalyzed published experiments, which we used to extend the calibration to higher and lower temperatures. The final calibration data set spans a range of conditions relevant to crustal and upper mantle processes: 1174–1606°C, 0.1–1350 MPa, QFM − 2.5 to QFM + 7.2 (oxygen fugacity, fO2, reported in log units relative to the quartz–fayalite–magnetite buffer, QFM), and 0–7.4 wt % H2Omelt. We propose three new models. The first is thermodynamically self-consistent, based on spinel Fe, Mg, Al, and Cr compositions and Al exchange between olivine and spinel. The second and third are empirical models that consider fewer elemental exchanges: the second uses only Al exchange and spinel compositions, whereas the third considers olivine–spinel Al and Cr exchange. All models include the modest effect of pressure on olivine-spinel equilibrium chemistry, whereas fO2 and water content have negligible effects. In general, as fewer elements are considered in the olivine–spinel exchange, the fit to experimental data worsens. Conversely, the associated decrease in model complexity improves their robustness against systematic errors when applied to natural crystal pairs: the thermodynamic model may underestimate crystallization temperatures in natural samples due to spinel subsolidus re-equilibration, whereas the empirical models (independent of Fe and Mg in spinel) are less sensitive to re-equilibration but yield temperatures with larger uncertainties. We applied a statistical test to select the most appropriate model for application to natural samples. When applied to lavas from mid-ocean ridges, Iceland, Skye, Emeishan, Etendeka, and Tortugal, our new temperature estimates are 30–100°C lower than previously proposed. The lower temperature estimates cause a lower mantle melting temperature and significant impacts on the mantle lithology constraints.

Progress Toward Medical Use of the Iseult Whole Body 11.7 T MRI: First Images

Neurospin is a neuroscience research center located in France at the Atomic Energy Commission (CEA Saclay). The facility is hosting an innovative whole-body 11.7 T MRI system that has delivered its first images in October 2021. The core part of the Iseult MRI is an actively shielded NbTi magnet cooled with a pressurized superfluid helium bath at 0.125 MPa and 1.8 K, providing a homogeneous magnetic field of 11.7 T within a 90 cm warm bore. After nearly twenty years of work and efforts, the magnet successfully reached its nominal field for the first time in July 2019. The field homogeneity has been adjusted and the control system tested against internal and external faults that could affect the future MRI operation. MRI peripheral equipment has been integrated and interactions between the gradient coils and the magnet and their impact on cryogenics and on the magnet safety system have also been studied. The MR scanner is now kept permanently at nominal field and the final calibration is on-going to prepare the first acquisition on a human volunteer. The paper will present the Iseult MRI commissioning status and the first images obtained, as well as a first feedback on the cryogenic plant operation after three years and a half at 1.8 K.

Self-expansion full information optimization strategy: Convenient and efficient method for near infrared spectrum auto-analysis

An essential step in the application of near infrared spectroscopy technology is the spectrum preprocessing. A reasonable implementation of it ensures that the effective spectral information is correctly extracted and, also that the model's accuracy is increased. However, some analysts' research still uses the manual approach of trial and error, particularly those less skilled ones. Previous papers have provided preprocessing optimization algorithms for NIR, but there are still some problems that need to be resolved, such as the unwieldy sequence determination of preprocessing method or, the fluctuated optimization outcomes or, lack of sufficient statistical information. This research suggests a spectrum auto-analysis methodology named self-expansion full information optimization strategy, a new powerful open-source technique for concurrently addressing all of these above issues simultaneously. For the first time in the field of chemometrics, this algorithm offers a reliable and effective automatic near infrared auto-modelling method based on the statistical informatics. With the aid of its built-in modules, such as information generators, spectrum processors, etc., it is able to fully search the common preprocessing techniques, which is determined by Monte Carlo cross validation. Then the final ensemble calibration model is built by employing the optimized preprocessing schemes, along with the wavelength variables screening algorithm. The optimization strategy can offer the user objective useful statistics information created throughout the modeling process to further examine the model's effectiveness. The results demonstrate that the suggested method can easily and successfully extract spectrum information and develop calibration models by putting it to the test on two groups of actual near-infrared spectral data. Additionally, this optimization strategy can also be applied to other spectrum analysis areas, such Raman spectroscopy or infrared spectroscopy, by changing a few of its parameters, and has extraordinary application value.

Process Window and Repeatability of Thermomechanical Tangential Ring Rolling

International objectives towards improved resource and energy efficiency require new manufacturing processes, such as the proposed thermomechanical tangential profiled ring rolling process. A rapid cooling-down for microstructural adjustment and a final calibration step via cold forming are combined into one single step. The reduction of process steps and the reduced number of heating cycles present opportunities for improved energy efficiency compared to traditional processes. Based on a series of experiments, this paper aims at showing the potential of this new process in terms of obtainable hardness and microstructure. The influence of the active cooling system and the rolling feed is demonstrated. They are identified as essential with regard to both the geometry and the microstructural properties of the produced part. Finally, the repeatability of the process is analyzed, and potential disturbances are identified and ranked.

Modifying the social responsiveness scale for adaptive administration.

The social responsiveness scale (SRS) is frequently used to quantify the autism-related phenotype and is gaining use in health outcomes research. However, it has a high respondent burden (65 items) for large-scale studies. Further, most evaluations of it have focused on the school-age form, not the preschool form. More validity evidence of shortened forms is necessary in the general population to support the broader health outcomes context of use. We evaluated the psychometrics of the SRS in 7030 individuals from multiple predominantly neurotypical samples in order to shorten it based on non-autistic sample metrics. Analyses included item factor analysis, differential item functioning (DIF), and multiple-group item response theory (IRT) to place the SRS items on a comparable scale, which was then simulated via computer adaptive testing (CAT) administration. The SRS was broadly unidimensional with few methodological residual dependencies. On average, males had more autistic characteristics than females, and preschoolers had fewer characteristics than school-age children. The final IRT calibration included 45 items equated across forms, and each form had 11 with significant wording discrepancies and 9 items with near-identical wording that exhibited form-related DIF. The CAT simulation suggested a median of 14 items was sufficient to reach a reliable score, demonstrating its feasibility across the range of impairments. IRT allows practitioners the ability to get highly reliable scores with fewer items than the full-length SRS. This supports the future application of the SRS in a computer adaptive testing mode in both neurotypical and ASD samples.

A New Approach for NaI(Tl) Detector Efficiency Calibration Using 41Ar Radionuclide for Air Exhaust Systems

Argon-41 is an essential gaseous radionuclide that must be monitored in gaseous effluents from nuclear facilities. Therefore, a precise evaluation of 41Ar activity is highly desired. Gamma spectroscopy with a NaI(Tl) scintillation detector coupled with a multichannel analyzer (MCA) is one of the widely used techniques for the identification and activity measurements of radioisotopes. However, the efficiency calibration of these kinds of monitoring systems highly depends on the source-detector geometry, and a large amount of uncertainty may complicate the calibration. This paper presents the evaluation of the full peak efficiency of a 2 × 2-in. NaI(Tl) scintillation detector coupled with a stable MCA for a 41Ar source with 1293.5 keV energy in two different source-detector geometries, duct and Marinelli beaker, using the FLUKA code. A new experimental technique is considered to produce 41Ar in a controlled geometry, like a Marinelli beaker, through neutron irradiation of natural argon inside a cyclotron bunker. The simulation data were compared with the experimental results for Marinelli beaker geometry, and the ratio was evaluated as 0.99 ± 0.07. The ratio was considered a scaling factor for the final efficiency calibration of duct geometry.

A Combined Physical and Mathematical Calibration Method for Low-Cost Cameras in the Air and Underwater Environment.

Low-cost camera calibration is vital in air and underwater photogrammetric applications. However, various lens distortions and the underwater environment influence are difficult to be covered by a universal distortion compensation model, and the residual distortions may still remain after conventional calibration. In this paper, we propose a combined physical and mathematical camera calibration method for low-cost cameras, which can adapt to both in-air and underwater environments. The commonly used physical distortion models are integrated to describe the image distortions. The combination is a high-order polynomial, which can be considered as basis functions to successively approximate the image deformation from the point of view of mathematical approximation. The calibration process is repeated until certain criteria are met and the distortions are reduced to a minimum. At the end, several sets of distortion parameters and stable camera interior orientation (IO) parameters act as the final camera calibration results. The Canon and GoPro in-air calibration experiments show that GoPro owns distortions seven times larger than Canon. Most Canon distortions have been described with the Australis model, while most decentering distortions for GoPro still exist. Using the proposed method, all the Canon and GoPro distortions are decreased to close to 0 after four calibrations. Meanwhile, the stable camera IO parameters are obtained. The GoPro Hero 5 Black underwater calibration indicates that four sets of distortion parameters and stable camera IO parameters are obtained after four calibrations. The camera calibration results show a difference between the underwater environment and air owing to the refractive and asymmetric environment effects. In summary, the proposed method improves the accuracy compared with the conventional method, which could be a flexible way to calibrate low-cost cameras for high accurate in-air and underwater measurement and 3D modeling applications.