Projection Error Research Articles

Skin deformation analysis for pre-operative planning of DIEAP flap reconstruction surgery.

Deep inferior epigastric artery perforator (DIEAP) flap reconstruction surgeries can potentially benefit from augmented reality (AR) in the context of surgery planning and outcomes improvement. Although three-dimensional (3D) models help visualize and map the perforators, the anchorage of the models to the patient's body during surgery does not consider eventual skin deformation from the moment of computed tomography angiography (CTA) data acquisition until the position of the patient while in surgery. In this work, we compared the 3D deformation registration from supine arms down (CTA position) to supine with arms at 90° degrees (surgical position), estimating the patient's skin deformation. We processed the data sets of 20 volunteers with a 3D rigid registration tool and performed a descriptive statistical analysis and statistical inference. With 2.45mm of root mean square and 2.89mm of standard deviation, results include 30% cases of deformation above 3mm and 15% above 4mm. Pose transformation deformation indicates that 3D surface data from the CTA scan position differs from data acquired in loco at the surgical table. Such results indicate that research should be conducted to construct accurate 3D models using CTA data to display on the patient, while considering projection errors when using AR technology.

Description and evaluation of the JULES-ES set-up for ISIMIP2b

Abstract. Global studies of climate change impacts that use future climate model projections also require projections of land surface changes. Simulated land surface performance in Earth system models is often affected by the atmospheric models' climate biases, leading to errors in land surface projections. Here we run the Joint UK Land Environment Simulator Earth System configuration (JULES-ES) land surface model with the Inter-Sectoral Impact Model Intercomparison Project second-phase future projections (ISIMIP2b) bias-corrected climate model data from four global climate models (GCMs). The bias correction reduces the impact of the climate biases present in individual models. We evaluate the performance of JULES-ES against present-day observations to demonstrate its usefulness for providing required information for impacts such as fire and river flow. We include a standard JULES-ES configuration without fire as a contribution to ISIMIP2b and JULES-ES with fire as a potential future development. Simulations for gross primary productivity (GPP), evapotranspiration (ET) and albedo compare well against observations. Including fire improves the simulations, especially for ET and albedo and vegetation distribution, with some degradation in shrub cover and river flow. This configuration represents some of the most current Earth system science for land surface modelling. The suite associated with this configuration provides a basis for past and future phases of ISIMIP, providing a simulation set-up, postprocessing and initial evaluation, using the International Land Model Benchmarking (ILAMB) project. This suite ensures that it is as straightforward, reproducible and transparent as possible to follow the protocols and participate fully in ISIMIP using JULES.

Evaluating the OECD–FAO and USDA agricultural baseline projections

Abstract The Organization for Economic Cooperation and Development (OECD) in collaboration with the Food and Agricultural Organization (FAO), and the US Department of Agriculture (USDA) produce long-term agricultural projections for key indicators of agricultural commodities. These baseline projections play a key role in agricultural decision-making and policies. Our study compares the OECD–FAO and USDA baseline projections for harvested acres, production, and yield in the European Union, the US, and China for rice and wheat. We evaluate the accuracy by examining the projection error, including the magnitude of bias and variance as well as the significance of bias. We also compare the OECD–FAO and USDA projections’ predictive abilities using the modified Diebold–Mariano test. We find that the accuracy of the projections decreases as the projection horizon increases. Both agencies underestimate wheat projections in the European Union and China. USDA projections generally outperform OECD–FAO projections for most rice variables, while the reverse is found for longer-horizon EU wheat projections. This assessment may enable agencies to make more accurate projections and enable market participants to better understand these projections.

Accuracy assessment of energy projections for China by Energy Information Administration and International Energy Agency

Energy projections are of great importance to energy policies but have consistently shown noticeable and repeated errors, and thus require accuracy assessment for improvement. International Energy Outlook (IEO) of the US Energy Information Administration and World Energy Outlook (WEO) of the International Energy Agency publish widely used energy projections, whose accuracy for China – the largest energy consuming economy and carbon dioxide emitter – has been rarely explored. This study investigates accuracy of China's reference energy projections in the annual reports of IEO and WEO from 2004 to 2019. Results show that most projections in IEO and WEO underestimated China's total energy consumption, particularly over longer projection horizons. The use of coal, natural gas and renewable energy tended to be underestimated, and nuclear energy was overestimated. The errors of industry and transport sectors were comparable and higher than for the other sectors. WEO showed substantially better accuracy than IEO in projections of total energy consumption, primary energy resources (except for nuclear energy) and end-use sectors. Projection horizon, errors in projected population's size, oil price and gross domestic product per capita were four leading factors related to the projection errors and hence they require particular attention in future modeling. For policy makers, this study shows that, if IEO and WEO projections are used to guide the policy making, China needs more aggressive policies in order to achieve the carbon neutrality goal.

Improving localization accuracy for non-invasive automated early left ventricular origin localization approach.

Background: We previously developed a non-invasive approach to localize the site of early left ventricular activation origin in real time using 12-lead ECG, and to project the predicted site onto a generic LV endocardial surface using the smallest angle between two vectors algorithm (SA). Objectives: To improve the localization accuracy of the non-invasive approach by utilizing the K-nearest neighbors algorithm (KNN) to reduce projection errors. Methods: Two datasets were used. Dataset #1 had 1012 LV endocardial pacing sites with known coordinates on the generic LV surface and corresponding ECGs, while dataset #2 included 25 clinically-identified VT exit sites and corresponding ECGs. The non-invasive approach used "population" regression coefficients to predict the target coordinates of a pacing site or VT exit site from the initial 120-m QRS integrals of the pacing site/VT ECG. The predicted site coordinates were then projected onto the generic LV surface using either the KNN or SA projection algorithm. Results: The non-invasive approach using the KNN had a significantly lower mean localization error than the SA in both dataset #1 (9.4 vs. 12.5mm, p < 0.05) and dataset #2 (7.2 vs. 9.5mm, p < 0.05). The bootstrap method with 1,000 trials confirmed that using KNN had significantly higher predictive accuracy than using the SA in the bootstrap assessment with the left-out sample (p < 0.05). Conclusion: The KNN significantly reduces the projection error and improves the localization accuracy of the non-invasive approach, which shows promise as a tool to identify the site of origin of ventricular arrhythmia in non-invasive clinical modalities.

Shear resistance of an anchoring coupler applied in demountable concrete connection at varying shear positions

Demountable structures are designed to reduce carbon emissions and energy consumption in the construction sector by reusing structural components. This paper focuses on an anchoring coupler with demountable property used in the demountable precast concrete structure. The shear resistance of the anchoring coupler at varying shear positions was investigated, considering the influence of installation and fabrication errors in actual projects. Firstly, several shear tests of the anchoring coupler were conducted. The ultimate capacities and failure modes of anchoring couplers with varying shear positions were obtained. The bearing capacity of the anchoring coupler without the gap between the beam and column is about 1.34 times that with 45 mm gap. The shear model of the anchoring coupler was fitted by the bilinear curves. Secondly, the numerical model of the anchoring coupler was established and verified. And the influence of the shear position on the ultimate strength was further analyzed by parametric analysis. A simplified calculation method of the shear resistance of the anchoring coupler with varying shear positions was proposed. The design equation for predicting the tension-shear interaction was recommended, considering the similarity between the anchoring coupler and conventional bolt. The aim is to provide theoretical guidance for the safety design of the anchoring coupler and optimize the beam-column connection, which is conducive to expanding the application of the demountable precast concrete structure.

Physics Guided Machine Learning for Variational Multiscale Reduced Order Modeling

We propose a new physics guided machine learning (PGML) paradigm that leverages the variational multiscale (VMS) framework and available data to dramatically increase the accuracy of reduced order models (ROMs) at a modest computational cost. The hierarchical structure of the ROM basis and the VMS framework enable a natural separation of the resolved and unresolved ROM spatial scales. Modern PGML algorithms are used to construct novel models for the interaction among the resolved and unresolved ROM scales. Specifically, the new framework builds ROM operators that are closest to the true interaction terms in the VMS framework. Finally, machine learning is used to reduce the projection error and further increase the ROM accuracy. Our numerical experiments for a two-dimensional vorticity transport problem show that the novel PGML-VMS-ROM paradigm maintains the low computational cost of current ROMs while significantly increasing the ROM accuracy.

Trust–Region Nonlinear Optimization Algorithm for Orientation Estimator and Visual Measurement of Inertial–Magnetic Sensor

This paper proposes a novel robust orientation estimator to enhance the accuracy and robustness of orientation estimation for inertial–magnetic sensors of the small consumer–grade drones. The proposed estimator utilizes a trust–region strategy within a nonlinear optimization framework, transforming the orientation fusion problem into a nonlinear optimization problem based on the maximum likelihood principle. The proposed estimator employs a trust–region Dogleg gradient descent strategy to optimize orientation precision and incorporates a Huber robust kernel to minimize interference caused by acceleration during the maneuvering process of the drone. In addition, a novel method for evaluating the performance of orientation estimators is also presented based on visuals. The proposed method consists of two parts: offline calibration of the basic cube using Augmented Reality University of Cordoba (ArUco) markers and online orientation measurement of the sensor carrier using a nonlinear optimization solver. The proposed measurement method’s accuracy and the proposed estimator’s performance are evaluated under low–dynamic (rotation) and high–dynamic (shake) conditions in the experiment. The experimental findings indicate that the proposed measurement method obtains an average re–projection error of less than 0.1 pixels. The proposed estimator has the lowest average orientation error compared to conventional orientation estimation algorithms. Despite the time–consuming nature of the proposed estimator, it exhibits greater robustness and precision, particularly in highly dynamic environments.

An improved method of concentric circle positioning in visual measurement

Target with concentric circles has been widely used in artificial target and camera calibration of visual measurement systems. The accuracy of concentric circle target directly affects the accuracy of vision measurement system. In this study, an improved method for accurate positioning of concentric circles projection center is proposed. Compared with similar methods, the proposed method has high accuracy and does not need to know the radius of the concentric circle in advance. The influence of inner and outer circle radius and rotation angle of concentric circles on the position error of circular projection center is analyzed by single camera experiment. The experimental results show that the position error of this method is lower than that of similar methods. The concentric circles calibration board is designed as an application to calibrate the binocular vision system. It is shown that the calibration results of the camera’s intrinsic parameters are almost the same as those of PPAGC. However, compared with PPAGC, the total projection error of the left camera is reduced by 30.77% and that of the right camera by 28.29%.

Optimal \(\boldsymbol{{L^2}}\) Error Estimates of Unconditionally Stable Finite Element Schemes for the Cahn–Hilliard–Navier–Stokes System

The paper is concerned with the analysis of a popular convex-splitting finite element method (FEM) for the Cahn–Hilliard–Navier–Stokes system, which has been widely used in practice. Since the method is based on a combined approximation to multiple variables involved in the system, the approximation to one of the variables may seriously affect the accuracy for others. Optimal-order error analysis for such combined approximations is challenging. The previous works failed to present optimal error analysis in -norm due to the weakness of the traditional approach. Here we first present an optimal error estimate in -norm for the convex-splitting FEMs. We also show that optimal error estimates in the traditional (interpolation) sense may not always hold for all components in the coupled system due to the nature of the pollution/influence from lower-order approximations. Our analysis is based on two newly introduced elliptic quasi-projections and the superconvergence of negative norm estimates for the corresponding projection errors. Numerical examples are also presented to illustrate our theoretical results. More important is that our approach can be extended to many other FEMs and other strongly coupled phase field models to obtain optimal error estimates.

Stability and discretization error analysis for the Cahn–Hilliard system via relative energy estimates

The stability of solutions to the Cahn–Hilliard equation with concentration dependent mobility with respect to perturbations is studied by means of relative energy estimates. As a by-product of this analysis, a weak-strong uniqueness principle is derived on the continuous level under realistic regularity assumptions on strong solutions. The stability estimates are further inherited almost verbatim by appropriate Galerkin approximations in space and time. This allows to derive sharp bounds for the discretization error in terms of certain projection errors and to establish order-optimal a priori error estimates for semi- and fully discrete approximation schemes. Numerical tests are presented for illustration of the theoretical results.

Towards transcervical ultrasound image guidance for transoral robotic surgery.

Trans-oral robotic surgery (TORS) using the da Vinci surgical robot is a new minimally-invasive surgery method to treat oropharyngeal tumors, but it is a challenging operation. Augmented reality (AR) based on intra-operative ultrasound (US) has the potential to enhance the visualization of the anatomy and cancerous tumors to provide additional tools for decision-making in surgery. We propose a US-guided AR system for TORS, with the transducer placed on the neck for a transcervical view. Firstly, we perform a novel MRI-to-transcervical 3D US registration study, comprising (i) preoperative MRI to preoperative US registration, and (ii) preoperative to intraoperative US registration to account for tissue deformation due to retraction. Secondly, we develop a US-robot calibration method with an optical tracker and demonstrate its use in an AR system that displays anatomy models in the surgeon's console in real-time. Our AR system achieves a projection error from the US to the stereo cameras of 27.14 and 26.03 pixels (image is 540[Formula: see text]960) in a water bath experiment. The average target registration error (TRE) for MRI to 3D US is 8.90mm for the 3D US transducer and 5.85mm for freehand 3D US, and the TRE for pre-intra operative US registration is 7.90mm. We demonstrate the feasibility of each component of the first complete pipeline for MRI-US-robot-patient registration for a proof-of-concept transcervical US-guided AR system for TORS. Our results show that trans-cervical 3D US is a promising technique for TORS image guidance.

Is the road to hell paved with good intentions? An empirical analysis of budgetary follow-up in the EU

We study the one-year-ahead budgetary projections from the Stability and Convergence Programmes (SCPs) of EU Member States since the start of the Economic and Monetary Union (EMU) until the start of the coronavirus crisis. First, we consider errors of the general government’s headline budget balance, which we then split into expenditure and revenue errors. Next, we split the latter two into “base”, “growth” and “denominator” effects. We find that the most important explanatory variable is the GDP growth error: more optimism in GDP growth projections produces more optimistic budgetary projections. This effect goes beyond a mechanical denominator effect on spending and revenues as shares of GDP; it also works through the numerator of these ratios. Our findings may call for delegating the construction of output projections to adequately equipped national independent fiscal institutions. Finally, we explore how independent fiscal institutions shape projection errors. Those with high media impact producing or assessing the macroeconomic forecast appear to lead to actual budgetary improvement relative to projections.

Motion Correction for Separate Mandibular and Cranial Movements in Cone Beam CT Reconstructions.

Patient motions are a repeatedly reported phenomenon in oral and maxillofacial cone beam CT scans, leading to reconstructions of limited usability. In certain cases, independent movements of the mandible induce unpredictable motion patterns. Previous motion correction methods are not able to handle such complex cases of patient movements. Our goal was to design a combined motion estimation and motion correction approach for separate cranial and mandibular motions, solely based on the 2D projection images from a single scan. Our iterative three-step motion correction algorithm models the two articulated motions as independent rigid motions. First of all, we segment cranium and mandible in the projection images using a deep neural network. Next, we compute a 3D reconstruction with the poses of the object's trajectories fixed. Third, we improve all poses by minimizing the projection error while keeping the reconstruction fixed. Step two and three are repeated alternately. We find that our marker-free approach delivers reconstructions of up to 85% higher quality, with respect to the projection error, and can improve on already existing techniques, which model only a single rigid motion. We show results of both synthetic and real data created in different scenarios. The reconstruction of motion parameters in a real environment was evaluated on acquisitions of a skull mounted on a hexapod, creating a realistic, easily reproducible motion profile. The proposed algorithm consistently enhances the visual quality of motion impaired CBCT scans, thus eliminating the need for a re-scan in certain cases, considerably lowering radiation dosage for the patient. It can flexibly be used with differently sized regions of interest and is even applicable to localtomography. This article is protected by copyright. All rights reserved.

Distinguishing inter- and pangenerational food trends

While food trends are usually described over an entire population, this paper suggests distinguishing between inter- and pangenerational food trends. To classify the food trends for the total population as inter- or pangenerational, we used disaggregated household-based consumption data on 60 food categories over the period from 1990 to 2020 in Switzerland. We followed six different cohorts with a range of 10 birth years each and estimated robust trends for each generation and each product. Our results show that especially for meat, different generations follow different trends and form ‘intergenerational’ trends for the total population, whereas beans and peas would be an example of products with an increasing consumption for every single generation and a ‘pangenerational’ trend. Our study is the first to suggest distinguishing inter- and pangenerational food trends and to cover the most disaggregated available food consumption data in Switzerland for the period from 1990 to 2020. Managers and policymakers should consider the mentioned differences in food consumption to mitigate errors in consumption projections, target consumers more effectively, and promote healthier food consumption.

Deep residual constrained reconstruction via learned convolutional sparse coding for low-dose CT imaging

Low-dose computed tomography (LDCT) holds great potential to reduce radiation dose damage. However, LDCT degrades the signal-to-noise ratio (SNR) of projection and compromises the reconstruction image quality. This paper proposes a deep residual-constrained reconstruction (DRCR) framework via learned convolutional sparse coding (LCSC) for LDCT imaging. DRCR consists of multiple iteration blocks, and each iteration contains three modules: image update, residual correction and convolution representation modules. First, the image update module updates the reconstruction image with a DL-based prior constraint. Then, the residual correction module attempts to accurately estimate the residual features after mapping the projection errors into the image domain. Finally, the LCSC network is applied for feature map and filter updating to further constrain the image update process. To further improve the performance of DRCR, the perceptual losses of the image domain and projection domain are considered in the optimized model. The results obtained on two datasets show the competitive performance of the proposed framework, with a 0.5 dB PSNR margin, 0.007 SSIM margin, and 1.7 FID margin on the MAYO dataset and a 0.5 dB PSNR margin, 0.011 SSIM margin and 2.4 FID margin on the UIH dataset.

Estimates of the permeability of extra-cellular pathways through the astrocyte endfoot sheath

BackgroundAstrocyte endfoot processes are believed to cover all micro-vessels in the brain cortex and may play a significant role in fluid and substance transport into and out of the brain parenchyma. Detailed fluid mechanical models of diffusive and advective transport in the brain are promising tools to investigate theories of transport.MethodsWe derive theoretical estimates of astrocyte endfoot sheath permeability for advective and diffusive transport and its variation in microvascular networks from mouse brain cortex. The networks are based on recently published experimental data and generated endfoot patterns are based on Voronoi tessellations of the perivascular surface. We estimate corrections for projection errors in previously published data.ResultsWe provide structural-functional relationships between vessel radius and resistance that can be directly used in flow and transport simulations. We estimate endfoot sheath filtration coefficients in the range L_p=2times 10^{-11},hbox {m},hbox {Pa}^{-1},hbox {s}^{-1} to 3times 10^{-10} ,hbox {m},hbox {Pa}^{-1},hbox {s}^{-1}, diffusion membrane coefficients for small solutes in the range C_M= 5 times 10^{2},hbox {m}^{-1} to 6times 10^{3},hbox {m}^{-1}, and gap area fractions in the range 0.2–0.6%, based on a inter-endfoot gap width of 20 nm.ConclusionsThe astrocyte endfoot sheath surrounding microvessels forms a secondary barrier to extra-cellular transport, separating the extra-cellular space of the parenchyma and the perivascular space outside the endothelial layer. The filtration and membrane diffusion coefficients of the endfoot sheath are estimated to be an order of magnitude lower than those of the extra-cellular matrix while being two orders of magnitude higher than those of the vessel wall.

A Novel Scene Matching Navigation System for UAVs Based on Vision/Inertial Fusion

Scene matching navigation system (SMNS) remains challenging in many navigation tasks, which rely heavily on accuracy, computational efficiency, and robustness. Due to the different generation conditions of the matching images, it is difficult for traditional methods to cover every aspect of the three navigation performances. This article aims at developing an accurate, fast, and robust SMNS based on vision/inertial fusion to provide complete navigation information for unmanned aerial vehicles (UAVs). Using the mechanization results of the low-cost micro-electro-mechanical system (MEMS), the proposed system first completes the georeferencing of the real-time aerial images, in which the projection errors are reduced greatly by introducing an optimized factor to the homography matrix. Then, applying a robust noise processing strategy, an improved feature extraction algorithm is designed to eliminate most of the features that vary with climate, time, and season, which lays a solid foundation for the accuracy of the following matching procedure. Under the framework of the SMNS, a novel matching strategy based on logic graphs is designed, which can facilitate the matching procedure. Eventually, by combining the mechanization results of the MEMS and the matching results of the SMNS, the proposed system can provide complete navigation results. Experiments in typical and complex scenarios are carried out, respectively, to verify the effectiveness and robustness of the proposed system. Experimental results demonstrate that the proposed SMNS possesses accuracy, computational efficiency, and robustness, which outperforms the state-of-the-art strategies [i.e., histogram of orientated phase congruency (HOPC), chanel feature of orientated gradient (CFOG), phase congruency (PC)] in terms of matching aerial and satellite images.

Computational Lower Bounds of the Maxwell Eigenvalues

A method to compute guaranteed lower bounds to the eigenvalues of the Maxwell system in two or three space dimensions is proposed as a generalization of the method of Liu and Oishi [SIAM J. Numer. Anal., 51 (2013), pp. 1634–1654] for the Laplace operator. The main tool is the computation of an explicit upper bound to the error of the Galerkin projection. The error is split into two parts. One part is controlled by a hypercircle principle and an auxiliary eigenvalue problem. The second part requires a perturbation argument for the right-hand side replaced by a suitable piecewise polynomial. The latter error is controlled through the use of the commuting quasi-interpolation by Falk and Winther and computational bounds on its stability constant. This situation is different from the Laplace operator where such a perturbation is easily controlled through local Poincaré inequalities. The practical viability of the approach is demonstrated in test cases for two and three space dimensions.

Non-Destructive Testing in the Strategic Research on the Performance of Bridges

The main purpose of bridges is to overcome physical obstacles and improve territorial mobility, therefore, knowledge of their particularities is required, such as project design, inspection and execution, maintenance service plans and others. The aim of the research is to carry out a survey of the main pathological manifestations in three reinforced concrete highway bridges (HB) &amp;ndash; built in different years, namely: 1927, 1960 and 2018. The qualification and quantification of the anomalies were carried out and, after that, the prioritization matrix was used, which allows obtaining strategies to ensure the safeguard of the HBs. The methodology used, data collection and detailed inspection of the structure, i.e., non-destructive testing, proved to be appropriate to assess the conditions of the HBs, and also allowed to present results related to recovery services and the necessary maintenance. The pathological manifestations in older bridges are the result of project and building errors, as well as lack of maintenance service.