Refinement Algorithm Research Articles

An Open-Source Processing Pipeline for Quad-Dominant Mesh Generation for Class-Compliant Ship Structural Analysis

We present an algorithm for the fully automatic generation of a class-compliant mesh for ship structural analysis. Our algorithm is implemented as an end-to-end solution. It starts from a description of a geometry and produces a class conforming surface mesh as a result. The algorithm consists of two parts, the automatic geometry refinement and the preconditioned Delaunay frontal quad dominant mesh generator. A geometry is described by a dictionary of elements and it contains points, rods, plates, and openings. A dictionary can contain modeling errors such as unintended overlaps or an unintended loss of connectivity between elements. The main contribution of the paper is the automatic geometry refinement algorithm and the virtual stiffener procedure designed to control the local mesh orientation of a marching front meshing algorithm. The geometry refinement algorithm guarantees that the output dictionary will be such that intersections of the boundary edges of plates are guaranteed to be nodes of any mesh generated by tessellating such geometry. The algorithm is implemented in Python, using the open-source Gmsh system together with the Open CASCADE kernel which is used to implement the automatic geometry refinement. We present several benchmark models from an engineering practice to illustrate our claims as well as to benchmark the efficiency of the various stages of the processing pipeline.

Computational and image processing methods for analysis and automation of anatomical alignment and joint spacing in reconstructive surgery.

Reconstructive surgeries to treat a number of musculoskeletal conditions, from arthritis to severe trauma, involve implant placement and reconstructive planning components. Anatomically matched 3D-printed implants are becoming increasingly patient-specific; however, the preoperative planning and design process requires several hours of manual effort from highly trained engineers and clinicians. Our work mitigates this problem by proposing algorithms for the automatic re-alignment of unhealthy anatomies, leading to more efficient, affordable, and scalable treatment solutions. Our solution combines global alignment techniques such as iterative closest points with novel joint space refinement algorithms. The latter is achieved by a low-dimensional characterization of the joint space, computed from the distribution of the distance between adjacent points in a joint. Experimental validation is presented on real clinical data from human subjects. Compared with ground truth healthy anatomies, our algorithms can reduce misalignment errors by 22% in translation and 19% in rotation for the full foot-and-ankle and 37% in translation and 39% in rotation for the hindfoot only, achieving a performance comparable to expert technicians. Our methods and histogram-based metric allow for automatic and unsupervised alignment of anatomies along with techniques for global alignment of complex arrangements such as the foot-and-ankle system, a major step toward a fully automated and data-driven re-positioning, designing, and diagnosing tool.

Infrared and visible image fusion based on QNSCT and Guided Filter

Image fusion is the process of fusing multiple images of the same scene to obtain a more informative image for human eye perception. In this paper, a new fusion framework based on Quaternion Non-Subsampled Contourlet Transform (QNSCT) and Guided Filter detail enhancement is designed to address the problems of inconspicuous infrared targets and poor background texture in Infrared and visible image fusion. The proposed method uses the quaternion wavelet transform for the first time instead of the traditional Non-Subsampled Pyramid Filter Bank structure in the Non-Subsampled Contourlet Transform (NSCT). The flexible multi-resolution of quaternion wavelet and the multi-directionality of NSCT are fully utilized to refine the multi-scale decomposition scheme. On the other hand, the coefficient matrix obtained from the proposed QNSCT algorithm is fused using a weight refinement algorithm based on the guided filter. The fusion scheme is divided into four steps. First, the Infrared and visible images are decomposed into multi-directional and multiscale coefficient matrices using QNSCT. The experimental results show that the proposed algorithm not only extracts important visual information from the source image, but also preserves the texture information in the scene better. Meanwhile, the scheme outperforms state-of-the-art methods in both subjective and objective evaluations.

Comparison between EWGSOP1 and EWGSOP2 criteria and modelling of diagnostic algorithm for sarcopenic obesity in over 70years old patients.

Sarcopenic obesity (SO) as a new diagnostic entity defined by presence of obesity in combination with sarcopenia represents serious health condition negatively affecting quality of life in old age. Despite the rapidly increasing incidence of SO associated with demographic aging, clear diagnostic criteria for SO have not yet been established. We describe here the applicability of the EWGSOP2 and EWGSOP1 diagnostic criteria in identifying sarcopenia and SO and the development of a refinement algorithm for SO detection. In total 156 subjects were pre-screened, 126 had a complete dataset and were included, 20.6% (n = 26) were men and 79.4% (n = 100) women, mean age 81 ± 6.3years in tertiary hospital, Prague, Czech Republic. Testing of physical performance (hand-grip test, 400m walk test, chair stand test, gait speed), anthropometric measures and SARC-F, SPPB and MNA-SF were used to determine physical, functional, and nutritional status, while muscle mass and fat mass were measured by DXA scans to confirm sarcopenia and SO diagnosis. The prevalence of sarcopenia (BMI adjusted ALM < 0.789 for men, < 0.512 for women) was 26.2% (n = 33), SO in 20.6% (n = 26). 78.8% of all sarcopenic subjects fulfilled the criteria of SO (FM > 27% for men and > 38% for women; waist circumference > 90cm for men and > 85cm for women). EWGSOP1 criteria for diagnosing sarcopenia showed better sensitivity of 97.0% than the EWGSOP2 66.7%, while specificity reached 100% for both criteria. According to DXA measurement, EWGSOP1 identified 3.0% cases (1 out of 33) as false negative meanwhile EWGSOP2 identified 33.3% cases as false negative and this difference was statistically significant (McNemar's test, p < 0.001). An algorithm for SO was developed (which uses sex, BMI, height, waist circumference and SPPB) with sensitivity and specificity of 88.5 and 91.0%, respectively. High prevalence of obesity among elderly people and rather low sensitivity of current diagnostic criteria for SO call for ongoing research. Broader international consensus for SO diagnostic criteria, screening and diagnosis algorithm are crucial for early detection of SO in older people in clinical practice so that optimal multi-component therapy can be initiated.

Wearable Surgical Optical Tracking System Based on Multi-Modular Sensor Fusion

Optical tracking system (OTS) is an important part of surgical navigation and has been commonly employed in clinical practice due to high accuracy and precision. However, current OTS is bulky, which reduces the flexibility for clinical procedures. To address this drawback, we propose a wearable surgical instrument tracking system based on a multi-modular sensor fusion approach. The proposed tracking system consists of a multispectrum red–green–blue-depth (RGB-D) sensor and the accompanying tracking target. It significantly increases the agility of the tracking device while obtaining high tracking accuracy. We propose a plane-based refinement algorithm (PRA) to estimate marker centers, which ensures the accuracy and precision for the short baseline of the wearable stereo camera. A 2-D marker-based instrument identification algorithm (MIIA) has also been proposed to identify different instruments. Both simulations and real experiments have been carried out to validate the feasibility and performance of the proposed system. These accurate and robust experimental results have verified the feasibility of the proposed wearable surgical instrument tracking system.

A Novel Sleep Stage Contextual Refinement Algorithm Leveraging Conditional Random Fields

Automatic sleep stage classification has gained much attention in recent researches. Various classification algorithms have been proposed for automatic sleep staging, including deep neural networks and traditional machine learning models. However, the output of those models has unreasonable sleep stage transitions, as temporal dependence of sleep stage label of adjacent data segment is ignored. In this article, we propose a novel sleep stage contextual refinement algorithm based on conditional random fields (CRFs). The algorithm works as a post-processing step to rectify the hypnogram produced by sleep staging pre-classifiers. Unreasonable sleep stage transitions can be corrected via our algorithm to further improve the classification performance. We use CNN-based, CNN-LSTM-based, random forest, and two existing sleep staging models UTSN and UTSN-L as pre-classifiers. Our algorithm is evaluated on three sleep datasets, Sleep-EDF-20, DRM-SUB, and SVUH-UCD datasets. Results demonstrate that our CRF contextual refinement algorithm can improve the classification performance of five sleep staging pre-classifiers, including overlapping-based and nonoverlapping-based models, and the algorithm works both on healthy subjects and patients with sleep disorder. When using CNN as pre-classifiers, our algorithm improves the overall accuracy and macro F1-score by 2.5% and 4.7% on Sleep-EDF-20, by 3.6% and 6.6% on DRM-SUB, and by 5.5% and 7.8% on the SVUH-UCD dataset.

Certain linear and weakly linear systems of matrix equations over semirings. Applications in a state reduction of weighted automata

In this paper we study particular linear and weakly linear systems of matrix equations over semirings, with the aim of describing and computing functions as solutions to those systems. Our special attention is devoted to solutions whose ranks are as small as possible. We prove the existence of solutions with the smallest ranks, give their characterizations, and present a method for their computations. Regarding weakly linear systems, the method is based on the well known partition refinement algorithm by Kanellakis and Smolka, adapted to work with weighted labeled transition systems. Moreover, we give a state reduction procedure of weighted automata based on a decomposition of solutions to particular linear and weakly linear systems.

On the algorithmic solution of optimization problems subject to probabilistic/robust (probust) constraints

We present an adaptive grid refinement algorithm to solve probabilistic optimization problems with infinitely many random constraints. Using a bilevel approach, we iteratively aggregate inequalities that provide most information not in a geometric but in a probabilistic sense. This conceptual idea, for which a convergence proof is provided, is then adapted to an implementable algorithm. The efficiency of our approach when compared to naive methods based on uniform grid refinement is illustrated for a numerical test example as well as for a water reservoir problem with joint probabilistic filling level constraints.

A simpler linear-time algorithm for the common refinement of rooted phylogenetic trees on a common leaf set

BackgroundThe supertree problem, i.e., the task of finding a common refinement of a set of rooted trees is an important topic in mathematical phylogenetics. The special case of a common leaf set L is known to be solvable in linear time. Existing approaches refine one input tree using information of the others and then test whether the results are isomorphic.ResultsAn O(k|L|) algorithm, LinCR, for constructing the common refinement T of k input trees with a common leaf set L is proposed that explicitly computes the parent function of T in a bottom-up approach.ConclusionLinCR is simpler to implement than other asymptotically optimal algorithms for the problem and outperforms the alternatives in empirical comparisons.AvailabilityAn implementation of LinCR in Python is freely available at https://github.com/david-schaller/tralda.

Double shear layer evolution on the non-uniform computational mesh

This paper considers the problem of a thin shear layer evolution at Reynolds number rmRe = 400000 using the novel Compact Accurately Boundary Adjusting high-Resolution Technique (CABARET). The study is focused on the effect of the specific mesh refinement in the high shear rate areas on the flow properties under the influence of the developing instability. The original sequence of computational meshes (2562, 5122, 10242, 20482 cells) is modified using an iterative refinement algorithm based on the hyperbolic tangent. The properties of the solutions obtained are discussed in terms of the initial momentum thickness and the initial vorticity thickness, viscous and dilatational dissipation rates and also integral enstrophy. The growth rate for the most unstable mode depending on the mesh resolution is considered. In conclusion the accuracy of calculated mesh functions is estimated via L 1, L 2, L ∞ norms.

Ford-Fulkerson algorithm refinement for the cooperation effectiveness increase of intensive and low-density lines

The article describes tools of the railway control on intensive and low-density lines which is directed on the effectiveness increase of low-density line functioning, for the solution of perspective tasks of the railway network functioning and development. For the too realization the oriented graph with the Ford-Fulkerson algorithm which allows determining the maximum flow and the minimum cut for non-oriented graphs. Firstly as values of graph tops inverse values of the station rating and as graph edges inverse values of the railway line class are accepted. The use of this approach allows determining the maximum flow in the system and provides the clear view of relations of transportation capacities of railway lines and stations.

Cell‐based hybrid adaptive mesh refinement algorithm for immersed boundary method

AbstractThis article proposes a hybrid adaptive mesh refinement (AMR) algorithm for the immersed boundary method (IBM) combination, and the AMR code is split into mesh and physics codes to optimize each part individually. The uniform parent grid solver is used for AMR grids by constructing hanging cells for the high‐order extension. The restrictive spatial refinement in a fully threaded tree (FTT) data structure is explored, and a simplified stencil search algorithm for hanging cells construction is introduced, including current and following child AMR level cells, if any. The proposed AMR method was applied to IBM, which offers flexibility in treating complex geometries in the Cartesian grid, leading to algorithmic simplicity and computational efficiency. Local near immersed boundary refinement is proposed to avoid complex and computationally expensive IBM and AMR algorithms near the solid bodies. Finally, a high‐order flux scheme extension at AMR level transition cells and the proposed method's applicability for steady and transient flows are demonstrated. Besides state and flux variables storage, the proposed hybrid AMR method's additional cost is 4.5 words/cell instead of 3.5 words/cell for 2D and 3.75 words/cell instead of 2.75 words/cell for 3D compared to the conventional FTT‐based AMR method. By comparing the uniform grid, the AMR final grid distribution shows that the optimal cell distribution based on flow physics reduces the cells required by more than 40% to resolve the complex flow features in less computational time. The enhanced performance and accuracy of the proposed AMR method in resolving different scale flow features are validated through benchmark problems.

Phase-field modeling of dendritic growth of magnesium alloys with a parallel-adaptive mesh refinement algorithm

A two-dimensional phase field (PF) model was developed to simulate the dendritic solidification in magnesium alloy with hcp crystal structure. By applying a parallel-adaptive mesh refinement (Para-AMR) algorithm, the computational efficiency of the numerical model was greatly improved. Based on the PF model, a series of simulation cases were conducted and the results showed that the anisotropy coefficient and coupling coefficient had a great influence on the dendritic morphology of magnesium alloy. The dendritic growth kinetics was determined by the undercooling and equilibrium solute partition coefficient. A significant finding is acquired that with a large undercooling, the maximum solute concentration is located on both sides of the dendrite tip in the liquid, whereas the maximum solute concentration gradient is located right ahead of the dendrite tip in the liquid. The dendrite tip growth velocity decreases with the increase of the equilibrium solute partition coefficient, while the variation trend of the dendrite tip radius is the opposite. Quantitative analysis was carried out relating to the dendritic morphology and growth kinetics, and the simulated results are consistent with the theoretical models proposed in the previously published works.

A Robust Airport Runway Detection Network Based on R-CNN Using Remote Sensing Images

Autonomous vision-based taxi, takeoff, and landing systems are gaining popularity in fixed-wing commercial, military, and unmanned aerial vehicles presenting new technical and regulatory challenges. With ongoing advances in deep learning, machine learning, and big data management, the dangers of takeoff and landing operations have greatly decreased, and with less dependence on simultaneous and complex controllers. However, in a real-world scenario, airport runway detection for takeoff and landing operations is still deemed challenging, due to the vast amount of contextual information, complex backgrounds, and dangerous geographical scenarios. In this work, we propose an end-to-end airport runway detection network for automatic runway detection and localization centered on a two-stage region-based convolutional neural network that could significantly improve navigation efficiency and dependence on complex control systems. The first stage is an enhanced region proposal network with domain-specific constraints added, which automatically learns and extracts multiscale (high, mid, and low-level) features using a deep convolutional neural network and filters low-quality positive samples for robust runway detection. The second stage classifies the candidate regions and determines the precise localization of predictions using an enhanced anchor refinement and regression algorithm with a nonmaximum suppression function for bounding box optimization. Transfer learning and data augmentation techniques are used to propagate relevant domain-specific semantic and context information during the training phase to handle the problem of imbalances in the dataset classes. The results show the effectiveness of our algorithmic contributions as well as the benefits of the specific enhancements, in terms of, improved efficiency and accuracy of runway detection in remote sensing images.

A fixed grid based accurate phase-field method for dendritic solidification in complex geometries

In this paper, an easily implementable immersed interface method based high-resolution phase-field model is proposed for simulating dendritic growth related problems in complicated geometries. At first, resolution issues of different Fourier Pseudo-spectral based dealiasing schemes are shown for the anisotropic phase field equations at high thermal undercooling conditions using just adequate spatial grid resolution. The problems of aliasing error and spectral leakage are studied using high-order based exponential smoothing spectral filters, which generally occur at low sampling rate situations. A fully dealiased zero padding scheme based anisotropic phase-field method is also developed in conjunction with an optimal third order strong stability preserving Runge–Kutta (SSPRK3) time integration scheme for obtaining more accurate and stable numerical solutions. In the later part, a modified phase-field method is implemented with the indicator function which simulates different crystal growth problems in complex geometries without using non-trivial mesh refinement algorithm. Effect of non-interacting obstacles present in the computational domain can also be readily incorporated in the present numerical model by using the single order parameter based phase-field equations.

Automated semantic segmentation of bridge point cloud based on local descriptor and machine learning

In recent years, monitoring the health condition of existing bridges has become a common requirement. By providing an information management system, Bridge Information Model (BrIM) can highly improve the efficiency of health inspection and the reliability of condition evaluation. However, the current modeling processes still largely rely on manual work, where the cost outweighs the benefits. The main barrier lies in the challenging step of semantic segmentation of point clouds. Efforts have been made to identify and segment the structural components of bridges in existing research. But these methods are either dependent on manual data preprocessing or need big training dataset, which, however, has rendered them unpractical in real-world applications. This paper presents a combined local descriptor and machine learning based method to automatically detect structural components of bridges from point clouds. Based on the geometrical features of bridges, we design a multi-scale local descriptor, which is then used to train a deep classification neural network. In the end, a result refinement algorithm is adopted to optimize the segmentation results. Experiments on real-world reinforced concrete (RC) slab and beam-slab bridges show an average precision of 97.26%, recall of 98.00%, and intersection over union (IoU) of 95.38%, which significantly outperforms PointNet. This method has provided a potential solution to semantic segmentation of infrastructures by small sample learning and will contribute to the fulfillment of the automatic BrIM generation of typical highway bridges from the point cloud in the future.

A solver for simulating shock-induced combustion on curvilinear adaptive meshes

A generic solver in a structured Cartesian adaptive mesh refinement framework is extended to simulate unsteady shock-induced combustion problems on a structured curvilinear mesh. A second-order accurate finite volume method is used with a grid-aligned Riemann solver for inviscid thermally perfect gas mixtures. To solve these reactive problems, detailed chemical kinetic mechanisms are employed with a splitting approach. The prolongation and restriction operators are modified to implement the adaptive mesh refinement algorithm on a mapped mesh. The developed solver is verified with several benchmark tests and is then used to simulate unsteady shock-induced combustion. The results show that the computed stand-off distance of waves and oscillation frequencies of mass fraction of products observed at the stagnation point are in good agreement with the results from experiments.

An Automated Line-of-Therapy Algorithm for Adults With Metastatic Non-Small Cell Lung Cancer: Validation Study Using Blinded Manual Chart Review.

BackgroundExtraction of line-of-therapy (LOT) information from electronic health record and claims data is essential for determining longitudinal changes in systemic anticancer therapy in real-world clinical settings.ObjectiveThe aim of this retrospective cohort analysis is to validate and refine our previously described open-source LOT algorithm by comparing the output of the algorithm with results obtained through blinded manual chart review.MethodsWe used structured electronic health record data and clinical documents to identify 500 adult patients treated for metastatic non–small cell lung cancer with systemic anticancer therapy from 2011 to mid-2018; we assigned patients to training (n=350) and test (n=150) cohorts, randomly divided proportional to the overall ratio of simple:complex cases (n=254:246). Simple cases were patients who received one LOT and no maintenance therapy; complex cases were patients who received more than one LOT and/or maintenance therapy. Algorithmic changes were performed using the training cohort data, after which the refined algorithm was evaluated against the test cohort.ResultsFor simple cases, 16 instances of discordance between the LOT algorithm and chart review prerefinement were reduced to 8 instances postrefinement; in the test cohort, there was no discordance between algorithm and chart review. For complex cases, algorithm refinement reduced the discordance from 68 to 62 instances, with 37 instances in the test cohort. The percentage agreement between LOT algorithm output and chart review for patients who received one LOT was 89% prerefinement, 93% postrefinement, and 93% for the test cohort, whereas the likelihood of precise matching between algorithm output and chart review decreased with an increasing number of unique regimens. Several areas of discordance that arose from differing definitions of LOTs and maintenance therapy could not be objectively resolved because of a lack of precise definitions in the medical literature.ConclusionsOur findings identify common sources of discordance between the LOT algorithm and clinician documentation, providing the possibility of targeted algorithm refinement.

Mathematical simulation of adaptive vector finite element method for the analysis of electromagnetic vibration spectrum field response

Mathematical simulation plays a vital role in the analysis of electromagnetic vibration spectrum field response. This article realizes a three-dimensional adaptive vector finite meta-acting algorithm of controlled source electromagnetic vibration spectrum (CSEM) field to address 3D meshing for the simulation of terrain fluctuations and complex electrical anomalies. The adaptive methods utilized in this article is employed for one-time field and secondary field separation in order to calculate electromagnetic vibration spectrum field response. This response can effectively solve the source singularity in finite meta-simulation and improves the numerical accuracy of electromagnetic vibration spectrum field near the field source. The two approaches analysed in this article are CSEM one-dimensional positive algorithm and finite meta-method. The adaptive mesh refinement algorithm based on post-test error estimation is used in this paper to guide the mesh refinement to reduce man-made errors caused by designing a grid. The validity of the proposed algorithm is verified through numerical simulation of one-dimensional and three-dimensional models. The outcomes obtained reveals that the finite solution of one-dimensional model coincides well with the analytical solution. The relative error of electromagnetic vibration spectrum field amplitude is about 1 %, and the overall phase difference of less than 1 degree is observed. It is analysed that the three-dimensional model finite solution also fits well with the finite volume solution and the controlled source electromagnetic vibration spectrum response with three-dimensional tilt plate abnormality is simulated. This experimental analysis shows the ability and effectiveness of the algorithm to simulate the electromagnetic vibration spectrum field of complex geoelectrical structure.

Python-based Helix Indexer: A graphical user interface program for finding symmetry of helical assembly through Fourier-Bessel indexing of electron microscopic data.

Many macromolecules form helical assemblies to carry out their functions. Helical reconstruction from electron microscopic images is a powerful approach for solving high-resolution structures of such assemblies. Determination of the symmetry parameters of the helical assemblies is a prerequisite step in helical reconstruction. The most widely used method for deducing the symmetry is through Fourier-Bessel indexing the diffraction pattern of the helical assemblies. This method, however, often leads to incorrect solutions, due to intrinsic ambiguities in indexing helical diffraction patterns. Here, we present Python-based Helix Indexer (PyHI), which provides a graphical user interface (GUI) to guide the users through the process of symmetry determination. Diffraction patterns can be read into the program directly or calculated on the fly from two-dimensional class averages of helical assemblies. PyHI allows deducing the Bessel orders of diffraction peaks by using both the amplitudes and phases of the diffraction data. Based on the Bessel orders of two unit vectors, the Fourier space lattice is constructed with minimal user inputs. The program then uses a refinement algorithm to optimize the Fourier space lattice, and subsequently generate the helical assembly in real space. The program provides both a publication-quality graphic representation of the helical assembly and the symmetry parameters required for subsequent helical reconstruction steps.