Refinement Algorithm Research Articles

Fast Direct Localization for Millimeter Wave MIMO Systems via Deep ADMM Unfolding

Massive arrays deployed in millimeter-wave systems enable high angular resolution performance, which in turn facilitates sub-meter localization services. Albeit suboptimal, up to now the most popular localization approach has been based on a so-called two-step procedure, where triangulation is applied upon aggregation of the angle-of-arrival (AoA) measurements from the collaborative base stations. This is mainly due to the prohibitive computational cost of the existing direct localization approaches in large-scale systems. To address this issue, we propose a deep unfolding based fast direct localization solver. First, the direct localization is formulated as a joint <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{2,1}$ </tex-math></inline-formula> norm sparse recovery problem, which is then solved by using alternating direction method of multipliers (ADMM). Next, we develop a deep ADMM unfolding network (DAUN) to learn the ADMM parameter settings from the training data and a position refinement algorithm is proposed for DAUN. Finally, simulation results showcase the superiority of the proposed DAUN over the baseline solvers in terms of better localization accuracy, faster convergence and significantly lower computational complexity.

A Feature-Extraction-Based Adaptive Refinement Method for Solving the Reynolds Equation in Piston–Cylinder System

A fast local refinement algorithm based on feature extraction is developed. In the mesh-based Reynolds equation solutions, two refinement features based on the physical parameters of fluid lubrication are firstly defined, namely, pressure value feature and pressure gradient feature. Then, a fast adaptive strategy different from the traditional methods based on residuals or recovery errors is constructed according to the features, which are expected to determine the element needed to be refined. Considering the update requirement of the feature parameters, an adaptive update strategy for feature parameters is also developed. Finally, the feasibility of the scheme is verified on a single-cylinder gasoline engine. Results show that the current algorithm can effectively reduce the computational scale while ensuring the computational accuracy of the mesh-based model, compared with the traditional global and local refinement strategy.

Integrated Sensing and Communication With mmWave Massive MIMO: A Compressed Sampling Perspective

Integrated sensing and communication (ISAC) has opened up numerous game-changing opportunities for realizing future wireless systems. In this paper, we propose an ISAC processing framework relying on millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Specifically, we provide a compressed sampling (CS) perspective to facilitate ISAC processing, which can not only recover the high-dimensional channel state information or/and radar imaging information, but also significantly reduce pilot overhead. First, an energy-efficient widely spaced array (WSA) architecture is tailored for the radar receiver, which enhances the angular resolution of radar sensing at the cost of angular ambiguity. Then, we propose an ISAC frame structure for time-varying ISAC systems considering different timescales. The pilot waveforms are judiciously designed by taking into account both CS theories and hardware constraints induced by hybrid beamforming (HBF) architecture. Next, we design the dedicated dictionary for WSA that serves as a building block for formulating the ISAC processing as sparse signal recovery problems. The orthogonal matching pursuit with support refinement (OMP-SR) algorithm is proposed to effectively solve the problems in the existence of the angular ambiguity. We also provide a framework for estimating the Doppler frequencies during payload data transmission to guarantee communication performances. Simulation results demonstrate the good performances of both communications and radar sensing under the proposed ISAC framework.

Development and Validation of ICD-10-CM-based Algorithms for Date of Last Menstrual Period, Pregnancy Outcomes, and Infant Outcomes.

Validation studies of algorithms for pregnancy outcomes based on International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes are important for conducting drug safety research using administrative claims databases. To facilitate the conduct of pregnancy safety studies, this exploratory study aimed to develop and validate ICD-10-CM-based claims algorithms for date of last menstrual period (LMP) and pregnancy outcomes using medical records. Using a mother-infant-linked claims database, the study included women with a pregnancy between 2016-2017 and their infants. Claims-based algorithms for LMP date utilized codes for gestational age (Z3A codes). The primary outcomes were major congenital malformations (MCMs) and spontaneous abortion; additional secondary outcomes were also evaluated. Each pregnancy outcome was identified using a claims-based simple algorithm, defined as presence of ≥ 1 claim for the outcome. Positive predictive values (PPV) and 95% confidence intervals (CI) were calculated. Overall, 586 medical records were sought and 365 (62.3%) were adjudicated, including 125 records each for MCMs and spontaneous abortion. Last menstrual period date was validated among maternal charts procured for pregnancy outcomes and fewer charts were adjudicated for the secondary outcomes. The median difference in days between LMP date based on Z3A codes and adjudicated LMP date was 4.0 (interquartile range: 2.0-10.0). The PPV of the simple algorithm for spontaneous abortion was 84.7% (95% CI 78.3, 91.2). The PPV for the MCM algorithm was < 70%. The algorithms for the secondary outcomes pre-eclampsia, premature delivery, and low birthweight performed well, with PPVs > 70%. The ICD-10-CM claims-based algorithm for spontaneous abortion performed well and may be used in pregnancy studies. Further algorithm refinement for MCMs is needed. The algorithms for LMP date and the secondary outcomes would benefit from additional validation in a larger sample.

A mesh refinement method for low‐thrust orbit transfer problems

AbstractThis paper researches the fuel‐optimal geocentric orbit transfer problem using a low‐thrust, electric propulsion. First, the transfer trajectory is divided into N burn‐coast arcs via introducing (N−1) intermediate orbits between the initial and the target orbits. By restricting the time duration of each burn, the changes in orbital states caused by one time of ignition can be represented by a discrete velocity impulse; therefore, a considerable large N, a great many optimization variables, and a months and revolutions long trajectory will be calculated to solve the optimization problem. By proposing a new transfer coordinate system, the performance index is analytically calculated, and strict constraints derived from the continuous condition between adjacent orbits are removed from the mathematical optimization model. Meanwhile, an improved particle swarm optimization (PSO) method and a mesh refinement method are proposed to address the optimization problem. The mesh refinement algorithm begins with a simple bi‐impulse transfer problem, automatically increases/decreases N, and finally converges to the optimal solution. Numerical simulation shows the efficiency of the method with some classical and representative scenarios.

Influence of longitudinal obstacle spacing on the deflagration-to-detonation transition through a bank of obstacles in a hydrogen-air mixture

Flame acceleration and deflagration-to-detonation transition (DDT) in a channel containing an array of staggered cylindrical obstacles and a stoichiometric hydrogen-air mixture were studied by solving the fully-compressible reactive Navier-Stokes equations using a high-order numerical algorithm and adaptive mesh refinement. Four different longitudinal spacings (ls) of the neighboring obstacle rows (i.e., ls = 15.28, 19.1, 25.4, and 38.2 mm, corresponding to 1.2, 1.5, 2 and 3 times of obstacle diameter, respectively) were used to examine the effect of obstacle spacing on flame acceleration and DDT. The results show that the main mechanisms of flame acceleration and transition to detonation in all the cases studied are consistent. While the flame acceleration is caused by the growth of flame surface area in the initial stage, it is governed by shock-flame interactions in the later stage when shock waves are generated. The focusing of strong shocks at flame front is responsible for the initiation of detonation. It was found that the flame propagation speed and the DDT run-up distance and time are highly dependent on ls. Specifically, the flame acceleration declines as ls increases, since a larger ls leads to less disturbance of flow by obstacles per unit channel length. For detonation initiation, both the run-up distance and time increase with the increase of ls. It is interesting to note that the DDT distance and time increase significantly as ls increases from 19.1 mm to 25.4 mm. This is related to the slowdown of the increase rate of energy release over a period before DDT occurs under large ls condition, because every time the flame passes over an obstacle row the shock-flame interaction is delayed and numerous isolated pockets of unburned gas material are formed.

Isogeometric analysis of stress concentrations and failure strength in composite plates with circular holes using RHT-splines

This paper studies the stress concentration factors (SCFs) to predict the first ply failure (FPF) of cross and angle-ply laminate composite plates with single circular holes. The rational splines over hierarchical T-meshes (RHT-splines) were chosen as a surface design methodology for Isogeometric Analysis (IGA) due to their ability to exactly represent the geometry, efficient and simple local refinement algorithm, and reduction of the superfluous control points of the NURBS surface. The Bézier extraction concept was used as an element structure for IGA that allows mapping the Bernstein polynomials basis functions on Bezier elements to be performed on RHT-splines geometries. Several failure criteria and composite materials are employed in numerical examples for different fiber orientation angles through Matlab® implementation. The results obtained by the IGA based on RHT-splines showed very good conformity with those obtained by the analytic approach and finite element method (FEM) published in the literature, which demonstrates the robustness and accuracy of the IGA using RHT-splines and provides an effective and realistic way to determine SCF and failure strength. The Matlab® program for the analytical approach and failure theories is made an open-source and can be downloaded from (Github Website).

Modified refinement algorithm to construct Lyapunov functions using meshless collocation

&lt;p style='text-indent:20px;'&gt;Lyapunov functions are functions with negative derivative along solutions of a given ordinary differential equation. Moreover, sublevel sets of a Lyapunov function are subsets of the domain of attraction of the equilibrium. One of the numerical construction methods for Lyapunov functions uses meshless collocation with radial basis functions.&lt;/p&gt;&lt;p style='text-indent:20px;'&gt;Recently, this method was combined with a grid refinement algorithm (GRA) to reduce the number of collocation points needed to construct Lyapunov functions. However, depending on the choice of the initial set of collocation point, the algorithm can terminate, failing to compute a Lyapunov function. In this paper, we propose a modified grid refinement algorithm (MGRA), which overcomes these shortcomings by adding appropriate collocation points using a clustering algorithm. The modified algorithm is applied to two- and three-dimensional examples.&lt;/p&gt;

Research on Optimization of 3D Printing Manufacturing Technology Based on Segmentation Algorithm

3D printing technology is a relatively mature processing technology. In actual production, external support needs to be added to ensure the smooth progress of the processing process. Tree support is a new type of external support method. Combined with instance segmentation technology in artificial intelligence, it can better solve the problems of collapse, deformation, and lack of precision in the 3D printing process and improve production efficiency. For the feature loss problem in the printing model, a new instance segmentation algorithm is adopted, including deepening the feature extraction architecture and using the nonlinear activation function Mish. For the feature reconstruction after instance segmentation, this paper optimizes and improves the classical concealment method, including the spatial range concealment method Z-buffer algorithm and the area scan line algorithm of the plane range concealment method to realize the projection mapping transformation of the 3D object model by combining coordinate refinement and Z-buffer algorithm.

Efficient Dynamic Mesh Refinement Technique for Simulation of HPM Breakdown-Induced Plasma Pattern Formation

Numerical simulation of the complex plasma dynamics associated with high power, high-frequency microwave breakdown at high pressures, leading to the formation of filamentary plasma structures such as self-organized plasma arrays, is a computationally challenging problem. The widely used 2-D electromagnetic (EM)–plasma fluid model, which accurately captures the experimental observations, requires a runtime of several days to months to simulate standard problems due to stringent numerical requirements in terms of cell size and time step. This article presents a self-aware mesh refinement (MR) algorithm that uses a coarse mesh and a fine mesh that dynamically expand based on the plasma profile topology to resolve the sharp gradients in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula> -fields and plasma density in the breakdown region. The dynamic MR (DMR) technique is explained in detail, and its performance has been evaluated using a standard benchmark microwave breakdown problem. We observe a speedup of 8 (of the order of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(r^{3})$ </tex-math></inline-formula> , when the refinement factor ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> ) is 2) compared with a traditional single uniform fine-mesh-based simulation. The technique is scalable and performs better when the problem size increases. We also present a comprehensive spatio-temporal visual analysis to explain the complex physics of high-power microwave (HPM) breakdown, leading to self-organized plasma filaments as an application of the DMR technique.

Two Algorithms for Refinement of Approximate Solutions in the Regularization Method

Two Algorithms for Refinement of Approximate Solutions in the Regularization Method

Collaborative Learning-Based Network for Weakly Supervised Remote Sensing Object Detection

Existing object detection algorithms rely excessively on instance-level labels, which are both time-consuming and expensive. In particular, for remote sensing images (RSI) with small and dense objects, the labeling cost is much higher than that of general images. Moreover, the propagation process of the labels over the noisy channel results in blurred and noisy information. To address the problem of obtaining RSI instance-level labels, we aim to propose a collaborative learning-based network for weakly supervised remote sensing object detection (CLN-RSOD). Compared with the state-of-the-art, the proposed model combines the advantages of two object detection sub-networks by jointly training. This improves the model's capability and thereby enhances the detection effect for multi-object in RSI. Moreover, we employ a mask-based proposal refinement algorithm for remote sensing images (MPR-RS) to optimize the candidate boxes. In addition, according to the data distribution characteristics of RSI, we introduce a new joint pooling module (JPM) in CLN-RSOD to enhance the backbone network's characterization of RSI. Finally, the experimental results on two public remote sensing datasets illustrate that the proposed weakly supervised learning method is superior to other weakly supervised methods and demonstrates the effectiveness of the proposed CLN-RSOD.

An Enhanced Pignistic Transformation-Based Fusion Scheme With Applications in Image Segmentation

The traditional Pignistic transformation is limited in the context of “betting”, which faces information loss and is inconvenient for multi-source information fusion. To tackle this challenge, an Enhanced Pignistic transformation is proposed for the first time. New divergence and information volume measures are tailor-made for the enhanced Pignistic probability, and a novel information fusion algorithm is developed. To further prove the fusion algorithm’s advantages in conflict management, it is applied in a new semi-automatic image segmentation scheme. Two uncertain decision-support techniques named adaptive belief assignment and scalable information extraction are raised, and a fuzzy heuristic refinement algorithm is conducted, fulfilling the gap between evidential decision-making and segmentation refinement. Experimental analysis shows the proposed segmentation algorithm is superior on four metrics each and can enhance the robustness of foreground segmentation, indicating the effectiveness of the proposal in solving the decision inaccuracy of evidential segmentation schemes.

Detection and localization of multi-scale and oriented objects using an enhanced feature refinement algorithm.

Object detection is a fundamental aspect of computer vision, with numerous generic object detectors proposed by various researchers. The proposed work presents a novel single-stage rotation detector that can detect oriented and multi-scale objects accurately from diverse scenarios. This detector addresses the challenges faced by current rotation detectors, such as the detection of arbitrary orientations, objects that are densely arranged, and the issue of loss discontinuity. First, the detector also adopts a progressive regression form (coarse-to-fine-grained approach) that uses both horizontal anchors (speed and higher recall) and rotating anchors (oriented objects) in cluttered backgrounds. Second, the proposed detector includes a feature refinement module that helps minimize the problems related to feature angulation and reduces the number of bounding boxes generated. Finally, to address the issue of loss discontinuity, the proposed detector utilizes a newly formulated adjustable loss function that can be extended to both single-stage and two-stage detectors. The proposed detector shows outstanding performance on benchmark datasets and significantly outperforms other state-of-the-art methods in terms of speed and accuracy.

Development of an algorithm for refineding parameters vehicle suspensions

The research results of the oscillations for sprung and unsprung masses in the frequency range are presented. The amplitude-phase frequency characteristics of individual masses during vibrations with follow-up presentation of transient processes, and the main results on the refinement of vehicle suspension parameters are determined. The function of the damping time of vehicle oscillations is considered. The time function a program algorithm that can be used when designing a vehicle to find the minimum value is developed. The purpose of the work is to rice the comfort of the vehicle by minimizing the damping time of the sprung mass oscillations. Keywords oscillation frequency, suspension system, static and dynamic suspension deflection, oscillation damping time, suspension parameters refinement algorithm

Neighborhood Preserving Kernels for Attributed Graphs.

We describe the design of a reproducing kernel suitable for attributed graphs, in which the similarity between two graphs is defined based on the neighborhood information of the graph nodes with the aid of a product graph formulation. Attributed graphs are those which contain a piece of vector information and a discrete label over the nodes and edges. We represent the proposed kernel as the weighted sum of two other kernels of which one is an R-convolution kernel that processes the attribute information of the graph and the other is an optimal assignment kernel that processes label information. They are formulated in such a way that the edges processed as part of the kernel computation have the same neighborhood properties and hence the kernel proposed makes a well-defined correspondence between regions processed in graphs. These concepts are also extended to the case of the shortest paths. We identified the state-of-the-art kernels that can be mapped to such a neighborhood preserving framework. We found that the kernel value of the argument graphs in each iteration of the Weisfeiler-Lehman color refinement algorithm can be obtained recursively from the product graph formulated in our method. By incorporating the proposed kernel on support vector machines we analyzed the real-world data sets and it showed superior performance in comparison with that of the other state-of-the-art graph kernels.

Formula omitted]-[formula omitted]: Computationally efficient adaptive mesh refinement of Monte Carlo mesh based tallies

The reactor physics community is always focused on reducing the computational time and memory required for simulations. χ-MeRA, which stands for flux-based-(χ)-Mesh tally Refinement Adaptively, was built to reduce the computational time and memory required to solve the neutronics side of a multiphysics problem when compared to traditional methods for mesh based tallies in Monte Carlo (MC) simulations. χ-MeRA couples a MC code with an adaptive mesh refinement (AMR) algorithm to take advantage of the accuracy of a MC code and the efficiency of an AMR algorithm. Also developed within χ-MeRA was a set of metrics to assess the effects of the refinement on various parameters in the simulation space. For a plutonium sphere, χ-MeRA shows a reduction in memory usage and computation time when compared to a fully refined mesh by a factor of 14.7 and 6.7, respectively. When compared to an unstructured mesh, improvement of 1.3 and 4.8 was achieved for memory usage and computation time. The development of χ-MeRA helps solve the neutronics side of a multiphysics problem in a faster, more computationally efficient manner than traditional methods, and the final mesh created contains accurate results that can be passed onto the next physics code.

Loaded contact pressure distribution prediction for spiral bevel gear

Contact pressure and load distribution are key indices used in the performance evaluation of spiral bevel gears, and their calculation accuracy and efficiency continue to be the focus of active research. An accurate and efficient numerical approach for predicting the contact pressure and load distribution of a spiral bevel gear is presented based on multi-tooth deformation compatibility. First, an improved discrete tooth surface contact analysis (DTCA) method is developed to obtain the contact characteristics of the gear set under the no-load condition. This method is a geometric approach that avoids numerical instability. A local triangular mesh refinement algorithm is proposed to improve the accuracy and efficiency of the DTCA method. Subsequently, the deformation compatibility relationships of a spiral bevel gear set are derived for single-tooth and multi-tooth meshing. By combining the deformation compatibility relationship and the influence coefficient method, a multi-tooth loaded contact analysis (MLTCA) model of the spiral bevel gear set is established. Moreover, an iterative algorithm based on the conjugate gradient and fast Fourier transform method is proposed to determine the mesh force and contact pressure distribution. Finally, the proposed method is tested using numerical examples to demonstrate its accuracy and efficiency by comparison with finite element simulations and experiment results.

Verification, validation, and parameter study of a computational model for corrosion pit growth adopting the level-set method. Part I: Corrosion

Corrosion is a phenomenon observed in structural components in corrosive environments such as pipelines, bridges, aircrafts, turbines, etc. The computational model of corrosion should enjoy two features: a) accurately considering the electrochemistry of corrosion and b) properly dealing with the moving interface between solid and electrolyte. There are several approaches to model corrosion such as using FEM with mesh refinement algorithms, combining FEM and level-set method, employing finite volume methods, adopting peridynamic formulation, and utilizing phase field models. Because of its accuracy, lower computational cost, and robust dealing with multiple pit merging, the model which combines FEM with level-set method is selected to be more extensively assessed in this paper. Part I focuses on demonstrating the model’s capabilities of simulating pitting corrosion through a set of numerical examples which include numerical solution verification, experimental validation, and uncertainty quantification of model parameters and properties.

Vortexlet formation in Schardin's problem

The present study focuses on the shock diffraction problem over a triangle wedge for Mach numbers of M=1.3, 1.5, 1.7, and 2.0 by using a two-dimensional, high-order, in-house Euler solver. The solver is based on a family of advection upstream splitting method in combination with a central essentially non-oscillatory scheme and benefits a block-based adaptive mesh refinement algorithm to resolve the regions that contain discontinuities. High accuracies in time and space, and adaptive mesh refinement capabilities of the solver allow us to investigate vortexlet formation mechanism in detail. Our results reveal that there are two different types of vortexlet formation mechanisms. While the first type of formation is observed at all Mach numbers considered here, the second type arises when the Mach number is greater than 1.3. This difference results from their driving mechanisms, which are the upward moving accelerated shock and embedded shock in the primary vortex. In addition to their driving mechanisms, two types are also different in terms of their locations.