Re-initialization Method Research Articles

High resolution Tibetan Plateau regional reanalysis 1961-present

With the rapid global warming in recent decades, the Tibetan Plateau (TP) has suffered severe impacts, such as glacier retreat, glacial lake expansion, and permafrost degradation, which threaten the lives and properties of the local and downstream populations. Regional Reanalysis (RR) is vital for TP due to the limitations of observations. In this work, a 62-year (1961–2022) long atmospheric regional reanalysis with spatial resolution of 9 km (convective gray-zone scale) and temporal resolution of 1 hour over the TP (TPRR) was developed using the Weather Research and Forecasting (WRF) model, combined with re-initialization method, spectral nudging (SN), and several optimizations. TPRR is forced by ERA5 at hourly intervals. TPRR outperforms ERA5, realistically capturing climatological characteristics and seasonal variations of precipitation and T2m (air temperature at 2m above ground level). Moreover, TPRR better reproduces the frequency and intensity of precipitation, as well as the diurnal cycle of precipitation. This study also quantifies the wetting trend of 0.0071 mm/year over the TP amid global warming using TPRR.

A novel mode of INS-aided BDS real-time high-rate and precise kinematic relative positioning between two moving platforms

ABSTRACT For kinematic relative positioning users between two moving platforms, limited communication bandwidth and computation ability usually cannot support real-time transmission of high-rate (≥10 Hz) BeiDou Navigation Satellite System (BDS) data. The performance of Ambiguity Resolution (AR) is also a major challenge in signal blocked and loss of lock environments. Based on BDS and Inertial Navigation System (INS) data, we develop a novel mode of INS-aided BDS kinematic relative positioning between two moving platforms, aiming to realize real-time, high-rate and precise positioning with low-cost communication modules in challenge environments. To achieve this goal, the INS-aided high-rate relative kinematic positioning and INS-aided BDS AR re-initialization methods are proposed. In this mode, the baseline bias caused by the different position datum is first defined and resolved. Then, high-rate INS data are assisted to obtain kinematic relative position when real-time kinematic positioning results are unavailable within 1 second. Once the BDS data are available, the predicted relative position is used as additional constraint to facilitate AR re-initialization and improve the kinematic positioning performance. The performance of these methods is discussed in a set of experiments with 1 Hz BDS data and 100 Hz INS data, and compared with the conventional method by sending raw BDS/INS measurements. The results show that the proposed methods can achieve an accuracy of about 5 cm for the INS-aided 100 Hz relative positioning in baseline components and lengths, which is equal to the conventional method, but the transmitted data have been sharply reduced by an average of nearly 80%. With the assistance of INS-predicted baseline constraint, the relative positioning performance has been further improved. The accuracy of baseline length errors is less than 4 cm, and the AR fixing rates keep larger than 95% during the experiment, while the wrongly fixed rates are reduced to less than 0.5%.

An enhanced estimation of distribution algorithm with problem-specific knowledge for distributed no-wait flowshop group scheduling problems

With the trend of economic globalization, distributed manufacturing widely exists in modern manufacturing systems. As an extension of the distributed flowshop scheduling problem, the distributed no-wait flowshop group scheduling problem with sequence-dependent setup times (DNFGSP_SDSTs) is investigated in this article. To address DNFGSP_SDSTs with the criterion of minimizing makespan, this study proposes an enhanced estimation of distribution algorithm·(EEDA) with problem-specific knowledge. First, a mixed integer linear programming(MILP) model of DNFGSP_SDSTs is established. Second, based on the characteristics of DNFGSP_SDSTs, five problem-specific properties about local search operators are derived as prior knowledge to reduce computational cost. Third, two NEH-based two-stage heuristics are presented to construct a high-quality population with diversity. Fourth, a probability model with problem-specific knowledge and a family-based updating mechanism are developed to accumulate valuable pattern information from high-quality solutions, while a sampling strategy is designed to generate new populations with the accumulated information. Fifth, several local search operators are devised to refine the obtained solutions. Furthermore, perturbation and reinitialization methods are developed to avoid premature convergence. Finally, the validity of the MILP model is verified by using the Gurobi solver. The parameters of EEDA are tuned through a design of experiments. The effectiveness of key components in EEDA is confirmed through extensive experiments, and the computational comparisons with the state-of-the-art algorithms indicate the effectiveness of the proposed EEDA for solving DNFGSP_SDSTs.

DSD²: Can We Dodge Sparse Double Descent and Compress the Neural Network Worry-Free?

Neoteric works have shown that modern deep learning models can exhibit a sparse double descent phenomenon. Indeed, as the sparsity of the model increases, the test performance first worsens since the model is overfitting the training data; then, the overfitting reduces, leading to an improvement in performance, and finally, the model begins to forget critical information, resulting in underfitting. Such a behavior prevents using traditional early stop criteria. In this work, we have three key contributions. First, we propose a learning framework that avoids such a phenomenon and improves generalization. Second, we introduce an entropy measure providing more insights into the insurgence of this phenomenon and enabling the use of traditional stop criteria. Third, we provide a comprehensive quantitative analysis of contingent factors such as re-initialization methods, model width and depth, and dataset noise. The contributions are supported by empirical evidence in typical setups. Our code is available at https://github.com/VGCQ/DSD2.

Comparative Analysis of Forest Fire Spread Simulation and Reinitialization Methods Based on FARSITE and Cell2Fire

Comparative Analysis of Forest Fire Spread Simulation and Reinitialization Methods Based on FARSITE and Cell2Fire

Multi-population cooperative multi-objective evolutionary algorithm for sequence-dependent group flow shop with consistent sublots

Inspired by the production model of pressure vessels for spacecraft, i.e., tanks and cylinders, this study addresses the sequence-dependent group flow shop scheduling problem with consistent sublots (SDGFSP_CS) to minimize makespan and total energy consumption. In the problem under consideration, there are several coupling sub-problems, namely, the group sequencing, job sequencing, lot assignment, and machine speed assignment. To solve these problems, a multi-population cooperative multi-objective evolutionary algorithm (MPCMOEA) is proposed. In the MPCMOEA, a hybrid initial method that combines two problem-specific heuristics is designed to generate high-quality initial solutions. Then, considering the problem features, a cooperative mechanism considering the co-evolution of multi-population and the archive set is designed to accelerate the optimization process. In the co-evolutionary stage, to deepen the exploitation ability of local search, an enhanced search with multiple problem-specific operators is implemented. Furthermore, a re-initialization method is developed to improve the global search abilities. Finally, 27 different scale instances are generated for a series of numerical experiments. For the hypervolume and inverse generational distance metrics, MPCMOEA gets 20/27 and 21/27 optimal values, respectively. It verifies that the MPCMOEA outperforms efficient algorithms in terms of the diversity and convergence performance.

High-order level set reinitialization for multiphase flow simulations based on unstructured grids

Reinitialization is essential to maintaining the accuracy of the level set method at capturing interfaces. In this paper, a high-order reinitialization method for the level set function which has previously been developed for structured grids was extended to unstructured grids. The proposed method involves constructing a stencil of an unstructured grid to define a high-order polynomial that approximates a piecewise segment of the interface. Then, the closest point method is adopted to estimate the signed distance function at grid points. The proposed method was validated by solving some benchmark problems of static cases and then the evolution of moving interfacial problems subject to prescribed velocity fields for various grid resolutions consisting of triangular elements. The accuracy of the proposed method was evaluated for computing geometric quantities such as the normal vector and curvature field. The proposed method proved to have high-order accuracy for estimating not only the signed distance function but also geometric quantities for a smooth interface. Finally, a local correction procedure was developed to apply the proposed method to level set problems involving the interface with kinks. The proposed method can accurately estimate normal vector and curvature fields including singularities both for static cases and for dynamic cases where an interface experiences topological changes. • High-order reinitialization of level set method on unstructured grid is developed. • We conduct a high-order estimation of normal vector and curvature on unstructured grid. • We propose a local correction procedure to manage kinks in level set method. • We successfully solve level set problems involving topological changes with kinks.

Finite Difference and Reinitialization Methods with Level Set to Interfacial Area Transport Equations for Gas–Liquid Two-Phase Flows

For the direct simulation of liquid atomization processes affected by surface tension, the level-set method was suitable, owing to the ease of estimating normal vectors and interface curvatures. However, the mass is not conserved in the original method because of errors in the interfacial advection evaluation. Therefore, previous studies have developed the interfacial advection equation and the interfacial evaluation method for reinitialization. The reinitialization method can be organized in terms of control parameters. Therefore, we modified and optimized the control parameters of the high-order constrained reinitialization scheme developed previously. Furthermore, the difference method in the convection term was adapted to the interfacial advection equation to evaluate the best method for simulating the droplet atomization process. Consequently, the fifth-order accuracy weighted essentially non-oscillatory scheme was observed to provide the best mass conservation for both the reinitialization and the advection equations.

A parallel cell-centered adaptive level set framework for efficient simulation of two-phase flows with subcycling and non-subcycling

We develop a unified adaptive level set (LS) framework using the multi-level collocated grid for incompressible two-phase flows. This framework allows us to advance all variables level by level using either the subcycling or the non-subcycling method such that the data advancement on each level is fully decoupled. A series of synchronization operations are designed to keep the momentum and mass conserved across all levels. A multi-level re-initialization method for the LS function is also proposed, which greatly improves the mass conservation of the two-phase flows. The collocated grid allows the use of a single set of differential schemes and interpolation operations for all variables, which greatly simplifies the numerical implementation. The capability and robustness of the computational framework are validated by a variety of canonical problems, including the inviscid shear layer, gravity wave, rising bubble, and Rayleigh-Taylor instability. It is shown that the present multi-level scheme can accurately resolve the interfaces of the two-phase flows with gravitational and surface tension effects while having good momentum and energy conservation. At last, a three-dimensional dam breaking problem is simulated to show the efficiency and significant speedup of the proposed framework.

An Improved Conservative Direct Re-Initialization Method (ICDR) for Two-Phase Flow Simulations

We introduce an improved conservative direct re-initialization (ICDR) method (for two-phase flow problems) as a new and efficient geometrical re-distancing scheme. The ICDR technique takes advantage of two mass-conserving and fast re-distancing schemes, as well as a global mass correction concept to reduce the extent of the mass loss/gain in two- and three-dimensional (2D and 3D) problems. We examine the ICDR method, at the first step, with two 2D benchmarks: the notched cylinder and the swirling flow vortex problems. To do so, we (for the first time) extensively analyze the dependency of the regenerated interface quality on both time-step and element sizes. Then, we quantitatively assess the results by employing a defined norm value, which evaluates the deviation from the exact solution. We also present a visual assessment by graphical demonstration of original and regenerated interfaces. In the next step, we investigate the performance of the ICDR in three-dimensional (3D) problems. For this purpose, we simulate drop deformation in a simple shear flow field. We describe our reason for this choice and show that, by employing the ICDR scheme, the results of our analysis comply with the existing numerical and experimental data in the literature.

A new finite element level set reinitialization method based on the shifted boundary method

We propose an efficient method to reinitialize a level set function to a signed distance function by solving an elliptic problem using the finite element method. The original zero level set interface is preserved by means of applying modified boundary conditions to a surrogate/approximate interface weakly with a penalty method. Narrow band technique is adopted to reinforce the robustness of the proposed method where a smooth initial guess for the nonlinear equation is given by solving a Laplace's equation. Numerical benchmarks in both two dimensions and three dimensions confirm the optimal convergence rates under several different error measures including an interface error measure, showing that our method is accurate and robust. A three-dimensional genus 2 surface is adopted to demonstrate the capability of the method for the reinitialization of complicated geometries.

Hierarchical Aggregation/Disaggregation for Adaptive Abstraction-Level Conversion in Digital Twin-Based Smart Semiconductor Manufacturing

In smart manufacturing, engineers typically analyze unexpected real-time problems using digitally cloned discrete-event (DE) models for wafer fabrication. To achieve a faster response to problems, it is essential to increase the speed of DE simulations because making optimal decisions for addressing the issues requires repeated simulations. This paper presents a hierarchical aggregation/disaggregation (A/D) method that substitutes complex event-driven operations with two-layered abstracted models—single-group mean-delay models (SMDMs) and multi-group MDMs (MMDMs)—to gain simulation speedup. The SMDM dynamically abstracts a DE machine group’s behaviors into observed mean-delay constants when the group converges into a steady state. The MMDM fast-forwards the input lots by bypassing the chained processing steps in multiple steady-state groups until it schedules the lots for delivery to subsequent unsteady groups after corresponding multi-step mean delays. The key component, the abstraction-level converter (ALC), has the roles of MMDM allocation, deallocation, extension, splitting, and controls the flow of each group’s input lot by deciding the destination DE model, SMDM, and MMDMs. To maximize the reuse of previously computed multi-step delays for the dynamically changing MMDMs, we propose an efficient method to manage the delays using two-level caches. Each steady-state group’s ALC performs statistical testing to detect the lot-arrival change to reactivate the DE model. However, fast-forwarding (FF) results in incorrect test results of the bypassed group’s ALCs due to the missed observations of the bypassed lots. Thus, we propose a method for test-sample reinitialization that considers the bypassing. Moreover, since a bypassed group’s unexpected divergence can change the multi-step delays of previously scheduled events, a method for examination of FF history is designed to trace the highly influenced events. This proposed method has been applied in various case studies, and it has achieved speedups of up to about 5.9 times, with 2.5 to 8.3% degradation in accuracy.

Discrete African Buffalo Optimization Algorithm for the Low-carbon Flexible Job Shop Scheduling Problem

In the area of production scheduling, some traditional indicators are always treated as the optimization objectives such as makespan, earliness/tardiness and workload, and so on. However, with the increasing amount of energy consumption, the low-carbon scheduling problem has gained more and more attention from scholars and engineers. In this paper, a low-carbon flexible job shop scheduling problem (LFJSP) is studied to minimize the earliness/tardiness cost and the energy consumption cost. In this paper, a low-carbon flexible job shop scheduling. Due to the NP-hard nature of the problem, a swarm-based intelligence algorithm, named discrete African buffalo optimization (DABO), is developed to deal with the problem under study effectively. The original ABO was proposed for continuous problems, but the problem is a discrete scheduling problem. Therefore, some individual updating methods are proposed to ensure the algorithm works in a discrete search domain. Then, some neighborhood structures are designed in terms of the characteristics of the problem. A local search procedure is presented based on some neighborhood structures and embedded into the algorithm to enhance its searchability. In addition, an aging-based population re-initialization method is proposed to enhance the population diversity and avoid trapping into the local optima. Finally, several experimental simulations have been carried out to test the effectiveness of the DABO. The comparison results demonstrate the promising advantages of the DABO for the considered LFJSP.

Non-Eulerian Newmark Methods: A Powerful Tool for Free-Boundary Continuum Mechanics Problems

In this paper we introduce a new procedure to solve free-surface problems based on applying the Newmark family of time integration schemes to non-Eulerian formulations of the problem (i.e., in non-current domains). The methods obtained within this framework present important advantages over other methods in literature, as for instance, that the computational domain is independent of the current unknowns, the convective term disappears, and modelling and tracking of the free surface is straightforward. Moreover, the Newmark algorithm is convenient to obtain accurate and stable methods for solving continuum mechanics models which can be written in terms of either displacement, velocity or acceleration. We consider a viscous Newtonian fluid in a time dependent domain which may undergo large deformations along the time but not topological changes of interfaces. A unified formulation providing the general framework of the proposed schemes is stated in this context. They are combined with finite elements methods for space discretization. In particular, the Newmark family of pure Lagrange–Galerkin methods is stated. The non-Eulerian formulations are advantageous to obtain linear methods. More precisely, linearized versions of the standard (non-linear) Newmark schemes that present good approximation properties are also proposed. Moreover, we deal with problems associated with high mesh distortion by proposing a reinitialization method to be applied in the non-Eulerian Newmark framework that preserves the order of convergence. In order to assess the performance of the proposed numerical methods, we solve different problems in two space dimensions.

Enhancing multi-GNSS time and frequency transfer using a refined stochastic model of a receiver clock

The precise point positioning (PPP) technique has been widely used for remote time and frequency transfer. With the development of the multiple global navigation satellite system (multi-GNSS), the PPP technique with multi-GNSS has been studied in order to improve the performance of time and frequency transfer. However, the day boundary problem, model correction residuals, and noise may affect the clock estimation in multi-GNSS PPP. In this study, we refine the receiver clock parameter using a stochastic model of an atomic clock to solve these problems. First, the receiver clock modeling method is proposed to reduce the additional noise in the clock estimation. Second, we present the clock instantaneous reinitialization method to solve the day boundary problem. Finally, an enhanced multi-GNSS time transfer method can be obtained. We combine the observations of the global positioning system, the global navigation satellite system, the BeiDou satellite navigation system, and Galileo for the experiment. Standard deviation (STD) and modified Allan deviation (MDEV) are employed to evaluate our approach. The experimental results showed that after using the enhanced transfer method, the STD values of the time transfer results with respect to the International GNSS Service final products were reduced, with an average reduction ratio of 18.3% for all time links. Meanwhile, the stability of frequency transfer results was effectively improved, especially in the short term. The average reduction ratios of MDEV at 30, 60, and 120 s were up to 55.5%, 39.1%, and 23.2%, respectively. Our approach can effectively solve the problem of discontinuities, model correction residuals, and noise in clock estimation, and significantly improve the performance of multi-GNSS time and frequency transfer.

Ionosphere-Constrained Triple-Frequency Cycle Slip Fixing Method for the Rapid Re-Initialization of PPP.

The re-initialization of precise point positioning (PPP) can be avoided by cycle slip detection and correction. Ionospheric delay is critical for cycle slip detection and correction, especially for a long data gap. The frequency diversity from GNSS modernization provides the potential for mitigating the impact of ionospheric delay on cycle slip detection and correction. The proposed method constructs the extra-wide lane (EWL), the wide lane (WL), and the narrow lane (NL) epoch-differenced linear combinations based on the ionosphere constrain criterion, so as to determine the undifferenced cycle slips from the cascading ambiguity resolution. The experiment results show that the cycle slips can be fixed correctly even though cycle slips occur in all the available carrier phase observations, and the 3 min data gaps can be merged without high precision positioning continuity loss. The kinematic experiment shows that the instantaneous re-initialization can be achieved with the proposed method.

A high-order and interface-preserving discontinuous Galerkin method for level-set reinitialization

A high-order numerical method for interface-preserving level-set reinitialization is presented in this paper. In the interface cells, the gradient of the level-set function is determined by a weighted local projection scheme and the missing additive constant is determined such that the position of the zero level set is preserved. In the non-interface cells, we compute the gradient of the level-set function by solving a Hamilton–Jacobi equation as a conservation law system using the discontinuous Galerkin method, following the work by Hu and Shu [SIAM J. Sci. Comput. 21 (1999) 660–690]; the missing constant is then recovered by the continuity of the level-set function while taking into account the characteristics. To handle highly distorted initial conditions, we develop a hybrid numerical flux that combines the Lax–Friedrichs flux and the penalty flux. Our method is stable for non-trivial test cases and handles singularities away from the interface very well. When derivative singularities are present on the interface, a second-derivative limiter is designed to suppress the oscillations. At least (N+1)th order accuracy in the interface cells and Nth order in the whole domain are observed for smooth solutions when Nth degree polynomials are used. Two dimensional test cases are presented to demonstrate superior properties such as accuracy, long-term stability, interface-preserving capability, and easy treatment of contact lines. We also show some preliminary results on the pinch-off process of a pendant drop, where topological changes of the fluid interface are involved. Our method is readily extendable to three dimensions and adaptive meshes.

Protein folding optimization using differential evolution extended with local search and component reinitialization

This paper presents a novel Differential Evolution algorithm for protein folding optimization that is applied to a three-dimensional AB off-lattice model. The proposed algorithm includes two new mechanisms. A local search is used to improve convergence speed and to reduce the runtime complexity of the energy calculation. For this purpose, a local movement is introduced within the local search. The designed evolutionary algorithm has fast convergence speed and, therefore, when it is trapped into the local optimum or a relatively good solution is located, it is hard to locate a better similar solution. The similar solution is different from the good solution in only a few components. A component reinitialization method is designed to mitigate this problem. Both the new mechanisms and the proposed algorithm were analyzed on well-known amino acid sequences that are used frequently in the literature. Experimental results show that the employed new mechanisms improve the efficiency of our algorithm and that the proposed algorithm is superior to other state-of-the-art algorithms. It obtained a hit ratio of 100% for sequences up to 18 monomers, within a budget of 1011 solution evaluations. New best-known solutions were obtained for most of the sequences. The existence of the symmetric best-known solutions is also demonstrated in the paper.

On the re-initialization of fluid interfaces in diffuse interface method

In this paper, a re-initialization method of fluid interfaces is introduced into the Cahn–Hilliard (C–H) model for reducing the continuous numerical and modeling diffusion in the simulation of complex multiphase flows. In this method, the re-initialization process is achieved through three simple steps. Specifically, the order parameter C obtained from the C–H model is first transformed into a roughly estimated distance function d. After that, correction of d is made by using the Hamilton–Jacobian equation. Finally, reconstruction of the fluid interface is then performed to enforce the profile of C to its equilibrium state. The re-initialization of the fluid interface is only required to be implemented occasionally so that the overall computational efficiency is comparable with the original C–H model. In addition to the phase field, the flow field is solved by using the recently-proposed multiphase lattice Boltzmann flux solver. Numerical validations of the proposed method have been carried out by simulating a shearing droplet, Rayleigh–Taylor instability of binary-layered fluids and droplet splashing on a thin film. Good agreements have been achieved with the data published in the literature. The obtained results also show that the present method not only reduces numerical and modeling diffusion substantially but also is able to accurately capture small yet important interfacial structures, such as entrapped air bubbles for the droplet splashing on a thin film.

An Improved Adaptive Level Set Method for Image Segmentation

In order to improve the accuracy of image segmentation, an improved adaptive level set method is proposed based on level set evolution without re-initialization method and adaptive distance preserving level set evolution method. A new definition of weight coefficient in evolution equations is the main innovation of this paper. The improved method can detect certain object boundaries, interior and exterior contours of an object, edges of multi-objects and weak boundaries of an object by synthetic and real images numerical experiments. Numerical results show that the improved adaptive level set method has faster segmentation speed and higher segmentation accuracy compared with the previous two methods, especially in weak boundaries and edges of multi-objects segmentation problems.