Fast Iteration Research Articles

Fast iterative position estimation method of permanent magnet synchronous linear motor

Abstract In this paper, a Fast Iterative Phase Estimation (FIPE) algorithm is proposed for robotic grinding force control devices using Permanent Magnet Synchronous Linear Motors (PMSLM), requiring high positional accuracy and response speed. FIPE obtains optimal phase estimates through iterative searching and exhibit excellent dynamic performance. The proposed method is optimized by iterative optimization algorithms, which significantly reduces the number of iterations and enhance the response speed of position estimation. In addition, the effects of harmonics, amplitude mismatch, noise and DC errors in the raw signal on the FIPE algorithm are discussed, and a shallow neural network is introduced to compensate the periodic errors. The experimental results show that the FIPE algorithm has higher calculation accuracy and dynamic performance than the traditional PLL such as SRF-PLL, ANF-PLL and LKF-PLL, and has faster iteration speed than the traditional position estimator such as FPS-PLL.

Bi-Level Game Strategy for Virtual Power Plants Based on an Improved Reinforcement Learning Algorithm

To address the issue of economic dispatch imbalance in virtual power plant (VPP) systems caused by the influence of operators and distribution networks, this study introduces an optimized economic dispatch method based on bi-level game theory. Firstly, a bi-level game model is formulated, which integrates the operational and environmental expenses of VPPs with the revenues of system operators. To avoid local optima during the search process, an enhanced reinforcement learning algorithm is developed to achieve rapid convergence and obtain the optimal solution. Finally, case analyses illustrate that the proposed method effectively accomplishes multi-objective optimization for various decision-making stakeholders, including VPP and system operators, while significantly reducing curtailment costs associated with the extensive integration of distributed renewable energy. Furthermore, the proposed algorithm achieves fast iteration and yields superior dispatch outcomes under the same modeling conditions.

Airborne Multi-Channel Forward-Looking Radar Super-Resolution Imaging Using Improved Fast Iterative Interpolated Beamforming Algorithm

Radar forward-looking imaging is critical in many civil and military fields, such as aircraft landing, autonomous driving, and geological exploration. Although the super-resolution forward-looking imaging algorithm based on spectral estimation has the potential to discriminate multiple targets within the same beam, the estimation of the angle and magnitude of the targets are not accurate due to the influence of sidelobe leakage. This paper proposes a multi-channel super-resolution forward-looking imaging algorithm based on the improved Fast Iterative Interpolated Beamforming (FIIB) algorithm to solve the problem. First, the number of targets and the coarse estimates of angle and magnitude are obtained from the iterative adaptive approach (IAA). Then, the accurate estimates of angle and magnitude are achieved by the strategy of iterative interpolation and leakage subtraction in FIIB. Finally, a high-resolution forward-looking image is obtained through non-coherent accumulation. The simulation results of point targets and scenes show that the proposed algorithm can distinguish multiple targets in the same beam, effectively improve the azimuthal resolution of forward-looking imaging, and attain the accurate reconstruction of point targets and the contour reconstruction of extended targets.

Innovative Design and Fast Iterative Manufacturing of Micro-Turbojet Engines at Low Cost

The turbojet is the foundation of the gas turbine engine, which combines knowledge of fluid dynamics, thermodynamics, heat transfer, mechanical design, manufacturing, and jet propulsion. This paper describes in detail the inspiration source, design process, fluid dynamics analysis, manufacturing steps, ignition test method and test process of small jet engines. 3D modeling software SolidWorks was used for the preliminary modeling and fluid simulation was performed to optimize the design. Part of the model was produced by 3D resin printing technology, and the turbine engine model was driven by compressed air for no-load testing to verify the feasibility and structural stability of the resin model. Subsequently, some parts were printed with metal materials, combined with traditional machining techniques to complete production and assembly, and finally successfully carried out static ignition tests. The micro turbojet engine designed in this paper has successfully achieved static ignition test and demonstrated the feasibility of efficient development with limited resources and low manufacturing cost. This exploration proves the feasibility of rapid iteration technology route. The market prospect and manufacturing cost of micro turbojet are also analyzed.

3D-printed microfluidic and millifluidic devices for cell culture and mechanotransduction studies: A review

The practice of conventional cell culture techniques affects the quality of biological research in mechanobiology by not replicating the cell’s microenvironment adequately. Microfluidic devices resembling 3D in vivo cell culture offer viable alternatives for mechanobiology studies and other applications including cell screening, cell separation, and point-of-care diagnostics. This review highlights recent advances in additive manufacturing (AM) to fabricate microfluidic and millifluidic devices through fast design iterations and cost reduction compared with conventional device manufacturing. Key advances in AM technologies such as fused deposition modeling (FDM), stereolithography apparatus (SLA), and digital light processing (DLP) have allowed the direct manufacture of complex microfluidic devices with master molds and sacrificial structures using multiple material components. Currently, available 3D printed devices for the precise fabrication of milli- and microfluidic devices, essential to mimicking the cellular microenvironment, while economically reducing production costs by eliminating expensive clean-room environments and reducing material waste are highlighted.

Fast and Unconditional Convergent MRMHSS Iteration Method for Solving Complex Symmetric Linear Systems

Fast and Unconditional Convergent MRMHSS Iteration Method for Solving Complex Symmetric Linear Systems

A novel multi-layer discriminative dictionary learning approach for image classification

Discriminative dictionary learning (DDL) has been confirmed to be effective for image classification. However, existing DDL approaches often fail to extract deep hierarchical information due to the single-layer dictionary learning framework. Moreover, they overlook the atoms-label information in the dictionary, leading to reduced feature distinctiveness and lower classification accuracy. To overcome the above problem, a novel DDL method, called the Multi-layer local constraint and label embedding dictionary learning (M-LCLEDL), is proposed. Specifically, the novel multi-layer DDL framework, which is formed by stacking the DL process one by one, is designed to learn the deep hierarchical and nonlinear features. The layer-by-layer stacking of the DL process in the multi-layer DDL framework allows for the elimination of redundant and interfering features. This step-by-step elimination process enhances the stability and robustness of the framework. Additionally, to leverage the label information carried by the labeled training samples, atoms with label embedding and locality structure are introduced. The proposed approach includes a fast iteration strategy for efficient optimization. Experimental results demonstrate that the approach is relatively insensitive to dictionary size, achieving promising performance and greater stability compared to most DDL-based image classification approaches.

Digital Twin-Enhanced Control for Fuel Cell and Lithium-Ion Battery Hybrid Vehicles

With the development of lithium-ion batteries and fuel cells, the application of hybrid power systems is becoming more and more widespread. To better optimize the energy management problem of fuel cell hybrid systems, the accuracy of system modeling and simulation is very important. The hybrid system is formed by connecting the battery to the fuel cell through an active topology. Digital twin technology is applicable to the mapping of physical entities to each other with high interactivity and fast optimization iterations. In this paper, a relevant model based on mathematical logic is established by collecting actual operational data; subsequently, the accuracy of the model is verified by combining relevant operating conditions and simulating the model. Subsequently, a three-dimensional visualization model of a hybrid power system-based sightseeing vehicle and its operating environment was established using digital twin technology to improve the model simulation of the fuel cell hybrid power system. At low speeds, the simulation results of the hybrid power system-based sightseeing vehicle have a small error compared with the actual running state, and the accuracy of the data related to each internal subcomponent is high. In the simple interaction between the model display vehicle and the environment, the communication state can meet the basic requirements of the digital twin model because the amount of data to be transferred is small. This study makes a preliminary attempt at digital parallelism by combining mathematical logic with visualization models and can be used as a basis for the subsequent development of more mature digital twin models.

An absolutely convergent fixed-point fast sweeping WENO method on triangular meshes for steady state of hyperbolic conservation laws

High order accuracy fast sweeping methods have been developed in the literature to efficiently solve steady state solutions of hyperbolic partial differential equations (PDEs) on rectangular meshes, while they were not available yet on unstructured meshes. In this paper, we extend high order accuracy fast sweeping methods to unstructured triangular meshes by applying fixed-point iterative sweeping techniques to a fifth-order finite volume unstructured WENO scheme, for solving steady state solutions of hyperbolic conservation laws. Similar as other fast sweeping methods, fixed-point fast sweeping methods use the Gauss-Seidel iterations and alternating sweeping strategy to cover characteristics of hyperbolic PDEs in each sweeping order to achieve fast convergence rate to steady state solutions. An advantage of fixed-point fast sweeping methods which distinguishes them from other fast sweeping methods is that they are explicit and do not require inverse operation of nonlinear local systems. This also provides certain convenience in designing high order fast sweeping methods on unstructured meshes. As in the first order fast sweeping methods on triangular meshes, we introduce multiple reference points to determine alternating sweeping directions on unstructured meshes. All the cells on the mesh are ordered according to their centroids' distances to those reference points, and the resulted orderings provide sweeping directions for iterations. To make the residue of the fast sweeping iterations converge to machine zero / round off errors, we follow the approach in our early work of developing the absolutely convergent fixed-point fast sweeping WENO methods on rectangular meshes, and adopt high order WENO scheme with unequal-sized sub-stencils, specifically here a fifth-order finite volume unstructured WENO scheme for spatial discretization. Extensive numerical experiments, including problems with complex domain geometries, are performed to show the accuracy, computational efficiency, and absolute convergence of the presented fifth-order fast sweeping scheme on triangular meshes. Furthermore, the proposed method is compared with the forward Euler time marching method and the popular third order total variation diminishing Rung-Kutta (TVD-RK3) time-marching method for steady state computations. Numerical examples show that the developed fixed-point fast sweeping WENO method is the most efficient scheme among them, and especially it can save up to 70% CPU time costs than TVD-RK3 in order to converge to steady state solutions.

A goal-conditioned policy search method with multi-timescale value function tuning

PurposeCurrent reinforcement learning (RL) algorithms are facing issues such as low learning efficiency and poor generalization performance, which significantly limit their practical application in real robots. This paper aims to adopt a hybrid model-based and model-free policy search method with multi-timescale value function tuning, aiming to allow robots to learn complex motion planning skills in multi-goal and multi-constraint environments with a few interactions.Design/methodology/approachA goal-conditioned model-based and model-free search method with multi-timescale value function tuning is proposed in this paper. First, the authors construct a multi-goal, multi-constrained policy optimization approach that fuses model-based policy optimization with goal-conditioned, model-free learning. Soft constraints on states and controls are applied to ensure fast and stable policy iteration. Second, an uncertainty-aware multi-timescale value function learning method is proposed, which constructs a multi-timescale value function network and adaptively chooses the value function planning timescales according to the value prediction uncertainty. It implicitly reduces the value representation complexity and improves the generalization performance of the policy.FindingsThe algorithm enables physical robots to learn generalized skills in real-world environments through a handful of trials. The simulation and experimental results show that the algorithm outperforms other relevant model-based and model-free RL algorithms.Originality/valueThis paper combines goal-conditioned RL and the model predictive path integral method into a unified model-based policy search framework, which improves the learning efficiency and policy optimality of motor skill learning in multi-goal and multi-constrained environments. An uncertainty-aware multi-timescale value function learning and selection method is proposed to overcome long horizon problems, improve optimal policy resolution and therefore enhance the generalization ability of goal-conditioned RL.

A Low-Cost 3D SLAM System Integration of Autonomous Exploration Based on Fast-ICP Enhanced LiDAR-Inertial Odometry

Advancements in robotics and mapping technology have spotlighted the development of Simultaneous Localization and Mapping (SLAM) systems as a key research area. However, the high cost of advanced SLAM systems poses a significant barrier to research and development in the field, while many low-cost SLAM systems, operating under resource constraints, fail to achieve high-precision real-time mapping and localization, rendering them unsuitable for practical applications. This paper introduces a cost-effective SLAM system design that maintains high performance while significantly reducing costs. Our approach utilizes economical components and efficient algorithms, addressing the high-cost barrier in the field. First, we developed a robust robotic platform based on a traditional four-wheeled vehicle structure, enhancing flexibility and load capacity. Then, we adapted the SLAM algorithm using the LiDAR-inertial Odometry framework coupled with the Fast Iterative Closest Point (ICP) algorithm to balance accuracy and real-time performance. Finally, we integrated the 3D multi-goal Rapidly exploring Random Tree (RRT) algorithm with Nonlinear Model Predictive Control (NMPC) for autonomous exploration in complex environments. Comprehensive experimental results confirm the system’s capability for real-time, autonomous navigation and mapping in intricate indoor settings, rivaling more expensive SLAM systems in accuracy and efficiency at a lower cost. Our research results are published as open access, facilitating greater accessibility and collaboration.

Fast simulation for interacting four-level Rydberg atoms: electromagnetically induced transparency and Autler-Townes splitting

Quantum sensing using Rydberg atoms is an emerging technology for precise measurement of electric fields. However, most existing computational methods are all based on a single-particle model and neglect Rydberg-Rydberg interaction between atoms. In this study, we introduce the interaction term into the conventional four-level optical Bloch equations. By incorporating fast iterations and solving for the steady-state solution efficiently, we avoid the computation of a massive 4 N × 4 N dimensional matrix. Additionally, we apply the Doppler frequency shift to each atom used in the calculation, eliminating the requirement for an additional Doppler iteration. These schemes allow for the calculation of the interaction between 7000 atoms around one minute. Based on the many-body model, we investigate the Rydberg-Rydberg interaction of Rydberg atoms under different atomic densities. Furthermore, we compare our results with the literature data of a three-level system and the experimental results of our own four-level system. The results demonstrate the validity of our model, with an effective error of 4.59% compared to the experimental data. Finally, we discover that the many-body model better predicts the linear range for measuring electric fields than the single-particle model, making it highly applicable in precise electric field measurements.

Efficient knowledge model for whale optimization algorithm to solve large-scale problems

In the process of digital transformation and development in various industries, there are more and more large-scale optimization problems. Currently, swarm intelligence optimization algorithms are the best method to solve such problems. However, previous experimental research has found that there is still room for improvement in the performance of using existing swarm intelligence optimization algorithms to solve such problems. To obtain the high-precision optimal value of whale optimization algorithm (WOA) for solving large-scale optimization problems, the optimization problem knowledge model is studied to guide the iterative process of WOA algorithm, and a novel whale optimization algorithm based on knowledge model guidance (KMGWOA) is proposed. First, a population update strategy based on multiple elite individuals is proposed to reduce the impact of the local optimal values, and the knowledge model to guide population update is constructed by combining the proposed population update strategy with the population update strategy based on global optimal individual. Second, a collaborative reverse learning knowledge model with multiple elite and poor individuals in the solution space is proposed to prevent long-term non-ideal region search. The above two knowledge models guide the iterative process of WOA algorithm in solving large-scale optimization problems. The performance of the KMGWOA algorithm guided by the proposed knowledge models is tested through the well-known classical test functions. The results demonstrate that the proposed KMGWOA algorithm not only has good search ability for the theoretical optimal value, but also achieves higher accuracy in obtaining the optimal value when it is difficult to obtain the theoretical optimal value. Moreover, KMGWOA algorithm has fast convergence speed and high effective iteration percentage.

Parametric Design Method and Lift/Drag Characteristics Analysis for a Wide-Range, Wing-Morphing Glide Vehicle

The parametric design method is widely utilized in the preliminary design stage for hypersonic vehicles; it ensures the fast iteration of configuration, generation, and optimization. This study proposes a novel parametric method for a wide-range, wing-morphing glide vehicle. The whole configuration, including a waverider fuselage, a rotating wing, a blunt leading edge, rudders, etc., can be easily described using 27 key parameters. In contrast to the typical parametric method, the new method takes internal payloads into consideration during the shape optimization process. That is, the vehicle configuration can be flexibly adjusted depending on the internal payloads; these payloads may be of random amounts and have different shapes. The code for the new parametric design method is developed using the secondary development tools of UG (UG 10.0) commercial software. The lift and drag characteristics over a wide operational range (H = 6–25 km, M = 2.5–8.5, AOA = 0–10°) were numerically investigated, as was the influence of the retracting angle of the morphing wings. It was found that, for the mode of the fully deployed wings, the lift-to-drag ratio (L/D) remained at a high level (≥4.7) over a Mach range of 4.0–8.5 and an AOA range of 4–7°. For the mode of the fully retracted wings, the drag coefficient remained smaller than 0.02 over a Mach range of 4.0–8.5 and an AOA range of 0–5°. A wide L/D of 0.3–4.7 could be achieved by controlling the retracting angle of the wings, thus demonstrating a good potential for flight maneuverability. The flexible change in L/D proved to be a combined result of varying pressure distribution and edge-flow spillage. This will aid in the further optimization of lift/drag characteristics.

Ultracompact wavefront characterization of femtosecond 3D printed microlenses using double-frequency Ronchi interferometry.

3D printed microoptics have become important tools for miniature endoscopy, novel CMOS-based on-chip sensors, OCT-fibers, among others. Until now, only image quality and spot diagrams were available for optical characterization. Here, we introduce Ronchi interferometry as ultracompact and quick quantitative analysis method for measuring the wavefront aberrations after propagating coherent light through the 3D printed miniature optics. We compare surface shapes by 3D confocal microscopy with optical characterizations by Ronchi interferograms. Phase retrieval gives us the transversal wave front aberration map, which indicates that the aberrations of our microlenses that have been printed with a Nanoscribe GT or Quantum X printer exhibit RMS wavefront aberrations as small as λ/20, Strehl ratios larger than 0.91, and near-diffraction limited modulation transfer functions. Our method will be crucial for future developments of 3D printed microoptics, as the method is ultracompact, ultra-stable, and very fast regarding measurement and evaluation. It could fit directly into a 3D printer and allows for in-situ measurements right after printing as well as fast iterations for improving the shape of the optical surface.

Design of fast braiding process based on reconstructing mesh with complex and irregular mandrels

Circular braiding of large-sized and complex irregular structural components is widely used in fields such as aerospace and rail transit. However, existing braiding process design and simulation algorithms lack applicability to mandrels with complex features, such as flanges, concave corners, holes, and hollows, which also have low calculation efficiency in the braiding process simulation. This study first proposes an optimization algorithm for the complex features on the mandrel surface, improving the applicability for the braiding process design of complex and irregular mandrels. Then the mandrel mesh was reconstructed, greatly improving the computational efficiency of the braiding process design process. Finally, a fast iteration method based on the mandrel reconstructed mesh was proposed to achieve the fast calculation of process control parameters, including traction trajectory and traction speed, as well as the prediction of forming quality for the braiding angle and coverage rate. The experiment shows that the algorithm improves the applicability of the multi-braiding angle process and shortens the sampling cycle of braiding process design by more than 60%.

Fast multi-level iteration schemes with compression technique for eigen-problems of compact integral operators

In this paper, we present multi-level iteration schemes to solve the eigen-problems of compact integral operators based on the multiscale Galerkin methods. By using the compression technique, the wavelet coefficient matrix is truncated into sparse. This technique results in a fast algorithm. To further minimize computational complexity, we establish Jacobi, Gauss-Seidel and L-H iteration schemes for solving the final algebraic eigenvalue problems obtained from discretizing the integral equation using fast multiscale Galerkin methods. We show that the proposed schemes require only linear computational complexity and obtain the desired convergence order for the approximate eigenvalues and eigenvectors. Numerical experiments are conducted and analyzed, with comparisons to the standard routine and the power iteration algorithm included. Ultimately, our multi-level iteration schemes yield faster solutions with fewer iterations, which is independent of the dimension of approximate space.

Development and application of a predictive model for advanced tokamak scenario design

Advanced tokamak (AT) scenarios applying additional heating during the current ramp (early-heating) usually require many iterations if developed fully empirically. To reduce the required experimental time, a model has been developed in the ASTRA framework, capable of doing predictive simulations of the relevant parameters. As scenario development requires fast iterations and inter-discharge runs, a sufficiently short run-time is required. While using a simplified transport model to achieve this, comparisons to experimental data from ASDEX-Upgrade (AUG) still show good agreement. Using this model, a new high performance early-heating AT scenario has been developed and successfully run on AUG with the results matching the predictions.

Fast Coupling Iterative Algorithm for Media Rough Surface with Multiple Targets Below

This paper proposes a fast mutual coupling iteration algorithm for analyzing the composite electromagnetic scattering characteristics of a Gaussian rough surface with multiple targets below it. Firstly, an electromagnetic scattering model of Gaussian rough surface with multiple targets underneath is established. To improve the efficiency of electromagnetic scattering calculations, the (banded matrix iterative approach canonical grid - conjugate gradient method (BMIA/CAG-CMG) algorithm is used to calculate the surface scattering of the rough surface, while for the scattering of the targets, the traditional numerical algorithm moment of methods (MoM) is used for the calculation. Its main acceleration principle is to decompose the matrix into banded and Toeplitz matrices during the solution process, and then use fast Fourier transform (FFT) for fast solution based on their characteristics. By comparing the calculation results of this algorithm with those of the MoM algorithm, the correctness of this algorithm is verified. Several examples are studied, and the impact of different parameters on the surface current and composite scattering characteristics is discussed, which have important significance for deepening the understanding of scattering characteristics in complex electromagnetic environments.

Wasserstein Dictionaries of Persistence Diagrams.

This article presents a computational framework for the concise encoding of an ensemble of persistence diagrams, in the form of weighted Wasserstein barycenters Turner et al. (2014), Vidal et al. (2020) of a dictionary of atom diagrams. We introduce a multi-scale gradient descent approach for the efficient resolution of the corresponding minimization problem, which interleaves the optimization of the barycenter weights with the optimization of the atom diagrams. Our approach leverages the analytic expressions for the gradient of both sub-problems to ensure fast iterations and it additionally exploits shared-memory parallelism. Extensive experiments on public ensembles demonstrate the efficiency of our approach, with Wasserstein dictionary computations in the orders of minutes for the largest examples. We show the utility of our contributions in two applications. First, we apply Wassserstein dictionaries to data reduction and reliably compress persistence diagrams by concisely representing them with their weights in the dictionary. Second, we present a dimensionality reduction framework based on a Wasserstein dictionary defined with a small number of atoms (typically three) and encode the dictionary as a low dimensional simplex embedded in a visual space (typically in 2D). In both applications, quantitative experiments assess the relevance of our framework. Finally, we provide a C++ implementation that can be used to reproduce our results.