Non-iterative Method Research Articles

Deep Plug-and-Play Non-Iterative Cluster for 3D Global Feature Extraction

Efficient and accurate point cloud feature extraction is crucial for critical tasks such as 3D recognition and semantic segmentation. However, existing global feature extraction methods for 3D data often require designing different models for different input types (point clouds, voxels, and maps). This article proposes an efficient plug-and-play non-iterative clustering method (NICM) to establish a unified point cloud global feature extraction paradigm suitable for any input type to solve the above problems. The core idea of the NICM is to construct the connection between a single point and other global points based only on the cosine similarity between center points to achieve global feature extraction, which has linear complexity characteristics and can be combined with any existing feature extraction model. Additionally, to better integrate the features extracted by NICM and the original model, this article designs an adaptive feature fusion module is designed based on the gate unit, which retains similar features and effectively fuses dissimilar features based on their importance to downstream tasks. We have applied our method to downstream tasks such as point cloud recognition, part segmentation and scene segmentation. Sufficient experiments have proven that our method can provide comprehensive and robust features for the original model, and effectively improve the performance of downstream tasks.

Nonlinear dynamics of continuous steady-state tunable mechanical metamaterials based on planetary gears

Mechanical metamaterials with tunable mechanical properties have received extensive attention. In this work, a continuous steady-state metamaterial with tunable mechanical based on planetary gear cells is designed. Firstly, the excellent tunable properties of metamaterials are studied, which shows the designed metamaterials has a wide range of programmable stiffness and a significantly tunning band gap. The configuration relationship between tunability and structural parameters is analyzed. Then, a rigid-flexible coupling nonlinear dynamic model of planetary gear metamaterials is established and the dynamic characteristics of planetary gear metamaterials in the process of dynamic tunning are explored by numerical simulation. Moreover, the distribution characteristics of the system vibration response with the coupling parameter plane are disclosed, and the influence of the system dynamic parameters on its bifurcation characteristics and stability under coupling excitation is explored. Furthermore, an improved non-iterative cell mapping method is used to analyze the dependence of the vibration response of the system on the initial conditions. This work shows that the planetary gear metamaterials have a wide range of stiffness tunability and significantly variable band gap interval. Under the influences of internal and external excitations, the system exhibits various dynamic characteristics. The dynamic characteristics of flexible construction are mainly affected by structural parameters, and the dynamic characteristics of rigid construction are affected by multiple sets of coupling parameters and initial conditions.

A long-term power system state calculation method using sequential power and holomorphic embedding

Traditional Power Flow (PF) calculations struggle to support the data analysis requirements as the power system develops. Therefore, this paper introduces a non-sequential and non-iterative PF calculation method to enhance the computational speed and analytical efficiency of power system state variables over long time scales. Firstly, a Power Sequential Division (PSD) method is proposed, representing the power curve using hierarchical energy with temporal characteristics. Next, based on the Fast and Flexible Holomorphic Embedding (FFHE) method, the paper explores the incremental properties of Voltage Power Series Coefficients (VPSC) and proposes an Incremental Holomorphic Embedding (IHE) method. Finally, by combining PSD and IHE, this study introduces a Power Sequential Flow (PSPF) method for rapid computation of power system state variables over long time scales. Unlike traditional PF methods, PSPF method shifts away from time-series calculations and instead analyzes the distribution of power system state variables over long time scales from an energy perspective. Various test cases are set up to analyze the performance characteristics of IHE and PSPF from multiple perspectives. The results indicate that the proposed methods significantly outperform traditional PF methods in terms of computational speed, adaptability to test cases, and analytical efficiency.

A non-iterative compensation method for machining errors of thin-walled parts considering coupling effect of tool-workpiece deformation

Thin-walled parts have significant application value in the aerospace industry due to their high strength-to-weight ratio. However, their low rigidity makes them susceptible to cutting force-induced error, which may seriously affect their machining accuracy. Error compensation is an effective method for addressing this issue. However, the commonly used mirror compensation method can cause residual errors due to the coupling effect of the tool-workpiece deformation. The influence mechanism of this coupling effect on error compensation is analyzed through an iterative compensation method. This method efficiently reduces residual errors. However, its computation efficiency is insufficient to meet the requirements of real-time compensation. Therefore, a non-iterative compensation method is proposed to directly calculate the compensation values considering the coupling effect of the tool-workpiece deformation. Through the approximate invariance of the overall cutting coefficient matrix and the pre-given system parameter, the proposed method avoids the repeated calculation of cutting forces and improves the computation efficiency. Experiment results of milling thin-walled blades show that after compensation using the proposed method, the machining accuracy of the thin-walled blade has seen a further increase of 18.1% in comparison to the mirror compensation method. Moreover, the proposed method achieves comparable compensation accuracy to the iterative method with a 66% reduction in computation time. The proposed method has significant potential for real-time compensation in the machining of complex 5-axis thin-walled parts.

Evaluation of finite element modeling methods for predicting compression screw failure in a custom pelvic implant.

Introduction: Three-dimensional (3D)-printed custom pelvic implants have become a clinically viable option for patients undergoing pelvic cancer surgery with resection of the hip joint. However, increased clinical utilization has also necessitated improved implant durability, especially with regard to the compression screws used to secure the implant to remaining pelvic bone. This study evaluated six different finite element (FE) screw modeling methods for predicting compression screw pullout and fatigue failure in a custom pelvic implant secured to bone using nine compression screws. Methods: Three modeling methods (tied constraints (TIE), bolt load with constant force (BL-CF), and bolt load with constant length (BL-CL)) generated screw axial forces using functionality built into Abaqus FE software; while the remaining three modeling methods (isotropic pseudo-thermal field (ISO), orthotropic pseudo-thermal field (ORT), and equal-and-opposite force field (FOR)) generated screw axial forces using iterative physics-based relationships that can be implemented in any FE software. The ability of all six modeling methods to match specified screw pretension forces and predict screw pullout and fatigue failure was evaluated using an FE model of a custom pelvic implant with total hip replacement. The applied hip contact forces in the FE model were estimated at two locations in a gait cycle. For each of the nine screws in the custom implant FE model, likelihood of screw pullout failure was predicted using maximum screw axial force, while likelihood of screw fatigue failure was predicted using maximum von Mises stress. Results: The three iterative physics-based modeling methods and the non-iterative Abaqus BL-CL method produced nearly identical predictions for likelihood of screw pullout and fatigue failure, while the other two built-in Abaqus modeling methods yielded vastly different predictions. However, the Abaqus BL-CL method required the least computation time, largely because an iterative process was not needed to induce specified screw pretension forces. Of the three iterative methods, FOR required the fewest iterations and thus the least computation time. Discussion: These findings suggest that the BL-CL screw modeling method is the best option when Abaqus is used for predicting screw pullout and fatigue failure in custom pelvis prostheses, while the iterative physics-based FOR method is the best option if FE software other than Abaqus is used.

Accurate Ultrasonic Thickness Measurement for Arbitrary Time-Variant Thermal Profile.

Ultrasonic thickness measurement of mechanical structures is one of the most popular and commonly used nondestructive methods for various kinds of process control and corrosion monitoring. With ultrasonic propagation speed being temperature-dependent, the thickness measurement can be performed reliably only when the thermal profile is completely known. Most conventional techniques assume the temperature of the test structure is uniform and at room temperature across its thickness. Such assumptions may lead to large errors in the thickness measurement, especially when there are significant temperature variations across the thickness. State-of-the-art techniques use external temperature measurements or implement iterative methods to compensate for the unknown thermal profiles. However, such techniques produce unsatisfactory results when the heat distribution is complex or varies rapidly with time. In this work, we propose a two-sensors technique, using both compressive and shear excitations, with a non-iterative rapid data processing method for accurate thickness measurement under arbitrary time-variant thermal profile. The independent behavior of shear and compressive waves is used to formulate a real-time thickness estimation technique. The developed technique is experimentally validated on a steel plate with fixed acoustic sensors. Test results show that the error in thickness estimation can be reduced by up to 98% compared to conventional thickness gauging methods.

Best Scanline Determination of Pushbroom Images for a Direct Object to Image Space Transformation Using Multilayer Perceptron

Working with pushbroom imagery in photogrammetry and remote sensing presents a fundamental challenge in object-to-image space transformation. For this transformation, accurate estimation of Exterior Orientation Parameters (EOPs) for each scanline is required. To tackle this challenge, Best Scanline Search or Determination (BSS/BSD) methods have been developed. However, the current BSS/BSD methods are not efficient for real-time applications due to their complex procedures and interpolations. This paper introduces a new non-iterative BSD method specifically designed for line-type pushbroom images. The method involves simulating a pair of sets of points, Simulated Control Points (SCOPs), and Simulated Check Points (SCPs), to train and test a Multilayer Perceptron (MLP) model. The model establishes a strong relationship between object and image spaces, enabling a direct transformation and determination of best scanlines. This proposed method does not rely on the Collinearity Equation (CE) or iterative search. After training, the MLP model is applied to the SCPs for accuracy assessment. The proposed method is tested on ten images with diverse landscapes captured by eight sensors, exploiting five million SCPs per image for statistical assessments. The Root Mean Square Error (RMSE) values range between 0.001 and 0.015 pixels across ten images, demonstrating the capability of achieving the desired sub-pixel accuracy within a few seconds. The proposed method is compared with conventional and state-of-the-art BSS/BSD methods, indicating its higher applicability regarding accuracy and computational efficiency. These results position the proposed BSD method as a practical solution for transforming object-to-image space, especially for real-time applications.

Development and Application of a Novel Non-Iterative Balancing Method for Hydronic Systems

The improvement of efficiency in new and existing buildings is one of the key aspects in achieving the climate change targets promoted by international regulatory and technical bodies, and among the measures that deserve renewed attention is the balancing of hydronic systems. However, the balancing procedures currently applied have not been updated for decades and are still largely unimplemented, as they are mainly based on cumbersome and iterative procedures. This paper deals with the proposal and advanced adaptation of a non-iterative balancing method previously developed for air systems, known as the progressive flow method (PFM). The application to water systems of the PFM’s concepts includes some aspects of an existing empirical method called the compensated method (CM) and overcomes its main limitations; moreover, the original PFM has been radically rethought in its implementation aspects, taking advantage of the tightness of water distribution systems, minimising instrumentation and the number of measurement operations, to definitively overcome the iterative nature of the currently applied methods. Experimental validation was carried out. Compared with a standard method, the enhanced PFM reduced the number of measurements by 48% and the number of balancing operations by 41%, achieving final flow rates within tolerances and the same configuration of balancing devices.

Fatigue life prediction of corroded hangers with parallel steel wire in a network arch bridge under vehicle-bridge interaction based on a novel noniterative simplified method

Fatigue damage is a main concern during the life-cycle maintenance of bridges with corroded hangers under vehicle-bridge interaction (VBI). In this study, a novel noniterative simplified method for VBI is proposed to analyze the responses of hangers in a network arch bridge, considering the effects of vehicle types, driving lanes, and road surface roughness. Furthermore, the fatigue life of corroded hangers is investigated, considering the actual traffic flow and uneven distribution of stress in the hanger’s cross-section. The results show that longitudinal and transverse bending moments of the hanger significantly affect the stress of steel wires in one hanger but have limited influence on stress amplitudes. The fatigue lives of steel wires are considerably reduced when subjected to both uniform and pitting corrosion. The fatigue life of steel wires is reduced under deteriorating road surface conditions. Moreover, as the fatigue life of steel wires in the hangers declines, the variations in fatigue life among individual steel wires within a single hanger gradually decrease. Consequently, this trend leads to a heightened likelihood of continuous wire breakage.

Full-angle single-shot quantitative phase imaging based on Kramers-Kronig relations.

As a non-interference and non-iterative method, annular-illumination quantitative phase imaging based on Kramers-Kronig relations (AIKK) can realize phase measurement with full-angle resolution enhancement under multiple exposures. In order to completely record the object spectrum with a single shot, we proposed a colorful complementary illumination method in the recording process. The angle of this illumination mode is not symmetrical with each other, so the spectrum between the three channels can complement each other to avoid spectrum loss caused by spectrum conjugation. Meanwhile, the three spectral segments of full-angle information spectrum respectively carried by three wavelengths can be recorded. Additionally, the numerical filter is applied to correct the overlapped spectrum in the reconstruction process. Simulation and experimental results show that this method can achieve high spatiotemporal resolution quantitative phase measurement.

A Non-Gradient and Non-Iterative Method for Mapping 3D Mesh Objects Based on a Summation of Dependent Random Values

A Non-Gradient and Non-Iterative Method for Mapping 3D Mesh Objects Based on a Summation of Dependent Random Values

A Noniterative Supervised On-Chip Training Circuitry for Reservoir Computing Systems.

Artificial neural networks (ANNs) is an exponentially growing field, mainly because of its wide range of applications to everyday life such as pattern recognition or time series forecasting. In particular, reservoir computing (RC) arises as an optimal computational framework suited for temporal/sequential data analysis. The direct on-silicon implementation of RCs may help to minimize power and maximize processing speed, that is especially relevant in edge intelligence applications where energy storage is considerably restricted. Nevertheless, most of the RC hardware solutions present in the literature perform the training process off-chip at the server level, thus increasing processing time and overall power dissipation. Some studies integrate both learning and inference on the same chip, although these works are normally oriented to implement unsupervised learning (UL) [with a lower expected accuracy than supervised learning (SL)], or propose iterative solutions (with a subsequent higher power consumption). Therefore, the integration of RC systems including both inference and a fast noniterative SL method is still an incipient field. In this article, we propose a noniterative SL methodology for RC systems that can be implemented on hardware either sequentially or fully parallel. The proposal presents a considerable advantage in terms of energy efficiency (EE) and processing speed if compared to traditional off-chip methods. In order to prove the validity of the model, a cyclic echo state NN with on-chip learning capabilities for time series prediction has been implemented and tested in a field-programmable gate array (FPGA). Also, a low-cost audio processing method is proposed that may be used to optimize the sound preprocessing steps.

Efficient non-iterative multi-point method for solving the Riemann problem

Efficient non-iterative multi-point method for solving the Riemann problem

Non-iterative generation of binary amplitude holograms applied to holographic projection with digital micromirror devices

In this work, we implement a fast non-iterative method for the generation of binary amplitude Fresnel holograms and demonstrate their application in a holographic projection scheme based on a digital micromirror device. To achieve this, we perform the binarization of phase-only holograms generated using an optimized Fresnel random phase. We analyze the quality of the resulting binary amplitude holograms and compare them with holograms obtained with the conventional iterative Fresnel algorithm as a function of the number of iterations and the propagation distance. Additionally, we evaluate the diffraction efficiency in both methods. We demonstrate that the holograms produced with our method present significantly advantages in computation speed without a significant reduction in the reconstruction quality. Both experimental and numerical results confirm the effectiveness of our proposal.

Tomography of nonlinear materials via the monotonicity principle

In this paper we present a first non-iterative imaging method for nonlinear materials, based on Monotonicity Principle. Specifically, we deal with the inverse obstacle problem, where the aim is to retrieve a nonlinear anomaly embedded in linear known background. The Monotonicity Principle (MP) is a general property for various class of PDEs, that has recently generalized to nonlinear elliptic PDEs. Basically, it states a monotone relation between the point-wise value of the unknown material property and the boundary measurements. It is at the foundation of a class of non-iterative imaging methods, characterized by a very low execution time that makes them ideal candidates for real-time applications. In this work, we develop an inversion method that overcomes some of the peculiar difficulties in practical application of MP to imaging of nonlinear materials, preserving the feasibility for real-time applications. For the sake of clarity, we focus on a specific application, i.e. the Magnetostatic Permeability Tomography where the goal is retrieving the unknown (nonlinear) permeability by boundary measurements in DC operations. This choice is motivated by applications in the inspection of boxes and containers for security. Reconstructions from simulated data prove the effectiveness of the presented method.

Imaging of conducting materials via the Kernel Method

In this work, we present a new non-iterative imaging method for Electrical Resistance Tomography (ERT). The problem in ERT is retrieving the spatial behaviour of the electrical conductivity by means of boundary measurements in steady-state conditions. Specifically, the interest is focused on the inverse obstacle problem, that consists in reconstructing the shape, position and dimension of one or more anomalies embedded in a known background. The proposed method, called Kernel Method, is based on the idea that if there exists a current density Jn that applied at the boundary ∂𝛺 of the domain under investigation 𝛺 produces the same scalar potential (on ∂𝛺), with and without anomalies, then the power density corresponding to Jn, evaluated on a configuration without anomalies, is vanishing in the region occupied by the latter. The proposed method has a very low computational cost. Indeed, the evaluation of the desired current density Jn on ∂𝛺 requires a negligible computational effort, and the reconstructions require only one forward problem.

A Non-Iterative Method for Design of Radio Frequency Energy Harvesters

A Non-Iterative Method for Design of Radio Frequency Energy Harvesters

A noniterative reconstruction method for the inverse potential problem for a time-fractional diffusion equation

This paper is concerned with the reconstruction of the support of the potential term for a time-fractional diffusion equation from the final measured data. The aim of this paper is to propose an accurate approach based on the topological derivative method. The idea is to formulate the reconstruction problem as a topology optimization one minimizing a given cost function. We derive a topological asymptotic expansion for the fractional model. The unknown support is reconstructed using the level-set curve of the topological gradient. We finally make some numerical examples proving the efficiency and accuracy of the proposed algorithm.

Non-iterative reconstruction of time-domain sound pressure and rapid prediction of large-scale sound field based on IG-DRBEM and POD-RBF

In this paper, a novel non-iterative reconstruction method of time-domain sound pressure and rapid prediction scheme of large-scale sound field are proposed respectively based on the isogeometric dual reciprocity boundary element method (IG-DRBEM) and proper orthogonal decomposition - radial basis functions (POD-RBF). By establishing the non-iterative inversion theory in the meaning of least squares, infinite domain acoustic holography is achieved with a small number of measured sound pressure information. Furthermore, the POD-RBF network is adopted to predict large-scale sound pressure. The presented work further expands the scope of application of IG-DRBEM, and gives full play to its advantage of not having to calculate the coefficient matrix repeatedly when analyzing the sound field at different moments. Moreover, in order to satisfy the integral regularization conditions, a suitable convergence condition about variation coefficients is investigated. Numerical results show that the proposed method has high accuracy and good stability even when the sound pressure of complex airship model is predicted with large-scale freedom.

RESEARCH ON LIDAR SLAM METHOD WITH FUSED POINT CLOUD INTENSITY INFORMATION

Abstract. Under the current trend of intelligence and automation, simultaneous positioning and mapping technology has become one of the research hotspots. The main problems of SLAM technology research are to improve the robustness of mapping and positioning, establish an efficient back-end optimization system, and improve the generalization of SLAM technology. This paper proposes to fuse the intensity information of the point cloud and the geometric information of the environment scene to construct a globally consistent environment feature descriptor and use the non-iterative two-step method to perform the nearest neighbour search on the point cloud in the point cloud registration stage. Then use the globally consistent descriptor that has been constructed to extract the laser point cloud descriptor by using the ring partition method, combine the method based on domain search to search for the closest point cloud frame, and finally use Intensity-ICP to complete the loopback frame. Fine registration, outputs the optimal pose transformation, to complete the loop detection. We use our self-built mobile platform to verify the robustness and generalization of the improved laser SLAM algorithm in public datasets and campus datasets. Experimental results show that the improved algorithm reduces the trajectory drift of the mobile platform and improves the efficiency of point cloud registration and loop closure detection.