High Computational Efficiency Research Articles

Position and orientation estimation method based on 3D digital morphology contour registration

Abstract Accurately and quickly obtaining the positions and orientations of mechanical parts based on the digital morphologies of mechanical parts are the key to achieving efficient and accurate assembly of mechanical parts. However, due to poor robustness and compactness in extracting digital morphology contours of mechanical parts, the accuracy of assembly positions and orientations obtained by using digital morphology contours cannot meet the requirements of high-precision assembly. Therefore, this paper proposes a position and orientation estimation method based on 3D digital morphology contour registration. This method extracts and optimizes digital morphology contours of mechanical parts and obtains the assembly positions and orientations by using an improved Iterative Closest Point (ICP) method to register the extracted digital morphology contours with those of the mechanical parts in assembly targets with desired positions and orientations. Experiments are conducted using mechanical parts from the ABC dataset and inertial confinement fusion micro-target. From the experimental results, when using the assembly positions and orientations obtained through the method proposed in this paper to assemble mechanical parts, it can achieve translation absolute errors of 8 μm, 5 μm, and 9 μm along the X-, Y-, and Z-axes, respectively. Similarly, the angular absolute errors in rotations around the Z-, Y-, and X-axes can be less than or equal to 0.16°, 0.15°, and 0.11°, respectively. The results prove that the proposed method in this paper exhibits high computational efficiency and accuracy, providing an effective approach for digital assembly of mechanical parts.

A Brain-Inspired Decision-Making method for upper limb exoskeleton based on Multi-Brain-Region structure and multimodal information fusion

For the challenges faced by upper limb exoskeleton in meeting human motion intention, this article proposes a novel brain-inspired decision-making model based on multi-brain-region information transmission mechanism and multimodal information fusion method to provide accurate prediction of motion trajectory. Firstly, a multimodal information perception and fusion method is proposed. Based on surface electromyographic (sEMG) signals and angle signals collected by sensors. Then a random forest algorithm is applied to screen the key features of sEMG signal for trajectory prediction. A multimodal information hierarchical fusion method based on dual-period mechanism is designed to solve the problem of fusion defect of information with different frequency. Finally, a multi-brain-region decision-making model based on hybrid hierarchical liquid state machine (HHLSM) is established to predict motion trajectory for the exoskeleton to meet human motion intention. Experiments show that the established model has a high computational efficiency and improves the effectiveness of exoskeleton assistance. The brain-inspired decision-making model based on HHLSM established in this paper is of great significance for further research on brain-inspired control and improving the human-robot interaction effect of upper limb exoskeleton.

A generalized approach for pricing american options under a regime-switching model

Abstract Regime-switching models gained their popularity over the past decade because of their distinctive advantage of modelling different financial market statuses in a discrete manner rather than a continuous manner as in stochastic volatility models. When they are used in option pricing, the clear advantage is that they enable a larger parameter space for models to be calibrated for a specific market dynamics and thus allow a better quantitative risk management in terms of utilizing financial derivatives. However, when they are used to price American-style financial derivatives, a large number of economic statuses result in a demand for improved computational efficiency. This paper provides a new algorithm of high computational efficiency supplemented with a theorem that pre-analyzes the associated matrices and their eigenvalues, without concern about the possibility of duplicated roots being mistakenly identified as simple roots due to rounding errors or the presence of two extremely closely positioned simple roots within the original system.

Hybrid Transformer and Convolution for Image Compressed Sensing

In recent years, deep unfolding networks (DUNs) have received widespread attention in the field of compressed sensing (CS) reconstruction due to their good interpretability and strong mapping capabilities. However, existing DUNs often improve the reconstruction effect at the expense of a large number of parameters, and there is the problem of information loss in long-distance feature transmission. Based on the above problems, we propose an unfolded network architecture that mixes Transformer and large kernel convolution to achieve sparse sampling and reconstruction of natural images, namely, a reconstruction network based on Transformer and convolution (TCR-Net). The Transformer framework has the inherent ability to capture global context through a self-attention mechanism, which can effectively solve the challenge of long-range dependence on features. TCR-Net is an end-to-end two-stage architecture. First, a data-driven pre-trained encoder is used to complete the sparse representation and basic feature extraction of image information. Second, a new attention mechanism is introduced to replace the self-attention mechanism in Transformer, and a hybrid Transformer and convolution module based on optimization-inspired is designed. Its iterative process leads to the unfolding framework, which approximates the original image stage by stage. Experimental results show that TCR-Net outperforms existing state-of-the-art CS methods while maintaining fast computational speed. Specifically, when the CS ratio is 0.10, the average PSNR on the test set used in this paper is improved by at least 0.8%, the average SSIM is improved by at least 1.5%, and the processing speed is higher than 70FPS. These quantitative results show that our method has high computational efficiency while ensuring high-quality image restoration.

Weight-adaptive augmented Lagrange multiplier sequential convex programming for nonlinear trajectory optimization

Sequential convex programming has become a prominent research focus in aerospace trajectory optimization due to its high computational efficiency. One critical challenge in sequential convex programming is the infeasibility of subproblems, which significantly impacts its stability. To address this issue, certain constraints must be relaxed using relaxation variables, which are then incorporated into the objective function as a penalty term. However, the choice of penalty weight greatly influences the optimality and convergence of sequential convex programming. Therefore, this paper proposes a weight-adaptive augmented Lagrange multiplier sequential convex programming algorithm to tackle the challenge. First, this paper analyzes the difficulty of determining an appropriate penalty weight from the perspective of its penalty effect. Based on this analysis, an augmented penalty term is introduced, and the update rule for the augmented Lagrange multiplier is formulated using the Karush-Kuhn-Tucker conditions. Subsequently, a method with adaptive weight adjustment is designed based on the change in the penalty term to further improve the algorithm's solving speed. Finally, the proposed method is validated through numerical simulations in a Mars-powered landing problem and a small glide vehicle reentry problem. The results show that the proposed method is 20.59 % and 25.16 % faster compared to the general sequential convex programming algorithm, respectively. Furthermore, the proposed method is demonstrated to be robust to the initial choice of penalty weight.

Foundation model-driven distributed learning for enhanced retinal age prediction.

The retinal age gap (RAG) is emerging as a potential biomarker for various diseases of the human body, yet its utility depends on machine learning models capable of accurately predicting biological retinal age from fundus images. However, training generalizable models is hindered by potential shortages of diverse training data. To overcome these obstacles, this work develops a novel and computationally efficient distributed learning framework for retinal age prediction. The proposed framework employs a memory-efficient 8-bit quantized version of RETFound, a cutting-edge foundation model for retinal image analysis, to extract features from fundus images. These features are then used to train an efficient linear regression head model for predicting retinal age. The framework explores federated learning (FL) as well as traveling model (TM) approaches for distributed training of the linear regression head. To evaluate this framework, we simulate a client network using fundus image data from the UK Biobank. Additionally, data from patients with type 1 diabetes from the UK Biobank and the Brazilian Multilabel Ophthalmological Dataset (BRSET) were utilized to explore the clinical utility of the developed methods. Our findings reveal that the developed distributed learning framework achieves retinal age prediction performance on par with centralized methods, with FL and TM providing similar performance (mean absolute error of 3.57 ± 0.18 years for centralized learning, 3.60 ± 0.16 years for TM, and 3.63 ± 0.19 years for FL). Notably, the TM was found to converge with fewer local updates than FL. Moreover, patients with type 1 diabetes exhibited significantly higher RAG values than healthy controls in all models, for both the UK Biobank and BRSET datasets (P < .001). The high computational and memory efficiency of the developed distributed learning framework makes it well suited for resource-constrained environments. The capacity of this framework to integrate data from underrepresented populations for training of retinal age prediction models could significantly enhance the accessibility of the RAG as an important disease biomarker.

An Innovative and Efficient Implementation of Matrix-Free Newton Krylov Method for Neutronics/Thermal-hydraulics Coupling Simulation

The core physical behavior of reactors is essentially the result of multi-physical fields coupling feedback. High-fidelity neutronics/thermal-hydraulics (N/TH) analysis can simulate and predict nuclear reactor core phenomena realistically, providing advanced and reliable technical means during the design and safety analysis of nuclear reactor. In this work, an efficient and robustness coupling method using power density as the coupling parameter, Matrix-Free Newton Krylov (MFNK) method, is successfully developed and innovatively implemented in HNET for high-fidelity N/TH coupling simulation. To enhance the efficiency and stability, the multi-level generalized equivalence theory-based CMFD (ML-gCMFD) iterative acceleration method and ML-gCMFD coupling acceleration method are proposed. In addition, the nonlinear preconditioning and hybrid perturbation size formula are implemented to further improve the convergence. Finally, to evaluate the numerical accuracy, convergence, efficiency and stability of MFNK method, a series of representative problems, including a three-dimensional (3D) single fuel pin problem, VERA Benchmark Problem 6, and VERA Benchmark Problem 7, are analyzed by comparing with the current N/TH coupling methods. Numerical results indicate that MFNK method can obtain strong stability, high convergence performance, and relatively high computational efficiency while ensuring high accuracy. It demonstrates that MFNK method has significant performance advantages and potential for high-fidelity N/TH coupling simulation.

Optimal two-stage arbitraging and scheduling strategy for distributed energy resource aggregators

Given the rapid development of the distributed energy resources (DER), involving DERs into the wholesale market under the market and renewable uncertainties to achieve economic benefits is necessary but challenging. In this work, an arbitraging strategy is proposed for DER aggregators that bridge DERs with the wholesale market through energy trading. Besides, a novel self-adaptive minimax regret (MMR)-based optimal offering model is proposed for the DER aggregator to handle the uncertainties in both renewable generations and market prices. Additionally, an exact and communication-free solution methodology is proposed to resolve the formulated optimization problem with high computational efficiency. In the numerical results, the proposed methods can achieve near-to-optimal profits. Moreover, the performance of the proposed approach remains satisfactory even if the environment becomes very volatile.

Worst-case robust optimization based on an adaptive incremental Kriging metamodel

Worst-case robust optimization problem is concerned with finding a candidate solution that is insensitive to uncertainty. This problem involves nested-loop structure based on the worst-case analysis, it is an expensive optimization problem which has large computational complexity. This paper presents a robust optimization method of reduced computational complexity. Firstly, the state transition algorithm (STA) is utilized to explore and exploit the candidate solutions of the search space. Then, an adaptive incremental Kriging (IKriging) metamodel is proposed to replace the evaluation functions for evaluating the robustness of candidate solutions. Finally, a preferential selection strategy is presented to select the optimal solution in terms of objective function value, constraint and robustness violation. Four engineering examples are studied to analyze the performance of the proposed robust optimization method. The experimental results illustrate that the proposed method can find better robust solution with higher computational efficiency.

Adaptive Model Predictive Control of Yaw Rate and Lateral Acceleration for an Active Steering Vehicle Based on Tire Stiffness Estimation

Dynamic systems for vehicles are commonly established based on physical rules that are simplified and are not accurately reflective of their dynamic characteristics under certain operating conditions, affecting their accuracy and safety. This paper presents an adaptive model predictive controller (AMPC) with an estimator that controls the yaw rate and lateral acceleration of a realistic ground vehicle. A number of vehicle parameters are estimated using different advanced estimators, of which recursive least squares is one that is widely used. AMPCs and estimators are used to manage the driving process in order for vehicles to remain stable and controllable. In this research, experiments were conducted on a realistic vehicle based on a nonlinear brush tire model. In this estimator, lateral force measurements are analyzed to estimate the tire cornering stiffnesses that are used in the AMPC from a linear bicycle model (lateral force model), where each parameter describes the nonlinearity of the vehicle model. The results demonstrate that the controlled vehicle's performance is improved by combining a recursive least squares estimator with an AMPC in the simulation process. As tire stiffness estimates become more accurate, AMPC performance improves. Yet, AMPC controllers are described in terms of a table of design parameters. As different steering inputs are applied to different vehicles, the yaw rate and lateral acceleration are varied, while tire stiffness is determined. According to the results of this paper, the proposed method for estimating tire-cornering stiffness exhibits high estimation accuracy, robustness, computational efficiency, stability, comfort, and accuracy and reliability under a variety of steering input maneuvers using an AMPC controller.

Online chatter detection via lightweight deep learning framework with efficient signal pre-processing

Deep learning methods have attracted considerable attention in the field of chatter detection due to their exceptional feature extraction and identification capabilities. However, it is challenging to adapt these methods to diverse machining conditions with limited sensor resources. In addition, the computational efficiency of these methods is compromised by their complexity, resulting in a reduced real-time performance for online chatter detection. To address them, this paper proposes a novel deep learning-based framework that combines an efficient signal pre-processing method with a novel lightweight parallel convolutional neural network (LPNN). A fast continuous wavelet transform algorithm is applied to the raw signal, effectively generating time-frequency maps that represent chatter characteristics. The LPNN incorporates the condensed block as a feature extraction unit, which is constructed using depthwise separable convolution. The introduction of the condensed block and the parallel structure increases the depth and width of the designed model while maintaining fewer parameters and higher computational efficiency. A transfer learning strategy is adopted to train the model, reducing the training time and data requirements for online chatter detection. Experimental results using large open-source turning datasets demonstrate the effectiveness of the proposed method in identifying chatter under various machining conditions. The proposed method outperforms other methods with a classification accuracy of 96.5% on the test set. Furthermore, the performance of the proposed method for online chatter detection is validated in our milling experiments, exhibiting enhanced real-time performance compared to other methods.

A novel data-driven 3D site characterisation method: Tucker decomposition-Bayesian compressive sensing and benchmarking study

ABSTRACT Site characterisation plays a pivotal role in geotechnical design and analysis. With advancements in machine learning and other digital technologies, data-driven site characterisation (DDSC) has garnered substantial interest in data-centric geotechnics. This paper proposes a novel and competitive 3D DDSC method, called Tucker decomposition-Bayesian compressive sensing (TD-BCS), which employs Tucker decomposition to factorise a 3D high-order tensor into a low-rank core tensor along with three associated factor matrices. The method comprises three key components: (1) 3D sparse representation using Tucker decomposition and discrete cosine transform; (2) Bayesian compressive sensing, and its algorithm implementation; and (3) Determination of three effective factor matrices. The proposed TD-BCS method is evaluated through its application to two benchmark examples, involving virtual ground and actual ground scenario based on real CPT data. And the benchmarking results demonstrate that the method not only yields accurate estimates with quantified uncertainty from sparse measurements but also exhibits high computational efficiency compared to other DDSC methods. Indeed, the TD-BCS method can be effectively applied to other 3D geotechnical engineering cases, and its framework is readily extendable to higher dimensions.

A digital twin-based assembly model for multi-source variation fusion on vision transformer

For the manufacture and assembly of the mechanical products, quality management is the key feature to improve the service performance, reduce the overall costs and enhance the sustainability of the manufacturing systems. While, with the emergence of advanced data-driven and digital twin technologies, the Zero-Defect Manufacturing (ZDM), which corrects, predicts, and prevents product defects based on multi-sources of data, is of great significance in improving assembly quality. As the specific application for the utilization of ZDM in product assembly, the critical performance index of the assembly results is predicted by taking into account the multi-source factors such as geometric deviation of the parts, material properties, assembly sequences and process boundary conditions during assembly. In this paper, we address the high computational cost and low computational efficiency of numerical simulation methods under multi-source factors, and propose a data-driven approach named DTA-VIT based on the fusion of heterogeneous variables for digital twin assembly modeling of products. Firstly, the geometric and performance variables of the assembly process are analyzed and modelled. Secondly, a multi-source assembly data fusion network under the Vision Transformer framework is developed. This network takes the parameter space, which fuses multi-source variables from the assembly process as input and the assembly result as output. Finally, a case study of the assembly process of composite bolted joint structures in aircraft assembly is conducted to verify the effectiveness and feasibility of the proposed method. The methodology provides a solid foundation for subsequent assembly quality control and prevent by predicting assembly performance efficiently, ultimately enabling the production of high-quality products.

Automatic unberthing for underactuated unmanned surface vehicle: Model-based planning and control approaches in constricted harbors

Due to limitations in maneuverability, Unmanned Surface Vehicle (USV) often struggles to achieve automatic unberthing through a single cohesive operation in constricted harbors. To address this problem, this paper proposed a novel Two-Layer Trajectory Planning Method (TL-TPM) and two Model Predictive Control (MPC) methods. Throughout the trajectory planning process, the TL-TPM considers the USV dynamic constraints along with the cumulative risk in viable areas. It incorporates the residual velocity and yaw angle deviation from the backward phase to derive the USV’s retreating trajectory and optimizes sailing time in the forward phase for a smooth forward trajectory, forming a complete automatic unberthing trajectory. The Efficiency-Priority MPC (EP-MPC) and Accuracy-Priority MPC (AP-MPC) are designed for trajectory tracking, considering tracking positional deviations, yaw angle deviations and surge force variations. The proposed EP-MPC demonstrates high computational efficiency and robustness, while AP-MPC shows superior control accuracy under various environmental disturbances. Extensive simulations validate the effectiveness of these methods in achieving automatic unberthing under wind and current impacts.

Analytical calculation method for development dynamics of water-flooding reservoir considering rock and fluid compressibility

The existing development dynamics calculation methods for water-flooding reservoir are difficult to comprehensively consider factors such as injection-production imbalance, irregular well pattern, reservoir heterogeneity and compressibility. Therefore, leveraging reservoir engineering and material balance theory, and considering the influence of reservoir compressibility on reservoir development dynamics, a set of analytical calculation method for development dynamics of water-flooding reservoir considering rock and fluid compressibility water-flooding with clear theories and high computational efficiency were derived and constructed. Based on the oil-water two-phase Darcy law, the reservoir compression coefficient is introduced, and the development dynamics calculation method of a single injection-production unit considering compressibility is derived and established by combining the material balance theory, which can quantitatively describe the relationship between the oil and water production rates per unit. On this basis, the injection-production splitting strategy is introduced, which is extended to the calculation of development dynamics for multiple well groups in the whole reservoir. In this study, seven working conditions with various injection-production ratios were designed to validate the reliability of the new method. Subsequently, the method was applied to actual block for further verification, and the prediction accuracies of oil and water production rates were improved by 67.48% and 19.07%, respectively.

CAWE-ACNN Algorithm for Coprime Sensor Array Adaptive Beamforming.

This paper presents a robust adaptive beamforming algorithm based on an attention convolutional neural network (ACNN) for coprime sensor arrays, named the CAWE-ACNN algorithm. In the proposed algorithm, via a spatial and channel attention unit, an ACNN model is constructed to enhance the features contributing to beamforming weight vector estimation and to improve the signal-to-interference-plus-noise ratio (SINR) performance, respectively. Then, an interference-plus-noise covariance matrix reconstruction algorithm is used to obtain an appropriate label for the proposed ACNN model. By the calculated label and the sample signals received from the coprime sensor arrays, the ACNN is well-trained and capable of accurately and efficiently outputting the beamforming weight vector. The simulation results verify that the proposed algorithm achieves excellent SINR performance and high computation efficiency.

PathMLP: Smooth path towards high-order homophily

Real-world graphs exhibit increasing heterophily, where nodes no longer tend to be connected to nodes with the same label, challenging the homophily assumption of classical graph neural networks (GNNs) and impeding their performance. Intriguingly, from the observation of heterophilous data, we notice that certain high-order information exhibits higher homophily, which motivates us to involve high-order information in node representation learning. However, common practices in GNNs to acquire high-order information mainly through increasing model depth and altering message-passing mechanisms, which, albeit effective to a certain extent, suffer from three shortcomings: (1) over-smoothing due to excessive model depth and propagation times; (2) high-order information is not fully utilized; (3) low computational efficiency. In this regard, we design a similarity-based path sampling strategy to capture smooth paths containing high-order homophily. Then we propose a lightweight model based on multi-layer perceptrons (MLP), named PathMLP, which can encode messages carried by paths via simple transformation and concatenation operations, and effectively learn node representations in heterophilous graphs through adaptive path aggregation. Extensive experiments demonstrate that our method outperforms baselines on 16 out of 20 datasets, underlining its effectiveness and superiority in alleviating the heterophily problem. In addition, our method is immune to over-smoothing and has high computational efficiency. The source code will be available in https://github.com/Graph4Sec-Team/PathMLP.

Target-oriented image-domain elastic least-squares reverse time migration

Elastic least-squares reverse time migration (ELSRTM), as an imaging method, offers advantages over conventional elastic reverse time migration (ERTM), including higher resolution, better amplitude balancing, reduced crosstalk, and broader bandwidth. However, conventional ELSRTM involves iterative processes in the data domain, resulting in high computational costs. Moreover, since time is continuous during data domain extrapolation, it cannot solely focus on the target area within the subsurface medium. In contrast, image-domain ELSRTM (IDELSRTM) exhibits high computational efficiency and the ability to image target area. Currently, research on image-domain least-squares reverse time migration is predominantly focused on the acoustic wave assumption, despite elastic waves being closer to the actual subsurface medium and providing richer imaging information. In this study, within the framework of data domain ELSRTM, we derived the objective function for the IDELSRTM and introduced an L1 regularization term under the L2 norm to enhance inversion stability. We devised an inversion strategy employing the fast iterative shrinkage-thresholding algorithm (FISTA). Furthermore, drawing from the point spread functions theory in optics, we derived the mapping relationship between the elastic multi-parameter point spread functions (PSF) and the elastic multi-parameter Hessian matrix, and the relationship between the Hessian matrix and the ERTM images. We provided the computational method for the elastic multi-parameter Hessian matrix and utilized it as the linearized forward operator for IDELSRTM. Through numerical experiments, we further elucidated the relationship between the ERTM images and the Hessian matrix under the framework of IDELSRTM, along with the sources of crosstalk in ERTM. Applying our proposed target-oriented IDELSRTM method to layered models and the Marmousi2 model, we demonstrated its effectiveness in improving imaging resolution and quality with only a marginal increase in computational overhead compared to conventional ERTM.

MGMI: A novel deep learning model based on short-term thermal load prediction

Accurate heat load prediction is the key to stable operation and control of district heating system. However, current heat load forecasting methods tend to treat individual buildings as isolated entities, ignoring the temporal and spatial correlation between buildings. In this paper, a heat load forecasting model is proposed which fully considers spatiotemporal information. Firstly, the spatial and temporal relationship between buildings is analyzed by statistical method. Secondly, synchronous wavelet transform (SWT) is used to denoise the heat load data to reduce the difficulty of prediction. Then, a hybrid model of multi-modal graph attention network (MG) and multi-scale Informer (MI) is constructed to capture spatiotemporal information in the data. Finally, an example of DHS in a campus in Liaoyang is analyzed. The results showed that compared with LSTM, CNN-Informer, GCN-LSTM and GAT-Informer, the MAE of MGMI on 168 h test set was reduced by 71.4%, 61.5%, 58% and 41.6%, respectively. And has the highest computational efficiency. The validity of the model is verified, which provides an important basis for the operation control of the thermal system.

Hybrid uncertainty propagation for mechanical dynamics problems via polynomial chaos expansion and Legendre interval inclusion function

This paper investigates a non-intrusive hybrid uncertainty propagation framework in mechanical dynamic systems, utilizing polynomial chaos expansion (PCE) and Legendre inclusion function. Uncertainties with substantial knowledge and information are conceptualized as stochastic parameters and described using PCE, while Legendre polynomials are employed to represent uncertain, bounded parameters, specifically interval uncertainties. During the computation, the PCE model for each time step is developed through Galerkin projection and sparse grid numerical integration. Consequently, the statistical moments of the dynamic responses relative to the stochastic parameters are derived from the orthogonality of the PCE and transformed into functions reflecting the interval parameters. The interval bounds for these statistical moments are further determined using the Legendre inclusion function, which is obtained through optimal Latin hypercube sampling and collocation methods. Three dynamics examples, respectively, modeled by differential–algebraic equations, finite element method, and commercial software proves its applicability and superiority. Detailed assessment demonstrates that this method offers high computational efficiency and accuracy. As a non-intrusive approach, it poses no particular limitations on numerical methods for solving differential equations, making it universally applicable.