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Aerodynamic forces hold significant promise for controlling small-satellite formations in low Earth orbit. However, significant challenges still exist regarding complex input constraints and their application to multi-satellite formations. This paper proposes a constraint tightening-based control scheme for small-satellite formations, utilizing differential drag and lift as actuating forces. Two attitude adjustment strategies, namely the reorientation strategy and the single-radial rotation strategy, are compared in terms of their control effectiveness and applicability. Relative aerodynamic coefficients are employed as control inputs in view of their solid reachable region, followed by the development of a decoupling algorithm for determining pointing angles. Through a method that tightens the reachable region using liner models, the input constraints are linearized and can be explicitly expressed. Subsequently, an observer-based Model Predictive Control (MPC) algorithm is formulated to meet these linearized input constraints and provide feedforward compensation for system disturbances. This discrete algorithm enables practical onboard implementations owing to its low computational complexity. Additionally, a concept of overall computation for all satellites' attitudes is proposed to adapt the control scheme for multi-satellite formations. Hardware-in-the-loop simulations are conducted to evaluate the control scheme's performance in along-track and circular formations, accounting for uncertainties in aerodynamic forces and attitude dynamics. [ABSTRACT FROM AUTHOR]

This paper proposes a low-cost, penalty parameter-free, and pressure-robust Stokes solver based on the enriched Galerkin (EG) method with a discontinuous velocity enrichment function. The EG method employs the interior penalty discontinuous Galerkin (IPDG) formulation to weakly impose the continuity of the velocity function. However, despite its advantage of symmetry, the symmetric IPDG formulation requires a lot of computational effort to choose an optimal penalty parameter and compute different trace terms. To reduce such effort, we replace the derivatives of the velocity function with its weak derivatives computed by the geometric data of elements. Therefore, our modified EG (mEG) method is a penalty parameter-free numerical scheme that has reduced computational complexity and conserves the optimal convergence orders. Moreover, we achieve pressure robustness for the mEG method by employing a velocity reconstruction operator on the load vector on the right-hand side of the discrete system. The theoretical results are confirmed through numerical experiments with two- and three-dimensional examples. [ABSTRACT FROM AUTHOR]

As we know that, the parameterized Uzawa (PU) method can be very efficient when used to solve the standard saddle point problem, especially, when we have good and accurate estimation of preconditioned Schur complement matrix. In this paper, by taking full use of the special structure of coefficient matrix arising from complex Helmholtz equations, two types of optimized PU (OPU) methods are discussed theoretically and experimentally. Specifically, the convergence factors of these two OPU methods are less than 0.172, and the optimal result of first OPU method can reach 0.0396 with σ 1 ≥ σ 2 > 0 , which is currently the best theoretical result in the literatures. Moreover, the second OPU method has better computational advantages compared with the first OPU method as it avoids the inverse computation of W at each step, indicating the less CPU time will be costed by the second OPU method. In addition, the optimal parameters involved in our algorithms consist of constants, reducing the computational complexity associated with parameter selection. Finally, numerical results are given, not only show the effectiveness of OPU methods, but also confirm the rationality of theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper develops two-filter particle smoothing (TFPS) algorithms for the nonlinear fixed-interval smoothing problem of one generalized hidden Markov model (GHMM), where the current observation depends not only on the current state, but also on one-step previous state. Firstly, by Bayesian approach, the two-filter smoothing (TFS) formula for GHMM is established to calculate smoothing densities. In this TFS formula, the backward information prediction density is generally not a density of the state. This results in a difficulty that the normal sequential Monte Carlo (SMC) sampling technique cannot be directly applied to design corresponding TFPS algorithms based on the TFS formula. To solve this difficulty, a generalized TFS formula for GHMM is then proposed by introducing a sequence of artificial densities. By combining this generalized TFS formula, SMC, and the auxiliary variable sampling technique, a basic auxiliary TFPS (ATFPS) algorithm with quadratic computational complexity is proposed, and a simplified ATFPS algorithm with linear computational complexity is further developed. Finally, the effectiveness and superiority of the two proposed ATFPS algorithms for GHMM are verified via simulation examples and real experimental data. • It studies a generalized HMM where observation depends on two-step states. • Two-filter smoothing formula and its generalization are established. • Auxiliary variable sampling technique is applied to draw samples. • It proposes two particle smoothing algorithms by using sequential Monte Carlo. [ABSTRACT FROM AUTHOR]

In this paper, some new approaches for stability and stabilizability determination as well as state feedback stabilization controllers of linear time-invariant (LTI) interval systems are proposed. The presented stability conditions are less conservative than those of Kharitonov's theorem, and Gerschgorin's disc theorem methods. Moreover, some of the proposed stability, stabilizability, and feedback stabilization control methods for LTI interval systems are proved to be sufficient and necessary conditions. Compared with some traditional stability analysis and feedback stabilization design methods for LTI interval systems, these new approaches have lower computational complexity because of a special form of parameter vertex matrices developed in this work. Some numerical and practical examples are given to demonstrate the effectiveness and advantages of the proposed methods. • The proposed stability conditions are less conservative compared to traditional methods. • One of the state feedback stabilization control methods is proven to be both a sufficient and necessary condition. • The computational complexity of the proposed strategies is substantially reduced, courtesy of the KCV matrix. [ABSTRACT FROM AUTHOR]

In this paper, several local and parallel finite element algorithms are proposed and analyzed for the 2D/3D stationary inductionless incompressible magnetohydrodynamic (MHD) equations. The core concept is to guarantee the charge-conservation property by choosing mixed finite element spaces in H 0 (div , Ω) × L 0 2 (Ω) to approximate (J , ϕ) , meantime combining the idea of domain decomposition method to realize parallel operation. The characteristic of the proposed algorithms is that the computational complexity is greatly reduced while ensuring the accuracy of the numerical simulation. With the local a prior estimate as the technical means of theoretical analysis, we give the error estimates of the algorithms. Finally, several numerical experiments are presented to verify the theoretical results. [ABSTRACT FROM AUTHOR]

In visual tracking, the Transformer architecture is widely used because it can capture the global dependencies of sequence data without inductive bias. However, the attention mechanism of Transformer will bring ultra-high computational complexity and space occupancy, so that the tracking task cannot meet the real-time requirements. In this paper, we explore a sparsity region-aware attention mechanism. The sparse attention mechanism retains the regions with semantic relevance, and performs fine-grained attention calculation in this region. In the region-aware attention mechanism, a DropKey technique is introduced to reduce model over-fitting and improve the generalization ability of the model. Using region-aware attention as the basic building block, we design a dynamic region-aware Transformer backbone for visual tracking. This backbone network can effectively reduce the computational complexity while exploring global context dependencies. Based on the region-aware Transformer backbone network, this paper proposes a dynamic region-aware Transformer backbone visual tracking algorithm, which uses an optimization based model predictor to fully fuse object appearance and background information, so as to achieve more robust object tracking. The proposed tracker is trained in an end-to-end manner and experimentally evaluated on eight tracking benchmarks. Experimental results show that the algorithm has good tracking performance, especially in the application of unmanned aerial vehicle (UAV) tracking, our proposed tracker achieves an area under curve (AUC) score of 66.5% on the UAV123 dataset. Code is available at https://github.com/YSGFF/RTDiMP. [ABSTRACT FROM AUTHOR]

Analyzing and predicting the dynamics of opinion formation in the context of social environments are problems that attracted much attention in literature. While grounded in social psychology, these problems are nowadays popular within the artificial intelligence community, where opinion dynamics are often studied via game-theoretic models in which individuals/agents hold opinions taken from a fixed set of discrete alternatives, and where the goal is to find those configurations where the opinions expressed by the agents emerge as a kind of compromise between their innate opinions and the social pressure they receive from the environments. As a matter of facts, however, these studies are based on very high-level and sometimes simplistic formalizations of the social environments, where the mental state of each individual is typically encoded as a variable taking values from a Boolean domain. To overcome these limitations, the paper proposes a framework generalizing such discrete preference games by modeling the reasoning capabilities of agents in terms of weighted propositional logics. It is shown that the framework easily encodes different kinds of earlier approaches and fits more expressive scenarios populated by conformist and dissenter agents. Problems related to the existence and computation of stable configurations are studied, under different theoretical assumptions on the structural shape of the social interactions and on the class of logic formulas that are allowed. Remarkably, during its trip to identify some relevant tractability islands, the paper devises a novel technical machinery whose significance goes beyond the specific application to analyzing opinion formation and diffusion, since it significantly enlarges the class of Integer Linear Programs that were known to be tractable so far. [ABSTRACT FROM AUTHOR]

The integer motion estimation (IME) involves high computational complexity and large amount of computation data due to the variable block sizes, and it is one of the most critical bottlenecks in video coding. The traditional algorithm for integer motion estimation loses search accuracy when simplifying the motion search process, or it becomes complex to pursue search accuracy, which is not conducive to video encoding and transmission. This paper presents a data reuse algorithm based on search window division, block splitting, the arrangement of search points in the Test Zone (TZ) search algorithm and block division. which divides pixel blocks of different sizes into several 8x8 pixel blocks and then reuses the overlapping 8x8 pixel blocks between adjacent search points of the Test Zone (TZ) search algorithm to reduce hardware resource consumption. By comparing the Sum of Absolute Differences (SAD) values of left and top pixel blocks with current pixel block, the integer motion estimation (IME) can be terminated in advance, reducing the computation complexity of integer motion estimation by over 70 %. Furthermore, the paper also presents a hardware architecture based on the data reuse algorithm, which can process 7680 × 4320@60fps videos at an operating clock frequency of 182.99 MHz, with lower resource consumption compared to similar hardware designs. [ABSTRACT FROM AUTHOR]

Motion matching has become a widely adopted technique for generating high-quality interactive animation systems in video games. However, its current implementations suffer from significant computational and memory resource overheads, limiting its scalability in the context of modern video game performance profiles. Our method significantly reduces the computational complexity of approaches to motion synthesis, such as "Learned Motion Matching", while simultaneously improving the compactness of the data that can be stored and the robustness of pose output. As a result, our method enables the efficient execution of motion matching that significantly outperforms other implementations, by 8.5× times in CPU execution cost and at 80% of the memory requirements of "Learned Motion Matching", on contemporary video game hardware, thereby enhancing its practical applicability and scalability in the gaming industry and unlocking the ability to apply on large numbers of animated in-game characters. In this paper, we expand upon our published paper "Learning Robust and Scalable Motion with Lipschitz Continuity and Sparse Mixture of Experts", where we successfully proposed a novel method for learning motion matching that combines a Sparse Mixture of Experts model architecture and a Lipschitz-continuous latent space for representation of poses. We present further details on our method, with extensions to our approach to expert utilization within our neural networks. [Display omitted] • Applies the Sparse Mixture of Experts architecture to animation synthesis for the first time. • Significantly reduce theoretical and practical compute requirements, 8.5 times that of prior art. • Lipschitz-continuous encoding neural networks producing a linearly interpolatable manifold. • SMoE Networks express a higher density of animation data than Feed-Forward Neural Networks. [ABSTRACT FROM AUTHOR]

In this paper, we propose an effective online portfolio selection strategy by integrating expert opinions, which are obtained based on mean reversion and trading volume. Existing studies have found that mean reversion and high volume premium exist in the stock market in the short term. Some online portfolio strategies have been proposed that rely upon mean reversion, but it is rare to consider both mean reversion and high volume premium, which is what we will do in this paper. First, a portfolio based on mean reversion and high volume premium is constructed using recent window data. Second, a pool of portfolios, also known as a pool of expert strategies or expert opinions, is established by changing the window size. Finally, the online gradient update algorithm is adopted to integrate a pool of expert strategies, and the MRvol strategy in this paper is proposed. Theoretically, we prove that the regret of MRvol is bounded. Empirically, we use the actual stock price and trading volume data from different markets to test the performance of MRvol. The results show that MRvol performs better than other online strategies in terms of final cumulative wealth and risk-adjusted return metrics in most cases, among which the annualized percentage yield on all datasets is 16%–37%, and the average values of Sharpe ratio, Calmar ratio and Information ratio are 1.113, 0.9216 and 0.0299, respectively. Additionally, MRvol has linear computational time complexity, and the running time of test results is all less than 1 s. Furthermore, it maintains strong robustness under different parameters and can bear reasonable transaction costs. • Mean reversion and high volume premium phenomena are used to design expert advice. • Online gradient update algorithm is applied to establish portfolio strategy MRvol. • It is proved that the MRvol strategy has a theoretical regret bound. • Extensive experiments show that MRvol performs well in multiple evaluation metrics. [ABSTRACT FROM AUTHOR]

In computed tomography (CT) imaging, artifacts caused by metals can seriously interfere with the accurate judgment, evaluation, and analysis of object structures. Deep learning presented new opportunities for minimizing metal artifacts. The currently proposed supervised methods mainly resort to simulated data for network training, which is difficult to be applied in real scenarios. Unsupervised methods do not completely suppress artifacts and are prone to instability in the corrected results. Therefore, this paper proposes an unsupervised CycleGAN model based on the efficient Transformer for metal artifact reduction in CT images. The encoder and decoder based on the efficient Transformer achieve the extraction and processing of global image content features and artifact features. By correlating global features through attention mechanisms, vital features are generated to enhance the consistency of recovery information. The efficient Transformer framework reduces the computational complexity of the attention mechanism, allowing efficient processing of standard medical CT images with 512 × 512 pixels. Projection preserving loss uses the metal trace outer projection as a fidelity constraint to overcome the problem that unsupervised networks are difficult to train and prone to spurious information. Experimental results indicate that the proposed method outperforms the original CycleGAN and ADN networks in suppressing metal artifacts while avoiding introducing spurious structures. Specifically, the proposed method improved the PSNR of the correction results by 9.23% and reduced the NMAD by 13.21% compared to the original CycleGAN, and improved the PSNR by 5.17% and reduced the NMAD by 5.57% compared to the ADN. [Display omitted] This paper uses the CycleGAN architecture as the base framework to achieve unsupervised reduction of metal artifacts by introducing an efficient Transformer-based generator and a relative discriminator. The generation model consists of a feature extraction and encoding module and a feature decoding module, which consists of two parts: a forward loop and a backward loop. A relative discriminator is used to effectively discriminate and classify the generated image from the real image to more effectively facilitate artifact correction by the generator. The efficient Transformer-based encode enables the extraction and processing of global image content and artifact features and the correlation of global feature relevance through an attention mechanism. This approach produces stronger features to enhance the consistency of the recovered information. Using metal trace outer projection as a projection preserving loss enables more accurate recovery of image information and prevents the generation of spurious structures, further enhancing the fidelity of the recovered information. • Efficient Transformer Unsupervised MAR network effectively extracts CT features. • MAR based on global features improves the consistency of the recovered information. • Efficient Transformer module optimizes the computational efficiency and memory usage. • Projection preserving loss improves the recovery fidelity of image content. [ABSTRACT FROM AUTHOR]

Physics-informed neural network (PINN) is an innovative universal function approximator which adds physical constraints to neural network to make the fitting results satisfy the physical laws better. In this paper, a physics-informed residual network (PIResNet) is proposed to solve the single-phase seepage equation without labeled data. The loss function is constructed by summarizing the residuals of the discretized seepage equation based on the finite volume method (FVM), and the boundary conditions are embedded in the PDE residuals in a "hard constraint" way. The PIResNet is simple in network structure, fast in convergence and easy to optimize. Furthermore, the convergence of the residual structure used in this paper is proved and the theoretical analysis of time complexity illustrates the computational efficiency of PIResNet. The correctness of the proposed method is proved and the experiments display that the solution time of PIResNet is reduced by more than six times compared to physics-informed convolutional neural networks (PICNN). [ABSTRACT FROM AUTHOR]

In applications of AI systems where exact definitions of the important notions of the application domain are hard to come by, the use of traditional logic-based knowledge representation languages such as Description Logics may lead to very large and unintuitive definitions, and high complexity of reasoning. To overcome this problem, we define new concept constructors that allow us to define concepts in an approximate way. To be more precise, we present a family τ EL (m) of extensions of the lightweight Description Logic EL that use threshold constructors for this purpose. To define the semantics of these constructors we employ graded membership functions m , which for each individual in an interpretation and concept yield a number in the interval [ 0 , 1 ] expressing the degree to which the individual belongs to the concept in the interpretation. Threshold concepts C ⋈ t for ⋈ ∈ { < , ≤ , > , ≥ } then collect all the individuals that belong to C with degree ⋈ t. The logic τ EL (m) extends EL with threshold concepts whose semantics is defined relative to a function m. To construct appropriate graded membership functions, we show how concept measures ∼ (which are graded generalizations of subsumption or equivalence between concepts) can be used to define graded membership functions m ∼. Then we introduce a large class of concept measures, called simi-d , for which the logics τ EL (m ∼) have good algorithmic properties. Basically, we show that reasoning in τ EL (m ∼) is NP/coNP-complete without TBox, PSpace-complete w.r.t. acyclic TBoxes, and ExpTime-complete w.r.t. general TBoxes. The exception is the instance problem, which is already PSpace-complete without TBox w.r.t. combined complexity. While the upper bounds hold for all elements of simi-d , we could prove some of the hardness results only for a subclass of simi-d. This article considerably improves on and generalizes results we have shown in three previous conference papers and it provides detailed proofs of all our results. [ABSTRACT FROM AUTHOR]

Due to the inevitable light absorption and scattering, underwater images always suffer from severe quality degradation, leading to significant performance decline for various maritime engineering-related applications. In recent years, deep neural networks (DNNs) have been proven to achieve high-quality enhancement of underwater images, which aims to extract and learn abstract features to exhibit superior performance. However, existing solutions often stack multilayer processing units to enrich features, which not only significantly burdens computational load but also encounters difficulties in fully utilizing and interacting between features at different depths. To address the above problems, this paper presents a cost-effective underwater image enhancement network via cascaded feature extraction, termed CFENet. Specifically, we develop a cost-effective cascaded structure for sufficient feature extraction and multiscale establishment of pixel-level long-range dependencies. In addition, we construct a dual-branch structure for effective feature fusion, so that the detailed texture and semantic information in the image can be simultaneously enhanced. Extensive experiments reveal the superiority of our proposed CFENet in both underwater image enhancement effects and computational complexity. Sufficient ablation study is conducted to demonstrate the effectiveness of each component in the network. [ABSTRACT FROM AUTHOR]

In the paper, we present a new transfer subspace learning algorithm termed discriminative transfer regression (DTR) for cross-domain image recognition, in which low-rank representation (LRR), discriminative regression, local geometry preserving, and different norm minimization are integrated in to a united framework for transfer learning. Firstly, both the global structure and the local geometry information of the original data are preserved by imposing low-rank and sparse constraints on the reconstruction coefficient matrix. Secondly, DTR algorithm can overcome the disturbance of outliers and noises. Thirdly, the local manifold structure of the observed samples with the same semantics from the source and target domains is captured by the adaptive weight graph. Fourthly, the discriminative information of the samples from the source domain is encoded to the target domain based on ridge regression (RR). Meanwhile, the small-class problem confronted by RR and its extensions, that the obtained projection matrix is limited by the number of classes, can be effectively solved. In addition, the convergence and computational complexity of DTR algorithm is analyzed. An extensive range of experiments on several cross-domain image databases demonstrate the superiority of the DTR algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a global probability distribution structure-sparsity filter pruning is proposed to address the problem of difficult deployment of diagnostic models in resource constrained wireless sensor networks (WSNs) for edge fault diagnosis. Firstly, local and global weight distributions are analyzed. A global probability distribution weight sparsity method is proposed to obtain the global sparse boundary and the important features of the model. Secondly, according to the distribution of weight amplitude, the problem of uneven weight distribution is disclosed. A computational acceleration sensing method is proposed to reduce the high computational cost caused by small weight amplitude with low importance estimation. Moreover, a novel layer filter clustering pruning scheme is proposed. The intra-layer filters are classified and redundant filters are removed by using the clustering idea according to the predetermined pruning rate. Thus, the structure-sparsity filter pruning is realized. Compared with other advanced filter pruning methods, the proposed method analyzes the global and local model weight distribution and the influence of weight importance on the computational complexity of the model. The proposed method then performs filter pruning on the importance of overall filters for each network layer rather than individual filters. The accuracy of the proposed method on experimental dataset for pruned ResNet8 model under pruning rate at 0.95 is 99.12% with 3.668K parameters and 3.636MFLOPs. The experimental results elucidate that the proposed method is more suitable for deployment in resource-constrained WSNs for fault diagnosis of rotating machinery. This provides a potential solution for practical engineering applications. • A global probability distribution weight sparsity is proposed. Sparse boundary is derived to identify unimportant weights. • The computational acceleration sensing is proposed to tackle that low-importance estimates consume unnecessary computations. • A novel layer filter clustering pruning is proposed to remove redundant filters and retain the overall important feature. • The results show that compared with other methods, the proposed method obtains tradeoff in accuracy, parameters and FLOPs. [ABSTRACT FROM AUTHOR]

The image noise generated by the welding process, such as arc light, splash, and smoke, brings significant challenges for the laser vision sensor-based welding robot to locate the weld seam and accurately conduct automatic welding. Currently, deep learning-based approaches surpass traditional methods in flexibility and robustness. However, their significant computational cost leads to a mismatch with the real-time requirement of automated welding. In this paper, we propose an efficient hybrid architecture of Convolutional Neural Network (CNN) and transformer, referred to as Dynamic Squeeze Network (DSNet), for real-time weld seam segmentation. More precisely, a lightweight segmentation framework is developed to fully leverage the advantages of the transformer structure without significantly increasing computational overhead. In this respect, an efficient encoder, which aims to increase its features diversity, has been designed and resulted in substantial improvement of encoding performance. Moreover, we propose a plug-and-play lightweight attention module that generates more effective attention weights by exploiting statistical information of weld seam data and introducing linear priors. Extensive experiments on weld seam images using NVIDIA GTX 1050Ti show that our approach reduces the number of parameters by 54x, decreases computational complexity by 34x, and improves inference speed by 33x compared to the baseline method TransUNet. DSNet achieves superior accuracy (78.01% IoU, 87.64% Dice) and speed performance (100 FPS) with lower model complexity and computational burden than most state-of-the-art methods. The code is available at https://github.com/hackerschen/DSNet. [ABSTRACT FROM AUTHOR]

Acoustic-based diagnosis (ABD) is a promising method for rotating machinery fault detection in real-industrial fields due to its advantage of non-contact measurement by air-couple. However, most ABD methods, which rely on single function module of denoising or anti-noise diagnosis, lacks sufficient ability against the strong and highly non-stationary background noise interference in practical industrial application. To deal with the obstacle, a novel two-stage ABD system, which performs denoising and anti-noise diagnosis in a step-by-step fashion, is proposed in this paper. In our method, the denoising sub-system can adaptively track and suppress real-industrial background noise from collected multi-channel signal by our designed recursive multi-head self-attention (RMHSA) mechanism, which take into account the global and long-range information of signal to recursively exclude noise component at each position in signal sequence for overcoming the limitation of traditional method in balancing denoising performance and computational complexity and discontinuity in diagnosis procedure. Subsequently, a novel anti-noise method is explored to automatically track the residual noise components after initial suppression and estimating the noise interference probability within time-frequency (T-F) unit of each signal sample by our designed spatial multi-head self-attention (SMHSA) mechanism in recursive manner. Simultaneously, the anti-noise protocol interacts with diagnosis model based on estimated probability in anti-noise diagnosis sub-system to further improve the diagnosis performance of ABD system under the interference of background noise. Experiment result in both real-industrial noise condition and simulated noise conditions with extremely worse SNRs indicate that the proposed two-stage ABD system is effective in dealing with gear fault diagnosis task under noise condition. • Design RMHSA for multi-channel noise suppression. • Explore SMHSA against residual noise. • Develop two-stage ABD system for noise conditions. [ABSTRACT FROM AUTHOR]

Fuel cell vehicles are predicted to play an important role in the electrification of heavy-duty transportation, hence the efficient operation of their powertrains is critical. Since the system dynamics of fuel cell systems are characterized by widespread time-scales (from electrochemistry to thermal processes), the implementation of optimization-based control methods poses several challenges. Control of systems with dynamics covering multiple time-scales leads to a trade-off between control performance and computational effort if solved with a conventional single-layer Model Predictive Control (MPC) scheme. One approach is to partition the problem into different time-scales and to construct a hierarchical architecture combining multiple control layers. In this paper, a two-layer hierarchical MPC structure is presented that optimizes the operation of a fuel cell truck. Its power trajectory and temperature are optimized in a planning layer MPC over a prediction horizon that captures the entire remaining driving route and can therefore be hours long. On a secondary operational layer, the operation of the fuel cell system, namely its electrical current as well as its pressure and stoichiometry at the cathode, are optimized with a sampling time that is around one order of magnitude faster than on the planning layer. In simulation, the proposed approach achieves operation that only consumes 1.4% more hydrogen than the benchmark solution, while demonstrating real-time feasibility and the benchmark can only be computed in hindsight. • Hierarchical MPC scheme is proposed for energy management and fuel cell operation. • Simulative comparison between hierarchical and single layer MPC. • Computational complexity of approaches is compared and discussed. • Variation of battery capacity shows predictive capabilities regarding constraints. [ABSTRACT FROM AUTHOR]

In the task of health monitoring of helicopter transmission systems, data transmission is often abnormal due to harsh working environments, sparse wire connections, and other factors. This paper addresses the problem of recovering abnormal vibration data, focusing on common Bias anomalies and precision degradation anomalies in the collected vibration data. The Transformer model has gained popularity to address time series for its ability to capture strong long-range correlations. However, it also poses intensive demands on memory and computation resources. To address this issue, an efficient model called Variable Scale Multilayer Perceptron is proposed for recovering abnormal vibration data. Initially, the input data is divided into patches of varying scales to alter the model's input view and reduce computational complexity. Subsequently, compute the relative variations in the input sequence to capture more motion information in the vibration data. Then, a simple Multilayer Perceptron (MLP) layer is employed to learn the latent relationships among the vibration data, followed by a linear mapping layer to reconstruct normal vibration data from the latent representations. Experimental evaluations were conducted on vibration datasets of bearings and gears, and the results demonstrate that our proposed method outperforms the Transformer-based and MLP-based models in terms of both performance and efficiency. [ABSTRACT FROM AUTHOR]

This paper considers the problem of optimal coordination of trajectory tracking performance and handling stability for autonomous equipped with distributed drive electric vehicle. Therefore, a hierarchical frame for multi-mode chassis dynamics torque vector allocation strategy is proposed, which aimed to solve the contradictory issues between vehicles' trajectory tracking accuracy and handling stability under extreme working conditions. Firstly, in a hierarchical architecture, the upper-level trajectory tracking controller is designed by using model predictive control theory, which is used to solve the front wheel angle and the additional yaw moment of the vehicle. Secondly, the lower-level multimode torque distribution controller severs the sum of tire force utilization in every wheel as the objective function, and designs three distribution modes of chassis dynamic torque vectors based on the response of the longitudinal force and yaw moment obtained from the upper-level controller. Thirdly, the switching mechanism between the three chassis torque vector distribution modes is set according to the road adhesion condition and the requirements of the upper-level controller. Then, an analysis is conducted on the computational time complexity and robustness of the algorithm, confirming the potential for real-world application of the algorithm. Finally, Simulink/CarSim co-simulation test and hardware-in-the-loop test platform are carried out. And a vehicle trajectory tracking controller with single-mode torque vectors distribution by MPC is used as the baseline algorithm. The test results show that the proposed method show better trajectory tracking performance and handling stability than the baseline algorithm under the conditions of low adhesion surfaces and split-friction surfaces. Therefore, this study provides a solution for the safe driving of autonomous vehicles under extreme working conditions. • A hierarchical architecture for autonomous driving trajectory tracking is proposed. • Multi-mode torque vector allocation is appropriate for extreme operating conditions. • HiL test verify the performance of handling stability and trajectory tracking. [ABSTRACT FROM AUTHOR]

• A novel FL-based VPP modeling approach. • A novel multi-task FL method improvement for non-IID data sets such as data sets in VPP. • The proposed algorithm's superiority is its accuracy and calculation velocity. • Presenting the performance analysis of the optimization process of the proposed method. The Virtual Power Plant (VPP) is a promising solution to power systems' challenges. However, its energy management system (EMS) faces challenges due to centralized Big-Data analysis, i.e., complexity and high computational cost. Decentralized strategies, e.g., federated learning (FL), have been proposed to mitigate these challenges. Although conventional FL can be implemented in VPPs, its effectiveness is limited by its typical structure, which relies on single-pattern modeling. Indeed, this particular structure leads to delays and inaccuracies in optimizing the heterogeneous generator dispatch patterns, particularly renewables. Hence, addressing this critical hindrance is essential for FL's efficient and effective deployment in VPPs. To overcome this drawback, this paper proposes novel VPP modeling utilizing multi-task FL modernized by multi-pattern modeling to decentralize the EMS processing. The proposed FL defines electricity generators' agents as clients collaborating to train the decentralized local data coordinated by a central server. The innovative idea is to create a novel multi-pattern-multi-task FL for electricity generation prediction so that the generator's agents learn to provide an optimized dispatch. Thus, the conventional multi-task FL is improved by proposing an optimized neural-network clustering technique. Simulation results demonstrate that multi-pattern-multi-task FL is 39%-41% faster than the best-reported methods, i.e., teaching–learning-based and honey-bee-mating optimization algorithms. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

Flexible job shop scheduling problem (FJSSP), as a variant of the job shop scheduling problem, has a larger solution space. Researchers are always looking for good methods to solve this problem. In recent years, the deep reinforcement learning (DRL) has been applied to solve various shop scheduling problems due to its advantages that fast solving speed and strong generalization ability. In this paper, we first propose a new DRL framework to realize representation learning and policy learning. The new framework adopts a lightweight multi-layer perceptron (MLP) as the state embedding network to extract state information, which reduces the computational complexity of the algorithm to some extent. Next, we design a new state representation and define a new action space. The new state representation can directly reflect the state features of candidate actions, which is conducive for the agent to capture more effective state information and improve its decision-making ability. The new definition of action space can solve the two subproblems of the FJSSP simultaneously with only one action space. Finally, we evaluate the performance of the policy model on four public datasets: Barnes dataset, Brandimarte dataset, Dauzere dataset and Hurink dataset. Extensive experimental results on these public datasets show that the proposed method achieves a better compromise in terms of optimization ability and applicability compared to the composite priority dispatching rules and the existing state-of-the-art models. [ABSTRACT FROM AUTHOR]

This paper presents the neutrosophic genetic algorithm (NGA) to address the research gap in the application of neutrosophic theory in conjunction with genetic algorithms. NGA introduces three distinct solution spaces—truth, falsity, and indeterminacy—enabling it to entirely encompass neutrosophic solution spaces in the operational process. Fine-tuning in the true solution space (TSS), adaptive regeneration in the false solution space (FSS), and modified crossover and mutation operations in the indeterminate solution space (ISS) enhance NGA ability to navigate away from local optima while reducing computational complexity. Evaluation against several prior algorithms based on the CEC2017 test suites demonstrates the superior performance of NGA, achieving the highest overall score of 92.11% in various problems and conditions. Sensitivity analysis of NGA parameters provides significant insights into algorithm performance variations, emphasizing the substantial impact of these parameters on the NGA's performance. The application of NGA to optimize the K-means method for clustering analysis of rock discontinuity sets showcases its efficiency and potential for practical applications in related fields, highlighting its advantages over other methods. This research establishes NGA as an innovative and efficient approach to address imprecision, incompleteness, and uncertainty in practical data scenarios, with significant implications for future development and applications. [ABSTRACT FROM AUTHOR]

Inter-turn Short Circuit (ITSC) faults in Permanent Magnet Synchronous Motor (PMSM) have gained significant attention due to the growing demand for enhanced reliability and safety in actuation systems. This paper presents a adaptive fault diagnosis approach specifically designed for ITSC faults in PMSM used in oil-drilling applications, where sparse actual data and varying speed conditions pose considerable sparse training data and ITSC feature extraction challenges respectively. To address these challenges, we first construct a physical fault model of PMSM-ITSC and establish a simulation experiment platform to replicate downhole environments with high temperatures and rapidly changing PMSM speeds, ensuring a reliable data source for analysis. Subsequently, we propose a novel adaptive peak-to-peak self-finding method (APPS) that leverages frequency-domain prior knowledge to adaptively extract ITSC fault characteristics, even amidst drastic changes in PMSM speed. Furthermore, we introduce the time-sequence efficient moving window self-attention network (EMWSAN) data model for inferring the stator phase winding state, incorporating Half-sandwich and Cascaded windows group attention operations. This approach significantly reduces computational complexity and network parameters compared to traditional self-attention mechanisms. To expedite the fitting process, we apply transfer learning (TL) theory, transferring knowledge from the physical knowledge to the data model, enabling EMWSAN to be trained more efficiently. As a result, our proposed ITSC fault diagnosis scheme achieves an impressive 96.72% classification accuracy, achieving existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel surface reconstruction method for unoriented points by establishing and solving a nonlinear equation system. By treating normals as unknown parameters and imposing the conditions that the implicit field is constant and its gradients parallel to the normals on the input point cloud, we establish a nonlinear equation system involving the oriented normals. To simplify the system, we transform it into a 0-1 integer programming problem solely focusing on orientation by incorporating inconsistent oriented normal information through PCA. We solve the simplified problem using flipping-based iterative algorithms and propose two novel criteria for flipping based on theoretical analysis. Extensive experiments on renowned datasets demonstrate that our flipping-based method with wavelet surface reconstruction achieves state-of-the-art results in orientation and reconstruction. Furthermore, it exhibits linear computational and storage complexity by leveraging the orthogonality and compact support properties of wavelet bases. The source code is available at https://github.com/mayueji/FISR_code. • We propose a novel surface reconstruction method by establishing and solving nonlinear equation systems numerically. • To tackle the nonlinear equation system, we innovatively propose the gradient-based criterion and value-based criterion. • Our method achieves state-of-the-art performance in orientation and reconstruction. Its linear complexity overcomes computational and storage bottlenecks. [ABSTRACT FROM AUTHOR]

• An effective screening algorithm is derived by extracting angle measurements. • An exclusion strategy of association results is proposed by building statistics. • Angle association is calculated by least square to get coordinates of fused measurements. • Another exclusion strategy is developed using unique position measurements. Multi-target tracking among different types of sensors is facing great challenge in fully utilizing various types of measurements. To this end, this paper presents a measurement fusion algorithm of single active and multi-passive sensors (SAMPS) based on angle association (AA), named SAMPS-AA algorithm, for multi-target tracking. Firstly, in order to narrow down the association range, the common angle measurements of two types of sensors are extracted by the proposed effective screening algorithm. Then, an exclusion strategy of wrong association groups is developed by building statistics, which is based on angle measurements. Subsequently, coordinates of fused measurements are obtained by angle association, based on least squares (LS). Finally, another exclusion strategy of wrong measurement points is proposed via measurement characteristics of active sensor. Experimental results indicate that the proposed SAMPS-AA algorithm can fully combine advantages of these two types of sensors, effectively exclude as many wrong association groups as possible, efficiently reduce the computational complexity, and obviously improve the tracking accuracy. [ABSTRACT FROM AUTHOR]

Feature extraction is one of the fundamental problems in pattern recognition research. For image recognition, extracting effective image features is the key to accomplish the recognition task. In this paper, a partial differential equations-based multi-rank two-dimensional canonical correlation analysis (PDEs-MR2DC2A) is proposed for multi-view feature extraction and pattern classification. Unlike most of the previous researches on multi-view algorithms that work directly on the original 2D representation, in our approach, we first utilize the evolution process of PDEs to extract the feature matrix of per-view. In addition, we employ multi-rank left and right projecting matrices to maximize the correlation. The computational complexity of PDEs-MR2DC2A is also analyzed. To evaluate the effectiveness of the proposed algorithm, we conducted a series of performance comparisons with some existing methods on several popular datasets. The experimental results showed that our proposed algorithm performed very well on these datasets and outperformed the existing related methods on some metrics. • A novel multi-rank multi-view algorithm based on PDEs is proposed. • This approach can extract rotation and translation invariant features. • Numerous experimental results demonstrate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

The rapid development of computer and database technologies has led to the high growth of large-scale datasets. This produces an important issue for data mining applications called the curse of dimensionality, where the number of features is much higher than the number of patterns. One of the dimensionality reduction approaches is feature selection, which can increase the accuracy of these applications and reduce their computational complexity. This paper proposes a novel feature selection method to reduce the dimensionality and computational complexity in high-dimensional data processing. First, based on the centrality and Fisher score, a probabilistic strategy metric is proposed to measure the influence of features. Second, a new discriminant function is proposed to determine whether one feature should be selected. It can automatically calculate weight parameters to balance the relevance of a feature to class labels and the redundancy of the selected feature subset. Finally, a new method is proposed by combining the maximum information coefficient (MIC), total information (TI) and centrality technique, named MTC_FS. The experimental results show that the average accuracy of the MTC_FS improves by 1.8% compared with the best baseline, and the comprehensive performance of the MTC_FS is superior to all baselines on 12 public datasets. MTC_FS has a shorter runtime on all datasets than all baselines. In addition, the performance of the MTC_FS method is the most stable on the NBayes classifier. • A metric is proposed to measure the influence of features themselves. • The new discriminant function is proposed to select the optimal feature subset. • Our method can effectively reduce the dimensionality and computational complexity. [ABSTRACT FROM AUTHOR]

Traditional virtual power plants (VPPs) with fixed resource composition and coordination strategies struggle to cost-effectively exploit the flexibility of large-scale resources for adapting variable regulation requirements and resources characteristics. To this end, this paper proposes a dynamic aggregation mechanism to flexibly select and coordinate individual resources for forming aggregators according to grids regulation requirements and resource characteristics. The proposed mechanism is operated through a two-stage dynamic aggregation model comprising resource selection and coordination. Considering the two-stage dynamic aggregation model is a combinational optimization problem with high computational complexity, the submodular optimization method is utilized to swiftly address this problem. First, the complementarity and submodularity of the dynamic aggregation process are formulated to elaborate how the aggregation regulation characteristics (ARCs) evolve with flexible resource composition and coordination. Next, a submodularity-based algorithm is developed to promptly solve dynamic aggregation model under three scenarios, where aggregation operators focus on the resources quantity, quality, and cost-effectiveness, respectively. The polynomial computational complexity of the proposed algorithm has also been evaluated. Simulations using the IEEE 39-bus (New England) system consists of 10,000 flexible resources were executed to assess the submodularity approach. The proposed algorithm demonstrates superior computing speed and better performance guaranteed results (90%, 97%, 90% in three scenarios) compared to other methods—making it more suitable for implementation in practice. • The complementarity and submodularity of dynamic aggregation process are revealed and proved. • A two-stage dynamic aggregation model consists of resources selection and coordination is established. • A dynamic aggregation algorithm is proposed to deal with different aggregation scenarios. • The approximate guarantees and polynomial computational complexity of algorithm are proved. [ABSTRACT FROM AUTHOR]

In recent years, the field of underwater image enhancement has evolved from physics-based approaches to various neural network-based methods, all of which have achieved promising results. However, these state-of-the-art algorithms often demand substantial computational resources, posing challenges for real-time applications and deployment on underwater mobile devices. To tackle this issue, this paper introduces a lightweight network called LiteEnhanceNet for single underwater image enhancement. Specifically, the network adopts depthwise separable convolution as the main building block to reduce computational complexity. The one-shot aggregation connection is employed to effectively extract features from both low-level and intermediate layers. Additionally, appropriate activation functions and the squeeze-and-excitation module are incorporated at suitable positions within the network to reduce computational complexity. Quantitative and qualitative experiments demonstrate that the proposed network achieves superior running speed while maintaining enhancement performance compared to the current state-of-the-art techniques. Our code is available at https://github.com/zhangsong1213/LiteEnhanceNet. [ABSTRACT FROM AUTHOR]

• The sequential fusion issue of non-Gaussian multi-sensor systems is studied. • Global iteration SFF & independent iteration SFF are designed in MEE criterion. • Homology and convergence of the two proposed SFF algorithms are analyzed. According to the minimum error entropy (MEE) criterion in information theory learning (ITL), the fusion filtering problem of non-Gaussian system is studied in this paper. Combined with the advantages of sequential fusion filtering (SFF) in dealing with asynchronous sampling and communication delay, two SFF algorithms are designed under MEE criterion. Firstly, the non-Gaussian multi-sensor system is transformed into a group of non-Gaussian single sensor subsystems. Then, by solving the optimal solution of the cost function corresponding to each subsystem, a set of fixed-point equations relating to the subsystems state is obtained. By using the strategies of global iteration and independent iteration to solve the fixed-point equations, two SFF algorithms based on the MEE criterion are designed, respectively. In addition, the performance including the computational complexity, the correlation between two iteration algorithms and their convergence is analyzed. Finally, simulation results indicate that the proposed SFF methods can effectively deal with the state estimation problem of non-Gaussian multi-sensor system, and can achieve similar fusion filtering accuracy. [ABSTRACT FROM AUTHOR]

Pure quaternion matrix has been widely employed to represent data in real-world applications, such as colour images. Additionally, the imaginary part of quaternion matrix is usually nonnegative due to the natural nonnegativity of real-world data. In this paper, we propose an alternating projection-based algorithm for low rank nonnegative pure quaternion matrix approximation, which can exactly calculate the optimal fixed rank approximation while preserve the pure and nonnegative properties from the given data. More concretely, the proposed algorithm alternatively projects the given quaternion matrix onto the fixed rank quaternion matrix set and nonnegative pure quaternion matrix set in an iterative fashion. Moreover, we establish the theoretical convergence guarantee of the proposed algorithm. To extend the proposed algorithm to large-scaled data, we further propose a randomized algorithm with significant lower computational complexity and comparable accuracy. Numerical experiments on colour images show that our algorithms outperform the other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Despite the great theoretical advancements in the area of Belief Revision, there has been limited success in terms of implementations. One of the hurdles in implementing revision operators is that their specification (let alone their computation), requires substantial resources. On the other hand, implementing a specific revision operator, like Dalal's operator, would be of limited use. In this paper we generalise Dalal's construction, defining a whole family of concrete revision operators, called Parametrised Difference revision operators or PD operators for short. This family is wide enough to cover a wide range of different applications, and at the same time it is easy to represent. In addition to its semantic definition, we characterise the family of PD operators axiomatically (including a characterisation specifically for Dalal's operator), we prove its' compliance with Parikh's relevance-sensitive postulate (P), we study its computational complexity, and discuss its benefits for belief revision implementations. [ABSTRACT FROM AUTHOR]

During the multi-attribute group decision-making (MAGDM) processing, the individuals often hold different opinions about the alternatives. It is necessary to aggregate the different individual opinions into a unified group opinion. In the real world, experts sometimes use linguistic expressions to evaluate attributes in uncertain environments. To address the problem of reducing the information loss of expert opinion aggregation in MAGDM, this paper proposes a MAGDM approach based on linguistic concept lattices in the context of uncertain linguistic expression. A linguistic concept lattice for multi-expert linguistic formal context is first constructed based on linguistic truth-valued lattice implication algebra, which can express both comparable and incomparable linguistic information in the decision-making process. Different expert opinions are aggregated via the extent of fuzzy linguistic concepts, which can reduce information loss in the aggregation process. Second, meet-irreducible elements in the linguistic concept lattice are introduced to reduce the computational complexity of obtaining all fuzzy linguistic concepts in the decision-making process. the distance between the intents of different fuzzy linguistic concepts is considered to enhance the rationality of linguistic decision results. In addition, the expert's decision-making process for each alternative is visualized via linguistic concept lattices. Finally, the case study and comparative analysis illustrate the validity and rationality of the proposed approach in MAGDM with linguistic information. • The fuzzy linguistic concepts are used to aggregate expert opinions. • The comparable and incomparable linguistic information is handled in MAGDM. • Visualize the decision-making process of experts by constructing concept lattices. • A novel linguistic concept lattice-based MAGDM approach is proposed. [ABSTRACT FROM AUTHOR]

Streaming services have become a central platform for music consuming. Often, they rely on large archives on which playlists are created. While many playlists are handcrafted, automatic playlist generation is a rising star, as it lets people explore new music easily. Playlist generation relays on some underlying song organization, which is regularly based on embedding methods. The reliability and flexibility of the constructed embedding reflects on the quality of the generated playlists. One type of embedding is content-based, which organizes the songs using raw audio features. Content-based embeddings typically result in a static representation that captures pairwise song similarity. This paper proposes a content-based embedding process that builds a dynamic, instead of a static embedding that is centered around an artist of interest. This embedding mutually organizes seed artist songs into several clusters as well as the rest of the songs in the dataset. The radio is dynamically generated using the distances in the embedded space, while also taking diversity into account. Moreover, it is scalabile in terms of computational complexity. Empirical results are demonstrated on a public dataset named AcousticBrainz, and evaluated by utilizing the Melon dataset, a novel public dataset for playlists. We show that the proposed dynamic embedding and radio generation algorithm yields a more accurate and diverse sequence when compared to other embedding techniques. The proposed methods may be utilized for other recommendation systems tasks that aim to offer the users a diverse recommendation list based on their initial choice. • An song similarity metric based on artists and acoustic features. • A low-dimensional embedding is created with respect to the artist. • Artist-based playlists are dynamically produced from the embedding. • Playlist generation incorporated genre diversity. • Experimental results highlight the accuracy and scalability of the algorithm. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of finding the shortest Dubins path between three consecutive via-points with prescribed initial and final orientations and without a prescribed orientation at the intermediate via-point. The problem plays a crucial role for online path planning in many marine applications, as for example, it is instrumental to solve the Dubins Traveling Salesman Problem. A novel solution is proposed using simple tools borrowed from analytic geometry, and an efficient algorithm is presented as a basic routine for real-time path planning algorithms. Extensive simulations confirmed the efficiency of the proposed strategy in terms of both computational complexity and accuracy of the solution. Moreover, a comparative analysis with recent existing approaches is performed showing the effectiveness of the proposed solution. • A novel method based on analytic geometry is adopted to solve the 3PDP Dubins problem. • The resulting algorithm provides fast and accurate solutions. • It is well fit for real time applications. • The solution gives insightful views to solve the Dubins Traveling Salesman Problem. [ABSTRACT FROM AUTHOR]

As the feature size of semiconductor technology shrinks to 7 nm and beyond, the multiple patterning lithography (MPL) attracts more attention from the industry. In this paper, we model the layout decomposition of MPL as a generalized graph coloring problem, which is addressed by a distribution evolutionary algorithm based on a population of probabilistic model (DEA-PPM). DEA-PPM can strike a balance between decomposition quality and running time, being scalable for varied settings of mask number and lithography resolution. Thanks to the robustness of the DEA-PPM, we get an alternative technique for multiple patterning layout decomposition in next-generation technology nodes. • Scalable multiple patterning layout decomposition framework by minimizing both the conflict number and the stitch number. • Efficient metaheuristics for multiple patterning decomposition of layouts with varied settings of lithography resolution and mask number. • Tailored exploitation strategy to accelerate convergence of the decomposition algorithm. • Compared with the ILP and the SDP method, good balance is struck between the computational complexity and the decomposition quality. [ABSTRACT FROM AUTHOR]

While the non-optimizing variant of multi-agent pathfinding on undirected graphs is known to be a polynomial-time problem since almost forty years, a similar result has not been established for directed graphs. In this paper, it will be shown that this problem is NP-complete. For strongly connected directed graphs, however, the problem is polynomial. And both of these results hold even if one allows for synchronous rotations on fully occupied cycles. Interestingly, the results apply also to the so-called graph motion planning feasibility problem on directed graphs. [ABSTRACT FROM AUTHOR]

In this paper, the minimum-time strategy optimization problem for NEGs with bankruptcy mechanism is considered based on semi-tensor product of matrices. Firstly, in order to describe the impact of bankruptcy mechanism on evolution, a novel matrix called controlled state transition matrix is constructed by deleting all invalid states, which will also reduce the computational complexity of subsequent analysis and design. Secondly, by assorting to the redefined matrix, the minimum-time strategy optimization problem for NEGs with bankruptcy mechanism can be solved by designing state feedback control which can guarantee the convergence of states without violating the bankruptcy mechanism. Meanwhile, the verification condition is derived to verify the existence of the state feedback control, and an algorithm for seeking all possible minimum-time state feedback controllers is constructed. Finally, a typical example is illustrated to validate the effectiveness of the proposed method. • The minimum-time control is first introduced into the study of NEGs. • The introduction of heterogeneous memory expands NEGs with bankruptcy mechanism. • A new approach is proposed to study NEGs with bankruptcy mechanism. • The new approach can study the "global" convergence problem. • The new approach can also appropriately reduce the computational cost. [ABSTRACT FROM AUTHOR]

The working environment of industrial fields where the Particulate Reinforced Composites (PRCs) are commonly used is complex. In these harsh environments, PRCs produce some nonlinear strains, such as plastic, thermal, and creep strains, which affect the performance of PRCs to some extent. In addition, the actual structure of PRCs contains a massive amount of inclusions, and the number of inclusions could reach the magnitude of millions or even tens of millions. For the large-scale numerical simulation of PRCs, ordinary displacement finite element requires a large number of meshes, which increases their computational complexity and unsolvability. For real PRCs with random particle distribution, homogenisation theory does not accurately capture the true state of stress concentration in PRCs. The Voronoi cell finite element method (VCFEM) can be a good solution to the drawbacks of the above two numerical simulation methods. The subject of the present work explores a modified complementary energy generalized function that takes the constitutive relationship of nonlinear strain into account, proposing a two-dimensional VCFEM, formulated with plastic, thermal, and creep strain. The simulation results are compared with the computed results of a commercial finite element software Marc to verify the validity and accuracy of the VCFEM. Results of the comparison show that the VCFEM has the advantages of simple meshing and faster computation under the same precision. The VCFEM, considering plastic, thermal, and creep strains proposed in this paper, lays the foundation for future large-scale numerical simulations of PRCs containing a large quantity of inclusion structures and can realistically capture the stress concentration state of PRCs. At the end of the article, the impact of the order of stress function on the calculation results is discussed. [ABSTRACT FROM AUTHOR]

The distance-minimizing data-driven computational mechanics has great potential in engineering applications by eliminating material modeling error and uncertainty. In this computational framework, the solution-seeking procedure relies on minimizing the distance between the constitutive database and the conservation law. However, the distance calculation is time-consuming and often takes up most of the computational time in the case of a huge database. In this paper, we show how to use quantum computing to enhance data-driven computational mechanics by exponentially reducing the computational complexity of distance calculation. The proposed method is not only validated on the quantum computer simulator Qiskit, but also on the real quantum computer from OriginQ. We believe that this work represents a promising step towards integrating quantum computing into data-driven computational mechanics. [ABSTRACT FROM AUTHOR]

"Black box" models created by modern machine learning techniques are typically hard to interpret. Thus, the necessity of explainable artificial intelligence (XAI) has grown for understanding the rationale behind those models and converting them into white boxes. Random Forest is a black box model essential in various domains due to its flexibility, ease of use, and remarkable predictive performance. One method for explaining a Random Forest is transforming it into a self-explainable Decision Tree using Forest-Based Tree (FBT) algorithm. It basically consists of three main phases, pruning, conjunction set generation, and Decision Tree construction. In this paper, we examine six state-of-the-art pruning approaches and analyze their effect on FBT performance through pruned FBT (PFBT) in order to minimize its computational complexity. This would make it appropriate for forests and datasets of any size. They are assessed on 30 datasets, and the results show that UMEP and Hybrid pruning methods can be effectively used in the pruning stage of the PFBT algorithm in terms of pruning time and predictive performance. However, the AUC-Greedy method achieves good performance with small-size datasets. [ABSTRACT FROM AUTHOR]