Seismic anisotropy has been extensively acknowledged as a crucial element that influences the wave propagation characteristic during wavefield simulation, inversion and imaging. Transversely isotropy (TI) and orthorho...
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Seismic anisotropy has been extensively acknowledged as a crucial element that influences the wave propagation characteristic during wavefield simulation, inversion and imaging. Transversely isotropy (TI) and orthorhombic anisotropy (OA) are two typical categories of anisotropic media in exploration geophysics. In comparison of the elastic wave equations in both TI and OA media, pseudo-acoustic wave equations (PWEs) based on the acoustic assumption can markedly reduce computational cost and complexity. However, the presently available PWEs may experience SV-wave contamination and instability when anisotropic parameters cannot satisfy the approximated condition. Exploiting pure-mode wave equations can effectively resolve the above-mentioned issues and generate pure P-wave events without any artifacts. To further improve the computational accuracy and efficiency, we develop two novel pure qP-wave equations (PPEs) and illustrate the corresponding numerical solutions in the time-space domain for 3D tilted TI (TTI) and tilted OA (TOA) media. First, the rational polynomials are adopted to estimate the exact pure qP-wave dispersion relations, which contain complicated pseudo-differential operators with irrational forms. The polynomial coefficients are produced by applying a linear optimization algorithm to minimize the objective function difference between the expansion formula and the exact one. Then, the developed optimized PPEs are efficiently implemented using the finite-difference (FD) method in the time-space domain by introducing a scalar operator, which can help avoid the problem of spectral-based algorithms and other calculation burdens. Structures of the new equations are concise and corresponding implementation processes are straightforward. Phase velocity analyses indicate that our proposed optimized equations can lead to reliable approximation results. 3D synthetic examples demonstrate that our proposed FD-based PPEs can produce accurate and stable P-wave resp
This study investigates the fissure development law of soil contaminated with varying degrees of lead ions under the influence of dry-wet cycles. The effects of different lead ion concentrations on the development of ...
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This study investigates the fissure development law of soil contaminated with varying degrees of lead ions under the influence of dry-wet cycles. The effects of different lead ion concentrations on the development of fissures in silty clay, red clay, and expansive soil were systematically investigated through dry-wet cycle tests, and parameters such as fissure area and fractal dimension were precisely quantified and analyzed using image processing techniques. The study's results indicate that the presence of lead ions significantly promotes the development of fissure areas in three soils. Based on this, four fractal dimension prediction models, namely, bidirectional long short-term memory network (BiLSTM), gated recurrent unit (GRU), extreme gradient boosting (XGBoost), and regression by relevance vector machine (RVM), are constructed, among which the RVM model exhibits optimal performance. To further improve the prediction accuracy, the sparrow search algorithm (SSA) and particle swarm optimization algorithm (PSO) are introduced to optimize the model parameters, and it is found that the SSA- RVM model performs the best, and its mean squared error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) metrics are reduced by 3.55%, 6.98%, and 16.79% compared with that of the RVM model, respectively. This study supports the optimization of ecological remediation techniques for contaminated soils and the risk assessment of heavy metal-contaminated sites.
Robotic arm is a complex system with multiple inputs and outputs, strong nonlinearity and strong coupling, and the research of high precision trajectory tracking control technology for robotic arm has been an importan...
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Robotic arm is a complex system with multiple inputs and outputs, strong nonlinearity and strong coupling, and the research of high precision trajectory tracking control technology for robotic arm has been an important issue for scholars at home and abroad. This paper takes the six-degree-of-freedom (6-DOF) robotic arm as its study object and designs a fractional-order PID (FOPID) control method. To improve its control accuracy, a parameter tuning method of fractional-order beetle antennae particle swarm algorithm (FBPA) optimized FOPID controller is proposed. This method puts the beetle antennae search (BAS) algorithm together with the particle swarm optimization (PSO) algorithm, introduces the concept of fractional-order calculus into the algorithm, dynamically adjusts the inertial weights and fractional order and finally improves the optimization effect of the algorithm. The simulation experiments of MATLAB/Simulink indicate that in comparison with the traditional PID control method, the FOPID control method optimized by the FBPA has high control accuracy and small overshooting, which meets the high-precision control requirements of the 6-DOF robotic arm.
The aim of Influence Maximization (IM) in social networks is to identify an optimal subset of users to maximize the spread of influence across the network. Fair Influence Maximization (FIM) develops the IM problem wit...
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The aim of Influence Maximization (IM) in social networks is to identify an optimal subset of users to maximize the spread of influence across the network. Fair Influence Maximization (FIM) develops the IM problem with the aim of equitable distribution of influence across communities and enhancing the fair propagation of information. Among the solutions for FIM, community-based techniques enhance performance by effectively capturing the structural properties and ensuring a more equitable influence spread. However, these techniques often ignore the overlapping nature of communities and suffer from a trade-off between complexity and fairness. With this motivation, this study handles the FIM based on Overlapping Community detection under optimization algorithms (FIMOC). FIMOC includes an overlapping community detection approach that can consider the importance of influential overlapping nodes in communities. Meanwhile, FIMOC uses a non-overlapping and overlapping node selection module based on communities to identify potential candidate nodes. Subsequently, FIMOC uses the Open-Source Development Model algorithm (ODMA) as an optimization algorithm to identify the set of influential nodes. Our method considers the dynamic and overlapping nature of social communities, ensuring that the influence spread is not only maximized but also equitably distributed across diverse groups. By leveraging real-world social networks, we demonstrate the effectiveness of our method compared to state-of-the-art methods through extensive experiments. The results show that our method achieves a more balanced influence spread, providing a fairer solution, while also enhancing the overall reach of information dissemination.
This paper presents a fast projected primal-dual method for solving linear-quadratic optimal control problems. The computational efficiency comes from a heavy-ball acceleration and specific (sparse) choices of precond...
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This paper presents a fast projected primal-dual method for solving linear-quadratic optimal control problems. The computational efficiency comes from a heavy-ball acceleration and specific (sparse) choices of preconditioning matrices. To analyse convergence, we first assume that the weighing matrices in the linear quadratic optimal control problems are diagonal, allowing us to propose the preconditioning matrices and study the convergence of the resulting algorithm by writing it a Lur'etype dynamic system. We then employ this preconditioned algorithm for the case that weighting matrices are nondiagonal by applying the preconditioned algorithm repeatedly in a sequentialquadratic programming fashion. Furthermore, it is shown that infeasibility of the optimal control problem can be detected using the Theorem of the Alternatives and the iterates produced by the algorithm. The resulting algorithm is simple, while also achieving competitive computational times. (c) 2025 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).
In this work, we present transformer-based powered descent guidance (T-PDG), a scalable algorithm for reducing the computational complexity of the direct optimization formulation of the spacecraft powered descent guid...
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In this work, we present transformer-based powered descent guidance (T-PDG), a scalable algorithm for reducing the computational complexity of the direct optimization formulation of the spacecraft powered descent guidance problem. T-PDG uses data from prior runs of trajectory optimization algorithms to train a transformer neural network, which accurately predicts the relationship between problem parameters and the globally optimal solution for the powered descent guidance problem. The solution is encoded as the set of tight constraints corresponding to the constrained minimum-cost trajectory and the optimal final landing time. By leveraging the attention mechanism of transformer neural networks, large sequences of time series data can be accurately predicted when given only the spacecraft state and landing site parameters. When applied to the real problem of Mars-powered descent guidance, T-PDG reduces the time for computing the 3-degree-of-freedom fuel-optimal trajectory when compared to lossless convexification, improving solution times by up to an order of magnitude. A safe and optimal solution is guaranteed by including a feasibility check in T-PDG before returning the final trajectory.
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