In response to the issues of low solution efficiency, poor path planning quality, and limited search completeness in narrow passage environments associated with Rapidly-exploring Random Tree (RRT), this paper proposes...
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In response to the issues of low solution efficiency, poor path planning quality, and limited search completeness in narrow passage environments associated with Rapidly-exploring Random Tree (RRT), this paper proposes a Grid-based Variable Probability Rapidly-exploring Random Tree algorithm (GVP-RRT) for narrow passages. The algorithm introduced in this paper preprocesses the map through gridization to extract features of different path regions. Subsequently, it employs random growth with variable probability density based on the features of path regions using various strategies based on grid, probability, and guidance to enhance the probability of growth in narrow passages, thereby improving the completeness of the algorithm. Finally, the planned route is subjected to path re-optimization based on the triangle inequality principle. The simulation results demonstrate that the planning success rate of GVP-RRT in complex narrow channels is increased by 11.5-69.5% compared with other comparative algorithms, the average planning time is reduced by more than 50%, and the GVP-RRT has a shorter average planning path length.
This paper proposes a metasurface filter integrated with a base station antenna for sub-6 GHz 5G communications. The metasurface filter, composed of a pixel-based geometric structure, is optimized using the conformati...
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ISBN:
(纸本)9781665494182
This paper proposes a metasurface filter integrated with a base station antenna for sub-6 GHz 5G communications. The metasurface filter, composed of a pixel-based geometric structure, is optimized using the conformational space annealing (CSA) algorithm. The proposed metasurface filter exhibits bandpass characteristics around the 3.5 GHz frequency, commonly used in sub-6 GHz 5G networks. The performance of the metasurface filter combined with a 4x1 patch antenna array was measured in a fully anechoic chamber. The measured gain pattern shows that the metasurface filter passes the signal with a small insertion loss in the passband and effectively suppresses out-of-band frequencies, providing frequency-selective performance.
The active metamodel is hard to represent the creep-fatigue failure, which hinders the application of efficient active metamodeling technique in creep-fatigue reliability estimation. To improve the computing efficienc...
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The active metamodel is hard to represent the creep-fatigue failure, which hinders the application of efficient active metamodeling technique in creep-fatigue reliability estimation. To improve the computing efficiency and accuracy of creep-fatigue reliability assessment for turbine disk involving complex coupling of multi-layer, multi-disciplinary, and multi-uncertainty, the efficient active metamodeling technique is first pushed deep into complex creep-fatigue reliability evaluation. By integrating the synergic surrogate strategy into the active metamodeling, a multi-layer surrogate control-based synergic enhanced Kriging (MSC-SEK) approach is proposed: Firstly, to precisely describe the complicated creep-fatigue strong-coupling relationships, a synergic enhanced Kriging (SEK) is established by organically synergizing multiple Kriging models, where a multi-colony multi-mutation artificial bee colony algorithm is designed to enhance the Kriging surrogate quality;further, to obtain high-quality modeling dataset, a novel MSC learning function is developed by synthetically considering multi-surrogate entropy and reliability-sensitive information. The superiority of MSC-SEK is validated by studying the creep-fatigue reliability evaluation of a typical aeroengine turbine disk. The current efforts open up an effective way to achieve high-accuracy and high-efficiency engineering creep-fatigue reliability evaluation.
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.
Accurate identification of coal and gangue is a crucial guarantee for efficient and safe mining of top coal caving face. This article proposes a coal-gangue recognition method based on an improved beluga whale optimiz...
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Accurate identification of coal and gangue is a crucial guarantee for efficient and safe mining of top coal caving face. This article proposes a coal-gangue recognition method based on an improved beluga whale optimization algorithm (IBWO), convolutional neural network, and long short-term memory network (CNN-LSTM) multi-modal fusion model. First, the mutation and memory library mechanisms are introduced into the beluga whale optimization to explore the solution space fully, prevent falling into local optimum, and accelerate the convergence process. Subsequently, the image mapping of the audio signal and vibration signal is performed to extract Mel-Frequency Cepstral Coefficients (MFCC) features, generating rich sample data for CNN-LSTM. Then the multi-head attention mechanism is introduced into CNN-LSTM to speed up the training speed and improve the classification accuracy. Finally, the IBWO-CNN-LSTM coal-gangue recognition model is constructed by the optimal hyperparameter combination obtained by IBWO to realize the automatic recognition of coal-gangue. The benchmark function proves that IBWO is superior to other optimization algorithms. By building an experimental platform for the impact of coal and gangue falling on the tail beam of hydraulic support, multiple experimental data collection is carried out. The experimental results show that the proposed coal-gangue recognition model has better performance than other recognition models, and the accuracy rate reaches 95.238%. The multi-modal fusion strategy helps to improve the accuracy and robustness of coal-gangue recognition.
Switched reluctance motors (SRMs) offer several advantages, including a magnet- and winding-free rotor, high mechanical strength, and exceptional output efficiency. However, the doubly salient pole structure and high-...
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Switched reluctance motors (SRMs) offer several advantages, including a magnet- and winding-free rotor, high mechanical strength, and exceptional output efficiency. However, the doubly salient pole structure and high-frequency switching power supply result in significant torque ripple and electromagnetic noise, which limit the application in the field of new energy vehicles. To address these issues, this paper proposes a direct instantaneous torque control (DITC) strategy based on an optimal switching angle torque sharing function (TSF). Firstly, an improved cosine TSF is designed to reasonably distribute the total reference torque among the phases, stabilizing the synthesized torque of SRM during the commutation interval. Subsequently, an improved artificial bee colony (ABC) algorithm is used to obtain the optimal switching angle data at various speeds, integrating these data into the torque distribution module to derive the optimal switching angle model. Finally, the effectiveness of the proposed control strategy is validated through simulations of an 8/6-pole SRM. Simulation results demonstrate that the proposed control strategy effectively suppresses torque ripple during commutation and reduces the peak current at the beginning of phase commutation.
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 letter, we consider an air-and-ground cooperative network, where several aerial base stations (ABS) help terrestrial base stations (TBS) for coverage enhancement. In this network, we first quantify the space-t...
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In this letter, we consider an air-and-ground cooperative network, where several aerial base stations (ABS) help terrestrial base stations (TBS) for coverage enhancement. In this network, we first quantify the space-time coverage ratio (STCR) by fully considering the antenna models and the dynamic of the ABS, and then formulate a joint ABS deployment and TBS antenna downtilt optimization problem with the objective to maximize the STCR of the concerned area. The objective function involves many control variables and judgement operations, which make the problem very complex. To solve the problem effectively, we first adopt the genetic algorithm (GA). Using the solutions of the GA as training samples, we propose a deep neural network architecture to further reduce the computational time. Simulation results indicate that the proposed GA significantly improves the coverage ratio and the deep neural network (DNN) architecture achieves orders of magnitude acceleration in computational time with acceptable performance.
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