The modern complicated manufacturing industry and smart manufacturing tendency have imposed new requirements on the scheduling method,such as self-regulation and self-learning *** traditional scheduling methods cannot...
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The modern complicated manufacturing industry and smart manufacturing tendency have imposed new requirements on the scheduling method,such as self-regulation and self-learning *** traditional scheduling methods cannot meet these needs due to their ***-learning is an inherent ability of reinforcement learning(RL) algorithm inhered from its continuous learning and trial-and-error ***-regulation of scheduling could be enabled by the emerging digital twin(DT) technology because of its virtual-real mapping and mutual control *** paper proposed a DT-enabled adaptive scheduling based on the improved proximal policy optimization RL algorithm,which was called explicit exploration and asynchronous update proximal policy optimization algorithm(E2APPO).Firstly,the DT-enabled scheduling system framework was designed to enhance the interaction between the virtual and the physical job shops,strengthening the self-regulation of the scheduling ***,an innovative action selection strategy and an asynchronous update mechanism were proposed to improve the optimization algorithm to strengthen the self-learning ability of the scheduling ***,the proposed scheduling model was extensively tested in comparison with heuristic and meta-heuristic algorithms,such as wellknown scheduling rules and genetic algorithms,as well as other existing scheduling methods based on reinforcement *** comparisons have proved both the effectiveness and advancement of the proposed DT-enabled adaptive scheduling strategy.
The development of new energy represents a significant area of interest and activity at the national and international levels. The advent of new energy prediction technology has the potential to revolutionize real-tim...
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This paper is a basic study for the development of a simulator algorithm to generate optimal robot motion and path planning for a multi-D.O.F(Degree of Freedom) gantry-type welding robot system. The main idea of the s...
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The inherent sparsity of LiDAR data often leads to extremely sparse depth maps, which poses a challenge for the development of LiDAR-based egocentric vehicles, such as self-driving cars and mobile robots. To overcome ...
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The inherent sparsity of LiDAR data often leads to extremely sparse depth maps, which poses a challenge for the development of LiDAR-based egocentric vehicles, such as self-driving cars and mobile robots. To overcome this limitation, guided depth completion methods use calibrated camera images to create precise, dense depth predictions from sparse LiDAR data. However, the extreme reliance on camera image limits its generalization of guided depth completion, especially its robustness to weather and light. In this paper, we aim to utilize camera images only during training phase to improve unguided depth completion, and discard camera in the inference phase. We comprehensively analyze the pivotal role of camera images in the depth completion task and emphasize the significance of the frequency distribution within the local windows, quantitatively demonstrating its substantial contribution. Subsequently, we introduce cross-modality knowledge distillation to align LiDAR features with camera features in the frequency domain, yielding corresponding guidance features. We devise a guidance and selection module to mitigate unavoidable inaccuracies in knowledge distillation, while it can enhance depth features and adeptly selects more precise encoded values from both the guidance branch and the unguided input. To further refine the completion result, we propose a progressive depth completion module incorporating two sub-networks connected by an attention for refinement module. This module produces weighted features from the decoder of the first stage to enhance the features in the encoder of the second stage. We denominate our method as Better Unguided Network (BUNet) and evaluate its efficacy on the KITTI depth completion benchmark and NYUv2 dataset, demonstrating its superiority over methods that exclude camera images during the inference phase. IEEE
In recent years, the impedance modeling of offshore wind power through flexible direct transmission system has been extensively studied, but the influence of impedance coupling is often ignored. Meanwhile, the rise of...
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Automatic access control system based on Raspberry Pi and Arduino UNO development board uses the CSI camera of Raspberry Pi to capture the face information, compare it with the face database, judge whether it meets th...
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In this paper, a predefined-time non-singular terminal sliding mode controller is proposed to control the trajectory of quadrotor unmanned aerial *** the position loop, the nonlinear disturbance observer compensates f...
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The identification of key nodes plays an important role in improving the robustness of the transportation *** different types of transportation networks,the effect of the same identification method may be *** is of pr...
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The identification of key nodes plays an important role in improving the robustness of the transportation *** different types of transportation networks,the effect of the same identification method may be *** is of practical significance to study the key nodes identification methods corresponding to various types of transportation *** on the knowledge of complex networks,the metro networks and the bus networks are selected as the objects,and the key nodes are identified by the node degree identification method,the neighbor node degree identification method,the weighted k-shell degree neighborhood identification method(KSD),the degree k-shell identification method(DKS),and the degree k-shell neighborhood identification method(DKSN).Take the network efficiency and the largest connected subgraph as the effective *** results show that the KSD identification method that comprehensively considers the elements has the best recognition effect and has certain practical significance.
In this research,a novel dynamic and heterogeneous identity based cooperative co-evolutionary algorithm(DHICCA)is proposed for addressing the distributed lot-streaming flowshop scheduling problem(DLSFSP)with the objec...
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In this research,a novel dynamic and heterogeneous identity based cooperative co-evolutionary algorithm(DHICCA)is proposed for addressing the distributed lot-streaming flowshop scheduling problem(DLSFSP)with the objective to minimize the makespan.A two-layer-vector representation is devised to bridge the solution space of DLSFSP and the search space of *** the evolution of DHICCA,population individuals are endowed with heterogeneous identities according to their quality,including superior individuals,ordinary individuals,and inferior individuals,which serve local exploitation,global exploration,and diversified restart,*** individuals with different identities require different evolutionary mechanisms to fully unleash their respective potentials,identity-specific evolutionary operators are devised to evolve them in a cooperative co-evolutionary *** is important to use limited population resources to solve complex optimization ***,exploitation is carried out on superior individuals by devising three exploitative operators with different intensities based on techniques of variable neighborhood,destruction-construction,and gene *** is executed on ordinary individuals by a newly constructed discrete Jaya algorithm and a probability crossover *** addition,restart is performed on inferior individuals to introduce new evolutionary individuals to the *** the cooperative co-evolution,all individuals with different identities are merged as a population again,and their identities are dynamically adjusted by new *** influence of parameters on the algorithm is investigated based on design-of-experiment and comprehensive computational experiments are used to evaluate the performance of all *** results validate the effectiveness of special designs and show that DHICCA performs more efficient than the existing state-of-the-art algorithms in solving the DLSFSP.
The formation of impurity bridges in transformer oil can be altered by the oil flow, which in turn influences the breakdown progress of contaminated oil. In this paper, a solid–liquid two-phase flow model that incorp...
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