With the continuous increase of IoT devices, multiple devices desire to use network resources simultaneously during peak hours and overload conditions, leading to collisions and a reduction in the network's effect...
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With the vigorous development of the integrated transportation network, the choice of passenger travel route is becoming more and more diversified. Therefore, it is important to build the integrated transportation net...
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Automatically solving math word problems,which involves comprehension,cognition,and reasoning,is a crucial issue in artificial intelligence *** math word problem solvers mainly work on word-level relationship extracti...
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Automatically solving math word problems,which involves comprehension,cognition,and reasoning,is a crucial issue in artificial intelligence *** math word problem solvers mainly work on word-level relationship extraction and the generation of expression solutions while lacking consideration of the clause-level *** this end,inspired by the theory of two levels of process in comprehension,we propose a novel clause-level relationship-aware math solver(CLRSolver)to mimic the process of human comprehension from lower level to higher ***,in the lower-level processes,we split problems into clauses according to their natural division and learn their *** the higher-level processes,following human′s multi-view understanding of clause-level relationships,we first apply a CNN-based module to learn the dependency relationships between clauses from word relevance in a local ***,we propose two novel relationship-aware mechanisms to learn dependency relationships from the clause semantics in a global ***,we enhance the representation of clauses based on the learned clause-level dependency *** expression generation,we develop a tree-based decoder to generate the mathematical *** conduct extensive experiments on two datasets,where the results demonstrate the superiority of our framework.
At present, the freight train formation plan cannot dynamically reflect the changes in freight demand in the transportation market, which makes it difficult to guide the actual transportation production work and ...
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The intermodal travel characteristics and combination selection behaviors of passengers are key issues to providing reasonable, efficient, and safe solutions, ensuring the traffic order, and improving quality. With th...
The rapid development of multi-modal large language models (MLLMs) has positioned visual storytelling as a crucial area in content creation. However, existing models often struggle to maintain temporal, spatial, and n...
This paper attempts first to solve the discrete-time modified algebraic Riccati equation (MARE) when the system model is completely unavailable. To achieve this, a new iterative algorithm is proposed to solve the MARE...
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Extensive studies have explored designing pricing schemes for single-mode or homogeneous networks. The development of bi-modal Macroscopic Fundamental Diagrams (3D-MFD) facilitates real-time coordinated control strate...
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ISBN:
(数字)9798331505929
ISBN:
(纸本)9798331505936
Extensive studies have explored designing pricing schemes for single-mode or homogeneous networks. The development of bi-modal Macroscopic Fundamental Diagrams (3D-MFD) facilitates real-time coordinated control strategies for bi-modal multi-regional urban networks. This research explores the traffic dynamics of mixed networks with urban networks and expressways, proposing two 3D-MFD-based coordinated pricing schemes for bi-modal multi-region urban networks. The first scheme utilizes a Model Predictive Control (MPC) approach to integrating toll rates into the 3D-MFD-based optimization problem. The second scheme applies feedback control theory to simultaneously determine the dynamic price and estimate the value of Time (VOT) and trip length distribution of commuters. The proposed coordinated pricing schemes are further compared with uncoordinated pricing schemes. Numerical examples indicate that coordinated pricing by comprehensively considering the traffic flow and demand across different regions of the urban network achieves more effective congestion alleviation.
Device activity detection and channel estimation are central problems in massive Machine Type Communication (mMTC) scenarios. Given the sparse distribution of active devices, compressive sensing (CS) emerges as a viab...
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Traffic flow forecasting task plays an essential role in intelligent transportation systems. Accurately capturing the intricate spatio-temporal dependencies in traffic network signals is the core of precise prediction...
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