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A Bi–LSTM and k-NN Based Method for Detecting Major ...
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由 S Park 著作2019被引用 7 次 — In the second step, we propose a new method of automatically identifying top-k time zones with many overloaded vehicles under the high traffic condition. It ...
[PDF] A Bi-LSTM and k-NN Based Method for Detecting Major ...
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A new method of automatically identifying top-k time zones with many overloaded vehicles under the high traffic condition and a new bridge traffic ...
A Bi–LSTM and k-NN Based Method for Detecting Major Time ...
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In the second step, we propose a new method of automatically identifying top-k time zones with many overloaded vehicles under the high traffic condition. It ...
A Bi–LSTM and k-NN Based Method for Detecting Major Time ...
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It first uses the k-Nearest Neighbor (NN) algorithm to find the sensing value, most similar to the actual sensing value of the overloaded vehicle, in the high ...
Overall diagram of the proposed model.
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Park et al. [26] used Bi-LSTM neural network, and K-NN to discover the major time zones of overloaded vehicles to reduce accidents. A pathbased deep learning ...
Vehicle overload detection system based on ...
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The experiment shows that the system can detect vehicle load and realize overload alarm and the system sends the alarm message to the driver's handheld ...
Concerning the Investigation of Overloaded Bridges
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A Bi–LSTM and k-NN Based Method for Detecting Major Time Zones of Overloaded Vehicles · Sang Min Park 1,. Byung-Won On 2,. Ryong Lee 3. et al. 2019. Symmetry.
Ryong Lee | Scholar Profiles and Rankings
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A Bi–LSTM and k-NN Based Method for Detecting Major Time Zones of Overloaded Vehicles (journal article). Park, Sangmin | On, Byung-Won | Lee, Ryong | Park ...
Recognition of ships based on vector sensor and ...
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由 S Li 著作2020被引用 27 次 — A Bi-LSTM and k-NN Based Method for Detecting Major Time Zones of Overloaded. Vehicles. Symmetry, 2019, 11(9), 1160:1-24. [15] Wei Z, Peng J, Cai X, and He G ...
Research on Weigh-in-Motion Algorithm of Vehicles Based ...
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由 S Xu 著作2022被引用 4 次 — This paper proposes a WIM model based on the beetle swarm optimization (BSO) algorithm and the error back propagation (BP) neural network.