3,480 research outputs found
DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting
Predicting traffic conditions has been recently explored as a way to relieve
traffic congestion. Several pioneering approaches have been proposed based on
traffic observations of the target location as well as its adjacent regions,
but they obtain somewhat limited accuracy due to lack of mining road topology.
To address the effect attenuation problem, we propose to take account of the
traffic of surrounding locations(wider than adjacent range). We propose an
end-to-end framework called DeepTransport, in which Convolutional Neural
Networks (CNN) and Recurrent Neural Networks (RNN) are utilized to obtain
spatial-temporal traffic information within a transport network topology. In
addition, attention mechanism is introduced to align spatial and temporal
information. Moreover, we constructed and released a real-world large traffic
condition dataset with 5-minute resolution. Our experiments on this dataset
demonstrate our method captures the complex relationship in temporal and
spatial domain. It significantly outperforms traditional statistical methods
and a state-of-the-art deep learning method
Modeling and identification of the dynamic behavior of stranded wire helical springs
A stranded wire helical spring is a cylindrical helical spring wound by a wire strand. Owing to its unique structure, the spring features special dynamic behavior such as nonlinear stiffness, hysteresis and hardening overlap. The dynamic response model, which gives an accurate description of the dynamic behavior, of the spring is a very important tool for designing systems using the spring as well as evaluating the responses of such systems. However, no accurate model has been reported. In the present study, a modified normalized Bouc-Wen model is proposed to model the dynamic behavior of the spring. A simple yet effective identification method is developed for identifying the model parameters using experimental data. Numerical simulations and periodic loading experiments were carried out to validate the proposed model and identification method. The results verify that the proposed model and method are effective for modeling and identifying the dynamic behavior of stranded wire helical springs
Why torus-unstable solar filaments experience failed eruption?
To investigate the factors that control the success and/or failure of solar
eruptions, we study the magnetic field and 3-Dimensional (3D) configuration of
16 filament eruptions during 2010 July - 2013 February. All these events, i.e.,
erupted but failed to be ejected to become a coronal mass ejection (CME), are
failed eruptions with the filament maximum height exceeding . The
magnetic field of filament source regions is approximated by a potential field
extrapolation method. The filament 3D configuration is reconstructed from three
vantage points by the observations of STEREO Ahead/Behind and SDO spacecraft.
We calculate the decay index at the apex of these failed filaments and find
that in 7 cases, their apex decay indexes exceed the theoretical threshold
() of the torus instability. We further determine the
orientation change or rotation angle of each filament top during the eruption.
Finally, the distribution of these events in the parameter space of rotation
angle versus decay index is established. Four distinct regimes in the parameter
space are empirically identified. We find that, all the torus-unstable cases
(decay index ), have a large rotation angles ranging from . The possible mechanisms leading to the rotation and failed eruption
are discussed. These results imply that, besides the torus instability, the
rotation motion during the eruption may also play a significant role in solar
eruptions
Bayesian Calibration of GPU–based DEM meso-mechanics Part II:Calibration of the granular meso-structure
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