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Creators/Authors contains: "Wang, P."

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  1. ; ; ; ; ; ( , Science of The Total Environment)
    Free, publicly-accessible full text available August 1, 2022
  2. ; ; ( , IEEE transactions on audio speech and language processing)
    Full Text Available 
  3. ; ; ; ; ( , Proceedings of the International Conference on Information Fusion (Fusion 2020))
    This paper proposes a computer vision framework aimed to segment hot steel sections and contribute to rolling precision. The steel section dimensions are calculated for the purposes of automating a high temperature rolling process. A structured forest algorithm along with the developed steel bar edge detection and regression algorithms extract the edges of the high temperature bars in optical videos captured by a GoPror camera. To quantify the impact of noises that affect the segmentation process and the final diameter measurements, a weighted variance is calculated, providing a level of trust in the measurements. The results show an accuracy whichmore »is in line with the rolling standards, i.e. with a root mean square error less than 2:5 mm.« less
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  4. ; ; ; ; ; ( , the International Joint Conference on Neural Networks)
    Bayesian neural networks are powerful inference methods by accounting for randomness in the data and the network model. Uncertainty quantification at the output of neural networks is critical, especially for applications such as autonomous driving and hazardous weather forecasting. However, approaches for theoretical analysis of Bayesian neural networks remain limited. This paper makes a step forward towards mathematical quantification of uncertainty in neural network models and proposes a cubature-rule-based computationally efficient uncertainty quantification approach that captures layerwise uncertainties of Bayesian neural networks. The proposed approach approximates the first two moments of the posterior distribution of the parameters by propagating cubaturemore »points across the network nonlinearities. Simulation results show that the proposed approach can achieve more diverse layer-wise uncertainty quantification results of neural networks with a fast convergence rate.« less
    Accepted Manuscript Full Text Available
  5. ; ; ; ; ; ; ; ; ; ; et al ( , Nature)
    Free, publicly-accessible full text available October 14, 2022
  6. ; ; ; ; ; ; ; ; ; ( , Bulletin of the Seismological Society of America)
    We present a database and analyze ground motions recorded during three events that occurred as part of the July 2019 Ridgecrest earthquake sequence: a moment magnitude (M) 6.5 foreshock on a left‐lateral cross fault in the Salt Wells Valley fault zone, an M 5.5 foreshock in the Paxton Ranch fault zone, and the M 7.1 mainshock, also occurring in the Paxton Ranch fault zone. We collected and uniformly processed 1483 three‐component recordings from an array of 824 sensors spanning 10 seismographic networks. We developed site metadata using available data and multiple models for the time‐averaged shear‐wave velocity in the uppermore »30 m (⁠VS30⁠) and for basin depth terms. We processed ground motions using Next Generation Attenuation (NGA) procedures and computed intensity measures including spectral acceleration at a number of oscillator periods and inelastic response spectra. We compared elastic and inelastic response spectra to seismic design spectra in building codes to evaluate the damage potential of the ground motions at spatially distributed sites. Residuals of the observed spectral accelerations relative to the NGA‐West2 ground‐motion models (GMMs) show good average agreement between observations and model predictions (event terms between about −0.3 and 0.5 for peak ground acceleration to 5 s). The average attenuation with distance is also well captured by the empirical NGA‐West2 GMMs, although azimuthal variations in attenuation were observed that are not captured by the GMMs. An analysis considering directivity and fault‐slip heterogeneity for the M 7.1 event demonstrates that the dispersion in the near‐source ground‐motion residuals can be reduced.« less
    Full Text Available 
  7. ; ; ; ; ; ; ; ; ; ; et al ( , Journal of Geophysical Research: Solid Earth)
    Full Text Available 
  8. ; ; ( , NeurIPS workshop: TDA and beyond)
    Accepted Manuscript Full Text Available
  9. ; ; ( , International Conference on Machine Learning)
    Accepted Manuscript Full Text Available
  10. ; ; ( , International Conference on Machine Learning)
    Accepted Manuscript Full Text Available