Immediately after a destructive earthquake, the real-time seismological community has a major focus on rapidly estimating the felt area and the extent of ground shaking. This estimate provides critical guidance for government emergency response teams to
The field of RL theory is dominated by attempts to arrive at rigorous convergent proofs for their methods. However, several theoretically sound machine learning algorithms cannot be used without alterations in praxis as their proven convergence is far too slow in real world problems with large state...
This release of CO22 comes, primarily, from the combustion of fossil fuels, which has led to a gradual rise in global temperatures. Since the Paris Climate Agreement in 2015 and the Kyoto Protocol in 2005, governments around the world have taken action to address climate change. The carbon ...
As shown in these results, the MLEs are convergent. Table 1. The maximum likelihood estimators (MLEs) and their mean squared errors (MSEs). 6.2. Test Statistic Y 2 For testing the null hypothesis H 0 that the “right censored” data become from the RG-LL model, we computed the ...
As shown in these results, the MLEs are convergent. Table 1. The maximum likelihood estimators (MLEs) and their mean squared errors (MSEs). 6.2. Test Statistic Y 2 For testing the null hypothesis H 0 that the “right censored” data become from the RG-LL model, we computed the ...
In order to determine the transformation relationship between 2D image pixel coordinates and world 3D coordinates, it is necessary to obtain the parameters related to the transformation through camera calibration to complete the reconstruction of the 3D scene. Figure 6 shows the imaging process of a ...
Compute the batch gradient of D0. for t = 1, 2, … do Gradient arrives from batch set Dt; Update the parameters; Send θt to batch set Dt; Compute the batch gradient of Dt. end for Output: Convergent deep learning model. When using SGD-based optimization algorithms to update the para...
Firstly, we elect a tiny version of YOLO as the backbone and integrate the VaryBlock module into the network structure. Secondly, WGAN is applied to expand the training dataset of small objects. Finally, we use the unsupervised learning algorithm k-means++ to obtain the best-preset boundary ...
robot location), or a uniform flow (defined as a flow with constant speed in a prespecified direction) plus a doublet (representing the obstacle), and their superposition, or (2) velocity potential solution to the Laplace's Equation with appropriate boundary conditions, to create a new HPF. ...