Anderson accelerated augmented Lagrangian for extended waveform inversion

The augmented Lagrangian (AL) method provides a flexible and efficient framework for solving extended-space full-waveform inversion (FWI), a constrained nonlinear optimization problem whereby we seek model parameters and wavefields that minimize the data residuals and satisfy the wave equation constraint. The AL-based wavefield reconstruction inversion, also known as iteratively refined wavefield reconstruction inversion, extends the search space of FWI in the source dimension and decreases sensitivity of the inversion to the initial model accuracy. Furthermore, it benefits from the advantages of the alternating direction method of multipliers (ADMM), such as generality and decomposability for dealing with non-differentiable regularizers, e.g., total variation regularization, and large scale problems, respectively. In practice any extension of the method aiming at improving its convergence and decreasing the number of wave-equation solves would have a great importance. To achieve this goal, we recast the method as a general fixed-point iteration problem, which enables us to apply sophisticated acceleration strategies like Anderson acceleration. The accelerated algorithm stores a predefined number of previous iterates and uses their linear combination together with the current iteration to predict the next iteration. We investigate the performance of the proposed accelerated algorithm on a simple checkerboard model and the benchmark Marmousi II and 2004 BP salt models through numerical examples. These numerical results confirm the effectiveness of the proposed algorithm in terms of convergence rate and the quality of the final estimated model.

A new feature extraction approach based on non linear source separation

A new feature extraction approach is proposed in this paper to improve the classification performance in remotely sensed data. The proposed method is based on a primary sources subset (PSS) obtained by nonlinear transform that provides lower space for land pattern recognition. First, the underlying sources are approximated using multilayer neural networks. Given that, Bayesian inferences update unknown sources’ knowledge and model parameters with information’s data. Then, a source dimension minimizing technique is adopted to provide more efficient land cover description. The support vector machine (SVM) scheme is developed by using feature extraction. The experimental results on real multispectral imagery demonstrates that the proposed approach ensures efficient feature extraction by using several descriptors for texture identification and multiscale analysis. In a pixel based approach, the reduced PSS space improved the overall classification accuracy by 13% and reaches 82%. Using texture and multi resolution descriptors, the overall accuracy is 75.87% for the original observations, while using the reduced source space the overall accuracy reaches 81.67% when using jointly wavelet and Gabor transform and 86.67% when using Gabor transform. Thus, the source space enhanced the feature extraction process and allow more land use discrimination than the multispectral observations.

Transceiver error reduction by design prototype system based on neural network analysis method

<p><span>Code words traditional can be decoding when applied in artificial neural network. Nevertheless, explored rarely for encoding of artificial neural network so that it proposed encoder for artificial neural network forward with major structure built by Self Organizing Feature Map (SOFM). According to number of bits codeword and bits source mentioned the dimension of forward neural network at first then sets weight of distribution proposal choosing after that algorithm appropriate using for sets weight initializing and finally sets code word uniqueness check so that matching with existing. The spiking neural network (SNN) using as decoder of neural network for processing of decoding where depending on numbers of bits codeword and bits source dimension the spiking neural network structure built at first then generated sets codeword by network neural forward using for train spiking neural network after that when whole error reached minimum the process training stop and at last sets code word decode accepted. In tests simulation appear that feasible decoding and encoding neural network while performance better for structure network neural forward a proper condition is achieved with γ node output degree. The methods of mathematical traditional can not using for decoding generated Sets codeword by encoder network of neural so it is prospect good for communication security. </span></p>

Field surveys and numerical modeling of the December 2018 Anak Krakatau volcanic tsunami

&lt;p&gt;We report results of field surveys and numerical modeling of the tsunami generated by the Anak Krakatau volcano eruption on 22 December 2018. We conducted two sets of field surveys of the coastal areas destroyed by the Anak Krakatau tsunami in 26-30 December 2018 and 4-10 January 2020. Field surveys provided information about the maximum tsunami height as well as the most damaged area. The maximum tsunami height was up to 13 m. Most locations registered a wave height of 3-4 m. Tsunami inundation was limited to approximately 100 m. For modeling, we considered 12 source models and conducted numerical modeling. The scenarios have source dimensions of 1.5&amp;#8211;4&amp;#8239;km and initial tsunami amplitudes of 10&amp;#8211;200&amp;#8239;m. By comparing observed and simulated waveforms, we constrained the tsunami source dimension and initial amplitude in the ranges of 1.5&amp;#8211;2.5&amp;#8239;km and 100&amp;#8211;150&amp;#8239;m, respectively. The best source model involves potential energy of 7.14&amp;#8239;&amp;#215;&amp;#8239;10&lt;sup&gt;13&lt;/sup&gt;&amp;#8211;1.05&amp;#8239;&amp;#215;&amp;#8239;10&lt;sup&gt;14&lt;/sup&gt;&amp;#8239;J which is equivalent to an earthquake of magnitude 6.0&amp;#8211;6.1.&lt;/p&gt;

Sensitivity Analysis of Deviation Source for Fast Assembly Precision Optimization

Assembly precision optimization of complex product has a huge benefit in improving the quality of our products. Due to the impact of a variety of deviation source coupling phenomena, the goal of assembly precision optimization is difficult to be confirmed accurately. In order to achieve optimization of assembly precision accurately and rapidly, sensitivity analysis of deviation source is proposed. First, deviation source sensitivity is defined as the ratio of assembly dimension variation and deviation source dimension variation. Second, according to assembly constraint relations, assembly sequences and locating, deviation transmission paths are established by locating the joints between the adjacent parts, and establishing each part’s datum reference frame. Third, assembly multidimensional vector loops are created using deviation transmission paths, and the corresponding scalar equations of each dimension are established. Then, assembly deviation source sensitivity is calculated by using a first-order Taylor expansion and matrix transformation method. Finally, taking assembly precision optimization of wing flap rocker as an example, the effectiveness and efficiency of the deviation source sensitivity analysis method are verified.