529 research outputs found
Application of Artificial Neural Networks in Predicting Abrasion Resistance of Solution Polymerized Styrene-Butadiene Rubber Based Composites
Abrasion resistance of solution polymerized styrene-butadiene rubber (SSBR)
based composites is a typical and crucial property in practical applications.
Previous studies show that the abrasion resistance can be calculated by the
multiple linear regression model. In our study, considering this relationship
can also be described into the non-linear conditions, a Multilayer Feed-forward
Neural Networks model with 3 nodes (MLFN-3) was successfully established to
describe the relationship between the abrasion resistance and other properties,
using 23 groups of data, with the RMS error 0.07. Our studies have proved that
Artificial Neural Networks (ANN) model can be used to predict the SSBR-based
composites, which is an accurate and robust process
Establishment of China Information Technology Outsourcing Early Warning Index Based on SVR
AbstractInformation technology outsourcing in China has developed fast, it plays a more and more important role in economic development of China. Economic analysis and early warning system of information technology outsourcing, which reflect the status of ITO, can promote the healthy development of the industry. This paper constructed the indicator system by the method of time difference relevance and peak-valley. The weight vector of each indicator is attained by using support vector regression. It also calculated the comprehensive early warning index and established the early warning index system. At last, we used a group of signal lamps to reflect the status at every time. Based on the reality of ITO in China, this paper found that the development speed of ITO is slowing in recent months, the government should take out some positive measures
Delving StyleGAN Inversion for Image Editing: A Foundation Latent Space Viewpoint
GAN inversion and editing via StyleGAN maps an input image into the embedding
spaces (, , and ) to simultaneously
maintain image fidelity and meaningful manipulation. From latent space
to extended latent space to feature space
in StyleGAN, the editability of GAN inversion decreases while its
reconstruction quality increases. Recent GAN inversion methods typically
explore and rather than to improve
reconstruction fidelity while maintaining editability. As and
are derived from that is essentially the foundation
latent space of StyleGAN, these GAN inversion methods focusing on
and spaces could be improved by stepping back to
. In this work, we propose to first obtain the precise latent code
in foundation latent space . We introduce contrastive learning to
align and the image space for precise latent code discovery. %The
obtaining process is by using contrastive learning to align and
the image space. Then, we leverage a cross-attention encoder to transform the
obtained latent code in into and ,
accordingly. Our experiments show that our exploration of the foundation latent
space improves the representation ability of latent codes in
and features in , which yields state-of-the-art
reconstruction fidelity and editability results on the standard benchmarks.
Project page: \url{https://github.com/KumapowerLIU/CLCAE}
A hybrid Hermite WENO scheme for hyperbolic conservation laws
In this paper, we propose a hybrid finite volume Hermite weighted essentially
non-oscillatory (HWENO) scheme for solving one and two dimensional hyperbolic
conservation laws. The zeroth-order and the first-order moments are used in the
spatial reconstruction, with total variation diminishing Runge-Kutta time
discretization. The main idea of the hybrid HWENO scheme is that we first use a
shock-detection technique to identify the troubled cell, then, if the cell is
identified as a troubled cell, we would modify the first order moment in the
troubled cell and employ HWENO reconstruction in spatial discretization;
otherwise, we directly use high order linear reconstruction. Unlike other HWENO
schemes, we borrow the thought of limiter for discontinuous Galerkin (DG)
method to control the spurious oscillations, after this procedure, the scheme
would avoid the oscillations by using HWENO reconstruction nearby
discontinuities and have higher efficiency for using linear approximation
straightforwardly in the smooth regions. In addition, the hybrid HWENO scheme
still keeps the compactness. A collection of benchmark numerical tests for one
and two dimensional cases are performed to demonstrate the numerical accuracy,
high resolution and robustness of the proposed scheme.Comment: 38 page
Joint Optimization of Preventive Maintenance and Spare Parts Inventory with Appointment Policy
Under the background of the wide application of condition-based maintenance (CBM) in maintenance practice, the joint optimization of maintenance and spare parts inventory is becoming a hot research to take full advantage of CBM and reduce the operational cost. In order to avoid both the high inventory level and the shortage of spare parts, an appointment policy of spare parts is first proposed based on the prediction of remaining useful lifetime, and then a corresponding joint optimization model of preventive maintenance and spare parts inventory is established. Due to the complexity of the model, the combination method of genetic algorithm and Monte Carlo is presented to get the optimal maximum inventory level, safety inventory level, potential failure threshold, and appointment threshold to minimize the cost rate. Finally, the proposed model is studied through a case study and compared with both the separate optimization and the joint optimization without appointment policy, and the results show that the proposed model is more effective. In addition, the sensitivity analysis shows that the proposed model is consistent with the actual situation of maintenance practices and inventory management
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