A deep deformable residual learning network for SAR images segmentation

Reliable automatic target segmentation in Synthetic Aperture Radar (SAR) imagery has played an important role in the SAR fields. Different from the traditional methods, Spectral Residual (SR) and CFAR detector, with the recent adavance in machine learning theory, there has emerged a novel method for SAR target segmentation, based on the deep learning networks. In this paper, we proposed a deep deformable residual learning network for target segmentation that attempts to preserve the precise contour of the target. For this, the deformable convolutional layers and residual learning block are applied, which could extract and preserve the geometric information of the targets as much as possible. Based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set, experimental results have shown the superiority of the proposed network for the precise targets segmentation

Accelerating Deep Reinforcement Learning With the Aid of Partial Model: Energy-Efficient Predictive Video Streaming

Predictive power allocation is conceived for energy-efficient video streaming over mobile networks using deep reinforcement learning. The goal is to minimize the accumulated energy consumption of each base station over a complete video streaming session under the constraint that avoids video playback interruptions. To handle the continuous state and action spaces, we resort to deep deterministic policy gradient (DDPG) algorithm for solving the formulated problem. In contrast to previous predictive power allocation policies that first predict future information with historical data and then optimize the power allocation based on the predicted information, the proposed policy operates in an on-line and end-to-end manner. By judiciously designing the action and state that only depend on slowly-varying average channel gains, we reduce the signaling overhead between the edge server and the base stations, and make it easier to learn a good policy. To further avoid playback interruption throughout the learning process and improve the convergence speed, we exploit the partially known model of the system dynamics by integrating the concepts of safety layer, post-decision state, and virtual experiences into the basic DDPG algorithm. Our simulation results show that the proposed policies converge to the optimal policy that is derived based on perfect large-scale channel prediction and outperform the first-predict-then-optimize policy in the presence of prediction errors. By harnessing the partially known model, the convergence speed can be dramatically improved

Logistics Forecasting Using Improved Fuzzy Neural Networks System

In this paper, we proposed and trained a fuzzy neural network system to estimate future logistics demand. The structure of neural network in the system is similar to that of BP network, except that here the nonlinear sigmoid functions in the networks are replaced by fuzzy reasoning process and wavelet functions respectively. Moreover, the trained network system is put into practical logistics demand forecasting. The experimental results show that it has good properties such as a fast convergence, high precision and strong function approximation ability and is good at predicting future logistics amount

Exploring the relationship between teacher growth mindset, grit, mindfulness, and EFL teachers’ well-being

IntroductionThis study examines the relationship between teacher growth mindset, mindfulness, grit, and teacher well-being, with a particular emphasis on the mediating role of grit.MethodsThe study involved 547 Chinese EFL teachers as participants. Data collection utilized validated measures of growth mindset, mindfulness, grit, and occupational well-being. Structural equation modeling was employed to analyze the data and investigate the proposed relationships.ResultsThe findings reveal several important relationships. Firstly, both teacher growth mindset and teacher grit exhibit a direct positive influence on teacher well-being. Secondly, teacher grit acts as a mediator in the connection between teacher mindfulness and teacher occupational well-being. This suggests that the positive impact of mindfulness on well-being is, in part, explained by the presence of grit.DiscussionThese findings significantly contribute to our comprehension of the factors influencing teacher well-being. They underscore the importance of cultivating growth mindset, mindfulness, and grit in educational contexts. Moreover, the implications of these findings for teacher training and support programs are discussed

Impacts of FDI Renewable Energy Technology Spillover on China's Energy Industry Performance

Environmental friendly renewable energy plays an indispensable role in energy industry development. Foreign direct investment (FDI) in advanced renewable energy technology spillover is promising to improve technological capability and promote China’s energy industry performance growth. In this paper, the impacts of FDI renewable energy technology spillover on China’s energy industry performance are analyzed based on theoretical and empirical studies. Firstly, three hypotheses are proposed to illustrate the relationships between FDI renewable energy technology spillover and three energy industry performances including economic, environmental, and innovative performances. To verify the hypotheses, techniques including factor analysis and data envelopment analysis (DEA) are employed to quantify the FDI renewable energy technology spillover and the energy industry performance of China, respectively. Furthermore, a panel data regression model is proposed to measure the impacts of FDI renewable energy technology spillover on China’s energy industry performance. Finally, energy industries of 30 different provinces in China based on the yearbook data from 2005 to 2011 are comparatively analyzed for evaluating the impacts through the empirical research. The results demonstrate that FDI renewable energy technology spillover has positive impacts on China’s energy industry performance. It can also be found that the technology spillover effects are more obvious in economic and technological developed regions. Finally, four suggestions are provided to enhance energy industry performance and promote renewable energy technology spillover in China

Experimental and numerical simulation study of perforation effect of steel pipes subject to the impact loadings of ASC and LSC jets

The perforation effect of steel pipes subjected to the circular-shaped charge (ASC) and linear-shaped charge (LSC) jet were studied by experimental research, and the explicit nonlinear dynamic finite element computer code LS-DYNA was adapted to study the nonlinear responses of the steel pipes, which subjected to the impact of the two different jets, using Lagrangian-Eulerian coupling method. The deformation process and the stress of the steel pipes were described and analyzed, and the simulation results are in good agreement with the experiment data. The studies indicated that under the impact of ASC jet, the steel pipe got a circular incision and a deformation process of local perforation, flocculent shear lip forming and axial shock. Under the impact of LSC jet, the steel pipe got a ship-type incision and a deformation process of coupling of local perforation and dent, whole bending and radial shock. The formation of flocculent shear lip attributes to the radial stress concentration. Under the impact of LSC jet, the whole bending leads to the axial stretch and tearing of the cut tip, and there is a bigger radial plastic deformation area than the damage effect for the impact of ASC jet