Enhanced CNN for image denoising

Owing to flexible architectures of deep convolutional neural networks (CNNs), CNNs are successfully used for image denoising. However, they suffer from the following drawbacks: (i) deep network architecture is very difficult to train. (ii) Deeper networks face the challenge of performance saturation. In this study, the authors propose a novel method called enhanced convolutional neural denoising network (ECNDNet). Specifically, they use residual learning and batch normalisation techniques to address the problem of training difficulties and accelerate the convergence of the network. In addition, dilated convolutions are used in the proposed network to enlarge the context information and reduce the computational cost. Extensive experiments demonstrate that the ECNDNet outperforms the state-of-the-art methods for image denoising.Comment: CAAI Transactions on Intelligence Technology[J], 201

A Distributed Computation Model Based on Federated Learning Integrates Heterogeneous models and Consortium Blockchain for Solving Time-Varying Problems

The recurrent neural network has been greatly developed for effectively solving time-varying problems corresponding to complex environments. However, limited by the way of centralized processing, the model performance is greatly affected by factors like the silos problems of the models and data in reality. Therefore, the emergence of distributed artificial intelligence such as federated learning (FL) makes it possible for the dynamic aggregation among models. However, the integration process of FL is still server-dependent, which may cause a great risk to the overall model. Also, it only allows collaboration between homogeneous models, and does not have a good solution for the interaction between heterogeneous models. Therefore, we propose a Distributed Computation Model (DCM) based on the consortium blockchain network to improve the credibility of the overall model and effective coordination among heterogeneous models. In addition, a Distributed Hierarchical Integration (DHI) algorithm is also designed for the global solution process. Within a group, permissioned nodes collect the local models' results from different permissionless nodes and then sends the aggregated results back to all the permissionless nodes to regularize the processing of the local models. After the iteration is completed, the secondary integration of the local results will be performed between permission nodes to obtain the global results. In the experiments, we verify the efficiency of DCM, where the results show that the proposed model outperforms many state-of-the-art models based on a federated learning framework

Image Super-resolution with An Enhanced Group Convolutional Neural Network

CNNs with strong learning abilities are widely chosen to resolve super-resolution problem. However, CNNs depend on deeper network architectures to improve performance of image super-resolution, which may increase computational cost in general. In this paper, we present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture by fully fusing deep and wide channel features to extract more accurate low-frequency information in terms of correlations of different channels in single image super-resolution (SISR). Also, a signal enhancement operation in the ESRGCNN is useful to inherit more long-distance contextual information for resolving long-term dependency. An adaptive up-sampling operation is gathered into a CNN to obtain an image super-resolution model with low-resolution images of different sizes. Extensive experiments report that our ESRGCNN surpasses the state-of-the-arts in terms of SISR performance, complexity, execution speed, image quality evaluation and visual effect in SISR. Code is found at https://github.com/hellloxiaotian/ESRGCNN

(Dynamic response of reinforced masonry structure under blast load)

Based on the numerical analysis with LS-DYNA, the deformation and damage of the reinforced masonry walls were investigated by considering the following factors：ｗall-borne constraint, masonry material, strength grade, vertical reinforcement ratio, height-to-span ratio, load peak, distance from explosive center to wall, a hole in the wall and GFRP on the wall. And the distance-time curves of the walls were obtained as well as the stress-time curves of the masonry materials and steel bars. Meanwhile, the anti-explosion performances of the reinforced masonry walls were compared in the different cases and the important factors influencing the structure response were determinded. It is helpful for the anti-explosion design about the reinforced masonry structure

(Research on anti-explosion performance of masonry buildings with bottom frames under blast loading)

The three dimensional model of masonry buildings with bottom frames and seven-storey was set up with explicit dynamic analytical software LS-DYNA. GSA criteria analysis method was carried out in the numerical analysis of the masonry buildings with bottom frames’ progressive collapse. There are some disadvantages of GSA criteria analysis method in the aspect of the simulative truth, so two-stage analysis method was created against the disadvantages of GSA criteria analysis methods. The structural damage was first at local, and the local damage was the reason why the progressive collapse of the structure happened. If we use some countermeasures to prevent the structural local damages, the progressive collapse of the structure may not happen. Various masonry material strengths, reinforced ratios of the vertical bar, GFRP strengthening, steel plate strengthening, arrangement of columns, wall openings and building storey number of the bottom frames were selected to verify the model. The dynamic response laws and destructive condition of the masonry buildings with bottom frames under explosive shock were obtained. The rule of the stress and the displacement of the masonry and reinforcing bars were given at the same time. All these works offer the theoretical principle and meritorious reference data for anti-explosion design of masonry buildings with bottom frames