Though the interconnected active suspension enhances the performances of roll dynamics along with the primary functions of passive suspension, it requires an external pressurized fluid supply. Also, higher energy is required to attain the variable damping force in an active interconnected system. Therefore, this research attempts to control the vehicle roll without an external fluid supply by novel semi-active based roll-resistant interconnected suspension system (SARR-HIS). The variable damping is achieved by utilizing the pressure developed in the chambers and hydraulic control valve (HCV), while the suspension compresses. The amount of the flow passes through the HCV is controlled by a model predictive controller. In addition, GA based optimization were performed to identify the optimal passive roll-resistant hydraulic interconnected suspension (PRR-HIS) parameters. For the controller design, vehicle plant model is estimated by the simple identification method in Matlab environment. On the AMESim platform, vehicle with SARR-HIS system integrated model was built and controlled in a co-simulation environment. To assess the effectiveness of the proposed suspension and its control strategy, the anti-roll bar (ARB) integrated passive standalone suspension, optimized PRR-HIS, and proposed SARR-HIS systems are tested under C class road roughness without steer and double lane change maneuver.

This paper presents Deep Rain Streaks Removal Convolutional Neural Network (Derain SRCNN) based post-processing optimization algorithm for High-Efficiency Video Coder (HEVC). Earlier, the CNN-based denoising optimization algorithm faced overfitting issues and large convergence time when training the CNN for rain streaks affected High Definition (HD) video sequences. To address these problems, Deep rain streaks removal CNN-based post-processing block is introduced in HEVC encoder. Derain SRCNN architecture consists of a parallel two residual block layer and Dual Channel Rectification Linear Unit (DCReLU) activation function with various sizes of the convolutional layer. By reducing the validate error and training the error of CNN, the overfitting issue is solved. Also, convergence time is reduced using proper learning rate and kernel weight of optimization algorithm. The proposed network provides a higher bit rate reduction and higher convergence speed for corrupted high-definition video sequences. The experiment result shows that proposed DerainSRCNN-based post-processing filtering method achieves 6.8% and 4.1% -bit rate reduction for random access (RA) and low delay [Formula: see text] frame (LDP) configuration, respectively.

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Rainy weather conditions are challenging issues for many computer vision applications. Rain streaks and rain patterns are two crucial environmental factors that degrade the visual appearance of high-definition images. A deep attention network-based single-image deraining algorithm is more famous for handling the image with the statistical rain pattern. However, the existing deraining network suffers from the false detection of rain patterns under heavy rain conditions and ineffective detection of directional rain streaks. In this paper, we have addressed the above issues with the following contributions. We propose a multilevel shearlet transform-based image decomposition approach to identify the rain pattern on different scales. The rain streaks in various dimensions are enhanced using a residual recurrent rain feature enhancement module. We adopt the Rain Pattern Absorption Attention Network (RaPaat-Net) to capture and eliminate the rain pattern through the four-dilation factor network. Experiments on synthetic and real-time images demonstrate that the proposed single-image attention network performs better than existing deraining approaches.

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