This paper proposes a new method for encryption of RGB color images by combining two encryption approaches: the spatial approach and the transformation approach. The proposed method uses the 3D fractional modified Hen...
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This paper proposes a new method for encryption of RGB color images by combining two encryption approaches: the spatial approach and the transformation approach. The proposed method uses the 3D fractional modified Henon map (3D FrMHM) and the discrete fractional Krawtchouk moments (FrDKM). We have also proposed a new hybrid optimization algorithm (H-SSAOA) to optimize the parameters of the proposed Henon map and the parameters of the Krawtchouk fractional moments. This algorithm is based on the hybridization of two metaheuristic algorithms: the "Salp swarmalgorithm " (SSA) and the "Arithmetic Optimization algorithm " (AOA). The simulation results reveal the optimization efficiency of the proposed hybrid algorithm H-SSAOA compared to other meta-heuristic algorithms and the efficiency of the suggested encryption method for encrypting RGB color images in terms of sensitivity to the security key and resistance to different attacks.
Deep learning (DL) has been widely used for celllevel traffic prediction and achieved state-of-the-art prediction accuracy in recent years. Though hyper-parameters seriously impact the DL-based prediction models' ...
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ISBN:
(纸本)9798350377859;9798350377842
Deep learning (DL) has been widely used for celllevel traffic prediction and achieved state-of-the-art prediction accuracy in recent years. Though hyper-parameters seriously impact the DL-based prediction models' performance, finding the best hyper-parameters for various prediction models is a significant challenge in the fifth generation (5G) and beyond mobile networks because optimizing each model's hyper-parameters manually with expert experience or with the exhaustively searching method is highly time and computational resource consuming. This work formulates the hyper-parameter optimization problem (HPO) related to every cell-level traffic prediction task into a combinatorial programming (CP) problem to address this issue. To solve it, we propose a salp swarmalgorithm with chaotic mapping and adaptive learning (SSA-CMAL). Our numerical results demonstrate that compared with the benchmarks, the proposed algorithm has a breakneck convergence speed and can provide better hyper-parameters for the cell-level traffic prediction models to obtain higher prediction accuracy.
Aggregate drying is a crucial step in asphalt mixture production. Enhancing the model accuracy and controller performance of aggregate dryer burners is essential for consistent flame characteristics, reducing NOx emis...
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Aggregate drying is a crucial step in asphalt mixture production. Enhancing the model accuracy and controller performance of aggregate dryer burners is essential for consistent flame characteristics, reducing NOx emissions, and minimizing fuel consumption. This paper introduces a second-order nonlinear parametric model for coal powder burners that includes delay and noise. Model parameters were determined through experimental data using the Salp swarmalgorithm, showing higher accuracy than models based on the least square method. A dual-layer model predictive control (MPC) based on this mathematical model was developed to improve the economic efficiency of the aggregate drying process. Simulation results showed that the dual-layer MPC saves 1.08 tons of coal every 10 h compared to a standard MPC. A full-scale prototype demonstrated average flame length, flame temperature, and NOx emissions of 4242.6 mm, 1729.4 degrees C, and 460.2 mg/m(3), respectively, validating the accuracy of the proposed mathematical model and controller.
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