Image dehazing is a rapidly progressing research concept to enhance image contrast and resolution in computer vision applications.Owing to severe air dispersion,fog,and haze over the environment,hazy images pose speci...Image dehazing is a rapidly progressing research concept to enhance image contrast and resolution in computer vision applications.Owing to severe air dispersion,fog,and haze over the environment,hazy images pose specific challenges during information retrieval.With the advances in the learning theory,most of the learning-based techniques,in particular,deep neural networks are used for single-image dehazing.The existing approaches are extremely computationally complex,and the dehazed images are suffered from color distortion caused by the over-saturation and pseudo-shadow phenomenon.However,the slow convergence rate during training and haze residual is the two demerits in the conventional image dehazing networks.This article proposes a new architecture“Atrous Convolution-based Residual Deep Convolutional Neural Network(CNN)”method with hybrid Spider Monkey-Particle Swarm Optimization for image dehazing.The large receptive field of atrous convolution extracts the global contextual information.The swarm based hybrid optimization is designed for tuning the neural network parameters during training.The experiments over the standard synthetic dataset images used in the proposed network recover clear output images free from distortion and halo effects.It is observed from the statistical analysis that Mean Square Error(MSE)decreases from 74.42 to 62.03 and Peak Signal to Noise Ratio(PSNR)increases from 22.53 to 28.82.The proposed method with hybrid optimization algorithm demonstrates a superior convergence rate and is a more robust than the current state-of-the-art techniques.展开更多
PV power production is highly dependent on environmental and weather conditions,such as solar irradiance and ambient temperature.Because of the single control condition and any change in the external environment,the f...PV power production is highly dependent on environmental and weather conditions,such as solar irradiance and ambient temperature.Because of the single control condition and any change in the external environment,the first step response of the converter duty cycle of the traditional MPPT incremental conductance algorithm is not accurate,resulting in misjudgment.To improve the efficiency and economy of PV systems,an improved incremental conductance algorithm of MPPT control strategy is proposed.From the traditional incremental conductance algorithm,this algorithm is simple in structure and can discriminate the instantaneous increment of current,voltage and power when the external environment changes,and so can improve tracking efficiency.MATLAB simulations are carried out under rapidly changing solar radiation level,and the results of the improved and conventional incremental conductance algorithm are compared.The results show that the proposed algorithm can effectively identify the misjudgment and avoid its occurrence.It not only optimizes the system,but also improves the efficiency,response speed and tracking efficiency of the PV system,thus ensuring the stable operation of the power grid.展开更多
文摘Image dehazing is a rapidly progressing research concept to enhance image contrast and resolution in computer vision applications.Owing to severe air dispersion,fog,and haze over the environment,hazy images pose specific challenges during information retrieval.With the advances in the learning theory,most of the learning-based techniques,in particular,deep neural networks are used for single-image dehazing.The existing approaches are extremely computationally complex,and the dehazed images are suffered from color distortion caused by the over-saturation and pseudo-shadow phenomenon.However,the slow convergence rate during training and haze residual is the two demerits in the conventional image dehazing networks.This article proposes a new architecture“Atrous Convolution-based Residual Deep Convolutional Neural Network(CNN)”method with hybrid Spider Monkey-Particle Swarm Optimization for image dehazing.The large receptive field of atrous convolution extracts the global contextual information.The swarm based hybrid optimization is designed for tuning the neural network parameters during training.The experiments over the standard synthetic dataset images used in the proposed network recover clear output images free from distortion and halo effects.It is observed from the statistical analysis that Mean Square Error(MSE)decreases from 74.42 to 62.03 and Peak Signal to Noise Ratio(PSNR)increases from 22.53 to 28.82.The proposed method with hybrid optimization algorithm demonstrates a superior convergence rate and is a more robust than the current state-of-the-art techniques.
基金The Project Supported by Natural Science Basic Research Plan in Shaanxi Province of China(Program No.2019JM-544).
文摘PV power production is highly dependent on environmental and weather conditions,such as solar irradiance and ambient temperature.Because of the single control condition and any change in the external environment,the first step response of the converter duty cycle of the traditional MPPT incremental conductance algorithm is not accurate,resulting in misjudgment.To improve the efficiency and economy of PV systems,an improved incremental conductance algorithm of MPPT control strategy is proposed.From the traditional incremental conductance algorithm,this algorithm is simple in structure and can discriminate the instantaneous increment of current,voltage and power when the external environment changes,and so can improve tracking efficiency.MATLAB simulations are carried out under rapidly changing solar radiation level,and the results of the improved and conventional incremental conductance algorithm are compared.The results show that the proposed algorithm can effectively identify the misjudgment and avoid its occurrence.It not only optimizes the system,but also improves the efficiency,response speed and tracking efficiency of the PV system,thus ensuring the stable operation of the power grid.
