电容式电压互感器存在电磁单元,当过电压达一定值时,中间变压器及速饱和电抗器可能饱和,在低频振荡下CVT的分压比降低,二次绕组测量到的电压值将高于实际值,导致过压保护误动。文中以500 k V多乐永丰I回线在带高抗切除空线时过压保护动...电容式电压互感器存在电磁单元,当过电压达一定值时,中间变压器及速饱和电抗器可能饱和,在低频振荡下CVT的分压比降低,二次绕组测量到的电压值将高于实际值,导致过压保护误动。文中以500 k V多乐永丰I回线在带高抗切除空线时过压保护动作为切入点,通过过电压电磁暂态仿真及CVT在系统电压偏离工频时的传变特性计算,分析CVT在低频饱和状态下二次侧测量电压被放大的原因,研究CVT饱和后的电压测量偏差。该线路空载分闸过电压现场测试数据,充分验证了CVT低频下传变特性改变引起测量值大于实际值是500 k V多永I回线在空载分闸时过压保护动作的真正原因。展开更多
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
This paper presented a novel adaptive cascade nonlinear trajectory tracking control scheme of over-actuated autonomous electric vehicles involving input saturation. First, a nonlinear vehicle dynamic model with input ...This paper presented a novel adaptive cascade nonlinear trajectory tracking control scheme of over-actuated autonomous electric vehicles involving input saturation. First, a nonlinear vehicle dynamic model with input saturation is established, which can accurately describe the features of uncertainties and coupling of autonomous electric vehicles, and the hyperbolic tangent function is designed to estimate the saturation function for dealing with the input saturation problem. Then, a novel adaptive cascade trajectory tracking control scheme is designed. An adaptive neural network-based terminal sliding control law is proposed for producing the generalized force/moment in real-time, the asymptotic stability of this adaptive control system is proven by Lyapunov theory, and a quasi-newton distribution law is designed to determine the optimum tire forces that guarantee the actual generalized forces/moment are close to the desired values. Finally, simulation results demonstrate the effectiveness of the proposed control scheme.展开更多
文摘电容式电压互感器存在电磁单元,当过电压达一定值时,中间变压器及速饱和电抗器可能饱和,在低频振荡下CVT的分压比降低,二次绕组测量到的电压值将高于实际值,导致过压保护误动。文中以500 k V多乐永丰I回线在带高抗切除空线时过压保护动作为切入点,通过过电压电磁暂态仿真及CVT在系统电压偏离工频时的传变特性计算,分析CVT在低频饱和状态下二次侧测量电压被放大的原因,研究CVT饱和后的电压测量偏差。该线路空载分闸过电压现场测试数据,充分验证了CVT低频下传变特性改变引起测量值大于实际值是500 k V多永I回线在空载分闸时过压保护动作的真正原因。
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supported by the National Basic Research Project of China(Grant Nos.2016YFB0100900&2016YFB0101101)the National Natural Science Foundation of China(Grant Nos.U1564208,61803319&61304193)the Natural Science Foundation of Fujian Province(Grant No.2017J01100)
文摘This paper presented a novel adaptive cascade nonlinear trajectory tracking control scheme of over-actuated autonomous electric vehicles involving input saturation. First, a nonlinear vehicle dynamic model with input saturation is established, which can accurately describe the features of uncertainties and coupling of autonomous electric vehicles, and the hyperbolic tangent function is designed to estimate the saturation function for dealing with the input saturation problem. Then, a novel adaptive cascade trajectory tracking control scheme is designed. An adaptive neural network-based terminal sliding control law is proposed for producing the generalized force/moment in real-time, the asymptotic stability of this adaptive control system is proven by Lyapunov theory, and a quasi-newton distribution law is designed to determine the optimum tire forces that guarantee the actual generalized forces/moment are close to the desired values. Finally, simulation results demonstrate the effectiveness of the proposed control scheme.
