快速准确的锁相环技术是保证并网系统安全、可靠并网的关键。针对传统EPLL的固有缺陷,设计了一种改进型EPLL算法,适用于以分布式电源为主的微网并网控制技术。首先,推导出输出电压频率和输入电压幅值之间的耦合关系,使用数学公式进行近...快速准确的锁相环技术是保证并网系统安全、可靠并网的关键。针对传统EPLL的固有缺陷,设计了一种改进型EPLL算法,适用于以分布式电源为主的微网并网控制技术。首先,推导出输出电压频率和输入电压幅值之间的耦合关系,使用数学公式进行近似解耦。其次,搭建误差信号的成本函数,利用梯度下降法设计直流偏移量的估算环路,通过闭环负反馈回路消去输入信号中的直流偏置。然后,在锁相算法的所有估算环路中引入滑动平均值滤波器MAF(moving average filter),以增强控制系统的高频谐波抗干扰能力。最后,在Matlab/Simulink软件中搭建了单相锁相环算法的仿真模型,进行对比分析。仿真结果验证了所提算法的正确性和可行性。展开更多
Digital images have been applied to various areas such as evidence in courts.However,it always suffers from noise by criminals.This type of computer network security has become a hot issue that can’t be ignored.In th...Digital images have been applied to various areas such as evidence in courts.However,it always suffers from noise by criminals.This type of computer network security has become a hot issue that can’t be ignored.In this paper,we focus on noise removal so as to provide guarantees for computer network security.Firstly,we introduce a well-known denoising method called Expected Patch Log Likelihood(EPLL)with Gaussian Mixture Model as its prior.This method achieves exciting results in noise removal.However,there remain problems to be solved such as preserving the edge and meaningful details in image denoising,cause it considers a constant as regularization parameter so that we denoise with the same strength on the whole image.This leads to a problem that edges and meaningful details may be oversmoothed.Under the consideration of preserving edges of the image,we introduce a new adaptive parameter selection based on EPLL by the use of the image gradient and variance,which varies with different regions of the image.Moreover,we add a gradient fidelity term to relieve staircase effect and preserve more details.The experiment shows that our proposed method proves the effectiveness not only in vision but also on quantitative evaluation.展开更多
In recent years,image restoration has become a huge subject,and finite hybrid model has been widely used in image denoising because of its easy modeling and strong explanatory results.The gaussian mixture model is the...In recent years,image restoration has become a huge subject,and finite hybrid model has been widely used in image denoising because of its easy modeling and strong explanatory results.The gaussian mixture model is the most common one.The existing image denoising methods usually assume that each component of the natural image is subject to the gaussian mixture model(GMM).However,this approach is not entirely reasonable.It is well known that most natural images are complex and their distribution is not entirely gaussian.As a result,there are still many problems that GMM cannot solve.This paper tries to improve the finite mixture model and introduces the asymmetric gaussian mixture model into it.Since the asymmetric gaussian mixture model can simulate the asymmetric distribution on the basis of the gaussian mixture model,it is more consistent with the natural image data,so the denoising effect of the natural complex image is better.We carried out image denoising experiments under different noise scales and types,and found that the asymmetric gaussian mixture model has better denoising effect and performance.展开更多
文摘快速准确的锁相环技术是保证并网系统安全、可靠并网的关键。针对传统EPLL的固有缺陷,设计了一种改进型EPLL算法,适用于以分布式电源为主的微网并网控制技术。首先,推导出输出电压频率和输入电压幅值之间的耦合关系,使用数学公式进行近似解耦。其次,搭建误差信号的成本函数,利用梯度下降法设计直流偏移量的估算环路,通过闭环负反馈回路消去输入信号中的直流偏置。然后,在锁相算法的所有估算环路中引入滑动平均值滤波器MAF(moving average filter),以增强控制系统的高频谐波抗干扰能力。最后,在Matlab/Simulink软件中搭建了单相锁相环算法的仿真模型,进行对比分析。仿真结果验证了所提算法的正确性和可行性。
基金This paper is partly supported by the National Natural Science Foundation of China(GRANT No.61672293).
文摘Digital images have been applied to various areas such as evidence in courts.However,it always suffers from noise by criminals.This type of computer network security has become a hot issue that can’t be ignored.In this paper,we focus on noise removal so as to provide guarantees for computer network security.Firstly,we introduce a well-known denoising method called Expected Patch Log Likelihood(EPLL)with Gaussian Mixture Model as its prior.This method achieves exciting results in noise removal.However,there remain problems to be solved such as preserving the edge and meaningful details in image denoising,cause it considers a constant as regularization parameter so that we denoise with the same strength on the whole image.This leads to a problem that edges and meaningful details may be oversmoothed.Under the consideration of preserving edges of the image,we introduce a new adaptive parameter selection based on EPLL by the use of the image gradient and variance,which varies with different regions of the image.Moreover,we add a gradient fidelity term to relieve staircase effect and preserve more details.The experiment shows that our proposed method proves the effectiveness not only in vision but also on quantitative evaluation.
基金This work was partly supported by the National Natural Science Foundation of China under Grants 61672293.
文摘In recent years,image restoration has become a huge subject,and finite hybrid model has been widely used in image denoising because of its easy modeling and strong explanatory results.The gaussian mixture model is the most common one.The existing image denoising methods usually assume that each component of the natural image is subject to the gaussian mixture model(GMM).However,this approach is not entirely reasonable.It is well known that most natural images are complex and their distribution is not entirely gaussian.As a result,there are still many problems that GMM cannot solve.This paper tries to improve the finite mixture model and introduces the asymmetric gaussian mixture model into it.Since the asymmetric gaussian mixture model can simulate the asymmetric distribution on the basis of the gaussian mixture model,it is more consistent with the natural image data,so the denoising effect of the natural complex image is better.We carried out image denoising experiments under different noise scales and types,and found that the asymmetric gaussian mixture model has better denoising effect and performance.
