滤环上微局部化模的正则奇点

滤环Ｒ上的模在微局部化下的性质是许多文献讨论的问题．Ｅｓｓｅｎ证明了Ｚａｒｉｓｋｉ滤环Ｒ上的模Ｍ若具有正则奇点，则它的微局部化ＱμＳ（Ｍ）作为ＱμＳ（Ｒ）－模仍具有正则奇点，但ＱμＳ（Ｍ）作为Ｒ－模是否仍具有正则奇点则不知道．对这一问题进行了讨论，并证明了若Ｍ是有正则奇点的Ｒ－模且Ｍ上的局部滤是良滤，则ＱμＳ（Ｍ）作为Ｒ－模是具正则奇点的模．在一定条件下解决了该问题．

偏序集上的局部极大滤子

在偏序集上引入局部极大滤子的概念,讨论局部极大滤子在格、分配格、Heyting代数、Boole代数中的相关性质,得到一些等价条件,进一步地丰富偏序集的内容.

根滤子与谱的连通性

研究环的理想的根滤子与Rosenberg左理想的关系,并研究含有Rosenberg左理想的拓扑空间的一些连通性质。

Approach of simultaneous localization and mapping based on local maps for robot

An extended Kalman filter approach of simultaneous localization and mapping(SLAM) was proposed based on local maps. A local frame of reference was established periodically at the position of the robot, and then the observations of the robot and landmarks were fused into the global frame of reference. Because of the independence of the local map, the approach does not cumulate the estimate and calculation errors which are produced by SLAM using Kalman filter directly. At the same time, it reduces the computational complexity. This method is proven correct and feasible in simulation experiments.

Local edge direction based non-local means for image denoising

Classic non-local means (CNLM) algorithm uses the inherent self-similarity in images for noise removal. The denoised pixel value is estimated through the weighted average of all the pixels in its non-local neighborhood. In the CNLM algorithm, the differences between the pixel value and the distance of the pixel to the center are both taken into consideration to calculate the weighting coefficients. However, the Gaussian kernel cannot reflect the information of edge and structure due to its isotropy, and it has poor performance in flat regions. In this paper, an improved non-local means algorithm based on local edge direction is presented for image denoising. In edge and structure regions, the steering kernel regression (SKR) coefficients are used to calculate the weights, and in flat regions the average kernel is used. Experiments show that the proposed algorithm can effectively protect edge and structure while removing noises better when compared with the CNLM algorithm.

Detection of P2P botnet based on network behavior features and Dezert-Smarandache theory

In order to improve the accuracy of detecting the new P2P(peer-to-peer)botnet,a novel P2P botnet detection method based on the network behavior features and Dezert-Smarandache theory is proposed.It focuses on the network behavior features,which are the essential abnormal features of the P2P botnet and do not change with the network topology,the network protocol or the network attack type launched by the P2P botnet.First,the network behavior features are accurately described by the local singularity and the information entropy theory.Then,two detection results are acquired by using the Kalman filter to detect the anomalies of the above two features.Finally,the above two detection results are fused with the Dezert-Smarandache theory to obtain the final detection results.The experimental results demonstrate that the proposed method can effectively detect the new P2P botnet and that it considerably outperforms other methods at a lower degree of false negative rate and false positive rate,and the false negative rate and the false positive rate can reach 0.09 and 0.12,respectively.

Application of a joint algorithm based on L-T to pulse pressure detection signal of fiber Fabry-Perot nano pressure sensor

An improved denoising method and its application in pulse beat signal denoising are studied.The proposed denoising algorithm takes the advantages of local mean decomposition(LMD)and time-frequency peak filtering(TFPF),called L-T algorithm.As a classical time-frequency filtering method,TFPF can effectively suppress random noise with signal amplitude retained when selecting a longer window length,while the signal amplitude will be seriously attenuated when selecting a shorter window length.In order to maintain effective signal amplitude and suppress random noise,LMD and TFPF are improved.Firstly,the original signal is decomposed into progression-free survival(PFS)by LMD,and then the standard error of mean(SEM)of each product function is calculated to classify many PFSs into useful component,mixed component and noise component.Secondly,by using the shorter window TFPF for useful component and the longer window TFPF for mixed component,noise component is removed and the final signal is obtained after reconstruction.Finally,the proposed algorithm is used for noise reduction of an Fabry-Perot(F-P)pressure sensor.Experimental results show that compared with traditional wavelet,L-T algorithm has better denoising effect on sampled data.

A novel denoising method for infrared image based on bilateral filtering and non-local means

This paper presents an image denoising method based on bilateral filtering and non-local means. The non-local region texture or structure of the image has the characteristics of repetition, which can be used to effectively preserve the edge and detail of the image. And compared with classical methods, bilateral filtering method has a better performance in denosing for the reason that the weight includes the geometric closeness factor and the intensity similarity factor. We combine the geometric closeness factor with the weight of non-local means, and construct a new weight. Experimental results show that the modified algorithm can achieve better performance. And it can protect the image detail and structure information better.

A Dwindling Filter Line Search Algorithm for Nonlinear Equality Constrained Optimization

This paper proposes a dwindling filter line search algorithm for nonlinear equality constrained optimization. A dwindling filter, which is a modification of the traditional filter, is employed in the algorithm. The envelope of the dwindling filter becomes thinner and thinner as the step size approaches zero. This new algorithm has more flexibility for the acceptance of the trial step and requires less computational costs compared with traditional filter algorithm. The global and local convergence of the proposed algorithm are given under some reasonable conditions. The numerical experiments are reported to show the effectiveness of the dwindling filter algorithm.