New self-adaptive method for image denoising based on sparse decomposition and clustering

2013 ◽  
Vol 33 (2) ◽  
pp. 476-479
Author(s):  
Yali WEI ◽  
Xianbin WEN ◽  
Yongliao ZOU ◽  
Yongchun ZHENG
Keyword(s):  
Image Denoising ◽  
Adaptive Method ◽  
Sparse Decomposition ◽  
Self Adaptive

A fast and adaptive method for complex-valued SAR image denoising based on l k norm regularization

2009 ◽  
Vol 52 (1) ◽  
pp. 138-148 ◽  
Author(s):  
WeiWei Wang ◽  
ZhengMing Wang ◽  
ZhenYu Yuan ◽  
MingShan Li
Keyword(s):  
Image Denoising ◽  
Adaptive Method ◽  
Sar Image ◽  
Complex Valued

Algorithm of Noise Reduction for Adaptive Dictionary Learning Research Based on Brain MRI Images

2014 ◽  
Vol 687-691 ◽  
pp. 4123-4127 ◽  
Author(s):  
Jia Qing Miao
Keyword(s):  
Sparse Representation ◽  
Image Denoising ◽  
Brain Mri ◽  
Image Representation ◽  
Training Algorithm ◽  
Redundant Dictionary ◽  
Popular Method ◽  
Sparse Decomposition ◽  
Learning Research ◽  
Dictionary Training

Recent years, the image sparse representation has been the popular method in the study of image representation, which has put forward a new idea in the image denoising. Its basic principle is that the original image has the sparse representation under the proper over-complete dictionary. Filter out the noise, we should find out the sparse representation of the image through the design of the dictionary. Its mechanism is that one hand the useful information of the image would be effectively expressed because of the sparse decomposition algorithm based on the redundant dictionary. The other the noise would not be expressed through the dictionary atoms. We do the image denoising according to the image sparse representation. Because of the superiority of the adaptive dictionary algorithm in the image, in this paper, we discuss the over-complete dictionary training algorithm. And we prove the effectiveness through the MATLAB.

scholarly journals A Self-Adaptive Method for Solving a System of Nonlinear Variational Inequalities

2007 ◽  
Vol 2007 ◽  
pp. 1-7
Author(s):  
Chaofeng Shi
Keyword(s):  
Iterative Method ◽  
Variational Inequalities ◽  
Adaptive Method ◽  
New Method ◽  
Computational Results ◽  
Computation Cost ◽  
Self Adaptive

The system of nonlinear variational inequalities (SNVI) is a useful generalization of variational inequalities. Verma (2001) suggested and analyzed an iterative method for solving SNVI. In this paper, we present a new self-adaptive method, whose computation cost is less than that of Verma's method. The convergence of the new method is proved under the same assumptions as Verma's method. Some preliminary computational results are given to illustrate the efficiency of the proposed method.

scholarly journals МЕТОД ЗАБЕЗПЕЧЕННЯ РЕЗИЛЬЄНТНОСТІ КОМП’ЮТЕРНИХ СИСТЕМ В УМОВАХ КІБЕРЗАГРОЗ НА ОСНОВІ САМОАДАПТИВНОСТІ

2019 ◽  
pp. 4-16
Author(s):  
Сергій Миколайович Лисенко
Keyword(s):  
Computer Systems ◽  
Adaptive Method ◽  
Attack Detection ◽  
Training Data ◽  
Network Activity ◽  
Network Reconfiguration ◽  
Corporate Networks ◽  
Feature Vectors ◽  
Cyber Threats ◽  
Self Adaptive

The dynamic expansion of cyber threats poses an urgent need for the development of new methods, methods, and systems for their detection. The subject of the study is the process of ensuring the resilience of computer systems in the presence of cyber threats. The goal is to develop a self-adaptive method for computer systems resilience in the presence of cyberattacks. Results. The article presents a self-adaptive system to ensure the resilience of corporate networks in the presence of botnets’ cyberattacks. Resilience is provided by adaptive network reconfiguration. It is carried out using security scenarios selected based on a cluster analysis of the collected network features inherent cyberattacks. To select the necessary security scenarios, the proposed method uses fuzzy semi-supervised c-means clustering. To detect host-type cyberattacks, information about the hosts’ network activity and reports of host antiviruses are collected. To detect the network type attacks, the monitoring of network activity is carried out, which may indicate the appearance of a cyberattack. According to gathered in the network information concerning possible attacks performed by botnet the measures for the resilient functioning of the network are assumed. To choose the needed scenario for network reconfiguration, the clustering is performed. The result of the clustering is the scenario with the list of the requirement for the reconfiguration of the network parameters, which will assure the network’s resilience in the situation of the botnet’s attacks. As the mean of the security scenario choice, the semi-supervised fuzzy c-means clustering was used. The clustering is performed based on labeled training data. The objects of the clustering are the feature vectors, obtained from a payload of the inbound and outbound traffic and reports of the antiviral tool about possible hosts’ infection. The result of clustering is a degree of membership of the feature vectors to one of the clusters. The membership of feature vector to cluster gives an answer to question what scenario of the network reconfiguration is to be applied in the situation of the botnet’s attack. The system contains the clusters that indicate the normal behavior of the network. The purpose of the method is to select security scenarios following cyberattacks carried out by botnets to mitigate the consequences of attacks and ensure a network functioning resilience. Conclusions. The self-adaptive method for computer systems resilience in the presence of cyberattacks has been developed. Based on the proposed method, a self-adaptive attack detection, and mitigation system has been developed. It demonstrates the ability to ensure the resilient functioning of the network in the presence of botnet cyberattacks at 70 %.

scholarly journals EMP: Extended Kalman Filter Based Self-Adaptive Mold Protection Method on a Toggle Mechanism

2020 ◽  
Vol 10 (3) ◽  
pp. 940
Author(s):  
Baiping Chen ◽  
Huifeng Wu ◽  
Hongwei Zhou ◽  
Danfeng Sun
Keyword(s):  
Kalman Filter ◽  
Injection Molding ◽  
Extended Kalman Filter ◽  
High Performance ◽  
Foreign Bodies ◽  
Adaptive Method ◽  
Protection Method ◽  
Current Curve ◽  
Maintenance Process ◽  
Self Adaptive

Nowadays, the plastic injection molding industry is ever-growing, crucial, and its plastic products can be seen everywhere. However, the mold damage problem still frustrates operators because of its high maintenance price and time-consuming maintenance process. This damage is commonly caused by foreign bodies in mold area, and the conventional mold protection method is insufficient for high-performance injection molding machines because of the uncertainty from many setting parameters. To improve detection precision of mold protection driven by a toggle mechanism ( T M ), this paper puts forward E M P , i.e., an extended Kalman filter ( E K F ) based self-adaptive mold protection method, wherein the E K F is used in current curve optimization, and the self-adaptive method ( S A M ) is proposed to gain an safety range of current curve. The E M P was verified in a 140-ton electric injection molding machine. Compared with a general method, the proposed method decreases the detected distance of mold protection by 22% under different thickness foreign bodies.

scholarly journals Extreme Interval Entropy Based on Symbolic Analysis and a Self-Adaptive Method

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 238
Author(s):  
Zhuofei Xu ◽  
Yuxia Shi ◽  
Qinghai Zhao ◽  
Wei Li ◽  
Kai Liu
Keyword(s):  
Pattern Recognition ◽  
Fault Diagnosis ◽  
Extreme Values ◽  
Rolling Bearing ◽  
Adaptive Methods ◽  
Adaptive Method ◽  
Printing Press ◽  
Mode Decomposition ◽  
Empirical Wavelet Transform ◽  
Self Adaptive

Self-adaptive methods are recognized as important tools in signal process and analysis. A signal can be decomposed into a serious of new components with these mentioned methods, thus the amount of information is also increased. In order to use these components effectively, a feature set is used to describe them. With the development of pattern recognition, the analysis of self-adaptive components is becoming more intelligent and depend on feature sets. Thus, a new feature is proposed to express the signal based on the hidden property between extreme values. In this investigation, the components are first simplified through a symbolization method. The entropy analysis is incorporated into the establishment of the characteristics to describe those self-adaptive decomposition components according to the relationship between extreme values. Subsequently, Extreme Interval Entropy is proposed and used to realize the pattern recognition, with two typical self-adaptive methods, based on both Empirical Mode Decomposition (EMD) and Empirical Wavelet Transform (EWT). Later, extreme interval entropy is applied in two fault diagnosis experiments. One experiment is the fault diagnosis for rolling bearings with both different faults and damage degrees, the other experiment is about rolling bearing in a printing press. The effectiveness of the proposed method is evaluated in both experiments with K-means cluster. The accuracy rate of the fault diagnosis in rolling bearing is in the range of 75% through 100% using EMD, 95% through 100% using EWT. In the printing press experiment, the proposed method can reach 100% using EWT to distinguish the normal bearing (but cannot distinguish normal samples at different speeds), with fault bearing in 4 r/s and in 8 r/s. The fault samples are identified only according to a single proposed feature with EMD and EWT. Therefore, the extreme interval entropy is proved to be a reliable and effective tool for fault diagnosis and other similar applications.

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