Detection of Network Intrusion Signal in Deep Camouflage Based on Chaotic Synchronization

2013 ◽  
Vol 380-384 ◽  
pp. 2695-2698
Author(s):  
Cai Tian Zhang ◽  
Yi Bo Zhang
Keyword(s):  
Signal Detection ◽  
Expectation Maximization ◽  
Detection Method ◽  
Expectation Maximization Algorithm ◽  
Gaussian Mixture ◽  
Chaotic Synchronization ◽  
New Method ◽  
Network Data ◽  
Network Intrusion ◽  
Better Than

For detecting the network intrusion signal in deep camouflage precisely and effectively, a new detection method based chaotic synchronization is proposed in this paper. The Gaussian mixture model of the network data combined with expectation maximization algorithm is established firstly for the afterwards detection, the chaotic synchronization concept is proposed to detect the intrusion signals. According to the simulation result, the new method which this paper proposed shows good performance of detection the intrusion signals. The detection ROC is plotted for the chaotic synchronization detection method and traditional ARMA method, and it shows that the detection performance of the chaotic synchronization algorithm is much better than the traditional ARMA detection method. It shows good application prospect of the new method in the network intrusion signal detection.

Gaussian Mixture Model of Ground Filtering Based on Hierarchical Curvature Constraints for Airborne Lidar Point Clouds

2021 ◽  
Vol 87 (9) ◽  
pp. 615-630
Author(s):  
Longjie Ye ◽  
Ka Zhang ◽  
Wen Xiao ◽  
Yehua Sheng ◽  
Dong Su ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Expectation Maximization ◽  
Latent Variables ◽  
Expectation Maximization Algorithm ◽  
Point Clouds ◽  
Gaussian Mixture ◽  
Airborne Lidar ◽  
Height Difference ◽  
Hierarchical Constraints

This paper proposes a Gaussian mixture model of a ground filtering method based on hierarchical curvature constraints. Firstly, the thin plate spline function is iteratively applied to interpolate the reference surface. Secondly, gradually changing grid size and curvature threshold are used to construct hierarchical constraints. Finally, an adaptive height difference classifier based on the Gaussian mixture model is proposed. Using the latent variables obtained by the expectation-maximization algorithm, the posterior probability of each point is computed. As a result, ground and objects can be marked separately according to the calculated possibility. 15 data samples provided by the International Society for Photogrammetry and Remote Sensing are used to verify the proposed method, which is also compared with eight classical filtering algorithms. Experimental results demonstrate that the average total errors and average Cohen's kappa coefficient of the proposed method are 6.91% and 80.9%, respectively. In general, it has better performance in areas with terrain discontinuities and bridges.

The Noisy Expectation-Maximization Algorithm for Multiplicative Noise Injection

2016 ◽  
Vol 15 (01) ◽  
pp. 1650007 ◽  
Author(s):  
Osonde Osoba ◽  
Bart Kosko
Keyword(s):  
Expectation Maximization ◽  
Multiplicative Noise ◽  
Expectation Maximization Algorithm ◽  
Gaussian Mixture ◽  
Important Special Case ◽  
Positivity Condition ◽  
The Em Algorithm ◽  
Noise Injection ◽  
Speed Up ◽  
Special Case

We generalize the noisy expectation-maximization (NEM) algorithm to allow arbitrary modes of noise injection besides just adding noise to the data. The noise must still satisfy a NEM positivity condition. This generalization includes the important special case of multiplicative noise injection. A generalized NEM theorem shows that all measurable modes of injecting noise will speed the average convergence of the EM algorithm if the noise satisfies a generalized NEM positivity condition. This noise-benefit condition has a simple quadratic form for Gaussian and Cauchy mixture models in the case of multiplicative noise injection. Simulations show a multiplicative-noise EM speed-up of more than [Formula: see text] in a simple Gaussian mixture model. Injecting blind noise only slowed convergence. A related theorem gives a sufficient condition for an average EM noise benefit for arbitrary modes of noise injection if the data model comes from the general exponential family of probability density functions. A final theorem shows that injected noise slows EM convergence on average if the NEM inequalities reverse and the noise satisfies a negativity condition.

Multilevel Image Thresholding Based on Improved Expectation Maximization (EM) and Differential Evolution Algorithm

2021 ◽  
pp. 2150013
Author(s):  
Ehsan Ehsaeyan ◽  
Alireza Zolghadrasli
Keyword(s):  
Em Algorithm ◽  
Differential Evolution ◽  
Expectation Maximization ◽  
Differential Evolution Algorithm ◽  
Gaussian Mixture ◽  
Standard Test ◽  
Computational Time ◽  
Image Thresholding ◽  
Evolution Algorithm ◽  
Better Than

Multilevel image thresholding is an essential step in the image segmentation process. Expectation Maximization (EM) is a powerful technique to find thresholds but is sensitive to the initial points. Differential Evolution (DE) is a robust metaheuristic algorithm that can find thresholds rapidly. However, it may be trapped in the local optimums and premature convergence occurs. In this paper, we incorporate EM algorithm to DE and introduce a novel algorithm called EM+DE which overcomes these shortages and can segment images better than EM and DE algorithms. In the proposed method, EM estimates Gaussian Mixture Model (GMM) coefficients of the histogram and DE tries to provide good volunteer solutions to EM algorithm when EM converges in local areas. Finally, DE fits GMM parameters based on Root Mean Square Error (RMSE) to reach the fittest curve. Ten standard test images and six famous metaheuristic algorithms are considered and result on global fitness. PSNR, SSIM, FSIM criteria and the computational time are given. The experimental results prove that the proposed algorithm outperforms the EM and DE as well as EM+ other natural-inspired algorithms in terms of segmentation criteria.

scholarly journals A new method of Bayesian causal inference in non-stationary environments

2019 ◽  
Author(s):  
Shuji Shinohara ◽  
Nobuhito Manome ◽  
Kouta Suzuki ◽  
Ung-il Chung ◽  
Tatsuji Takahashi ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Causal Inference ◽  
Expectation Maximization ◽  
Moving Average ◽  
Sudden Change ◽  
Expectation Maximization Algorithm ◽  
Gaussian Mixture ◽  
Learning Task ◽  
Trade Off ◽  
The One

AbstractBayesian inference is the process of narrowing down the hypotheses (causes) to the one that best explains the observational data (effects). To accurately estimate a cause, a considerable amount of data is required to be observed for as long as possible. However, the object of inference is not always constant. In this case, a method such as exponential moving average (EMA) with a discounting rate is used to improve the ability to respond to a sudden change; it is also necessary to increase the discounting rate. That is, a trade-off is established in which the followability is improved by increasing the discounting rate, but the accuracy is reduced. Here, we propose an extended Bayesian inference (EBI), wherein human-like causal inference is incorporated. We show that both the learning and forgetting effects are introduced into Bayesian inference by incorporating the causal inference. We evaluate the estimation performance of the EBI through the learning task of a dynamically changing Gaussian mixture model. In the evaluation, the EBI performance is compared with those of the EMA and a sequential discounting expectation-maximization algorithm. The EBI was shown to modify the trade-off observed in the EMA.

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