Communication Network Alarm Correlation Based on the Confidence Fusion Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 2237-2240
Author(s):  
Hui Sheng Gao ◽  
Ying Min Li
Keyword(s):  
Communication Network ◽  
Correlation Analysis ◽  
Association Rules ◽  
Traditional Method ◽  
Sliding Window ◽  
Experimental Results ◽  
Fusion Algorithm ◽  
Alarm Correlation ◽  
Calculation Process ◽  
Calculation Results

In this article we achieve SDH communication network correlation analysis by the confidence fusion algorithm. We take the effect of the alarm topology relationship on the alarm timing relationship into the calculation process of confidence fusion algorithm .Thus it can get alarm correlation results more in line with objective facts. According to the correlation calculation results it forms the alarm correlation transactions, then applying the alarm correlation transactions to the alarm association rules mining. We compare the mining results with the traditional method based on sliding window. The experimental results show that the mining results are more effective after introducing the confidence fusion algorithm.

Research of a De-Noising Algorithm Based on Sliding Window

2012 ◽  
Vol 461 ◽  
pp. 355-359
Author(s):  
Wen Chuan Yang ◽  
Ying Hua Song ◽  
Ting Xi Gou
Keyword(s):  
Knowledge Discovery ◽  
Association Rules ◽  
Sliding Window ◽  
Experimental Results ◽  
Data Set ◽  
Model Based ◽  
Frequent Item ◽  
Telecom Service ◽  
Mining Algorithms ◽  
Frequent Item Sets

Top-quality and efficient service increases in importance in the telecom service. One of its challenging issues is to deal with the atypical incidents. While the traditional mining algorithms are focus on the high-frequent item sets, a de-noising algorithm related to the atypical incidents still remains unsettled. This paper proposed a de-noising model based on the sliding window. In this model, FP-tree and multi-association rules are introduced to fix the thresholds of the sliding window. Experimental results demonstrate that the proposed algorithm can apply an appropriate data set to the knowledge discovery of the atypical incidents

A New Algorithm for the Fuzziness of Alarms in Network Faults Diagnosis

2012 ◽  
Vol 198-199 ◽  
pp. 1539-1544 ◽  
Author(s):  
Pan Liu ◽  
Xing Ming Li ◽  
Jian Wu
Keyword(s):  
Fault Diagnosis ◽  
Correlation Analysis ◽  
Communication Networks ◽  
Association Rules ◽  
Association Rules Mining ◽  
Fuzzy Association Rules ◽  
Edge Field ◽  
Alarm Correlation ◽  
Faults Diagnosis ◽  
Network Fault

The alarm correlation analysis based on fuzzy association rules mining is the popular and cutting-edge field of the network fault diagnosis research. In the application environment of alarms in communication networks, a new algorithm of the fuzziness of alarms which is called FKMA (Fuzzy K-Means of Alarms algorithm) is proposed .During the process of fuzziness, there are two methods of sorting the center. Simulations are carried out to the comparison of the two methods. The fuzziness of alarms is effectively realized. And fuzzy association rules mining are achieved. The advantages and efficiency of FKMA are demonstrated by experiments.

Optimization of a Specimen Size for the Determination of the Diffusible Hydrogen Content in Metals

2014 ◽  
Vol 698 ◽  
pp. 466-471
Author(s):  
Oleg V. Panchenko ◽  
Alexey M. Levchenko ◽  
Victor A. Karkhin
Keyword(s):  
Hydrogen Content ◽  
Calculation Method ◽  
Specimen Size ◽  
Experimental Results ◽  
Diffusible Hydrogen ◽  
Measuring Methods ◽  
Deposited Metal ◽  
Calculation Results

Specimens of various sizes are used to determine hydrogen content in deposited metals in such standards as ISO 3690, AWS A 4.3, and GOST 23338 while measuring methods are the same. It causes problems in comparison of experimental results and brings up the following question: what kind of specimen size is optimal to determine hydrogen content? An optimal specimen size was estimated using a calculation method. Experimental and calculation results obtained by using specimens with estimated dimensions were compared to the results obtained by using the specimen with dimensions of 100*25*8 mm to determine hydrogen content in a deposited metal.

scholarly journals Random Forest with Adaptive Local Template for Pedestrian Detection

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Tao Xiang ◽  
Tao Li ◽  
Mao Ye ◽  
Zijian Liu
Keyword(s):  
Computer Vision ◽  
Random Forest ◽  
Classification Accuracy ◽  
Template Matching ◽  
Detection Method ◽  
State Of The Art ◽  
Pedestrian Detection ◽  
Sliding Window ◽  
Experimental Results ◽  
Training Samples

Pedestrian detection with large intraclass variations is still a challenging task in computer vision. In this paper, we propose a novel pedestrian detection method based on Random Forest. Firstly, we generate a few local templates with different sizes and different locations in positive exemplars. Then, the Random Forest is built whose splitting functions are optimized by maximizing class purity of matching the local templates to the training samples, respectively. To improve the classification accuracy, we adopt a boosting-like algorithm to update the weights of the training samples in a layer-wise fashion. During detection, the trained Random Forest will vote the category when a sliding window is input. Our contributions are the splitting functions based on local template matching with adaptive size and location and iteratively weight updating method. We evaluate the proposed method on 2 well-known challenging datasets: TUD pedestrians and INRIA pedestrians. The experimental results demonstrate that our method achieves state-of-the-art or competitive performance.

Pedestrian Detection Based on Histograms of Oriented Gradients in ROI

2012 ◽  
Vol 542-543 ◽  
pp. 937-940
Author(s):  
Ping Shu Ge ◽  
Guo Kai Xu ◽  
Xiu Chun Zhao ◽  
Peng Song ◽  
Lie Guo
Keyword(s):  
Traditional Method ◽  
Detection Method ◽  
Feature Vector ◽  
Pedestrian Detection ◽  
Region Of Interest ◽  
Experimental Results ◽  
Process Time ◽  
Vertical Edge ◽  
Histograms Of Oriented Gradients

To locate pedestrian faster and more accurately, a pedestrian detection method based on histograms of oriented gradients (HOG) in region of interest (ROI) is introduced. The features are extracted in the ROI where the pedestrian's legs may exist, which is helpful to decrease the dimension of feature vector and simplify the calculation. Then the vertical edge symmetry of pedestrian's legs is fused to confirm the detection. Experimental results indicate that this method can achieve an ideal accuracy with lower process time compared to traditional method.

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