With the increment of focal elements number in discernment framework,the computation amount in Dezert-Smarandache Theory (DSmT) will exponentially go up. This has been the bottleneck problem to block the wide applicat...With the increment of focal elements number in discernment framework,the computation amount in Dezert-Smarandache Theory (DSmT) will exponentially go up. This has been the bottleneck problem to block the wide application and development of DSmT. Aiming at this difficulty,in this paper,a kind of fast approximate reasoning method in hierarchical DSmT is proposed. Presently,this method is only fit for the case that there are only singletons with assignment in hyper-power set. These singletons in hyper-power set are forced to group through bintree or tri-tree technologies. At the same time,the assignments of singletons in those different groups corresponding to each source are added up respectively,in order to realize the mapping from the refined hyper-power set to the coarsened one. And then,two sources with the coarsened hyper-power set are combined together according to classical DSm Combination rule (DSmC) and Proportional Conflict Redistribution rule No. 5 (PCR5). The fused results in coarsened framework will be saved as the connecting weights between father and children nodes. And then,all assignments of singletons in different groups will be normalized respectively. Tree depth is set,in order to decide the iterative times in hierarchical system. Finally,by comparing new method with old one from different views,the superiority of new one over old one is testified well.展开更多
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 netw...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.展开更多
针对姿态多变化的飞机自动目标识别中的低识别率问题,提出了一种基于DSm T(Dezert-Smarandache theory)与隐马尔可夫模型(Hidden Markov model,HMM)的飞机多特征序列信息融合识别算法(Multiple features and sequential information fus...针对姿态多变化的飞机自动目标识别中的低识别率问题,提出了一种基于DSm T(Dezert-Smarandache theory)与隐马尔可夫模型(Hidden Markov model,HMM)的飞机多特征序列信息融合识别算法(Multiple features and sequential information fusion,MFSIF).其创新性在于将单幅图像的多特征信息融合识别和序列图像信息融合识别进行有机结合.首先,对图像进行二值化预处理,并提取目标的Hu矩和轮廓局部奇异值特征;然后,利用概率神经网络(Probabilistic neural networks,PNN)构造基本信度赋值(Basic belief assignment,BBA);接着,利用DSm T对该图像的不同特征进行融合,从而获得HMM的观察值序列;再接着,利用隐马尔可夫模型对飞机序列信息融合,计算观察值序列与各隐马尔可夫模型之间的相似度,从而实现姿态多变化的飞机目标自动识别;最后,通过仿真实验,验证了该算法在飞机姿态发生较大变化时,依然可以获得较高的正确识别率,同时在实时性方面也可以满足飞机目标识别的要求.另外,在飞机序列发生连续遮挡帧数τ≤6的情况下,也具有较高的飞机目标正确识别率.展开更多
基金Supported by the National Natural Science Foundation of China (No. 60804063)
文摘With the increment of focal elements number in discernment framework,the computation amount in Dezert-Smarandache Theory (DSmT) will exponentially go up. This has been the bottleneck problem to block the wide application and development of DSmT. Aiming at this difficulty,in this paper,a kind of fast approximate reasoning method in hierarchical DSmT is proposed. Presently,this method is only fit for the case that there are only singletons with assignment in hyper-power set. These singletons in hyper-power set are forced to group through bintree or tri-tree technologies. At the same time,the assignments of singletons in those different groups corresponding to each source are added up respectively,in order to realize the mapping from the refined hyper-power set to the coarsened one. And then,two sources with the coarsened hyper-power set are combined together according to classical DSm Combination rule (DSmC) and Proportional Conflict Redistribution rule No. 5 (PCR5). The fused results in coarsened framework will be saved as the connecting weights between father and children nodes. And then,all assignments of singletons in different groups will be normalized respectively. Tree depth is set,in order to decide the iterative times in hierarchical system. Finally,by comparing new method with old one from different views,the superiority of new one over old one is testified well.
基金The National High Technology Research and Development Program of China(863 Program)(No.2011AA7031024G)the National Natural Science Foundation of China(No.61133011,61373053,61472161)
文摘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.
文摘针对姿态多变化的飞机自动目标识别中的低识别率问题,提出了一种基于DSm T(Dezert-Smarandache theory)与隐马尔可夫模型(Hidden Markov model,HMM)的飞机多特征序列信息融合识别算法(Multiple features and sequential information fusion,MFSIF).其创新性在于将单幅图像的多特征信息融合识别和序列图像信息融合识别进行有机结合.首先,对图像进行二值化预处理,并提取目标的Hu矩和轮廓局部奇异值特征;然后,利用概率神经网络(Probabilistic neural networks,PNN)构造基本信度赋值(Basic belief assignment,BBA);接着,利用DSm T对该图像的不同特征进行融合,从而获得HMM的观察值序列;再接着,利用隐马尔可夫模型对飞机序列信息融合,计算观察值序列与各隐马尔可夫模型之间的相似度,从而实现姿态多变化的飞机目标自动识别;最后,通过仿真实验,验证了该算法在飞机姿态发生较大变化时,依然可以获得较高的正确识别率,同时在实时性方面也可以满足飞机目标识别的要求.另外,在飞机序列发生连续遮挡帧数τ≤6的情况下,也具有较高的飞机目标正确识别率.
