A new integrity metric for navigation systems is proposed based on the measurement domain. Proba-hilistic optimization design offers tools for fault detection by considering the required navigation performance （RNP）...A new integrity metric for navigation systems is proposed based on the measurement domain. Proba-hilistic optimization design offers tools for fault detection by considering the required navigation performance （RNP） parameter and the uncertainty noise. The choice of the proper performance parameter provided the single-valued mapping with the missed detection probability estimates the probability of failure. The desirable characteristics of the residual sensitivity matrix are exploited to increase the efficiency for identifying erroneous observations. The algorithm can be used to support the performance specification and the efficient calculation of the integrity monitoring process. The simulation for non-precision approach （NPA） validates both the viability and the effectiveness of the proposed algorithm.展开更多
Intrusion detection aims to detect intrusion behavior and serves as a complement to firewalls.It can detect attack types of malicious network communications and computer usage that cannot be detected by idiomatic fire...Intrusion detection aims to detect intrusion behavior and serves as a complement to firewalls.It can detect attack types of malicious network communications and computer usage that cannot be detected by idiomatic firewalls.Many intrusion detection methods are processed through machine learning.Previous literature has shown that the performance of an intrusion detection method based on hybrid learning or integration approach is superior to that of single learning technology.However,almost no studies focus on how additional representative and concise features can be extracted to process effective intrusion detection among massive and complicated data.In this paper,a new hybrid learning method is proposed on the basis of features such as density,cluster centers,and nearest neighbors(DCNN).In this algorithm,data is represented by the local density of each sample point and the sum of distances from each sample point to cluster centers and to its nearest neighbor.k-NN classifier is adopted to classify the new feature vectors.Our experiment shows that DCNN,which combines K-means,clustering-based density,and k-NN classifier,is effective in intrusion detection.展开更多
Proximal point algorithms （PPA） are attractive methods for solving monotone variational inequalities （MVI）. Since solving the sub-problem exactly in each iteration is costly or sometimes impossible, various approx...Proximal point algorithms （PPA） are attractive methods for solving monotone variational inequalities （MVI）. Since solving the sub-problem exactly in each iteration is costly or sometimes impossible, various approximate versions ofPPA （APPA） are developed for practical applications. In this paper, we compare two APPA methods, both of which can be viewed as prediction-correction methods. The only difference is that they use different search directions in the correction-step. By extending the general forward-backward splitting methods, we obtain Algorithm Ⅰ; in the same way, Algorithm Ⅱ is proposed by spreading the general extra-gradient methods. Our analysis explains theoretically why Algorithm Ⅱ usually outperforms Algorithm Ⅰ. For computation practice, we consider a class of MVI with a special structure, and choose the extending Algorithm Ⅱ to implement, which is inspired by the idea of Gauss-Seidel iteration method making full use of information about the latest iteration. And in particular, self-adaptive techniques are adopted to adjust relevant parameters for faster convergence. Finally, some numerical experiments are reported on the separated MVI. Numerical results showed that the extending Algorithm II is feasible and easy to implement with relatively low computation load.展开更多
Discovery of service nodes in flows is a challenging task, especially in large ISPs or campus networks where the amount of traffic across net-work is rmssive. We propose an effective data structure called Round-robin ...Discovery of service nodes in flows is a challenging task, especially in large ISPs or campus networks where the amount of traffic across net-work is rmssive. We propose an effective data structure called Round-robin Buddy Bloom Filters （RBBF） to detect duplicate elements in flows. A two-stage approximate algorithm based on RBBF which can be used for detecting service nodes from NetFlow data is also given and the perfonmnce of the algorithm is analyzed. In our case, the proposed algorithm uses about 1% memory of hash table with false positive error rate less than 5%. A proto-type system, which is compatible with both IPv4 and IPv6, using the proposed data structure and al-gorithm is introduced. Some real world case studies based on the prototype system are discussed.展开更多
This paper presents a constructive design of new controllers that force underactuated ships under constant or slow time-varying sea loads to asymptotically track a parameterized reference path, that guarantees the dis...This paper presents a constructive design of new controllers that force underactuated ships under constant or slow time-varying sea loads to asymptotically track a parameterized reference path, that guarantees the distance from the ship to the reference path always be within a specified value. The control design is based on a global exponential disturbance observer, a transformation of the ship dynamics to an almost spherical form, an interpretation of the tracking errors in an earth-fixed frame, an introduction of dynamic variables to compensate for relaxation of the reference path generation, p-times differentiable step functions, and backstepping and Lyapunov＇s direct methods. The effectiveness of the proposed results is illustrated through simulations.展开更多
基金Supported by the National High Technology Research and Development Program of China (‘863’Program) (2006AA12Z313)~~
文摘A new integrity metric for navigation systems is proposed based on the measurement domain. Proba-hilistic optimization design offers tools for fault detection by considering the required navigation performance （RNP） parameter and the uncertainty noise. The choice of the proper performance parameter provided the single-valued mapping with the missed detection probability estimates the probability of failure. The desirable characteristics of the residual sensitivity matrix are exploited to increase the efficiency for identifying erroneous observations. The algorithm can be used to support the performance specification and the efficient calculation of the integrity monitoring process. The simulation for non-precision approach （NPA） validates both the viability and the effectiveness of the proposed algorithm.
文摘Intrusion detection aims to detect intrusion behavior and serves as a complement to firewalls.It can detect attack types of malicious network communications and computer usage that cannot be detected by idiomatic firewalls.Many intrusion detection methods are processed through machine learning.Previous literature has shown that the performance of an intrusion detection method based on hybrid learning or integration approach is superior to that of single learning technology.However,almost no studies focus on how additional representative and concise features can be extracted to process effective intrusion detection among massive and complicated data.In this paper,a new hybrid learning method is proposed on the basis of features such as density,cluster centers,and nearest neighbors(DCNN).In this algorithm,data is represented by the local density of each sample point and the sum of distances from each sample point to cluster centers and to its nearest neighbor.k-NN classifier is adopted to classify the new feature vectors.Our experiment shows that DCNN,which combines K-means,clustering-based density,and k-NN classifier,is effective in intrusion detection.
基金Project (No. 1027054) supported by the National Natural Science Foundation of China
文摘Proximal point algorithms （PPA） are attractive methods for solving monotone variational inequalities （MVI）. Since solving the sub-problem exactly in each iteration is costly or sometimes impossible, various approximate versions ofPPA （APPA） are developed for practical applications. In this paper, we compare two APPA methods, both of which can be viewed as prediction-correction methods. The only difference is that they use different search directions in the correction-step. By extending the general forward-backward splitting methods, we obtain Algorithm Ⅰ; in the same way, Algorithm Ⅱ is proposed by spreading the general extra-gradient methods. Our analysis explains theoretically why Algorithm Ⅱ usually outperforms Algorithm Ⅰ. For computation practice, we consider a class of MVI with a special structure, and choose the extending Algorithm Ⅱ to implement, which is inspired by the idea of Gauss-Seidel iteration method making full use of information about the latest iteration. And in particular, self-adaptive techniques are adopted to adjust relevant parameters for faster convergence. Finally, some numerical experiments are reported on the separated MVI. Numerical results showed that the extending Algorithm II is feasible and easy to implement with relatively low computation load.
基金supported by the National Basic Research Program of China under Grant No. 2009CB320505
文摘Discovery of service nodes in flows is a challenging task, especially in large ISPs or campus networks where the amount of traffic across net-work is rmssive. We propose an effective data structure called Round-robin Buddy Bloom Filters （RBBF） to detect duplicate elements in flows. A two-stage approximate algorithm based on RBBF which can be used for detecting service nodes from NetFlow data is also given and the perfonmnce of the algorithm is analyzed. In our case, the proposed algorithm uses about 1% memory of hash table with false positive error rate less than 5%. A proto-type system, which is compatible with both IPv4 and IPv6, using the proposed data structure and al-gorithm is introduced. Some real world case studies based on the prototype system are discussed.
基金Supported in Part by the Australian Research Council Under Grant No.DP0988424
文摘This paper presents a constructive design of new controllers that force underactuated ships under constant or slow time-varying sea loads to asymptotically track a parameterized reference path, that guarantees the distance from the ship to the reference path always be within a specified value. The control design is based on a global exponential disturbance observer, a transformation of the ship dynamics to an almost spherical form, an interpretation of the tracking errors in an earth-fixed frame, an introduction of dynamic variables to compensate for relaxation of the reference path generation, p-times differentiable step functions, and backstepping and Lyapunov＇s direct methods. The effectiveness of the proposed results is illustrated through simulations.
