We study the power of local computations on labelled edges (which allow two adjacent vertices to synchronize and to modify their states simultaneaously in function of their previous states) through the classical elect...
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We study the power of local computations on labelled edges (which allow two adjacent vertices to synchronize and to modify their states simultaneaously in function of their previous states) through the classical election problem. We characterize the graphs for which this problem has a solution. As corollaries we characterize graphs which admit an election algorithm for two seminal models: Angluin's model and asynchronous systems where processes communicate with synchronous message passing (i.e., there is a synchronization between the process sending the message and the one receiving it).
A methodology is presented to construct an expectation robust algorithm for principal component regression. The presented method is the first multivariate regression method which can resist outliers and which can cope...
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A methodology is presented to construct an expectation robust algorithm for principal component regression. The presented method is the first multivariate regression method which can resist outliers and which can cope with missing elements in the data simultaneously. Simulations and an example illustrate the good statistical properties of the method. (C) 2009 Elsevier B.V. All rights reserved.
The operational reliability of the space manipulator is closely related to the control method. However the existing control methods seldom consider the operational reliability from the system level. A method to constr...
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The operational reliability of the space manipulator is closely related to the control method. However the existing control methods seldom consider the operational reliability from the system level. A method to construct the operational reliability system control model based on particle filter for the space manipulator is presented in this paper. Firstly, the definition of operational reliability and the degree of operational reliability are given and the state space equations of the control system are established as well. Secondly, based on the particle filter algorithm, a method to estimate the distribution of the end position error and calculate the degree of operational reliability with any form of noise distribution in real time is established. Furthermore, a performance model based on quality loss theory is built and a performance function is obtained to evaluate the quality of the control process. The adjustment value of the end position of the space manipulator can be calculated by using the performance function. Finally, a large number of simulation results show that the control method proposed in this paper can improve the task success rate effectively compared to the simulation results using traditional control methods and control methods based on Bayesian estimation.
Standard pattern-matching methods used for deep packet inspection and network security can be evaded by means of TCP and IP fragmentation. To detect such attacks, intrusion detection systems must reassemble packets be...
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Standard pattern-matching methods used for deep packet inspection and network security can be evaded by means of TCP and IP fragmentation. To detect such attacks, intrusion detection systems must reassemble packets before applying matching algorithms, thus requiring a large amount of memory and time to respond to the threat. In the literature, only a few efforts proposed a method to detect evasion attacks at high speed without reassembly. The aim of this article is to introduce an efficient system for anti-evasion that can be implemented in real devices. It is based on counting Bloom filters and exploits their capabilities to quickly update the string set and deal with partial signatures. In this way, the detection of attacks and almost all of the traffic processing is performed in the fast data path, thus improving the scalability of intrusion detection systems.
In this paper, a rapid and automatic color image segmentation method for the serialized slices of the Visible Human is proposed. The main strategy is based on region growing and pixel color difference. A rapid color s...
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In this paper, a rapid and automatic color image segmentation method for the serialized slices of the Visible Human is proposed. The main strategy is based on region growing and pixel color difference. A rapid color similarity computing method is improved and applied for classifying different pixels. An algorithm based on corrosion from four directions is proposed to automatically extract the seed points for the serialized slices. Utilizing this method, the color slice images of the Visible Human body can be segmented in series automatically. Also, the multithreading frame of parallel computing is introduced in the entire segmentation process. This method is simple but rapid and automatic. The primary organs of the Visible Human can be segmented clearly and accurately. The 3D models of these organs after 3D reconstruction are satisfactory. This novel method can provide support to the Visible Human research. (C) 2013 Elsevier Ltd. All rights reserved.
This paper considers a topology formation problem for wireless ad hoc networks. There are wireless nodes located on a plane. Each node can adjust its transmission power in the dynamic mode. The global objective lies i...
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This paper considers a topology formation problem for wireless ad hoc networks. There are wireless nodes located on a plane. Each node can adjust its transmission power in the dynamic mode. The global objective lies in assigning an optimal transmission power to each node so that the resulting topology is connected and minimizes the total power cost. The topology formation problem is studied as a noncooperative game. The author proposes two algorithms of collective behavior and network formation based on the so-called "double best response" decision rule. This decision rule originates from the reflexive game framework and describes the behavior of an agent with reflexion rank 1. The efficiency of the suggested algorithms is evaluated by simulation and compared with the standard best response algorithm.
Output consensus problem is investigated for mixed-order linear multi-agent systems composed of two-type agents with one and two poles at the origin respectively, and usual output-coupled consensus algorithm is adopte...
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Output consensus problem is investigated for mixed-order linear multi-agent systems composed of two-type agents with one and two poles at the origin respectively, and usual output-coupled consensus algorithm is adopted. According to generalized Nyquist stability criterion, consensus conditions are gained for the mixed-order multi-agent systems to achieve an asymptotic stationary consensus without and with communication delay respectively. Besides, output consensus is also discussed for a simple mixed-order multi-agent system, which consists of single and double integrators. Numerical examples show the correctness of theoretical results. (C) 2015 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
Particle swarm optimization (PSO) is a stochastic optimization method sensitive to parameter settings. The paper presents a modification on the comprehensive learning particle swarm optimizer (CLPSO), which is one of ...
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Particle swarm optimization (PSO) is a stochastic optimization method sensitive to parameter settings. The paper presents a modification on the comprehensive learning particle swarm optimizer (CLPSO), which is one of the best performing PSO algorithms. The proposed method introduces a self-adaptive mechanism that dynamically changes the values of key parameters including inertia weight and acceleration coefficient based on evolutionary information of individual particles and the swarm during the search. Numerical experiments demonstrate that our approach with adaptive parameters can provide comparable improvement in performance of solving global optimization problems.
Social insect colonies have survived over evolutionary time in part due to the success of their collaborative methods: using local information and distributed decision making algorithms to detect and exploit critical ...
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Social insect colonies have survived over evolutionary time in part due to the success of their collaborative methods: using local information and distributed decision making algorithms to detect and exploit critical resources in their environment. These methods have the unusual and useful ability to detect anomalies rapidly, with very little memory, and using only very local information. Our research investigates the potential for a self-organizing anomaly detection system inspired by those observed naturally in colonies of honey bees. We provide a summary of findings from a recently presented algorithm for a nonparametric, fully distributed coordination framework that translates the biological success of these methods into analogous operations for use in cyber defense and discuss the features that inspired this translation. We explore the impacts on detection performance of the defined range of distributed communication for each node and of involving only a small percentage of total nodes in the network in the distributed detection communication. We evaluate our algorithm using a software-based testing implementation, and demonstrate up to 20 percent improvement in detection capability over parallel isolated anomaly detectors.
As is well known, traditional spectral clustering (SC) methods are developed based on the manifold assumption, namely, that two nearby data points in the high-density region of a low-dimensional data manifold have the...
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As is well known, traditional spectral clustering (SC) methods are developed based on the manifold assumption, namely, that two nearby data points in the high-density region of a low-dimensional data manifold have the same cluster label. But, for some high-dimensional and sparse data, such an assumption might be invalid. Consequently, the clustering performance of SC will be degraded sharply in this case. To solve this problem, in this paper, we propose a general spectral embedded framework, which embeds the true cluster assignment matrix for high-dimensional data into a nonlinear space by a predefined embedding function. Based on this framework, several algorithms are presented by using different embedding functions, which aim at learning the final cluster assignment matrix and a transformation into a low dimensionality space simultaneously. More importantly, the proposed method can naturally handle the out-of-sample extension problem. The experimental results on benchmark datasets demonstrate that the proposed method significantly outperforms existing clustering methods.
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