The relationship between structural controllability and observability of complex systems is studied. Algebraic and graph theoretic tools are combined to prove the extent of some controller/observer duality results. Tw...
详细信息
The relationship between structural controllability and observability of complex systems is studied. Algebraic and graph theoretic tools are combined to prove the extent of some controller/observer duality results. Two types of control design problems are addressed and some fundamental theoretical results are provided. In addition new algorithms are presented to compute optimal solutions for monitoring large scale real networks.
Kernel Fisher discriminant analysis (KFDA) method has demonstrated its success in extracting facial features for face recognition. Compared to linear techniques, it can better describe the complex and nonlinear variat...
详细信息
Kernel Fisher discriminant analysis (KFDA) method has demonstrated its success in extracting facial features for face recognition. Compared to linear techniques, it can better describe the complex and nonlinear variations of face images. However, a single kernel is not always suitable for the applications of face recognition which contain data from multiple, heterogeneous sources, such as face images under huge variations of pose, illumination, and facial expression. To improve the performance of KFDA in face recognition, a novel algorithm named multiple data-dependent kernel Fisher discriminant analysis (MDKFDA) is proposed in this paper. The constructed multiple data-dependent kernel (MDK) is a combination of several base kernels with a data-dependent kernel constraint on their weights. By solving the optimization equation based on Fisher criterion and maximizing the margin criterion, the parameter optimization of data-dependent kernel and multiple base kernels is achieved. Experimental results on the three face databases validate the effectiveness of the proposed algorithm.
The low-rate denial of service (LDoS) attacks reduce network services capabilities by periodically sending high intensity pulse data flows. For their concealed performance, it is more difficult for traditional DoS det...
详细信息
The low-rate denial of service (LDoS) attacks reduce network services capabilities by periodically sending high intensity pulse data flows. For their concealed performance, it is more difficult for traditional DoS detection methods to detect LDoS attacks;at the same time the accuracy of the current detection methods for LDoS attacks is relatively low. As the fact that LDoS attacks led to abnormal distribution of the ACK traffic, LDoS attacks can be detected by analyzing the distribution characteristics of ACK traffic. Then traditional EWMA algorithm which can smooth the accidental error while being the same as the exceptional mutation may cause some misjudgment;therefore a new LDoS detection method based on adaptive EWMA (AEWMA) algorithm is proposed. The AEWMA algorithm which uses an adaptive weighting function instead of the constant weighting of EWMA algorithm can smooth the accidental error and retain the exceptional mutation. So AEWMA method is more beneficial than EWMA method for analyzing and measuring the abnormal distribution of ACK traffic. The NS2 simulations show that AEWMA method can detect LDoS attacks effectively and has a low false negative rate and a false positive rate. Based on DARPA99 datasets, experiment results show that AEWMA method is more efficient than EWMA method.
The authors present a local stereo matching algorithm whose performance is insensitive to changes in radiometric conditions between the input images. First, a prior on the disparities is built by combining the DAISY d...
详细信息
The authors present a local stereo matching algorithm whose performance is insensitive to changes in radiometric conditions between the input images. First, a prior on the disparities is built by combining the DAISY descriptor and Census filtering. Then, a Census-based cost aggregation with a self-adaptive window is performed. Finally, the maximum a-posteriori estimation is carried out to compute the disparity. The authors' algorithm is compared with both local and global stereo matching algorithms (NLCA, ELAS, ANCC, Adapt Weight and CSBP) by using Middlebury datasets. The results show that the proposed algorithm achieves high-accuracy dense disparity estimations and is more robust to radiometric differences between input images than other algorithms.
The conventional interacting multiple model (IMM) algorithm will increase the computational load when applying a large number of models, meanwhile, it cannot yield accurate estimation results with a small number of mo...
详细信息
The conventional interacting multiple model (IMM) algorithm will increase the computational load when applying a large number of models, meanwhile, it cannot yield accurate estimation results with a small number of models. Furthermore, the unknown target acceleration is regarded as an additional process noise to the target model, and its time-varying variance is hard to be approximated. The paper proposes a fuzzy-logic adaptive variable structure multiple model (FAVSMM) algorithm for tracking a high maneuvering target. The algorithm can optimize the model parameters using the model probability and construct an optimal model set quickly, and the fuzzy-logic IMM algorithm included in the FAVSMM algorithm is adopted for states estimation. The simulation results show that the proposed algorithm can match well with the actual target trajectory with less computational complexity and better accuracy. (C) 2013 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
After an eventful decade of live-coding activities, this article seeks to explore the practice with the aim of situating it in the history of contemporary arts and music. The article introduces several key points of i...
详细信息
After an eventful decade of live-coding activities, this article seeks to explore the practice with the aim of situating it in the history of contemporary arts and music. The article introduces several key points of investigation in live-coding research and discusses some examples of how live-coding practitioners engage with these points in their system design and performances. In light of the extremely diverse manifestations of live-coding activities, the problem of defining the practice is discussed, and the question is raised whether live coding is actually necessary as an independent category.
A novel improved particle swarm algorithm named competition particle swarm optimization (CPSO) is proposed to calibrate the Underwater Transponder coordinates. To improve the performance of the algorithm, TVAC algorit...
详细信息
A novel improved particle swarm algorithm named competition particle swarm optimization (CPSO) is proposed to calibrate the Underwater Transponder coordinates. To improve the performance of the algorithm, TVAC algorithm is introduced into CPSO to present an extension competition particle swarm optimization (ECPSO). The proposed method is tested with a set of 10 standard optimization benchmark problems and the results are compared with those obtained through existing PSO algorithms, basic particle swarm optimization (BPSO), linear decreasing inertia weight particle swarm optimization (LWPSO), exponential inertia weight particle swarm optimization (EPSO), and time-varying acceleration coefficient (TVAC). The results demonstrate that CPSO and ECPSO manifest faster searching speed, accuracy, and stability. The searching performance for multimodulus function of ECPSO is superior to CPSO. At last, calibration of the underwater transponder coordinates is present using particle swarm algorithm, and novel improved particle swarm algorithm shows better performance than other algorithms.
Hammerstein model has been popularly applied to identify the nonlinear systems. In this paper, a Hammerstein-type neural network (HTNN) is derived to formulate the well-known Hammerstein model. The HTNN consists of a ...
详细信息
Hammerstein model has been popularly applied to identify the nonlinear systems. In this paper, a Hammerstein-type neural network (HTNN) is derived to formulate the well-known Hammerstein model. The HTNN consists of a nonlinear static gain in cascade with a linear dynamic part. First, the Lipschitz criterion for order determination is derived. Second, the backpropagation algorithm for updating the network weights is presented, and the stability analysis is also drawn. Finally, simulation results show that HTNN identification approach demonstrated identification performances.
A multi-scale framework is proposed for more realistic molecular dynamics simulations in continuum solvent models by coupling a molecular mechanics treatment of solute with a fluid mechanics treatment of solvent. This...
详细信息
A multi-scale framework is proposed for more realistic molecular dynamics simulations in continuum solvent models by coupling a molecular mechanics treatment of solute with a fluid mechanics treatment of solvent. This article reports our initial efforts to formulate the physical concepts necessary for coupling the two mechanics and develop a 3D numerical algorithm to simulate the solvent fluid via the Navier-Stokes equation. The numerical algorithm was validated with multiple test cases. The validation shows that the algorithm is effective and stable, with observed accuracy consistent with our design.
Orthogonal frequency division multiplexing (OFDM) has been widely adopted in radar and communication systems. High sensitivity to carrier frequency offset (CFO) is one of the major drawbacks of OFDM. CFO estimation fo...
详细信息
Orthogonal frequency division multiplexing (OFDM) has been widely adopted in radar and communication systems. High sensitivity to carrier frequency offset (CFO) is one of the major drawbacks of OFDM. CFO estimation for OFDM systems had been extensively studied and various algorithms had been proposed. however, the established algorithms may be compromised by the adoption of direct-conversion architecture and multi-mode low noise amplifier in the OFDM receiver, which introduces time-varying direct current offset (TV-DUO) into the system. In our previous study, we developed an eigen-decomposition based estimation algorithm, which is robust to TV-DCO but suffers from performance degradation under low to medium signal-to-noise ratio and requires high computation efforts. To address those issues, we in this paper propose a novel blind CFO estimation algorithm. By making use of the second order differential filtering and subspace method, the proposed algorithm achieves great performance improvement with reduced complexity. The performance of the proposed algorithm is demonstrated by simulations. (C) 2013 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
暂无评论