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TOC Alert for Publication# 6221036 2014October 23<![CDATA[Table of contents]]>4411C11993188<![CDATA[IEEE Transactions on Cybernetics publication information]]>4411C2C2138<![CDATA[Guest Editorial: Introduction to the Special Issue on Resilient Control Architectures and Systems]]>[1]. Threats are those elements that counter normalcy and destabilize control system networks, including human error and malicious human attacks, complex latencies and interdependencies.]]>441119941996165<![CDATA[Adaptive GSA-Based Optimal Tuning of PI Controlled Servo Systems With Reduced Process Parametric Sensitivity, Robust Stability and Controller Robustness]]>4411199720095200<![CDATA[Resilient Monitoring Systems: Architecture, Design, and Application to Boiler/Turbine Plant]]>441120102023830<![CDATA[Distributed Fault Detection and Isolation Resilient to Network Model Uncertainties]]>441120242037871<![CDATA[Resilient Distributed Control in the Presence of Misbehaving Agents in Networked Control Systems]]>44112038204911938<![CDATA[Studies on Resilient Control Through Multiagent Consensus Networks Subject to Disturbances]]>4411205020645885<![CDATA[FN-DFE: Fuzzy-Neural Data Fusion Engine for Enhanced Resilient State-Awareness of Hybrid Energy Systems]]>4411206520752551<![CDATA[Wireless Sensing and Vibration Control With Increased Redundancy and Robustness Design]]>44112076208720076<![CDATA[Learning Locality Preserving Graph from Data]]>(n(n-1)/2) variables (n is the number of data points). Exploiting the sparsity of the graph, we further propose a more efficient cutting plane algorithm to solve the problem, making the method better scalable in practice. In the context of clustering and semi-supervised learning, we demonstrated the advantages of our proposed method by experiments.]]>4411208820989074<![CDATA[Human Body Segmentation via Data-Driven Graph Cut]]>44112099210810647<![CDATA[Onboard Centralized Frame Tree Database for Intelligent Space Operations of the Mars Science Laboratory Rover]]>44112109212119453<![CDATA[Distributed Object Detection With Linear SVMs]]>44112122213316448<![CDATA[Robust Object Tracking With Reacquisition Ability Using Online Learned Detector]]>44112134214211016<![CDATA[Learning From Errors in Super-Resolution]]>44112143215433837<![CDATA[Topological Coding and Its Application in the Refinement of SIFT]]>44112155216613792<![CDATA[Fast and Accurate Hashing Via Iterative Nearest Neighbors Expansion]]>$r_{h}$ , where $r_{h}$ is a constant. However, in order to find the true nearest neighbors, both the locating time and the linear scan time are proportional to $O(sum_{i=0}^{r_{h}}{cchoose i})$ ($c$ is the code length), which increase exponentially as $r_{h}$ increases. To address this limitation, we propose a novel algorithm named iterative expanding hashing in this paper, which builds an auxiliary index based on an offline constructed nearest neighbor table to avoid large $r_{h}$ . This auxiliary index can be easily combined with all the traditional hashing methods. Extensive experimental results over various real large-scale datasets demonstrate the superiority of the proposed approach.]]>44112167217716075<![CDATA[Mathematical and Experimental Analyses of Oppositional Algorithms]]>44112178218913239<![CDATA[Novel Neural Networks-Based Fault Tolerant Control Scheme With Fault Alarm]]>4411219022017505<![CDATA[A Novel Strategy for Solving the Stochastic Point Location Problem Using a Hierarchical Searching Scheme]]>^{1} This paper proposes a dramatically distinct strategy, namely, that of partitioning the line in a hierarchical tree-like manner, and of moving to relatively distant points, as characterized by those along the path of the tree. We are thus attempting to merge the rich fields of stochastic optimization and data structures. Indeed, as in the original discretized solution to the SPL, in one sense, our solution utilizes the concept of discretization and operates a uni-dimensional controlled random walk (RW) in the discretized space, to locate the unknown parameter. However, by moving to nonneighbor points in the space, our newly proposed hierarchical stochastic searching on the line (HSSL) solution performs such a controlled RW on the discretized space structured on a superimposed binary tree. We demonstrate that the HSSL solution is orders of magnitude faster than the original SPL solution proposed by -
ommen. By a rigorous analysis, the HSSL is shown to be optimal if the effectiveness (or credibility) of the environment, given by $p$ , is greater than the golden ratio conjugate. The solution has been both analytically solved and simulated, and the results obtained are extremely fascinating, as this is the first reported use of time reversibility in the analysis of stochastic learning. The learning automata extensions of the scheme are currently being investigated.

As we shall see later, hierarchical solutions have been proposed in the field of LA.]]>
44112202222010824<![CDATA[Active Robust Optimization: Enhancing Robustness to Uncertain Environments]]>4411222122318419<![CDATA[A Multiple-Feature and Multiple-Kernel Scene Segmentation Algorithm for Humanoid Robot]]>44112232224115647<![CDATA[IEEE Systems, Man, and Cybernetics Society Information]]>4411C3C3100<![CDATA[IEEE Transactions on Cybernetics information for authors]]>4411C4C4108