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Analysis Strategy

The Experts below are selected from a list of 17796 Experts worldwide ranked by ideXlab platform

Jeffrey V. Nickerson – 1st expert on this subject based on the ideXlab platform

  • Intruder Detection: An Optimal Decision Analysis Strategy
    IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2012
    Co-Authors: Tal Ben-zvi, Jeffrey V. Nickerson

    Abstract:

    This study considers a situation in which a sensor network aims to protect a stationary target (e.g., a large battleship at anchor) and detects signals from approaching objects (e.g., small boats traveling in a harbor). Once the network detects a sufficient number of signals from the object (for example, through video surveillance), it may classify the object’s intention as hostile and act accordingly by informing responders. The objective is to design an optimal Strategy for recognition that guarantees accurate and timely intruder detection. We show that the optimal policy is of a control limit threshold.

J D Andrews – 2nd expert on this subject based on the ideXlab platform

  • a fault tree Analysis Strategy using binary decision diagrams
    Reliability Engineering & System Safety, 2002
    Co-Authors: Karen A Reay, J D Andrews

    Abstract:

    Abstract The use of binary decision diagrams (BDDs) in fault tree Analysis provides both an accurate and efficient means of analysing a system. There is a problem, however, with the conversion process of the fault tree to the BDD. The variable ordering scheme chosen for the construction of the BDD has a crucial effect on its resulting size and previous research has failed to identify any scheme that is capable of producing BDDs for all fault trees. This paper proposes an Analysis Strategy aimed at increasing the likelihood of obtaining a BDD for any given fault tree, by ensuring the associated calculations are as efficient as possible. The method implements simplification techniques, which are applied to the fault tree to obtain a set of ‘minimal’ subtrees, equivalent to the original fault tree structure. BDDs are constructed for each, using ordering schemes most suited to their particular characteristics. Quantitative Analysis is performed simultaneously on the set of BDDs to obtain the top event probability, the system unconditional failure intensity and the criticality of the basic events.

Xin Song – 3rd expert on this subject based on the ideXlab platform

  • CBD – A Data Streams Analysis Strategy Based on Hoeffding Tree with Concept Drift on Hadoop System
    2016 International Conference on Advanced Cloud and Big Data (CBD), 2016
    Co-Authors: Xin Song, Huiyuan He

    Abstract:

    The massive sensor data streams Analysis in the monitoring application of internet of things is very important, especially in the environments where supporting such kind of real time streaming data storage and management. In order to support the classification of the massive sensor data streams, in this paper, a massive sensor data streams Analysis Strategy is proposed based on Hoeffding tree with concept drift for event monitoring application on Hadoop system. The proposed Strategy is sufficient for sensor data streams classification tasks using map-reduce platform of Hadoop system. Finally, the possibilities of the Strategy are demonstrated on spatial sensing data streams processing operations in comparison with existing solutions in the cloud computing environment. The simulation results show that the Strategy achieves more energy savings and also ensures few amounts of sensor data retained in memory.

  • A Data Streams Analysis Strategy Based on Hoeffding Tree with Concept Drift on Hadoop System
    2016 International Conference on Advanced Cloud and Big Data (CBD), 2016
    Co-Authors: Xin Song, Huiyuan He

    Abstract:

    The massive sensor data streams Analysis in the monitoring application of internet of things is very important, especially in the environments where supporting such kind of real time streaming data storage and management. In order to support the classification of the massive sensor data streams, in this paper, a massive sensor data streams Analysis Strategy is proposed based on Hoeffding tree with concept drift for event monitoring application on Hadoop system. The proposed Strategy is sufficient for sensor data streams classification tasks using map-reduce platform of Hadoop system. Finally, the possibilities of the Strategy are demonstrated on spatial sensing data streams processing operations in comparison with existing solutions in the cloud computing environment. The simulation results show that the Strategy achieves more energy savings and also ensures few amounts of sensor data retained in memory.

  • FAW – A Data Streams Analysis Strategy Based on Hadoop Scheduling Optimization for Smart Grid Application
    Frontiers in Algorithmics, 2015
    Co-Authors: Fengquan Zhou, Xin Song

    Abstract:

    The massive data streams Analysis in the Smart Grids data processing system is very important, especially in the high-concurrent read and write environments where supporting the massive real-time streaming data storage and management. The computational and stored requirements for Smart Grids can be met by utilizing the Cloud computing. In order to support the robust, affordable and reliable power streaming data Analysis and storage, in this paper, we propose a power data streams Analysis Strategy based on Hadoop scheduling optimization for smart grid monitoring application. The proposed Strategy combined with the flexible resources and services shared in network, omnipresent access and parallel processing features of cloud computing. Finally, the simulation results show that proposed Strategy can effectively improve the efficiency of computing resource utilization and achieve the massive information concurrent processing ability.