Home / Journals / CSSE / Vol.33, No.1, 2018
  • Open Access

    ARTICLE

    A Dynamic Independent Component Analysis Approach To Fault Detection With New Statistics

    M. Teimoortashloo1, A. Khaki Sedigh2,*
    Computer Systems Science and Engineering, Vol.33, No.1, pp. 5-20, 2018, DOI:10.32604/csse.2018.33.005
    Abstract This paper presents a fault detection method based on Dynamic Independent Component Analysis (DICA) with new statistics. These new statistics are statistical moments and first characteristic function that surrogate the norm operator to calculate the fault detection statistics to determine the control limit of the independent components (ICs). The estimation of first characteristic function by its series is modified such that the effect of series remainder on estimation is reduced. The advantage of using first characteristic function and moments, over second characteristic function and cumulants, as fault detection statistics is also presented. It is shown that the proposed method can… More >

  • Open Access

    ARTICLE

    Probabliistic Analysis Of Electrocardiogram (Ecg) Heart Signal

    Amjad Gawanmeh1,3,∗, Usman Pervez2, Osman Hasan2,3
    Computer Systems Science and Engineering, Vol.33, No.1, pp. 21-29, 2018, DOI:10.32604/csse.2018.33.021
    Abstract Electrocardiography (ECG) is a heart signal wave that is recorded using medical sensors, which are normally attached to the human body by the heart. ECG waves have repetitive patterns that can be efficiently used in the diagnosis of heart problems as they carry several characteristics of heart operation. Traditionally, the analysis of ECG waves is done using informal techniques, like simulation, which is in-exhaustive and thus the analysis results may lead to ambiguities and life threatening scenarios in extreme cases. In order to overcome such problems, we propose to analyze ECG heart signals using probabilistic model checking, which is a… More >

  • Open Access

    ARTICLE

    A Scheduling Extension Scheme of the Earliest Deadline First Policy for Hard Real-Time Uniprocessor Systems Integrated on Posix Threads Based on Linux

    Vidblain Amaro-Ortega1,∗, Arnoldo Díaz-Ramírez2, Brenda Leticia Flores-Ríos1, Félix Fernando González-Navarro1, Frank Werner3, Larysa Burtseva1
    Computer Systems Science and Engineering, Vol.33, No.1, pp. 31-40, 2018, DOI:10.32604/csse.2018.33.031
    Abstract The Linux operating system has been employed to execute numerous real-time applications. However, it is limited to support soft real-time systems by two scheduling policies: First-In-First-Out and Round Robin. For real-time systems with critical constraints, the soft real-time support and these scheduling policies are still insufficient. In this work, the Earliest Deadline First scheduling policy, which has been shown in theory to be an optimal one in uniprocessor systems, is introduced as an extension of the Linux kernel. This policy is implemented into the real-time class, without the necessity of defining an additional class. The Linux kernel affords capabilities of… More >

  • Open Access

    ARTICLE

    Rank-Order Correlation-Based Feature Vector Context Transformation for Learning to Rank for Information Retrieval

    Jen-Yuan Yeh
    Computer Systems Science and Engineering, Vol.33, No.1, pp. 41-52, 2018, DOI:10.32604/csse.2018.33.041
    Abstract As a crucial task in information retrieval, ranking defines the preferential order among the retrieved documents for a given query. Supervised learning has recently been dedicated to automatically learning ranking models by incorporating various models into one effective model. This paper proposes a novel supervised learning method, in which instances are represented as bags of contexts of features, instead of bags of features. The method applies rank-order correlations to measure the correlation relationships between features. The feature vectors of instances, i.e., the 1st-order raw feature vectors, are then mapped into the feature correlation space via projection to derive the context-level… More >

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