FHUQI-Miner: Fast high utility quantitative itemset mining

Applied Intelligence - High utility itemset mining is a popular pattern mining task, which aims at revealing all sets of items that yield a high profit in a transaction database. Although this task...     

Synergistic verification and validation of systems and software engineering models

In this paper, we present a unified approach for the verification and validation of software and systems engineering design models expressed in UML 2.0 and SysML 1.0. The approach is based on three well-established techniques, namely formal analysis, programme analysis and software engineering (SwE) techniques. More precisely, our contribution consists of the synergistic combination of model checking, static analysis and SwE metrics that enables the automatic and efficient assessment of design models from static and dynamic perspectives. Additionally, we present the design and implementation of an automated computer-aided assessing framework integrating the proposed approach. Moreover, we discuss the related technical details and the underlying synergism. Finally, we illustrate the proposed approach by assessing a design case study that is composed of state machine and sequence diagrams.     

An evolutionary algorithm for generalized comparison-based self-diagnosis of multiprocessor systems

In this article, we consider the problem of self-diagnosis of multiprocessor and multicomputer systems under the generalized comparison model. In this approach, a system consists of a collection n independent heterogeneous processors (or units) interconnected via point-to-point communication links, and it is assumed that at most t of these processors are permanently faulty. For the purpose of diagnosis, system tasks are assigned to pairs of processors and the results are compared. The agreements and disagreements among units are the basis for identifying faulty processors. Such a system is said to be t-diagnosable if, given any complete collection of comparison results, the set of faulty processors can be unambiguously identified. We present an efficient fault identification method based on genetic algorithms. Analysis and simulations are provided, first, to evaluate the genetic parameters of the diagnosis algorithm; second, to show the efficiency of the genetic approach. The new strategy is shown to correctly identify the set of faulty processors, making it an attractive and viable addition or alternative to present fault diagnosis techniques.     

A novel classification approach based on Extreme Learning Machine and Wavelet Neural Networks

Multimedia Tools and Applications - In this paper, we present a novel classification approach based on Extreme Learning Machine (ELM) and Wavelet Neural Networks. We introduce two novel...     

Positive sliding mode control for blood glucose regulation

Biological systems involving positive variables as concentrations are some examples of so-called positive systems. This is the case of the glycemia–insulinemia system considered in this paper. To cope with these physical constraints, it is shown that a positive sliding mode control (SMC) can be designed for glycemia regulation. The largest positive invariant set (PIS) is obtained for the insulinemia subsystem in open and closed loop. The existence of a positive SMC for glycemia regulation is shown here for the first time. Necessary conditions to design the sliding surface and the discontinuity gain are derived to guarantee a positive SMC for the insulin dynamics. SMC is designed to be positive everywhere in the largest closed-loop PIS of plasma insulin system. Two-stage SMC is employed; the last stage SMC2 block uses the glycemia error to design the desired insulin trajectory. Then the plasma insulin state is forced to track the reference via SMC1. The resulting desired insulin trajectory is the required virtual control input of the glycemia system to eliminate blood glucose (BG) error. The positive control is tested in silico on type-1 diabetic patients model derived from real-life clinical data.     

Partial syndrome-based system-level fault diagnosis using game theory

This paper introduces a novel diagnosis approach, using game theory, to solve the comparison-based system-level fault identification problem in distributed and parallel systems based on the asymmetric comparison model. Under this diagnosis model tasks are assigned to pairs of nodes and the results of executing these tasks are compared. Using the agreements and disagreements among the nodes’ outputs, i.e. the input syndrome, the fault diagnosis algorithm identifies the fault status of the system’s nodes, under the assumption that at most t of these nodes can permanently fail simultaneously. Since the introduction of the comparison model, significant progress has been made in both theory and practice associated with the original model and its offshoots. Nevertheless, the problem of efficiently identifying the set of faulty nodes when not all the comparison outcomes are available to the fault identification algorithm prior to initiating the diagnosis phase, i.e. partial syndromes, remains an outstanding research issue. In this paper, we first show how game theory can be adapted to solve the fault diagnosis problem by maximising the payoffs of all players (nodes). We then demonstrate, using results from a thorough simulation, the effectiveness of this approach in solving the fault identification problem using partial syndromes from randomly generated diagnosable systems of different sizes and under various fault scenarios. We have considered large diagnosable systems, and we have experimented extreme faulty situations by simulating all possible fault sets even those that are less likely to occur in practice. Over all the extensive simulations we have conducted, the new game-theory-based diagnosis algorithm performed very well and provided good diagnosis results, in terms of correctness, latency, and scalability, making it a viable addition or alternative to existing diagnosis algorithms.     

Doubly-striped disk mirroring: Reliable storage for video servers

Disk striping is essential in Video-On-Demand servers to allow concurrent access for a large number of users to the same copy of a video object. On the other hand, disk striping makes the system more vulnerable to component failures. We propose a replication scheme for video content and an associated I/O scheduling algorithm that enables the system to tolerate a component failure. The scheme, called doubly-striped mirrors, compares favorably to other approaches for implementing redundancy such as RAID in terms of cost, bandwidth efficiency, buffer usage and system availability.     

A two-phase framework for quality-aware Web service selection

Service-oriented computing is gaining momentum as the next technological tool to leverage the huge investments in Web application development. The expected large number of Web services poses a set of new challenges for efficiently accessing these services. We propose an integrated service query framework that facilitates users in accessing their desired services. The framework incorporates a service query model and a two-phase optimization strategy. The query model defines service communities that are used to organize the large and heterogeneous service space. The service communities allow users to use declarative queries to retrieve their desired services without worrying about the underlying technical details. The two-phase optimization strategy automatically generates feasible service execution plans and selects the plan with the best user-desired quality. In particular, we present an evolutionary algorithm that is able to “co-evolve” multiple feasible execution plans simultaneously and allows them to compete with each other to generate the best plan. We conduct a set of experiments to assess the performance of the proposed algorithms.     

Decentralized Networked Control System Design Using Takagi-Sugeno (TS) Fuzzy Approach

This paper proposes a new method for control of continuous large-scale systems where the measures and control functions are distributed on calculating members which can be shared with other applications and connected to digital network communications.At first, the nonlinear large-scale system is described by a Takagi-Sugeno(TS) fuzzy model. After that, by using a fuzzy LyapunovKrasovskii functional, sufficient conditions of asymptotic stability of the behavior of the decentralized networked control system(DNCS),are developed in terms of linear matrix inequalities(LMIs). Finally, to illustrate the proposed approach, a numerical example and simulation results are presented.     

Study of planar structure applying reciprocity technique combined with MGEC and analysis of discontinuity

The 3D structure electromagnetic computation presents several difficulties related to the volume mesh. In fact, the entire volume space must be taken into account even the smallest details. In this article, we propose a formulation based on the reciprocity theorem combined with the generalized equivalent circuit method to model a planar 3D structure with both coaxial and planar excitation. The major advantage of this formulation is the fact to reduce the computational volume into 2D ones in the discontinuity plane. In addition, we focused on the calculation of the discontinuity between the excitation source and the planar structure to determine the exact behavior of the electric coaxial excitation model. The obtained current density, electric field distributions, and the input impedance are presented and discussed in the following sections. An approximately good agreement of input impedance with those obtained by the simulator and measurement is shown.