Research on semantic webs has become increasingly widespread in the computer science community. The core technology of a semantic web is an artefact called an ontology. The major problem in constructing an ontology is the long period of time required. Another problem is the large number of possible meanings for the knowledge in the ontology. In this paper, we present a novel ontology construction based on artificial neural networks and a Bayesian network. First, we collected web pages related to the problem domain using search engines. The system then used the labels of the HTML tags to select keywords, and used WordNet to determine the meaningful keywords, called terms. Next, it calculated the entropy value to determine the weight of the terms. After the above steps, the projective adaptive resonance theory neural network clustered the collected web pages and found the representative term of each cluster of web pages using the entropy value. The system then used a Bayesian network to insert the terms and complete the hierarchy of the ontology. Finally, the system used a resource description framework to store and express the ontology results.
The empirical entropy of the network flow attributes is an essential measure for identifying anomalous network traffic. However, computing the exact entropy values for high-speed networks in realtime is computationally expensive. Accordingly, the present study replaces the complex computations of existing stable random projection methods for entropy estimation with a simple table lookup procedure. Notably, the size of the lookup table is reduced through a piece-wise linear interpolation heuristic in order to facilitate the implementation of the proposed scheme in resource-constrained pipeline environments. The proposed architecture enables entropy estimation to be performed using both the Log-Mean Estimator (LME) method and the New Estimator of Compressed Counting (NECC) algorithm reported in the literature. The feasibility of the proposed approach is verified empirically using both real-world network traffic traces and synthetic data streams. Moreover, the practical applicability is demonstrated via stream-based implementation in the programmable data planes of the NetFPGA-Plus framework and a Tofino P4 switch, respectively. The results indicate that the proposed tabulation-based entropy estimation scheme allows minimum-sized Ethernet frames to be processed with a wire speed of up to several hundred gigabits per second.
A de-loaded real power control strategy is proposed to decrease the real power output and increase the reactive power output of a grid-connected offshore wind farm in order to improve the voltage profile when the wind farm is subject to a grid fault. A simplified linear model of the wind farm is first derived and a fixed-gain proportional-integral (PI) real power controller is designed based on the pole-zero cancellation method. To improve the dynamic voltage response when the system is subject to a major disturbance such as a three-phase fault in the grid, a self-tuning controller based on particle swarm optimization (PSO) is proposed to adapt the PI controller gains based on the on-line measured system variables. Digital simulations using MATLAB/SIMULINK were performed on an offshore wind farm connected to the power grid in central Taiwan in order to validate the effectiveness of the proposed PSO controller. It is concluded from the simulation results that a better dynamic voltage response can be achieved by the proposed PSO self-tuning controller than the fixed-gain controller when the grid is subject to a three-phase fault. In addition, low voltage ride through (LVRT) requirements of the local utility can be met by the wind farm with the proposed power controller.
Load changes in a microgrid comprising diesel generators and wind turbines cause oscillations in system frequency. In this paper, an analytical model that takes wind generator speed deviation into account is proposed to evaluate the oscillation frequency and damping ratio of the frequency mode oscillation when the wind turbines participate in ancillary frequency control. Because the frequency mode oscillation may excite torsional modes, a complete linearized model comprising the diesel generators and the five‐mass drivetrain of the wind turbine is formulated, and the mode shapes as well as the frequencies and damping ratios of the torsional modes are analyzed. To improve damping for the torsional modes, a constant damping ratio design method is proposed to move the eigenvalues of poorly damped modes to points along a line with a constant damping ratio on the complex plane. Dynamic simulations are performed on the microgrid subject to load changes and wind fluctuations in order to validate the results from mode shape analyses as well as demonstrate the effectiveness of the designed torsional damper.
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