Multiple sensors fusion plays a major role in situation awareness, especially in modern dynamic security monitoring and tracking applications. In this paper, we focus on a specific effective set of complementary sensors [Laser (for speed measurements), Sonar (for space scanning) and RF (for access rights)]. A novel multi-agent system is obtained by fusing the above types of sensory data taking advantages of similarity and complementarity concepts. Furthermore, Kalman Filter is utilized to track next state estimates of agent(s) in uncertain environment. Finally, our proposed system transforms system state to be able to make a security awareness decision, using type-2 fuzzy logic system to handle exhibited uncertainty, for asset scenery under surveillance. It is shown that the system performance can exhibit promising improvements for this dynamic security monitoring situation as a result of using the above complementary heterogeneous set of sensors.
This paper describes a technique for modeling nonlinear systems using multiple piecewise linear equations. The technique provides a means for linearizing the nonlinear system in such a way as to not limit the large signal behavior of the target system. The nonlinearity in the target system must be able to be represented as a piecewise linear function. A simple third order nonlinear system is used to demonstrate the technique. The behavior of the modeled system is compared to the behavior of the nonlinear system.
Use of wind energy as a renewable source of energy for electric utility systems is increasing around the world. The major challenges of wind energy generation are natural intermittency, unpredictability, and uncertainty due to wind variations. In this paper, five different adaptive neuro-fuzzy wind predictors are proposed and compared to forecast the speed of wind blowing in the East Coast of Egypt, a very promising location to generate more than 20 GW of wind power. The first and second proposed models are based on real wind-speed data of the selected site for the same month aided by the corresponding real wind-speed data for the same site for the past 4 and 6 years, respectively. The results that have been obtained from these models show more accuracy with respect to the previous work in the literature. Furthermore, three new models are proposed based on only 20% of the real data used for the first model obtaining similar accuracy to predict the average wind speed one day, half a day, and quarter day ahead.
Ab&act-Variable structure systems are designed so that desirable performance is achieved by judicious input switching. In this note, we introduce a new approach for the design of large variable structure systems subject to control bounds. The method includes a new switching hyperplane design based on generalized inverses and system decomposition. To ensure reaching the hyperplane and achieving a sliding condition, the control is switched between local equivalent control and bounded corrective control. The design of the corrective control component is completed using system decomposition into smaller subsystems. An estimate of the domain of attraction corresponding to the bounded control is obtained and used to select the appropriate controller bounds. The method is illustrated using a fifth-order numerical example.
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