Missouri University of Science and Technology

About: Missouri University of Science and Technology is a education organization based out in Rolla, Missouri, United States. It is known for research contribution in the topics: Control theory & Artificial neural network. The organization has 9380 authors who have published 21161 publications receiving 462544 citations. The organization is also known as: Missouri S&T & University of Missouri–Rolla.

Acoustic nonlinearities and power losses at orifices

Abstract: Acoustic power losses occur when high-amplitude sound waves impinge on an orifice in the absence of mean flow. Described is a simple theoretical model of these losses, in which a harmonic, linear acoustic field is coupled to a nonlinear hydrodynamic flowfield at the orifice. Experimental and theoretical data on power losses at orifices with both pc and flanged acoustic terminations are compared, and fairly good agreement is noted. The structure of the flowfield in the neighborhood of the orifice is broadly described and some quantitative comparisons are made between the measured translational velocity of the ring vortices shed from both sides of the orifice and theoretical predictions. Again, the theory is seen to give generally reasonable results.

A novel fuzzy adaptive configuration of particle swarm optimization to solve large-scale optimal reactive power dispatch

Abstract: Display Omitted Introducing a novel heuristic algorithm with extra exploration and exploitation processes namely FAHCLPSO in order to solve the ORPD problem.Applying the advantages of Fuzzy Logic (FL) and Fuzzy Interface System (FIS) for dynamic adopting the inertia weight of particles in the proposed algorithm.Considering active power transmission losses as well as voltage deviation of the system.Reporting the simulation results related to three different types of power systems such as IEEE 30-bus, 118-bus and 354-bus for small-, medium- and large-scale test systems. Management and scheduling of reactive power resources is one of the important and prominent problems in power system operation and control. It deals with stable and secure operation of power systems from voltage stability and voltage profile improvement point of views. To this end, a novel Fuzzy Adaptive Heterogeneous Comprehensive-Learning Particle Swarm Optimization (FAHCLPSO) algorithm with enhanced exploration and exploitation processes is proposed to solve the Optimal Reactive Power Dispatch (ORPD) problem. Two different objective functions including active power transmission losses and voltage deviation, which play important roles in power system operation and control, are considered in this paper. In order to authenticate the accuracy and performance of the proposed FAHCLPSO, it applied on three different standard test systems including IEEE 30-bus, IEEE 118-bus and IEEE 354-bus test systems with six, fifty-four and one-hundred-sixty-two generation units, respectively. Finally, outcomes of the proposed algorithm are compared with the results of the original PSO and those in other literatures. The comparison proves the supremacy of the proposed algorithm in solving the complex optimization problem.

Access control policy combining: theory meets practice

Abstract: Many access control policy languages, e.g., XACML, allow a policy to contain multiple sub-policies, and the result of the policy on a request is determined by combining the results of the sub-policies according to some policy combining algorithms (PCAs). Existing access control policy languages, however, do not provide a formal language for specifying PCAs. As a result, it is difficult to extend them with new PCAs. While several formal policy combining algebras have been proposed, they did not address important practical issues such as policy evaluation errors and obligations; furthermore, they cannot express PCAs that consider all sub-policies as a whole (e.g., weak majority or strong majority). We propose a policy combining language PCL, which can succinctly and precisely express a variety of PCAs. PCL represents an advancement both in terms of theory and practice. It is based on automata theory and linear constraints, and is more expressive than existing approaches. We have implemented PCL and integrated it with SUN's XACML implementation. With PCL, a policy evaluation engine only needs to understand PCL to evaluate any PCA specified in it.