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Bells Corners, Canada

Wojtaszek D.,Chalk River Laboratories | Wesolkowski S.,DRDC
IEEE Transactions on Systems, Man, and Cybernetics: Systems

The ability of an organization to perform some critical military tasks in a timely manner may depend on the availability of a sufficient number of appropriate vehicles. Therefore, the decision of which is the best military fleet-mix for a given set of requirements should take into consideration, in addition to cost, the ability of the fleet to perform tasks when some of its vehicles are unavailable. In this paper, a measure of the flexibility of military air mobility fleets is presented that evaluates their ability to perform tasks in a timely manner taking into account the possibility that some aircraft in the fleet may be unavailable at any given time. This measure computes the number of aircraft that must be unavailable in order to render a fleet incapable of performing each task in a timely manner. The utility of the flexibility measure is demonstrated by using it as an objective in a multiobjective optimization framework to compute nondominated fleets with respect to cost and flexibility. An artificial data set that is representative of real military air mobility data is used to illustrate how the new flexibility measure may be used to aid decision makers with their fleet mix problems. © 2013 IEEE. Source

Sinha A.,A.U.G. Signals Ltd. | Stolpner S.,A.U.G. Signals Ltd. | Mukherjee A.,A.U.G. Signals Ltd. | Monckton S.,DRDC

This article considers the problem of accurately modeling the kinematic state transition of an Unmanned Aerial Vehicle (UAV). The full 3D range of motion is accurately captured using compact equations for position update derived in this work. This derivation makes use of the independence of the rotation and translation components of a 3D rigid motion. The proposed motion model is transparent to the sensors used in the system; it is particularly useful in GPS-denied environments and can contribute to different aspects of robust navigation, such as accurate state estimation, sensor fault tolerance and sensor bias estimation. Experimental results comparing the performance of the proposed kinematic model with those typically used demonstrate its superiority. © 2014 GEOMATICA All rights reserved. Source

Wojtaszek D.,DRDC | Wesolkowski S.,DRDC
IEEE Computational Intelligence Magazine

A major financial expense for any military is the acquisition, operation, and maintenance of vehicles such as ships [1] and aircraft [2]. For example, the U.S. Air Force estimates that the acquisition of the F-35 fighter aircraft will cost $156 million each [3], hence even slight improvements in fleet efficiency and/or effectiveness can save governments large amounts of money or, using the same budget, can buy better equipment. Such high costs have driven the development and application of optimization and simulation methodologies to problems of military fleet mix computation and analysis. The complexity of military fleet mix problems, due in large part to the uncertainty, multi-objectivity, and temporal criticality of military missions, has resulted in the increased use of computational intelligence (CI) methods for solving them. © 2012 IEEE. Source

Willick K.,University of Waterloo | Storer B.,University of Waterloo | Wesolkowski S.,DRDC
2013 IEEE Congress on Evolutionary Computation, CEC 2013

Principal curves are a study of the underlying structure of a data cloud. We modify Kégl's [2] polygonal line algorithm by assuming that data points are vertices on different continuous curves which implies data ordering. We also develop a representation of curve deviation from the polygonal path by creating a deviation cloud based on computing a measure of the variance of the curves from the polygonal path. For the purposes of this paper, we consider the input curves to be vertex representations of independent polygonal paths. Comparisons of the presented algorithm on various data sets with that of Verbeek et al. [3] are given to illustrate differences when using ordered data represented as multiple continuous curves. We further consider applications of this algorithm to the evaluation of multiobjective optimization algorithm convergence for bi-objective optimization.We present preliminary results for NSGA-II on ZDT1, ZDT2, and ZDT3 in order to show how this methodology could be used. © 2013 IEEE. Source

Wesolkowski S.,DRDC | Wojtaszek D.,DRDC
2012 IEEE Congress on Evolutionary Computation, CEC 2012

Militaries involved in transportation of people and cargo need to be able to assess which tasks they can or cannot do given a specified fleet of heterogeneous platforms (such as vehicles or aircraft). We introduce the Stochastic Fleet Estimation under Steady State Tasking (SaFESST) model to determine which tasks will not be achievable. SaFESST is a bin-packing model which uses a fleet configuration (the assignment of specific platforms to each of the tasks) to fit each task from a scenario within the platform bins (the height of the bin represents the number of platforms). Each individual platform is represented by a strip of scenario length which is packed by sub-tasks it can carry out. SaFESST is run on a set of 10,000 scenarios for a single fleet configuration. Results are reported on various statistics of tasks that are unachievable. © 2012 IEEE. Source

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