Abstract: A model-based egress support system (10) employs models to generate an optimal egress route for occupants in a region. The system includes an egress controller (16) that is connected to receive detection data. The egress controller (16) executes an optimization algorithm (22) to select an egress route based on model-based egress estimates generated by an egress prediction model (18) in response to the detection data.

Abstract: A system generates occupancy estimates based on a Kinetic-Motion (KM)-based model that predicts the movements of occupants through a region divided into a plurality of segments. The system includes a controller for executing an algorithm representing the KM-based model. The KM-based model includes state equations that define each of the plurality of segments as containing congested portions and uncongested portions. The state equations define the movement of occupants based, in part, on the distinctions made between congested and uncongested portions of each segment.

Abstract: A model-based egress support system (10) employs models to generate an optimal egress route for occupants in a region. The system includes an egress controller (16) that is connected to receive detection data. The egress controller (16) executes an optimization algorithm (22) to select an egress route based on model-based egress estimates generated by an egress prediction model (18) in response to the detection data.

Abstract: An alarm filter (22) for use in a security system (14) to reduce the occurrence of nuisance alarms receives sensor signals (S1-Sn, Sv) from a plurality of sensors (18, 20) included in the security system (14). The alarm filter (22) produces an opinion output as a function of the sensor signals and selectively modifies the sensor signals as a function of the opinion output to produce verified sensor signals (S1?-Sn?).

Abstract: A system generates occupancy estimates based on a Kinetic-Motion (KM)-based model that predicts the movements of occupants through a region divided into a plurality of segments. The system includes a controller for executing an algorithm representing the KM-based model. The KM-based model includes state equations that define each of the plurality of segments as containing congested portions and uncongested portions. The state equations define the movement of occupants based, in part, on the distinctions made between congested and uncongested portions of each segment.

Abstract: An occupancy estimator calculates an occupancy estimate (x) of a region based on sensor data (z) provided by one or more sensor devices and a model-based occupancy estimate generated by an occupant traffic model (f). The occupant traffic model (f) is based on predicted movement of occupants throughout a region. The occupancy estimation system includes an occupancy estimator algorithm (20) that combines the sensor data (z) and the model-based occupancy estimate generated by the occupant traffic model (f) to generate an occupancy estimate (x) for the region.

Abstract: An alarm filter (22) for use in a security system (14) to reduce the occurrence of nuisance alarms receives sensor signals (S1-Sn, Sv) from a plurality of sensors (18, 20) included in the security system (14). The alarm filter (22) produces an opinion output as a function of the sensor signals and selectively modifies the sensor signals as a function of the opinion output to produce verified sensor signals (S1?-Sn?).

Abstract: The optimization algorithm described herein combines stochastic programming and intelligent enumeration schemes that exploit the problem structure to avoid evaluating solutions that on their face are known to be non-optimal. The optimization algorithm finds the module workscope decisions and LLP replacement decisions that minimize expected future maintenance cost per engine flight cycle. The optimization algorithm considers the enormous number of possible solutions, and determines the best one.

Abstract: A method and system for scheduling using a facet ascending algorithm or a reduced complexity bundle method for solving an integer programming problem is presented. A Lagrangian dual function of an integer scheduling problem is maximized (for a primal minimization problem) to obtain a good near-feasible solution, and to provide a lower bound to the optimal value of the original problem. The dual function is a polyhedral concave function made up of many facets. The facet ascending algorithm of the present invention exploits the polyhedral concave nature of the dual function by ascending facets along intersections of facets. At each iteration, the algorithm finds the facets that intersect at the current dual point, calculates a direction of ascent along these facets, and then performs a specialized line search which optimizes a scaler polyhedral concave function in a finite number of steps.