Behavioral Learning of Aircraft Landing Sequencing Using a Society of Probabilistic Finite State Machines

Air Traffic Control (ATC) is a complex safety critical environment. A tower controller would be making many decisions in real-time to sequence aircraft. While some optimization tools exist to help the controller in some airports, even in these situations, the real sequence of the aircraft adopted by the controller is significantly different from the one proposed by the optimization algorithm. This is due to the very dynamic nature of the environment. The objective of this paper is to test the hypothesis that one can learn from the sequence adopted by the controller some strategies that can act as heuristics in decision support tools for aircraft sequencing. This aim is tested in this paper by attempting to learn sequences generated from a well-known sequencing method that is being used in the real world. The approach relies on a genetic algorithm (GA) to learn these sequences using a society Probabilistic Finite-state Machines (PFSMs). Each PFSM learns a different sub-space; thus, decomposing the learning problem into a group of agents that need to work together to learn the overall problem. Three sequence metrics (Levenshtein, Hamming and Position distances) are compared as the fitness functions in GA. As the results suggest, it is possible to learn the behavior of the algorithm/heuristic that generated the original sequence from very limited information

Evolutionary-Computation Based Risk Assessment of Aircraft Landing Sequencing Algorithms

Abstract. Usually, Evolutionary Computation (EC) is used for optimisation and machine learning tasks. Recently, a novel use of EC has been proposedMultiobjective Evolutionary Based Risk Assessment (MEBRA). MEBRA characterises the problem space associated with good and inferior performance of computational algorithms. Problem instances are represented ("scenario Representation") and evolved ("scenario Generation") in order to evaluate algorithms ("scenario Evaluation"). The objective functions aim at maximising or minimising the success rate of an algorithm. In the "scenario Mining" step, MEBRA identifies the patterns common in problem instances when an algorithm performs best in order to understand when to use it, and in instances when it performs worst in order to understand when not to use it. So far, MEBRA has only been applied to a limited number of problems. Here we demonstrate its viability to efficiently detect hot spots in an algorithm's problem space. In particular, we apply the basic MEBRA rationale in the area of Air Traffic Management (ATM). We examine two widely used algorithms for Aircraft Landing Sequencing: First Come First Served (FCFS) and Constrained Position Shifting (CPS). Through the use of three different problem ("scenario") representations, we identify those patterns in ATM problems that signal instances when CPS performs better than FCFS, and those when it performs worse. We show that scenario representation affects the quality of MEBRA outputs. In particular, we find that the variable-length chromosome representation of aircraft scheduling sequence scenarios converges fast and finds all relevant risk patterns associated with the use of FCFS and CPS

Femtogram Doubly Clamped Nanomechanical Resonators Embedded in a High-Q Two-Dimensional Photonic Crystal Nanocavity

We demonstrate a new optomechanical device system which allows highly efficient transduction of femtogram nanobeam resonators. Doubly clamped nanomechanical resonators with mass as small as 25 fg are embedded in a high-finesse two-dimensional photonic crystal nanocavity. Optical transduction of the fundamental flexural mode around 1 GHz was performed at room temperature and ambient conditions, with an observed displacement sensitivity of 0.94 fm/Hz^(1/2). Comparison of measurements from symmetric and asymmetric double-beam devices reveals hybridization of the mechanical modes where the structural symmetry is shown to be the key to obtain a high mechanical quality factor. Our novel configuration opens the way for a new category of "NEMS-in-cavity" devices based on optomechanical interaction at the nanoscale.Comment: Nano Lett. 201