Current Issue : January - March Volume : 2014 Issue Number : 1 Articles : 4 Articles
The present paper focuses on the development of an algorithm for safely and\r\noptimally managing the routing of aircraft on an airport surface in future airport operations.\r\nThis tool is intended to support air traffic controllers� decision-making in selecting the\r\npaths of all aircraft and the engine startup approval time for departing ones. Optimal routes\r\nare sought for minimizing the time both arriving and departing aircraft spend on an airport\r\nsurface with engines on, with benefits in terms of safety, efficiency and costs. The\r\nproposed algorithm first computes a standalone, shortest path solution from runway to\r\napron or vice versa, depending on the aircraft being inbound or outbound, respectively. For\r\ntaking into account the constraints due to other traffic on an airport surface, this solution is\r\namended by a conflict detection and resolution task that attempts to reduce and possibly\r\nnullify the number of conflicts generated in the first phase. An example application on a\r\nsimple Italian airport exemplifies how the algorithm can be applied to true-world\r\napplications. Emphasis is given on how to model an airport surface as a weighted and\r\ndirected graph with non-negative weights, as required for the input to the algorithm....
We consider a two-sided market under incomplete preference lists with ties, where\r\nthe goal is to find a maximum size stable matching. The problem is APX-hard, and a\r\n3/2-approximation was given by McDermid [1]. This algorithm has a non-linear running\r\ntime, and, more importantly needs global knowledge of all preference lists. We present\r\na very natural, economically reasonable, local, linear time algorithm with the same ratio,\r\nusing some ideas of Paluch [2]. In this algorithm every person make decisions using only\r\ntheir own list, and some information asked from members of these lists (as in the case of\r\nthe famous algorithm of Gale and Shapley). Some consequences to the Hospitals/Residents\r\nproblem are also discussed....
The tag identification efficiency of a reader in an RFID system with Frame Slotted Aloha\r\n(FSA) based Anti-Collision Algorithm (ACA) can be maximized by selecting the optimal\r\nframe length with respect to the number of interrogating tags. Conventional analytical models\r\nthat have been used widely to derive such an optimal frame length are inaccurate because\r\nthey lack either precise characterization of the timing details of the underlying ACA or do not\r\nconsider the physical layer capture effect. In this study, one of the most popular conventional\r\nanalytical models has been extended not only to deliberate the exact timing details of the\r\nunderlying ACA but also to consider the physical layer capture effect. Rigorous numerical\r\nanalysis shows that the optimal frame length derived from the new extended model is precise,\r\nwhereas that of from the conventional model deviates significantly from the true optimal\r\nvalue, particularly when the number of tags is high or the capture probability is low....
The Quantitative Trait Loci (QTL) mapping problem aims to identify regions\r\nin the genome that are linked to phenotypic features of the developed organism that vary\r\nin degree. It is a principle step in determining targets for further genetic analysis and is\r\nkey in decoding the role of specific genes that control quantitative traits within species.\r\nApplications include identifying genetic causes of disease, optimization of cross-breeding\r\nfor desired traits and understanding trait diversity in populations. In this paper a new\r\nmulti-objective evolutionary algorithm (MOEA) method is introduced and is shown to\r\nincrease the accuracy of QTL mapping identification for both independent and epistatic\r\nloci interactions. The MOEA method optimizes over the space of possible partial least\r\nsquares (PLS) regression QTL models and considers the conflicting objectives of model\r\nsimplicity versus model accuracy. By optimizing for minimal model complexity, MOEA\r\nhas the advantage of solving the over-fitting problem of conventional PLS models. The\r\neffectiveness of the method is confirmed by comparing the new method with Bayesian\r\nInterval Mapping approaches over a series of test cases where the optimal solutions are\r\nknown. This approach can be applied to many problems that arise in analysis of genomic data sets where the number of features far exceeds the number of observations and where\r\nfeatures can be highly correlated....
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