Student Paper Award Winners Announced for ICNS 2010

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The following two papers received the Student Paper Award for the 2010 ICNS Conference. The awards were announced at the Thursday, May 13, plenary session.

  • Innovative Idea Award: Ontology-based CNS Software Development. Eduard Gringinger, Dieter Eier, Dieter Merkel (Vienna University of Technology).
  • Best Paper Award: Rigorous Bounding of Position Error Estimates for Aircraft Surface Movement. Kyle O’Brien, Jason Rife. (Tufts University)

Innovative Idea Award

Ontology-based CNS Software Development

  • Eduard Gringinger, FREQUENTIS AG, Vienna, Austria
  • Dieter Eier, FREQUENTIS USA Inc., Columbia, Maryland
  • Dieter Merkl, Vienna University of Technology, Vienna, Austria

Why Awarded: This paper describes an approach to leverage designs, procedures and technologies from command and control centers in other domains (e.g. emergency response) for application in aviation command and control (e.g., Tower, TRACON, Center, TFM). Cost savings and improved speed-to-market are the benefits of this excellent research. Congratulations on this good work.

Abstract: The heart of Air Traffic Control (ATC) lays in the Control Room (CR) in the ATC en route center, Terminal Radar Approach Control (TRACON), and ATC Tower (ATCT) facilities. However, CRs are also used in other mission critical domains such as 911, or Emergency control centers. In the past this led to the development of domain specific control rooms resulting in different solutions for each specific environment. This raises the cost for efficient software development and increases the time-to-market.

A modern Ontology-Based Control Room Framework (ONTOCOR) could dramatically improve this Air Traffic Management (ATM) situation. Uniform and open standards build up ontologies described by the Web Ontology Language (OWL). Information Management (IM) and the development of uniform and open standards are key components of the Next Generation Air Transportation System (NextGen) and Europe’s SESAR Program. ONTOCOR increases productive code usage and reduces software development. It focuses on improving efficiency and gain effort by code reusability, thus contributing to reduction of deployment cost of such solutions.

This paper analyzes and compares different ontology languages as well as relevant semantic tools for ontology development and management. The present paper will also give a brief survey on ontology-based software engineering, before the ongoing research of ONTOCOR is introduced.

Best Paper Award

Rigorous Bounding of Position Error Estimates for Aircraft Surface Movement

  • Kyle O’Brien, Tufts University, Medford, MA
  • Jason Rife, Tufts University, Medford, MA

Why Awarded: This paper used an innovative new method (biased-Gaussian error bounding) to bounding of position error of aircraft. The paper described research on a relevant problem, applied a sound theoretical approach, and included results of a physical experiment. Congratulations on this excellent research.

Abstract: NextGen will require new navigation and surveillance capabilities to support safe and efficient surface operations based on tightly-coordinated 4D trajectories. In developing these new technologies, such as Automatic Dependent Surveillance-Broadcast (ADS-B) and the Ground Based Augmentation System (GBAS), it is essential to remember that all sensing technologies are prone to rare but potentially hazardous errors. Accordingly, the development of new navigation and surveillance technologies must be complemented by the development of rigorous integrity algorithms that allow pilots and controllers to determine when a sensor system should or should not be trusted.

This paper describes the application of a new state-prediction methodology to developing conservative position-error bounds for aircraft ground movement. These position-error bounds can be compared to operational limits in order to generate an alert if the risk of a large navigation error becomes unacceptably high. To demonstrate the conservatism of our approach, we have conducted a series of experiments in a lab setting using a surrogate (robotic) vehicle. These experiments indicate that our method, which we call biased-Gaussian prediction, generates a conservative position-error bound even when more conventional prediction methods do not.