Evaluating Performance of UAS Detect-And-Avoid System Using a Fast-Time Simulation Tool

Most unmanned aircraft systems will be required to be equipped with a Detect-and-Avoid (DAA) system. The surveillance performance of the DAA system to detect and track intruder aircraft will depend on the encounter geometries that unmanned aircraft are expected to have with other aircraft in the airspace. The performance of DAA alerting and avoidance system is also dependent on the timeliness of alerting for UAS pilots to give a sufficient time to determine and command a resolution maneuver to avoid well clear separation violations. This presentation introduces general background of UAS DAA systems and concept of well clear separation standard to satisfy see and avoid regulations. The presentation shows the several UAS mission profiles and analysis of the encounter geometries that were simulated using historical VFR traffic data and some proposed UAS missions. This presentation introduces several potential metrics for evaluating the performance of a DAA system and shows the results that measured through fast-time simulation with traffic scenarios that include NAS-wide VFR manned aircraft and IFR UAS flights. At the end, some research areas will be briefly discussed

A conceptual and computational framework for identifying and predicting the performance of novel airspace concept of operations

Issued as final reportUnited States. National Aeronautics and Space Administratio

Observations on Human Performance in Air Traffic Control Operations: Preliminaries to a Cognitive Model

We have previously described a computational approach to modeling human performance, built using the Apex architecture [1],in which extended behavioral sequences are automatically constructed from simpler CPMGOMS templates [2]. Here we report on efforts to extend that method to the more complex domain of air traffic control operations. We describe the overall approach and our initial analysis of patterns of human performance observed in a simulation of air traffic control operations conducted by the Federal Aviation Administration. Of particular interest are patterns that characterize how operators manage multiple tasks, and distribute their attention over items. We show how templates in our compositional approach might be structured to accommodate these patterns

Ensuring Interoperability between UAS Detect-and-Avoid and Manned Aircraft Collision Avoidance

The UAS community in the United States has identified the need for a collision avoidance region in which UAS Detect-and-Avoid (DAA) vertical guidance is restricted to preclude interoperability issues with manned aircraft collision avoidance system vertical resolution advisories (RAs). This paper documents the process by which the collision avoidance region was defined. Three candidate definitions were evaluated on 1.3 million simulated pairwise encounters between UAS and manned aircraft covering a wide range of horizontal and vertical closure rates, angles, and miss distances. They were evaluated with regard to UAS DAA interoperability with manned aircraft collision avoidance systems in terms of: 1) the primary objective of restricting DAA vertical guidance before RAs when the aircraft are close, and 2) the secondary objective of avoiding unnecessary restrictions of DAA vertical guidance at a DAA alert when the aircraft are further apart. The collision avoidance region definition that fully achieves the primary objective and best achieves the secondary objective was recommended to and accepted by the UAS community in the United States. By this definition, UAS and manned aircraft are in the collision avoidance region--during which DAA vertical guidance is restricted--when the time to closest point of approach is less than 50 seconds and either the time to co-altitude is less than 50 seconds or the current vertical separation is less than 800 feet

A machine-learning approach to predict postprandial hypoglycemia

Background For an effective artificial pancreas (AP) system and an improved therapeutic intervention with continuous glucose monitoring (CGM), predicting the occurrence of hypoglycemia accurately is very important. While there have been many studies reporting successful algorithms for predicting nocturnal hypoglycemia, predicting postprandial hypoglycemia still remains a challenge due to extreme glucose fluctuations that occur around mealtimes. The goal of this study is to evaluate the feasibility of easy-to-use, computationally efficient machine-learning algorithm to predict postprandial hypoglycemia with a unique feature set. Methods We use retrospective CGM datasets of 104 people who had experienced at least one hypoglycemia alert value during a three-day CGM session. The algorithms were developed based on four machine learning models with a unique data-driven feature set: a random forest (RF), a support vector machine using a linear function or a radial basis function, a K-nearest neighbor, and a logistic regression. With 5-fold cross-subject validation, the average performance of each model was calculated to compare and contrast their individual performance. The area under a receiver operating characteristic curve (AUC) and the F1 score were used as the main criterion for evaluating the performance. Results In predicting a hypoglycemia alert value with a 30-min prediction horizon, the RF model showed the best performance with the average AUC of 0.966, the average sensitivity of 89.6%, the average specificity of 91.3%, and the average F1 score of 0.543. In addition, the RF showed the better predictive performance for postprandial hypoglycemic events than other models. Conclusion In conclusion, we showed that machine-learning algorithms have potential in predicting postprandial hypoglycemia, and the RF model could be a better candidate for the further development of postprandial hypoglycemia prediction algorithm to advance the CGM technology and the AP technology further.11Ysciescopu

Regulatory gaps between LNG carriers and LNG fuelled ships

Given a number of marine vessels treating the liquefied natural gas either as cargo or fuel, this paper examined the regulatory gaps of two different international Codes – the InternationalCodeof the Construction and Equipment of Ships Carrying Liquefied Gases in Bulk and the International Code of Safety for Ships Using Gases or Other Low-flashpoint Fuels – from the regulatory standpoint. Results of the gap analysis have identified and discussed the key areas encountered with regulatory discrepancies or ambiguities that might interrupt the proper design, construction and operation of LNG carrier and LNG fuelled ship. A systematic investigation and harmonisation process across the Codes was proposed to mitigate the potential issues that may arise from the discordant regulations. Also, the International Maritime Organization was suggested to take proactive action to improve such dissonances while a general insight into the importance of filling those gaps was provided for rule-makers and stakeholders

Investigating Detect-and-Avoid Surveillance Performance for Unmanned Aircraft Systems

Most unmanned aircraft systems will be required to be equipped with a Detect-and-Avoid (DAA) system with a surveillance component. The surveillance performance requirements of the DAA system to detect and track intruder aircraft will depend on the encounter geometries that unmanned aircraft are expected to have with other aircraft in the airspace. This presentation shows the analysis of the encounter geometries that were simulated using historical low-altitude traffic data and some proposed UAS missions. This analysis suggests how the overall safety and performance of a surveillance system may relate to surveillance parameters such as surveillance range, horizontal and vertical fields or regard. This study proposed and investigated potential safety and performance metrics for evaluating the performance of a surveillance system, such as the ratio of undetected and late-detected separation violations, and the time to violation at first detection for given sets of surveillance parameters