Ground Risk Assessment Service Provider (GRASP) Development Effort as a Supplemental Data Service Provider (SDSP) for Urban Unmanned Aircraft System (UAS) Operations

NASAs Unmanned Aircraft System (UAS) Traffic Management (UTM) project aims to enable the integration of new aviation paradigms such as Unmanned Aircraft Systems (UAS) while providing the necessary infrastructure for future concepts such as On-Demand Mobility (ODM) and Urban Air Mobility (UAM) operations in the National Airspace System (NAS). In order to do so, the UTM project has developed an architecture to allow communication among UAS operators, UAS Service Suppliers (USS), Air Navigation Service Providers (ANSP), and the public. As part of this framework, the Supplemental Data Service Providers (SDSP) are envisioned as model and/or data based services that disseminate essential or enhanced information to ensure safe operations within low-altitude airspace. These services include terrain and obstacle data, specialized weather data, surveillance, constraint information, risk monitoring, etc. This paper highlights the development efforts of a non-participant casualty risk assessment SDSP called Ground Risk Assessment Service Provider (GRASP) which assists operators with preflight planning. GRASP is based on the previously introduced UTM Risk Assessment Framework (URAF) and allows UAS operators to simulate and visualize potential non-participant casualty risks associated with their proposed flight. The risk assessment capability also allows operators to revise their flight plans if the casualty risks are determined to be above acceptable thresholds. GRASP is configured to account for future improvements including servicing airborne aircraft as part of NASAs System-Wide Safety (SWS) project

Architecture and Information Requirements to Assess and Predict Flight Safety Risks During Highly Autonomous Urban Flight Operations

As aviation adopts new and increasingly complex operational paradigms, vehicle types, and technologies to broaden airspace capability and efficiency, maintaining a safe system will require recognition and timely mitigation of new safety issues as they emerge and before significant consequences occur. A shift toward a more predictive risk mitigation capability becomes critical to meet this challenge. In-time safety assurance comprises monitoring, assessment, and mitigation functions that proactively reduce risk in complex operational environments where the interplay of hazards may not be known (and therefore not accounted for) during design. These functions can also help to understand and predict emergent effects caused by the increased use of automation or autonomous functions that may exhibit unexpected non-deterministic behaviors. The envisioned monitoring and assessment functions can look for precursors, anomalies, and trends (PATs) by applying model-based and data-driven methods. Outputs would then drive downstream mitigation(s) if needed to reduce risk. These mitigations may be accomplished using traditional design revision processes or via operational (and sometimes automated) mechanisms. The latter refers to the in-time aspect of the system concept. This report comprises architecture and information requirements and considerations toward enabling such a capability within the domain of low altitude highly autonomous urban flight operations. This domain may span, for example, public-use surveillance missions flown by small unmanned aircraft (e.g., infrastructure inspection, facility management, emergency response, law enforcement, and/or security) to transportation missions flown by larger aircraft that may carry passengers or deliver products. Caveat: Any stated requirements in this report should be considered initial requirements that are intended to drive research and development (R&D). These initial requirements are likely to evolve based on R&D findings, refinement of operational concepts, industry advances, and new industry or regulatory policies or standards related to safety assurance

Quadcopter Trajectory Prediction and Wind Estimation Using Machine Learning

Small unmanned aerial systems are heavily impacted by wind disturbances. Wind causes deviations from desired trajectories, potentially leading to crashes. In this thesis, we consider two inherently related problems: predicting quadcopter trajectory deviations due to wind disturbances and estimating wind velocity based on quadcopter trajectory deviations. The former is addressed using linear difference equation identification as well as neural network (NN) modeling. Simulations validate the use of linear difference equation identification as a tool to predict trajectory deviations in crosswinds and machine learning (specifically, long short-term memory (LSTM) NNs) as an approach to predict trajectory deviations in multidimensional wind. We approach the wind estimation problem from a machine learning perspective due to easier generalization of the NN to multidimensional winds. As in the trajectory prediction case, we use LSTM NNs to identify a model. The trained NN is deployed to estimate the turbulent winds as generated by the Dryden gust model as well as a realistic large eddy simulation of a near-neutral atmospheric boundary layer over flat terrain. The resulting NN predictions are compared to a wind triangle approach that uses tilt angle as an approximation of airspeed. Results from this study indicate that the LSTM NN based approach results in lower errors in both the mean and variance of the local wind field as compared to the wind triangle approach

Is Inflation Persistence Over?

We analyze inflation persistence in several industrial and emerging countries in the recent past by estimating reduced-form models of inflation dynamics. We select a very representative group of 23 industrial and 17 emerging economies. Our sample period is comprised of quarterly data and starts in the first quarter of 1995. Our results show that inflation persistence is low and stable for all countries in our sample. It seems to be lower in industrial relative to emerging countries. Finally, even countries that have had “hyperinflation” experience in the recent past showed low levels of inflation persistence, albeit apparently higher than the other countries in our sample.

The Brazilian Interbank Network Structure and Systemic Risk

We explore the structure and dynamics of interbank exposures in Brazil using a unique data set of all mutual exposures of financial institutions in Brazil, as well as their capital reserves, at various periods in 2007 and 2008. We show that the network of exposures can be adequately modeled as a directed scale-free (weighted) graph with heavy-tailed degree and weight distributions. We also explore the relationship between connectivity of a financial institution and its capital buffer. Finally, we use the network structure to explore the extent of systemic risk generated in the system by the individual institutions.

Atmospheric Sampling on Ascension Island Using Multirotor UAVs

As part of an NERC-funded project investigating the southern methane anomaly, a team drawn from the Universities of Bristol, Birmingham and Royal Holloway flew small unmanned multirotors from Ascension Island for the purposes of atmospheric sampling. The objective of these flights was to collect air samples from below, within and above a persistent atmospheric feature, the Trade Wind Inversion, in order to characterise methane concentrations and their isotopic composition. These parameters allow the methane in the different air masses to be tied to different source locations, which can be further analysed using back trajectory atmospheric computer modelling. This paper describes the campaigns as a whole including the design of the bespoke eight rotor aircraft and the operational requirements that were needed in order to collect targeted multiple air samples up to 2.5 km above the ground level in under 20 min of flight time. Key features of the system described include real-time feedback of temperature and humidity, as well as system health data. This enabled detailed targeting of the air sampling design to be realised and planned during the flight mission on the downward leg, a capability that is invaluable in the presence of uncertainty in the pre-flight meteorological data. Environmental considerations are also outlined together with the flight plans that were created in order to rapidly fly vertical transects of the atmosphere whilst encountering changing wind conditions. Two sampling campaigns were carried out in September 2014 and July 2015 with over one hundred high altitude sampling missions. Lessons learned are given throughout, including those associated with operating in the testing environment encountered on Ascension Island

Fluctuation Dynamics in US Interest Rates and the Role of Monetary Policy

This paper presents empirical evidence suggesting that the degree of long-range dependence in interest rates depends on the conduct of monetary policy. We study the term structure of interest rates for the US and find evidence that global Hurst exponents change dramatically according to Chairman Tenure in the Federal Reserve Board and also with changes in the conduct of monetary policy. In the period from 1960's until the monetarist experiment in the beginning of the 1980's interest rates had a significant long-range dependence behavior. However, in the recent period, in the second part of the Volcker tenure and in the Greenspan tenure, interest rates do not present long-range dependence behavior. These empirical findings cast some light on the origins of long-range dependence behavior in financial assets.