Rotorcraft In-Flight Simulation Research at NASA Ames Research Center: A Review of the 1980's and plans for the 1990's

A new flight research vehicle, the Rotorcraft-Aircrew System Concepts Airborne Laboratory (RASCAL), is being developed by the U.S. Army and NASA at ARC. The requirements for this new facility stem from a perception of rotorcraft system technology requirements for the next decade together with operational experience with the Boeing Vertol CH-47B research helicopter that was operated as an in-flight simulator at ARC during the past 10 years. Accordingly, both the principal design features of the CH-47B variable-stability system and the flight-control and cockpit-display programs that were conducted using this aircraft at ARC are reviewed. Another U.S Army helicopter, a Sikorsky UH-60A Black Hawk, was selected as the baseline vehicle for the RASCAL. The research programs that influence the design of the RASCAL are summarized, and the resultant requirements for the RASCAL research system are described. These research programs include investigations of advanced, integrated control concepts for achieving high levels of agility and maneuverability, and guidance technologies, employing computer/sensor-aiding, designed to assist the pilot during low-altitude flight in conditions of limited visibility. The approach to the development of the new facility is presented and selected plans for the preliminary design of the RASCAL are described

An integrated Rotorcraft Avionics/Controls Architecture to support advanced controls and low-altitude guidance flight research

Salient design features of a new NASA/Army research rotorcraft--the Rotorcraft-Aircrew Systems Concepts Airborne Laboratory (RASCAL) are described. Using a UH-60A Black Hawk helicopter as a baseline vehicle, the RASCAL will be a flying laboratory capable of supporting the research requirements of major NASA and Army guidance, control, and display research programs. The paper describes the research facility requirements of these programs together with other critical constraints on the design of the research system. Research program schedules demand a phased development approach, wherein specific research capability milestones are met and flight research projects are flown throughout the complete development cycle of the RASCAL. This development approach is summarized, and selected features of the research system are described. The research system includes a real-time obstacle detection and avoidance system which will generate low-altitude guidance commands to the pilot on a wide field-of-view, color helmet-mounted display and a full-authority, programmable, fault-tolerant/fail-safe, fly-by-wire flight control system

From Rankings to Ratings: Rank Scoring Via Active Learning

In this paper we present RaScAL, an active learning approach to predicting real-valued scores for items given access to an oracle and knowledge of the overall item-ranking. In an experiment on six different datasets, we find that RaScAL consistently outperforms the state-of-the-art. The RaScAL algorithm represents one step within a proposed overall system of preference elicitations of scores via pairwise comparisons

Program Analysis Scenarios in Rascal

Rascal is a meta programming language focused on the implementation of domain-specific languages and on the rapid construction of tools for software analysis and software transformation. In this paper we focus on the use of Rascal for software analysis. We illustrate a range of scenarios for building new software analysis tools through a number of examples, including one showing integration with an existing Maude-based analysis. We then focus on ongoing work on alias analysis and type inference for PHP, showing how Rascal is being used, and sketching a hypothetical solution in Maude. We conclude with a high-level discussion on the commonalities and differences between Rascal and Maude when applied to program analysis

The Rascal meta-programming language - a lab for software analysis, transformation, generation & visualization

National audienceThis paper summarizes the goals and features of a do- main specific programming language called Rascal. On the one hand it is designed to facilitate software research -- research about software in general. On the other hand Rascal is applied to specific software portfolios as well, as a means to improve them and as a means to learn to understand them. Specifically, Rascal is used create tools that analyze, transform, generate or visualize source code of software products. Such tools are motivated by the need to im- prove quality of existing software or the need to lower its cost-of-ownership. More generally such tools are cre- ated to build laboratory experiments that observe and measure quality, or try and improve software quality, etc. In this paper we provide an overview of Rascal as a "domain specific language for meta programming". We first explain its goals and then its features. We end by highlighting some example applications in the area of software analysis and transformation

A Generalized Method for Integrating Rule-based Knowledge into Inductive Methods Through Virtual Sample Creation

Hybrid learning methods use theoretical knowledge of a domain and a set of classified examples to develop a method for classification. Methods that use domain knowledge have been shown to perform better than inductive learners. However, there is no general method to include domain knowledge into all inductive learning algorithms as all hybrid methods are highly specialized for a particular algorithm. We present an algorithm that will take domain knowledge in the form of propositional rules, generate artificial examples from the rules and also remove instances likely to be flawed. This enriched dataset then can be used by any learning algorithm. Experimental results of different scenarios are shown that demonstrate this method to be more effective than simple inductive learning

A New Game Invariant of Graphs: the Game Distinguishing Number

The distinguishing number of a graph $G$ is a symmetry related graph invariant whose study started two decades ago. The distinguishing number $D(G)$ is the least integer $d$ such that $G$ has a $d$-distinguishing coloring. A distinguishing $d$-coloring is a coloring $c:V(G)\rightarrow\{1,...,d\}$ invariant only under the trivial automorphism. In this paper, we introduce a game variant of the distinguishing number. The distinguishing game is a game with two players, the Gentle and the Rascal, with antagonist goals. This game is played on a graph $G$ with a set of $d\in\mathbb N^*$ colors. Alternately, the two players choose a vertex of $G$ and color it with one of the $d$ colors. The game ends when all the vertices have been colored. Then the Gentle wins if the coloring is distinguishing and the Rascal wins otherwise. This game leads to define two new invariants for a graph $G$, which are the minimum numbers of colors needed to ensure that the Gentle has a winning strategy, depending on who starts. These invariants could be infinite, thus we start by giving sufficient conditions to have infinite game distinguishing numbers. We also show that for graphs with cyclic automorphisms group of prime odd order, both game invariants are finite. After that, we define a class of graphs, the involutive graphs, for which the game distinguishing number can be quadratically bounded above by the classical distinguishing number. The definition of this class is closely related to imprimitive actions whose blocks have size $2$. Then, we apply results on involutive graphs to compute the exact value of these invariants for hypercubes and even cycles. Finally, we study odd cycles, for which we are able to compute the exact value when their order is not prime. In the prime order case, we give an upper bound of $3$