Towards a standard benchmark for phenotype-driven variant and gene prioritisation algorithms: PhEval - Phenotypic inference Evaluation framework
Not provided.
Biochemistry & Molecular Biology↗
Engineering topics
Robinson, Peter
Publications and source records attributed to Robinson, Peter.
Piloted Simulator Evaluation Results of Flight Physics Based Stall Recovery Guidance
In recent studies, it has been observed that loss of control in flight is the most frequent primary cause of accidents. A significant share of accidents in this category can be remedied by upset prevention if possible, and by upset recovery if necessary, in this order of priorities. One of the most important upsets to be recovered from is stall. Recent accidents have shown that a correct stall recovery maneuver remains a big challenge in civil aviation, partly due to a lack of pilot training. A possible strategy to support the flight crew in this demanding context is calculating a recovery guidance signal, and showing this signal in an intuitive way on one of the cockpit displays, for example by means of the flight director. Different methods for calculating the recovery signal, one based on fast model predictive control and another using an energy based approach, have been evaluated in four relevant operational scenarios by experienced commercial as well as test pilots in the Vertical Motion Simulator at NASA Ames Research Center. Evaluation results show that this approach could be able to assist the pilots in executing a correct stall recovery maneuver.
Lombaerts, Thomas↗
Vertical Motion Simulator Experiment on Stall Recovery Guidance
A stall recovery guidance system was designed to help pilots improve their stall recovery performance when the current aircraft state may be unrecognized under various complicating operational factors. Candidate guidance algorithms were connected to the split-cue pitch and roll flight directors that are standard on large transport commercial aircraft. A new thrust guidance algorithm and cue was also developed to help pilots prevent the combination of excessive thrust and nose-up stabilizer trim. The overall system was designed to reinforce the current FAA recommended stall recovery procedure. A general transport aircraft model, similar to a Boeing 757, with an extended aerodynamic database for improved stall dynamics simulation fidelity was integrated into the Vertical Motion Simulator at NASA Ames Research Center. A detailed study of the guidance system was then conducted across four stall scenarios with 30 commercial and 10 research test pilots, and the results are reported.
Schuet, Stefan↗
Software Testbed for Developing and Evaluating Integrated Autonomous Subsystems
To implement fault tolerant autonomy in future space systems, it will be necessary to integrate planning, adaptive control, and state estimation subsystems. However, integrating these subsystems is difficult, time-consuming, and error-prone. This paper describes Intelliface/ADAPT, a software testbed that helps researchers develop and test alternative strategies for integrating planning, execution, and diagnosis subsystems more quickly and easily. The testbed's architecture, graphical data displays, and implementations of the integrated subsystems support easy plug and play of alternate components to support research and development in fault-tolerant control of autonomous vehicles and operations support systems. Intelliface/ADAPT controls NASA's Advanced Diagnostics and Prognostics Testbed (ADAPT), which comprises batteries, electrical loads (fans, pumps, and lights), relays, circuit breakers, invertors, and sensors. During plan execution, an experimentor can inject faults into the ADAPT testbed by tripping circuit breakers, changing fan speed settings, and closing valves to restrict fluid flow. The diagnostic subsystem, based on NASA's Hybrid Diagnosis Engine (HyDE), detects and isolates these faults to determine the new state of the plant, ADAPT. Intelliface/ADAPT then updates its model of the ADAPT system's resources and determines whether the current plan can be executed using the reduced resources. If not, the planning subsystem generates a new plan that reschedules tasks, reconfigures ADAPT, and reassigns the use of ADAPT resources as needed to work around the fault. The resource model, planning domain model, and planning goals are expressed using NASA's Action Notation Modeling Language (ANML). Parts of the ANML model are generated automatically, and other parts are constructed by hand using the Planning Model Integrated Development Environment, a visual Eclipse-based IDE that accelerates ANML model development. Because native ANML planners are currently under development and not yet sufficiently capable, the ANML model is translated into the New Domain Definition Language (NDDL) and sent to NASA's EUROPA planning system for plan generation. The adaptive controller executes the new plan, using augmented, hierarchical finite state machines to select and sequence actions based on the state of the ADAPT system. Real-time sensor data, commands, and plans are displayed in information-dense arrays of timelines and graphs that zoom and scroll in unison. A dynamic schematic display uses color to show the real-time fault state and utilization of the system components and resources. An execution manager coordinates the activities of the other subsystems. The subsystems are integrated using the Internet Communications Engine (ICE). an object-oriented toolkit for building distributed applications.
Testbed↗
A Framework to Debug Diagnostic Matrices
Diagnostics is an important concept in system health and monitoring of space operations. Many of the existing diagnostic algorithms utilize system knowledge in the form of diagnostic matrix (D-matrix, also popularly known as diagnostic dictionary, fault signature matrix or reachability matrix) gleaned from physical models. But, sometimes, this may not be coherent to obtain high diagnostic performance. In such a case, it is important to modify this D-matrix based on knowledge obtained from other sources such as time-series data stream (simulated or maintenance data) within the context of a framework that includes the diagnostic/inference algorithm. A systematic and sequential update procedure, diagnostic modeling evaluator (DME) is proposed to modify D-matrix and wrapper logic considering least expensive solution first. This iterative procedure includes conditions ranging from modifying 0s and 1s in the matrix, or adding/removing the rows (failure sources) columns (tests). We will experiment this framework on datasets from DX challenge 2009.
Model-based↗
Web Based Prognostics and 24/7 Monitoring
We created a general framework for analysts to store and view data in a way that removes the boundaries created by operating systems, programming languages, and proximity. With the advent of HTML5 and CSS3 with JavaScript the distribution of information is limited to only those who lack a browser. We created a framework based on the methodology: one server, one web based application. Additional benefits are increased opportunities for collaboration. Today the idea of a group in a single room is antiquated. Groups will communicate and collaborate with others from other universities, organizations, as well as other continents across times zones. There are many varieties of data gathering and condition-monitoring software available as well as companies who specialize in customizing software to individual applications. One single group will depend on multiple languages, environments, and computers to oversee recording and collaborating with one another in a single lab. The heterogeneous nature of the system creates challenges for seamless exchange of data and ideas between members. To address these limitations we designed a framework to allow users seamless accessibility to their data. Our framework was deployed using the data feed on the NASA Ames' planetary rover testbed. Our paper demonstrates the process and implementation we followed on the rover.
CSS3↗
Advanced Caution and Warning System, Final Report - 2011
The work described in this report is a continuation of the ACAWS work funded in fiscal year (FY) 2010 under the Exploration Technology Development Program (ETDP), Integrated Systems Health Management (ISHM) project. In FY 2010, we developed requirements for an ACAWS system and vetted the requirements with potential users via a concept demonstration system. In FY 2011, we developed a working prototype of aspects of that concept, with placeholders for technologies to be fully developed in future phases of the project. The objective is to develop general capability to assist operators with system health monitoring and failure diagnosis. Moreover, ACAWS was integrated with the Discrete Controls (DC) task of the Autonomous Systems and Avionics (ASA) project. The primary objective of DC is to demonstrate an electronic and interactive procedure display environment and multiple levels of automation (automatic execution by computer, execution by computer if the operator consents, and manual execution by the operator).
Spirkovska, Lilly↗
Two Trees: Migrating Fault Trees to Decision Trees for Real Time Fault Detection on International Space Station
We started from ISS fault trees example to migrate to decision trees, presented a method to convert fault trees to decision trees. The method shows that the visualizations of root cause of fault are easier and the tree manipulating becomes more programmatic via available decision tree programs. The visualization of decision trees for the diagnostic shows a format of straight forward and easy understands. For ISS real time fault diagnostic, the status of the systems could be shown by mining the signals through the trees and see where it stops at. The other advantage to use decision trees is that the trees can learn the fault patterns and predict the future fault from the historic data. The learning is not only on the static data sets but also can be online, through accumulating the real time data sets, the decision trees can gain and store faults patterns in the trees and recognize them when they come.
Lee, Charles↗
Applying Model-Based Reasoning to the FDIR of the Command and Data Handling Subsystem of the International Space Station
All of the International Space Station (ISS) systems which require computer control depend upon the hardware and software of the Command and Data Handling System (C&DH) system, currently a network of over 30 386-class computers called Multiplexor/Dimultiplexors (MDMs)[18]. The Caution and Warning System (C&W)[7], a set of software tasks that runs on the MDMs, is responsible for detecting, classifying, and reporting errors in all ISS subsystems including the C&DH. Fault Detection, Isolation and Recovery (FDIR) of these errors is typically handled with a combination of automatic and human effort. We are developing an Advanced Diagnostic System (ADS) to augment the C&W system with decision support tools to aid in root cause analysis as well as resolve differing human and machine C&DH state estimates. These tools which draw from sources in model-based reasoning[ 16,291, will improve the speed and accuracy of flight controllers by reducing the uncertainty in C&DH state estimation, allowing for a more complete assessment of risk. We have run tests with ISS telemetry and focus on those C&W events which relate to the C&DH system itself. This paper describes our initial results and subsequent plans.
Robinson, Peter↗
[ ] or SUCCESS is Not Enough: Current Technology and Future Directions in Proof Presentation
Automated theorem provers for first order logic are now around for several decades. Over the last few years, their deductive power to solve hard problems has increased tremendously. The annual CASC system competitions [Se97] give a clear picture of this situation. However, today's automated theorem provers are restricted "more by general usability than by raw deductive power." As a result of this, there are only very few serious applications of automated theorem provers. There are numerous features which a theorem prover lacks for real-world applicability. An automated theorem prover (as it is currently seen) is nothing more than a fast and elaborate search procedure. In that sense, an ATP can compared to a formulated race car, cool and fast, but virtually unusable for shopping groceries around the corner. Many important features are missing, or are optimized for speed rather than for applicability. [Schol] identifies important features which are needed for practical usability like detection of non-theorems, handling of modal/inductive proof tasks, control of the prover, and proof output. In this paper, we will focus solely on the last point, the presentation of the ATP's result to the user. In the rest of this paper, we will first discuss the general importance of providing feedback to the user, then we will describe the system ExplainIt!, a part of the deductive synthesis system AMPHION/NAV. In the conclusions we will relate proof presentation to other ways of post-processing a proof found by an ATP and stress their role in the future of automated deduction.
Schumann, Johann↗
Transformation Systems at NASA Ames
In this paper, we describe the experiences of the Automated Software Engineering Group at the NASA Ames Research Center in the development and application of three different transformation systems. The systems span the entire technology range, from deductive synthesis, to logic-based transformation, to almost compiler-like source-to-source transformation. These systems also span a range of NASA applications, including solving solar system geometry problems, generating data analysis software, and analyzing multi-threaded Java code.
Buntine, Wray↗
Explaining Synthesized Software
Motivated by NASA's need for high-assurance software, NASA Ames' Amphion project has developed a generic program generation system based on deductive synthesis. Amphion has a number of advantages, such as the ability to develop a new synthesis system simply by writing a declarative domain theory. However, as a practical matter, the validation of the domain theory for such a system is problematic because the link between generated programs and the domain theory is complex. As a result, when generated programs do not behave as expected, it is difficult to isolate the cause, whether it be an incorrect problem specification or an error in the domain theory. This paper describes a tool we are developing that provides formal traceability between specifications and generated code for deductive synthesis systems. It is based on extensive instrumentation of the refutation-based theorem prover used to synthesize programs. It takes augmented proof structures and abstracts them to provide explanations of the relation between a specification, a domain theory, and synthesized code. In generating these explanations, the tool exploits the structure of Amphion domain theories, so the end user is not confronted with the intricacies of raw proof traces. This tool is crucial for the validation of domain theories as well as being important in everyday use of the code synthesis system. It plays an important role in validation because when generated programs exhibit incorrect behavior, it provides the links that can be traced to identify errors in specifications or domain theory. It plays an important role in the everyday use of the synthesis system by explaining to users what parts of a specification or of the domain theory contribute to what pieces of a generated program. Comments are inserted into the synthesized code that document these explanations.
VanBaalen, Jeffrey↗
Reactive control and reasoning assistance for scientific laboratory instruments
Scientific laboratory instruments that are involved in chemical or physical sample identification frequently require substantial human preparation, attention, and interactive control during their operation. Successful real-time analysis of incoming data that supports such interactive control requires: (1) a clear recognition of variance of the data from expected results; and (2) rapid diagnosis of possible alternative hypotheses which might explain the variance. Such analysis then aids in decisions about modifying the experiment protocol, as well as being a goal itself. This paper reports on a collaborative project at the NASA Ames Research Center between artificial intelligence researchers and planetary microbial ecologists. Our team is currently engaged in developing software that autonomously controls science laboratory instruments and that provides data analysis of the real-time data in support of dynamic refinement of the experiment control. the first two instruments to which this technology has been applied are a differential thermal analyzer (DTA) and a gas chromatograph (GC). coupled together, they form a new geochemicstry and microbial analysis tool that is capable of rapid identification of the organiz and mineralogical constituents in soils. The thermal decomposition of the minerals and organics, and the attendance release of evolved gases, provides data about the structural and molecular chemistry of the soil samples.
Thompson, David E.↗
Automated Simulation For Analysis And Design
Design Assistant Workstation (DAWN) software being developed to facilitate simulation of qualitative and quantitative aspects of behavior of life-support system in spacecraft, chemical-processing plant, heating and cooling system of large building, or any of variety of systems including interacting process streams and processes. Used to analyze alternative design scenarios or specific designs of such systems. Expert system will automate part of design analysis: reason independently by simulating design scenarios and return to designer with overall evaluations and recommendations.
Cantwell, E.↗
An expert system for monitoring dynamic aspects of RCS
An expert system (ARC) based on the MIMIC(2) framework is being developed for assistance in monitoring the Space Shuttle reaction control system (RCS) and in training RCS mission controllers. ARC has four components: a hypothesis generator, a model-constructor, a model-simulator and a matcher. The hypothesis generator examines current readings of sensors to generate a set of hypotheses. The model constructor generates models from these hypotheses. The simulator predicts future behaviors of the system. The matcher validates hypotheses by testing the predicted states against real data. The hypothesis generator postulates faults using model-based reasoning (MBR), learned rules from real data, and expert-defined rules. The simulator uses a qualitative QSIM model of the RCS which can handle both nominal modes as well as a variety of fault models. This allows the simulator to simulate the system even when one or more components have degraded. Finally, the matcher is responsible for comparing readings against predictions and calculating confidence in a particular hypothesis.
Kulkarni, Deepak↗
Data analysis using scale-space filtering and Bayesian probabilistic reasoning
This paper describes a program for analysis of output curves from Differential Thermal Analyzer (DTA). The program first extracts probabilistic qualitative features from a DTA curve of a soil sample, and then uses Bayesian probabilistic reasoning to infer the mineral in the soil. The qualifier module employs a simple and efficient extension of scale-space filtering suitable for handling DTA data. We have observed that points can vanish from contours in the scale-space image when filtering operations are not highly accurate. To handle the problem of vanishing points, perceptual organizations heuristics are used to group the points into lines. Next, these lines are grouped into contours by using additional heuristics. Probabilities are associated with these contours using domain-specific correlations. A Bayes tree classifier processes probabilistic features to infer the presence of different minerals in the soil. Experiments show that the algorithm that uses domain-specific correlation to infer qualitative features outperforms a domain-independent algorithm that does not.
Kulkarni, Deepak↗