119 research outputs found

    A general graphical user interface for automatic reliability modeling

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    Reported here is a general Graphical User Interface (GUI) for automatic reliability modeling of Processor Memory Switch (PMS) structures using a Markov model. This GUI is based on a hierarchy of windows. One window has graphical editing capabilities for specifying the system's communication structure, hierarchy, reconfiguration capabilities, and requirements. Other windows have field texts, popup menus, and buttons for specifying parameters and selecting actions. An example application of the GUI is given

    Automatic specification of reliability models for fault-tolerant computers

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    The calculation of reliability measures using Markov models is required for life-critical processor-memory-switch structures that have standby redundancy or that are subject to transient or intermittent faults or repair. The task of specifying these models is tedious and prone to human error because of the large number of states and transitions required in any reasonable system. Therefore, model specification is a major analysis bottleneck, and model verification is a major validation problem. The general unfamiliarity of computer architects with Markov modeling techniques further increases the necessity of automating the model specification. Automation requires a general system description language (SDL). For practicality, this SDL should also provide a high level of abstraction and be easy to learn and use. The first attempt to define and implement an SDL with those characteristics is presented. A program named Automated Reliability Modeling (ARM) was constructed as a research vehicle. The ARM program uses a graphical interface as its SDL, and it outputs a Markov reliability model specification formulated for direct use by programs that generate and evaluate the model

    Assessment team report on flight-critical systems research at NASA Langley Research Center

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    The quality, coverage, and distribution of effort of the flight-critical systems research program at NASA Langley Research Center was assessed. Within the scope of the Assessment Team's review, the research program was found to be very sound. All tasks under the current research program were at least partially addressing the industry needs. General recommendations made were to expand the program resources to provide additional coverage of high priority industry needs, including operations and maintenance, and to focus the program on an actual hardware and software system that is under development

    Software-implemented fault insertion: An FTMP example

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    This report presents a model for fault insertion through software; describes its implementation on a fault-tolerant computer, FTMP; presents a summary of fault detection, identification, and reconfiguration data collected with software-implemented fault insertion; and compares the results to hardware fault insertion data. Experimental results show detection time to be a function of time of insertion and system workload. For the fault detection time, there is no correlation between software-inserted faults and hardware-inserted faults; this is because hardware-inserted faults must manifest as errors before detection, whereas software-inserted faults immediately exercise the error detection mechanisms. In summary, the software-implemented fault insertion is able to be used as an evaluation technique for the fault-handling capabilities of a system in fault detection, identification and recovery. Although the software-inserted faults do not map directly to hardware-inserted faults, experiments show software-implemented fault insertion is capable of emulating hardware fault insertion, with greater ease and automation

    Learning an Orchestra Conductor's Technique Using a Wearable Sensor Platform

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    Our study focuses on finding new input devices for a system allowing users with any skill to configure and conduct a virtual orchestra in real-time. As a first step, we conducted a user study to learn more about the interaction between a conductor's gestures and the orchestra 's reaction. During an orchestra rehearsal session, we observed a conductor's timing and gestures using the eWatch, a wrist-worn wearable computer and sensor platform. The gestures are analyzed and compared to the music of the orchestra

    Fault-free performance validation of fault-tolerant multiprocessors

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    A validation methodology for testing the performance of fault-tolerant computer systems was developed and applied to the Fault-Tolerant Multiprocessor (FTMP) at NASA-Langley's AIRLAB facility. This methodology was claimed to be general enough to apply to any ultrareliable computer system. The goal of this research was to extend the validation methodology and to demonstrate the robustness of the validation methodology by its more extensive application to NASA's Fault-Tolerant Multiprocessor System (FTMP) and to the Software Implemented Fault-Tolerance (SIFT) Computer System. Furthermore, the performance of these two multiprocessors was compared by conducting similar experiments. An analysis of the results shows high level language instruction execution times for both SIFT and FTMP were consistent and predictable, with SIFT having greater throughput. At the operating system level, FTMP consumes 60% of the throughput for its real-time dispatcher and 5% on fault-handling tasks. In contrast, SIFT consumes 16% of its throughput for the dispatcher, but consumes 66% in fault-handling software overhead

    Report of the IEEE Workshop on Measurement and Modeling of Computer Dependability

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    Coordinated Science Laboratory was formerly known as Control Systems LaboratoryNASA Langley Research Center / NASA NAG-1-602 and NASA NAG-1-613ONR / N00014-85-K-000

    An exploratory study on techniques for quantitative assessment of stroke rehabilitation exercises

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    Technology-assisted systems to monitor and assess rehabilitation exercises have an opportunity of enhancing rehabilitation practices by automatically collecting patient’s quantitative performance data. However, even if a complex algorithm (e.g. Neural Network) is applied, it is still challenging to develop such a system due to pa tients with various physical conditions. The system with a complex algorithm is limited to be a black-box system that cannot provide explanations on its predictions. To address these challenges, this paper presents a hybrid model that integrates a machine learn ing (ML) model with a rule-based (RB) model as an explainable artificial intelligence (AI) technique for quantitative assessment of stroke rehabilitation exercises. For evaluation, we collected thera pist’s knowledge on assessment as 15 rules from interviews with therapists and the dataset of three upper-limb stroke rehabilitation exercises from 15 post-stroke and 11 healthy subjects using a Kinect sensor. Experimental results show that a hybrid model can achieve comparable performance with a ML model using Neural Network, but also provide explanations on a model prediction with a RB model. The results indicate the potential of a hybrid model as an explainable AI technique to support the interpretation of a model and fine-tune a model with user-specific rules for personalization.info:eu-repo/semantics/publishedVersio
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