84 research outputs found

    Scheduling lessons learned from the Autonomous Power System

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    The Autonomous Power System (APS) project at NASA LeRC is designed to demonstrate the applications of integrated intelligent diagnosis, control, and scheduling techniques to space power distribution systems. The project consists of three elements: the Autonomous Power Expert System (APEX) for Fault Diagnosis, Isolation, and Recovery (FDIR); the Autonomous Intelligent Power Scheduler (AIPS) to efficiently assign activities start times and resources; and power hardware (Brassboard) to emulate a space-based power system. The AIPS scheduler was tested within the APS system. This scheduler is able to efficiently assign available power to the requesting activities and share this information with other software agents within the APS system in order to implement the generated schedule. The AIPS scheduler is also able to cooperatively recover from fault situations by rescheduling the affected loads on the Brassboard in conjunction with the APEX FDIR system. AIPS served as a learning tool and an initial scheduling testbed for the integration of FDIR and automated scheduling systems. Many lessons were learned from the AIPS scheduler and are now being integrated into a new scheduler called SCRAP (Scheduler for Continuous Resource Allocation and Planning). This paper will service three purposes: an overview of the AIPS implementation, lessons learned from the AIPS scheduler, and a brief section on how these lessons are being applied to the new SCRAP scheduler

    Autonomous power system: Integrated scheduling

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    The Autonomous Power System (APS) project at NASA Lewis Research Center is designed to demonstrate the abilities of integrated intelligent diagnosis, control and scheduling techniques to space power distribution hardware. The project consists of three elements: the Autonomous Power Expert System (APEX) for fault diagnosis, isolation, and recovery (FDIR), the Autonomous Intelligent Power Scheduler (AIPS) to determine system configuration, and power hardware (Brassboard) to simulate a space-based power system. Faults can be introduced into the Brassboard and in turn, be diagnosed and corrected by APEX and AIPS. The Autonomous Intelligent Power Scheduler controls the execution of loads attached to the Brassboard. Each load must be executed in a manner that efficiently utilizes available power and satisfies all load, resource, and temporal constraints. In the case of a fault situation on the Brassboard, AIPS dynamically modifies the existing schedule in order to resume efficient operation conditions. A database is kept of the power demand, temporal modifiers, priority of each load, and the power level of each source. AIPS uses a set of heuristic rules to assign start times and resources to each load based on load and resource constraints. A simple improvement engine based upon these heuristics is also available to improve the schedule efficiency. This paper describes the operation of the Autonomous Intelligent Power Scheduler as a single entity, as well as its integration with APEX and the Brassboard. Future plans are discussed for the growth of the Autonomous Intelligent Power Scheduler

    Autonomous power expert system

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    The goal of the Autonomous Power System (APS) program is to develop and apply intelligent problem solving and control technologies to the Space Station Freedom Electrical Power Systems (SSF/EPS). The objectives of the program are to establish artificial intelligence/expert system technology paths, to create knowledge based tools with advanced human-operator interfaces, and to integrate and interface knowledge-based and conventional control schemes. This program is being developed at the NASA-Lewis. The APS Brassboard represents a subset of a 20 KHz Space Station Power Management And Distribution (PMAD) testbed. A distributed control scheme is used to manage multiple levels of computers and switchgear. The brassboard is comprised of a set of intelligent switchgear used to effectively switch power from the sources to the loads. The Autonomous Power Expert System (APEX) portion of the APS program integrates a knowledge based fault diagnostic system, a power resource scheduler, and an interface to the APS Brassboard. The system includes knowledge bases for system diagnostics, fault detection and isolation, and recommended actions. The scheduler autonomously assigns start times to the attached loads based on temporal and power constraints. The scheduler is able to work in a near real time environment for both scheduling and dynamic replanning

    Autonomous power system intelligent diagnosis and control

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    The Autonomous Power System (APS) project at NASA Lewis Research Center is designed to demonstrate the abilities of integrated intelligent diagnosis, control, and scheduling techniques to space power distribution hardware. Knowledge-based software provides a robust method of control for highly complex space-based power systems that conventional methods do not allow. The project consists of three elements: the Autonomous Power Expert System (APEX) for fault diagnosis and control, the Autonomous Intelligent Power Scheduler (AIPS) to determine system configuration, and power hardware (Brassboard) to simulate a space based power system. The operation of the Autonomous Power System as a whole is described and the responsibilities of the three elements - APEX, AIPS, and Brassboard - are characterized. A discussion of the methodologies used in each element is provided. Future plans are discussed for the growth of the Autonomous Power System

    Design and implementation of an experiment scheduling system for the ACTS satellite

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    The Advanced Communication Technology Satellite (ACTS) was launched on the 12th of September 1993 aboard STS-51. All events since that time have proceeded as planned with user operations commencing on December 6th, 1993. ACTS is a geosynchronous satellite designed to extend the state of the art in communication satellite design and is available to experimenters on a 'time/bandwidth available' basis. The ACTS satellite requires the advance scheduling of experimental activities based upon a complex set of resource, state, and activity constraints in order to ensure smooth operations. This paper describes the software system developed to schedule experiments for ACTS

    Blur detection is unaffected by cognitive load

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    Blur detection is affected by retinal eccentricity, but is it also affected by attentional resources? Research showing effects of selective attention on acuity and contrast sensitivity suggests that allocating attention should increase blur detection. However, research showing that blur affects selection of saccade targets suggests that blur detection may be pre-attentive. To investigate this question, we carried out experiments in which viewers detected blur in real-world scenes under varying levels of cognitive load manipulated by the N-back task. We used adaptive threshold estimation to measure blur detection thresholds at 0°, 3°, 6°, and 9° eccentricity. Participants carried out blur detection as a single task, a single task with to-be-ignored letters, or an N-back task with four levels of cognitive load (0, 1, 2, or 3-back). In Experiment 1, blur was presented gaze-contingently for occasional single eye fixations while participants viewed scenes in preparation for an easy picture recognition memory task, and the N-back stimuli were presented auditorily. The results for three participants showed a large effect of retinal eccentricity on blur thresholds, significant effects of N-back level on N-back performance, scene recognition memory, and gaze dispersion, but no effect of N-back level on blur thresholds. In Experiment 2, we replicated Experiment 1 but presented the images tachistoscopically for 200 ms (half with, half without blur), to determine whether gaze-contingent blur presentation in Experiment 1 had produced attentional capture by blur onset during a fixation, thus eliminating any effect of cognitive load on blur detection. The results with three new participants replicated those of Experiment 1, indicating that the use of gaze-contingent blur presentation could not explain the lack of effect of cognitive load on blur detection. Thus, apparently blur detection in real-world scene images is unaffected by attentional resources, as manipulated by the cognitive load produced by the N-back task

    Project HyBuJET

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    A conceptual Hypersonic Business Jet (HyBuJet) was examined. The main areas of concentration include: aerodynamics, propulsion, stability and control, mission profile, and atmospheric heating. In order to optimize for cruise conditions, a waverider configuration was chosen for the high lift drag ratio and low wave drag. The leading edge and lower surface of a waverider was mapped out from a known flow field and optimized for cruising at Mach 6 and at high altitudes. The shockwave generated by a waverider remains attached along the entire leading edge, allowing for a larger compression along the lower surface. Three turbofan ramjets were chosen as the propulsion of the aircraft due to the combination of good subsonic performance along with high speed propulsive capabilities. A combination of liquid silicon convective cooling for the leading edges with a highly radiative outer skin material was chosen to reduce the atmospheric heating to acceptable level

    Seasonally variant low cloud adjustment over cool oceans

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    The Earth’s solar reflectance is reduced through rapid climate adjustments to increasing CO2, via a decrease in total cloud cover over ocean. Perturbations to marine boundary-layer clouds are essentially important for the global radiative balance at the top of the atmosphere. However, the physical robustness of low cloud adjustments to increasing CO2 has not been assessed systematically. Here we show that low cloud adjustment is distinct from that in total cloud and is seasonally variant. Among multiple climate models, marine boundary-layer clouds over the subtropics and extratropics (especially over the Northern Hemisphere) are consistently increased in the rapid adjustment, while middle and high clouds are greatly reduced. The increase in low cloud cover is only found during summer, associated with a summertime enhancement of lower tropospheric stability. We further examine mechanisms behind the rapid adjustments of low cloud and inversion strength of the boundary layer, using land surface temperature prescribing experiments in an atmospheric general circulation model (AGCM). Summertime increases in low cloud and enhanced inversion strength over the ocean simulated in this AGCM are attributed to (1) CO2-induced land warming; and (2) reduced radiative cooling in the lower troposphere due to increased CO2. The seasonality in the cloud adjustment implies an importance of seasonal variations in background cloud and atmospheric circulation related to the Hadley and monsoon circulations for radiative forcing, feedback and climate sensitivity
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