Autonomous power system: Intelligent diagnosis and control

Ringer, Mark J.; Quinn, Todd M.; Merolla, Anthony

doi:10.1016/s0736-5853(05)80060-7

Cited by 8 publications

(2 citation statements)

References 3 publications

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“…As for fault tolerance, the SRM and Server Groups are currently being enhanced for greater reliability via an automated recovery design based on standard checkpointing and messagelogging techniques (Strom, 1985). Control extensions to support fault tolerant transparency are alsobeinginvestigated, involving automated detection and management of group member server failures (Birman, 1993 (Ringer, 1991;Walls, 1990 …”

Section: Requests For Reliable Replicated Servicesmentioning

confidence: 99%

Group-oriented coordination models for distributed client-server computing

Adler¹,

Hughes²

1994

Telematics and Informatics

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Section: Requests For Reliable Replicated Servicesmentioning

confidence: 99%

Group-oriented coordination models for distributed client-server computing

Adler¹,

Hughes²

1994

Telematics and Informatics

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“…Automatic retry is only necessary for devices required to support ISS Survival Mode. Manual retry may be performed for functions without redundancy and may be controlled by either the crew or ground (Ringer 1991). This process provides the approach for the identification, development, and documentation of EPS off-nominal scenarios.…”

Section: Off-nominal Scenarios Handling Processmentioning

confidence: 99%

Off-nominal conditions and caution and warning techniques for the electric power system for the International Space Station

Aghabarari

Adams²

IECEC-97 Proceedings of the Thirty-Second Intersociety Energy Conversion Engineering Conference (Cat. No.97CH6203)

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In this paper the latest Electric Power System (EPS) off-nominal operations and caution and waming techniques for the Intemational Space Station (ISS) are presented. The EPS off-nominal operations development consists of a compilation of analyses that provide plans and procedures for continued operation of the EPS in the event of a failure or anomaly. The off-nominal conditions encompass the following: ( I ) expected system performance changes (Le., contributions due to aging), ( 2 ) failures that are automatically detected, isolated,and reconfigured, (3) failures that are manually reconfigured (extravehicular activity [EVA] or via command and control by an operator), and (4) unexpected system anomalies. Off-nominal scenarios including those associated with failure detection, isolation, and recovery (FDIR) operations within the ISS EPS are performed by FDIR functionality allocated to individual FDIR domains resident in equipment that implements the EP functionality (Aghabarari 1995).The off-nominal conditions were isolated by estimating EPS effect and risk assessment for loss of function andlor hazard relation. After off-nominal scenarios are identified, the following products are developed to support EPS testing and verification:(1) detailed procedures, ( 2 ) test plans, and (3) test requirements. The identification and development of off-nominal scenarios was performed on a flight-by-flight basis.Additional objectives of off-nominal and contingency analysis reviewed in this paper include (1) initial verification that the EPS design supports continued operation after a single failure for every station build, (2) to provide a solid basis for the development of test plans, requirements, and procedures to be used at testing facilities, (3) feedback on requirements compliance and the adequacy of the design to support off-nominal and contingency operations, (4) operational constraints that exist after a failure occurs, (5) the effects of given failures based on the accomplishment of mission objectives, and (6) the relative risk of continued operation with the failed condition, and recommended plans for future flights based on the relative risk.

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A systematic review and taxonomy of explanations in decision support and recommender systems

Nunes

Jannach

2017

User Model User-Adap Inter

296

193

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With the recent advances in the field of artificial intelligence, an increasing number of decision-making tasks are delegated to software systems.A key requirement for the success and adoption of such systems is that users must trust system choices or even fully automated decisions. To achieve this, explanation facilities have been widely investigated as a means of establishing trust in these systems since the early years of expert systems. With today's increasingly sophisticated machine learning algorithms, new challenges in the context of explanations, accountability, and trust towards such systems constantly arise. In this work, we systematically review the literature on explanations in advice-giving systems. This is a family of systems that includes recommender systems, which is one of the most successful classes of advicegiving software in practice. We investigate the purposes of explanations as well as how they are generated, presented to users, and evaluated. As a result, we derive a novel comprehensive taxonomy of aspects to be considered when designing explanation facilities for current and future decision support systems. The taxonomy includes a variety of different facets, such as explanation objective, responsiveness, content and presentation. Moreover, we identified several challenges that remain unaddressed so far, for example related to fine-grained issues associated with the presentation of explanations and how explanation facilities are evaluated.

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Autonomous power system: Intelligent diagnosis and control

Cited by 8 publications

References 3 publications

Group-oriented coordination models for distributed client-server computing

Group-oriented coordination models for distributed client-server computing

Off-nominal conditions and caution and warning techniques for the electric power system for the International Space Station

A systematic review and taxonomy of explanations in decision support and recommender systems

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