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27 results for “event-based”
Dataset for Event-based architecture for enabling multi-modal reasoning on loosely coupled Linked Data services
<p>Dataset with the raw UNIX timestamps for start/stop time points for each sample of each evaluated configuration as well as produced plots.</p>
Shortcomings of Event-Based Metrics [Supplement to PhD "Machine-Actionable Assessment of Research Data Products"]
<p>This deposition includes a tabular overview of all publications analyzed by my PhD to identify and classify shortcomings of event-based metrics for research data products. The tabular overview is linked via its field "Key" to the bibtex file which holds the bibliographic information to replicate the results shown in the table.</p> <p>The deposition is supplementary material to the dissertation "Machine-Actionable Assessment of Research Data Products" (Tobias Weber, yet unpublished), especially chapter 3.</p>
Qualitative Event-based Diagnosis with Possible Conflicts Applied to Spacecraft Power Distribution Systems
Model-based diagnosis enables efficient and safe operation of engineered systems. In this paper, we describe two algorithms based on a qualitative event-based fault isolation framework augmented with model-based fault identification that are applied to spacecraft power distribution systems. Although based on a common framework, the fundamental difference between the two algorithms is that one uses a global model for residual generation, fault isolation, and fault identification; whereas the other uses a set of minimal submodels computed using Possible Conflicts. We describe the implementation of the two algorithms and compare their diagnosis results on a representative spacecraft power distribution system.
Qualitative Event-based Diagnosis with Possible Conflicts: Case Study on the Third International Diagnostic Competition
We describe two model-based diagnosis algo- rithms entered into the Third International Diag- nostic Competition. We focus on the first diag- nostic problem of the industrial track of the com- petition in which a diagnosis algorithm must de- tect, isolate, and identify faults in an electrical power distribution testbed in order to provide cor- rect abort recommendations. Both diagnosis al- gorithms are based on a qualitative event-based fault isolation framework augmented with model- based fault identification. Although based on a common framework, the fundamental difference between the two algorithms is that one is based on a global model for residual generation, fault iso- lation, and fault identification, whereas the other uses a set of minimal submodels computed using Possible Conflicts. We describe, compare, and contrast the two algorithms in terms of practical implementation and their diagnosis results.
An Event-based Approach to Hybrid Systems Diagnosability
Diagnosability is an important issue in the design of diagnostic systems, because it helps identify whether sufficient information is available to distinguish all the faults. Diagnosability of hybrid systems, however, is challenging, because mode transitions may occur during fault isolation. We present an event-based framework for hybrid systems diagnosis based on a qualitative abstraction of measurement deviations from nominal behavior. We derive event-based fault models that describe the possible measurement deviations sequences due to faults, which, coupled with the mode transition structure of the system, are used to automatically synthesize an event-based diagnoser for hybrid systems. We introduce notions of diagnosability for hybrid systems and show how the event-based diagnoser can be used to verify the diagnosability of the system. We apply our diagnosability analysis scheme to a real-world electrical power distribution system.
An Event-based Distributed Diagnosis Framework using Structural Model Decomposition
Complex engineering systems require efficient on-line fault diagnosis methodologies to improve safety and reduce maintenance costs. Traditionally, diagnosis approaches are centralized, but these solutions do not scale well. Also, centralized diagnosis solutions are difficult to implement on increasingly prevalent distributed, networked embedded systems. This paper presents a distributed diagnosis framework for physical systems with continuous behavior. Using Possible Conflicts, a structural model decomposition method from the Artificial Intelligence model-based diagnosis (DX) community, we develop a distributed diagnoser design algorithm to build local event-based diagnosers. These diagnosers are constructed based on global diagnosability analysis of the system, enabling them to generate local diagnosis results that are globally correct without the use of a centralized coordinator. We also use Possible Conflicts to design local parameter estimators that are integrated with the local diagnosers to form a comprehensive distributed diagnosis framework. Hence, this is a fully distributed approach to fault detection, isolation, and identification. We evaluate the developed scheme on a four-wheeled rover for different design scenarios to show the advantages of using Possible Conflicts, and generate on-line diagnosis results in simulation to demonstrate the approach.
Event-based surveillance corpus annotated for infectious events and their novelty
<p>Corpus of 305 event-based surveillance documents, in English (n=274) and in French (n=36). Epidemiological events related to infectious diseases were annotated in each document. The novelty of the events described in the 110 most recent documents was also annotated.</p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.