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Record W7030439087

A network model control system (NMCS) for model and full scale tests

2015· article· en· W7030439087 on OpenAlexvenueaboutno aff

Bibliographic record

VenueNPARC · 2015
Typearticle
Languageen
FieldEngineering
TopicControl Systems and Identification
Canadian institutionsnot available
Fundersnot available
KeywordsData acquisitionEthernetSoftwareModular designSynchronization (alternating current)Full scaleComponent (thermodynamics)Control systemInterfacingComponent-based software engineering
DOInot available

Abstract

fetched live from OpenAlex

This paper describes the integrated model and full scale Control and Data Acquisition (NMCS) technology used in the model and full scale tests in the Ocean Coastal and River Engineering Portfolio of National Research Council of Canada. The NMCS includes in a highly integrated suite of hardware and software all components required to: • Acquire real-time data from multiple analog and digital instruments • Store this data on digital media, • Use the real-time data as inputs to real-time control functions, such as autopilots and Dynamic positioning components, •Provide drive signals for multiple steering and propulsion elements, as well as other synchronized commands to devices such as winches, ballast systems, and roll compensation systems The NMCS is based almost entirely 011 Commercial off the Shelf (COTS) components. These include; • Power sub-system components, power sources including batteries for free-running models and remote systems, • Charging Systems, • Power Safety interlock systems, E-Stop functions, • Computers, • Computer networking equipment all communications is handled via standard Ethernet devices. • Motor Controllers and support components, • Data Acquisition, • Synchronization system, that coordinates, synchronizes all elements of acquisition and control, • NRC written custom software provides integration for all of the various hardware functions. The underlying principle of the design was to integrate complex functions into a very flexible system that can be applied to any of NRC's model testing requirements, field trials with models or full scale trials systems. The modularity of the system includes hardware and software aspects, that allow the experiment designer to tailor component content to their exact requirements, and makes it efficient to implement. The core system design allows for the continuous addition of new functions , ongoing improvement of functions , as new requirements are defined or new technologies become available.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0230.008

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.207
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2015
Admission routes2
Has abstractyes

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