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Record W2096944874 · doi:10.1109/im-m.2008.4449013

Control output devices - actuators and displays for process automation - part 12 in a series of tutorials in instrumentation and measurement

2008· article· en· W2096944874 on OpenAlex
C.W. de Silva, Ying Wang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Instrumentation & Measurement Magazine · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAutomationActuatorStepperProcess (computing)Control engineeringInstrumentation (computer programming)EngineeringTorqueServomotorRealization (probability)Process controlMachiningSolenoidControl systemComputer scienceAutomotive engineeringElectrical engineeringMechanical engineering

Abstract

fetched live from OpenAlex

The principles and applications of dc motors, ac induction motors, stepper motors, solenoid actuators, and micromotors used in process automation are discussed. Their operating principles, characteristics control approaches are outlined. Several output devices typically used in industrial applications are defined. Microactuators, new members of the output equipment family, are also introduced. Several real industrial actuator products and their specifications are described. Computer-based displays and data loggers are introduced and and important technical trends are pointed out. It is clear that future applications of output devices will focus on the development of industrial actuators of smaller size, lighter weight, higher output power and torque, and better static and dynamic characteristics. Novel mechanisms for force generation and more sophisticated machining technique to facilitate the realization of these goals are expected.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.912

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.250
Teacher spread0.216 · 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