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Record W3150545993 · doi:10.1109/wsc48552.2020.9383937

Cell-DEVS Models for CO<sub>2</sub> Sensors Locations in Closed Spaces

2020· article· en· W3150545993 on OpenAlexaff
Hoda Khalil, Gabriel Wainer, Zachary Dunnigan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTraffic control and management
Canadian institutionsCarleton University
Fundersnot available
KeywordsDEVSOccupancyEnergy consumptionComputer scienceLatency (audio)Real-time computingFormalism (music)Efficient energy useAir conditioningGlobal warmingSimulationModeling and simulationArchitectural engineeringClimate changeEngineeringTelecommunicationsEcologyElectrical engineering

Abstract

fetched live from OpenAlex

With the global warming crisis and its correlation to levels of energy consumption, it is paramount to find ways to reduce energy consumption in closed spaces with minimal disruption to occupants' comfort. Thus, researchers are working to improve methodologies for occupant-based demand-control heating, ventilation, and air conditioning. Sensor usage for occupancy detection is among the methodologies researched for controlling consumption. Carbon dioxide sensors proved to be effective but overly sensitive to configuration. Research also proved that there is an undetermined latency period between the changes of the number of occupants and the carbon dioxide sensors detection of that change. We present a work in progress method to determine the best placement of carbon dioxide sensors for the accurate occupants' detection and calculation of latency using the Cellular Discrete-Event Specifications formalism. We present several case studies showing resemblance between physical closed spaces and the models and how the simulation replicates real-life scenarios.

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 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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.577
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.014
GPT teacher head0.198
Teacher spread0.183 · 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 teacher head, 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

Citations9
Published2020
Admission routes1
Has abstractyes

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