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LoRaWAN-WiFi device semiconductor technology-based for airflow measurements in HVAC systems

2021· article· en· W3200781783 on OpenAlex
Jihen Souifi, Yassine Bouslimani, Mohsen Ghribi, Azeddine Kaddouri

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIoT Networks and Protocols
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsThermistorAirflowHVACCompensation (psychology)Realization (probability)Temperature measurementTemperature coefficientComputer scienceTransducerAutomotive engineeringWirelessElectrical engineeringElectronic engineeringEnvironmental scienceAcousticsAir conditioningEngineeringMechanical engineeringTelecommunications

Abstract

fetched live from OpenAlex

This paper focuses on designing and implementing an IoT device capable of air flow measurements. The transducer is based on a Positive Temperature Coefficient (PTC) Thermistor and intended to be used with ventilation systems. The PTC Temperature depends on the power level dissipated into the surrounding medium and the airflow velocity. A PCB design and realization are proposed for a device with LoRaWAN and Wi-Fi dual capabilities. In this device and in addition to the PTC, a negative temperature coefficient (NTC) sensor is also considered for temperature compensation. A database and IoT platform are used to collect and store the realtime data of the airflow measurements.

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)0.000
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: Empirical
Teacher disagreement score0.522
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.054
GPT teacher head0.271
Teacher spread0.218 · 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

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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