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Record W2786012660 · doi:10.1109/icm.2017.8268861

IWSN under an industrial wireless channel in the context of Industry 4.0

2017· article· en· W2786012660 on OpenAlexaff
Safa Saadaoui, Mohamed Tabaa, Fabrice Monteiro, Mouahamd Chehaitly, Abbas Dandache, Aziz Oukaira

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsIndustrial InternetContext (archaeology)WirelessWireless sensor networkTransmitterTransceiverChannel (broadcasting)TelecommunicationsComputer scienceComputer networkManufacturing engineeringEngineeringEmbedded systemInternet of Things

Abstract

fetched live from OpenAlex

In the coming next years, the industrial wireless sensor network (IWSN), witch constitutes the main part of the Industrial Internet of Things (IIoT), plays a crucial role in transforming industrial world by opening up a new era of economic growth and competitiveness in digital Industry 4.0. The IWSN is able to help organizations to gain greater profits in industrial manufacturing markets by increasing productivity, reducing the costs, and developing new services and products. However, requirements in the industrial systems differ from the general WSN requirements due to the complex environment propagation. In this work, performance for an architecture of wide-band wireless sensor network under an industrial channel is proposed. Transceiver for IWSN is based on IDWPT and DWPT for transmitter and receiver respectively for multi user applications. A model of industrial channel is described and performance are presented compared with AWGN channel.

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: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.437

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 science0.0010.000
Research integrity0.0000.001
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 head0.058
GPT teacher head0.263
Teacher spread0.205 · 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
Published2017
Admission routes1
Has abstractyes

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