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Record W4389377983 · doi:10.23977/acss.2023.071007

Internet of Things Based on Parking Lot System Design

2023· article· en· W4389377983 on OpenAlexvenueno aff
Yuanqi Zhang, Yun Bai

Bibliographic record

VenueAdvances in Computer Signals and Systems · 2023
Typearticle
Languageen
FieldEngineering
TopicSmart Parking Systems Research
Canadian institutionsnot available
Fundersnot available
KeywordsParking guidance and informationPaymentParking spaceParking lotEconomic shortageTransport engineeringManagement systemControl (management)Internet of ThingsSpace (punctuation)Quality (philosophy)Computer scienceComputer securityEngineeringOperations managementGovernment (linguistics)

Abstract

fetched live from OpenAlex

With the rapid development of China's economy and the improvement of science and technology level, the continuous increase in car ownership leads to a serious shortage of parking space supply, and it is difficult to find a parking space and park a car, etc.; at the same time, the parking lot has a low capacity of information management, and there is no supporting parking space management system. To build a safe underground parking lot, carry out effective operations, and provide the public with high-quality parking lot management services, the establishment of a complete set of operation and management programs for the long-term development of the parking lot is essential. This thesis aims to deeply explore the application and development of IoT technology in the field of intelligent parking lots. This system adopts LoRa wireless communication technology to realize parking space control and collection of parking space detection information, which can realize automatic payment, automated parking management, improve parking lot management efficiency, reduce manual operation, and ensure the safety of parking lots and users.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.953
Threshold uncertainty score0.674

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 communication0.0000.000
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 head0.040
GPT teacher head0.270
Teacher spread0.231 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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