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Automated Monorail Integrated with Solar and Piezoelectric Power Generating System

2022· article· en· W4281839971 on OpenAlexaff
Nageswara Rao Atyam, D. Kodandapani, P. Vijayapriya, Pavan Kumar, Shaik Mohammad Rafee, V Agalya

Bibliographic record

Venue2022 8th International Conference on Smart Structures and Systems (ICSSS) · 2022
Typearticle
Languageen
FieldEngineering
TopicThermal Radiation and Cooling Technologies
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsMonorailAutomotive engineeringEngineeringSolar powerSolar energyRenewable energyElectrical engineeringPower (physics)Civil engineering

Abstract

fetched live from OpenAlex

This article spotlights on integration of solar and piezoelectric energy in a beneficiary manner to power the transportation system like Monorail and metro trains in Bengaluru. Monorail is a new mode of transport which can be developed in future as there is an improvement in technology. The main objective is to reduce the power load on the power grid and apply solar and piezoelectric energy which is clean, eco-friendly and a rich source of energy. Also, Implementation of the Automated operating system in rail makes it safer and more comfortable. Urban rail system operation consumes huge amount of power and this can be decreased by using solar and piezoelectric energy. The design of the automated rail powered by solar and piezoelectric network for DC electrified 750V third rail systems for Mono and metro rails. Solar and piezoelectric powered automated rail utilizes a smart combination of energy produced by Solar panels, Piezoelectric Transducers and power grid. The recommended system can be developed in monorail Transport systems or otherwise it can be integrated into the third rail 750V technology in a beneficiary way.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score0.701

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 head0.217
Teacher spread0.203 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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