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Record W4396797019 · doi:10.3390/vehicles6020040

New Design of an Electrical Excavator and Its Path Generation for Energy Saving and Obstacle Avoidance

2024· article· en· W4396797019 on OpenAlexaff
Omid Ahmadi Khiyavi, Jaho Seo, Xianke Lin

Bibliographic record

VenueVehicles · 2024
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsExcavatorObstacle avoidancePath (computing)DiggingComputer scienceObstacleActuatorEnergy consumptionEnergy (signal processing)Automotive engineeringSimulationEngineeringMathematicsMechanical engineeringArtificial intelligenceRobotStatisticsElectrical engineeringMobile robot

Abstract

fetched live from OpenAlex

This study’s goals are divided into two categories. The first is to design and build an excavator equipped with parallel electrical linear actuators. The second is to generate and test a PSO-based and a PFM-based path for this excavator in order to save energy by reducing energy consumption, improve the digging accuracy by minimizing the deviation between the desired and dug surfaces of the ground, and prevent colliding with subsurface objects. For this purpose, computer vision was employed to improve monitoring and verification. Five types of experiments were carried out in this investigation. The first two and the other three examined the impact of energy conservation in PSO- and PFM-based path generation, respectively. Finally, the results from these experiments were compared to identify and show the effect of optimal path generation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.225
Teacher spread0.201 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations3
Published2024
Admission routes1
Has abstractyes

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