Results 231 to 240 of about 22,224 (372)

A review on rockburst prediction and prevention to shape an ontology‐based framework for better decision‐making for underground excavations

open access: yesDeep Underground Science and Engineering, EarlyView.
A scientometric analysis of 2449 journal articles and a comprehensive review of 336 papers were conducted, discussing and identifying challenges and research gaps in rockburst prediction and prevention and proposing an ontology‐based framework for better decision‐making in underground excavations. Abstract With underground engineering projects becoming
Hongchuan Yan   +6 more
wiley   +1 more source

SpiceFuzz: LLM-Based Fuzzing for Spice Circuit Simulator Tools Bug Detection

open access: hybrid
Zhilei Ren   +5 more
openalex   +1 more source

WuppieFuzz: Coverage-Guided, Stateful REST API Fuzzing [PDF]

open access: green
Thomas Rooijakkers   +5 more
openalex   +1 more source

Real‐time lithology identification while drilling based on drill cuttings image analysis with ensemble learning

open access: yesDeep Underground Science and Engineering, EarlyView.
A lithology identification while drilling method was developed, integrating an automated cuttings sampling system, a smart drilling rig, and an ensemble learning model. Underground trials achieved 97.42% accuracy in real‐time identification of cuttings lithology and composition, enhancing hazard management and supporting unmanned drilling technology in
Kun Li   +7 more
wiley   +1 more source

Motion planning and control of an installation robot for attitude adjustment of arc parts in underground shield tunneling

open access: yesDeep Underground Science and Engineering, EarlyView.
Inspired by spiders, the multilegged walk‐through assembling robot for arc parts achieves high‐precision synchronous control under heavy loads through dual‐layer hydraulic pose dynamics modeling and hierarchical pressure optimization, significantly enhancing shield tunneling assembly efficiency and precision.
Quan Xiao   +5 more
wiley   +1 more source

Advances in vital‐sign prediction and early‐warning models for underground coal mine workers integrating environmental factors

open access: yesDeep Underground Science and Engineering, EarlyView.
This review synthesizes advances in predicting miners' vital signs by integrating environmental monitoring (dust, temperature, and gas) with physiological data. It highlights multi‐source data fusion techniques and early‐warning models for enhanced occupational safety in underground coal mines.
Junji Zhu   +4 more
wiley   +1 more source

A comprehensive guide to CAN IDS data and introduction of the ROAD dataset. [PDF]

open access: yesPLoS One
Verma ME   +7 more
europepmc   +1 more source

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