Pull diesel to the excavator

  • Introduction to Backhoe Operation | TEEX.ORG

    HEP002 | The Introduction to Backhoe Operation course provides entry-level operators with instruction on the proper operation of a backhoe and/or loader. Traditional classroom training provides participants with safe work practices, an understanding of machine mechanical systems, and operational techniques. Hands-on exercises utilizing the TEEX Heavy Equipment Field provide students with

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  • (PDF) Occupant behavior monitoring and emergency event

    This paper proposes a vision-based action recognition framework that considers the sequential working patterns of earthmoving excavators for automated cycle time and productivity analysis.

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  • Data, Sensing, and Analytics

    Sequential Pattern Learning of Visual Features and Operation Cycles for Vision-Based Action Recognition of Earthmoving Excavators ..298 Jinwoo Kim, Seokho Chi, and Minji Choi Modelling and Controlling Unmanned Excavation Equipment on

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  • WE2350 wheel loader | Komatsu

    The world's largest wheel loader, it's designed to center load ultra-class 400+ ton trucks with ease. And it does so with remarkably fast cycle times, and at a fraction of the initial cost of large excavators. Up to a 45% fuel consumption reduction*. 35% CO 2 reduction*. Up to a 10-15% TCO advantage*.

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  • Frontiers | Visual Analytics for Operation-Level

    Nov 27, 2020 · Action recognition of earthmoving excavators based on sequential pattern analysis of visual features and operation cycles. Automat. Constr. 104, 255–264. doi: 10.1016/j.autcon.2019.03.025

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  • Action recognition of earthmoving excavators based on

    This paper proposes a vision-based action recognition framework that considers the sequential working patterns of earthmoving excavators for automated cycle time and productivity analysis. The sequential patterns of visual features and operation cycles are incorporated into the action recognition framework, which includes three main processes: excavator detection, excavator tracking, and excavator action …

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  • Automated Benchmarking and Monitoring of an Earthmoving

    The recognized time-series of equipment actions are placed in an emission and carbon footprint estimation model where based on the amount of emission for each equipment action, the overall Green House Gas emissions are analyzed. The proposed method is validated for several videos collected on an on-going construction project.

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  • A Deep Learning-Based Approach to Enable Action

    J. Kim and S. Chi, "Action recognition of earthmoving excavators based on sequential pattern analysis of visual features and operation cycles," Automation in Construction, vol. 104, pp. 255–264, 2019. View at: Publisher Site | Google Scholar

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  • Action recognition of earthmoving excavators based on

    Sep 26, 2021 · The recognition based on the sequential pattern method associates atomic actions in a Atomic actions of an earthmoving excavator intercepted the precision level for the recognition

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  • Development of a Comprehensive Driving Cycle for

    Aug 01, 2019 · Such visual information can be an important cue for classifying the operation types of an excavator; for instance, the 'digging' actions of excavators can be recognized easily based on the geometric relationship between their bodies, arms, and buckets (e.g., …

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  • Vision-based action recognition of earthmoving equipment

    In contrast, equipment posture recognition has mainly involved recognizing single actions of earthmoving construction equipment based on computer vision algorithms [13]. Automatic equipment

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  • Development of a Comprehensive Driving Cycle for

    Feb 23, 2021 · J. Kim and S. Chi, "Action recognition of earthmoving excavators based on sequential pattern analysis of visual features and operation cycles," …

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  • Construction of Stretching-Bending Sequential Pattern to

    May 14, 2021 · Counting the number of work cycles per unit of time of earthmoving excavators is essential in order to calculate their productivity in earthmoving projects. The existing methods b

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  • Automated Vision-Based Tracking and Action Recognition of

    Automated Vision-Based Tracking and Action Recognition of Earthmoving Construction Operations Arsalan Heydarian Thesis submitted to the faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Master of Science In Civil Engineering Mani Golparvar-Fard, Committee Chair

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  • ‪Seokho Chi (지석호)‬ - ‪Google Scholar‬

    Action recognition of earthmoving excavators based on sequential pattern analysis of visual features and operation cycles J Kim, S Chi Automation in Construction 104, 255-264, 2019

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  • Vision-based action recognition of earthmoving equipment

    We present a computer vision based method for equipment action recognition.Our vision-based method is based on a multiple binary SVM classifier and spatio-temporal features.A comprehensive real-wor

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  • Multi-camera vision-based productivity monitoring of

    Apr 01, 2020 · Action recognition. Based on the tracking results, this module recognizes the individual actions of earthmoving equipment (excavators: 'digging', 'swinging full', 'dumping', 'swinging empty', 'moving', and 'stopping'; dump trucks: 'moving' and 'stopping') via sequential pattern analysis.

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  • Construction of Stretching-Bending Sequential Pattern to

    Recognition based on sequential patterns associates the atomic actions in the work cycle with each other according to the operation sequence based on the actual work of the earthmoving excavator at the construction site to achieve recognition of the work cycle.

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  • (PDF) Interaction analysis for vision-based activity

    Mar 01, 2018 · mework included four main processes: equipment tracking, action recognition of individual equipment, inter- action analysis, and post-processing. The …

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  • Computing in Civil Engineering 2019 | Proceedings

    Sequential Pattern Learning of Visual Features and Operation Cycles for Vision-Based Action Recognition of Earthmoving Excavators Jinwoo Kim, Seokho Chi and Minji Choi pp. 298 - 304

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