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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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VIS4ML: An Ontology for Visual Analytics Assisted Machine Learning.

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    This study introduces VIS4ML, an ontology for Visual Analytics (VA)-assisted Machine Learning (ML). VIS4ML helps understand and improve ML workflows by identifying gaps and opportunities for advanced VA techniques.

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    Area of Science:

    • Visual Analytics (VA)
    • Machine Learning (ML)
    • Ontology Engineering

    Background:

    • Machine-learned models are crucial for analytical tasks in VA.
    • VA workflows are increasingly vital for understanding and enhancing ML processes.
    • Existing VA-ML interactions lack a structured framework for analysis and optimization.

    Purpose of the Study:

    • Propose VIS4ML, an ontology for VA-assisted ML.
    • Describe and understand current VA workflows in ML.
    • Identify gaps in ML processes and potential for advanced VA integration.

    Main Methods:

    • Adopted scholarly ontology construction methodologies (specification, conceptualization, formalization, implementation, validation).
    • Reinterpreted the traditional VA pipeline for model-development workflows.
    • Utilized semantic web technologies and Web Ontology Language (OWL) for implementation.

    Main Results:

    • Developed VIS4ML, capturing high-level knowledge of VA-assisted ML workflows.
    • Ensured consistency with established VA concepts.
    • Provided definitions, rules, syntaxes, and visual notations for VIS4ML formulation.

    Conclusions:

    • VIS4ML establishes a theoretical foundation for VA in ML.
    • Enables practitioners to optimize model-development workflows by assessing machine and human capabilities.
    • Designed to be extensible for future advancements in VA and ML.