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Related Concept Videos

Structural Classification of Joints01:20

Structural Classification of Joints

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
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Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
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Functional Classification of Joints01:09

Functional Classification of Joints

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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
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Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

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In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
205
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

140
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
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Source Transformation01:15

Source Transformation

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Source transformation is a fundamental technique employed in circuit analysis, offering a valuable tool for simplifying complex electrical circuits. This technique involves the replacement of either a voltage source in series with a resistor by a current source in parallel with a resistor, or vice versa. The key concept here is that when the original sources are deactivated (turned off), the equivalent resistance at the circuit's end terminals remains the same.
It is essential to note that when...
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Related Experiment Video

Updated: Sep 6, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Published on: July 5, 2024

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Video Joint Modelling Based on Hierarchical Transformer for Co-Summarization.

Haopeng Li, Qiuhong Ke, Mingming Gong

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |June 27, 2022
    PubMed
    Summary

    This study introduces a novel Hierarchical Transformer model for video co-summarization, effectively leveraging correlations between similar videos. The approach enhances video understanding and improves summarization accuracy by modeling cross-video semantic dependencies.

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    Area of Science:

    • Artificial Intelligence
    • Computer Vision
    • Machine Learning

    Background:

    • Video summarization aids video retrieval and browsing by creating concise representations.
    • Existing methods often overlook valuable correlations among similar videos, limiting understanding.
    • Cross-video semantic dependencies are crucial for robust video summarization.

    Purpose of the Study:

    • To propose a novel method for video co-summarization that utilizes semantic dependencies across similar videos.
    • To enhance individual video summarization by incorporating information from related videos.
    • To improve the accuracy and relevance of automatically generated video summaries.

    Main Methods:

    • Introduced Video Joint Modelling based on Hierarchical Transformer (VJMHT) for co-summarization.
    • Employed a two-layer Transformer architecture: one for individual shot representation and another for cross-video joint modeling.
    • Incorporated Transformer-based video representation reconstruction to maximize summary-video similarity.

    Main Results:

    • Demonstrated the effectiveness of the proposed modules through extensive experiments.
    • Achieved superior performance in video summarization compared to existing methods.
    • Validated the model's superiority using F-measure and rank-based evaluation metrics.

    Conclusions:

    • The proposed VJMHT model effectively captures and leverages cross-video semantic dependencies for improved summarization.
    • Joint modeling of similar videos significantly enhances the quality of individual video summaries.
    • The approach offers a promising direction for advanced video understanding and summarization systems.