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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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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.
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Related Experiment Video

Updated: Oct 1, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

642

Online Multi-Agent Forecasting With Interpretable Collaborative Graph Neural Networks.

Maosen Li, Siheng Chen, Yanning Shen

    IEEE Transactions on Neural Networks and Learning Systems
    |March 4, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a collaborative prediction unit (CoPU) for predicting agent statuses in dynamic systems. The novel collaborative graph neural network (CoGNN) method significantly improves prediction accuracy and efficiency.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Predicting future agent statuses in dynamic systems is crucial for various applications.
    • Existing methods often struggle to effectively capture complex inter-agent dynamics.

    Purpose of the Study:

    • To propose a novel collaborative prediction unit (CoPU) for enhanced agent status prediction.
    • To develop a collaborative graph neural network (CoGNN) that leverages dynamic interactions.

    Main Methods:

    • Introduced the collaborative prediction unit (CoPU) aggregating predictions via a collaborative graph.
    • Developed an online multiplicative update mechanism for adjusting graph edge weights.
    • Stacked multiple CoPUs to form a collaborative graph neural network (CoGNN).

    Main Results:

    • CoPU demonstrated theoretical interpretability with regret analysis, matching the best predictor in hindsight.
    • CoGNN significantly outperformed state-of-the-art methods in online trajectory, human motion, and traffic speed prediction (28.6%, 17.4%, 21.0% average improvement).
    • The proposed CoGNNs exhibited lower average time costs during online training/testing.

    Conclusions:

    • The novel CoPU and CoGNN framework effectively predicts future agent statuses by exploiting dynamic interactions.
    • The method offers superior accuracy and efficiency compared to existing approaches in real-world online prediction tasks.