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Transformers in Distribution System01:27

Transformers in Distribution System

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Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
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Secondary distribution systems provide electrical energy at the utilization voltage levels from distribution transformers to customer meters. Typical secondary voltages in the United States include 120/240 V for residential use, 208Y/120 V for residential and commercial use, and 480Y/277 V for industrial and high-rise commercial use.
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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
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QDN: A Quadruplet Distributor Network for Temporal Knowledge Graph Completion.

Jiapu Wang, Boyue Wang, Junbin Gao

    IEEE Transactions on Neural Networks and Learning Systems
    |May 24, 2023
    PubMed
    Summary
    This summary is machine-generated.

    The Quadruplet Distributor Network (QDN) advances temporal knowledge graph completion (TKGC) by independently modeling entities, relations, and timestamps. This approach overcomes semantic loss and improves accuracy over existing methods.

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    Area of Science:

    • Artificial Intelligence
    • Data Science

    Background:

    • Temporal knowledge graph completion (TKGC) extends static knowledge graph completion by incorporating timestamps.
    • Current TKGC methods often integrate timestamps into entities or relations, leading to limited expressiveness and semantic loss.

    Purpose of the Study:

    • To propose a novel TKGC method, the Quadruplet Distributor Network (QDN), that addresses the limitations of existing approaches.
    • To independently model embeddings for entities, relations, and timestamps to preserve semantic integrity.

    Main Methods:

    • The QDN models entities, relations, and timestamps in separate spaces for comprehensive semantic capture.
    • A quadruplet-specific decoder integrates interactions, transforming a third-order tensor to a fourth-order tensor for TKGC.
    • A temporal regularization technique imposes a smoothness constraint on temporal embeddings.

    Main Results:

    • The proposed QDN method demonstrates superior performance compared to existing state-of-the-art TKGC techniques.
    • Independent modeling preserves the semantic richness of temporal information.

    Conclusions:

    • The QDN offers a more effective approach to temporal knowledge graph completion.
    • The method successfully addresses semantic loss and enhances the expressive power of temporal information.