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In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
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There is variation in the electrical conductivity of materials - metals, semiconductors, and insulators that are showcased with the help of the energy band diagrams.
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The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
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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: Sep 26, 2025

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Research on Product Core Component Acquisition Based on Patent Semantic Network.

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Summary

This study uses natural-language processing (NLP) and complex networks to identify core product components from patent data. This helps companies overcome R&D bottlenecks by clarifying design priorities and innovation ideas.

Keywords:
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Area of Science:

  • Intellectual Property Analysis
  • Natural Language Processing
  • Complex Network Theory

Background:

  • Enterprises face R&D challenges due to a lack of innovative ideas and insufficient understanding of product components.
  • Patent data offers valuable insights but requires advanced methods for effective extraction of actionable information.

Purpose of the Study:

  • To introduce a methodology for extracting core product components from patent data to enhance R&D efficiency.
  • To provide enterprises and designers with a clearer understanding of R&D directions and design priorities.

Main Methods:

  • Utilized natural-language processing (NLP) techniques, including part-of-speech (POS) tagging and subject-action-object (SAO) classification.
  • Applied complex network analysis, incorporating structural holes and eigenvector centrality, to identify core components from extracted patent keywords.
  • Validated the methodology using US shower patent data.

Main Results:

  • Successfully extracted core components from patent data, demonstrating the methodology's effectiveness and feasibility.
  • The approach aids in clarifying R&D ideas and establishing design priorities for product development.

Conclusions:

  • The proposed NLP and complex network strategy is effective for identifying critical product components from patents.
  • This method can significantly improve product R&D efficiency and strategic decision-making for enterprises.