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Reducing Line Loss01:18

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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
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Forgetting is an intrinsic aspect of human memory, characterized by the gradual loss or inaccessibility of information over time. Hermann Ebbinghaus, a pioneering psychologist, extensively studied this phenomenon and formulated the forgetting curve. This curve illustrates that memory loss occurs rapidly immediately after learning and then decelerates over time. Several mechanisms contribute to forgetting, including encoding failure, storage decay, retrieval failure, and interference.
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LostNet: A smart way for lost and find.

Meihua Zhou1, Ivan Fung2, Li Yang3

  • 1School of Medical Information, Wannan Medical Collage, Wuhu City, China.

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This study introduces an efficient image matching system for lost and found items, improving recovery times. The proposed network achieves high accuracy, making lost item retrieval faster and more accessible.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Information Systems

Background:

  • Urban population growth increases lost items in public spaces.
  • Current tracking methods are limited to specific devices, delaying recovery of other valuables.
  • Efficient systems are needed to reunite owners with lost property.

Purpose of the Study:

  • To develop a streamlined method for identifying and recovering lost and found items using image comparison.
  • To create an efficient and accurate online lost and found image identification system.

Main Methods:

  • A photo matching network integrating MobileNetV2 transfer learning and the Convolutional Block Attention Module (CBAM).
  • Incorporation of perceptual hashing algorithms for speed and simplicity.
  • Development of an online system using a Spring Boot framework.

Main Results:

  • Achieved a testing accuracy of 96.8% for image recognition.
  • The system is computationally efficient, using only 0.67 GFLOPs and 3.5M training parameters.
  • Demonstrated operability on standard laptops for real-world scenarios.

Conclusions:

  • The proposed system effectively streamlines the lost and found item search process.
  • High accuracy and efficiency enable practical application in real-world environments.
  • The system offers a scalable solution for managing lost and found items in urban settings.