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Imperfections in Crystal Structure: Stoichiometric Point Defects

Imperfections in Crystal Structure: Stoichiometric Point Defects

Schottky defects arise when some lattice points in a crystal, such as those in NaCl, remain unoccupied, creating lattice vacancies without disturbing the overall electrical neutrality of the crystal. This defect is common in ionic crystals where the positive and negative ions are similar in size, as seen in sodium chloride and cesium chloride. The presence of Schottky defects enables the crystal to conduct electricity to a small extent through an ionic mechanism. Electric fields cause nearby...

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Updated: Jun 19, 2026

In Situ Time-dependent Dielectric Breakdown in the Transmission Electron Microscope: A Possibility to Understand the Failure Mechanism in Microelectronic Devices

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Insulator Defect Detection via a Residual Denoising Diffusion Mechanism.

Li Zhang^1,2,3, Mengyang Song², Huaping Guo²

¹School of Computer Science and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China.

Materials (Basel, Switzerland)

|May 7, 2025

Summary

This summary is machine-generated.

A new diffusion-based Insulator Defect Detector (IDDet) accurately locates defects on transmission lines, even with environmental noise. This method improves power supply safety by reliably identifying small, hard-to-detect insulator flaws.

Keywords:

diffusion model insulator defect object detection residual denoising diffusion

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Area of Science:

Electrical Engineering
Computer Vision
Artificial Intelligence

Background:

Defective insulators on transmission lines endanger power supply safety.
Environmental noise (rain, fog, dirt) complicates the detection of small insulator defects.
Diffusion models' denoising process can simulate and overcome environmental noise challenges.

Purpose of the Study:

To develop a robust method for detecting insulator defects under noisy conditions.
To leverage diffusion models for precise defect localization in power transmission systems.

Main Methods:

Proposed a novel diffusion-based Insulator Defect Detector (IDDet).
Trained IDDet by introducing noise to true bounding boxes and recovering them via residual denoising diffusion.
Refined defect localization during inference by progressively removing noise from random bounding boxes.

Main Results:

IDDet demonstrated significantly improved detection in noisy environments.
Achieved a state-of-the-art mean average precision (mAP) of 92.3%.
Validated the feasibility and effectiveness of the denoising-based defect localization approach.

Conclusions:

The proposed IDDet method is effective for detecting insulator defects amidst complex environmental noise.
This approach enhances the safety and reliability of power supply systems.
Diffusion models offer a promising direction for robust object detection in challenging conditions.