Terahertz Imaging Method for Composite Insulator Defects Based on Edge Detection Algorithm
Bibliographic record
Abstract
Composite insulator is prone to internal defects such as air gaps which seriously endanger the safety of power systems. To detect the internal defects in composite insulators, a terahertz (THz) imaging method based on edge detection algorithm was proposed in this article. First, air gap defect sample consisting of planar multilayers was imaged based on the envelope area of the THz reflection waveform. Then, Canny operator was applied to extract the edges of the defects. After that, the time interval of defect characteristic pulses was used to calculate the defect depth and then 3-D imaging of defects was, therefore, obtained. The experimental results showed that the imaging method based on envelope area was very effective in determining the edges of air gap defect and it had better performance compared with those using pulse amplitude or time interval as parameter for defect imaging. According to the results, the accuracy was quite satisfactory when the defect depth was above 0.339 mm. To verify the effectiveness of the proposed method, the developed THz imaging system was also applied to the rod of composite insulator. It was found that the relative error was smaller along the axial direction of the rod than the lateral direction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".