Home ›
Entities
› academia
› Denoising technique for partial discharge signal : A comparison performance between artificial neural network, fast fourier transform and discrete wavelet transform
Denoising technique for partial discharge signal : A comparison performance between artificial neural network, fast fourier transform and discrete wavelet transform
Research article (2016 IEEE International Conference on Power and Energy (PECon), 2016) · cited 29× · AI/ML
Denoising technique for partial discharge signal : A comparison performance between artificial neural network, fast fourier transform and discrete wavelet transform
Summary
Denoising technique for partial discharge signal : A comparison performance between artificial neural network, fast fourier transform and discrete wavelet transform is a scholarly article[1].
Key Facts
Denoising technique for partial discharge signal : A comparison performance between artificial neural network, fast fourier transform and discrete wavelet transform's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Denoising technique for partial discharge signal : A comparison performance between artificial neural network, fast fourier transform and discrete wavelet transform. Retrieved May 24, 2026, from https://4ort.xyz/entity/denoising-technique-for-partial-discharge-signal-a-comparison-performance-between-artificial-neural-network-fast-fourier
MLA“Denoising technique for partial discharge signal : A comparison performance between artificial neural network, fast fourier transform and discrete wavelet transform.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/denoising-technique-for-partial-discharge-signal-a-comparison-performance-between-artificial-neural-network-fast-fourier.
BibTeX@misc{4ortxyz_denoising-technique-for-partial-discharge-signal-a-comparison-performance-between-artificial-neural-network-fast-fourier_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Denoising technique for partial discharge signal : A comparison performance between artificial neural network, fast fourier transform and discrete wavelet transform}}, year = {2026}, url = {https://4ort.xyz/entity/denoising-technique-for-partial-discharge-signal-a-comparison-performance-between-artificial-neural-network-fast-fourier}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Denoising technique for partial discharge signal : A comparison performance between artificial neural network, fast fourier transform and discrete wavelet transform — https://4ort.xyz/entity/denoising-technique-for-partial-discharge-signal-a-comparison-performance-between-artificial-neural-network-fast-fourier (retrieved 2026-05-24)
