Study on corona discharge spatial structure and stages division based on visible digital image colorimetry information

Guo, Zeqing; Ye, Qizheng; Li, Feixing; Wang, Yuwei

doi:10.1109/tdei.2019.008054

Cited by 17 publications

(5 citation statements)

References 19 publications

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“…Corona detection methods based on visible light are becoming attractive due to the advances in photoelectric detection methods because the colorimetric information informs about the spatial distribution and the status of the discharge [37]. In [38] a micro Si photomultiplier sensor, sensitive to the visible and UV spectra, is used to detect PDs in SF6 gas.…”

Section: Pressure Effect On Electrical Breakdown Partial Discharges and Coronamentioning

confidence: 99%

Sensor Comparison for Corona Discharge Detection Under Low Pressure Conditions

2020

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Low pressure environments, situate insulation systems in a challenging position since partial discharges (PDs), corona and arc tracking are more likely to develop. Therefore, specific solutions are required to detect such harmful phenomena before major failure occurrence. This paper deals with three low-cost and small-size sensing methods, i.e., a single loop antenna, a visible-UV imaging sensor and the measurement of the leakage current to detect corona in the early stage, thus anticipating the appearance of severer effects such as arc tracking or disruptive breakdown. The three studied methods can be applied for an on-line monitoring of corona activity under low pressure environments, thus being compatible with predictive maintenance approaches. This on-line monitoring can be used to develop improved electrical protection devices able to detect such effects in an initial stage, thus improving current solutions which are unable to do so. All three studied sensors give consistent linear responses within the studied pressure range, i.e., 10-100 kPa, with almost no drift. The sensitivity of the visible-UV imaging sensor is slightly lower than that of the others, but it has the advantage of directly locating the discharge points. Results presented in this paper can be very useful for the more electrical aircraft (MEA), which is forcing electrical distribution systems to operate at higher voltage levels. Due to the little experience and scarcity of published data, the experimental results presented in this paper can be valuable for a better understanding of the combined action of high voltage and low pressure environments.

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Section: Pressure Effect On Electrical Breakdown Partial Discharges and Coronamentioning

confidence: 99%

Sensor Comparison for Corona Discharge Detection Under Low Pressure Conditions

2020

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“…A digital image is a combination of color space data, and many researchers had performed colorimetry studies based on digital image color space data for a few applications and areas ( 37–41 ). Since digital image colorimetry is a well-known method for describing perceived color, this technique was used to extract the color component of the chicken comb at pixel level and the average pixel color component bounded on the comb area.…”

Section: Image Processing and Machine Learning Algorithmsmentioning

confidence: 99%

Translating conventional wisdom on chicken comb color into automated monitoring of disease-infected chicken using chromaticity-based machine learning models

et al. 2023

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Bacteria- or virus-infected chicken is conventionally detected by manual observation and confirmed by a laboratory test, which may lead to late detection, significant economic loss, and threaten human health. This paper reports on the development of an innovative technique to detect bacteria- or virus-infected chickens based on the optical chromaticity of the chicken comb. The chromaticity of the infected and healthy chicken comb was extracted and analyzed with International Commission on Illumination (CIE) XYZ color space. Logistic Regression, Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), and Decision Trees have been developed to detect infected chickens using the chromaticity data. Based on the X and Z chromaticity data from the chromaticity analysis, the color of the infected chicken’s comb converged from red to green and yellow to blue. The development of the algorithms shows that Logistic Regression, SVM with Linear and Polynomial kernels performed the best with 95% accuracy, followed by SVM-RBF kernel, and KNN with 93% accuracy, Decision Tree with 90% accuracy, and lastly, SVM-Sigmoidal kernel with 83% accuracy. The iteration of the probability threshold parameter for Logistic Regression models has shown that the model can detect all infected chickens with 100% sensitivity and 95% accuracy at the probability threshold of 0.54. These works have shown that, despite using only the optical chromaticity of the chicken comb as the input data, the developed models (95% accuracy) have performed exceptionally well, compared to other reported results (99.469% accuracy) which utilize more sophisticated input data such as morphological and mobility features. This work has demonstrated a new feature for bacteria- or virus-infected chicken detection and contributes to the development of modern technology in agriculture applications.

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“…Machine Learning (ML) gives several approaches, ways, and gadget that helps in solving identification and prediction problems in various medical domains. ML is used for the examination of the significance of clinical parameters and their combinations for diagnosis, e.g., prediction of disease progression, extraction of medical knowledge for outcome research, therapy planning and support, and for the overall management of the patient [7]. ML is used for data analysis too, for instance, observation of regularities in the data by appropriately dealing with the data which is not perfect, explanation of continuous data used in the Intensive Care Unit, and intelligent alarming resulting in helpful and systematic monitoring.…”

Section: Literature Surveymentioning

confidence: 99%

Risk Analysis and Prediction of Covid-19 Infection Using Machine Learning Techniques

2022

Proceedings of 2022 the 12th International Workshop on Computer Science and Engineering

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, the lethal contagious virus which has caused a pandemic, has metastasized all over the world starting from China. The figures observed of the number of casualties, is in millions and billions. This new malicious virus has caused panic amongst pubic, implanted fear and number of doubts in people's minds. There is lack of information as scientists are working on eradicating this deadly virus, less information has instilled doubts and people are panicking being helpless about how to cope up with the virus. Ways to protect oneself from getting infected, how could and where could one seek medical help when needed, these kinds of queries should be sorted out and the public needs to be educated about the virus. This will help calm down the public. This would also aid in keeping tranquil environment and even help in health and government sector workers to carry on with their duties without any obstacles.

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Study on corona discharge spatial structure and stages division based on visible digital image colorimetry information

Cited by 17 publications

References 19 publications

Sensor Comparison for Corona Discharge Detection Under Low Pressure Conditions

Sensor Comparison for Corona Discharge Detection Under Low Pressure Conditions

Translating conventional wisdom on chicken comb color into automated monitoring of disease-infected chicken using chromaticity-based machine learning models

Risk Analysis and Prediction of Covid-19 Infection Using Machine Learning Techniques

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