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- 实验方案
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - NIEL Dose Analysis on triple and single junction InGaP/GaAs/Ge solar cells irradiated with electrons, protons and neutrons
摘要： Quantitative grading of opals is a challenging task even for skilled opal assessors. Current opal evaluation practices are highly subjective due to the complexities of opal assessment and the limitations of human visual observation. In this paper, we present a novel machine vision system for the automated grading of opals—the gemological digital analyzer (GDA). The grading is based on statistical machine learning with multiple characteristics extracted from opal images. The assessment work-flow includes calibration, opal image capture, image analysis, and opal classification and grading. Experimental results show that the GDA-based grading is more consistent and objective compared with the manual evaluations conducted by the skilled opal assessors.
关键词： feature extraction,image analysis,machine vision,learning systems,Artificial intelligence
更新于2025-09-23 15:21:01
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Fault detection of photovoltaic array based on Grubbs criterion and local outlier factor
摘要： Quantitative grading of opals is a challenging task even for skilled opal assessors. Current opal evaluation practices are highly subjective due to the complexities of opal assessment and the limitations of human visual observation. In this paper, we present a novel machine vision system for the automated grading of opals—the gemological digital analyzer (GDA). The grading is based on statistical machine learning with multiple characteristics extracted from opal images. The assessment workflow includes calibration, opal image capture, image analysis, and opal classification and grading. Experimental results show that the GDA-based grading is more consistent and objective compared with the manual evaluations conducted by the skilled opal assessors.
关键词： feature extraction,image analysis,machine vision,learning systems,Artificial intelligence
更新于2025-09-23 15:21:01
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[IEEE 2018 11th UK-Europe-China Workshop on Millimeter Waves and Terahertz Technologies (UCMMT) - HangZhou, China (2018.9.5-2018.9.7)] 2018 11th UK-Europe-China Workshop on Millimeter Waves and Terahertz Technologies (UCMMT) - Compact Balun Filter Using Coupled Resonators
摘要： Quantitative grading of opals is a challenging task even for skilled opal assessors. Current opal evaluation practices are highly subjective due to the complexities of opal assessment and the limitations of human visual observation. In this paper, we present a novel machine vision system for the automated grading of opals—the gemological digital analyzer (GDA). The grading is based on statistical machine learning with multiple characteristics extracted from opal images. The assessment work-flow includes calibration, opal image capture, image analysis, and opal classification and grading. Experimental results show that the GDA-based grading is more consistent and objective compared with the manual evaluations conducted by the skilled opal assessors.
关键词： machine vision,Artificial intelligence,learning systems,image analysis,feature extraction
更新于2025-09-19 17:13:59