Journal
ISIJ INTERNATIONAL
Volume 48, Issue 2, Pages 186-193Publisher
IRON STEEL INST JAPAN KEIDANREN KAIKAN
DOI: 10.2355/isijinternational.48.186
Keywords
sinter ore mineragraphy; index of reflection; gray histogram; Gaussian distribution; feature extraction
Categories
Ask authors/readers for more resources
The intelligent recognizing and processing system make a great convenience for recognition and measurement of sinter ore mineragraphy. Specially, it's crucial for a successful intelligent system to extract the minerals' features accurately and sufficiently. The paper got the relatively position of peak of the gray histogram in theory by using the calculated model of index of reflection and peak-find model of gray histogram; the parameters like mu and theta(2) of familiar minerals were gotten by statistical averaging with Gaussian gray distribution model, the information about the category and content of minerals can also be got by fitting the curve of gray histogram with Gaussian gray distribution model; the feature curves of gray histogram of two minerals were gained by synthesizing two density functions; also the feature parameters such as the number and position of peak and valley of two minerals in different ratios were obtained by differentiating to the distribution functions. The feature parameters, feature curves, and other conclusions lay the foundation for artificial intelligence system of mineragraphy recognizing and processing in sinter ore.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available