A Dynamic Coal-rock Interface Recognition Model Based on Cutting Acoustic Emission and Fuzzy D-S Theory

The coal-rock interface recognition is one of the critical automated technologies in the fully mechanized mining face. The poor working conditions underground result in the seriously polluted edge information of the coal-rock interface, which affects the positioning precision of the shearer drum. The Gaussian filter parameters and the high-low thresholds are difficult to select in the traditional Canny algorithm, which causes the information loss of gradual edge and the phenomenon of false edge. Consequently, this paper presents an improved Canny edge detection algorithm, which adopts the adaptive median filtering algorithm to calculate the thresholds of Canny algorithm according to the grayscale mean and variance mean. This algorithm can protect the image edge details better and can restrain the blurred image edge. Experimental results show that this algorithm has improved the edge extraction effect under the case of noise interference and improved the detection precision and accuracy of the coal-rock image effectively.

Download Full-text

Attribute Recognition Model Based on Entropy Weight and Its Application to Evaluation of Groundwater Quality

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.29-32.2698 ◽

2010 ◽

Vol 29-32 ◽

pp. 2698-2702

Author(s):

Xian Qi Zhang ◽

Wen Hong Feng ◽

Nan Nan Li

Keyword(s):

Water Quality ◽

Groundwater Quality ◽

Information Entropy ◽

Simple Calculation ◽

Weight Coefficient ◽

Entropy Theory ◽

Entropy Weight ◽

Recognition Model ◽

Model Based ◽

Attribute Recognition

It is necessary to take into account synthetically attribute of every index because of independence and incompatibility resulted from single index evaluating outcomes. Through the information entropy theory and attribute recognition model being combined together, attribute recognition model based on entropy weight is constructed and applied to evaluating groundwater quality by a new method, weight coefficient by the law of entropy value is exercised so that it is more objective. The outcome from concrete application indicates that it is suitable to evaluate water quality with reasonable conclusion and simple calculation.

Download Full-text

Simulation of English text recognition model based on ant colony algorithm and genetic algorithm

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189807 ◽

2021 ◽

pp. 1-12

Author(s):

Fei Long

Keyword(s):

Machine Learning ◽

Genetic Algorithm ◽

Ant Colony Algorithm ◽

Ant Colony ◽

Text Recognition ◽

English Text ◽

Recognition Model ◽

Central Node ◽

Model Based ◽

Characteristic Points

The difficulty of English text recognition lies in fuzzy image text classification and part-of-speech classification. Traditional models have a high error rate in English text recognition. In order to improve the effect of English text recognition, guided by machine learning ideas, this paper combines ant colony algorithm and genetic algorithm to construct an English text recognition model based on machine learning. Moreover, based on the characteristics of ant colony intelligent algorithm optimization, a method of using ant colony algorithm to solve the central node is proposed. In addition, this paper uses the ant colony algorithm to obtain the characteristic points in the study area and determine a reasonable number, and then combine the uniform grid to select some non-characteristic points as the central node of the core function, and finally use the central node with a reasonable distribution for modeling. Finally, this paper designs experiments to verify the performance of the model constructed in this paper and combines mathematical statistics to visually display the experimental results using tables and graphs. The research results show that the performance of the model constructed in this paper is good.

Download Full-text