Short-Term Prediction of Sintering State Based on Improved Random Forest

Fubin Wang; Rui Wang; Chen Wu

doi:10.3788/LOP202259.1815018

Journals >Laser & Optoelectronics Progress >Volume 59 >Issue 18 >Page 1815018 > Article

Laser & Optoelectronics Progress
Vol. 59, Issue 18, 1815018 (2022)

Short-Term Prediction of Sintering State Based on Improved Random Forest

Fubin Wang¹, Rui Wang^1、*, and Chen Wu²

Author Affiliations

¹College of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, Hebei , China

²Tang Steel International Engineering Technology Co., Ltd., Tangshan 063000, Hebei , China

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DOI: 10.3788/LOP202259.1815018 Cite this Article Set citation alerts

Fubin Wang, Rui Wang, Chen Wu. Short-Term Prediction of Sintering State Based on Improved Random Forest[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1815018 Copy Citation Text

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Fig. 1. Flow chart of random forest algorithm

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Fig. 2. Pretreatment process

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Fig. 3. Geometric features

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Fig. 4. Relationship between classification accuracy and number of decision trees

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Fig. 5. Performance analysis of conventional random forest

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Fig. 6. Performance analysis of improved random forest (K-means)

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Fig. 7. Performance analysis of improved random forest(FCM)

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Category	Normal burning	Underburning	Overheating	Sum
Quantity	140	80	80	300

Table 1. Number of categories

Geometric feature	Area	Major	Minor	Eccentricity	Orientation
Accuracy	0.49	0.42	0.64	0.47	0.59
Geometric feature	Equiv diameter	Solidity	Extent	Perimeter	Thinness ratio
Accuracy	0.51	0.35	0.51	0.47	0.37

Table 2. Accuracy of K-means

Geometric feature	Area	Major	Minor	Eccentricity	Orientation
Accuracy	0.51	0.43	0.64	0.5	0.64
Geometric feature	Equiv diameter	Solidity	Extent	Perimeter	Thinness ratio
Accuracy	0.51	0.48	0.39	0.47	0.46

Table 3. Accuracy of FCM

Geometric feature	Area	Major	Minor	Eccentricity	Orientation
Probability	0.1	0.09	0.13	0.1	0.12
Geometric feature	Equiv diameter	Solidity	Extent	Perimeter	Thinness ratio
Probability	0.11	0.07	0.11	0.09	0.08

Table 4. K-means geometric feature probability

Geometric feature	Area	Major	Minor	Eccentricity	Orientation
Probability	0.1	0.08	0.13	0.1	0.13
Geometric feature	Equiv diameter	Solidity	Extent	Perimeter	Thinness ratio
Probability	0.1	0.1	0.08	0.09	0.09

Table 5. FCM geometric feature probability

Method	Normal burning	Underburning	Overheating
method Conventional	85.71	91.67	91.67
SVM（RBF kernel function）	95.24	91.67	91.67
Improvement（K-means）	97.62	95.83	100
Improvement（FCM）	97.56	100	95.83

Table 6. Accuracy of improved random forest algorithm for three categories of images

Method	Normal burning	Underburning	Overheating
Conventional method	94.74	81.48	88
SVM（RBF kernel function）	97.56	88	91.67
Improvement（K-means）	97.62	95.83	100
Improvement（FCM）	97.56	92.31	100

Table 7. Recall of improved random forest algorithm for three categories of images

Method	Accuracy /%	Precision /%	Recall /%	F1 value /%
Conventional method	88.89	89.68	88.07	88.87
SVM（RBF kernel function）	93.3333	92.86	92.41	92.63
Improvement（K-means）	97.78	97.82	97.82	97.82
Improvement（FCM）	96.67	97.80	96.62	97.21

Table 8. Comparison of overall result

Fubin Wang, Rui Wang, Chen Wu. Short-Term Prediction of Sintering State Based on Improved Random Forest[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1815018

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