A Blast Furnace Burden Surface Deeplearning Detection System Based on Radar Spectrum Restructured by Entropy Weight

Shi, Qiudong; Wu, Jiangxue; Ni, Ziming; Lv, Xin; Ye, Fenxian; Hou, Qingwen; Chen, Xianzhong

doi:10.1109/jsen.2020.3045973

Cited by 19 publications

(11 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Te entropy weight method is an objective weighting method [26]. Entropy is a physical concept of thermodynamics and a measure of the disorder degree or disorder degree of a system.…”

Section: Multiple Integration Empowermentmentioning

confidence: 99%

See 1 more Smart Citation

A New Ensemble Learning Method for Multiple Fusion Weighted Evidential Reasoning Rule

Zhang

Zhou

et al. 2023

Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

Ensemble learning, as a kind of method to improve the generalization ability of classifiers, is often used to improve the model effect in the field of deep learning. However, the present ensemble learning methods mostly adopt voting fusion in combining strategies. This strategy has difficulty mining effective information from the classifiers and cannot effectively reflect the relationship between different classifiers. Ensemble learning based on the evidential inference rule (ER rule) can effectively excavate the internal relationships among different classifiers and has a certain interpretability. However, the ER rule depends on the weight distribution of different combination strategies, and the setting of the evidence weight will affect the accuracy and stability of the model. Therefore, this paper proposes a new ensemble learning method based on multiple fusion weighted evidential reasoning rules and constructs an ensemble learning framework for data fusion and decision mapping. This framework takes the evidence weight, confidence, and feature data of each classifier as input and the integration results as output. The weight of evidence was determined by multiple fusion weights of the entropy weight method and order relation method. Finally, the integrated learning process is set up by the ER algorithm. The method proposed in this paper is verified by multiple datasets. Experimental results show that the surface construction model has good performance, and the defects of single weighting instability are greatly improved under the premise of improving the integration effect.

show abstract

“…Te entropy weight method is an objective weighting method [26]. Entropy is a physical concept of thermodynamics and a measure of the disorder degree or disorder degree of a system.…”

Section: Multiple Integration Empowermentmentioning

confidence: 99%

“…In the process of repeated experiments, the diference in the weight of evidence will lead to a gap of the accuracy of the integration results of more than 3%. Te weights used literature [17][18][19][20][21][22][23][24][25][26][27] are specifed by experts who actually evaluate industrial process indicators.…”

Section: Introductionmentioning

confidence: 99%

A New Ensemble Learning Method for Multiple Fusion Weighted Evidential Reasoning Rule

Zhang

Zhou

et al. 2023

Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

show abstract

“…where E is the rotation matrix, i s is the number of samples contained in each class, and i X is the sample data vector contained in the i th class represented by the i n s  matrix. For the partition  of the matrix X described by Equation (11), its correlation square sum cost function can be defined as:…”

Section: Eigen Qr Decomposition Direct Clustering Algorithm (Eqrdd)mentioning

confidence: 99%

“…This method is widely used due to its advantages of non-contact, high penetration, and real-time performance [9]. However, because the smelting environment in the blast furnace is extremely harsh [10], the high-speed airflow and high concentration of dust in the furnace often interfere with the radar directional echo signal, which makes the measurement accuracy fluctuate, and have poor stability and low reliability [11]. Therefore, overcoming the defects of the two methods for stockline measurement and using the advantages of the two methods, so as to obtain the stockline detection data continuously and with high precision in real-time, is a scientific problem that requires solving urgently.…”

Section: Introductionmentioning

confidence: 99%

High-Precision Real-Time Detection of Blast Furnace Stockline Based on High-Dimensional Spatial Characteristics

Liu

Chen

Gui

et al. 2022

Sensors

View full text Add to dashboard Cite

The real-time, continuity, and accuracy of blast furnace stockline information are of great significance in reducing energy consumption and improving smelting efficiency. However, the traditional mechanical measurement method has the problem of measuring point discontinuity, while the radar measurement method exhibits problems such as weak anti-interference ability, low accuracy, and poor stability. Therefore, a high-dimensional, spatial feature stockline detection method based on the maximum likelihood radial basis function model (MLRBFM) and structural dynamic self-optimization RBF neural network (SDSO-RBFNN) is proposed. Firstly, the discrete time series joint partition method is used to extract the time dimension periodic features of the blast furnace stockline. Based on MLRBFM, the high-dimensional spatial features of the stockline are then obtained. Finally, an SDSO-RBFNN is constructed based on an eigen orthogonal matrix and a right triangular matrix decomposition (QR) direct clustering algorithm with spatial–temporal features as input, so as to obtain continuous, high-precision stockline information. Both the simulation results and industrial validation indicate that the proposed method can provide real-time and accurate stockline information, and has great practical value for industrial production.

show abstract

“…Q.D. Shi et al combined high-temperature metallurgy, radar detection and image processing, and a new blast furnace surface deep learning detection method of a blast furnace smelting state visualization system for a burden surface based on energy weight is proposed, which realizes the visualization of blast furnace smelting state and digitization of burden surface information [ 20 ]. H. Wang et al proposed a key point estimation method based on learning combined with a key point-based connected region noise reduction algorithm (KP-CRNR) to reconstruct the key points in the BSP image measured by the radar probe.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Detection and Short-Term Prediction of Blast Furnace Burden Level Based on Space-Time Fusion Features

Yan-li

Chen

Gui

et al. 2022

Sensors

View full text Add to dashboard Cite

Real-time, continuous and accurate blast furnace burden level information is of great significance for controlling the charging process, ensuring a smooth operation of a blast furnace, reducing energy consumption and emissions and improving blast furnace output. However, the burden level information measured by conventional mechanical stock rods and radar probes exhibit problems of weak anti-interference ability, large fluctuations in accuracy, poor stability and discontinuity. Therefore, a space-time fusion prediction and detection method of burden level based on a long-term focus memory network (LFMN) and an efficient structure self-tuning RBF neural network (ESST-RBFNN) is proposed. First, the space dimensional features are extracted by the space regression model based on radar data. Then, the LFMN is designed to predict the burden level and extract the time dimensional features. Finally, the ESST-RBFNN based on a proposed fast eigenvector space clustering algorithm (ESC) is constructed to obtain reliable and continuous burden level information with high accuracy. Both the simulation results and industrial verification indicate that the proposed method can provide real-time and continuous burden level information in real-time, which has great practical value for industrial production.

show abstract

A Blast Furnace Burden Surface Deeplearning Detection System Based on Radar Spectrum Restructured by Entropy Weight

Cited by 19 publications

References 24 publications

A New Ensemble Learning Method for Multiple Fusion Weighted Evidential Reasoning Rule

A New Ensemble Learning Method for Multiple Fusion Weighted Evidential Reasoning Rule

High-Precision Real-Time Detection of Blast Furnace Stockline Based on High-Dimensional Spatial Characteristics

Real-Time Detection and Short-Term Prediction of Blast Furnace Burden Level Based on Space-Time Fusion Features

Contact Info

Product

Resources

About