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Anode effect prediction based on collaborative two-dimensional forecast model in aluminum electrolysis production
School of Information Science and Engineering, Central South University, Changsha 410083, China |
In this study, a new prediction algorithm is proposed, based on the collaborative two-dimensional forecast model (CTFM) that combines the traditional method and similarity search technique. The main idea of the algorithm is that the prediction of the change trend of the slope and the accumulated slope of the cell resistance as well as the useful knowledge obtained using the similarity search technique are used as the main criteria to calculate anode effect (AE)-prediction reliability. The accumulated mass deviation value is used as an auxiliary criterion to adjust the AE-prediction reliability. Finally, the current AE-process is marked according to the current AE-prediction reliability. The prediction model based on CTFM is tested on a real situation, in which multiple samples are extracted from the production of a 400 kA aluminum electrolysis cell. We observe that when the time advance of AE-prediction is within 20 ~ 40 min, the accuracy rate of the CTFM algorithm is greater than 95% and the applicability of the method is excellent, showing a high prediction accuracy for different aluminum electrolysis cells.
References:
[1] |
G. Bearne, M. Dupuis and G. Tarcy, On the anode effect in aluminum electrolysis, in Essential Readings in Light Metals: Aluminum Reduction Technology, (eds. J. Thonstad, T. A. Utigard and H. Vogt), Metals and Alloys, 2 (2013), 131-138. |
[2] |
B. Bardet, T. Foetisch, S. Renaudier, J. Rappaz, M. Flueck and M. Picasso,
Alumina dissolution modeling in aluminium electrolysis cell considering MHD driven convection and thermal impact, Light Metals, Springer International Publishing, (2016), 315-319.
|
[3] |
D. S. Wong, P. Fraser, P. Lavoie and J. Kim,
PFC emissions from detected versus nondetected anode effects in the aluminum industry, JOM, 67 (2015), 342-353.
doi: 10.1007/s11837-014-1265-8. |
[4] |
L. Dion, L. I. Kiss, S. Poncsák and C. L. Lagacé,
Prediction of low-voltage tetrafluoromethane emissions based on the operating conditions of an aluminium electrolysis cell, JOM, 68 (2016), 2472-2482.
doi: 10.1007/s11837-016-2043-6. |
[5] |
L. Kong, C. Yu, K. L. Teo and C. Yang,
Robust real-time optimization for blending operation of alumina production, Journal of Industrial and Management Optimization, 13 (2017), 1149-1167.
|
[6] |
M. Farrow,
Prediction of AEs in aluminum reduction cells, JOM, 36 (2013), 33-34.
|
[7] |
J. Li, F. Q. Ding, M. J. Li, J. Xiao and Z. Zou,
Intelligent anode effect prediction method for prebaked-anode aluminum reduction cells, Journal of Central South University of Technology, 32 (2001), 29-32.
|
[8] |
D. G. Bell, System for predicting impending anode effects in aluminum cells, US, US. 6132571[P], (2000). |
[9] |
Y. Zhang,
Study on anode effect prediction of aluminium reduction applying wavelet packet transform, International Conference on Intelligent Computing, Springer Berlin Heidelberg, (2010), 477-484.
doi: 10.1007/978-3-642-14831-6_62. |
[10] |
J. Xing and D. Y. Xiao,
Ordered neural network and its application to prediction of anode effect, Control Engineering of China, 14 (2007), 27-36.
|
[11] |
J. B. Harley and J. M. F. Moura,
Data-driven matched field processing for Lamb wave structural health monitoring, The Journal of the Acoustical Society of America, 135 (2014), 1231-1244.
|
[12] |
V. Y. Bazhin, A. A. Vlasov and A. V. Lupenkov,
Controlling the AE in an aluminum reduction cell, Metallurgist, 55 (2011), 463-468.
|
[13] |
Y. Song, J. P. Peng, Y. W. Wang, Y. Z. Di, B. K. Li and N. X. Feng,
Magneto-hydrodynamics simulation of 300 KA novel cell for aluminium electrolysis, Metalurgija, 55 (2016), 22-24.
|
[14] |
J. Yi, D. Huang, S. Fu, H. He and T. Li,
Multi-objective bacterial foraging optimization algorithm based on parallel cell entropy for aluminum electrolysis production process, IEEE Transactions on Industrial Electronics, 63 (2016), 2488-2500.
doi: 10.1109/TIE.2015.2510977. |
[15] |
H. Viumdal and S. Mylvaganam,
System identification of a non-uniformly sampled multi-rate system in aluminium electrolysis cells, Modeling Identification and Control, 35 (2014), 127-146.
doi: 10.4173/mic.2014.3.1. |
[16] |
A. Solheim,
Entropic heat effects in aluminum electrolysis cells with inert anodes, Metallurgical and Materials Transactions -B, 47 (2016), 1274-1279.
doi: 10.1007/s11663-015-0561-1. |
[17] |
F. Allard, G. Soucy and L. Rivoaland,
Formation of deposits on the cathode surface of aluminum electrolysis cells, Metallurgical and Materials Transactions -B, 45 (2014), 2475-2485.
doi: 10.1007/s11663-014-0118-8. |
[18] |
J. J. Li, Z. J. Wang and J. L. Zhu,
Aluminum electrolysis multi-objective control system based on quantum optimized, Advanced Materials, Technology and Application: Proceedings of the 2016 International Conference on Advanced Materials, Technology and Application (AMTA2016). World Scientific, (2016), 417-423.
doi: 10.1142/9789813200470_0049. |
[19] |
H. Zhang, T. Li, J. Li, S. Yang and Z. Zou,
Progress in aluminum electrolysis control and future direction for smart aluminum electrolysis plant, JOM, 69 (2017), 292-300.
doi: 10.1007/s11837-016-2150-4. |
[20] |
C. K. Hu, F. B. Liu and C. F. Hu,
Efficiency measures in fuzzy data envelopment analysis with common weights, Journal of Industrial and Management Optimization, 13 (2017), 237-249.
|
[21] |
H. Z. Haghighi, S. Adeli and F. H. Lotfi,
Revenue congestion: An application of data envelopment analysis, Journal of Industrial and Management Optimization, 12 (2016), 1311-1322.
doi: 10.3934/jimo.2016.12.1311. |
[22] |
A. Klos, J. Bogusz, M. Figurski and W. Kosek,
On the handling of outliers in the GNSS time series by means of the noise and probability analysis, Springer Berlin Heidelberg, 143 (2015), 657-664.
doi: 10.1007/1345_2015_78. |
[23] |
A. Katayev, J. K. Fleming, D. Luo, A. H. Fisher and T. M. Sharp,
Reference intervals data mining, American Journal of Clinical Pathology, 143 (2015), 134-142.
doi: 10.1309/AJCPQPRNIB54WFKJ. |
[24] |
Q. X. Chi and X. C. Si,
Discussion for radar signal sorting method based on the grubbs' criterion, Chinese Journal of Sensors and Actuators, 6 (2006), 2625-2629.
|
[25] |
Z. N. Qu and J. L. Xie,
Long-term periodicity variations of the solar radius, Astrophysical Journal, 762 (2012), 23-28.
doi: 10.1088/0004-637X/762/1/23. |
[26] |
F. Gürbüz and P. M. Pardalos,
A decision making process application for the slurry production in ceramics via fuzzy cluster and data mining, Journal of Industrial and Management Optimization, 8 (2013), 285-297.
|
[27] |
M. Kato, H. Masuyama, S. Kasahara and Y. Takahashi,
Effect of energy-saving server scheduling on power consumption for large-scale data centers, Journal of Industrial and Management Optimization, 12 (2016), 667-685.
|
[28] |
Z. Gong, C. Liu and Y. Wang,
Optimal control of switched systems with multiple time-delays and a cost on changing control, Journal of Industrial and Management Optimization, 14 (2018), 183-198.
doi: 10.3934/jimo.2017042. |
[29] |
F. M. Anuar, R. Setchi and Y. K. Lai,
Semantic retrieval of trademarks based on conceptual similarity, IEEE Transactions on Systems Man and Cybernetics Systems, 46 (2016), 220-233.
doi: 10.1109/TSMC.2015.2421878. |
[30] |
Y. Xia,
Convex hull of the orthogonal similarity set with applications in quadratic assignment problems, Journal of Industrial and Management Optimization, 9 (2013), 689-701.
doi: 10.3934/jimo.2013.9.689. |
[31] |
V. Satuluri and S. Parthasarathy,
Bayesian locality sensitive hashing for fast similarity search, Proceedings of the VLDB Endowment, 5 (2012), 430-441.
doi: 10.14778/2140436.2140440. |
[32] |
H. Xiao, Similarity Search and Outlier Detection in Time Series. Department of Computer and Information Technique, Ph. D thesis, FuDan University in shanghai, 2005. |
[33] |
L. Zhang, J. Lin and R. Karim,
Sliding window-based fault detection from high-dimensional data streams, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47 (2017), 289-303.
doi: 10.1109/TSMC.2016.2585566. |
[34] |
R. Faragher,
Understanding the basis of the Kalman filter via a simple and intuitive derivation, IEEE Signal processing magazine, 29 (2012), 128-132.
|
[35] |
T. Schuhmann, W. Hofmann and R. Werner,
Improving operational performance of active magnetic bearings using Kalman filter and state feedback control, IEEE Transactions on Industrial Electronics, 59 (2012), 821-829.
doi: 10.1109/TIE.2011.2161056. |
[36] |
V. F. De, A. Brandl, M. Battipede and P. Gili,
Joseph covariance formula adaptation to square-root sigma-point Kalman filters, Nonlinear Dynamics, 88 (2017), 1969-1986.
|
[37] |
B. Jia, M. Xin and Y. Cheng,
High-degree cubature Kalman filter, Automatica, 49 (2013), 510-518.
doi: 10.1016/j.automatica.2012.11.014. |
[38] |
J. Shawash and D. R. Selviah,
Real-time nonlinear parameter estimation using the Levenberg-Marquardt algorithm on field programmable gate arrays, IEEE Transactions on Industrial Electronics, 60 (2013), 170-176.
doi: 10.1109/TIE.2012.2183833. |
[39] |
V. López, S. delRío, J. M. Benítez and F. Herrera,
Cost-sensitive linguistic fuzzy rule based classification systems under the MapReduce framework for imbalanced big data, Fuzzy Sets and Systems, 258 (2015), 5-38.
doi: 10.1016/j.fss.2014.01.015. |
[40] |
C. C. Jiang, R. F. Zhu, G. Y. Xiao, L. L. Wang, Y. Z. Zheng and Y. P. Lu,
Communication-effect of nano-alumina concentration on the microstructure and corrosion resistance of phosphate chemical conversion coating, Journal of The Electrochemical Society, 163 (2016), C339-C341.
doi: 10.1149/2.0131607jes. |
[41] |
S. Zhang, X. Chen and Y. Yin, An ELM based online soft sensing approach for alumina concentration detection, Mathematical Problems in Engineering, 2015 (2015), Article ID 268132, 8 pages.
doi: 10.1155/2015/268132. |
[42] |
G. Bearne, M. Dupuis and G. Tarcy, Pseudo resistance curves for aluminium cell control -alumina dissolution and cell dynamics, in Essential Readings in Light Metals: Aluminum Reduction Technology, Volume 2 (eds. H. Kvande, B. P. Moxnes, J. Skaar and P. A. Solli), Metals and Alloys, (2013), 760-766. |
[43] |
Q. Zhai, J. Yang, M. Xie and Y. Zhao,
Generalized moment-independent importance measures based on Minkowski distance, European Journal of Operational Research, 239 (2014), 449-455.
doi: 10.1016/j.ejor.2014.05.021. |
[44] |
J. Torres-Sospedra, R. Montoliu, S. Trilles, $\mathit{Ó}$. Belmonte and J. Huerta,
Comprehensive analysis of distance and similarity measures for Wi-Fi fingerprinting indoor positioning systems, Expert Systems with Applications, 42 (2015), 9263-9278.
doi: 10.1016/j.eswa.2015.08.013. |
[45] |
G. H. B. Foo, X. Zhang and D. M. Vilathgamuwa,
A sensor fault detection and isolation method in interior permanent-magnet synchronous motor drives based on an extended Kalman filter, IEEE Transactions on Industrial Electronics, 60 (2013), 3485-3495.
doi: 10.1109/TIE.2013.2244537. |
show all references
References:
[1] |
G. Bearne, M. Dupuis and G. Tarcy, On the anode effect in aluminum electrolysis, in Essential Readings in Light Metals: Aluminum Reduction Technology, (eds. J. Thonstad, T. A. Utigard and H. Vogt), Metals and Alloys, 2 (2013), 131-138. |
[2] |
B. Bardet, T. Foetisch, S. Renaudier, J. Rappaz, M. Flueck and M. Picasso,
Alumina dissolution modeling in aluminium electrolysis cell considering MHD driven convection and thermal impact, Light Metals, Springer International Publishing, (2016), 315-319.
|
[3] |
D. S. Wong, P. Fraser, P. Lavoie and J. Kim,
PFC emissions from detected versus nondetected anode effects in the aluminum industry, JOM, 67 (2015), 342-353.
doi: 10.1007/s11837-014-1265-8. |
[4] |
L. Dion, L. I. Kiss, S. Poncsák and C. L. Lagacé,
Prediction of low-voltage tetrafluoromethane emissions based on the operating conditions of an aluminium electrolysis cell, JOM, 68 (2016), 2472-2482.
doi: 10.1007/s11837-016-2043-6. |
[5] |
L. Kong, C. Yu, K. L. Teo and C. Yang,
Robust real-time optimization for blending operation of alumina production, Journal of Industrial and Management Optimization, 13 (2017), 1149-1167.
|
[6] |
M. Farrow,
Prediction of AEs in aluminum reduction cells, JOM, 36 (2013), 33-34.
|
[7] |
J. Li, F. Q. Ding, M. J. Li, J. Xiao and Z. Zou,
Intelligent anode effect prediction method for prebaked-anode aluminum reduction cells, Journal of Central South University of Technology, 32 (2001), 29-32.
|
[8] |
D. G. Bell, System for predicting impending anode effects in aluminum cells, US, US. 6132571[P], (2000). |
[9] |
Y. Zhang,
Study on anode effect prediction of aluminium reduction applying wavelet packet transform, International Conference on Intelligent Computing, Springer Berlin Heidelberg, (2010), 477-484.
doi: 10.1007/978-3-642-14831-6_62. |
[10] |
J. Xing and D. Y. Xiao,
Ordered neural network and its application to prediction of anode effect, Control Engineering of China, 14 (2007), 27-36.
|
[11] |
J. B. Harley and J. M. F. Moura,
Data-driven matched field processing for Lamb wave structural health monitoring, The Journal of the Acoustical Society of America, 135 (2014), 1231-1244.
|
[12] |
V. Y. Bazhin, A. A. Vlasov and A. V. Lupenkov,
Controlling the AE in an aluminum reduction cell, Metallurgist, 55 (2011), 463-468.
|
[13] |
Y. Song, J. P. Peng, Y. W. Wang, Y. Z. Di, B. K. Li and N. X. Feng,
Magneto-hydrodynamics simulation of 300 KA novel cell for aluminium electrolysis, Metalurgija, 55 (2016), 22-24.
|
[14] |
J. Yi, D. Huang, S. Fu, H. He and T. Li,
Multi-objective bacterial foraging optimization algorithm based on parallel cell entropy for aluminum electrolysis production process, IEEE Transactions on Industrial Electronics, 63 (2016), 2488-2500.
doi: 10.1109/TIE.2015.2510977. |
[15] |
H. Viumdal and S. Mylvaganam,
System identification of a non-uniformly sampled multi-rate system in aluminium electrolysis cells, Modeling Identification and Control, 35 (2014), 127-146.
doi: 10.4173/mic.2014.3.1. |
[16] |
A. Solheim,
Entropic heat effects in aluminum electrolysis cells with inert anodes, Metallurgical and Materials Transactions -B, 47 (2016), 1274-1279.
doi: 10.1007/s11663-015-0561-1. |
[17] |
F. Allard, G. Soucy and L. Rivoaland,
Formation of deposits on the cathode surface of aluminum electrolysis cells, Metallurgical and Materials Transactions -B, 45 (2014), 2475-2485.
doi: 10.1007/s11663-014-0118-8. |
[18] |
J. J. Li, Z. J. Wang and J. L. Zhu,
Aluminum electrolysis multi-objective control system based on quantum optimized, Advanced Materials, Technology and Application: Proceedings of the 2016 International Conference on Advanced Materials, Technology and Application (AMTA2016). World Scientific, (2016), 417-423.
doi: 10.1142/9789813200470_0049. |
[19] |
H. Zhang, T. Li, J. Li, S. Yang and Z. Zou,
Progress in aluminum electrolysis control and future direction for smart aluminum electrolysis plant, JOM, 69 (2017), 292-300.
doi: 10.1007/s11837-016-2150-4. |
[20] |
C. K. Hu, F. B. Liu and C. F. Hu,
Efficiency measures in fuzzy data envelopment analysis with common weights, Journal of Industrial and Management Optimization, 13 (2017), 237-249.
|
[21] |
H. Z. Haghighi, S. Adeli and F. H. Lotfi,
Revenue congestion: An application of data envelopment analysis, Journal of Industrial and Management Optimization, 12 (2016), 1311-1322.
doi: 10.3934/jimo.2016.12.1311. |
[22] |
A. Klos, J. Bogusz, M. Figurski and W. Kosek,
On the handling of outliers in the GNSS time series by means of the noise and probability analysis, Springer Berlin Heidelberg, 143 (2015), 657-664.
doi: 10.1007/1345_2015_78. |
[23] |
A. Katayev, J. K. Fleming, D. Luo, A. H. Fisher and T. M. Sharp,
Reference intervals data mining, American Journal of Clinical Pathology, 143 (2015), 134-142.
doi: 10.1309/AJCPQPRNIB54WFKJ. |
[24] |
Q. X. Chi and X. C. Si,
Discussion for radar signal sorting method based on the grubbs' criterion, Chinese Journal of Sensors and Actuators, 6 (2006), 2625-2629.
|
[25] |
Z. N. Qu and J. L. Xie,
Long-term periodicity variations of the solar radius, Astrophysical Journal, 762 (2012), 23-28.
doi: 10.1088/0004-637X/762/1/23. |
[26] |
F. Gürbüz and P. M. Pardalos,
A decision making process application for the slurry production in ceramics via fuzzy cluster and data mining, Journal of Industrial and Management Optimization, 8 (2013), 285-297.
|
[27] |
M. Kato, H. Masuyama, S. Kasahara and Y. Takahashi,
Effect of energy-saving server scheduling on power consumption for large-scale data centers, Journal of Industrial and Management Optimization, 12 (2016), 667-685.
|
[28] |
Z. Gong, C. Liu and Y. Wang,
Optimal control of switched systems with multiple time-delays and a cost on changing control, Journal of Industrial and Management Optimization, 14 (2018), 183-198.
doi: 10.3934/jimo.2017042. |
[29] |
F. M. Anuar, R. Setchi and Y. K. Lai,
Semantic retrieval of trademarks based on conceptual similarity, IEEE Transactions on Systems Man and Cybernetics Systems, 46 (2016), 220-233.
doi: 10.1109/TSMC.2015.2421878. |
[30] |
Y. Xia,
Convex hull of the orthogonal similarity set with applications in quadratic assignment problems, Journal of Industrial and Management Optimization, 9 (2013), 689-701.
doi: 10.3934/jimo.2013.9.689. |
[31] |
V. Satuluri and S. Parthasarathy,
Bayesian locality sensitive hashing for fast similarity search, Proceedings of the VLDB Endowment, 5 (2012), 430-441.
doi: 10.14778/2140436.2140440. |
[32] |
H. Xiao, Similarity Search and Outlier Detection in Time Series. Department of Computer and Information Technique, Ph. D thesis, FuDan University in shanghai, 2005. |
[33] |
L. Zhang, J. Lin and R. Karim,
Sliding window-based fault detection from high-dimensional data streams, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47 (2017), 289-303.
doi: 10.1109/TSMC.2016.2585566. |
[34] |
R. Faragher,
Understanding the basis of the Kalman filter via a simple and intuitive derivation, IEEE Signal processing magazine, 29 (2012), 128-132.
|
[35] |
T. Schuhmann, W. Hofmann and R. Werner,
Improving operational performance of active magnetic bearings using Kalman filter and state feedback control, IEEE Transactions on Industrial Electronics, 59 (2012), 821-829.
doi: 10.1109/TIE.2011.2161056. |
[36] |
V. F. De, A. Brandl, M. Battipede and P. Gili,
Joseph covariance formula adaptation to square-root sigma-point Kalman filters, Nonlinear Dynamics, 88 (2017), 1969-1986.
|
[37] |
B. Jia, M. Xin and Y. Cheng,
High-degree cubature Kalman filter, Automatica, 49 (2013), 510-518.
doi: 10.1016/j.automatica.2012.11.014. |
[38] |
J. Shawash and D. R. Selviah,
Real-time nonlinear parameter estimation using the Levenberg-Marquardt algorithm on field programmable gate arrays, IEEE Transactions on Industrial Electronics, 60 (2013), 170-176.
doi: 10.1109/TIE.2012.2183833. |
[39] |
V. López, S. delRío, J. M. Benítez and F. Herrera,
Cost-sensitive linguistic fuzzy rule based classification systems under the MapReduce framework for imbalanced big data, Fuzzy Sets and Systems, 258 (2015), 5-38.
doi: 10.1016/j.fss.2014.01.015. |
[40] |
C. C. Jiang, R. F. Zhu, G. Y. Xiao, L. L. Wang, Y. Z. Zheng and Y. P. Lu,
Communication-effect of nano-alumina concentration on the microstructure and corrosion resistance of phosphate chemical conversion coating, Journal of The Electrochemical Society, 163 (2016), C339-C341.
doi: 10.1149/2.0131607jes. |
[41] |
S. Zhang, X. Chen and Y. Yin, An ELM based online soft sensing approach for alumina concentration detection, Mathematical Problems in Engineering, 2015 (2015), Article ID 268132, 8 pages.
doi: 10.1155/2015/268132. |
[42] |
G. Bearne, M. Dupuis and G. Tarcy, Pseudo resistance curves for aluminium cell control -alumina dissolution and cell dynamics, in Essential Readings in Light Metals: Aluminum Reduction Technology, Volume 2 (eds. H. Kvande, B. P. Moxnes, J. Skaar and P. A. Solli), Metals and Alloys, (2013), 760-766. |
[43] |
Q. Zhai, J. Yang, M. Xie and Y. Zhao,
Generalized moment-independent importance measures based on Minkowski distance, European Journal of Operational Research, 239 (2014), 449-455.
doi: 10.1016/j.ejor.2014.05.021. |
[44] |
J. Torres-Sospedra, R. Montoliu, S. Trilles, $\mathit{Ó}$. Belmonte and J. Huerta,
Comprehensive analysis of distance and similarity measures for Wi-Fi fingerprinting indoor positioning systems, Expert Systems with Applications, 42 (2015), 9263-9278.
doi: 10.1016/j.eswa.2015.08.013. |
[45] |
G. H. B. Foo, X. Zhang and D. M. Vilathgamuwa,
A sensor fault detection and isolation method in interior permanent-magnet synchronous motor drives based on an extended Kalman filter, IEEE Transactions on Industrial Electronics, 60 (2013), 3485-3495.
doi: 10.1109/TIE.2013.2244537. |
















Curve types | Similarity |
Red curve | 0.9419 |
Green curve | 0.9526 |
Blue curve | 0.9661 |
Turquoise curve | 0.9628 |
Carmine curve | 0.9587 |
Curve types | Similarity |
Red curve | 0.9419 |
Green curve | 0.9526 |
Blue curve | 0.9661 |
Turquoise curve | 0.9628 |
Carmine curve | 0.9587 |
Curve types | Similarity |
Red curve | 0.9278 |
Green curve | 0.9152 |
Blue curve | 0.9472 |
Turquoise curve | 0.9324 |
Carmine curve | 0.9461 |
Curve types | Similarity |
Red curve | 0.9278 |
Green curve | 0.9152 |
Blue curve | 0.9472 |
Turquoise curve | 0.9324 |
Carmine curve | 0.9461 |
Time advance ( |
DPMD | Minkowski distance | Euclidean distance |
Mean accuracy | Mean accuracy | Mean accuracy | |
|
55.4% | 47.9% | 45.9% |
51.1% | 42.3% | 38.5% | |
46.8% | 38.3% | 35.6% | |
43.9% | 32.6% | 31.2% | |
40.4% | 26.1% | 25.6% | |
38.4% | 21.4% | 22.1% | |
36.5% | 15.2% | 18.3% |
Time advance ( |
DPMD | Minkowski distance | Euclidean distance |
Mean accuracy | Mean accuracy | Mean accuracy | |
|
55.4% | 47.9% | 45.9% |
51.1% | 42.3% | 38.5% | |
46.8% | 38.3% | 35.6% | |
43.9% | 32.6% | 31.2% | |
40.4% | 26.1% | 25.6% | |
38.4% | 21.4% | 22.1% | |
36.5% | 15.2% | 18.3% |
Time advance ( | LMSRCKF | SRCKF | EKF |
Mean accuracy | Mean accuracy | Mean accuracy | |
|
75.6% | 57.1% | 51.6% |
72.3% | 51.2% | 45.3% | |
68.8% | 44.7% | 40.2% | |
62.9% | 35.3% | 32.7% | |
59.8% | 31.2% | 28.6% | |
55.4% | 25.7% | 23.4% | |
51.2% | 19.3% | 17.6% |
Time advance ( | LMSRCKF | SRCKF | EKF |
Mean accuracy | Mean accuracy | Mean accuracy | |
|
75.6% | 57.1% | 51.6% |
72.3% | 51.2% | 45.3% | |
68.8% | 44.7% | 40.2% | |
62.9% | 35.3% | 32.7% | |
59.8% | 31.2% | 28.6% | |
55.4% | 25.7% | 23.4% | |
51.2% | 19.3% | 17.6% |
Results of AE-predicting of the fused CA and CB | |
Mean accuracy | Time advance (min) |
93.3% | 0 ~ 40 |
91.5% | 5 ~ 40 |
89.2% | 10 ~ 40 |
88.6% | 15 ~ 40 |
85.1% | 20 ~ 40 |
83.9% | 25 ~ 40 |
80.2% | 30 ~ 40 |
Results of AE-predicting of the fused CA and CB | |
Mean accuracy | Time advance (min) |
93.3% | 0 ~ 40 |
91.5% | 5 ~ 40 |
89.2% | 10 ~ 40 |
88.6% | 15 ~ 40 |
85.1% | 20 ~ 40 |
83.9% | 25 ~ 40 |
80.2% | 30 ~ 40 |
Project names | cell numbers | |||||||
201 |
202 |
203 |
204 |
205 |
206 |
207 |
208 |
|
Total number of occurring AE | 34 | 41 | 37 | 41 | 29 | 35 | 38 | 47 |
Total number of successful AE-predicting | 32 | 39 | 36 | 41 | 27 | 34 | 36 | 45 |
Total number of AE-predicting | 35 | 41 | 39 | 45 | 30 | 35 | 39 | 51 |
Total number of underreporting | 2 | 2 | 1 | 2 | 2 | 1 | 2 | 2 |
Total number of underreporting | 3 | 2 | 3 | 4 | 3 | 1 | 3 | 6 |
Mean accuracy | 94.1% | 95.1% | 97.3% | 95.3% | 93.1% | 97.1% | 94.7% | 95.7% |
Project names | cell numbers | |||||||
201 |
202 |
203 |
204 |
205 |
206 |
207 |
208 |
|
Total number of occurring AE | 34 | 41 | 37 | 41 | 29 | 35 | 38 | 47 |
Total number of successful AE-predicting | 32 | 39 | 36 | 41 | 27 | 34 | 36 | 45 |
Total number of AE-predicting | 35 | 41 | 39 | 45 | 30 | 35 | 39 | 51 |
Total number of underreporting | 2 | 2 | 1 | 2 | 2 | 1 | 2 | 2 |
Total number of underreporting | 3 | 2 | 3 | 4 | 3 | 1 | 3 | 6 |
Mean accuracy | 94.1% | 95.1% | 97.3% | 95.3% | 93.1% | 97.1% | 94.7% | 95.7% |
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