The Experts below are selected from a list of 23340 Experts worldwide ranked by ideXlab platform
Yanping Wang - One of the best experts on this subject based on the ideXlab platform.
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A Method for Predicting the Remaining Useful Life of Lithium-Ion Batteries Based on Particle Filter Using Kendall Rank Correlation Coefficient
Energies, 2020Co-Authors: Diju Gao, Yong Zhou, Tianzhen Wang, Yanping WangAbstract:With the wide application of lithium batteries, battery fault prediction and healthmanagement have become more and more important. This article proposes a method for predictingthe remaining useful life (RUL) of lithium-ion batteries to avoid a series of safety problems causedby continuing to use the battery after reaching its service life threshold. Since the battery capacityis not easy to obtain online, we propose that some measurable parameters should be used in thebattery discharge cycle to estimate battery capacity. Then, the estimated capacity is used to replace themeasured value of the particle filter (PF) based on the Kendall Rank Correlation Coefficient (KCCPF) topredict the RUL of the lithium batteries. Simulation results show that the proposed method has highprediction accuracy, stability, and practical value.
Diju Gao - One of the best experts on this subject based on the ideXlab platform.
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A Method for Predicting the Remaining Useful Life of Lithium-Ion Batteries Based on Particle Filter Using Kendall Rank Correlation Coefficient
Energies, 2020Co-Authors: Diju Gao, Yong Zhou, Tianzhen Wang, Yanping WangAbstract:With the wide application of lithium batteries, battery fault prediction and healthmanagement have become more and more important. This article proposes a method for predictingthe remaining useful life (RUL) of lithium-ion batteries to avoid a series of safety problems causedby continuing to use the battery after reaching its service life threshold. Since the battery capacityis not easy to obtain online, we propose that some measurable parameters should be used in thebattery discharge cycle to estimate battery capacity. Then, the estimated capacity is used to replace themeasured value of the particle filter (PF) based on the Kendall Rank Correlation Coefficient (KCCPF) topredict the RUL of the lithium batteries. Simulation results show that the proposed method has highprediction accuracy, stability, and practical value.
Xudong Kang - One of the best experts on this subject based on the ideXlab platform.
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the effect of ground truth on accuracy indexes in hyperspectral image classification
International Geoscience and Remote Sensing Symposium, 2018Co-Authors: Qiaobo Hao, Xudong KangAbstract:In this paper, the effect of ground truths on performance evaluation of hyperspectral image classification is studied. The purpose is to investigate whether the accuracies in terms of three representative accuracy indexes, i.e., the overall accuracy (OA), the average accuracy (AA), and the Kappa Coefficient, can be completely responsible when the ground truth is insufficient. The major contribution of this work is designing several experiments so as to subjectively and objectively analysis the influences of ground truths on performance evaluation. Furthermore, four evaluation metrics, i.e., the Pearson linear Correlation Coefficient (PLCC), root mean square error (RMSE), Spearmans Rank Correlation Coefficient (SR-CC), and Kendalls Rank Correlation Coefficient (KRCC) have been adopted to measure the robustness of different classification methods to ground truths containing different numbers of labeled pixels and the location of ground truth in the image. Based on the designed experiments, a conclusion is obtained that insufficient ground truths may affect the performance of existing accuracy indexes. This underlines that overoptimistic performance evaluations may exist when the ground truth contains a small number of labeled pixels.
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the effect of ground truth on performance evaluation of hyperspectral image classification
IEEE Transactions on Geoscience and Remote Sensing, 2018Co-Authors: Qiaobo Hao, Guanghao Gao, Xudong KangAbstract:In the field of hyperspectral image classification, a widely used way for objective performance evaluation of different classification methods is calculating three accuracy indexes, i.e., the overall accuracy, the average accuracy, and the Kappa Coefficient. These accuracy indexes are obtained by comparing the classification results with the ground truth, i.e., a reference classification map labeled by human experts. In this paper, the effect of ground truths on the objective performance evaluation of hyperspectral image classification is studied. The purpose is to investigate, if the ground truth is insufficient, whether the above accuracy indexes can be completely responsible. Furthermore, in order to measure the robustness of different classification methods to those insufficient ground truths, four evaluation metrics, i.e., the Pearson linear Correlation Coefficient, root-mean-square error, Spearman’s Rank Correlation Coefficient, and Kendall’s Rank Correlation Coefficient have been adopted for further analysis. Based on these experiments, an interesting conclusion can be obtained that insufficient ground truths may limit the assessment capability of existing accuracy indexes. This underlines that overoptimistic performance evaluations may exist and stresses the demand of designing more appropriate accuracy indexes for objective performance evaluation with insufficient ground truths.
Tianzhen Wang - One of the best experts on this subject based on the ideXlab platform.
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A Method for Predicting the Remaining Useful Life of Lithium-Ion Batteries Based on Particle Filter Using Kendall Rank Correlation Coefficient
Energies, 2020Co-Authors: Diju Gao, Yong Zhou, Tianzhen Wang, Yanping WangAbstract:With the wide application of lithium batteries, battery fault prediction and healthmanagement have become more and more important. This article proposes a method for predictingthe remaining useful life (RUL) of lithium-ion batteries to avoid a series of safety problems causedby continuing to use the battery after reaching its service life threshold. Since the battery capacityis not easy to obtain online, we propose that some measurable parameters should be used in thebattery discharge cycle to estimate battery capacity. Then, the estimated capacity is used to replace themeasured value of the particle filter (PF) based on the Kendall Rank Correlation Coefficient (KCCPF) topredict the RUL of the lithium batteries. Simulation results show that the proposed method has highprediction accuracy, stability, and practical value.
Yong Zhou - One of the best experts on this subject based on the ideXlab platform.
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A Method for Predicting the Remaining Useful Life of Lithium-Ion Batteries Based on Particle Filter Using Kendall Rank Correlation Coefficient
Energies, 2020Co-Authors: Diju Gao, Yong Zhou, Tianzhen Wang, Yanping WangAbstract:With the wide application of lithium batteries, battery fault prediction and healthmanagement have become more and more important. This article proposes a method for predictingthe remaining useful life (RUL) of lithium-ion batteries to avoid a series of safety problems causedby continuing to use the battery after reaching its service life threshold. Since the battery capacityis not easy to obtain online, we propose that some measurable parameters should be used in thebattery discharge cycle to estimate battery capacity. Then, the estimated capacity is used to replace themeasured value of the particle filter (PF) based on the Kendall Rank Correlation Coefficient (KCCPF) topredict the RUL of the lithium batteries. Simulation results show that the proposed method has highprediction accuracy, stability, and practical value.
