Because of the short length of documents describing Web services on the Internet, the traditional modeling method is not ideal, which affects the clustering effect of Web services. To this end, we propose a word-embed...
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In the past few years, self-attention in Transformer has been widely used in natural language processing (NLP) and computer vision (CV) due to its excellent ability to capture global information, especially in image c...
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When the multi-object tracking (MOT) algorithm confronts complex scenarios such as target occlusion and blurring, the trajectory missing and identity switching problems frequently occur. To address this issue, a traje...
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A robust feature extraction method based on feature transfer for heterogeneous remote sensing images is proposed to address the problem of insufficient generalization ability of traditional edge feature extraction met...
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Visual and inertial navigation have obvious complementarity in navigation accuracy, and the combined navigation of the two has excellent anti-interference ability. In this paper, a visual-inertial-based satellite qual...
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In order to further improve the ability to deal with complex scenes, a visual tracking algorithm based on DenseNet features and model adaptive updating is proposed. Aiming to improve the feature representation ability...
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To address the problem of object miss detection and object false detection in single threshold-Non-Maximum Suppression algorithm,this paper proposed a GDT-NMS(Generalized Intersection over Union Dual Threshold NMS,GDT...
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To address the problem of object miss detection and object false detection in single threshold-Non-Maximum Suppression algorithm,this paper proposed a GDT-NMS(Generalized Intersection over Union Dual Threshold NMS,GDT-NMS)) algorithm which using GIoU(Generalized Intersection over Union).Using the GIoU indicator computing the similarity between objects,can better describe the relative position and overlap between the *** we proposed the dual-threshold NMS algorithm,which can balance the relationship between the object missed detection problem and the object false detection problem,reduce "false positive example" *** nonlinearly processing the weight function,the object is better *** algorithm uses Faster R-CNN as the *** experimental results show that the improved algorithm has outstanding performance.
With the large-scale application of lithium-ion batteries (LIB), using deep neural networks to predict the remaining useful life (RUL) of LIB has gradually become a hotshot in recent years. RUL prediction method based...
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ISBN:
(纸本)9781665442084
With the large-scale application of lithium-ion batteries (LIB), using deep neural networks to predict the remaining useful life (RUL) of LIB has gradually become a hotshot in recent years. RUL prediction method based on deep neural network can avoid studying electrochemical phenomena and manual extracting the features in battery. But single neural network has the different prediction accuracy and features extraction on different dataset. In this study, an ensemble method for the heterogeneous neural network is proposed, which integrates the prediction results of multiple heterogeneous neural networks with the adaptive weight. The weight of the neural network is higher with the closer correlation to the majority prediction results, vice versa. Furthermore, the weight of the neural network is adjusted via the predicting results for neural network on the different dataset, so that the computed weight of the neural network is adapted to the various dataset, and the effects of poor predictions of certain neural networks can be reduced sufficiently. The effectiveness of the ensemble method is verified on MIT-Stanford LIB degradation dataset, and the results show that the proposed method has higher accuracy than the existing ensemble methods for neural network.
Accurately predicting the Remaining Useful Life (RUL) of lithium-ion batteries is critical for accelerating the technology development. The neural network via data driven can avoid manual feature extraction and releas...
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ISBN:
(纸本)9781665405546
Accurately predicting the Remaining Useful Life (RUL) of lithium-ion batteries is critical for accelerating the technology development. The neural network via data driven can avoid manual feature extraction and release the difficulty of model construction. However, diverse aging mechanisms, significant device variability and dynamic operating conditions have remained major challenges. The single neural network cannot tackle these challenges efficiently. In this paper, the RUL prediction of lithium battery via neural network ensemble is developed to improve the accuracy and generalization of the prediction model. The predicting results of multiple neural networks are considered as the base vectors, and the liner combination on the base vectors are conducted to obtain the ensemble RUL prediction of lithium-ion battery. The experiment is performed on the MIT-Stanford University lithium battery accelerated degradation dataset, and the experimental results show that the developed method has achieved better generalization and higher accuracy comparing with the other methods.
The challenge of solving dynamic multi-objective optimization problems is to trace the varying Pareto optimal front and/or Pareto optimal set quickly and efficiently. This paper proposes a multi-direction prediction s...
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