Overcharging is an important safety issue in the charging process of electric vehicle power batteries,and can easily lead to accelerated battery aging and serious safety *** is necessary to accurately predict the vehi...
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Overcharging is an important safety issue in the charging process of electric vehicle power batteries,and can easily lead to accelerated battery aging and serious safety *** is necessary to accurately predict the vehicle’s charging time to effectively prevent the battery from *** to the complex structure of the battery pack and various charging modes,the traditional charging time prediction method often encounters modeling difficulties and low *** response to the above problems,data drivers and machine learning theories are *** the basis of fully considering the different electric vehicle battery management system(bMS)charging modes,a charging time prediction method with charging mode recognition is ***,an intelligent algorithm based on dynamic weighted density peak clustering(DWDPC)and random forest fusion is proposed to classify vehicle charging ***,on the basis of an improved simplified particle swarm optimization(ISPSO)algorithm,a high-performance charging time prediction method is constructed by fully integrating long short-term memory(LSTM)and a strong tracking ***,the data run by the actual engineering system are verified for the proposed charging time prediction *** results show that the new method can effectively distinguish the charging modes of different vehicles,identify the charging characteristics of different electric vehicles,and achieve high prediction accuracy.
In this paper, we address the problem of ship detection in PolSAR image. We firstly investigate the differences of scattering mechanism between ship targets and the sea surface based on the polarimetric similarity ana...
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In this paper, we address the problem of ship detection in PolSAR image. We firstly investigate the differences of scattering mechanism between ship targets and the sea surface based on the polarimetric similarity analysis. It is shown that, the sea surface scattering is dominated by the odd bounce (denoted as r 1 ), while the scattering of ship targets are both dominated by the even bounce scattering (r 2 ) which has been widely accepted, and the line bounce scattering (r 4 ) which is a new find from the experiments. based on those differences, a metric (r 2 +r 4 )/r 1 can be applied to distinguish ship targets from the sea surface. To further suppress the effects of sidelobes and imaging artifacts which have the same/similar scattering behaviors with ship targets, we introduce in the third eigenvalue λ 3 ) of the coherency matrix and obtain the metric (r 2 + r 4 )λ 3 /r 1 . The preliminary results show that a constant false alarm rate (CFAR) ship detector based on the proposed metric can obtain promising ship detection performance.
Emerging self-ensembling methods have achieved promising semi-supervised segmentation performances on medical images through forcing consistent predictions of unannotated data under different perturbations. However, t...
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Human parsing has been extensively studied recently (Yamaguchi et al. 2012;Xia et al. 2017) due to its wide applications in many important scenarios. Mainstream fashion parsing models (i.e., parsers) focus on parsing ...
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Human parsing has been extensively studied recently (Yamaguchi et al. 2012;Xia et al. 2017) due to its wide applications in many important scenarios. Mainstream fashion parsing models (i.e., parsers) focus on parsing the high-resolution and clean images. However, directly applying the parsers trained on benchmarks of high-quality samples to a particular application scenario in the wild, e.g., a canteen, airport or workplace, often gives non-satisfactory performance due to domain shift. In this paper, we explore a new and challenging cross-domain human parsing problem: Taking the benchmark dataset with extensive pixel-wise labeling as the source domain, how to obtain a satisfactory parser on a new target domain without requiring any additional manual labeling? To this end, we propose a novel and efficient crossdomain human parsing model to bridge the cross-domain differences in terms of visual appearance and environment conditions and fully exploit commonalities across domains. Our proposed model explicitly learns a feature compensation network, which is specialized for mitigating the cross-domain differences. A discriminative feature adversarial network is introduced to supervise the feature compensation to effectively reduces the discrepancy between feature distributions of two domains. besides, our proposed model also introduces a structured label adversarial network to guide the parsing results of the target domain to follow the high-order relationships of the structured labels shared across domains. The proposed framework is end-to-end trainable, practical and scalable in real applications. Extensive experiments are conducted where LIP dataset is the source domain and 4 different datasets including surveillance videos, movies and runway shows without any annotations, are evaluated as target domains. The results consistently confirm data efficiency and performance advantages of the proposed method for the challenging cross-domain human parsing problem. Copyright
In this paper, we propose a novel model for unsupervised segmentation of viewer's attention object from natural images based on localizing region-based active con-tour (LRAC). Firstly, we proposed the saliency det...
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Ruddlesden–Popper phase 2D perovskite solar cells (PSCs) exhibit improved lifetime while still facing challenges such as phase alignment and up-scaling to module-level devices. Herein, polyelectrolytes are explored t...
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Ruddlesden–Popper phase 2D perovskite solar cells (PSCs) exhibit improved lifetime while still facing challenges such as phase alignment and up-scaling to module-level devices. Herein, polyelectrolytes are explored to tackle this issue. The contact between perovskite and hole-transport layer (HTL) is important for decreasing interfacial non-radiative recombination and scalable fabrication of uniform 2D perovskite films. Through exploring compatible butylamine cations, we first demonstrate poly(3-(4-carboxybutyl)thiophene-2,5-diyl)-butylamine (P3CT-bA) as an efficient HTL for 2D PSCs due to its great hydrophilicity, relatively high hole mobility and uniform surface. More importantly, the tailored P3CT-bA has an anchoring effect and acts as the buried passivator for 2D perovskites. Consequently, a best efficiency approaching 18 % was achieved and we further first report large-area (2×3 cm 2 , 5×5 cm 2 ) 2D perovskite minimodules with an impressive efficiency of 14.81 % and 11.13 %, respectively.
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