Heterogeneous oxidization of graphene nanosheets damages membrane

Wang, QianChun; Zhai, Xiaobo; Crowe, Michael J.; Gou, Lu; Li, Yinfeng; Li, DeChang; Zhang, Lei; Diao, Jiajie; Ji, Baohua

doi:10.1007/s11433-018-9317-7

Cited by 17 publications

(18 citation statements)

References 86 publications

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“…CRAFTS is a drfit-scan survey which aims to observe the entire visible sky of the FAST for HI emission and search for the new pulsars utilizing the FAST Lband Array of 19-beams (FLAN; Zhang et al 2019). The early observation data with labels (Wang et al 2019b) for pulsar searching from CRAFTS has been public on https://github.com/ dzuwhf/FAST_label_data. This work uses this data set to train and test our model.…”

Section: Datamentioning

confidence: 99%

“…The validation accuracy curve peaks at the 41th epoch and keeps oscillating after that, which means that the model achieves its best performance at this epoch and it is saved as the final model used to make the classification on the test set. And two final models are compared to the state-of-the-art ML models: PICS (Zhu et al 2014) and PICS-ResNet (Wang et al 2019b), which were trained in the previous works and have been public 2 .…”

Section: The Experiments On Fast Datamentioning

confidence: 99%

“…It is expected to discover about 1500 new normal pulsars and 200 millisecond pulsars (Yue et al 2012). In practice, the FAST 19beam drift-scan survey generates more than one million pulsar candidates per night (Wang et al 2019b). However, the proportion of real pulsars among candidates is exceedingly small (approximately one in ten thousand; Lyon et al 2013) due to the presence of radio frequency interference (RFI) and noise.…”

Section: Introductionmentioning

confidence: 99%

“…These methods prompt the model to learn the "pulsar-like" patterns from the diagnostic subplots by themselves through data-driven learning. Zhu et al (2014) and Wang et al (2019b) proposed a two-layer ensemble model to identify the pulsars. For example, the model in Wang et al (2019b) is composed of five classifiers totally, including two Residual Neural Networks (ResNets), two Support Vector Machines (SVMs) and one Logistic regression (LR).…”

Section: Introductionmentioning

confidence: 99%

“…Zhu et al (2014) and Wang et al (2019b) proposed a two-layer ensemble model to identify the pulsars. For example, the model in Wang et al (2019b) is composed of five classifiers totally, including two Residual Neural Networks (ResNets), two Support Vector Machines (SVMs) and one Logistic regression (LR). Two ResNets are used to determine whether the time versus phase plot and frequency versus phase plot are "pulsar-like", respectively.…”

Section: Introductionmentioning

confidence: 99%

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Concat Convolutional Neural Network for pulsar candidate selection

Qing

Lin

2020

Monthly Notices of the Royal Astronomical Society

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Pulsar searching is essential for the scientific research in the field of physics and astrophysics. As the development of the radio telescope, the exploding volume and it growth speed of candidates growth have brought about several challenges. Therefore, there is an urgent demand for developing an automatic, accurate and efficient pulsar candidate selection method. To meet this need, this work designed a Concat Convolutional Neural Network (CCNN) to identify the candidates collected from the Five-hundred-meter Aperture Spherical Telescope (FAST) data. The CCNN extracts some "pulsar-like" patterns from the diagnostic subplots using Convolutional Neural Network (CNN) and combines these CNN features by a concatenate layer. Therefore, the CCNN is an end-to-end learning model without any need for any intermediate labels, which makes CCNN suitable for the online learning pipeline of pulsar candidate selection. Experimental results on FAST data show that the CCNN outperforms the available state-of-the-art models in similar scenario. It only misses 4 real pulsars out of 326 totally.

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Section: Datamentioning

confidence: 99%

Section: The Experiments On Fast Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Concat Convolutional Neural Network for pulsar candidate selection

Qing

Lin

2020

Monthly Notices of the Royal Astronomical Society

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Dissipative Particle Dynamics Simulation: A Review on Investigating Mesoscale Properties of Polymer Systems

Wang

Han

et al. 2021

Macro Materials & Eng

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Polymer systems have typical multiscale characteristics, both in space and time. The mesoscopic properties of polymers are difficult to describe through traditional experimental approaches. Dissipative particle dynamics (DPD) is a simulation method used for solving mesoscale problems of complex fluids and soft matter. The mesoscopic properties of polymer systems, such as conformation, dynamics, and transport properties, have been studied extensively using DPD. This paper briefly summarizes the application of DPD to research involving microchannel flow, electrospinning, free‐radical polymerization, polymer self‐assembly processes, polymer electrolyte fuel cells, and biomedical materials. The main features and possible development avenues of DPD are described as well.

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