Gold nanorods (AuNRs) that contained polyvinyl alcohol/chitosan (PVA/CS) hybrid nanofibers with dual functions are successfully fabricated by a simple electrospinning method. The results of ...transmission electron microscopy (TEM), X-ray diffraction (XRD) and energy dispersive X-ray (EDX) spectroscopy indicate that AuNRs are indeed encapsulated into the PVA/CS hybrid nanofibers. FTIR spectra results demonstrate that the chemical structures of PVA and CS are not affected when the AuNRs are introduced into the fibers. In vitro cytotoxicity test reveals that the hybrid fibers involving AuNRs are completely biocompatible. The as-prepared fibers can be used as a carrier for anticancer agent doxorubicin (DOX), and the drug is delivered into the cell nucleus. The AuNRs and DOX incorporated fibers are effective for inhibiting the growth and proliferation of ovary cancer cells and they can also be used as the cell imaging agent due to the unique optical properties of AuNRs. The nanofiber matrix combining two functions of cell imaging and drug delivery may be of great application potential in biomedical-related areas.
•The AuNRs contained PVA/CS nanofibers are fabricated by electrospinning.•The hybrid fibers involving AuNRs are completely biocompatible.•The DOX loaded fibers are effective for inhibiting the proliferation of cancer cells.•The nanofibers combined two functions of cell imaging and drug delivery.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
202.
Construction of Curriculum Knowledge Map based on Ontology Pei, Pei; Xuejing, Ding; Deqing, Zhang
Proceedings of the 2020 2nd International Conference on Robotics, Intelligent Control and Artificial Intelligence,
10/2020
Conference Proceeding
With the rapid development of science and technology, knowledge update is accelerating, which puts forward higher requirements for teachers and students in Colleges and universities, and needs to ...grasp the latest development trends of curriculum knowledge related fields faster, more comprehensive and more accurate. Many schools have digitized their educational resources. However, the traditional sharing of educational resources lacks a unified knowledge representation structure, which makes the sharing and reuse of learning resources unsatisfactory. Curriculum is the core of school knowledge teaching. It evaluates the curriculum system and discipline system in order to achieve certain teaching objectives. Curriculum knowledge includes explicit knowledge and tacit knowledge.
Knowledge map is a kind of model which can describe knowledge in semantic and knowledge level. Its purpose is to acquire, organize and present knowledge in a general and intuitive way, to search and match knowledge quickly, so as to improve the utilization of knowledge by learners and knowledge workers. Curriculum knowledge has obvious ontology characteristics, and there are many inconveniences and shortcomings in the presentation of traditional knowledge map. Using ontology technology to construct curriculum knowledge map can not only reflect the relationship between knowledge modules, but also realize the mining and representation of more knowledge relations and types such as tacit knowledge to a certain extent.
Several radar systems have been proposed in the past decades, including real aperture radar (RAR) and synthetic aperture radar (SAR). Spatial resolutions of different radar systems cannot be compared ...together because their work modes are different. In this paper, a normalized spatial resolution analysis model is proposed to deduce the spatial resolution of different systems. First, the normalized wavenumber spectra of different radar systems are deduced. Second, the relationship between spatial resolution and the wavenumber spectra distribution is analyzed. Finally, the point spread functions (PSFs) of different radar systems are simulated.
Convolutional neural networks (CNNs) have achieved high performance in synthetic aperture radar (SAR) automatic target recognition (ATR). However, the performance of CNNs depends heavily on a large ...amount of training data. The insufficiency of labeled training SAR images limits the recognition performance and even invalidates some ATR methods. Furthermore, under few labeled training data, many existing CNNs are even ineffective. To address these challenges, we propose a Semi-supervised SAR ATR Framework with transductive Auxiliary Segmentation (SFAS). The proposed framework focuses on exploiting the transductive generalization on available unlabeled samples with an auxiliary loss serving as a regularizer. Through auxiliary segmentation of unlabeled SAR samples and information residue loss (IRL) in training, the framework can employ the proposed training loop process and gradually exploit the information compilation of recognition and segmentation to construct a helpful inductive bias and achieve high performance. Experiments conducted on the MSTAR dataset have shown the effectiveness of our proposed SFAS for few-shot learning. The recognition performance of 94.18\% can be achieved under 20 training samples in each class with simultaneous accurate segmentation results. Facing variances of EOCs, the recognition ratios are higher than 88.00\% when 10 training samples each class.
Sufficient synthetic aperture radar (SAR) target images are very important for the development of researches. However, available SAR target images are often limited in practice, which hinders the ...progress of SAR application. In this paper, we propose an azimuth-controllable generative adversarial network to generate precise SAR target images with an intermediate azimuth between two given SAR images' azimuths. This network mainly contains three parts: generator, discriminator, and predictor. Through the proposed specific network structure, the generator can extract and fuse the optimal target features from two input SAR target images to generate SAR target image. Then a similarity discriminator and an azimuth predictor are designed. The similarity discriminator can differentiate the generated SAR target images from the real SAR images to ensure the accuracy of the generated, while the azimuth predictor measures the difference of azimuth between the generated and the desired to ensure the azimuth controllability of the generated. Therefore, the proposed network can generate precise SAR images, and their azimuths can be controlled well by the inputs of the deep network, which can generate the target images in different azimuths to solve the small sample problem to some degree and benefit the researches of SAR images. Extensive experimental results show the superiority of the proposed method in azimuth controllability and accuracy of SAR target image generation.
Spatial resolution of synthetic aperture radar (SAR) is a vital index to evaluate the performance of its observed image. However, high spatial resolution of SAR is achieved at the cost of system ...resources. Therefore, super-resolution methods can be applied in SAR systems to improve their spatial resolution without system resource increases. In this paper, we propose a new residual network-based structure for super-resolution of SAR images. The proposed method adopts the structure of global residuals and adds several convolutional layers before and after the residual module to take into account the depth and width of the network. The simulation results show that the proposed method is effective as the visual effect and data evaluation.
To design configuration parameters for coherent multistatic synthetic aperture radar (C-MuSAR), a wavenumber spectra projection (WSP) approach is proposed in this paper based on the relationship ...between the wavenumber support regions (WSRs) and configuration parameters, including synthetic aperture time, positions and flight directions of receivers. First, the projected pattern of multiple WSRs is deduced, and the relationship between multiple WSRs and the point spread function (PSF) is analyzed. Second, the primary WSR is designed based on the relationship between the transmitter and the leading receiver. A WSP method is proposed to quickly deduce the configuration parameters of the following receivers. Finally, based on the designed configuration parameters of C-MuSAR, an adaptive WSP method is adopted to reconstruct the targets. Simulations are carried out to testify the proposed method.
Target reconstruction is one of the most important missions in the fields of radar signal processing. In this paper, we propose a new deep learning-based approach to reconstruct the target ...information from the scanning radar returns. Unlike the traditional analytical methods, a deep neural network with a topology of linear chains of convolutional layers is designed, and the input radar signals will be learned layer by layer through the network, which a direct map from the radar echo to the reflectivity function of the targets is obtained during the learning procedure. Finally, we can get the optimal deep learning network as the reconstructing map to recover the scanning radar target information effectively. Simulation results have shown the superiority of the proposed method under different target scenes.
The echo data of real aperture radar in scanning mode is widely used in earth-observing, forward-looking super-resolution imaging and air-searching. However, the generation of the echo data is ...time-consuming. In this paper, a rapid generation method of the echo for the real aperture radar in scan mode is proposed. First, by analyzing the independent relationship between beam scanning and radar signal propagation, the radar echo signal is decomposed into two-dimensional convolution relationship along the distance direction and the azimuth direction. Then, according to the low-resolution characteristics of the scanning radar and the distribution of target position, the scene and the echo signal are mapped into a unified coordinate system. Finally, due to the two-dimensional convolution relationship along the distance direction and azimuth direction, the fast Fourier transform is used to realize the rapid generation of real aperture radar echo. Simulations verify the acceleration performance of the proposed method.
Two-proton relative momentum distributions from the break-up channels
23
Al→p+p+
21
Na and
22
Mg→p+p+
20
Ne at an energy of 60–70 A MeV have been measured together with two-proton opening angles at ...the projectile fragment separator beamline (RIPS) in the RIKEN Ring Cyclotron Facility. The results demonstrate the existence of diproton emission component from single-step
2
He for highly excited
23
Al and
22
Mg.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
