155 research outputs found
Research on Some Phenomenon of E-Government Service Capacity Distribution in Mainland China Based on Multi-channel Perspective
In the context of the government\u27s increasing emphasis on e-government services, this is an urgent need for empirical research of large sample and multi-channels. Therefore, based on the government website, WeChat, Micro-blog, app, by using the existing mature evaluation index system, this paper analyzes e-government service capacity of the city above prefecture- level and provincial. Then, this paper selects the administrative level, economic level, regional balance as the differentiation attribute. It is found that both administrative level and economic level are positively correlated with government service capacity in all the channels. The channel capacity distribution varies related to attribute of administrative and economic, government type of city and province, but it is not restricted by level and region. It provides direction and intensity management to balance and promote channel service capacity for China government
EWT: Efficient Wavelet-Transformer for Single Image Denoising
Transformer-based image denoising methods have achieved encouraging results
in the past year. However, it must uses linear operations to model long-range
dependencies, which greatly increases model inference time and consumes GPU
storage space. Compared with convolutional neural network-based methods,
current Transformer-based image denoising methods cannot achieve a balance
between performance improvement and resource consumption. In this paper, we
propose an Efficient Wavelet Transformer (EWT) for image denoising.
Specifically, we use Discrete Wavelet Transform (DWT) and Inverse Wavelet
Transform (IWT) for downsampling and upsampling, respectively. This method can
fully preserve the image features while reducing the image resolution, thereby
greatly reducing the device resource consumption of the Transformer model.
Furthermore, we propose a novel Dual-stream Feature Extraction Block (DFEB) to
extract image features at different levels, which can further reduce model
inference time and GPU memory usage. Experiments show that our method speeds up
the original Transformer by more than 80%, reduces GPU memory usage by more
than 60%, and achieves excellent denoising results. All code will be public.Comment: 12 pages, 11 figur
A Multifunctional Array System Based on Adjustable-Phase Antenna for Wireless communications
In this work, an innovative method for controlling the current distribution
of the radiating patch by adjusting the input phase is investigated to achieve
both pattern and polarization reconfigurable characteristics for the
multifunction. A compact and low-profile antenna with four fed ports is
designed to implement the proposed method, which can operate linear, right-hand
circular polarization (RHCP) and left-hand circular polarization (LHCP) with
different beam directions in the operating band from 4.0 to 5.0 GHz. Even more,
a four-by-four passive planar array is designed and fabricated based on this
antenna element, which can scan the coverage of 70{\deg} with low gain
fluctuation and low sidelobe with dual-polarization. Meanwhile, it can realize
the wide-angle scanning capability up to 60{\deg} with low sidelobe with RHCP
and LHCP. More important, the dual- and triple-beam with different directions
can be obtained by the proposed array. Good agreement has been shown between
measured and simulated results. Therefore, the proposed antenna is a good
solution for wireless communication systems because of its
simple-configuration, multifunction, and beamforming capability
The effect of calcium nitrate on the hydration of calcium aluminate cement at different curing temperatures
Phase conversion in calcium aluminate cements (CAC) induces significant volumetric instability; it would result in an increase in porosity and decrease in strength in CAC. In this study, calcium nitrate (CN) as a phase conversion inhibitor, the effect of CN on the hydration of CAC at different curing temperatures was studied. Xray diffraction, thermal analysis, SEM, isothermal calorimetry and the compressive strength were conducted on the CAC dosages of 0%, 5%, 10% and 15%CN cured at 20�, 30�, 40� and 50�. The results show CN can retard CAC hydration, alter the characters of the hydrates of CAC systems and avoid the conversion process. With increasing dosage of CN and curing temperature, the hydration products formed is different.in CAC systems with CN, NO3-AFm and NO3-AFt are more preferred than CAH10 and C2AH8 and are more thermostable than those typically hydrates. In the presence of CN, The phase conversion to a large extent can be avoided and the compressive strength is significantly improved. The CN dosage has a very important effect on CAC systems with CN. In this study, the optimum dosage for CN is 10 percent. This study may provide a new insight into avoiding the unstable phase conversion in calcium aluminate cements
CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution
Recently, deep convolution neural networks (CNNs) steered face
super-resolution methods have achieved great progress in restoring degraded
facial details by jointly training with facial priors. However, these methods
have some obvious limitations. On the one hand, multi-task joint learning
requires additional marking on the dataset, and the introduced prior network
will significantly increase the computational cost of the model. On the other
hand, the limited receptive field of CNN will reduce the fidelity and
naturalness of the reconstructed facial images, resulting in suboptimal
reconstructed images. In this work, we propose an efficient CNN-Transformer
Cooperation Network (CTCNet) for face super-resolution tasks, which uses the
multi-scale connected encoder-decoder architecture as the backbone.
Specifically, we first devise a novel Local-Global Feature Cooperation Module
(LGCM), which is composed of a Facial Structure Attention Unit (FSAU) and a
Transformer block, to promote the consistency of local facial detail and global
facial structure restoration simultaneously. Then, we design an efficient Local
Feature Refinement Module (LFRM) to enhance the local facial structure
information. Finally, to further improve the restoration of fine facial
details, we present a Multi-scale Feature Fusion Unit (MFFU) to adaptively fuse
the features from different stages in the encoder procedure. Comprehensive
evaluations on various datasets have assessed that the proposed CTCNet can
outperform other state-of-the-art methods significantly.Comment: 12 pages, 10 figures, 8 table
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