In adaptive optics systems, the bad spot detected by the wavefront detector affects the wavefront reconstruction accuracy. A convolutional neural network(CNN) model is established to estimate the missing information o...
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In adaptive optics systems, the bad spot detected by the wavefront detector affects the wavefront reconstruction accuracy. A convolutional neural network(CNN) model is established to estimate the missing information on bad points, reduce the reconstruction error of the distorted wavefront. By training 10,000 groups of spot array images and the corresponding 30th order Zernike coefficient samples, learns the relationship between the light intensity image and the Zernike coefficient, and predicts the Zernike mode coefficient based on the spot array image to restore the wavefront. Following the wavefront restoration of 1,000 groups of test set samples, the root mean square(RMS) error between the predicted value and the real value was maintained at approximately 0.2 μm. Field wavefront correction experiments were carried out on three links of 600 m, 1.3 km,and 10 km. The wavefront peak-to-valley values corrected by the CNN decreased from 12.964 μm, 13.958 μm,and 31.310 μm to 0.425 μm, 3.061 μm, and 11.156 μm, respectively, and the RMS values decreased from 2.156 μm,9.158 μm, and 12.949 μm to approximately 0.166 μm, 0.852μm, and 6.963 μm, respectively. The results show that the CNN method predicts the missing wavefront information of the sub-aperture from the bad spot image, reduces the wavefront restoration error, and improves the wavefront correction performance.
Opinion sentence classification of Chinese microblog comments aims to recognise those comments with opinions about the specific microblog content, which is the basis of internet public opinion analysis and opinion min...
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Entity alignment(EA)is an important technique aiming to find the same real entity between two different source knowledge graphs(KGs).Current methods typically learn the embedding of entities for EA from the structure ...
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Entity alignment(EA)is an important technique aiming to find the same real entity between two different source knowledge graphs(KGs).Current methods typically learn the embedding of entities for EA from the structure of KGs for *** EA models are designed for rich-resource languages,requiring sufficient resources such as a parallel corpus and pre-trained language ***,low-resource language KGs have received less attention,and current models demonstrate poor performance on those low-resource ***,researchers have fused relation information and attributes for entity representations to enhance the entity alignment performance,but the relation semantics are often *** address these issues,we propose a novel Semantic-aware Graph Neural Network(SGNN)for entity ***,we generate pseudo sentences according to the relation triples and produce representations using pre-trained ***,our approach explores semantic information from the connected relations by a graph neural *** model captures expanded feature information from *** results using three low-resource languages demonstrate that our proposed SGNN approach out performs better than state-of-the-art alignment methods on three proposed datasets and three public datasets.
THE development of agriculture faces significant challenges due to population growth, climate change, land depletion, and environmental pollution, threatening global food security [1]. This necessitates the developmen...
THE development of agriculture faces significant challenges due to population growth, climate change, land depletion, and environmental pollution, threatening global food security [1]. This necessitates the development of sustainable agriculture, where a fundamental step is crop breeding to improve agronomic or economic traits, e.g., increasing yields of crops while decreasing resource usage and minimizing pollution to the environment [2].
A broadband tunable instantaneous frequency measurement(IFM)system is designed based on the stimulated Brillouin scattering effect of the highly nonlinear fiber in which the carrier suppressed single sideband modulate...
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A broadband tunable instantaneous frequency measurement(IFM)system is designed based on the stimulated Brillouin scattering effect of the highly nonlinear fiber in which the carrier suppressed single sideband modulated signal of the Brillouin frequency shift acts as pump *** amplitude comparison function(ACF)is constructed by the power radio of the two paths in the *** frequency measurement range and measurement accuracy can be tuned by changing the frequency difference of the two phase modulation *** tunable frequency measurement ranges of 2—5 GHz,2—10 GHz,2—15 GHz,2—20 GHz,and 2—24 GHz are realized,and the corresponding measurement accuracies are 3.64 d B/GHz,2.17 d B/GHz,1.87 d B/GHz,1.22 d B/GHz,and 0.77 d B/GHz,respectively.
In recent years, automated program repair (APR) has been conducted to reduce the cost of debugging. When performing APR, it is necessary to perform fault localization (FL) to identify the location of bugs. A challenge...
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This study explores the application of single photon detection(SPD)technology in underwater wireless optical communication(UWOC)and analyzes the influence of different modulation modes and error correction coding type...
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This study explores the application of single photon detection(SPD)technology in underwater wireless optical communication(UWOC)and analyzes the influence of different modulation modes and error correction coding types on communication *** study investigates the impact of on-off keying(OOK)and 2-pulse-position modulation(2-PPM)on the bit error rate(BER)in single-channel intensity and polarization ***,it compares the error correction performance of low-density parity check(LDPC)and Reed-Solomon(RS)codes across different error correction coding *** effects of unscattered photon ratio and depolarization ratio on BER are also ***,a UWOC system based on SPD is constructed,achieving 14.58 Mbps with polarization OOK multiplexing modulation and 4.37 Mbps with polarization 2-PPM multiplexing modulation using LDPC code error correction.
The high growth rate of melanoma poses a huge challenge to healthcare delivery worldwide. At this stage, rapid and accurate diagnosis and timely treatment of melanoma is crucial. In this study, we utilized multiple me...
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Preserving privacy has become crucial in the field of location-based services (LBS) to leverage their full potential while protecting users from untrustworthy LBS providers. Given that LBS users typically use resource...
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The multitude of airborne point clouds limits the point cloud processing *** are grouped based on similar points,which can effectively alleviate the demand for computing resources and improve processing ***,existing s...
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The multitude of airborne point clouds limits the point cloud processing *** are grouped based on similar points,which can effectively alleviate the demand for computing resources and improve processing ***,existing superpoint segmentation methods focus only on local geometric structures,resulting in inconsistent spectral features of points within a *** feature inconsistencies degrade the performance of subsequent ***,this study proposes a novel Superpoint Segmentation method that jointly utilizes spatial Geometric and Spectral information for multispectral point cloud superpoint segmentation(GSI-SS).Specifically,a similarity metric that combines spatial geometry and spectral information is proposed to facilitate the consistency of geometric structures and object attributes within segmented *** the formation of the primary superpoints,an intersuperpoint pointexchange mechanism that maximizes feature consistency within the final superpoints is *** are conducted on two real multispectral point cloud datasets,and the proposed method achieved higher recall,precision,F score,and lower global consistency and feature classification *** experimental results demonstrate the superiority of the proposed GSI-SS over several state-of-the-art methods.
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