作者机构：
[He, Xuyan; Liu, Changhua] School of Mathematics and Computer Science, Wuhan Polytechnic University, Hubei, Wuhan, China;[Guan, Wenjie] Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Hubei, Wuhan, China
会议名称：
6th International Conference on Computer Science and Application Engineering, CSAE 2022
期刊：
INDIANA UNIVERSITY MATHEMATICS JOURNAL,2022年71(2):463-508 ISSN：0022-2518
作者机构：
[Fan, Lili] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.;[Ruan, Lizhi] Cent China Normal Univ, Sch Math & Stat, POB 71010, Wuhan 430079, Peoples R China.;[Ruan, Lizhi] Cent China Normal Univ, Hubei Key Lab Math Sci, POB 71010, Wuhan 430079, Peoples R China.;[Xiang, Wei] City Univ Hong Kong, Dept Math, Kowloon, Hong Kong, Peoples R China.
摘要：
Urban sound event detection can automatically preload relevant information for a robot to ensure that it can be applied to various scene-activity tasks. To address the limitations of timbre similarity and scene recognition by audio collection devices, a fusion model based on the self-attention mechanism is proposed in this paper. The model consists of scattering transform and self-attention model. The scattering transform computes modulation spectrum coefficients of multiple orders through cascades of wavelet convolutions and modulus operators. It is learnable compared with Mel-scale Frequency Cepstral Coefficients (MFCC), and can be used to better restore the semantic features of some sound scenes with similar timbres. The transformer has an outstanding effect on Natural Language Processing (NLP) owing to its self-attention mechanism. In this paper, the self-attention mechanism in its encoder was used in the model, mainly to make the feature granularity consistent to refine the features. In addition, Focal Loss function was adopted in the model to curb the sample distribution imbalance. The Google Command and ESC-50 were used to supplement the scene categories of dataset UrbanSound8K. The model parameters of the learnable filters that performed well on the dataset UrbanSound8K were preserved to fine-tune the other two datasets with insufficient data volume and more target categories. The length of slice duration was further explored the in the model. The experimental results show that the model can achieve better performance in a large range of scene models.
作者机构：
[Feng, Yun] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.;[Lin, Wensong] Southeast Univ, Sch Math, Nanjing 210096, Peoples R China.
通讯机构：
[Yun Feng] S;School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China<&wdkj&>Author to whom correspondence should be addressed.
期刊：
Cryptography and Communications,2022年14(1):145-159 ISSN：1936-2447
通讯作者：
Luo, Jinquan(luojinquan@mail.ccnu.edu.cn)
作者机构：
[Fang, Xiaolei] School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, 430048, China;[Jin, Renjie] College of Liberal Arts and Sciences, National University of Defense Technology, Changsha, 410073, China;[Ma, Wen; Luo, Jinquan] School of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Wuhan, 430079, China
通讯机构：
[Jinquan Luo] S;School of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Wuhan, China
作者机构：
[Wang, Zemin; An, Jiachun; Ma, Yuanyuan; Li, Fei; Liu, Shunlun; Li, Bing] Wuhan Univ, Chinese Antarctic Ctr Surveying & Mapping, Wuhan 430079, Peoples R China.;[Ma, Yuanyuan; Li, Fei] Wuhan Univ, Elect Informat Sch, Wuhan 430072, Peoples R China.;[Wang, Zemin; An, Jiachun; Ma, Yuanyuan; Li, Fei; Liu, Shunlun] MNR, Key Lab Polar Surveying & Mapping, Wuhan 430079, Peoples R China.;[Li, Bing] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430048, Peoples R China.;[Ma, Weifeng] Yunnan Normal Univ, Fac Geog, Kunming 650050, Yunnan, Peoples R China.
通讯机构：
[Wang, ZM ] W;Wuhan Univ, Chinese Antarctic Ctr Surveying & Mapping, Wuhan 430079, Peoples R China.;MNR, Key Lab Polar Surveying & Mapping, Wuhan 430079, Peoples R China.
关键词：
glacier mass balance;SAR interferometry;ice motion;ionospheric effects;reformulation of the split-spectrum method;ionospheric correction
摘要：
Ice motion is an essential element for accurately evaluating glacier mass balance. Interferometric synthetic aperture radar (InSAR) has been widely applied for monitoring ice motion with high precision and wide coverage in the Antarctic. However, the ionospheric effects can significantly impact InSAR-based ice-motion measurements. At low radar frequencies in particular, the ionospheric effects have been regarded as a serious source of noise in L-band SAR data. The split-spectrum method (SSM) is commonly used for correcting the ionospheric effects of the InSAR technique. However, it requires spatial filtering with the relatively large factors used to scale the sub-bands' interferograms, which often results in an unwrapped phase error. In this paper, a reformulation of the split-spectrum method (RSSM) is introduced to correct the ionospheric effects in the Grove Mountains of East Antarctica, which have slow ice flow and frequent ionosphere changes. The results show that RSSM can effectively correct the ionospheric effects of InSAR-based ice-motion measurements. To evaluate the ability of ionospheric correction using RSSM, the result of ionospheric correction derived from SSM is compared with the results of RSSM. In addition, ionosphere-corrected ice motion is also compared with GPS and MEaSUREs. The results show that the ionosphere-corrected ice velocities are in good agreement with GPS observations and MEaSUREs. The average ice velocity from the InSAR time series is compared to that from MEaSUREs, and the average ionosphere-corrected ice velocity error reduces 43.9% in SSM and 51.1% in RSSM, respectively. The ionosphere-corrected ice velocity error is the most significant, reducing 86.9% in SSM and 90.4% in RSSM from 1 November 2007 to 19 December 2007. The results show that the ability of RSSM to correct ionospheric effects is slightly better than that of SSM. Therefore, we deduce that the RSSM offers a feasible way to correct ionospheric effects in InSAR-based ice-motion measurements in Antarctica.
通讯机构：
[Shan Zeng] S;School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China<&wdkj&>Author to whom correspondence should be addressed.
关键词：
deep learning;hyperspectral image classification;attention mechanism;feature fusion;3D CNN
摘要：
With the continuous development of hyperspectral image technology and deep learning methods in recent years, an increasing number of hyperspectral image classification models have been proposed. However, due to the numerous spectral dimensions of hyperspectral images, most classification models suffer from issues such as breaking spectral continuity and poor learning of spectral information. In this paper, we propose a new classification model called the enhanced spectral fusion network (ESFNet), which contains two parts: an optimized multi-scale fused spectral attention module (FsSE) and a 3D convolutional neural network (3D CNN) based on the fusion of different spectral strides (SSFCNN). Specifically, after sampling the hyperspectral images, our model first implements the weighting of the spectral information through the FsSE module to obtain spectral data with a higher degree of information richness. Then, the weighted spectral data are fed into the SSFCNN to realize the effective learning of spectral features. The new model can maximize the retention of spectral continuity and enhance the spectral information while being able to better utilize the enhanced information to improve the model’s ability to learn hyperspectral image features, thus improving the classification accuracy of the model. Experiment results on the Indian Pines and Pavia University datasets demonstrated that our method outperforms other relevant baselines in terms of classification accuracy and generalization performance.
作者机构：
[Wang, Li; Huang, Wentao] East China Jiaotong Univ, Sch Basic Sci, Nanchang, Jiangxi, Peoples R China.;[Wang, Qingfang] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan, Peoples R China.
通讯机构：
[Wang, Qingfang] W;Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan, Peoples R China.
关键词：
35J20;35J62;58E05;invariant sets of descending flow;Quasilinear Schrödinger equation;sign-changing solutions
摘要：
This paper is motivated by the study of the following quasilinear Schrodinger equation - Delta u + V(x)u - [ Delta(1 + u(2))(1/2)] u/2(1 + u(2))(1/2) = lambda h(u), x is an element of R-N, where N >= 3, lambda > 0 is a parameter and V(x) is a given positive potential. As an example, the nonlinearity includes the pure power type of h(u) = vertical bar u vertical bar(p-2)u for the well-studied case 12 - 4 root 6 < p < 2*, and the case 2 < p < 12 - 4 root 6 in which few existence results are known. Distinguishing from the existing results in the literature, we are more interested in the existence and multiplicity of sign-changing solutions for the above problem.
摘要：
In recent years, various deep learning based methods have been successfully developed for change detection, such as Convolutional Neural Network (CNN) based U-Net and its variants, and Transformer based ones. However, CNNs lack the ability to effectively learn global representations, while Transformers neglect to learn local representations. Therefore, in this paper we propose a novel deep network, namely Multi-scale Attention based Transformer U-Net (MATU), to take advantages of CNNs and Transformers for learning both local and global features effectively. The backbone of our proposed MATU is a U-Net. In the encoder, a Siamese network is used to extract features from two input images, which is followed by a transformer module to further refine the feature pairs produced by the Siamese network. The difference of the refined feature pairs is fed into an Atrous Spatial Pyramid Pooling (ASSP) module to generate a distance map. Moreover, axial-attention blocks are integrated in the decoder with the corresponding multi-level feature differences of the encoder to progressively produce and improve the change map through attention upsampling. Extensive experiments on two widely used benchmark datasets SYSU-CD and LEVIR-CD demonstrate that the proposed MATU method achieves the state-of-the-art performance. Our code is available at https://github.com/easm002/MATU.
期刊：
Nonlinear Analysis: Real World Applications,2022年63:103411 ISSN：1468-1218
通讯作者：
Hou, Meichen
作者机构：
[Hou, Meichen] Northwest Univ, Sch Math, Xian 710069, Peoples R China.;[Hou, Meichen] Northwest Univ, CNS, Xian 710069, Peoples R China.;[Fan, Lili] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.
通讯机构：
[Hou, Meichen] N;Northwest Univ, Sch Math, Xian 710069, Peoples R China.;Northwest Univ, CNS, Xian 710069, Peoples R China.
关键词：
Inflow problem;Non-viscous;Contact wave
摘要：
This paper is devoted to studying the inflow problem governed by the non-viscous and heat-conductive gas dynamic system in the one-dimensional half space. We establish the unique global-in-time existence and the asymptotic stability of the viscous contact wave. The contact discontinuity in the linearly degenerate field is less stable, and the dissipative mechanism for non-viscous systems is also weaker than that of viscous systems, these all make the problem more challenging. We used the weighted energy estimates to overcome those difficulties. Some technical discussions were created carefully by taking good advantage of properties of the supersonic region and the viscous contact wave. (C) 2021 Elsevier Ltd. All rights reserved.
