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Fast Frequency Sweep Analysis of a Dielectric Rough Surface by Maehly Approximation

IEEE Journal on Multiscale and Multiphysics Computational Techniques 2017年 2卷 224-227页

作者： Bao, Ran Wang, Anqi Huang, Zhixiang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China

An efficient method for simulating electromagnetic scattering from a dielectric rough surface over a frequency band is proposed. The method is based on the Chebyshev series and the Maehly approximation. The tapered plane wave is chosen as the incident wave to avoid the artificial edge diffraction caused by the finite length of the simulated rough surface. The Gaussian rough surface is considered. Good agreement between the Maehly approximation and the exact results obtained by the method of moments (MoM) is found. The numerical results show that the Maehly approximation is superior to the MoM in terms of the CPU time required. © 2017 IEEE.

关键词： Method of moments

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Social Routing Based on Location Preference Prediction in DTN 11

Social Routing Based on Location Preference Prediction in DT...

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2017 11th IEEE International Conference on Anti-counterfeiting,Security,and Identification(ASID)

作者： GuiZhu Wang LuJie Yan Lei Zheng Jian Tao Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University

ISBN: (纸本)9781538605349;9781538605332

In modern society, with the pad, smart phones and other wireless devices have a large number of wireless communication interface, people carry these devices that act as network nodes, the random movement of such a network has many characteristics of social networks. The social DTN also has many human social characteristics, such as high predictability and high similarity, so in this paper, we focus on effective routing among different regions. As for the real world, we can adjust the Spray strategy according to the regional prediction and the similarity of the encounter nodes to reduce the noneffective duplicate and decrease the consumption of networks *** results show that compared with spray and wait routing, this algorithm could not only improve the delivery rate but also significantly reduce the average latency of network.

关键词： DTN spray and wait routing social network location preference prediction

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Probabilistic Routing Algorithm Based on Transmission Capability of Nodes in DTN 11

Probabilistic Routing Algorithm Based on Transmission Capabi...

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2017 11th IEEE International Conference on Anti-counterfeiting,Security,and Identification(ASID)

作者： Guizhu Wang Lei Zheng Lujie Yan Hui Zhang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University

ISBN: (纸本)9781538605349;9781538605332

Delay Tolerant Network(DTN) is a network of intermittent connectivity, and for most of the time there is no end-to-end full path between nodes. This is a huge challenge for the efficient transmission of packets from the source to the destination node. In order to solve this problem, we combine the actually situation of the network, consider the difference of nodes,and propose the concept of node transmission *** a reasonable assessment of node transmission capacity and combined with the traditional probabilistic routing, this paper proposes a probabilistic routing algorithm based on the node transmission capability(PROPHET-TC), which makes the selection of the next hop node more reasonable and efficient, and compensate the deficiency of the traditional probabilistic routing in selecting the next hop node. Simulation results show that compared with Epidemic routing and probabilistic routing, the proposed algorithm can improve the delivery rate and reduce the overhead of the network.

关键词： Delay Tolerant Networks Node difference Probabilistic routing Transmission capacity

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Singular Value Decomposition Compressed Ghost Imaging Based on Non-negative Constraints 11

Singular Value Decomposition Compressed Ghost Imaging Based ...

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2019第十一届数字图像处理国际会议

作者： Cheng Zhang Jun Tang Yuanyuan Zhu Meiqin Wang Qianwen Chen Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University

ISBN: (数字)9781510630765

ISBN: (纸本)9781510630758

Compressed ghost imaging can effectively enhance the quality of original image from far fewer measurements,but due to the non-negativity of the measurement matrix,the recover quality is thus *** this paper,singular value decomposition compressed ghost imaging is proposed;First,the singular value decomposition be used to decompose the measurement matrix,and then the optimized measurement matrix and measurements are used to recover the original *** experiments verify the superiority of our proposed singular value decomposition compression ghost imaging method.

关键词： ghost imaging compressed ghost imaging singular value decomposition compressed ghost imaging

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Degradation-Trend-Aware Deep Neural Network with Attention Mechanism for Bearing Remaining Useful Life Prediction

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2997-3011页

作者： Liu, Yongkang Pan, Donghui Zhang, Haifeng Zhong, Kai Anhui University Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education School of Mathematical Sciences Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China

Remaining useful life (RUL) prediction of bearings has extraordinary significance for prognostics and health management (PHM) of rotating machinery. RUL prediction approaches based on deep learning have been dedicated to finding a nonlinear mapping relationship between nonstationary monitoring data and RUL. However, most existing approaches pay little attention to the degradation trend of diverse health stages of bearing and lack the discriminative power of crucial degradation features, resulting in the loss of some important information associated with RUL. To address this challenge, this article proposes a novel RUL prediction framework based on degradation-trend-aware deep neural network with attention mechanism (DTADAN). First the multidirection features with evident degradation trend are extracted via the analysis of bearing vibration signal from both time-domain and time-frequency domain. Next, the deep neural network architecture with attention mechanism is utilized to adaptively learn the critical degradation features beneficial for RUL prediction. Distinct from the existing approaches, the proposed framework is able to dynamically extract key degradation features of the bearing including degradation trend information and effectively fuse multidirection information to improve RUL prediction accuracy. The performance of the proposed approach is evaluated via case studies on XJTU-SY bearing dataset and PRONOSTIA bearing dataset. Compared with other state-of-the-art approaches, the proposed framework has better predictive accuracy and robustness. Additionally, interpretable analysis is provided to reveal the process of model learning and data characteristics, and the analysis results are helpful in guiding model learning. © 2020 IEEE.

关键词： Deep neural networks

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Multilevel Characteristic Basis Function Method with ACA for Accelerated Solution of Electrically Large Scattering Problems

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Transactions of Nanjing University of Aeronautics and Astronautics 2018年第3期35卷 449-454页

作者： Li Chenlu Sun Yufa Wang Zhonggen Wang Guohua Key Laboratory of Intelligent Computing & Signal Processing Ministry of EducationAnhui University College of Electrical and Information Engineering Anhui University of Science and Technology

The multilevel characteristic basis function method(MLCBFM)with the adaptive cross approximation(ACA)algorithm for accelerated solution of electrically large scattering problems is studied in this *** the conventional MLCBFM based on Foldy-Lax multiple scattering equations,the improvement is only made in the generation of characteristic basis functions(CBFs).However,it does not provide a change in impedance matrix filling and reducing matrix calculation procedure,which is *** reality,all the impedance and reduced matrix of each level of the MLCBFM have low-rank property and can be calculated ***,ACA is used for the efficient generation of two-level CBFs and the fast calculation of reduced matrix in this *** results are given to demonstrate the accuracy and efficiency of the method.

关键词： multilevel characteristic basis function method(MLCBFM) adaptive cross approximation(ACA) characteristic basis functions(CBFs) electromagnetic scattering

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Optimal Ternary Cubic Two-Weight Codes

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Chinese Journal of Electronics 2018年第4期27卷 734-738页

作者： SHI Minjia HUANG Daitao SOL Patrick Key Laboratory of Intelligent Computing & Signal Processing Ministry of EducationAnhui University School of Mathematical Sciences Anhui University CNRS/LAGA University of Paris 8

We study trace codes with defining set L,a subgroup of the multiplicative group of an extension of degree m of a certain ring of order 27. These codes are abelian, and their ternary images are quasi-cyclic of coindex three(a.k.a. cubic codes). Their Lee weight distributions are computed by using Gauss sums. These codes have three nonzero weights when m is singly-even. When m is odd, under some hypothesises on the size of L, we obtain two new infinite families of two-weight codes which are optimal. Applications of the image codes to secret sharing schemes are also given.

关键词： Three-weight codes Two-weight codes Gauss sums Trace codes

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HyFit: Hybrid Fine-Tuning With Diverse Sampling for Abstractive Summarization

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IEEE Transactions on Big Data 2024年第03期11卷 1-12页

作者： Zhao, Shu Cheng, Yuanfang Zhang, Yanping Chen, Jie Duan, Zhen Sun, Yang Wang, Xinyuan Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei Anhui P.R.China

Abstractive summarization has made significant progress in recent years, which aims to generate a concise and coherent summary that contains the most important facts from the source document. Current fine-tuning approaches based on pre-training models typically rely on autoregressive and maximum likelihood estimation, which may result in inconsistent historical distributions generated during the training and inference stages, i.e., exposure bias problem. To alleviate this problem, we propose a hybrid fine-tuning model(HyFit), which combines contrastive learning and reinforcement learning in a diverse sampling space. Firstly, we introduce reparameterization and probability-based sampling methods to generate a set of summary candidates called candidates bank, which improves the diversity and quality of the decoding sampling space and incorporates the potential for uncertainty. Secondly, hybrid fine-tuning with sampled candidates bank, upweighting confident summaries and downweighting unconfident ones. Experiments demonstrate that HyFit significantly outperforms the state-of-the-art models on SAMSum and DialogSum. HyFit also shows good performance on low-resource summarization, on DialogSum dataset, using only approximate 8% of the examples exceed the performance of the base model trained on all examples. IEEE

关键词： Reinforcement learning

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Fusion of multi-spectral image and panchromatic image based on support vector regression

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Journal of Beijing Institute of Technology 2012年第2期21卷 269-277页

作者：胡根生梁栋 Key Laboratory of Intelligent Computing and Signal Processing Ministry of EducationAnhui University School of Electronics and Information Engineering Anhui University

In our study, support vector value contourlet transform is constructed by using support vector regression model and directional filter banks. The transform is then used to decompose source images at multi-scale, multi-direction and multi-resolution. After that, the super-resolved multi-spectral image is reconstructed by utilizing the strong learning ability of support vector regression and the correlation between multi-spectral image and panchromatic image. Finally, the super-resolved multi- spectral image and the panchromatic image are fused based on regions at different levels. Our experi- ments show that, the learning method based on support vector regression can improve the effect of super-resolution of multi-spectral image. The fused image preserves both high space resolution and spectrum information of multi-spectral image.

关键词： image processing image fusion support vector regression super-resolution

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An improved entity similarity measurement method

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Telkomnika (Telecommunication computing Electronics and Control) 2014年第4期12卷 1017-1022页

作者： Lv, Gang Zheng, Cheng Chen, Sheng-Bing Key of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei Anhui China Key Laboratory of Network and Intelligent Information Processing Hefei University Hefei Anhui China

To facilitate the integration of learning resources categorized under different ontology representations, the techniques of ontology mapping can be applied. Though many algorithms and systems have been proposed for ontology mapping, they do not have an automatic weighting strategy on class features to automate the ontology mapping process. A novel method of computing the feature weights is proposed. By feature semantic analysis, the different entities similarity calculation model and weight calculation model were defined. The results show that it makes the ontology mapping process more automatic while retaining satisfying accuracy. Improve ontology mapping effectiveness.

关键词： Semantics

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