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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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Dual Averaging for Distributed Unbalanced Optimization with Delayed Information

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IEEE Transactions on signal and Information processing over Networks 2025年 11卷 366-377页

作者： Huang, Qing Fan, Yuan Cheng, Songsong Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Anhui Hefei230601 China

In this paper, we study a category of distributed constrained optimization problems where each agent has access to local information, communicates with its neighbors, and cooperatively minimizes the aggregated cost functions over time-varying unbalanced graphs. To address the considered problems, we propose a distributed dual averaging algorithm based on a row-stochastic weighted matrix (DDAR), which improves the robustness of network topology compared to conventional push-sum algorithms. Moreover, we develop a modified version of DDAR with delayed information (DDARD), which considers the delays of both network communication and gradient calculation, enhancing the algorithm's flexibility in communication and iteration. Our analysis demonstrates that the DDAR and DDARD achieve the optimal value at rates of O(N/(1-λ)√T) and O(τ2N/(1-λ}})√T}), respectively. Finally, the theoretical results are confirmed by simulation on a logistic regression problem. © 2015 IEEE.

关键词： Cost functions

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DMFVAE:miRNA-disease associations prediction based on deep matrix factorization method with variational autoencoder

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Frontiers of Computer Science 2024年第6期18卷 259-270页

作者： Pijing WEI Qianqian WANG Zhen GAO Ruifen CAO Chunhou ZHENG Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230601China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial IntelligenceAnhui UniversityHefei 230601China

MicroRNAs(miRNAs)are closely related to numerous complex human diseases,therefore,exploring miRNA-disease associations(MDAs)can help people gain a better understanding of complex disease *** increasing number of computational methods have been developed to predict ***,the sparsity of the MDAs may hinder the performance of many *** addition,many methods fail to capture the nonlinear relationships of miRNA-disease network and inadequately leverage the features of network and neighbor *** this study,we propose a deep matrix factorization model with variational autoencoder(DMFVAE)to predict potential *** first decomposes the original association matrix and the enhanced association matrix,in which the enhanced association matrix is enhanced by self-adjusting the nearest neighbor method,to obtain sparse vectors and dense vectors,***,the variational encoder is employed to obtain the nonlinear latent vectors of miRNA and disease for the sparse vectors,and meanwhile,node2vec is used to obtain the network structure embedding vectors of miRNA and disease for the dense ***,sample features are acquired by combining the latent vectors and network structure embedding vectors,and the final prediction is implemented by convolutional neural network with channel *** evaluate the performance of DMFVAE,we conduct five-fold cross validation on the HMDD v2.0 and HMDD v3.2 datasets and the results show that DMFVAE performs ***,case studies on lung neoplasms,colon neoplasms,and esophageal neoplasms confirm the ability of DMFVAE in identifying potential miRNAs for human diseases.

关键词： miRNA-disease association deep matrix factorization self-adjusted nearest neighbor variational encoder network structure

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Dependable Pipeline Detection algorithms for GPRs in Obscuration Environments 7

Dependable Pipeline Detection algorithms for GPRs in Obscura...

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7th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2024

作者： Zhou, Hongyu Xu, Guanghui Li, Yingsong Huang, ZhiXiang Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (纸本)9798350384437

We propose a robust pipeline detection algorithm for obscuration environments, which includes two improvements: a pre-processing method called the Regional Adaptive Thresh-olding Algorithm (RATA) and a novel clustering method based on trend analysis and symmetric matching called as Symmetry Matching Clustering Algorithm (SMCA). This approach is highly effective in extracting targets from low-intensity regions and has been validated through simulation experiments. The gotten results demonstrate significant enhancements in the accuracy and reliability of underground pipeline detection in obscuration environments. Comparative analysis shows that, in some aspects, the proposed method outperforms existing algorithms. © 2024 IEEE.

关键词： Image enhancement

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Kernel Weibull M-Transform Least Mean Square Algorithm 12

Kernel Weibull M-Transform Least Mean Square Algorithm

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12th IEEE International Conference on Computer Science and Network Technology, ICCSNT 2024

作者： Bi, Chenchong Miao, Yongchun Li, Liping Li, Yingsong Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (纸本)9798350350791

The paper introduces a Kernel Weibull M-Transform Least-Mean Square (LMS) (KWMLMS) algorithm aimed to enhance filtering performance for a nonlinear system. By incorporating the Weibull M-Transformation into the cost function of the Kernel LMS (KLMS) algorithm, a weighting updated rule is gotten. Simulation experiments conducted for nonlinear-system identification under impulse noise conditions demonstrate that the created KWMLMS algorithm achieves significantly faster convergence compared to existing methods, indicating its superiority in dealing with impulsive-noises. © 2024 IEEE.

关键词： Wiener filtering

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A C-band quad-cluster low-profile broadband circularly polarized antenna array based on sequentially rotating metasurface-based

A C-band quad-cluster low-profile broadband circularly polar...

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2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024

作者： Bai, Rongxian Li, Minquan Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Hefei230601 China

ISBN: (纸本)9798350355581

This paper proposes a metasurface-based left-handed circularly polarized (CP) sequential rotating metasurface-based (MTS) antenna array for C-band. The array comprises a cluster of 4 × 4 periodically aligned MTS cells, which exhibit excellent performance at a low profile of 0.08 λ0. The antenna array is constructed on a Rogers RO4003 dielectric board in two layers. The upper surface of the upper layer is the MTS, the floor has a gap between the upper and lower layers, and the lower surface of the lower layer is the sequential feed network. The simulation conducted in HFSS software yielded an integrated bandwidth (IBW) of 95% (3.79-8.54 GHz) and an antenna radiation bandwidth (ARBW) of 49.8% (3.77-6.26 GHz), respectively. The dimensions of the antenna are 1.33λ0 × 1.33λ0 × 0.08λ0. This configuration results in a miniaturized, compact, and low-profile antenna array. This antenna configuration is applicable to satellite communications using circularly polarized waves through MTS units. © 2024 IEEE.

关键词： Circular polarization

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The Dual-3DCRU Model Based on Hemisphere Asymmetry for EEG Emotion Recognition 5

The Dual-3DCRU Model Based on Hemisphere Asymmetry for EEG E...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Dong, Hao Zhou, Jian Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui Hefei China

ISBN: (纸本)9798350385557

In this paper, we propose an innovative multi scale model called Dual-3DCRU, designed to extract more discriminative feature from both the left and right hemispheres. Byleveraging spatial relationships of electrode locations and the distinct frequency bands present in EEG signals from brain two hemispheres regions, we reconstructed comprehensive three-dimensional frequency space fusion feature representation. The Dual-3DCRU utilizes two different multi scale 3D -CNN to effectively capture both frequency and spatial information embedded in the 3D feature representations. Furthermore, we integrate a Gated Recursive Unit (GRU) to model the temporal dynamics across continuous time periods and extract critical temporal information. Ultimately, the Dual-3DGRU model delivers a holistic feature representation that incorporates time, frequency, and spatial information seamlessly. Our experiments showcase that the Dual-3DCRU model attains exceptional performance on DEAP dataset. Specifically, the accuracy in the Valence dimensionis 94.35%, F1-score is 93.24%, accuracy in the Arousal dimensionis 95.04%, and F1-score is 94.17%. © 2024 IEEE.

关键词： Emotion Recognition

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A compact, metasurface-based wideband circularly polarized patch antenna with L-shaped AR tuning stubs

A compact, metasurface-based wideband circularly polarized p...

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2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024

作者： Yuan, Yongkang Li, Minquan Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Hefei230601 China

ISBN: (纸本)9798350355581

A compact, wideband circularly polarized (CP) patch antenna based on the metasurface (MS) is presented. The antenna composed of a truncated corner square patch with four L-shaped axial ratio (AR) tuning stubs is sandwiched between the metasurface (MS) and the ground plane. The MS is composed of a lattice of 4 × 4 periodic metal plates. When surface waves on the MS are stimulated within the antenna structure, it will broaden the impedance bandwidth and AR bandwidth of the antenna. The proposed antenna adopts a stacked layout with an overall size of 40mm × 40mm × 4.5mm (0.71λ0 × 0.71λ0 × 0.08λ0, at 5.3 GHz). The simulation results show a |S11| © 2024 IEEE.

关键词： Slot antennas

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A SIR dual-band microstrip filter loaded with shaped branches

A SIR dual-band microstrip filter loaded with shaped branche...

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2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024

作者： Lv, Zhiyan Song, Xiaodi Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Hefei230601 China

ISBN: (纸本)9798350355581

In this paper, a SIR-coupled dual-band bandpass filter is proposed, which has lower insertion loss, better out-of-band rejection. The bandpass characteristics of the dual-band are obtained by bending the SIR and coupling it, and loading the shaped branches. By changing the shape of the shaped branches, a transmission pole is added in the first passband, which reduces the insertion loss of the filter and improves the rejection effect of the stopband. The simulation results show that the dual-frequency bandpass filter has a center frequency of 6.26GHz, an insertion loss of 0.17dB, a return loss of less than -27.8dB, and a relative bandwidth of 15.5% for the low band, and an insertion loss of 0.67dB, a return loss of less than -15.8dB, and a relative bandwidth of 11.6% for the high band, at a center frequency of 9.26GHz. © 2024 IEEE.

关键词： Microstrip filters

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Classification of Cervical Images Based On Deep Neural Network With Attention Mechanism 6

Classification of Cervical Images Based On Deep Neural Netwo...

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6th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2023

作者： Feng, Yun Chen, Lan Zhang, Xia Li, Yingsong Li, Liping Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (纸本)9798350399059

In this paper, we propose a DenseNet-CBAM model that utilizes DenseNet121 as the backbone network for feature extraction. The features are then weighted using the Convolutional Block Attention Module (CBAM), which incorporates both channel and spatial attention mechanisms. The features are further processed with ReLU activation and adaptive average pooling operations. Finally, a linear classifier is applied to output the final category predictions. Experiment results show that the proposed model performs the best in terms of the ability in detecting patients with high-grade squamous intraepithelial lesions (HSIL). © 2023 IEEE.

关键词： Image classification

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