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AASRI International Conference on Applied Engineering Science, ICAES 2014

作者： Peng, M. Li, M.Q. Rong, B. Ding, Y. Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (纸本)9781138026490

In this paper, a novel design of theWideband Bandstop Filter, based on Split Ring Resonators, (SRR) is proposed. The Defect Ground Structure (DGS) is achieved by opening the SRR-shaped slot in the ground plane, and the metal cylinder is added between micro-strip line and the ground. With the above two methods, the wideband bandstop characteristics can be realized. This proposed structure is simulated by the high frequency simulation software HFSS 13.0, and the effect on the performance of a wideband Band-stop Filter by adding a metal column is studied. The design of Band-stop Filter structure with three cascaded SRR-shaped DGS units is proposed. Both the simulated and measured results show that the attenuation at the resonance frequency center reaches −55 dB, and its relative bandwidth less than −10 dB is extended to 42% ranging from 5.6 GHz to 8.6 GHz. Moreover, the insertion loss of passband is limited to 1.2 dB. © 2015 Taylor & Francis Group, London.

关键词： Notch filters

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Design of Microstrip Type Broadband Directional Coupler

Design of Microstrip Type Broadband Directional Coupler

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023

作者： Yan, Hang Wu, Xianliang Wang, Ruikang Wang, Gang Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China

ISBN: (纸本)9781733509657

A microstrip line broadband directional coupler is designed based on microstrip line theory. The bandwidth of the coupler is extended by using a cascade of multiple coupled lines. The isolation of the directional coupler is greatly improved by covering the microstrip line with a dielectric layer [1] and by loading the branching compensation capacitor to compensate for the odd-even mode phase velocity difference. Simulation results of ANSYS HFSS 2019 show that the return loss (S11) is greater than 20 dB, the insertion loss (S21) is less than 1.7 dB, the coupling degree (S31) is about -6dB(±0.8dB), the isolation degree (S41) is greater than 23.4dB, and the minimum directionality is greater than 17dB during 700 MHz-3700 MHz. The physical size of coupler is 18 mm × 60mm, which meets the design requirements of 6dB coupler. © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Directional couplers

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Recognition algorithm for plant leaves based on adaptive supervised locally linear embedding

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International Journal of Agricultural and Biological Engineering 2013年第3期6卷 52-57页

作者： Yan Qing Liang Dong Zhang Dongyan Wang Xiu Key Laboratory of Intelligent Computing&Signal Processing Ministry of EducationAnhui UniversityHefei 230039China Beijing Research Center for Information Technology in Agriculture Beijing 100097China

Locally linear embedding(LLE)algorithm has a distinct deficiency in practical *** requires users to select the neighborhood parameter,k,which denotes the number of nearest neighbors.A new adaptive method is presented based on supervised LLE in this article.A similarity measure is formed by utilizing the Fisher projection distance,and then it is used as a threshold to select *** samples will produce different k adaptively according to the density of the data *** method is applied to classify plant *** experimental results show that the average classification rate of this new method is up to 92.4%,which is much better than the results from the traditional LLE and supervised LLE.

关键词： supervised locally linear embedding manifold learning Fisher projection adaptive neighbors leaf recognition Precision Agriculture

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Feature-Consistent Cyclic Domain Adversarial Network for Unsupervised Transfer Diagnosis of the Autoimmune Disease Igg4-Rd by Raman Spectroscopy

SSRN

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SSRN 2025年

作者： Zhang, Ziyang Chen, Cheng Qin, Wentao Wu, Xue Liu, Yang Wu, Lijun Lv, Xiaoyi Chang, Chenjie Chen, Xinya Chen, Zhiyuan Xu, Qianxi Li, Zhuoyu Ma, Mengsi Chen, Chen Xinjiang University College of Software Urumqi830046 China Xinjiang Cloud Computing Application Laboratory Xinjiang University Karamay834099 China School of Computer Science and Technology Xinjiang University Urumqi830046 China Key Laboratory of signal detection and processing Xinjiang University Urumqi830046 China Department of Rheumatology＆Immunology People's Hospital of Xinjiang Uygur Autonomous Region Xinjiang Urumqi China Xinjiang Clinical Research Center for Rheumatoid arthritis Xinjiang Urumqi China

Abstracts IgG4-related disease (IgG4-RD) is a multi-organ immune disorder characterized by systemic involvement, diverse pathogenesis, and rarity, which complicates its diagnosis. Traditional medical diagnostic models, predominantly based on supervised learning in machine learning or deep learning frameworks, struggle to address the scarcity of labeled data due to the diagnostic challenges of IgG4-RD. To overcome this limitation, this study proposes a novel framework based on Feature-Consistent Cyclic Domain Adversarial Networks (FCCDAN). The framework first trains a classification model on the source domain for diagnosing source domain diseases under supervised conditions. Subsequently, leveraging the source domain model, the encoder is guided to extract semantic features, while feature consistency loss is integrated into both the generator and discriminator to align the semantic features of the target domain with those of the source domain. Finally, the aligned features are processed through a pre-trained linear mapping to achieve unsupervised classification and diagnosis in the target domain. The proposed model utilizes common autoimmune diseases as a knowledge source, enabling unsupervised transfer diagnosis for IgG4-RD, and demonstrates generalizability through experiments on other disease datasets, achieving an average diagnostic accuracy of 90.62%. This represents improvements of 4.14%, 9.82%, and 10.51% over baseline models, respectively. FCCDAN expands the applicability of Raman spectroscopy in disease diagnosis, maintaining high performance even in label-scarce environments. The overall research workflow also establishes a robust paradigm for the application and advancement of domain adaptation techniques in this context. © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Protein surface Modeling using active contour model 1

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5th International Conference on Geometric Modeling and processing (GMP 2008)

作者： Xiang, Junping Hu, Maolin Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education and School of Mathematics and Computational Science Anhui University China

ISBN: (数字)9783540792468

ISBN: (纸本)9783540792451

A PDE (partial differential equation) based method, 3D active contour model, is presented to model the surface of protein structure. Instead of generating a single molecular surface, we create a series of surfaces associated with the atomic energy inside the protein, which describe different resolutions of molecular surface. Our results indicate that the surfaces we generated are suitable for shape analysis and visualization. So, when the solvent-accessible surface is not enough to represent the features of protein structure, the evolution surface sequence may be an alternative choice. Besides, if the initial surface is smooth enough, the generated surfaces will preserve this property partly, because the evolution of the surfaces is controlled by the PDE.

关键词： chan-Vese method solvent-accessible surface evolution partial differential equation finite difference atomic energy

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Short-Time Variational Mode Decomposition

arXiv

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arXiv 2025年

作者： Jia, Hao Cao, Pengfei Liang, Tong Caiafa, Cesar F. Sun, Zhe Kushihashi, Yasuhiro Grau, Antoni Bolea, Yolanda Duan, Feng Solé-Casals, Jordi School of Medicine Nankai University Tianjin China Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Tianjin China Universitat Politècnica de Catalunya Barcelona Spain Mechanical Systems Engineering Nippon Institute of Technology Saitama Japan Villa Elisa Argentina Computational Bioengineering Laboratory Faculty of Health Data Science Juntendo University Tokyo Japan Data and Signal Processing Research Group University of Vic-Central University of Catalonia Vic Spain

Variational mode decomposition (VMD) and its extensions like Multivariate VMD (MVMD) decompose signals into ensembles of band-limited modes with narrow central frequencies. These methods utilize Fourier transformations to shift signals between time and frequency domains. However, since Fourier transformations span the entire time-domain signal, they are suboptimal for non-stationary time series. We introduce Short-Time Variational Mode Decomposition (STVMD), an innovative extension of the VMD algorithm that incorporates the Short-Time Fourier transform (STFT) to minimize the impact of local disturbances. STVMD segments signals into short time windows, converting these segments into the frequency domain. It then formulates a variational optimization problem to extract band-limited modes representing the windowed data. The optimization aims to minimize the sum of the bandwidths of these modes across the windowed data, extending the cost functions used in VMD and MVMD. Solutions are derived using the alternating direction method of multipliers, ensuring the extraction of modes with narrow bandwidths. STVMD is divided into dynamic and non-dynamic types, depending on whether the central frequencies vary with time. Our experiments show that non-dynamic STVMD is comparable to VMD with properly sized time windows, while dynamic STVMD better accommodates non-stationary signals, evidenced by reduced mode function errors and tracking of dynamic central frequencies. This effectiveness is validated by steady-state visual-evoked potentials in electroencephalogram signals. Copyright © 2025, The Authors. All rights reserved.

关键词： Cost functions

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A Gradient-Guided Evolutionary Approach to Training Deep Neural Networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第9期33卷 4861-4875页

作者： Yang, Shangshang Tian, Ye He, Cheng Zhang, Xingyi Tan, Kay Chen Jin, Yaochu Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei China Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong SAR Department of Computer Science University of Surrey Guildford U.K

It has been widely recognized that the efficient training of neural networks (NNs) is crucial to classification performance. While a series of gradient-based approaches have been extensively developed, they are criticized for the ease of trapping into local optima and sensitivity to hyperparameters. Due to the high robustness and wide applicability, evolutionary algorithms (EAs) have been regarded as a promising alternative for training NNs in recent years. However, EAs suffer from the curse of dimensionality and are inefficient in training deep NNs (DNNs). By inheriting the advantages of both the gradient-based approaches and EAs, this article proposes a gradient-guided evolutionary approach to train DNNs. The proposed approach suggests a novel genetic operator to optimize the weights in the search space, where the search direction is determined by the gradient of weights. Moreover, the network sparsity is considered in the proposed approach, which highly reduces the network complexity and alleviates overfitting. Experimental results on single-layer NNs, deep-layer NNs, recurrent NNs, and convolutional NNs (CNNs) demonstrate the effectiveness of the proposed approach. In short, this work not only introduces a novel approach for training DNNs but also enhances the performance of EAs in solving large-scale optimization problems.

关键词： Artificial neural networks Training Optimization Search problems Scalability Complexity theory Genetic algorithms Deep neural networks (DNNs) evolutionary algorithm (EA) genetic operator gradient descent sparsity

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The sound and complete R-calculus for revising propositional theories

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Science China(Information Sciences) 2015年第9期58卷 5-16页

作者： LI Wei SUI YueFei SUN MeiYing State Key Laboratory of Software Development Environment Beihang University Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences University of Chinese Academy of Sciences

The AGM postulates are for the belief revision(revision by a single belief), and the DP postulates for the iterated revision(revision by a finite sequence of beliefs). Li gave an R-calculus for R-configurations?|Γ,where ? is a set of atomic formulas or the negations of atomic formulas, and Γ is a finite set of formulas. In this paper, two deduction systems for the revision of a theory by another theory are given such that the systems are sound and complete, that is, if ?|Γ■Γ′ is provable then Γ′■ ? is consistent and Γ′- ? is a maximal subset of Γ such that(Γ′- ?) ∪ ? is consistent; and for any finite theories ? and Γ, there is a finite theoryΓ′ such that Γ′- ? is a maximal subset of Γ such that(Γ′- ?) ∪ ? is consistent, and ?|Γ ? Γ′ is ***, if?|Γ■Γ′ is provable then Γ′ satisfies the AGM and the DP postulates.

关键词： belief revision R-calculus minimal change the AGM postulates the DP postulates

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Multi-views Emotional Knowledge Extraction for Emotion Recognition in Conversation

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Knowledge-Based Systems 2025年 322卷

作者： Jian, Zhongquan Wu, Daihang Wang, Shaopan He, Jiezhou Yao, Junfeng Liu, Kunhong Wu, Qingqiang Institute of Artificial Intelligence Xiamen University Xiamen Fujian 361005 China School of Informatics Xiamen University Xiamen Fujian 361005 China School of Film Xiamen University Xiamen Fujian 361005 China College of Management Mahidol University Phayathai Bangkok 10400 Thailand Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University Xiamen Fujian 361005 China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University Xiamen Fujian 361005 China

Emotion Recognition in Conversation (ERC) is a challenging task due to the scarcity and dispersion of contextual information across utterances. Most existing methods attempt to integrate comprehensive information to enhance utterance semantics, which, however, also introduces noise and irrelevant content, misleading the model and limiting its potential in emotion recognition. To this end, we introduce the concept of Conversational Clique (ConvClique) and propose CC-ERC, a multi-view emotional knowledge extraction method designed to capture the most relevant emotional cues within the ConvClique from complementary perspectives and collaboratively predict utterance emotions. Specifically, CC-ERC comprises two modules: 1) the Utterance Spatial Relationship (USR) module, which predicts emotions by modeling structural correlations among utterances, and 2) the Emotion Temporal Relationship (ETR) module, which captures emotion sequence patterns to determine utterance emotions. These modules are integrated to obtain the final prediction, contributing to the robustness and accuracy of emotion recognition. The effectiveness of CC-ERC is validated on three widely used ERC datasets, evaluated in both online and offline settings. Compared to the state-of-the-art methods, CC-ERC achieves average improvements of 0.63% in accuracy and 0.94% in weighted F1 scores. Ablation studies further validate the significance of ConvClique-based knowledge extraction and demonstrate the effectiveness of the USR and ETR modules in modeling utterance structural correlations and emotion sequence patterns. © 2025

关键词： Conversation clique Emotion recognition in conversation Graph attention network Hidden Markov model Natural language processing

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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...

引用

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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