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A novel miniaturized l-band filter with great stopband characteristics using interdigitated coupled lines CRLH-TL structure

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Progress In Electromagnetics Research C 2021年 114卷 57-67页

作者： Wang, Peng Duan, Kaiyue Li, Minquan Zhang, Man Jin, Baokun Anqing Normal University Anqing246011 China Key Lab of Ministry of Education of Intelligent Computing & Signal Processing Anhui University Hefei230601 China

This paper proposes a novel bandpass filter for L-band based on CRLH TL, which is mainly formed by coupling a high-pass characteristic module with a low-pass characteristic module in a cascade. The high-pass module consists of an interdigitated coupled line and a grounding via, owning to its singular characteristics, which the miniaturization is realized. The low-pass module is composed of a C-type resonator with high-low impedance lines, which can realize great sideband attenuation characteristics. To further improve its out-of-band rejection characteristics, a complementary split-ring resonator (CSRR) defective ground structure with single-pole attenuation characteristics is loaded, and a transmission zero is introduced at 2.5f0 out-of-band. The test results are in great agreement with the simulation ones, and the dimensions are only 0.20λg * 0.22λg. Compared with other similar types, the filter proposed in this paper has miniaturization, great passband selection characteristics, stopband characteristics, and the advantage of low insertion loss. © 2021, Electromagnetics Academy. All rights reserved.

关键词： Bandpass filters

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Distributed solving Sylvester equations with fractional order dynamics

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Control Theory and Technology 2021年第2期19卷 249-259页

作者： Songsong Cheng Shu Liang Yuan Fan Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and AutomationAnhui UniversityHefei230601AnhuiChina Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical EngineeringUniversity of Science and Technology BeijingBeijing100083China

This paper addresses distributed computation Sylvester equations of the form AX+XB=C with fractional order *** partitioning parameter matrices A,B and C,we transfer the problem of distributed solving Sylvester equations as two distributed optimization models and design two fractional order continuous-time algorithms,which have more design freedom and have potential to obtain better convergence performance than that of existing first order ***,rewriting distributed algorithms as corresponding frequency distributed models,we design Lyapunov functions and prove that proposed algorithms asymptotically converge to an exact or least squares ***,we validate the effectiveness of proposed algorithms by providing a numerical example.

关键词： Fractional order calculus Distributed optimization Sylvester equation Frequency distributed model Convergence

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Inductive Network Representation based on Neighbor Aggregation 3

Inductive Network Representation based on Neighbor Aggregati...

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2022 3rd International Conference on Computer Information and Big Data Applications, CIBDA 2022

作者： Xie, Jingsheng Chen, Xi Zhang, Yanping Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Anhui Hefei China School of Computer Science and Technology Anhui University HefeiAH551 China

ISBN: (纸本)9783800758760

Network representation learning is to learn low dimensional vectors for nodes. It plays a critical role in network analysis. However, most existing network embedding methods focus on embedding the nodes that already exist in the network, and cannot be generalized to newly-added nodes without retraining the learning model. To deal with this problem, the most common idea is to directly aggregate neighbor embeddings as the representation of new nodes, but this may lead to ignoring the particularity of node neighbors themselves. To fill this gap, we present INRL-NA, an innovative method of Inductive Network Representation Learning based on Neighbor Aggregation, which uses the hierarchical information to selectively aggregate neighbor node embeddings, preserving both the structural information and feature information of network. First we build a hierarchical network, then train a refinement model on the hierarchical network, finally after a new node joins the network, we use the hierarchical information and feature information to selectively aggregate neighbor embeddings. The experimental results show that compared with direct aggregation of all neighbor representations, INRLNA has higher representation quality. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Aggregates

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Generalized Self-Adaption Network for Domain Adaptation 2

Generalized Self-Adaption Network for Domain Adaptation

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2nd International Conference on Artificial Intelligence and intelligent Information processing, AIIIP 2023

作者： Bian, Yongheng Ke, Xiao College of Computer and Data Science Fuzhou University Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou350116 China Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fuzhou350003 China

ISBN: (纸本)9798350371451

The purpose of domain adaptation is to transfer knowledge learned in the labeled source domain to unlabeled but related target domains without requiring a large number of target domain labels. The latest method of domain adaptation mainly adjusts the distribution of the source and target domains to adapt to the target domain. Despite significant success, when there are significant distribution differences between the source and target domains, the adaptability in the target domain still decreases, which may be due to insufficient exploration of domain specific features leading to domain offset issues. To address this issue, we propose the GSA-NET framework in this article. We designed an adversarial dropout method to improve the generalization performance of the model in the target domain by pushing the decision boundary away from the feature dense region. At the same time, in order to effectively match the distribution of advanced features across domains, we deploy feature adaptation modules before the classification layer, which can significantly reduce domain differences. Our experiments on two cross domain benchmarks, Office Home and Office-31, have shown that the proposed method is significantly superior to existing methods. © 2023 IEEE.

关键词： Computer vision

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A Method to Verify Internal Parameters of Camera Calibration in World Coordinate System 6

A Method to Verify Internal Parameters of Camera Calibration...

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

作者： Xing, Zheng Wang, Qingchun Li, Yingsong Qi, Junwei College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Shanghai Yanding Information Technology Co. Ltd. Shanghai201210 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (纸本)9798350399059

Aiming at the problem that reprojection error (RE) cannot reflect the camera calibration accuracy intuitively, this paper proposes a method to verify the camera calibration results in world coordinate system. Firstly, the checkerboard calibration target is photographed with the camera under test, and the internal parameters and distortion parameters of the camera are calculated by using the pinhole model and the Kannala-Brandt (KB) fisheye model, respectively. Then, the same camera is used to shoot two targets in the field of view (FOV), and the pixel coordinates of the two target points are detected from the image. Thirdly, the inverse projection model is used to calculate the distance between the two objects and the angle between the camera and the two objects, and the real distance and angle are compared. The experimental results show that the average distance errors of the pinhole model and the KB fisheye model are 0.34% and 0.35%, respectively, and the angle errors are 0.40% and 0.12%, respectively, which proves that the verification method can effectively characterize the calibration accuracy of the camera. © 2023 IEEE.

关键词： Calibration

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Interpretable multi-view attention network for drug-drug interaction prediction

Interpretable multi-view attention network for drug-drug int...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Lin, Xuan Wen, Qi Yang, Sijie Yu, Zu-Guo Long, Yahui Zeng, Xiangxiang Xiangtan University College of Computer Science China Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education China Singapore Hunan University College of Information Science and Engineering China

ISBN: (纸本)9798350337488

Drug-drug interaction (DDI) plays an increasingly crucial role in drug discovery. Predicting potential DDI is also essential for clinical research. Given the high cost and risk of wet-lab experiments, in-silico DDI prediction is an alternative choice. Recently, deep learning methods have been developed for DDI prediction. However, most of existing methods focus on feature extraction from either molecular SMILES sequences or drug interactive networks, ignoring the valuable complementary information that can be derived from these two views. In this paper, we propose a novel interpretable Multi-View Attention network (MVA-DDI) for DDI prediction. MVA-DDI can effectively extracts drug representations from different perspectives to improve DDI prediction. Specifically, for a given drug, we design a transformer-based encoder and a graph convolutional networkbased encoder to learn sequence and graph representations from SMILES sequence and molecular graph, respectively. To fully exploit the complementary information between the sequence and molecular views, an attention mechanism is further adopted to adaptively aggregate the sequence and graph representations by taking the importance of different views into accounts, generating the final drug representations. Comparison experiments demonstrated that our MVA-DDI1 model achieved superior performance to state-of-the-art models on DDI prediction. © 2023 IEEE.

关键词： Contrastive learning Drug-drug interaction prediction Interpretable attention network Multi-view learning

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Femtosecond laser-induced flexible and highly sensitive capacitive pressure sensor with treefrog suckers bionic microstructure 2

Femtosecond laser-induced flexible and highly sensitive capa...

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2nd International Symposium on Sensor Technology and Control, ISSTC 2023

作者： Wang, Zhiyun Chu, Mengyang Su, Yahui Guo, Xiaohui Liu, Rui Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province School of Electronic and Information Engineering Hefei China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University School of Integrated Circuits Hefei China Anhui Xinhuai Electronics Co. Ltd Huaibei China

ISBN: (纸本)9798350314465

Flexible capacitive pressure sensors are optimal for wearable electronic equipment owing to their advantages of simplicity in architecture and low power consumption. However, fabricating a bionic flexible capacitive pressure sensor with high sensitivity and a wide dynamic range of detection is still a great challenge. In this article, we propose a flexible pressure sensor based on femtosecond laser ablation with a treefrog suckers bionic microstructure. The treefrog suckers capacitive pressure sensor (TSCPS) has high sensitivity (0-10 kPa, 0.0762 kPa-1), wide detection range (500 kPa), high repeatability (>12000 s at 10 kPa), and fast response time (62.5 ms). For practical applications, the TSCPS was positioned on the wrist, palm, and sole of the hand to detect the movements of each part accordingly. The experimental findings indicate that the TSCPS has broad promising applications in bionic wearable products such as human locomotion monitoring. © 2023 IEEE.

关键词： Pressure sensors

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A Tetrahedral Spectral Element Method for Thermo-Mechanical Analysis of Electronic Devices

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IEEE Transactions on Components, Packaging and Manufacturing Technology 2025年

作者： Feng, Naixing Zhang, Shuai Huang, Zhixiang Liu, Qi Qiang Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Electromagnetic Environmental Sensing of Anhui Higher Education Institutes Hefei China Hangzhou Dianzi University School of Electronics and Information Engineering Hangzhou310018 China

For the coupled thermo-mechanical simulation of complex electronic devices, we propose a Tetrahedral Spectral Element Method (TSEM), which truly combines the high accuracy of spectral methods with the strong geometric adaptability of Finite Element Methods (FEMs). The TSEM employs a collapse transformation method and successfully converts the orthogonal Gauss-Lobatto-Legendre (GLL) basis functions from a hexahedron to a tetrahedral reference element. This ensures numerical accuracy and stability for arbitrary high-order interpolation. We also generate non-equidistant GLL spectral interpolation nodes for tetrahedral elements through Warp&Blend transformation, enabling direct nodal numerical integration calculation through Vandermonde matrices. This approach replaces the classical Gaussian quadrature in the FEM, not only significantly enhancing the computational efficiency of element matrix but also avoiding potential numerical dissipation and dispersion associated with Gaussian integration, thus achieving rapid convergence. In the TSEM thermo-mechanical coupling, we further construct a separate solver based on the implicit Newmark-β method, which realizes unconditional stability for temporal integration for arbitrary time step increment. Moreover, it supports independent inversion of system matrices for temperature and stress fields, without increasing computational resource consumption due to the coupling of multi-physics equations. By quantitatively comparing with the COMSOL Multiphysics software, several typical case studies are used to demonstrate the significant advantages of the TSEM in numerical accuracy, efficiency, and stability. © 2025 IEEE. All rights reserved.

关键词： Orthogonal functions

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Spring pair method of finding saddle points using the minimum energy path as a compass

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Physical Review E 2024年第6期110卷 064123-064123页

作者： Gang Cui Kai Jiang Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education School of Mathematics and Computational Science Xiangtan University Xiangtan Hunan 411105 China

Finding index-1 saddle points is crucial for understanding phase transitions. In this work, we propose a simple yet efficient approach, the spring pair method (SPM), to accurately locate saddle points. Without requiring the Hessian information, the SPM evolves a single pair of spring-coupled particles on an energy surface. By designing complementary drifting and climbing dynamics based on gradient decomposition, the spring pair converges onto the minimum energy path (MEP) and spontaneously aligns its orientation with the MEP tangent, providing a reliable ascent direction for efficient convergence to saddle points. The SPM fundamentally differs from traditional surface walking methods that rely on the eigenvectors of the Hessian, which may deviate from the MEP tangent and potentially lead to convergence failure or undesired saddle points. The efficiency of the SPM for finding saddle points is verified by ample examples, including Lennard-Jones clusters, Morse clusters, water clusters, and the Landau energy functional involving quasicrystal phase transitions.

关键词： Lennard-Jones potential

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A Nonconformal SETI-DP Method for Transient Thermal Simulation

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IEEE Transactions on Components, Packaging and Manufacturing Technology 2025年

作者： Feng, Naixing Li, Hongyang Huang, Zhixiang Liu, Qi Qiang Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education The Information Materials and Intelligent Sensing Laboratory of Anhui Province The Key Laboratory of Electromagnetic Environmental Sensing of Anhui Higher Education Institutes Hefei China Hangzhou Dianzi University School of Electronics and Information Engineering Hangzhou310018 China

This paper proposes a novel nonconformal meshbased dual-primal spectral element tearing and interconnecting (SETI-DP) method designed for the efficient transient simulation of complex large-scale heat conduction problems utilizing a domain decomposition method (DDM). This method allows for the division of complex, large-scale problems into smaller subdomain problems. By applying Robin-type transmission conditions at the subdomain interfaces, rapid convergence of the global interface iterative solution is achieved for arbitrary non-conformal subdomain problems. This non-conformal DDM approach supports independent meshing of subdomains, eliminating the need to generate a globally continuous mesh for large-scale problems, thereby significantly enhancing meshing efficiency. In addition, this non-conformal meshing strategy enables high-contrast spatial discretization and mixed-order spectral interpolation between different subdomains. This significantly increases meshing flexibility, minimizes the number of degrees of freedom (DoF) in multi-scale problems, and reduces computational costs. In addition, by developing a subdomain-coupled time iteration scheme, the global corner problem of residual transfer is explicitly given, which greatly improves the versatility and flexibility of the algorithm. Finally, to demonstrate the accuracy, efficiency, and robustness of our algorithm, we conduct benchmark tests on several representative examples. © 2011-2012 IEEE.

关键词： Domain decomposition methods

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