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13th Asia-Pacific International Symposium on Electromagnetic Compatibility and Technical Exhibition, APEMC 2022

作者： He, Zhikang Zhu, Haoran Anhui Province Anhui University Information Materials and Intelligent Sensing Laboratory Hefei230039 China Anhui University Ministry of Education Key Lab of Intelligent Computing and Signal Processing Hefei230039 China

ISBN: (数字)9781665416719

ISBN: (纸本)9781665416719

a U-shaped structure, based on the equivalent lumped circuit of differential transmission line, is proposed to suppress the noise of differential-common-mode conversion. With the equivalent lumped circuit, the cause of mode conversion noise is analyzed. The U-shaped structure has reached the suppression level of 24.31 dB from DC to 10 GHz according to the simulated result. © 2022 IEEE.

关键词： Electric lines

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A Miniaturized Microwave Limiter by Using T-Type LC Network Topology with GaAs Technology 13

A Miniaturized Microwave Limiter by Using T-Type LC Network ...

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13th Asia-Pacific International Symposium on Electromagnetic Compatibility and Technical Exhibition, APEMC 2022

作者： Wang, Jun Zhu, Haoran Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Hefei230039 China Anhui University Ministry of Education Key Lab of Intelligent Computing & Signal Processing Hefei230039 China

ISBN: (数字)9781665416719

ISBN: (纸本)9781665416719

A miniaturized ultra-wideband microwave limiter with low insertion loss is presented in this paper. This limiter adopts a three-stage antiparallel diode structure. A T-type LC network topology consisting of two spiral inductors and a parallel grounded metal-insulator-metal (MIM) capacitor is utilized to replace the traditional λ/4 transmission line in series between the limiting circuits. Compared with the traditional topology, the proposed limiter has the advantages of miniaturization and ultra-wideband. The area of the presented microwave limiter chip is only 1.25×0.75mm2. The simulation results show that the limiter can work in 0.05-18GHz. Insertion loss is less than 0.65dB at 18GHz. The output power can be maintained below 15dBm with a maximum power input of 40dBm continuous wave. © 2022 IEEE.

关键词： Network topology

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Hierarchical Graph Convolutional Networks With Latent Structure Learning for Mechanical Fault Diagnosis

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IEEE/ASME Transactions on Mechatronics 2023年第6期28卷 3076-3086页

作者： Zhong, Kai Han, Bing Han, Min Chen, Hongtian Anhui University Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Shanghai Ship and Shipping Research Institute State Key Laboratory of Navigation and Safety Technology Shanghai200135 China Dalian University of Technology Key Lab. of Intelligent Contr. and Optimization for Indust. Equipment of the Ministry of Education Dalian116024 China University of Alberta Department of Chemical and Materials Engineering EdmontonABT6G 2V4 Canada

Industrial sensor signals are essentially non-Euclidean graph structures due to the interplay between process variables;thus, graph convolutional networks (GCNs) have been widely studied and applied. However, most of the existing GCN-based methods may suffer from two drawbacks: 1) it is difficult to characterize multiple interactions among nodes and 2) the input graph constructed from the original data may contain errors and missing edges, which will degenerate the fault diagnosis performance. To address the abovementioned issues, this article designs a hierarchical GCN with latent structure learning for industrial fault diagnosis, which can organize hierarchical networks to collaboratively improve the quality of latent graph structure, and enhanced diagnostic performance can be guaranteed. First, a high-quality updated graph is formed by incorporating the original graph with the new graph in the graph constructing layer, which can not only eliminate the adverse effects of noise and outliers but also characterize the multiple interactions among nodes. Then, the updated graph is fed into the multilayer GCN layer for better feature learning and enhances the node representation through intra- and inter-layer convolutional operations simultaneously. After that, the produced node embeddings are used to guide the latent structure learning process for optimal graph. Finally, the proposed method is verified in both the simulated and real industrial processes. The experimental results demonstrate that the new approach has better fault diagnosis accuracy and practicability than state-of-the-art methods. © 1996-2012 IEEE.

关键词： Convolution

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Determination of Continuous Dielectric Properties of Clothing Materials Using Hybrid Method

Determination of Continuous Dielectric Properties of Clothin...

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2023 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition, iWEM 2023

作者： Chen, Manni Chung, Kwok L. Huang, Zhining Wen, Jiating Li, Yingsong Huizhou University School of Computer Science and Engineering Guangdong516007 China Huizhou University Glorious Sun Guangdong School of Fashion Guangdong516007 China Ministry of Education Anhui University Key Laboratory of Intelligent Computing and Signal Processing China

ISBN: (纸本)9798350336740

This paper presents a hybrid method for determination of continuous dielectric properties of clothing materials. The dielectric constant and loss tangent of three types of materials are firstly investigated using open-ended coaxial probe from 0.5 to 20 GHz. The average measured values are input into the CST simulation of two CPW lines to obtain the two-port S-parameters. Finally, more reasonable yet reliable results are obtained by using two retrieval algorithms (TRACE and ABCD) for validation and comparative purposes. © 2023 IEEE.

关键词： Dielectric properties

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COMPLEX-VALUED CROSS-DOMAIN FEW-SHOT LEARNING NETWORK FOR POLSAR IMAGE CLASSIFICATION

COMPLEX-VALUED CROSS-DOMAIN FEW-SHOT LEARNING NETWORK FOR PO...

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IET International Radar Conference 2023, IRC 2023

作者： Cao, Yice Zhu, Dayi Wu, Zhenhua Chen, Jie Huang, Zhixiang Yang, Lixia Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Electronics and Information Engineering Anhui University Hefei230601 China 38th Research Institute China Electronics Technology Group Corporation Hefei230088 China

ISBN: (纸本)9781839539954

Gathering reliable labeled samples for polarimetric synthetic aperture (PolSAR) image classification is laborious. Moreover, applying a trained classifier to new domains often leads to noticeable performance degradation due to domain disparities. Therefore, this paper proposes the novel complex-valued cross-domain (CD) few-shot learning classification (CCFSLC) method for PolSAR images to address these issues. Firstly, the transferrable knowledge learning module (TKLM) with a complex-valued feature encoder (CVFE) is trained using source data with sufficient labeled samples. Then, the deep few-shot learning module (DFSLM), constructed using the pre-trained CVFE, is trained by episodes in both source and target domains, with only minimal target labeled samples. Meanwhile, the adversarial domain adaptation module (ADAM) is employed to eliminate domain shift. The proposed CCFSLC mainly focuses on exploring discriminative information from raw PolSAR data, while reducing the domain gap to recognize novel categories in unseen domains with only a few annotated samples. Experiments on typical PolSAR datasets validate the effectiveness of the proposed method. © The Institution of Engineering & Technology 2023.

关键词： Polarimeters

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An Optimization Method Based on Deep Learning for Electromagnetic Inverse Scattering Problems

An Optimization Method Based on Deep Learning for Electromag...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Wangchao Hu Guoda Xie Jinpeng Dai Guilin Hou Xiang Li Zhixiang Huang The Key Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei China Education of Anhui Province Anhui Province Key Laboratory of Target Recognition and Feature Extraction Luan China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China Key Laboratory of Electromagnetic Environmental Sensing Anhui Province Anhui University Hefei China Anhui University Hefei China

ISBN: (数字)9798350355581

ISBN: (纸本)9798350355598

In this paper, a deep learning optimization method combining U-net model and cycle generative adversarial network (Cycle-GAN) is proposed to efficiently solve the electromagnetic inverse scattering (EMIS) problems. Firstly, a U-net model is used to obtain a rough preliminary reconstruction. Through down-sampling and up-sampling processes, the basic features of the object are extracted and reconstructed from the measured scattered field, providing a coarse dielectric parameter distribution. Then, a Cycle-GAN model further refines the output of the U-net. The Cycle-GAN model, through its adversarial training mechanism involving a generator and a discriminator, enhances the details of the preliminary reconstruction, resulting in a finely detailed dielectric parameter distribution his method improves the problem of rough results obtained by using U-net method alone, and can effectively solve and optimize EMIS problems.

关键词： Deep learning Inverse problems Computational modeling Electromagnetic interference Electromagnetic scattering Optimization methods Feature extraction Dielectric measurement Electromagnetics Image reconstruction

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Blockchain-based Transaction Scheme for Massive MIMO Channel Estimation 11

Blockchain-based Transaction Scheme for Massive MIMO Channel...

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11th International Conference on Networks, Communication and computing, ICNCC 2022

作者： Huang, Yu Zhi, Hui Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei230601 China School of Electronic and Information Engineering Anhui University Hefei230601 China

ISBN: (纸本)9781450398039

In wireless communication systems, accurate channel estimation is essential to ensure the performance of wireless communication systems. Massive Multiple Input Multiple Output (M-MIMO) systems have a dramatic increase in the number of channels to be estimated due to the large number of antennas, which poses a great challenge to the computing resources of the base station, and one solution is offloading these computing tasks of channel estimation to other computing nodes with more computing resources (such as servers). However, in real life, computing nodes are not willing to participate in channel estimation because of their equipment and operation cost. In order to solve this problem, this paper introduces blockchain technology and designs a blockchain-based transaction scheme for channel estimation (BTS-CE) to solve the problem of computing resources transaction for massive MIMO channel estimation, and designs the specific process of the scheme. In order to solve the problem that computing nodes cannot complete the computation task due to failure or malicious disconnection, a task allocation model is designed to assign a computation task to multiple computation nodes. The security of the transactions is guaranteed through the blockchain. Practical Byzantine Fault Tolerance (PBFT) consensus mechanism cannot be applied to the public chain due to its low performance caused by a large number of nodes participating in consensus, this problem is solved by selecting some active nodes as consensus nodes. Simulation results show that improved PBFT(IPBFT) is lower in transaction delay and higher in throughput than PoW consensus mechanism. © 2022 ACM.

关键词： Blockchain

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Integrating Conjugate Gradients Into Evolutionary Algorithms for Large-Scale Continuous Multi-Objective Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1801-1817页

作者： Ye Tian Haowen Chen Haiping Ma Xingyi Zhang Kay Chen Tan Yaochu Jin the Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China the Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230601China IEEE the Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial IntelligenceAnhui UniversityHefei 230601China the Department of Computing The Hong Kong Polytechnic UniversityHong Kong SARChina the Faculty of Technology Bielefeld UniversityBielefeld 33619Germany

Large-scale multi-objective optimization problems(LSMOPs)pose challenges to existing optimizers since a set of well-converged and diverse solutions should be found in huge search *** evolutionary algorithms are good at solving small-scale multi-objective optimization problems,they are criticized for low efficiency in converging to the optimums of *** contrast,mathematical programming methods offer fast convergence speed on large-scale single-objective optimization problems,but they have difficulties in finding diverse solutions for ***,how to integrate evolutionary algorithms with mathematical programming methods to solve LSMOPs remains *** this paper,a hybrid algorithm is tailored for LSMOPs by coupling differential evolution and a conjugate gradient *** the one hand,conjugate gradients and differential evolution are used to update different decision variables of a set of solutions,where the former drives the solutions to quickly converge towards the Pareto front and the latter promotes the diversity of the solutions to cover the whole Pareto *** the other hand,objective decomposition strategy of evolutionary multi-objective optimization is used to differentiate the conjugate gradients of solutions,and the line search strategy of mathematical programming is used to ensure the higher quality of each offspring than its *** comparison with state-of-the-art evolutionary algorithms,mathematical programming methods,and hybrid algorithms,the proposed algorithm exhibits better convergence and diversity performance on a variety of benchmark and real-world LSMOPs.

关键词： Conjugate gradient differential evolution evolutionary computation large-scale multi-objective optimization mathematical programming

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Deep Learning-based AP Selection Method in Software-defined Networks 5

Deep Learning-based AP Selection Method in Software-defined ...

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5th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2022

作者： Duan, Miaomiao Zhi, Hui Yang, Lixia Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei230601 China School of Electronic and Information Engineering Anhui University Hefei230601 China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

In the Software Defined Network (SDN), the terminal has been connected to the network in the process when an access point failure or damage, if the access point is not properly selected, it will lead to a decline in the quality of service applications. To address this problem, most traditional AP selection methods use access point selection based on received signal strength without take into account the channel and channel capacity of the access point access, while this paper uses Deep Neural Networks (DNN)-based access point (AP) selection method and considers the channel and channel capacity as part of the parameters. In the SDN controller, the received signal strength, throughput, number of connected devices, channel, and the channel capacity and access point performance quality are used as the input and label of the DNN, respectively. The analysis and simulation results show that the algorithm can select AP with better performance quality compared with the traditional AP selection method based on received signal strength, and the correct rate is significantly improved by 5% when compared with the AP selection method based on feedforward neural network. Meanwhile, the DNN-based access point selection method in SDN achieves flexible control of network traffic and balances the load of access points in the network. © 2022 IEEE.

关键词： Deep neural networks

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A High Isolation Design Used Between Broadband Dual Polarization Transceiver Antennas

A High Isolation Design Used Between Broadband Dual Polariza...

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International Symposium on Antennas, Propagation and EM Theory (ISAPE)

作者： Yuke Guo Xiaosheng Zhang Luyu Zhao National Key Laboratory of Radar Detection and Sensing Xidian University Xi'an China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (数字)9798350353129

ISBN: (纸本)9798350353136

We proposed a high isolation design between two broadband dual polarization transceiver antennas in this paper. It employs periodic metal isolation walls with equal spacing and height to strongly suppress the low-frequency band (LB) coupling. Covering the vertical and horizontal surfaces of the metal with flexible graphene high resistive metasurfaces that easily conform to the structure improves 40 dB in isolation between different polarizations at 6–18 GHz. This device has less impact on the radiation pattern of the antenna. This design not only enhances the isolation between different polarizations but also optimizes equipment heat dissipation and improves device performance.

关键词： Performance evaluation Heating systems Couplings Graphene Metals Metasurfaces Transceivers Broadband communication Broadband antennas Antenna radiation patterns

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