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Data-Driven for Affine Nonlinear System via Adaptive Control and LDI technology 10

Data-Driven for Affine Nonlinear System via Adaptive Control...

引用

10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021

作者： Tu, Yidong Chen, Huan He, Shuping Key Laboratory of Intelligent Computing and Signal Processing School of Electrical Engineering and Automation Anhui University Ministry of Education Hefei230601 China

ISBN: (纸本)9781665424233

This paper investigates a novel optimal control scheme for affine nonlinear systems. With the complexity of Hamilton-Jacobi-Bellman function and the linear differential inclusion based on neural network model is used to linearize the affine system, Meanwhile, a data-driven policy iterative algorithm is proposed to solve the relevant optimal solution. Finally, a simulation example is given to demonstrates the feasibility and applicability of the designed algorithm. © 2021 IEEE.

关键词： Neural network models

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A single notch ultra-wideband filter with wide Stopband

A single notch ultra-wideband filter with wide Stopband

引用

International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Minquan Li Chen Li Xin Qu Shuang Xiao Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China

An ultra-wideband filter with wide stopband performance with single notch using E-shaped resonator is proposed. Based on a U-shaped MMR structure loaded by an open-circuited stub connected at its center, the loaded basic open-stub structure can generate transmission zeros on both sides of the passband to improve the in-band selectivity. Meanwhile, the symmetrical U-shaped open stub introduced not only strengthens the out-of-band suppression ability, but also optimizes the lower stopband. To suppress the in-band interference phenomenon, the proposed UWB BPF used E-shaped resonator coupled to the main transmission line of the basic UWB BPF to generate a notch band at 8GHz (Satellite communications X band). The filter has passband from 2.74 GHz to 11.5 GHz, the insertion loss is lower than 0.6dB and the filter has a relative high selectivity (30dB skirt factor is 0.891), Meanwhile, the filter has a wide stopband of up to 20.8GHz ($S21 \lt -20$dB).

关键词： Band-pass filters Power transmission lines Resonator filters Resonant frequency Interference Insertion loss Passband

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Enhanced Second-Harmonic Generation Driven by Dual-Quasi Bound States in the Continuum

Enhanced Second-Harmonic Generation Driven by Dual-Quasi Bou...

引用

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

作者： Liu, Yuhang Ren, Xingang Niu, Kaikun Huang, Zhixiang School of Electronic and Information Engineer Anhui University Hefei230601 China Engineering Technology Research Center of Specific Power Devices with Novel Semiconductor Materials Anhui Province Chizhou247100 China Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Hefei230601 China

ISBN: (纸本)9781733509657

Bound state in the continuum (BIC) is a dark mode with infinitely high quality factors(Q-factors), it has recently attracted considerable research attention in photonics. Here, we present a metasurface that can support two BICs, which consists of AlGaAs. By breaking the structural symmetry, the two BICs are transformed into quasi-bic modes because they are not protected by the symmetry. Furthermore, it is found that the two quasi-BICs resonate near the fundamental and the second harmonic respectively, which can be used to enhance efficiency of second harmonic generation. By exploiting the high Q characteristics of this structure at both frequencies, we can obtain a second harmonic generation efficiency of nearly 10-3 with 1GW/cm2of pump intensity. Our proposed metasurface will offer good prospects for nonlinear operation. © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Harmonic generation

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ABCD-HN: An Artificial Network Benchmark for Community Detection on Heterogeneous Networks 18th

ABCD-HN: An Artificial Network Benchmark for Community Dete...

引用

18th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2023

作者： Liu, Junjie Guo, Kun Wu, Ling Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of Computer and Data Science/College of Software Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819996360

Community detection is essential for identifying cohesive groups in complex networks. Artificial benchmarks are critical for evaluating community detection algorithms, offering controlled environments with known community structures. However, existing benchmarks mainly focus on homogeneous networks and overlook the unique characteristics of heterogeneous networks. This paper proposes a novel artificial benchmark, called ABCD-HN (Artificial Network Benchmark for Community Detection on Heterogeneous Networks), for community detection in heterogeneous networks. This benchmark enables the generation of artificial heterogeneous networks with controllable community quantity, node quantity, and community complexity. Additionally, an evaluation framework for artificial heterogeneous networks is proposed to assess their effectiveness. Experimental results demonstrate the effectiveness and usability of ABCD-HN as a benchmark for artificial heterogeneous networks. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Heterogeneous networks

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Few-Shot Object Detection Based on Generalized Features 2

Few-Shot Object Detection Based on Generalized Features

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

作者： Chen, Qiuqin 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 Fuzhou Fuzhou350003 China

ISBN: (纸本)9798350371451

Few-shot object detection aims to rapidly detect novel classes of objects using a minimal number of annotated instances. Compared to methods such as meta-learning, few-shot object detection based on transfer learning can achieve better performance. However, when training on base-class datasets, the model gradually improves its detection performance on base classes but does not enhance its generalization. A base-class model lacking generalization tends to result in lower detection performance for novel classes during fine-tuning. To address the aforementioned issues, this paper tackles the problem from two aspects: firstly, we introduce the Confusion-Dropblock Module (CDM) to perturb the model's features, thus enhancing feature generalization. Secondly, we incorporate Deformable Convolution Modules to mitigate the impact of object shapes and sizes on detection performance, thereby improving overall detection performance. Experimental results on the COCO dataset demonstrate the effectiveness of the proposed algorithm, outperforming the compared methods. © 2023 IEEE.

关键词： Object detection

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A Dual-passbands FSS with Wide Angular Stability and High Polarization Stability for 5G and EMC Pulse Shielding

A Dual-passbands FSS with Wide Angular Stability and High Po...

引用

International Workshop on Antenna Technology: Small Antennas and Novel Metamaterials, iWAT

作者： Yunshuai Gao Yingsong Li Luyu Zhao Zhixiang Huang Eugene Sinkevich Vladimir Mordachev Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China EMC R&D Laboratory Belarusian State University of Informatics and Radioelectronics Minsk Belarus

ISBN: (数字)9798350314755

ISBN: (纸本)9798350314762

An excellent frequency selective surface (FSS) with capacitance loading and multi-layer structure is proposed, primarily for application in the sub-6 and shielding. The proposed FSS has two different operational passbands to cover the frequencies located at 3.68 GHz and 5.85 GHz, respectively, which is also a multi-layered unit consisting of three metal layers and two dielectric layers. The unit size is 0.125λ, and the unit profile is 0.075λ, where λ corresponds to the resonance frequency of the first operating band. By adjusting the structural dimensions and parameters, the operating bands can be well-tuned. Simulation results demonstrate favorable polarization stability and angular stability, making it good for pulse shielding.

关键词： Frequency selective surfaces Simulation Capacitors Loading Resonant frequency Metals Interference

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SES: Bridging the Gap Between Explainability and Prediction of Graph Neural Networks

SES: Bridging the Gap Between Explainability and Prediction ...

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International Conference on Data Engineering

作者： Zhenhua Huang Kunhao Li Shaojie Wang Zhaohong Jia Wentao Zhu Sharad Mehrotra Anhui University Hefei China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Hefei China Amazon Research Seattle USA University of California Irvine Irvine USA

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Despite the Graph Neural Networks' (GNNs) pro-ficiency in analyzing graph data, achieving high-accuracy and interpretable predictions remains challenging. Existing GNN interpreters typically provide post-hoc explanations disjointed from GNNs' predictions, resulting in misrepresentations. Self-explainable GNNs offer built-in explanations during the training process. However, they cannot exploit the explanatory outcomes to augment prediction performance, and they fail to provide high-quality explanations of node features and require additional processes to generate explainable subgraphs, which is costly. To address the aforementioned limitations, we propose a self-explained and self-supervised graph neural network (SES) to bridge the gap between explainability and prediction. SES comprises two processes: explainable training and enhanced predictive learning. During explainable training, SES employs a global mask generator co-trained with a graph encoder and directly produces crucial structure and feature masks, reducing time consumption and providing node feature and subgraph explanations. In the enhanced predictive learning phase, mask-based positive-negative pairs are constructed utilizing the ex-planations to compute a triplet loss and enhance the node representations by contrastive learning. Extensive experiments demonstrate the superiority of SES on multiple datasets and tasks. SES outperforms baselines on real-world node classification datasets by notable margins of up to 2.59% and achieves state-of-the-art (SOTA) performance in explanation tasks on synthetic datasets with improvements of up to 3.0%. Moreover, SES delivers more coherent explanations on real-world datasets, has a fourfold increase in Fidelity+ score for explanation quality, and demonstrates faster training and expla-nation generating times. To our knowledge, SES is a pioneering GNN to achieve SOTA performance on both explanation and prediction tasks.

关键词： Training Bridges Accuracy Reliability engineering Data engineering Graph neural networks Generators

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Research and Implementation of Chinese Resume Named Entity Recognition Method Based on BLDC 4

Research and Implementation of Chinese Resume Named Entity R...

引用

4th International Conference on Image processing and intelligent Control, IPIC 2024

作者： Chen, Min Wei, Bin Yu, Hongzhi Wan, Fucheng Feng, Yulin Lu, Baoqing Key Laboratory of Linguistic and Cultural Computing Ministry of Education Northwest Minzu University Gansu Lanzhou730000 China Key Laboratory of Minzu Languages and Cultures Intelligent Information Processing Northwest Minzu University Gansu Province Gansu Lanzhou730000 China Lanzhou Technology and Business College Lanzhou China

ISBN: (纸本)9781510682313

Aiming at the semantic complexity and the difficulty of capturing long-distance dependencies in the task of named entity recognition in Chinese resume text, a BLDC (BERT-BiLSTM-Dense-CRF)-based named entity recognition method for Chinese resumes is investigated. The model first uses the pre-trained BERT-Base-Chinese model to obtain the semantic information of the text, then learns the contextual information of the sequence through the BiLSTM layer, followed by mapping the output of BiLSTM to the label space through the Dense layer, and finally uses the CRF layer to consider the dependencies between the labels for sequence annotation. Experimental results on public datasets show that the method outperforms other models in terms of precision, recall, and F1 value, and effectively improves the performance of named entity recognition in Chinese resume text. © 2024 SPIE.

关键词： Semantics

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Remaining Useful Life Prediction of Bearing via a Double Attention-Based Deep Neural Network 19

Remaining Useful Life Prediction of Bearing via a Double Att...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

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

ISBN: (纸本)9798350346558

Predicting the remaining useful life (RUL) of bearings is important for the secure running and reducing maintenance costs of rotating machinery. In this article, a novel feature extraction and RUL prediction framework of bearing is proposed by utilizing time-frequency representations (TFRs) and deep neural network with double attention (DA-DNN). Firstly, TFRs are employed to extract the degradation features associated with RUL prediction, which can effectively cope with the non-stationary properties of bearing vibration data. The reduced dimensional TFRs are then used as the DA-DNN model input. Afterwards, DA-DNN is developed by employing the double attention structure to extract the key degradation features more efficiently. The dual-attention structure has ability to capture the degradationrelated features from two aspects of channel attention and spatial attention, which enables the network to focus on high RUL-relevant information. Finally, the experimental results demonstrate the effectiveness of DA-DNN, which shows higher performance in terms of prediction accuracy compared to other deep learning (DL) methods. © 2022 IEEE.

关键词： Deep neural networks

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Super-Broadband Rectifier with Expanded Input Power Range for Microwave Power Transmission 5

Super-Broadband Rectifier with Expanded Input Power Range fo...

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

作者： Chen, Yan Liu, Xin Li, Yingsong Huang, Zhixiang The Fifth Research Institute of Miit Guangzhou511370 China College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

A super-broadband rectifier with expended input power range(IPR) for wireless power transfer (WPT) and radio frequency energy harvesting (RFEH) is proposed and analyzed. Meanwhile, using a simple impedance compression network consisting of quadruple microstrip lines (QMLICN) with the topology of the dickson charge pump, high power conversion efficiency (PCE) and super-broadband impedance matching are achieved. In addition, the IPRs of the rectifier are also extended. The simulation results indicate that an IPR of 12 - 25 dBm with a frequency range of 0.4 - 2.5 GHz (fractional bandwidth over 144%), the PEC exceeds 50% are realized, while at an input power of 19 dBm, the rectifier reaches a peak PCE of 74.2%. The rectifier covers the GSM 900, GSM 1800, 3G 2100 and WIFI 2400 bands, which design would have significant potential applications for the development of microwave power transmission systems. © 2022 IEEE.

关键词： Energy transfer

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