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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 518-532页

作者： Tian, Ye Ji, Luke Hu, Yiwei Ma, Haiping Wu, Le Zhang, Xingyi Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei230029 China

Represented by evolutionary algorithms and swarm intelligence algorithms, nature-inspired metaheuristics have been successfully applied to recommender systems and amply demonstrated effectiveness, in particular, for multiobjective recommendation. Owing to the population-based search paradigm, these algorithms can produce a number of recommendation lists, making diverse tradeoffs between multiple metrics and meeting the requirements of accuracy, novelty, diversity, and other user preferences. However, these algorithms are criticized for the low efficiency of the optimization process, especially when the number of users is large. To address this issue, this article proposes an evolutionary multitasking-based recommendation method, where each task corresponds to a user and all the tasks are optimized simultaneously, thus highly improving the efficiency of recommendation. To enhance the convergence speed, all the users are divided into multiple populations according to the similarity between their preferences, where each population evolves with internal knowledge transfer between users, and all the populations evolve with external knowledge transfer between populations. Experimental results on various datasets verify that the proposed method can better balance between multiple metrics than classical and deep neural network-based recommendation methods and exhibits significantly higher efficiency than evolutionary multiobjective optimization-based recommendation methods. © 2024 IEEE. All rights reserved.

关键词： Multiobjective optimization

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Meta-feature-based Concept Evolution Detection on Feature Streams 8

Meta-feature-based Concept Evolution Detection on Feature St...

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8th International Conference on intelligent computing and signal processing, ICSP 2023

作者： Guo, Yufeng Zhou, Peng Zhang, Yanping Jiang, Xin Anhui University School of Computer Science and Technology Hefei China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Hefei China

ISBN: (纸本)9798350302455

Data in the real world is often not static but generated and processed in streams, such as real-time adjustment of device setting parameters and real-time GPS positioning data. Feature streams means the number of samples is fixed, and their features are generated and arrive individually over time. A significant challenge of learning from online streaming data is a phenomenon known as concept evolution, that the concept of the data may change over time. In the streaming feature scenario, we define meta-features as univariate statistics describing data distribution and use meta-features to capture the data distribution and statistical properties of concepts. Therefore, an efficient Meta-Feature-based Concept Evolution Detection framework on Feature Streams (MF-CED-FS) is proposed, which consists of a sliding window, meta-feature vector similarity discrimination, and a concept detection method based on a weighted bipartite graph. Extensive experiments on real-world high-dimensional datasets verify the effectiveness of MF-CED-FS. © 2023 IEEE.

关键词： Feature extraction

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Non-uniform Compressive Sensing Imaging Based on Image Saliency

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Chinese Journal of Electronics 2023年第1期32卷 159-165页

作者： LI Hongliang DAI Feng ZHAO Qiang MA Yike CAO Juan ZHANG Yongdong University of Chinese Academy of Sciences Key Lab of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences University of Science and Technology of China

For more effective image sampling, compressive sensing(CS) imaging methods based on image saliency have been proposed in recent years. Those methods assign higher measurement rates to salient regions,but lower measurement rate to non-salient regions to improve the performance of CS imaging. However, those methods are block-based, which are difficult to apply to actual CS sampling, as each photodiode should strictly correspond to a block of the scene. In our work, we propose a non-uniform CS imaging method based on image saliency, which assigns higher measurement density to salient regions and lower density to non-salient regions,where measurement density is the number of pixels measured in a unit size. As the dimension of the signal is reduced, the quality of reconstructed image will be improved theoretically, which is confirmed by our experiments. Since the scene is sampled as a whole, our method can be easily applied to actual CS sampling. To verify the feasibility of our approach, we design and implement a hardware sampling system, which can apply our non-uniform sampling method to obtain measurements and reconstruct the images. To our best knowledge, this is the first CS hardware sampling system based on image saliency.

关键词： Measurement units Density measurement Imaging Image sampling Size measurement Hardware Sensors

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Design of a Broadband MIMO Antenna with Shared Radiator

Design of a Broadband MIMO Antenna with Shared Radiator

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

作者： Fang, Rongzhen Wu, Xianliang Anhui University Key Laboratory of Intelligent Computing and Signal Processing Hefei China Anhui University Department of Education of Anhui Province Hefei China

ISBN: (纸本)9781733509657

In this paper, a broadband 5G MIMO mobile antenna with a shared radiator is proposed. The working band range is 3.3-6.0GHz, covering n77, n78, n79 and WLAN 5G working bands, which can meet a large number of application scenarios of 5G mobile phones. In addition, the antenna is applied to self-decoupling, defect ground, direction diversity and other technologies and principles in the decoupling way, without the use of complex decoupling structure, to ensure that its structure is simple and easy to process, small size is suitable for today's 5G mobile phones. © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Mobile antennas

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Tiny Object Tracking: A Large-Scale Dataset and a Baseline

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第8期35卷 10273-10287页

作者： Zhu, Yabin Li, Chenglong Liu, Yao Wang, Xiao Tang, Jin Luo, Bin Huang, Zhixiang Anhui Univ Key Lab Electromagnet Environm Sensing Anhui Highe Sch Elect & Informat Engn Key Lab Intelligent Comp & Signal ProcessingMinis Hefei 230601 Peoples R China Anhui Univ Sch Artificial Intelligence Anhui Prov Key Lab Multimodal Cognit Computat Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China Anhui Univ Sch Comp Sci & Technol Anhui Prov Key Lab Multimodal Cognit Computat Informat Mat & Intelligent Sensing Anhui Prov Hefei 230601 Peoples R China

Tiny objects, frequently appearing in practical applications, have weak appearance and features, and receive increasing interests in many vision tasks, such as object detection and segmentation. To promote the research and development of tiny object tracking, we create a large-scale video dataset, which contains 434 sequences with a total of more than 217K frames. Each frame is carefully annotated with a high-quality bounding box. In data creation, we take 12 challenge attributes into account to cover a broad range of viewpoints and scene complexities, and annotate these attributes for facilitating the attribute-based performance analysis. To provide a strong baseline in tiny object tracking, we propose a novel multilevel knowledge distillation network (MKDNet), which pursues three-level knowledge distillations in a unified framework to effectively enhance the feature representation, discrimination, and localization abilities in tracking tiny objects. Extensive experiments are performed on the proposed dataset, and the results prove the superiority and effectiveness of MKDNet compared with state-of-the-art methods. The dataset, the algorithm code, and the evaluation code are available at https://***/mmic-lcl/Datasets-and-benchmark-code.

关键词： Object tracking Visualization Annotations Knowledge engineering Location awareness Target tracking Sensors Benchmark dataset knowledge distillation tiny objects visual tracking

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Enhanced Symplectic Geometric Mode Decomposition via K-means Clustering for LFM signal Denoising

Enhanced Symplectic Geometric Mode Decomposition via K-means...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Jincheng Ni Jin Zhao Xianliang Wu Ping Wu Key Lab of Intelligent Computing & Signal Processing Ministry of Education AnHui University Hefei China

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

This article presents a Symplectic Geometric Mode Decomposition (SGMD) method incorporating K-means clustering, coupled with wavelet denoising, for mitigating noise in Linear Frequency Modulation (LFM) signals. This method substitutes the original component recombination approach in SGMD with the K-means clustering algorithm, thus rendering the signal grouping more logically coherent. In the experiment, we compare the method proposed in this article with other denoising methods. The experimental findings demonstrate substantial advantages of the proposed method in mitigating noise in LFM signals, thereby enhancing both signal-to-Noise Ratio (SNR) and signal quality. The research outcomes underscore the feasibility and substantial potential of this method in mitigating noise in signals. In subsequent studies, additional investigation into its applicability across diverse domains can be pursued alongside the refinement of clustering and denoising algorithms.

关键词： Electric potential Chirp modulation Noise reduction Refining Clustering algorithms Rendering (computer graphics) Information and communication technology

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An ultra-wideband high-isolation dual-port MIMO antenna design

An ultra-wideband high-isolation dual-port MIMO antenna desi...

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

作者： Cao, Shuangqing Li, Minquan Zi, Zhenfa Li, Zhonghui Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Hefei230601 China Hefei Normal University School of Physics and Materials Engineering Hefei230601 China

ISBN: (纸本)9798350355581

In this paper, an antenna for ultra-wideband multiple-input and multiple-output (UWB-MIMO) is designed. The design comprises two hexagonal monopole antennas. In order to achieve ultra-wideband characteristics and mitigate the coupling, a stepped microstrip line structure for impedance matching and T-shaped defective ground structure are employed, which optimizes the broadband characteristics and achieves high isolation characteristics. The antenna achieves a wide impedance bandwidth of 3.5 GHz to 13.9 GHz (relative bandwidth of 119%). Furthermore, the isolation between the two feed ports of the antenna exceeds 18 dB, the envelope correlation coefficient (ECC) is less than 0.012, and it exhibits favorable radiation characteristics. © 2024 IEEE.

关键词： Bandwidth

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Coplanar waveguide multiband antenna based on open resonant ring

Coplanar waveguide multiband antenna based on open resonant ...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Li, Minquan Qu, Xin Xiao, Shuang Li, Chen Zhang, Zufeng Zhang, Weiqiang Ministry of Education Anhui University Key Laboratory of Intelligent Computing and Signal Processing Liaoyang Province China

ISBN: (纸本)9781665452366

A compact coplanar waveguide multiband antenna based on an open resonant ring is proposed in this paper. The proposed antenna is fabricated on FR-4 substrate with a dielectric constant of 4.4 and a small size of 23 × 23 × 1.6mm3. In the design process of the antenna, the deformed circular split ring metamaterial (RSRR) is introduced to realize the broadband and multi frequency characteristics of the antenna. The two frequency bands of the antenna are 2.91-3.63GHz (relative bandwidth 22%), 4.19-9.32GHz (relative bandwidth 75.9%), covering WiMax (3.4/5.5GHz), lower C-band (4.41GHz), upper C-band (6.9GHz), WLAN (5.4/5.8GHz), lower X-band (8.7GHz) wireless standards, S11 ≤ -10dB. High frequency structure simulator (HFSS) is used to simulate the antenna structure, which has uniform radiation characteristics. © 2022 IEEE.

关键词： Coplanar waveguides

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A One-to-Two Filter Power Divider Based on LTCC 12

A One-to-Two Filter Power Divider Based on LTCC

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12th International Conference on Electronics, Communications and Networks, CECNet 2022

作者： Li, Yuanyuan Wu, Xianliang Hu, Yunfeng Dong, Mengying Ma, Yue Geng, Xiaolei Fang, Rongzhen Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230001 China

ISBN: (纸本)9781643683683

In order to miniaturize microwave devices in RF systems, a power divider with filtering performance is designed. The filter power divider is based on the LTCC process, and miniaturization is achieved by cascading a bandpass filter and a one-to-two power divider. The band-pass filter adopts a fourth-order comb-line structure, and the one-to-two power divider adopts an LC lumped structure. The size of the one-to-two filter power divider designed is 10mm×4mm×5mm, which is working in 3.3GHz-3.6GHz. The simulation results show that the insertion loss(S21 and S31) are less than or equal to 4 dB, the return loss(S11) is greater than 15 dB, the isolation(S23) is greater than 17 dB, and the out-of-band suppression is greater than 18 dB in 3.8-4.3GHz and 22 dB in 0-3.2GHz and 4.3-6GHz. © 2022 The authors and IOS Press.

关键词： Bandpass filters

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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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