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IEEE Transactions on Artificial Intelligence 2025年

作者： Zhu, Zhilin Zhang, Chucai Dai, Jianhua Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Changsha410081 China

Feature selection is an important data preprocessing process in artificial intelligence, which aims to eliminate redundant features while retaining essential features. Measuring feature significance and relevance between features is a significant challenge. Fuzzy information entropy is an extension of Shannon entropy. It is widely used for quantifying the information of fuzzy divisions. However, it has significant limitations, notably the lack of monotonicity in fuzzy conditional entropy measure of decision uncertainty in the feature selection process. We introduce a novel measurement Macrogranular Entropy (ME) and construct some generalized forms, such as conditional macrogranular entropy, mutual macrogranular information, and joint macrogranular entropy. The conditional macrogranular entropy exhibits monotonicity when measuring decision uncertainty. Additionally, we propose two feature selection algorithms: one based on Monotonic Conditional Macrogranular Entropy (MCME), and the other based on the Degree of Symmetric Association (ADSA). The ADSA algorithm and the MCME algorithm are compared against eight other feature selection algorithms through a series of experiments. The comparison was conducted based on classification performance using SVM and NB classifiers, as well as evaluation metrics including F1-score and Recall. In terms of all four evaluation metrics, ADSA and MCME achieved the top two rankings, respectively. Specifically, on the NB and SVM classifiers, the ADSA algorithm improves the average accuracy by 12.22% and 2.88% compared to the original feature set, while MCME improves the accuracy by 10.07% and 1.01%, respectively. Experimental comparisons demonstrate that ADSA algorithm effectively removes redundant information from the dataset during feature selection. © 2025 IEEE.

关键词： Feature Selection

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A Solver for Devices of Subwavelength Lamellar Gratings

A Solver for Devices of Subwavelength Lamellar Gratings

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Optoelectronics Global Conference (OGC)

作者： Zhuang Wang Chuan Shen Liu Wang Bin Wang Daofeng He Sui Wei Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (纸本)9781665486996

A solver for subwavelength lamellar gratings is presented synchronized with the development of a light modulator for holographic video display Grating Liquid Crystal on Silicon (GLCoS) and is regarded as an important part of the whole R&D work. In this way it not only gives computational support in the whole design process including physical concept descriptions, verifications, predictions, fabrications and other experimental activities but also provides a support platform for further product development. Based on the generic Fourier modal method, we focus on the electrodynamics specific to conductors (Au, Al) and charge carriers in semiconductors (Indium Tin Oxide, ITO) in SPPs (Surface Plasmon Polaritons) and permittivity characteristics of the materials to make the solver oriented towards subwavelength (metal, semiconductor) lamellar gratings. Further, we analyze and calculate the optical characteristics of subwavelength lamellar gratings composed of ITO and Au. The results of these calculations not only agree with the results of actual GLCoS device tests to the same order of accuracy, but also demonstrate the validity and accuracy of the solver.

关键词： Gold Computational modeling Indium tin oxide Optical device fabrication Modulation Holography Writing

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Online Multi-label Streaming Feature Selection with Missing Features by Dual-Space Consistency Information Measurement

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Dai, Jianhua Wang, Jie Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Changsha410081 China

Multi-label feature selection is critical to address the challenges of high dimensionality and computational complexity in multi-label learning. However, in some practical applications, a more complex challenge is the dynamic generation of features, known as streaming features. Although some scholars have studied multi-label streaming feature selection methods, the problem of missing features during dynamic feature generation in multi-label data is rarely considered. Hence, we propose an online multi-label streaming feature selection method for multilabel data containing missing features by dual-space consistency information measurement. Firstly, we construct the dual-space consistency information measurement, which can effectively capture the relationships between features and labels. Specifically, we consider it from the perspective of instance correlation, and propose a fuzzy α-tolerance relation in the feature space to directly deal with missing features, and introduce an improved neighbour similarity relation in the label space to capture the instance similarity between the label combinations. Secondly, based on the dual-space consistency information, we further introduce the consistency gain index to help identify those features with important information during the streaming feature selection process. Thirdly, by combining online significance analysis, online relevance analysis, and online redundancy analysis, a new streaming feature selection framework is constructed, which can comprehensively evaluate the newly arrived features in terms of strong consistency, weak consistency and irrelevance, so as to identify the most representative features in the continuously arriving feature streams. Finally, a series of comparative experiments on multiple benchmark datasets demonstrate the effectiveness and superiority of our proposed method. © 2025 IEEE.

关键词： Data consistency

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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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International Applied Computational Electromagnetics Society Symposium (ACES)

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

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.

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Metasurface Antenna Near-field Computational Imaging with Generative Adversarial Network

Metasurface Antenna Near-field Computational Imaging with Ge...

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International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Fafa Zhao Zhenhua Wu Lixia Yang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (纸本)9781665482271

Considering the characteristics of electromagnetic scattering of radar observation targets, it is possible to introduce compressed sensing theory into computational imaging. For the sparse undersampling problem of scene data, it is required to achieve higher reconstruction accuracy with lower complexity. However, it is often difficult to construct and solve the sparse representation dictionary in complex scenarios, and it is difficult to fully exploit the correlation and sparsity between data. In addition, the design of observation matrix and reconstruction algorithm is also difficult. In this paper, the imaging problem is modeled as an inverse scattering problem based on the mathematical model of metasurface antenna imaging, and the feasibility of the near-field computational imaging method of generative adversarial network is analyzed from the perspective of solving the inverse problem. Using the measured radiation field data to conduct imaging experiments, the network can reconstruct the imaging target quickly and with high quality under the condition of low scene compressed ratio, which verifies the effectiveness and robustness of the method.

关键词： Microwave measurement Computational modeling Millimeter wave measurements Imaging Radar imaging Generative adversarial networks Metasurfaces

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Long Video Micro-expression Spotting Based On OCC Theory

Long Video Micro-expression Spotting Based On OCC Theory

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liu, BingTong Zhang, ZheYuan Zhou, Ju Wang, HanPu Chen, Tong Southwest University Institute of Legal Psychology and Intelligent Computing Chongqing400715 China Chongqing Key Laboratory of Non-Linear Circuit and Intelligent Information Processing Chongqing400715 China Southwest University School of Electronic and Information Engineering Chongqing400715 China

ISBN: (纸本)9798350337488

The generation of micro-expression (ME) is unconscious and unaffected by human subjective consciousness. It can reveal hidden emotions in humans, which has important applications in the domains of psychological diagnosis, lie detection, and so on. ME detection, which aims to locate ME apex frames in long videos, is the foundation of ME recognition. In this paper, we propose a ME detection method based on One-Class Classification (OCC) theory for long videos. The method is the first to use neutral expression frames to detect MEs, which addresses the problem of insufficient training samples in ME detection. We design a geometric motion feature based on the prior knowledge from the FACS and utilize it in conjunction with the features extracted by CNN to train separate one-class classifiers for ME detection. Finally, we evaluate the proposed method on publicly available datasets CASME I, CASME II, SAMM, and a mixed database comprising data from the three datasets, to verify the feasibility of the proposed method in both single-database and cross-database ME detection. The experimental results show that our method outperforms three other ME detection methods, demonstrating its ability to more accurately locate ME apex frames in long videos. © 2023 IEEE.

关键词： Micro-expression Detection OCC One-Class Classifier

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Community Evolution Tracking Based on Core Node Extension and Edge Variation Discerning 17th

Community Evolution Tracking Based on Core Node Extension a...

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17th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2022

作者： Zhuang, Qifeng Yu, Zhiyong Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819923557

Communities exist anywhere in various complex networks, and community evolution tracking is one of the most well-liked areas of inquiry in the study of dynamic complex networks. Community evolution tracking has many applications in daily life, such as predicting social network behaviors or analyzing the spread of infectious illnesses. However, the majority of existing evolution tracking algorithms obtain community detection results before matching the community in tracking evolution events, making it difficult to trace the whole evolution of communities because of community matching errors. In addition, the majority of evolution tracking algorithms do not adequately account for the potential scenarios in community evolution, resulting in erroneous detection of evolution events. In this research, a community evolution tracking algorithm based on edge variation discerning and core node extension is proposed. First, we detect communities based on the core node extension strategy, which avoids the problem of community matching errors. Second, we track community evolution based on the edge variation discerning strategy, which fully considers various situations that may occur during the community evolution process. According to the outputs of our experiments, our system can effectively track the evolution of communities in synthetic dynamic networks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Complex networks

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A beamforming microstrip array antenna for K-band applications

A beamforming microstrip array antenna for K-band applicatio...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Zijian Yu Wenwen Feng Xiaodi Song Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China

A beamforming microstrip array antenna for K-band is proposed. The antenna array is a 4*4 array, the array elements are composed of slotted rectangular microstrip antennas, and the feed network is a combination of series and parallel connections. The beamforming is achieved by using a microstrip phase shift network. The design results show that the VSWR of the antenna array is less than 1.8dB in the operating frequency bandwidth of 24.0 GHz-24.5 GHz. This antenna array can achieve a gain of more than 7.6dB in the beam range of 22.5° azimuth and 30^° pitch.

关键词： Phased arrays Microstrip antenna arrays Slot antennas Array signal processing K-band Microstrip antennas Radar antennas

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Online Multi-Label Streaming Feature Selection by Label Enhancement and Fuzzy Synergistic Discrimination Information

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IEEE Transactions on Fuzzy Systems 2025年

作者： Zou, Ligeng Zhou, Tong Dai, Jianhua Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Changsha410081 China

Online streaming feature selection is an effective approach for handling large-scale streaming data in real-world applications. However, many existing online streaming feature selection studies do not effectively leverage the correlation and hidden information in the label space. The measures used in streaming feature selection often face challenges such as computational complexity and insufficient information exploitation. To address these issues, we propose an online streaming feature selection method based on label enhancement and fuzzy synergistic discrimination information. Firstly, we define a novel label enhancement method based on label correlation, which does not require any prior knowledge of feature space. Secondly, we propose a new measure to evaluate the discriminative ability of multiple feature subsets for labels in fuzzy environment, called fuzzy synergistic discrimination information. It is bounded and has many interesting properties. The fuzzy synergistic discrimination information is computed directly based on fuzzy similarity relations, so the calculation is relatively simple. Thirdly, based on label enhancement and proposed fuzzy synergistic discrimination information, we construct a three-stage online multi-label streaming feature selection method, including online significance analysis, online relevance analysis and online redundancy analysis. Finally, comparative experiments on 12 datasets demonstrate the effectiveness and superiority of the proposed method. The influences of different feature sequences and these components on the algorithm's performance are further analyzed. © 1993-2012 IEEE.

关键词： Information hiding

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Resource Binding and Module Placement Algorithms for Continuous-Flow Microfluidic Biochip in intelligent Digital Healthcare 18th

Resource Binding and Module Placement Algorithms for Conti...

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18th International Conference on Green, Pervasive, and Cloud computing, GPC 2023

作者： Yang, Zhongliao Huang, Huichang Liu, Zeyi Dong, Chen Xu, Li College of Computer and Data Science Fuzhou University Fuzhou350116 China College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9789819998951

Continuous-Flow Microfluidic Biochip (CFMB), with their integrated features, bring traditional biochemical experiments on a single chip to accomplish complex operations and reactions through precise control, efficient reactions and emerging ways of saving reagents. In the field of intelligent digital healthcare, CFMB have attracted a lot of attention. However, traditional manual design schemes can no longer meet the needs of increasingly complex chip architecture design. Therefore, this paper proposes an automated design method for resource binding and module placement of CFMB based on a list scheduling algorithm and an improved Simulated Annealing algorithm. Through the resource binding and scheduling design based on the list scheduling algorithm, an effective scheduling strategy is generated, which effectively improves the biochip execution efficiency. In addition, the improved Simulated Annealing algorithm solves the module placement problem in the biochip in a limited physical space. Compared with some benchmark algorithms, the experimental results demonstrate the effectiveness of the method in the biochip design process and provide a practical framework for further development of CFMB in the field of intelligent digital healthcare. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Biochips

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