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Formulation of Optimization Problems with Radiation Field Phases as Design Variables for Pattern Synthesis of Linear Antenna Arrays

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China Communications 2022年第12期19卷 86-100页

作者： Fei Zhao Jun He Sanyou Zeng Changhe Li Qinghui Xu Zhigao Zeng School of Mechanical Engineering and Electronic Information China University of GeosciencesWuhan 430074China Science and Technology on Blind Signal Processing Laboratory Southwest Electronics and Telecommunication Technology Research InstituteChengdu 610041China School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China College of Computer Hunan University of TechnologyZhuzhou CityHunan Province412007China

Pattern synthesise of antenna arrays is usually complicated optimization problems,while evolutionary algorithms(EAs)are promising in solving these *** paper does not propose a new EA,but does construct a new form of optimization *** new optimization formulation has two differences from the common *** is the objective function is the field error between the desired and the designed,not the usual amplitude error between the desired and the *** difference is beneficial to decrease complexity in some *** second difference is that the design variables are changed as phases of desired radiation field within shaped-region,instead of excitation *** difference leads to the reduction of the number of design variables.A series of synthesis experiments including equally and unequally spaced linear arrays with different pattern shape requirements are applied,and the effectiveness and advantages of the proposed new optimization problems are *** results show that the proposing a new optimization formulation with less complexity is as significant as proposing a new algorithm.

关键词： pattern synthesis evolutionary algorithms linear antenna arrays

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A Generalized Subspace Distribution Adaptation Framework for Cross-Corpus Speech Emotion Recognition

A Generalized Subspace Distribution Adaptation Framework for...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Shaokai Li Peng Song Liang Ji Yun Jin Wenming Zheng School of Computer and Control Engineering Yantai University China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province School of Physics and Electronic Engineering Jiangsu Normal University China Key Laboratory of Child Development and Learning Science (Southeast University) Ministry of Education Research Center for Learning Science Southeast University China

In this paper, we propose a novel transfer learning framework, named generalized subspace distribution adaptation (GSDA), to tackle the challenging cross-corpus speech emotion recognition problem. First, we learn a common low-dimensional feature subspace by utilizing a generalized subspace learning method. Second, we develop a novel distance metric to reduce the divergence between the source and target corpora, which can efficiently explore the similarity and dissimilarity information in the process of knowledge transfer. Third, to demonstrate the effectiveness of our framework, we apply GSDA to the traditional subspace learning algorithms. Finally, we conduct extensive experiments by using the low-level features and deep features on three popular emotional databases, i.e., Berlin, IEMOCAP, and CVE. The results demonstrate that the proposed framework can achieve better performance than several state-of-the-art transfer learning approaches.

关键词： Measurement Learning systems Emotion recognition Databases Transfer learning Signal processing algorithms Speech recognition

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Optimizing Binary Sequence Excitation Signals for Rapid Impedance Spectrum Measurement in Proton Exchange Membrane Fuel Cells

Optimizing Binary Sequence Excitation Signals for Rapid Impe...

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第43届中国控制会议

作者： Jingzhi Lei Chuang Sheng Jingjing Wang Liansong Yu Xi Li School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education MinistryHuazhong University of Science and Technology State Grid Electric Power Research Institute Wuhan Nanrui Limited Liability Company Shenzhen Huazhong University of Science and Technology Research Institute

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

With the increasing demand for efficient and reliable renewable energy sources,Proton Exchange Membrane Fuel Cells(PEMFCs) have emerged as a promising *** experiments demonstrate that Electrochemical Impedance Spectroscopy(EIS) effectively differentiates various states of *** broadband signals as testing excitations significantly reduces EIS measurement time,facilitating real-time monitoring of PEMFCs' health under dynamic *** signal design requires maintaining adequate amplitude across frequency components for noise resistance while minimizing signal peak value to reduce operational *** paper introduces an EIS simulation environment,leveraging PEMFCs' impedance and electrochemical noise *** focuses on optimizing the excitation signal's amplitude-frequency distribution using discrete interval binary *** results indicate that our method achieves minimal perturbation amplitude while preserving measurement accuracy in amplitude-frequency signal design.

关键词： Proton exchange membrane fuel cell Electrochemical impedance spectroscopy Electrochemical noise Excitation signal optimization

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SMTC-CL: Continuous Learning via Selective Multi-Task Coordination for Adaptive Signal Classification

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IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1664-1681页

作者： Hao, Xiaoyang Yang, Shuyuan Liu, Ruoyu Feng, Zhixi Peng, Tongqing Huang, Bincheng Xidian University Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education School of Artificial Intelligence Xi'an710071 China Avic Xi'an Flight Automatic Control Research Institute China MeiTuan China China Electronics Technology Group Corporation Institute of Information Science and Technology China

Continuously adaptive signal classification in complex electromagnetic environments is a desired property of realistic intelligent systems. However, the limitation of most existing signal processing tasks and methods lies in their assumption of fixed categories and invariant distributions. In contrast, humans naturally acquire knowledge continuously from data streams, enhancing their capabilities through cross-task knowledge integration and learning from the past. Inspired by this, we propose a continuous learning (CL) method based on selective multi-task coordination (SMTC) to deal with ever-changing task categories and distributions in practical electromagnetic environments (denoted as SMTC-CL). Firstly, we propose a signal task distribution comparison (TDC) method that leverages intra-domain density and inter-domain divergence. TDC selectively activates multiple old tasks to collaboration with the current incremental new task. Secondly, we propose the SMTC method. By revisiting potential collaborative tasks from the past using TDC and formulating different selection strategies based on the context, continuous learners can achieve mutual benefits between past and current tasks in SMTC learning. SMTC can partially alleviate catastrophic forgetting and enable continuous learners to adapt to the changing electromagnetic environment. Based on the signal automatic modulation classification (AMC) and real-world specific emitter identification (SEI) datasets, extensive experimental results validate the effectiveness of our proposed method on a variety of CL scenarios, including varying signal task categories, distributions, and their combinations. © 2015 IEEE.

关键词： Contrastive Learning

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Provable Discriminative Hyperspherical Embedding for Out-of-Distribution Detection 39

Provable Discriminative Hyperspherical Embedding for Out-of-...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zou, Zhipeng Wan, Sheng Li, Guangyu Han, Bo Liu, Tongliang Zhao, Lin Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China Hong Kong Baptist University China Sydney AI Centre The University of Sydney Sydney Australia Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)157735897X

Out-of-distribution (OOD) detection aims to identify the test examples that do not belong to the distribution of training data. The distance-based methods, which identify OOD examples based on their distances from the centroids of in-distribution (ID) examples, have demonstrated promising OOD detection performance. However, the objectives utilized in prior approaches are typically designed for classification and thus might not yield sufficient discriminative power to distinguish between ID and OOD examples. Therefore, this paper proposes a prototype-based contrastive learning framework for OOD detection, which is termed provable Discriminative Hyperspherical Embedding (DHE). The proposed framework provides a theoretical analysis of inter-class dispersion, which is proved to be fundamental in reducing the false positive rate (FPR) on OOD examples. Based on this, we devise an angular spread loss to achieve the maximal dispersion of the prototypes of different classes prior to training. Subsequently, a prototype-enhanced contrastive loss is introduced to align embeddings of ID examples closely with their corresponding prototypes. In our proposed DHE, the maximal prototype dispersion is theoretically proved, thereby avoiding the pitfalls of local optima commonly encountered by most existing methods. Experimental results demonstrate the effectiveness of our proposed DHE, which showcases a remarkable reduction in FPR95 (i.e., 5.37% on CIFAR-100) and more than doubling the computational efficiency when compared with the state-of-the-art methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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Coordinated Navigation control of Cross-Domain Unmanned Systems via Guiding Vector Fields

arXiv

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arXiv 2023年

作者： Hu, Bin-Bin Zhang, Hai-Tao Liu, Bin Ding, Jianing Xu, Yifan Luo, Chuanshang Cao, Haosen The School of Artificial Intelligence and Automation The Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control The State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China Dongguan University of Technology Dongguan523106 China

This paper proposes a distributed guiding-vector-field (DGVF) controller for cross-domain unmanned systems (CDUSs) consisting of heterogeneous unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs), to achieve coordinated navigation whereas maneuvering along their prescribed paths. In particular, the DGVF controller provides a hierarchical architecture of an upper-level heterogeneous guidance velocity controller and a lower-level signal tracking regulator. Therein, the upper-level controller is to govern multiple heterogeneous USVs and UAVs to approach and maneuver along the prescribed paths and coordinate the formation simultaneously, whereas the low-level regulator is to track the corresponding desired guidance signals provided by the upper-level module. Significantly, the heterogeneous coordination among neighboring UAVs and USVs is achieved merely by the lightweight communication of a scalar (i.e., the additional virtual coordinate), which substantially decreases the communication and computational costs. Sufficient conditions assuring asymptotical convergence of the closed-loop system are derived in presence of the exponentially vanishing tracking errors. Finally, real-lake experiments are conducted on a self-established cross-domain heterogeneous platform consisting of three M-100 UAVs, two HUSTER-16 USVs, a HUSTER-12C USV, and a WiFi 5G wireless communication station to verify the effectiveness of the present DGVF controller. Copyright © 2023, The Authors. All rights reserved.

关键词： Air navigation

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Wind Speed Prediction Based on Combined Deep Learning Model Woa-Vmd-Gru-Tcn

SSRN

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SSRN 2024年

作者： Zeng, Chuxi Xia, Zhipnig Wang, Zhuo Zhou, Yihuan Zeng, Linghong Yin, Zishan Li, Xi Huang, Haizhou Qi, Xiaolan Deng, Zhonghua School of Artificial Intelligence and Automation Key Laboratory of Image Processing Intelligent Control of Education Ministry Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518055 China Huazhong University of Science and Technology China

Accurate wind speed prediction is essential for the integration of wind turbines into microgrids. This paper proposes a combined prediction model, firstly, the key parameters of the variational modal decomposition (VMD) is optimized by whale optimization algorithm (WOA), and the original wind speed sequence is decomposed by using the WOA-VMD to obtain the intrinsic mode functions (IMFs) with different frequencies;secondly, the Pearson correlation coefficient is performed between IMFs and the original wind speed sequence, classifying the IMFs into two groups according to their correlation coefficients, the IMFs with strong correlation are predicted using the temporal convolutional network (TCN), and the IMFs with weak correlation are predicted using the gate recurrent unit (GRU);finally, the prediction results of each component are superimposed to obtain the final prediction results. The combined model is applied to real data from six wind farms in China with different climatic conditions and geographic locations, and different comparison models are established for validation. The experimental results show that the combined WOA-VMD-GRU-TCN prediction model can significantly improve the prediction accuracy, reduce the wind speed sequence error, and have better robustness than other methods. © 2024, The Authors. All rights reserved.

关键词： Intrinsic mode functions

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A Survey of Machine Learning-based IoT Intrusion Detection Techniques 6

A Survey of Machine Learning-based IoT Intrusion Detection T...

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6th IEEE International Conference on Smart Cloud, SmartCloud 2021

作者： Long, Jing Fang, Fei Luo, Haibo College of Information Science and Engineering Hunan Normal University Changsha410081 China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin541004 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China

ISBN: (纸本)9781665443746

Research on data anomaly intrusion detection in the Internet of Things (IoT) environment is still insufficient, and there are still many practical problems that need to be solved urgently. At present, the deep integration of Artificial Intelligence (AI) technology and IoT, and intelligent technologies such as machine learning have gradually been applied to the field of intrusion detection of the Internet of Things. Therefore, this paper conducts the latest and in-depth research on the efficient and accurate intrusion detection technology used in computers, analyzes the security threats facing the Internet of Things, and summarizes the latest technology and evaluation indicators of intrusion detection. This paper can promote the improvement of large-scale industrial network security detection performance under the new situation to a certain extent and has important theoretical significance and practical application value for ensuring the rapid and healthy development of today's industrial IoT security environment. © 2021 IEEE.

关键词： Intrusion detection

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Unleashing Correlation and Continuity for Hyperspectral Reconstruction from RGB images

arXiv

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arXiv 2025年

作者： Feng, Fuxiang Cong, Runmin Wei, Shoushui Zhang, Yipeng Li, Jun Kwong, Sam Zhang, Wei The School of Control Science and Engineering Shandong University Jinan250061 China The Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China The University of California Los Angeles United States The Hubei Key Laboratory of Intelligent Geo-Information Processing The School of Computer Science China University of Geo-sciences Wuhan430078 China The Lingnan University Hong Kong

Reconstructing Hyperspectral images (HSI) from RGB images can yield high spatial resolution HSI at a lower cost, demonstrating significant application potential. This paper reveals that local correlation and global continuity of the spectral characteristics are crucial for HSI reconstruction tasks. Therefore, we fully explore these inter-spectral relationships and propose a Correlation and Continuity Network (CCNet) for HSI reconstruction from RGB images. For the correlation of local spectrum, we introduce the Group-wise Spectral Correlation Modeling (GrSCM) module, which efficiently establishes spectral band similarity within a localized range. For the continuity of global spectrum, we design the Neighborhood-wise Spectral Continuity Modeling (NeSCM) module, which employs memory units to recursively model the progressive variation characteristics at the global level. In order to explore the inherent complementarity of these two modules, we design the Patch-wise Adaptive Fusion (PAF) module to efficiently integrate global continuity features into the spectral features in a patch-wise adaptive manner. These innovations enhance the quality of reconstructed HSI. We perform comprehensive comparison and ablation experiments on the mainstream datasets NTIRE2022 and NTIRE2020 for the spectral reconstruction task. Compared to the current advanced spectral reconstruction algorithms, our designed algorithm achieves State-Of-The-Art (SOTA) performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Analysis of Big Data Complex Network Structure Based on Fuzzy Clustering Algorithm

Analysis of Big Data Complex Network Structure Based on Fuzz...

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2021 International Conference on Networking, Communications and information Technology, NetCIT 2021

作者： Shanshan, Fu Zhiqiang, Ruan Minjiang University Center of Modem Education Technology and Information Management Fujian350108 China Minjiang University College of Computer and Control Engineering Fujian350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fujian350108 China Digital Fujian IoT Laboratory of Intelligent Production Fujian350108 China

ISBN: (纸本)9781665400701

In the era of artificial intelligence with big data as the background, the statistical analysis method based on complex network has become a research hotspot and actively promoted the development of big data visualization. Complex network is a new discipline developed by cross research with other disciplines. Its network topology has obvious clustering characteristics, that is, complex network cluster structure. Together with small world and scale-free, it is called the three most important and basic attributes of complex network topology. The fundamental purpose of complex network clustering algorithm is to detect the actual network cluster structure in complex network, which has very important practical value for mining the cluster structure in network and understanding its characteristics, and is conducive to understanding the functions of network, such as predicting the development of complex network, analyzing the behavior of social network, discovering hot topics on network, etc. This paper combines the methods and ideas of complex network analysis, carries out the method design and research of organization analysis, and tries to solve the relationship and characteristics of inter departmental cooperation within the organization. © 2021 IEEE.

关键词： Complex networks

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