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学者姓名：徐宗本
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Abstract ：
The performance of an evolutionary algorithm (EA) is deeply affected by its control parameter's setting. It has become a trend in recent studies to treat the control parameter as a random variable. In these studies, the associated distribution of the control parameter is updated at each generation and new parameter setting is sampled from the distribution. The distribution's parameter (called hyper-parameter) is thus critical to the algorithmic performance. In this paper, we propose a variational learning framework to tune the hyper-parameters of EA, in which the expectation-maximization (EM) algorithm and a reinforcement learning algorithm are combined. To verify the effectiveness of the proposed method which is named Reinforcement EM (REM), we apply it to tune the hyper-parameters of the distributions of two important parameters, i.e. the scaling parameter (F) and crossover rate (CR), of differential evolution (DE) and an adaptive DE algorithm. In addition, we propose to use the meta-learning technique to learn good initial distributions for the hyper-parameters of F and CR so that the REM can effectively adapt to a new optimization problem. Experimental results obtained on the CEC 2018 test suite show that with the tuned hyper-parameters, DE and the adaptive DE can achieve significantly better performance than their counterparts with empirical parameter settings and with parameters tuned by some widely-used tuning methods, including ParamILS, F-Race and Bayesian optimization algorithm.
Keyword ：
evolutionary algorithm expectation-maximization parameter tuning reinforcement learning Variational inference
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| GB/T 7714 | Zhang, Haotian , Sun, Jianyong , Wang, Yuhao et al. Variational Reinforcement Learning for Hyper-Parameter Tuning of Adaptive Evolutionary Algorithm [J]. | IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE , 2022 . |
| MLA | Zhang, Haotian et al. "Variational Reinforcement Learning for Hyper-Parameter Tuning of Adaptive Evolutionary Algorithm" . | IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE (2022) . |
| APA | Zhang, Haotian , Sun, Jianyong , Wang, Yuhao , Shi, Jialong , Xu, Zongben . Variational Reinforcement Learning for Hyper-Parameter Tuning of Adaptive Evolutionary Algorithm . | IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE , 2022 . |
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Abstract ：
Sparse representation-based classification (SRC) has been widely used because it just relies on simple linear regression ideas to do classification, and it does not need learning. However, the performance of SRC is limited by needing sufficient labeled samples per class and the sensitivity to class imbalance. For tackling these problems, an improved SRC model is constructed in this paper. For alleviating the problem of insufficient labeled samples, an unlabeled data-driven inverse projection sparse representation-based classification model is constructed to achieve effective and stable representation and recognition results. The L-1/2 and S-1/2 regularizations are introduced to capture the sparsity of 1-D and 2-D, and to make the model have good statistical properties. Moreover, the cost-sensitive strategy is integrated into the model's classification criteria to improve the imbalance of class distribution adaptively, especially for multiclass imbalanced data. A solver utilizing the mixed Gauss-Seidel and Jacobian ADMM algorithm is developed to obtain the approximate solution. Experiments on common public test databases show that the proposed model achieves competitive results compared with the latest published results and some deep-learning algorithms.
Keyword ：
1 2 regularization cost-sensitive inverse projection sparse representation-based classification unlabeled-data driven
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| GB/T 7714 | Yang, Xiaohui , Wang, Zheng , Sun, Jian et al. Unlabeled data driven cost-sensitive inverse projection sparse representation-based classification with 1/2 regularization [J]. | SCIENCE CHINA-INFORMATION SCIENCES , 2022 , 65 (8) . |
| MLA | Yang, Xiaohui et al. "Unlabeled data driven cost-sensitive inverse projection sparse representation-based classification with 1/2 regularization" . | SCIENCE CHINA-INFORMATION SCIENCES 65 . 8 (2022) . |
| APA | Yang, Xiaohui , Wang, Zheng , Sun, Jian , Xu, Zongben . Unlabeled data driven cost-sensitive inverse projection sparse representation-based classification with 1/2 regularization . | SCIENCE CHINA-INFORMATION SCIENCES , 2022 , 65 (8) . |
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Abstract ：
Blind hyperspectral unmixing (HU) has long been recognized as a crucial component in analyzing the hyperspectral imagery (HSI) collected by airborne and spaceborne sensors. Due to the highly ill-posed problems of such a blind source separation scheme and the effects of spectral variability in hyperspectral imaging, the ability to accurately and effectively unmixing the complex HSI still remains limited. To this end, this article presents a novel blind HU model, called sparsity-enhanced convolutional decomposition (SeCoDe), by jointly capturing spatial-spectral information of HSI in a tensor-based fashion. SeCoDe benefits from two perspectives. On the one hand, the convolutional operation is employed in SeCoDe to locally model the spatial relation between the targeted pixel and its neighbors, which can be well explained by spectral bundles that are capable of addressing spectral variabilities effectively. It maintains, on the other hand, physically continuous spectral components by decomposing the HSI along with the spectral domain. With sparsity-enhanced regularization, an alternative optimization strategy with alternating direction method of multipliers (ADMM)-based optimization algorithm is devised for efficient model inference. Extensive experiments conducted on three different data sets demonstrate the superiority of the proposed SeCoDe compared to previous state-of-the-art methods. We will also release the code at https://github.com/danfenghong/IEEE_TGRS_SeCoDe to encourage the reproduction of the given results.
Keyword ：
Blind hyperspectral unmixing (HU) Context modeling Convolutional codes convolutional sparse coding (CSC) Encoding Hyperspectral imaging Optimization spectral bundles spectral variability (SV) Task analysis tensor decomposition Tensors
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| GB/T 7714 | Yao, Jing , Hong, Danfeng , Xu, Lin et al. Sparsity-Enhanced Convolutional Decomposition: A Novel Tensor-Based Paradigm for Blind Hyperspectral Unmixing [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2022 , 60 . |
| MLA | Yao, Jing et al. "Sparsity-Enhanced Convolutional Decomposition: A Novel Tensor-Based Paradigm for Blind Hyperspectral Unmixing" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60 (2022) . |
| APA | Yao, Jing , Hong, Danfeng , Xu, Lin , Meng, Deyu , Chanussot, Jocelyn , Xu, Zongben . Sparsity-Enhanced Convolutional Decomposition: A Novel Tensor-Based Paradigm for Blind Hyperspectral Unmixing . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2022 , 60 . |
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Abstract ：
Inspired by psychological principles, the authors designed a big data clustering algorithm with publicly available codes, which can scale to billion-level datasets and is highly effective across various domains. Clustering is the discovery of latent group structure in data and is a fundamental problem in artificial intelligence, and a vital procedure in data-driven scientific research over all disciplines. Yet, existing methods have various limitations, especially weak cognitive interpretability and poor computational scalability, when it comes to clustering massive datasets that are increasingly available in all domains. Here, by simulating the multi-scale cognitive observation process of humans, we design a scalable algorithm to detect clusters hierarchically hidden in massive datasets. The observation scale changes, following the Weber-Fechner law to capture the gradually emerging meaningful grouping structure. We validated our approach in real datasets with up to a billion records and 2000 dimensions, including taxi trajectories, single-cell gene expressions, face images, computer logs and audios. Our approach outperformed popular methods in usability, efficiency, effectiveness and robustness across different domains.
Keyword ：
clustering cognitive interpretability computational scalability massive data psychological observation Weber-Fechner law
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| GB/T 7714 | Yang, Shusen , Zhang, Liwen , Xu, Chen et al. Massive data clustering by multi-scale psychological observations [J]. | NATIONAL SCIENCE REVIEW , 2022 , 9 (2) . |
| MLA | Yang, Shusen et al. "Massive data clustering by multi-scale psychological observations" . | NATIONAL SCIENCE REVIEW 9 . 2 (2022) . |
| APA | Yang, Shusen , Zhang, Liwen , Xu, Chen , Yu, Hanqiao , Fan, Jianqing , Xu, Zongben . Massive data clustering by multi-scale psychological observations . | NATIONAL SCIENCE REVIEW , 2022 , 9 (2) . |
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Abstract ：
Seismic acoustic-impedance (AI) inversion, which estimates the AI of the reservoir from seismic and other geophysical data, is a type of nonlinear inverse problem that faces the local minima issue during optimization. Without requiring an accurate initial model, global optimization methods have the ability to jump out of local minima and search for the optimal global solution. However, the low-efficiency nature of global optimization methods hinders their practical application, especially in largescale AI inversion problems (AI inversion with a large number of traces). We have developed a new intelligent seismic AI inversion method based on global optimization and deep learning. In this method, global optimization is used to generate data sets for training a deep-learning network, and it is used to first accelerate and then surrogate global optimization. In other words, for large-scale seismic AI inversion, global optimization only inverts the AI model for a few traces, and the AI models of most traces are obtained by deep learning. The deep-learning architecture that we used to map from the seismic trace to its corresponding AI model is established based on U-Net. Because the time-consuming global optimization inversion procedure can be avoided for most traces, this method has a significant advantage over conventional global optimization methods in efficiency. To verify the effectiveness of our method, we compare its performance with the conventional global optimization method on 3D synthetic and field data examples. Compared to the conventional method, our method only needs approximately one-tenth of the computation time to build AI models with better accuracy.
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| GB/T 7714 | Gao, Zhaoqi , Yang, Wei , Tian, Yajun et al. Global optimization with deep-learning-based acceleration surrogate for large-scale seismic acoustic-impedance inversion [J]. | GEOPHYSICS , 2022 , 87 (1) : R35-R51 . |
| MLA | Gao, Zhaoqi et al. "Global optimization with deep-learning-based acceleration surrogate for large-scale seismic acoustic-impedance inversion" . | GEOPHYSICS 87 . 1 (2022) : R35-R51 . |
| APA | Gao, Zhaoqi , Yang, Wei , Tian, Yajun , Li, Chuang , Jiang, Xiudi , Gao, Jinghuai et al. Global optimization with deep-learning-based acceleration surrogate for large-scale seismic acoustic-impedance inversion . | GEOPHYSICS , 2022 , 87 (1) , R35-R51 . |
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Abstract ：
Adaptive parameter control is critical in the design and application of evolutionary algorithm (EA), so does in differential evolution. In the past decade, many adaptive evolutionary algorithms have been proposed, in which online information collected until current generation during the evolutionary search procedure is used to determine the algorithmic parameters for the next generation. Recent studies often assume that the algorithmic parameters follow some distributions, while the distributions' parameters (called hyper-parameters) are updated by the collected information. Performances of these adaptive EAs depend highly on the hyper-parameters. Notice that the experiences obtained from optimizing some related problems could provide useful guidelines on how to adaptively control the distributions' parameters. However, few existing studies sufficiently used such experiences. To fill the gap, we propose a general framework for adaptive parameter control by modeling its evolution procedure as a Markov decision process. In the framework, a neural network is employed to act as the controller. The natural evolution strategies is applied to train the neural network. The proposed framework is applied on two well-known differential evolutions (DEs), namely JADE and LSHADE. By incorporating the learned controller, two DEs, named JADE/AC and LSHADE/AC, are formed. Experimental results on the CEC 2018 benchmark suite show that in general JADE/AC and LSHADE/AC perform significantly better than their counterparts. Moreover, in comparison with some well-known EAs including three suggested best DEs in a review paper (including LSHADE, cDE and CoBiDE), the championship algorithm in the CEC 2018 competitions, a recently-developed learnable DE and recently proposed DEs, our study shows that LSHADE/AC performs the best amongst them without sacrificing much computation time.
Keyword ：
Evolutionary algorithm History learning to optimize Markov decision process natural evolution strategies Next generation networking Q-learning Search problems Sociology Statistics Tuning
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| GB/T 7714 | Zhang, Haotian , Sun, Jianyong , Tan, Kay Chen et al. Learning Adaptive Differential Evolution by Natural Evolution Strategies [J]. | IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE , 2022 . |
| MLA | Zhang, Haotian et al. "Learning Adaptive Differential Evolution by Natural Evolution Strategies" . | IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE (2022) . |
| APA | Zhang, Haotian , Sun, Jianyong , Tan, Kay Chen , Xu, Zongben . Learning Adaptive Differential Evolution by Natural Evolution Strategies . | IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE , 2022 . |
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Abstract ：
In this paper, we study the uniqueness of a virtual substrate for periodic metallic elements in a dielectric half-space. When the periodic metallic elements are placed at the interface of different substrates, they can be regarded to be embedded into a virtual substrate whose thickness approaches zero. However, the process of the mathematical limit of the thickness seems to be independent of the choice of the virtual substrate. Thereby, it is necessary to verify whether the arbitrary virtual substrate holds for the case. It is theoretically verified that the permittivity of the virtual substrate should be unique in order to satisfy the physical boundary condition of the periodic metallic elements. The root of the phenomenon is that the mathematical limit gives the alternative means to approach the actual physical situation, but the actual physical situation determines the way how the mathematical limit approaches zero. Finally, for comparison, two different virtual substrates are designed to validate the theory, for alternative substrate, incidence angle, and metallic elements. Besides, the finding can also be used to simplify the analysis and design of the metasurface by converting the periodic metallic elements in a dielectric half-space to the same periodic metallic elements in a uniform substrate.
Keyword ：
boundary condition mathematical limit periodic metallic elements relative permittivity virtual substrate
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| GB/T 7714 | Liu, Xiaobo , Xue, Wei , Chen, Xiaoming et al. On the uniqueness of virtual substrate for metasurface in a dielectric half-space [J]. | SCIENCE CHINA-INFORMATION SCIENCES , 2022 , 65 (1) . |
| MLA | Liu, Xiaobo et al. "On the uniqueness of virtual substrate for metasurface in a dielectric half-space" . | SCIENCE CHINA-INFORMATION SCIENCES 65 . 1 (2022) . |
| APA | Liu, Xiaobo , Xue, Wei , Chen, Xiaoming , Zhang, Anxue , Cheng, Qiang , Xu, Zongben . On the uniqueness of virtual substrate for metasurface in a dielectric half-space . | SCIENCE CHINA-INFORMATION SCIENCES , 2022 , 65 (1) . |
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Abstract ：
Ground roll, a source-generated surface wave, is a main type of coherent noise in a land seismic survey. Low-frequency and high-amplitude ground roll often overlays valid reflection events, resulting in obscuring seismic reflections. Ground-roll attenuation is an essential step for seismic data processing, which is based on the accurate separation of the ground roll and reflections without damaging their morphological characteristics. In this study, we effectively separate and suppress ground roll in shot gathers through a proposed workflow. We first identify the major components of the ground roll adopting the multichannel variational mode decomposition (MVMD), which shows significant improvements compared to the conventional single-channel VMD. Ground roll can be identified on the decomposed band-limited intrinsic mode functions (IMFs). Moreover, we propose an adaptive criterion to determine the number of decomposed IMFs. Due to the narrowly concentrated frequency components with the multichannel continuity constraint from MVMD, ground roll is mainly contained in low-frequency IMFs, which benefits the accurate ground-roll suppression. Next, we separate ground roll and reflections on the selected low-frequency IMFs through a curvelet based block-coordinate relaxation method. Afterward, we can obtain a filtered gather by removing the separated ground roll from the original shot gather. Finally, we apply the proposed workflow to synthetic and field gathers to testify its validity and effectiveness for simultaneously attenuating ground roll and preserving valid seismic reflector information.
Keyword ：
Attenuation Curvelet transform ground roll intrinsic mode functions (IMFs) multichannel variational mode decomposition (MVMD) Optimization Robustness Signal resolution Signal to noise ratio Transforms variational mode decomposition (VMD) Wavelet transforms
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| GB/T 7714 | Liu, Naihao , Li, Fangyu , Wang, Dehua et al. Ground-Roll Separation and Attenuation Using Curvelet-Based Multichannel Variational Mode Decomposition [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2022 , 60 . |
| MLA | Liu, Naihao et al. "Ground-Roll Separation and Attenuation Using Curvelet-Based Multichannel Variational Mode Decomposition" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60 (2022) . |
| APA | Liu, Naihao , Li, Fangyu , Wang, Dehua , Gao, Jinghuai , Xu, Zongben . Ground-Roll Separation and Attenuation Using Curvelet-Based Multichannel Variational Mode Decomposition . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2022 , 60 . |
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Abstract ：
Multispectral and hyperspectral image fusion (MS/HS fusion) aims to fuse a high-resolution multispectral (HrMS) and a low-resolution hyperspectral (LrHS) images to generate a high-resolution hyperspectral (HrHS) image, which has become one of the most commonly addressed problems for hyperspectral image processing. In this paper, we specifically designed a network architecture for the MS/HS fusion task, called MHF-net, which not only contains clear interpretability, but also reasonably embeds the well studied linear mapping that links the HrHS image to HrMS and LrHS images. In particular, we first construct an MS/HS fusion model which merges the generalization models of low-resolution images and the low-rankness prior knowledge of HrHS image into a concise formulation, and then we build the proposed network by unfolding the proximal gradient algorithm for solving the proposed model. As a result of the careful design for the model and algorithm, all the fundamental modules in MHF-net have clear physical meanings and are thus easily interpretable. This not only greatly facilitates an easy intuitive observation and analysis on what happens inside the network, but also leads to its good generalization capability. Based on the architecture of MHF-net, we further design two deep learning regimes for two general cases in practice: consistent MHF-net and blind MHF-net. The former is suitable in the case that spectral and spatial responses of training and testing data are consistent, just as considered in most of the pervious general supervised MS/HS fusion researches. The latter ensures a good generalization in mismatch cases of spectral and spatial responses in training and testing data, and even across different sensors, which is generally considered to be a challenging issue for general supervised MS/HS fusion methods. Experimental results on simulated and real data substantiate the superiority of our method both visually and quantitatively as compared with state-of-the-art methods along this line of research.
Keyword ：
generalization Hyperspectral imaging image restoration interpretable deep learning Multispectral and hyperspectral image fusion Network architecture Sensors Task analysis Testing Training
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| GB/T 7714 | Xie, Qi , Zhou, Minghao , Zhao, Qian et al. MHF-Net: An Interpretable Deep Network for Multispectral and Hyperspectral Image Fusion [J]. | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2022 , 44 (3) : 1457-1473 . |
| MLA | Xie, Qi et al. "MHF-Net: An Interpretable Deep Network for Multispectral and Hyperspectral Image Fusion" . | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 44 . 3 (2022) : 1457-1473 . |
| APA | Xie, Qi , Zhou, Minghao , Zhao, Qian , Xu, Zongben , Meng, Deyu . MHF-Net: An Interpretable Deep Network for Multispectral and Hyperspectral Image Fusion . | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2022 , 44 (3) , 1457-1473 . |
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Abstract ：
Owing to the powerful data representation ability of deep learning (DL) techniques, tremendous progress has been recently made in hyperspectral image (HSI) classification. Convolutional neural network (CNN), as a main part of the DL family, has been proven to be considerably effective to extract spatial-spectral features for HSIs. Nevertheless, its classification performance, to a great extent, depends on the quality and quantity of samples in the network training process. To select those samples, either labeled or unlabeled, which can be used to enhance the generalization ability of CNNs and further improve the classification accuracy, we propose an iterative semi-supervised CNNs framework by means of active learning and superpixel segmentation techniques, dubbed as semi-active CNNs (SA-CNNs), for HSI classification. More specifically, we start to pretrain a CNN-based model on a small-scale unbiased labeled set and infer unlabeled data using the trained model, i.e., generating pseudolabels. Then, the reliable samples, which consist of two parts: high label homogeneity and most informativeness, are actively selected from superpixel segments. These selected labeled and unlabeled samples with their labels and pseudolabels are refed into the next-round network training. Moreover, three different schedules, i.e., log-, exp-, and linear-schedules, are progressively adopted to fully explore their potentials in sample selection, until a labeling budget is finally reached. Extensive experiments are conducted on three benchmark HSI datasets, demonstrating substantial performance improvements of the proposed SA-CNNs over other similar competitors.
Keyword ：
Active learning classification convolutional neural network (CNN) Convolutional neural networks deep learning (DL) Feature extraction hyperspectral Hyperspectral imaging Image segmentation Iterative methods pseudolabel remote sensing Schedules semi-supervised learning superpixel segmentation Training
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| GB/T 7714 | Yao, Jing , Cao, Xiangyong , Hong, Danfeng et al. Semi-Active Convolutional Neural Networks for Hyperspectral Image Classification [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2022 , 60 . |
| MLA | Yao, Jing et al. "Semi-Active Convolutional Neural Networks for Hyperspectral Image Classification" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60 (2022) . |
| APA | Yao, Jing , Cao, Xiangyong , Hong, Danfeng , Wu, Xin , Meng, Deyu , Chanussot, Jocelyn et al. Semi-Active Convolutional Neural Networks for Hyperspectral Image Classification . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2022 , 60 . |
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