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Hyperspectral Remote Sensing Image Classification With CNN Based on Quantum Genetic-Optimized Sparse Representation

IEEE ACCESS 2020年 8卷 99900-99909页

作者： Chen, Huayue Miao, Fang Shen, Xu Chengdu Univ Technol Sch Geophys Chengdu 610059 Peoples R China China West Normal Univ Sch Comp Sci Nanchong 637002 Peoples R China Internet Things Percept & Big Data Anal Key Lab N Nanchong 637002 Peoples R China Chengdu Univ Key Lab Pattern Recognit & Intelligent Informat P Chengdu 610041 Peoples R China

Due to the characteristics of the spectrum integration, information redundancy, spectrum mixing phenomenon and nonlinearity of the hyperspectral remote sensing images, it is a major challenging task to classify the hyperspectral remote sensing images. Therefore, a hyperspectral remote sensing image classification method, named QGASR-CNN is proposed in this paper. In the QGASR-CNN, a quantum genetic-optimized sparse representation method is designed to obtain the over-complete dictionary with sparsity, and achieve the feature sparse representation to construct the sparse feature matrix of hyperspectral remote sensing image pixel groups. Then the convolution neural network(CNN) directly convolutes with image pixels to build the feature mapping relation by using convolution operation. Finally, in order to testify the effectiveness of the QGASR-CNN, the actual hyperspectral remote sensing image datasets are selected in here. The comparison results show that the QGASR-CNN sparsely represents the features of hyperspectral remote sensing images and improves the classification accuracy. It can effectively alleviate the problems of the small samples and 'salt and pepper misclassification'.

关键词： Hyperspectral imaging Feature extraction Convolution Genetic algorithms Data mining Hyperspectral remote sensing image classification sparse representation convolutional neural network quantum genetic

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SSANet-Novel Residual Network for Computer-Aided Diagnosis of pulmonary Nodules in Chest Computed Tomography

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2024年第5期34卷

作者： Gu, Yu Liu, Jiaqi Yang, Lidong Zhang, Baohua Wang, Jing Lu, Xiaoqi Li, Jianjun Liu, Xin Yu, Dahua Zhao, Ying Tang, Siyuan He, Qun Inner Mongolia Univ Sci & Technol Sch Digital & Intelligent Ind Inner Mongolia Key Lab Pattern Recognit & Intellig Baotou Peoples R China Inner Mongolia Univ Sci & Technol Sch Automat & Elect Engn Baotou Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing Peoples R China Inner Mongolia Univ Technol Coll Informat Engn Hohhot Peoples R China Inner Mongolia Univ Sci & Technol Baotou Med Coll Sch Comp Sci & Technol Baotou Peoples R China

The manifestations of early lung cancer in medical imaging often appear as pulmonary nodules, which can be classified as benign or malignant. In recent years, there has been a gradual application of deep learning-based computer-aided diagnosis technology to assist in the diagnosis of lung nodules. This study introduces a novel three-dimensional (3D) residual network called SSANet, which integrates split-based convolution, shuffle attention, and a novel activation function. The aim is to enhance the accuracy of distinguishing between benign and malignant lung nodules using convolutional neural networks (CNNs) and alleviate the burden on doctors when interpreting the images. To fully extract pulmonary nodule information from chest CT images, the original residual network is expanded into a 3D CNN structure. Additionally, a 3D split-based convolutional operation (SpConv) is designed and integrated into the feature extraction module to reduce redundancy in feature maps and improve network inference speed. In the SSABlock part of the proposed network, ACON (Activated or Not) function is also introduced. The proposed SSANet also incorporates an attention module to capture critical characteristics of lung nodules. During the training process, the polyLoss function is utilized. Once SSANet generates the diagnosis result, a heatmap displays using Score-CAM is employed to evaluate whether the network accurately identifies the location of lung nodules. In the final test set, the proposed network achieves an accuracy of 89.13%, an F1-score of 84.85%, and a G-mean of 86.20%. These metrics represent improvements of 5.43%, 5.98%, and 4.09%, respectively, compared with the original base network. The experimental results align with those of previous studies on pulmonary nodule diagnosis networks, confirming the reliability and clinical applicability of the diagnostic outcomes.

关键词： attention module class activation mapping computer-aided diagnosis of pulmonary nodules deep learning improved residual network

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Optical solitons to the coupled cubic-quartic Sasa-Satsuma equation with Kerr law nonlinearity in birefringent fibers

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OpTIK 2022年 269卷

作者： Li, Zhao Xie, Xinyu Jin, Changjiang Chengdu Univ Coll Comp Sci Chengdu 610106 Peoples R China Chengdu Univ Inst Higher Educ Sichuan Prov Key Lab Pattern Recognit & Intelligent Informat Pr Chengdu 610106 Peoples R China Chengdu Aeronaut Vocat & Tech Coll Chengdu 610100 Sichuan Peoples R China

This paper mainly studies the optical soliton solutions of the coupled cubic-quartic Sasa- Satsuma equation with Kerr law nonlinearity describing the propagation of femtosecond pulses in optical fibers. Firstly, using traveling wave transformation, the coupled cubic-quartic Sasa- Satsuma equation with Kerr law nonlinearity in birefringent fibers is simplified into the coupled nonlinear ordinary differential equations. Secondly, according to the trial method of polynomial of rank homogeneous equation, the optical soliton solutions of coupled cubic-quartic Sasa- Satsuma equation with Kerr law nonlinearity can be obtained. Finally, in order to explain the propagation of optical solitons, three-dimensional and two-dimensional diagrams of the obtained solutions are drawn.

关键词： Sasa-Satsuma equation Kerr law nonlinearity Optical soliton solutions Trial method of polynomial

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Single nighttime image dehazing based on unified variational decomposition model and multi-scale contrast enhancement

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ENGINEERING AppLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期116卷

作者： Liu, Yun Yan, Zhongsheng Ye, Tian Wu, Aimin Li, Yuche Southwest Univ Coll Artificial Intelligence Chongqing 400715 Peoples R China Inst Higher Educ Sichuan Prov Key Lab Pattern Recognit & Intelligent Informat P Chengdu Peoples R China Jimei Univ Coll Ocean Informat Engn Xiamen 361021 Peoples R China Chongqing Coll Int Business & Econ Coll Big Data & Intelligent Engn Chongqing 401520 Peoples R China China Univ Petr Coll Geosci Beijing 102249 Peoples R China

Most of existing dehazing methods are unable to deal with nighttime hazy scenarios well due to complex degraded factors such as non-uniform illumination, low light, glows and hazes. To obtain high-quality image under nighttime haze imaging conditions, we propose a single nighttime image dehazing framework based on a unified variational decomposition model and multi-scale contrast enhancement to simultaneously address these undesirable issues. First, a unified variational decomposition model consisting of three regularization terms is proposed to simultaneously decompose a nighttime hazy image into a structure layer, a detail layer and a noise layer. Concretely, we employ e(1) norm to constrain the structure component, adopt e(0) sparsity term to enforce the piece-wise continuous of the residual of the gradients between the detail layer and the modified glow-free image, and use the Frobenius norm to estimate the noise layer. Next, the hazes in the structure layer are removed by inversing the physical model and the effective details in the texture layers are enhanced while the amplified noises are suppressed in a multi-scale fashion. Finally, the dehazed structure layer and the enhanced detail layers are integrated into a haze-free image. Experiments demonstrate that the proposed framework achieves superior performance on nighttime haze removal and noise suppression compared with state-of-the-art dehazing techniques.

关键词： Single nighttime image dehazing Unified variational decomposition model Multi-scale Noise amplification

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Identifying and distinguishing of essential tremor and parkinson's disease with grouped stability analysis based on searchlight-based MVpA

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BIOMEDICAL ENGINEERING ONLINE 2022年第1期21卷 81-81页

作者： Cheng, FuChao Duan, YuMei Jiang, Hong Zeng, Yu Chen, XiaoDan Qin, Ling Zhao, LiQin Yi, FaSheng Tang, YiQian Liu, Chang Chengdu Univ Coll Comp Chengdu Peoples R China Chengdu Jincheng Coll Dept Comp & Software Chengdu Peoples R China Shanghai Jiao Tong Univ Rui Jin Hosp Dept Neurosurg Sch Med Shanghai Peoples R China Inst Higher Educ Sichuan Prov Key Lab Pattern Recognit & Intelligent Informat P Chengdu Peoples R China

Background Since both essential tremor (ET) and parkinson's disease (pD) are movement disorders and share similar clinical symptoms, it is very difficult to recognize the differences in the presentation, course, and treatment of ET and pD, which leads to misdiagnosed commonly. purpose Although neuroimaging biomarker of ET and pD has been investigated based on statistical analysis, it is unable to assist the clinical diagnosis of ET and pD and ensure the efficiency of these biomarkers. The aim of the study was to identify the neuroimaging biomarkers of ET and pD based on structural magnetic resonance imaging (MRI). Moreover, the study also distinguished ET from pD via these biomarkers to validate their classification performance. Methods This study has developed and implemented a three-level machine learning framework to identify and distinguish ET and pD. First of all, at the model-level assessment, the searchlight-based machine learning method has been used to identify the group differences of patients (ET/pD) with normal controls (NCs). And then, at the feature-level assessment, the stability of group differences has been tested based on structural brain atlas separately using the permutation test to identify the robust neuroimaging biomarkers. Furthermore, the identified biomarkers of ET and pD have been applied to classify ET from pD based on machine learning techniques. Finally, the identified biomarkers have been compared with the previous findings of the biology-level assessment. Results According to the biomarkers identified by machine learning, this study has found widespread alterations of gray matter (GM) for ET and large overlap between ET and pD and achieved superior classification performance (pCA + SVM, accuracy = 100%). Conclusions This study has demonstrated the significance of a machine learning framework to identify and distinguish ET and pD. Future studies using a large data set are needed to confirm the potential clinical application of machine

关键词： Grouped stability analysis Neuroimaging biomarkers MVpA Essential tremor parkinson's disease

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Joint Multisource Saliency and Exemplar Mechanism for Weakly Supervised Video Object Segmentation

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IEEE TRANSACTIONS ON IMAGE pROCESSING 2021年 30卷 8155-8169页

作者： En, Qing Duan, Lijuan Zhang, Zhaoxiang Beijing Univ Technol Fac Informat Technol Beijing Key Lab Trusted Comp Beijing 100124 Peoples R China Natl Engn Lab Crit Technol Informat Secur Classif Beijing 100124 Peoples R China Chinese Acad Sci Ctr Res Intelligent Percept & Comp Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Ctr Artificial Intelligence & Robot Hong Kong Inst Sci & Innovat Hong Kong Peoples R China

Weakly supervised video object segmentation (WSVOS) is a vital yet challenging task in which the aim is to segment pixel-level masks with only category labels. Existing methods still have certain limitations, e.g., difficulty in comprehending appropriate spatiotemporal knowledge and an inability to explore common semantic information with category labels. To overcome these challenges, we formulate a novel framework by integrating multisource saliency and incorporating an exemplar mechanism for WSVOS. Specifically, we propose a multisource saliency module to comprehend spatiotemporal knowledge by integrating spatial and temporal saliency as bottom-up cues, which can effectively eliminate disruptions due to confusing regions and identify attractive regions. Moreover, to our knowledge, we make the first attempt to incorporate an exemplar mechanism into WSVOS by proposing an adaptive exemplar module to process top-down cues, which can provide reliable guidance for co-occurring objects in intraclass videos and identify attentive regions. Our framework, which comprises the two aforementioned modules, offers a new perspective on directly constructing the correspondence between bottom-up cues and top-down cues when ground-truth information for the reference frames is lacking. Comprehensive experiments demonstrate that the proposed framework achieves state-of-the-art performance.

关键词： Spatiotemporal phenomena Feature extraction Motion segmentation Annotations Training Task analysis Object segmentation Weakly supervised learning video object segmentation exemplar mechanism spatiotemporal saliency

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Synchronization sampled-data control of uncertain neural networks under an asymmetric Lyapunov-Krasovskii functional method☆

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EXpERT SYSTEMS WITH AppLICATIONS 2024年 239卷

作者： Wang, Shuoting Shi, Kaibo Wang, Jun Yu, Yongbin Wen, Shiping Yang, Jin Han, Sheng Chengdu Univ Sch Comp Chengdu 610106 Peoples R China Chengdu Univ Inst Higher Educ Sichuan Prov Key Lab Pattern Recognit & Intelligent Informat Pr Chengdu 610106 Peoples R China Chengdu Univ Sch Elect Informat & Elect Engn Chengdu 610106 Peoples R China Southwest Minzu Univ Coll Elect & Informat Engn Chengdu 610041 Peoples R China Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu 610054 Peoples R China Univ Technol Sydney Australian AI Inst Fac Engn & Informat Technol Ultimo NSW 2007 Australia Univ Elect Sci & Technol China Sch Aeronaut & Astronaut Chengdu 611731 Peoples R China

This paper investigates the synchronization control of neural networks (NNs) considering parameter uncertainties by utilizing an improved asymmetric Lyapunov-Krasovskii functional (ALKF) method. Firstly, an uncertain NNs model with discrete and distributed delays is developed. To synchronize the master-slave system, a memory sampled-data controller is designed. Secondly, an improved ALKF is constructed, in which the positive-definite and symmetric restrictions of matrices are relaxed. Furthermore, improved synchronization criteria are established by linear matrix inequalities (LMIs), which are based on the ALKF method and the integral inequality technique. Finally, two simulation examples are conducted to demonstrate the feasibility and superiority of the established stability conditions in reaching a maximum sampling period.

关键词： Neural networks (NNs) parameter uncertainties Memory sampled-data control Distributed delays Asymmetric Lyapunov-Krasovskii functional (ALKF)

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Effect of diffusion interlayer and graded multi-interlayers on the reduction of thermal stress in tritium penetration barrier coating system

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FUSION ENGINEERING AND DESIGN 2024年 205卷

作者： Liu, Ze Zhang, Hong Tang, Lin Guo, Junxiong Zhang, Yafei Li, Yuepeng You, Lei Wang, Jian Dong, XiZe Liu, Qiaoling Cao, Zhi Chengdu Univ Sch Elect Informat & Elect Engn Chengdu 610106 Peoples R China Chengdu Univ Key Lab Pattern Recognit & Intelligent Informat Pr Chengdu 610106 Sichuan Peoples R China Panzhihua Univ Vanadium & Titanium Resource Comprehens Utilizat K Panzhihua 617000 Peoples R China Chengdu Univ Technol Coll Nucl Technol & Automat Engn Chengdu 610059 Peoples R China Shanghai Marine Diesel Engine Res Inst Cryogen Dept Shanghai 201203 Peoples R China CNNC 404 Co Ltd Jiayuguan 735100 Peoples R China

The effect of substrate roughness on the thermal stress of the Al2O3/RAFM tritium penetration barrier was investigated by using finite element method. Two solutions (single diffusion interlayer and graded multiinterlayers) were tried to implemented to minimize the thermal stress in the system. The results indicated that a single diffusion interlayer with an appropriate coefficient of thermal expansion (CTE) can effectively reduce the thermal stress of the system. Multi-interlayers, with a grade yield strength in the interlayer, mitigated thermal stress. Additionally, the third method was investigated the relationship between coating thickness and system thermal stress. The outcome indicated that as the coating thickness increased, the system thermal stress improved. By comparing these three approaches, the results suggested that the preparation of a single diffusion interlayer was the most effective means of reducing the thermal stress.

关键词： Tritium penetration barrier system Diffusion interlayer Graded multi-interlayers Film thickness

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phase portraits and optical soliton solutions of coupled Sasa-Satsuma model in birefringent fibers

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RESULTS IN pHYSICS 2022年 43卷

作者： Li, Zhao Fan, Wenjie Miao, Fang Jin, Changjiang Chengdu Univ Coll Comp Sci Chengdu 610106 Peoples R China Chengdu Univ Inst Higher Educ Sichuan Prov Key Lab Pattern Recognit & Intelligent Informat Pr Chengdu 610106 Peoples R China Chengdu Aeronaut Polytech Chengdu 610100 Peoples R China

The main purpose of this paper is to discuss the optical soliton solutions and phase portraits of the coupled Sasa-Satsuma model in nonlinear optics. This model is usually used to describe the propagation of femtosecond pulses in optical fibers. By using traveling wave transformation, the coupled Sasa-Satsuma model is simpli-fied into the coupled nonlinear ordinary differential equations. After that, the coupled nonlinear ordinary differential equations are transformed into two-dimensional planar dynamic system with the Hamiltonian system. According to the bifurcation theory of planar dynamical system, the phase portrait of two-dimensional dynamical system is drawn. What is more, some very important optical soliton solutions are also constructed. In order to explain the propagation of optical solitons, three-dimensional diagrams, two-dimensional diagrams and the contour plots of the obtained solutions are drawn by using Maple software.

关键词： Coupled Sasa-Satsuma equation phase portrait Optical soliton solution planar dynamic system method

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Generating adversarial examples with elastic-net regularized boundary equilibrium generative adversarial network

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pattern recognitION LETTERS 2020年 140卷 281-287页

作者： Hu, Cong Wu, Xiao-Jun Li, Zuo-Yong Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Jiangsu Prov Engn Lab Pattern Recognit & Computat Wuxi 214122 Jiangsu Peoples R China Minjiang Univ Fujian Prov Key Lab Informat Proc & Intelligent C Fuzhou 350121 Peoples R China

To improve the attack success rate and image perceptual quality of adversarial examples against deep neural networks(DNNs), we propose a new Generative Adversarial Network (GAN) based attacker, named Elastic-net Regularized Boundary Equilibrium Generative Adversarial Network(ERBEGAN). Recent studies have shown that DNNs are easy to attack by adversarial examples(AEs) where benign images with small-magnitude perturbations mislead DNNs to incorrect results. A number of methods are proposed to generate AEs, but how to generate them with high attack success rate and perceptual quality needs more effort. Most attackers generate AEs by restricting L-2-norm and L-infinity-norm of adversarial perturbations. However, very few works have been developed on L-1 distortion matrix which encourages sparsity in the perturbation. In this paper, we penalize both L-2-norm and L-1-norm of perturbation as Elastic-Net regularization to improve the diversity and robustness of AEs. We further improve GAN by minimizing the additional pixel-wise loss derived from the Wasserstein distance between benign and adversarial auto-encoder loss distributions. Extensive experiments and visualizations on several datasets show that the proposed ER-BEGAN can yield higher attack success rates than the state-of-the-art GAN-based attacker AdvGAN under the semi-whitebox and black-box attack settings. Besides, our method efficiently generates diverse adversarial examples that are more perceptually realistic. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Adversarial example Elastic-net regularization Generative adversarial network Semi-whitebox attack Blackbox attack

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