Volume 13 Issue 2
Apr.  2024
Turn off MathJax
Article Contents
Abstract
References
Article Navigation >  Journal of Radars  > 2024  >  13(2): 443-456
Yang Jin-long, Liu Feng-mei, Wang Dong, Ge Hong-wei. Affinity Propagation Based Measurement Partition Algorithm for Multiple Extended Target Tracking[J]. Journal of Radars, 2015, 4(4): 452-459. doi: 10.12000/JR15003
Citation: LI Yi, DU Lan, ZHOU Ke’er, et al. Deep network for SAR target recognition based on attribute scattering center convolutional kernel modulation[J]. Journal of Radars, 2024, 13(2): 443–456. doi: 10.12000/JR24001
shu

Deep Network for SAR Target Recognition Based on Attribute Scattering Center Convolutional Kernel Modulation

DOI: 10.12000/JR24001

  • National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

Funds:  The National Natural Science Foundation of China (U21B2039)
More Information
  • Corresponding author: DU Lan, dulan@mail.xidian.edu.cn
  • Received Date: 2024-01-04
  • Rev Recd Date: 2024-03-13
    • Available Online: 2024-03-19
  • Publish Date: 2024-03-27
    Abstract
  • The feature extraction capability of Convolutional Neural Networks (CNNs) is related to the number of their parameters. Generally, using a large number of parameters leads to improved feature extraction capability of CNNs. However, a considerable amount of training data is required to effectively learn these parameters. In practical applications, Synthetic Aperture Radar (SAR) images available for model training are often limited. Reducing the number of parameters in a CNN can decrease the demand for training samples, but the feature expression ability of the CNN is simultaneously diminished, which affects its target recognition performance. To solve this problem, this paper proposes a deep network for SAR target recognition based on Attribute Scattering Center (ASC) convolutional kernel modulation. Given the electromagnetic scattering characteristics of SAR images, the proposed network extracts scattering structures and edge features that are more consistent with the characteristics of SAR targets by modulating a small number of CNN convolutional kernels using predefined ASC kernels with different orientations and lengths. This approach generates additional convolutional kernels, which can reduce the network parameters while ensuring feature extraction capability. In addition, the designed network uses ASC-modulated convolutional kernels at shallow layers to extract scattering structures and edge features that are more consistent with the characteristics of SAR images while utilizing CNN convolutional kernels at deeper layers to extract semantic features of SAR images. The proposed network focuses on the electromagnetic scattering characteristics of SAR targets and shows the feature extraction advantages of CNNs due to the simultaneous use of ASC-modulated and CNN convolutional kernels. Experiments based on the studied SAR images demonstrate that the proposed network can ensure excellent SAR target recognition performance while reducing the demand for training samples.

     

    • FullText(HTML)
    • References(26)
  • [1]
    KRIZHEVSKY A, SUTSKEVER I, and HINTON G E. ImageNet classification with deep convolutional neural networks[C]. The 25th International Conference on Neural Information Processing Systems, Lake Tahoe, USA, 2012: 1006–1114.
    [2]
    SIMONYAN K and ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[C]. 3rd International Conference on Learning Representations, San Diego, USA, 2015: 1–14. doi: 10.48550/arXiv.1409.1556.
    [3]
    HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 770–778. doi: 10.1109/CVPR.2016.90.
    [4]
    TAN Mingxing and LE Q. EfficientNet: Rethinking model scaling for convolutional neural networks[C]. The 36th International Conference on Machine Learning, Long Beach, USA, 2019: 6105–6114.
    [5]
    LIU Ze, LIN Yutong, CAO Yue, et al. Swin transformer: Hierarchical vision transformer using shifted windows[C]. 2021 IEEE/CVF International Conference on Computer Vision, Montreal, Canada, 2021: 9992–10002. doi: 10.1109/ICCV48922.2021.00986.
    [6]
    DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16x16 words: Transformers for image recognition at scale[C]. 9th International Conference on Learning Representations, 2021: 1−22. https://iclr.cc/virtual/2021/index.html.
    [7]
    CHEN Sizhe, WANG Haipeng, XU Feng, et al. Target classification using the deep convolutional networks for SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(8): 4806–4817. doi: 10.1109/TGRS.2016.2551720.
    [8]
    喻玲娟, 王亚东, 谢晓春, 等. 基于FCNN和ICAE的SAR图像目标识别方法[J]. 雷达学报, 2018, 7(5): 622–631. doi: 10.12000/JR18066.

    YU Lingjuan, WANG Yadong, XIE Xiaochun, et al. SAR ATR based on FCNN and ICAE[J]. Journal of Radars, 2018, 7(5): 622–631. doi: 10.12000/JR18066.
    [9]
    赵鹏菲, 黄丽佳. 一种基于EfficientNet与BiGRU的多角度SAR图像目标识别方法[J]. 雷达学报, 2021, 10(6): 895–904. doi: 10.12000/JR20133.

    ZHAO Pengfei and HUANG Lijia. Target recognition method for multi-aspect synthetic aperture radar images based on EfficientNet and BiGRU[J]. Journal of Radars, 2021, 10(6): 895–904. doi: 10.12000/JR20133.
    [10]
    HUANG Xiayuan, YANG Qiao, and QIAO Hong. Lightweight two-stream convolutional neural network for SAR target recognition[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 18(4): 667–671. doi: 10.1109/LGRS.2020.2983718.
    [11]
    LIU Jiaming, XING Mengdao, YU Hanwen, et al. EFTL: Complex convolutional networks with electromagnetic feature transfer learning for SAR target recognition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5209811. doi: 10.1109/TGRS.2021.3083261.
    [12]
    ZHANG Tianwen, ZHANG Xiaoling, KE Xiao, et al. HOG-ShipCLSNet: A novel deep learning network with HOG feature fusion for SAR ship classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5210322. doi: 10.1109/TGRS.2021.3082759.
    [13]
    QOSJA D, WAGNER S, and BRÜGGENWIRTH S. Benchmarking convolutional neural network backbones for target classification in SAR[C]. 2023 IEEE Radar Conference, San Antonio, USA, 2023: 1–6. doi: 10.1109/RadarConf2351548.2023.10149802.
    [14]
    LIU Zhuang, MAO Hanzi, WU Chaoyuan, et al. A ConvNet for the 2020s[C]. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, 2022: 11966–11976. doi: 10.1109/CVPR52688.2022.01167.
    [15]
    张翼鹏, 卢东东, 仇晓兰, 等. 基于散射点拓扑和双分支卷积神经网络的SAR图像小样本舰船分类[J]. 雷达学报, 2024, 13(2): 411–427. doi: 10.12000/JR23172.

    ZHANG Yipeng, LU Dongdong, QIU Xiaolan, et al. Few-shot ship classification of SAR images via scattering point topology and dual-branch convolutional neural network[J]. Journal of Radars, 2024, 13(2): 411–427. doi: 10.12000/JR23172.
    [16]
    LUAN Shangzhen, CHEN Chen, ZHANG Baochang, et al. Gabor convolutional networks[J]. IEEE Transactions on Image Processing, 2018, 27(9): 4357–4366. doi: 10.1109/TIP.2018.2835143.
    [17]
    徐丰, 金亚秋. 微波视觉与SAR图像智能解译[J]. 雷达学报, 2024, 13(2): 285–306. doi: 10.12000/JR23225.

    XU Feng and JIN Yaqiu. Microwave vision and intelligent perception of radar imagery[J]. Journal of Radars, 2024, 13(2): 285–306. doi: 10.12000/JR23225.
    [18]
    GERRY M J, POTTER L C, GUPTA I J, et al. A parametric model for synthetic aperture radar measurements[J]. IEEE Transactions on Antennas and Propagation, 1999, 47(7): 1179–1188. doi: 10.1109/8.785750.
    [19]
    POTTER L C and MOSES R L. Attributed scattering centers for SAR ATR[J]. IEEE Transactions on Image Processing, 1997, 6(1): 79–91. doi: 10.1109/83.552098.
    [20]
    李飞. 雷达图像目标特征提取方法研究[D]. [博士论文], 西安电子科技大学, 2014.

    LI Fei. Study on target feature extraction based on radar image[D]. [Ph.D. dissertation], Xidian University, 2014.
    [21]
    ROSS T D, WORRELL S W, VELTEN V J, et al. Standard SAR ATR evaluation experiments using the MSTAR public release data set[C]. SPIE 3370, Algorithms for Synthetic Aperture Radar Imagery V, Orlando, USA, 1998: 566–573. doi: 10.1117/12.321859.
    [22]
    HUANG Lanqing, LIU Bin, LI Boying, et al. OpenSARShip: A dataset dedicated to sentinel-1 ship interpretation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(1): 195–208. doi: 10.1109/JSTARS.2017.2755672.
    [23]
    SUN Yongguang, DU Lan, WANG Yan, et al. SAR automatic target recognition based on dictionary learning and joint dynamic sparse representation[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(12): 1777–1781. doi: 10.1109/LGRS.2016.2608578.
    [24]
    DENG Sheng, DU Lan, LI Chen, et al. SAR automatic target recognition based on Euclidean distance restricted autoencoder[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(7): 3323–3333. doi: 10.1109/JSTARS.2017.2670083.
    [25]
    NI Jiacheng and XU Yuelei. SAR automatic target recognition based on a visual cortical system[C]. 2013 6th International Congress on Image and Signal Processing, Hangzhou, China, 2013: 778–782. doi: 10.1109/CISP.2013.6745270.
    [26]
    LI Yi, DU Lan, and WEI Di. Multiscale CNN based on component analysis for SAR ATR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5211212. doi: 10.1109/TGRS.2021.3100137.
    • Relative Articles

  • Relative Articles

    [1]WANG Bohong, SHEN Biao, MU Wenxing, LIU Tao. Research on Super-resolution Methods for Radar Targets Based on Bat-inspired Spectrogram Correlation and Transformation Models[J]. Journal of Radars, 2025, 14(2): 293-308. doi: 10.12000/JR24239
    [2]GUO Zhongyi, WANG Yanzhe, WANG Yunlai, GUO Kai. Research Advances on the Rotational Doppler Effect of Vortex Electromagnetic Waves[J]. Journal of Radars, 2021, 10(5): 725-739. doi: 10.12000/JR21109
    [3]PEI Jiazheng, HUANG Yong, CHEN Baoxin, GUAN Jian, CAI Mi, CHEN Xiaolong. Long Time Coherent Integration Method Based on Combining Pulse Compression and Radon-Fourier Transform[J]. Journal of Radars, 2021, 10(6): 956-969. doi: 10.12000/JR21068
    [4]WEI Wei, ZHU Daiyin, WU Di. Wavenumber Domain Algorithm Based on the Principle of Chirp Scaling for SAR Imaging[J]. Journal of Radars, 2020, 9(2): 354-362. doi: 10.12000/JR19112
    [5]Li Yuqian, Yi Jianxin, Wan Xianrong, Liu Yuqi, Zhan Weijie. Helicopter Rotor Parameter Estimation Method for Passive Radar[J]. Journal of Radars, 2018, 7(3): 313-319. doi: 10.12000/JR17125
    [6]He Qifang, Zhang Qun, Luo Ying, Li Kaiming. A Sinusoidal Frequency Modulation Fourier-Bessel Transform and its Application to Micro-Doppler Feature Extraction[J]. Journal of Radars, 2018, 7(5): 593-601. doi: 10.12000/JR17069
    [7]Chen Fangxiang, Yi Wei, Zhou Tao, Kong Lingjiang. Passive Direct Location Determination for Multiple Sources Based on FRFT[J]. Journal of Radars, 2018, 7(4): 523-530. doi: 10.12000/JR18027
    [8]Liu Yuqi, Yi Jianxin, Wan Xianrong, Cheng Feng, Rao Yunhua, Gong Ziping. Experimental Research on Micro-Doppler Effect of Multi-rotor Drone with Digital Television Based Passive Radar[J]. Journal of Radars, 2018, 7(5): 585-592. doi: 10.12000/JR18062
    [9]Liu Xiangyang, Yang Jungang, Meng Jin, Zhang Xiao, Niu Dezhi. Sparse Three-dimensional Imaging Based on Hough Transform for Forward-looking Array SAR in Low SNR[J]. Journal of Radars, 2017, 6(3): 316-323. doi: 10.12000/JR17011
    [10]Li Liechen, Li Daojing, Huang Pingping. Airship Sparse Array Antenna Radar Real Aperture Imaging Based on Compressed Sensing and Sparsity in Transform Domain[J]. Journal of Radars, 2016, 5(1): 109-117. doi: 10.12000/JR14159
    [11]Lin Chunfeng, Huang Chunlin, Su Yi. Target Integration and Detection with the Radon-Fourier Transform for Bistatic Radar[J]. Journal of Radars, 2016, 5(5): 526-530. doi: 10.12000/JR16049
    [12]Qin Yao, Huang Chun-lin, Lu Min, Xu Wei. Adaptive clutter reduction based on wavelet transform and principal component analysis for ground penetrating radar[J]. Journal of Radars, 2015, 4(4): 445-451. doi: 10.12000/JR15013
    [13]Du Lan, Li Lin-sen, Li Wei-lu, Wang Bao-shuai, Shi Hui-ruo. Aircraft Target Classification Based on Correlation Features from Time-domain Echoes[J]. Journal of Radars, 2015, 4(6): 621-629. doi: 10.12000/JR15117
    [14]Tian Rui-qi, Bao Qing-long, Wang Ding-he, Chen Zeng-ping. An Algorithm for Target Parameter Estimation Based on Fractional Fourier and Keystone Transforms[J]. Journal of Radars, 2014, 3(5): 511-517. doi: 10.3724/SP.J.1300.2014.14058
    [15]Zhe Xiao-qiang, Chou Xiao-lan, Han Bing, Lei Bin. An Improved Doppler Rate Estimation Approach for Sliding Spotlight SAR Data Based on the Transposition Domain[J]. Journal of Radars, 2014, 3(4): 419-427. doi: 10.3724/SP.J.1300.2014.14008
    [16]Lu Chuan-guo, Feng Xin-xi, Kong Yun-bo, Zeng Rong, Li Hong-Ying. Track Initiation Based on Parallel Hough Transform[J]. Journal of Radars, 2013, 2(3): 292-299. doi: 10.3724/SP.J.1300.2013.13036
    [17]Guan Xin, Zhong Li-hua, Hu Dong-hui, Ding Chi-biao. A Compensation Algorithm Based on RSPWVD-Hough Transform for Doppler Expansion in Passive Radar[J]. Journal of Radars, 2013, 2(4): 430-438. doi: 10.3724/SP.J.1300.2013.13073
    [18]Deng Dong-hu, Zhang Qun, Luo Ying, Li Song, Zhu Ren-Fei. Resolution and Micro-Doppler Effect in Bi-ISAR System (in English)[J]. Journal of Radars, 2013, 2(2): 152-167. doi: 10.3724/SP.J.1300.2013.13039
    [19]Zheng Jin, You Hong-jian. Change Detection with SAR Images Based on Radon Transform and Jeffrey Divergence[J]. Journal of Radars, 2012, 1(2): 182-189. doi: 10.3724/SP.J.1300.2012.10068
    [20]Yu Bin-bin, Liu Chang, Wang Yan-fei. A Non-linear Scaling Algorithm Based on chirp-z Transform for Squint Mode FMCW-SAR[J]. Journal of Radars, 2012, 1(1): 69-75. doi: 10.3724/SP.J.1300.2012.20005
    • Supplements(0)
    • Cited By

  • Cited by

    Periodical cited type(6)

    1. 王纪平，刘衍琦，邓钱钰，郭淑婷，毛新华. 基于PAST的毫米波雷达MIMO阵列幅相误差校正算法. 信号处理. 2024(09): 1633-1647 .
    2. 宁宝俊. 毫米波雷达在斜拉桥索力监测中的应用. 西部交通科技. 2024(07): 157-160 .
    3. 张春杰，陈奇，赵佳琦. 基于注意力机制CNN-LSTM的毫米波雷达点云特征数据预测生成. 电讯技术. 2024(11): 1718-1725 .
    4. 耿同乐. 电流反馈在毫米波雷达信号处理中的应用. 集成电路应用. 2024(11): 26-27 .
    5. 于丹阳，杜磊. 一种基于快速迭代自适应算法的毫米波雷达波达角高精度测量方法. 计量科学与技术. 2024(11): 3-9+21 .
    6. 孙航，张路，季国田. 智能网联汽车标准体系及重点标准研究与展望. 汽车安全与节能学报. 2024(06): 795-812 .

    Other cited types(29)

  • Created with Highcharts 5.0.7Amount of accessChart context menuAbstract Views, HTML Views, PDF Downloads StatisticsAbstract ViewsHTML ViewsPDF Downloads2024-052024-062024-072024-082024-092024-102024-112024-122025-012025-022025-032025-04020406080
    Created with Highcharts 5.0.7Chart context menuAccess Class DistributionFULLTEXT: 30.8 %FULLTEXT: 30.8 %META: 59.9 %META: 59.9 %PDF: 9.3 %PDF: 9.3 %FULLTEXTMETAPDF
    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 5.4 %其他: 5.4 %其他: 2.2 %其他: 2.2 %Central District: 0.0 %Central District: 0.0 %China: 0.3 %China: 0.3 %Herndon: 0.0 %Herndon: 0.0 %Kao-sung: 0.0 %Kao-sung: 0.0 %Malvern: 0.0 %Malvern: 0.0 %North Point: 0.1 %North Point: 0.1 %San Lorenzo: 0.1 %San Lorenzo: 0.1 %Seattle: 0.1 %Seattle: 0.1 %[]: 0.4 %[]: 0.4 %三亚: 0.1 %三亚: 0.1 %三明: 0.1 %三明: 0.1 %上海: 3.0 %上海: 3.0 %东京: 0.2 %东京: 0.2 %东京都: 0.0 %东京都: 0.0 %东莞: 0.3 %东莞: 0.3 %中卫: 0.1 %中卫: 0.1 %临汾: 0.1 %临汾: 0.1 %临沂: 0.1 %临沂: 0.1 %丹东: 0.1 %丹东: 0.1 %九江: 0.0 %九江: 0.0 %伊利诺伊州: 0.0 %伊利诺伊州: 0.0 %伦敦: 0.2 %伦敦: 0.2 %佛山: 0.0 %佛山: 0.0 %兰州: 0.1 %兰州: 0.1 %兰辛: 0.0 %兰辛: 0.0 %凤凰城: 0.1 %凤凰城: 0.1 %北京: 10.5 %北京: 10.5 %北海: 0.0 %北海: 0.0 %十堰: 0.0 %十堰: 0.0 %华沙: 0.1 %华沙: 0.1 %南京: 2.1 %南京: 2.1 %南充: 0.0 %南充: 0.0 %南昌: 0.1 %南昌: 0.1 %南通: 0.0 %南通: 0.0 %卡拉奇: 0.0 %卡拉奇: 0.0 %厦门: 0.0 %厦门: 0.0 %台北: 0.9 %台北: 0.9 %台州: 0.0 %台州: 0.0 %合肥: 0.5 %合肥: 0.5 %吉安: 0.0 %吉安: 0.0 %呼和浩特: 0.0 %呼和浩特: 0.0 %咸阳: 0.1 %咸阳: 0.1 %哈密: 0.1 %哈密: 0.1 %哈尔滨: 0.2 %哈尔滨: 0.2 %哥伦布: 0.0 %哥伦布: 0.0 %唐山: 0.3 %唐山: 0.3 %嘉兴: 0.1 %嘉兴: 0.1 %大克罗伊茨: 0.6 %大克罗伊茨: 0.6 %大连: 0.2 %大连: 0.2 %天津: 1.2 %天津: 1.2 %太原: 0.4 %太原: 0.4 %威海: 0.4 %威海: 0.4 %宁波: 0.1 %宁波: 0.1 %安山: 0.1 %安山: 0.1 %安康: 0.3 %安康: 0.3 %宝鸡: 0.0 %宝鸡: 0.0 %宣城: 0.2 %宣城: 0.2 %宿州: 0.0 %宿州: 0.0 %岳阳: 0.1 %岳阳: 0.1 %常州: 0.4 %常州: 0.4 %常德: 0.1 %常德: 0.1 %广元: 0.0 %广元: 0.0 %广州: 1.2 %广州: 1.2 %库比蒂诺: 0.4 %库比蒂诺: 0.4 %廊坊: 0.0 %廊坊: 0.0 %开普敦: 0.1 %开普敦: 0.1 %张家口: 0.3 %张家口: 0.3 %张家界: 0.1 %张家界: 0.1 %徐州: 0.1 %徐州: 0.1 %德里: 0.1 %德里: 0.1 %怀化: 0.0 %怀化: 0.0 %成都: 1.8 %成都: 1.8 %扬州: 0.5 %扬州: 0.5 %抚顺: 0.0 %抚顺: 0.0 %揭阳: 0.0 %揭阳: 0.0 %新乡: 0.0 %新乡: 0.0 %无锡: 0.1 %无锡: 0.1 %昆明: 0.6 %昆明: 0.6 %昌迪加尔: 0.0 %昌迪加尔: 0.0 %晋城: 0.1 %晋城: 0.1 %朝阳: 0.1 %朝阳: 0.1 %杜塞尔多夫: 0.0 %杜塞尔多夫: 0.0 %杭州: 0.9 %杭州: 0.9 %枣庄: 0.0 %枣庄: 0.0 %格兰特县: 0.0 %格兰特县: 0.0 %格林菲尔德: 0.0 %格林菲尔德: 0.0 %桂林: 0.1 %桂林: 0.1 %梅州: 0.0 %梅州: 0.0 %榆林: 0.3 %榆林: 0.3 %武汉: 2.8 %武汉: 2.8 %汕头: 0.1 %汕头: 0.1 %沈阳: 0.2 %沈阳: 0.2 %沧州: 0.1 %沧州: 0.1 %河内: 0.1 %河内: 0.1 %泉州: 0.0 %泉州: 0.0 %法兰克福: 0.1 %法兰克福: 0.1 %泰州: 0.0 %泰州: 0.0 %泰米尔纳德: 0.3 %泰米尔纳德: 0.3 %泸州: 0.1 %泸州: 0.1 %洛阳: 0.3 %洛阳: 0.3 %济南: 0.3 %济南: 0.3 %海口: 0.0 %海口: 0.0 %淄博: 0.2 %淄博: 0.2 %淮南: 0.0 %淮南: 0.0 %淮安: 0.1 %淮安: 0.1 %深圳: 1.7 %深圳: 1.7 %清远: 0.0 %清远: 0.0 %温州: 0.1 %温州: 0.1 %渭南: 0.1 %渭南: 0.1 %湖州: 0.0 %湖州: 0.0 %滁州: 0.0 %滁州: 0.0 %漯河: 0.8 %漯河: 0.8 %烟台: 0.3 %烟台: 0.3 %珠海: 0.1 %珠海: 0.1 %石家庄: 0.2 %石家庄: 0.2 %秦皇岛: 0.1 %秦皇岛: 0.1 %纳什维尔: 0.1 %纳什维尔: 0.1 %纽约: 0.0 %纽约: 0.0 %绍兴: 0.5 %绍兴: 0.5 %绵阳: 0.2 %绵阳: 0.2 %胡志明: 0.1 %胡志明: 0.1 %舟山: 0.0 %舟山: 0.0 %芒廷维尤: 32.6 %芒廷维尤: 32.6 %芝加哥: 0.3 %芝加哥: 0.3 %苏州: 0.4 %苏州: 0.4 %莫斯科: 0.0 %莫斯科: 0.0 %葫芦岛: 0.0 %葫芦岛: 0.0 %蒙彼利埃: 0.0 %蒙彼利埃: 0.0 %衡阳: 0.3 %衡阳: 0.3 %衢州: 0.1 %衢州: 0.1 %西宁: 9.3 %西宁: 9.3 %西安: 2.1 %西安: 2.1 %许昌: 0.0 %许昌: 0.0 %诺沃克: 0.2 %诺沃克: 0.2 %贵阳: 0.1 %贵阳: 0.1 %赣州: 0.1 %赣州: 0.1 %赤峰: 0.0 %赤峰: 0.0 %赫尔辛基: 0.1 %赫尔辛基: 0.1 %达州: 0.1 %达州: 0.1 %运城: 0.4 %运城: 0.4 %遵义: 0.1 %遵义: 0.1 %邢台: 0.0 %邢台: 0.0 %邯郸: 0.1 %邯郸: 0.1 %郑州: 0.7 %郑州: 0.7 %鄂州: 0.1 %鄂州: 0.1 %重庆: 0.4 %重庆: 0.4 %镇江: 0.1 %镇江: 0.1 %长春: 0.0 %长春: 0.0 %长沙: 1.1 %长沙: 1.1 %随州: 0.0 %随州: 0.0 %雷德蒙德: 0.1 %雷德蒙德: 0.1 %青岛: 0.4 %青岛: 0.4 %韦斯特罗斯: 0.0 %韦斯特罗斯: 0.0 %韦科: 0.1 %韦科: 0.1 %首尔: 0.0 %首尔: 0.0 %首尔特别: 0.0 %首尔特别: 0.0 %香港: 0.1 %香港: 0.1 %香港特别行政区: 0.1 %香港特别行政区: 0.1 %马鞍山: 0.0 %马鞍山: 0.0 %驻马店: 0.0 %驻马店: 0.0 %黄冈: 0.1 %黄冈: 0.1 %黄山: 0.0 %黄山: 0.0 %黄石: 0.3 %黄石: 0.3 %齐齐哈尔: 0.1 %齐齐哈尔: 0.1 %其他其他Central DistrictChinaHerndonKao-sungMalvernNorth PointSan LorenzoSeattle[]三亚三明上海东京东京都东莞中卫临汾临沂丹东九江伊利诺伊州伦敦佛山兰州兰辛凤凰城北京北海十堰华沙南京南充南昌南通卡拉奇厦门台北台州合肥吉安呼和浩特咸阳哈密哈尔滨哥伦布唐山嘉兴大克罗伊茨大连天津太原威海宁波安山安康宝鸡宣城宿州岳阳常州常德广元广州库比蒂诺廊坊开普敦张家口张家界徐州德里怀化成都扬州抚顺揭阳新乡无锡昆明昌迪加尔晋城朝阳杜塞尔多夫杭州枣庄格兰特县格林菲尔德桂林梅州榆林武汉汕头沈阳沧州河内泉州法兰克福泰州泰米尔纳德泸州洛阳济南海口淄博淮南淮安深圳清远温州渭南湖州滁州漯河烟台珠海石家庄秦皇岛纳什维尔纽约绍兴绵阳胡志明舟山芒廷维尤芝加哥苏州莫斯科葫芦岛蒙彼利埃衡阳衢州西宁西安许昌诺沃克贵阳赣州赤峰赫尔辛基达州运城遵义邢台邯郸郑州鄂州重庆镇江长春长沙随州雷德蒙德青岛韦斯特罗斯韦科首尔首尔特别香港香港特别行政区马鞍山驻马店黄冈黄山黄石齐齐哈尔

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML
    Article views(981) PDF downloads(253) Cited by(35)
    Proportional views
    Related

    京ICP备20021838号-8   Supported by: Beijing Renhe Information Technology Co. Ltd     百度统计

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return