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基于原始对偶内点法的EST图像重建

薛倩 刘婧 马敏 王化祥

薛倩, 刘婧, 马敏, 等 . 基于原始对偶内点法的EST图像重建[J]. 北京航空航天大学学报, 2019, 45(10): 1973-1981. doi: 10.13700/j.bh.1001-5965.2019.0013
引用本文: 薛倩, 刘婧, 马敏, 等 . 基于原始对偶内点法的EST图像重建[J]. 北京航空航天大学学报, 2019, 45(10): 1973-1981. doi: 10.13700/j.bh.1001-5965.2019.0013
XUE Qian, LIU Jing, MA Min, et al. EST image reconstruction based on primal dual interior point algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(10): 1973-1981. doi: 10.13700/j.bh.1001-5965.2019.0013(in Chinese)
Citation: XUE Qian, LIU Jing, MA Min, et al. EST image reconstruction based on primal dual interior point algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(10): 1973-1981. doi: 10.13700/j.bh.1001-5965.2019.0013(in Chinese)

基于原始对偶内点法的EST图像重建

doi: 10.13700/j.bh.1001-5965.2019.0013
基金项目: 

国家自然科学基金 61401466

中国民航大学科研启动基金 2013QD01S

详细信息
    作者简介:

    薛倩    女, 博士, 讲师。主要研究方向:电学层析成像

    通讯作者:

    薛倩, E-mail: xueqian@tju.edu.cn

  • 中图分类号: V221+.3;TH89

EST image reconstruction based on primal dual interior point algorithm

Funds: 

National Natural Science Foundation of China 61401466

Startup Scientific Research Foundation of Civil Aviation University of China 2013QD01S

More Information
  • 摘要:

    静电层析成像(EST)被动感应电荷的机理决定了其独立测量值数等于电极数目,远小于电容层析成像(ECT)等相对成熟的电学成像(ET)技术的测量值数,导致逆问题的欠定性更加严重。为此,对基于压缩感知理论的EST图像重建算法进行了研究。利用奇异值分解(SVD)处理灵敏度矩阵使其满足有限等距性质(RIP),采用l1范数正则化模型和原始对偶内点法(PDIPA)实现图像重建,并在迭代过程中针对荷电磨粒稀疏分布的特点,对图像向量中非零元素个数施加约束。仿真实验表明:该算法相对于基于"Circle of Appolonius"的反投影(BP)算法和Landweber迭代算法,明显改进了成像质量,对不同位置的单个电荷可准确重建;2个电荷距离不小于1 mm时可正确分辨电荷数目与位置;对10组随机分布的3个电荷模型进行测试,荷电磨粒数目监测的准确率约为80%。

     

  • 图 1  EST传感器仿真模型

    Figure 1.  EST sensor model used for simulation

    图 2  EST传感器敏感场分布

    Figure 2.  Sensitivity distribution of EST sensor

    图 3  单个电荷仿真中设置的位置

    Figure 3.  Positions used for single point charge simulation

    图 4  基于BP算法的单个电荷图像重建结果

    Figure 4.  Image reconstruction results for single point charge based on BP algorithm

    图 5  基于Landweber迭代算法的单个电荷图像重建结果

    Figure 5.  Image reconstruction results for single point charge based on Landweber iteration algorithm

    图 6  基于PDIPA的单个电荷图像重建结果

    Figure 6.  Image reconstruction results for single point charge based on PDIPA

    图 7  两个电荷仿真中设置的位置

    Figure 7.  Positions used for two point charges simulation

    图 8  基于BP算法的2个电荷图像重建结果

    Figure 8.  Image reconstruction results for two point charges based on BP algorithm

    图 9  基于Landweber迭代算法的2个电荷图像重建结果

    Figure 9.  Image reconstruction results for two point charges based on Landweber iteration algorithm

    图 10  基于PDIPA的2个电荷图像重建结果

    Figure 10.  Image reconstruction results for two point charges based on PDIPA

    图 11  基于PDIPA的3个随机分布电荷图像重建结果

    Figure 11.  Image reconstruction results for three randomly distributed point charges based on PDIPA

  • [1] 陈志雄, 左洪福, 詹志娟, 等.滑油系统全流量在线磨粒静电监测技术研究[J].航空学报, 2012, 33(3):446-452. http://d.old.wanfangdata.com.cn/Periodical/hkxb201203008

    CHEN Z X, ZUO H F, ZHAN Z J, et al.Study of oil system oil-line debris electrostatic monitoring technology[J].Acta Aeronautica et Astronautica Sinica, 2012, 33(3):446-452(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/hkxb201203008
    [2] 李绍成.基于静电感应和显微图像的油液磨粒监测技术研究[D].南京: 南京航空航天大学, 2009: 4-8. http://www.wanfangdata.com.cn/details/detail.do?_type=degree&id=Y1855041

    LI S C.Research on technologies of oil wear particle monitoring based on electrostatic induction and microscopical image[D].Nanjing: Nanjing University of Aeronautics and Astronautics, 2009: 4-8(in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=degree&id=Y1855041
    [3] TANG X, CHEN Z S, LI Y, et al.Compressive sensing-based electrostatic sensor array signal processing and exhausted abnormal debris detecting[J].Mechanical Systems and Signal Processing, 2018, 105:404-426. doi: 10.1016/j.ymssp.2017.12.022
    [4] GREEN R G, RAHMAT M F, EVANS K, et al.Concentration profiles of dry powders in a gravity conveyor using an electrodynamic tomography system[J].Measurement Science and Technology, 1997, 8(2):192-197. doi: 10.1088/0957-0233/8/2/014
    [5] MACHIDA M, SCARLETT B.Process tomography system by electrostatic charge carried by particles[J].IEEE Sensors Journal, 2005, 5(2):251-259. doi: 10.1109/JSEN.2005.843892
    [6] RAHMAT M F, ISA M D, RAHIM R A, et al.Electrodynamics sensor for the image reconstruction process in an electrical charge tomography system[J].Sensors, 2009, 9(12):10291-10308. doi: 10.3390/s91210291
    [7] THUKU I T, RAHMAT M F, WAHAB N A, et al. 2-D finite-element modeling of electrostatic sensor for tomography system[J].Sensor Review, 2013, 33(2):104-113. doi: 10.1108/02602281311299644
    [8] 高鹤明.管内气固两相流的静电层析成像技术[D].南京: 东南大学, 2011: 56-61. http://www.wanfangdata.com.cn/details/detail.do?_type=degree&id=Y2187653

    GAO H M.Electrostatic tomography in gas/solid two-phase flow within pipe[D].Nanjing: Southeast University, 2011: 56-61(in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=degree&id=Y2187653
    [9] ZHOU B, ZHANG J Y, XU C L, et al.Image reconstruction in electrostatic tomography using a priori knowledge from ECT[J].Nuclear Engineering and Design, 2011, 241(6):1952-1958. doi: 10.1016/j.nucengdes.2010.09.012
    [10] THUKU I T, RAHMAT M F A.Finite-element method modeling in 4 and 16 sensors electic-charge tomography systems for particles moving in pipeline[J].Flow Measurement and Instrumentation, 2014, 38:9-20. doi: 10.1016/j.flowmeasinst.2014.05.009
    [11] QIAN x, yAN Y, WANG L, et al.An integrated multi-channel electrostatic sensing and digital imaging system for the on-line measurement of biomasscoal particles in fuel injection pipelines[J].Fuel, 2015, 151:2-10. doi: 10.1016/j.fuel.2014.11.013
    [12] WANG S, LI J, XU C, et al.Local particle mean velocity measurement in pneumatic conveying pipelines using electrostatic sensor arrays[J].Particulate Science and Technology, 2015, 33(1):81-90. doi: 10.1080/02726351.2014.938380
    [13] 曹宏庆.某型发动机滑油颗粒监测试验研究[D].南京: 南京航空航天大学, 2012: 11-12. http://www.wanfangdata.com.cn/details/detail.do?_type=degree&id=D281639

    CAO H Q.Research on experiments of aero-engine lubricating oil debris monitoring[D].Nanjing: Nanjing University of Aeronautics and Astronautics, 2012: 11-12(in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=degree&id=D281639
    [14] TANG X, CHEN Z S, LI Y, et al.Analysis of the dynamic sensitivity of hemisphere-shaped electrostatic sensors' circular array for chargedparticle monitoring[J].Sensors, 2016, 16(9):1403. doi: 10.3390/s16091403
    [15] XUE Q, SUN B Y, CUI Z Q, et al. Online monitoring of oil film using electrical capacitance tomography and level set method[J].Review of Scientific Instruments, 2015, 86(8):085106. doi: 10.1063/1.4928060
    [16] DING M L, YUE S H, LI J, et al.Second-order sensitivity coefficient based electrical tomography imaging[J].Chemical Engineering Science, 2019, 199:40-49. doi: 10.1016/j.ces.2019.01.020
    [17] YU Y Y, HONG M J, LIU F, et al.Compressed sensing MRI using singular value decomposition based sparsity basis[C]//Annual International Conference of the IEEE Engineering in Medicine and Biology Society.Piscataway, NJ: IEEE Press, 2011: 5734-5737.
    [18] ZHANG Q B, LI X N, CHENG H, et al.SVD-based compressive sensing[J].AISS, 2013, 5(3):580-588. doi: 10.4156/aiss.vol5.issue3.68
    [19] 张立峰, 刘昭麟, 田沛.基于压缩感知的电容层析成像图像重建算法[J].电子学报, 2017, 45(2):353-358. doi: 10.3969/j.issn.0372-2112.2017.02.013

    ZHANG L F, LIU Z L, TIAN P.Image reconstruction algorithm for electrical capacitance tomography based on compressed sensing[J].Acta Electronica Sinica, 2017, 45(2):353-358(in Chinese). doi: 10.3969/j.issn.0372-2112.2017.02.013
    [20] WANG G Q, BAI Y Q. Primal-dual interior-point algorithm for convex quadratic semi-definite optimization[J].Nonlinear Analysis:Theory, Methods & Applications, 2009, 71(7-8):3389-3402. http://cn.bing.com/academic/profile?id=7d306414c4fd743460de9036bd9768f1&encoded=0&v=paper_preview&mkt=zh-cn
    [21] YANG A Y, ZHOU Z H, GANESH A, et al.Fast l1-minimization algorithms for robust face recognition[J].IEEE Transactions on Image Processing, 2013, 22(8):3234-3246. doi: 10.1109/TIP.2013.2262292
    [22] KIM S J, KOH K, LUSTIG M, et al.An interior-point method for large-scale l1-regularized least squares[J].IEEE Journal of Selected Topics in Signal Processing, 2007, 1(4):606-617. doi: 10.1109/JSTSP.2007.910971
    [23] LI Y, YANG W. Image reconstruction by nonlinear Landweber iteration for complicated distributions[J]. Measurement Science and Technology, 2008, 19(9):094014. doi: 10.1088/0957-0233/19/9/094014
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出版历程
  • 收稿日期:  2019-01-16
  • 录用日期:  2019-02-02
  • 刊出日期:  2019-10-20

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