中国电机工程学报
中國電機工程學報
중국전궤공정학보
ZHONGGUO DIANJI GONGCHENG XUEBAO
2013年
23期
118-125
,共8页
电阻层析成像%病态%图像重建算法%灵敏度矩阵%增益因子%相关系数
電阻層析成像%病態%圖像重建算法%靈敏度矩陣%增益因子%相關繫數
전조층석성상%병태%도상중건산법%령민도구진%증익인자%상관계수
electrical resistance tomography%ill-posedness%image reconstruction algorithm%sensitivity matrix%gain factor%correlation coefficient
由于电阻层析成像问题本身严重病态，导致重建图像分辨率较低。为了提高重建图像精度，提出一种改进Landweber 预迭代图像重建算法。首先，利用改进粒子群算法离线优化灵敏度矩阵，改善其病态程度，同时缩小满足算法收敛条件的增益因子取值范围；再设置4种典型流型，计算算法重建图像与其相关系数的平均值，并将平均值作为适应度函数，利用改进粒子群算法离线选择 Landweber 预迭代算法增益因子，并将改进算法应用于两相流典型流型与复合流型图像重建。实验结果表明，相同实验条件下，与在线预迭代(offline iteration online reconstruction，OIOR)算法、经验选择增益因子 Landweber 预迭代算法相比，新算法有效提高了图像重建质量，而与修正牛顿-拉夫逊算法相比，在不影响成像精度的前提下，提高了算法实时性。
由于電阻層析成像問題本身嚴重病態，導緻重建圖像分辨率較低。為瞭提高重建圖像精度，提齣一種改進Landweber 預迭代圖像重建算法。首先，利用改進粒子群算法離線優化靈敏度矩陣，改善其病態程度，同時縮小滿足算法收斂條件的增益因子取值範圍；再設置4種典型流型，計算算法重建圖像與其相關繫數的平均值，併將平均值作為適應度函數，利用改進粒子群算法離線選擇 Landweber 預迭代算法增益因子，併將改進算法應用于兩相流典型流型與複閤流型圖像重建。實驗結果錶明，相同實驗條件下，與在線預迭代(offline iteration online reconstruction，OIOR)算法、經驗選擇增益因子 Landweber 預迭代算法相比，新算法有效提高瞭圖像重建質量，而與脩正牛頓-拉伕遜算法相比，在不影響成像精度的前提下，提高瞭算法實時性。
유우전조층석성상문제본신엄중병태，도치중건도상분변솔교저。위료제고중건도상정도，제출일충개진Landweber 예질대도상중건산법。수선，이용개진입자군산법리선우화령민도구진，개선기병태정도，동시축소만족산법수렴조건적증익인자취치범위；재설치4충전형류형，계산산법중건도상여기상관계수적평균치，병장평균치작위괄응도함수，이용개진입자군산법리선선택 Landweber 예질대산법증익인자，병장개진산법응용우량상류전형류형여복합류형도상중건。실험결과표명，상동실험조건하，여재선예질대(offline iteration online reconstruction，OIOR)산법、경험선택증익인자 Landweber 예질대산법상비，신산법유효제고료도상중건질량，이여수정우돈-랍부손산법상비，재불영향성상정도적전제하，제고료산법실시성。
Due to the ill-posedness of the image reconstruction problem in electrical resistance tomography, the space resolution of the reconstructed image is relatively low. In order to improve the imaging quality, this paper proposed an improved pre-iteration Landweber image reconstruction algorithm. The improved particle swarm optimization was firstly used off-line to improve the ill-posedness of the sensitivity matrix, and to limit the range of the gain factor which could guarantee the convergence of the algorithm; secondly, set four typical flow regimes, then calculated mean value of the four image correlation coefficients between the four pre-determined resistance distribution and their reconstructions, and regarded the mean value as the fitness function. Thereafter, the improved particle swarm optimization was used to calculate gain factor, and the proposed algorithm was applied to image reconstruction for both typical and complicated flow regimes of two-phase flow. The experimental results demonstrate that, under the same experimental conditions, compared to the offline iteration online reconstruction (OIOR) algorithm, pre-iteration Landweber method with empirically-chosen gain factor, the new method improves the imaging quality obviously; compared to the modified Newton-Raphson method, the improved algorithm enhances the real-time performance without sacrificing the imaging quality.
