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Top2. Ert And Ect Sensors
<i>2.1.</i> The Structure of Sensors</div><p>The 16-electrode ERT and ECT sensors are depicted in Figures 1(a) and (b).</p><div class="xmlReaderFig"><i>Figure 1. </i><div class="xmlReaderFig"><p>The structure of sensor: (a) ERT sensor and (b) ECT sensor</p></div><div style="width: 100%;"><a href="https://igiprodst.blob.core.windows.net:443/source-content/9781613505403_47850/japuc.2011040104.f01.png?sv=2015-12-11&sr=c&sig=6PQj5nFGehu%2FknTdLCcYj%2FFIhl5SpdW4rTA8YHZHL8E%3D&se=2019-12-23T13%3A10%3A16Z&sp=r" target="_blank"><img src="https://igiprodst.blob.core.windows.net:443/source-content/9781613505403_47850/japuc.2011040104.f01.png?sv=2015-12-11&sr=c&sig=6PQj5nFGehu%2FknTdLCcYj%2FFIhl5SpdW4rTA8YHZHL8E%3D&se=2019-12-23T13%3A10%3A16Z&sp=r" alt="japuc.2011040104.f01" style="max-width: 100%;" /></a></div></graphic></div><p>In ERT, the electrodes are mounted equally on the interior of pipe. The measured objects in pipe must be conductive and the electrodes must contact with them. While in ECT, the electrodes are mounted equally on the exterior of pipe. The measured objects in pipe must be nonconductive or most of the mixture of the multi-phase material is nonconductive.</p></div><div><h3 id="2.2.-image-reconstruction-algorithms">2.2. Image Reconstruction Algorithms</h3><p>There are many different image reconstruction methods for ERT and ECT, which can be mainly classified into two categories, direct methods (linear back projection, Tikhonov regularization and truncated singular value decomposition) and iterative methods (Conjugate Gradient and Landweber) (Yang & Peng, 2003; Marashdeh, Warsito, Fan, & Teixeira, 2006; Wang, Tang, & Cao, 2007). In our study, Landweber iterative algorithm with optimal step length was adopted which is defined as (Liu, Fu, & Yang, 1999):<div style="width: 100%;"><a href="https://igiprodst.blob.core.windows.net:443/source-content/9781613505403_47850/japuc.2011040104.m01.png?sv=2015-12-11&sr=c&sig=6PQj5nFGehu%2FknTdLCcYj%2FFIhl5SpdW4rTA8YHZHL8E%3D&se=2019-12-23T13%3A10%3A16Z&sp=r" target="_blank"><img src="https://igiprodst.blob.core.windows.net:443/source-content/9781613505403_47850/japuc.2011040104.m01.png?sv=2015-12-11&sr=c&sig=6PQj5nFGehu%2FknTdLCcYj%2FFIhl5SpdW4rTA8YHZHL8E%3D&se=2019-12-23T13%3A10%3A16Z&sp=r" alt="japuc.2011040104.m01" style="max-width: 100%;" /></a></div><i> (1)</i><div style="width: 100%;"><a href="https://igiprodst.blob.core.windows.net:443/source-content/9781613505403_47850/japuc.2011040104.m02.png?sv=2015-12-11&sr=c&sig=6PQj5nFGehu%2FknTdLCcYj%2FFIhl5SpdW4rTA8YHZHL8E%3D&se=2019-12-23T13%3A10%3A16Z&sp=r" target="_blank"><img src="https://igiprodst.blob.core.windows.net:443/source-content/9781613505403_47850/japuc.2011040104.m02.png?sv=2015-12-11&sr=c&sig=6PQj5nFGehu%2FknTdLCcYj%2FFIhl5SpdW4rTA8YHZHL8E%3D&se=2019-12-23T13%3A10%3A16Z&sp=r" alt="japuc.2011040104.m02" style="max-width: 100%;" /></a></div><i> (2)</i><div style="width: 100%;"><a href="https://igiprodst.blob.core.windows.net:443/source-content/9781613505403_47850/japuc.2011040104.m03.png?sv=2015-12-11&sr=c&sig=6PQj5nFGehu%2FknTdLCcYj%2FFIhl5SpdW4rTA8YHZHL8E%3D&se=2019-12-23T13%3A10%3A16Z&sp=r" target="_blank"><img src="https://igiprodst.blob.core.windows.net:443/source-content/9781613505403_47850/japuc.2011040104.m03.png?sv=2015-12-11&sr=c&sig=6PQj5nFGehu%2FknTdLCcYj%2FFIhl5SpdW4rTA8YHZHL8E%3D&se=2019-12-23T13%3A10%3A16Z&sp=r" alt="japuc.2011040104.m03" style="max-width: 100%;" /></a></div><i> (3)</i> where <span class="xmlReaderInlineFormula"><span><a href="https://igiprodst.blob.core.windows.net:443/source-content/9781613505403_47850/japuc.2011040104.m04.png?sv=2015-12-11&sr=c&sig=6PQj5nFGehu%2FknTdLCcYj%2FFIhl5SpdW4rTA8YHZHL8E%3D&se=2019-12-23T13%3A10%3A16Z&sp=r" target="_blank"><img src="https://igiprodst.blob.core.windows.net:443/source-content/9781613505403_47850/japuc.2011040104.m04.png?sv=2015-12-11&sr=c&sig=6PQj5nFGehu%2FknTdLCcYj%2FFIhl5SpdW4rTA8YHZHL8E%3D&se=2019-12-23T13%3A10%3A16Z&sp=r" alt="japuc.2011040104.m04" style="max-width: 100%;" /></a></span></span>, <span class="xmlReaderInlineFormula"><span><a href="https://igiprodst.blob.core.windows.net:443/source-content/9781613505403_47850/japuc.2011040104.m05.png?sv=2015-12-11&sr=c&sig=6PQj5nFGehu%2FknTdLCcYj%2FFIhl5SpdW4rTA8YHZHL8E%3D&se=2019-12-23T13%3A10%3A16Z&sp=r" target="_blank"><img src="https://igiprodst.blob.core.windows.net:443/source-content/9781613505403_47850/japuc.2011040104.m05.png?sv=2015-12-11&sr=c&sig=6PQj5nFGehu%2FknTdLCcYj%2FFIhl5SpdW4rTA8YHZHL8E%3D&se=2019-12-23T13%3A10%3A16Z&sp=r" alt="japuc.2011040104.m05" style="max-width: 100%;" /></a></span></span> is the measurements, <span class="xmlReaderInlineFormula"><span><a href="https://igiprodst.blob.core.windows.net:443/source-content/9781613505403_47850/japuc.2011040104.m06.png?sv=2015-12-11&sr=c&sig=6PQj5nFGehu%2FknTdLCcYj%2FFIhl5SpdW4rTA8YHZHL8E%3D&se=2019-12-23T13%3A10%3A16Z&sp=r" target="_blank"><img src="https://igiprodst.blob.core.windows.net:443/source-content/9781613505403_47850/japuc.2011040104.m06.png?sv=2015-12-11&sr=c&sig=6PQj5nFGehu%2FknTdLCcYj%2FFIhl5SpdW4rTA8YHZHL8E%3D&se=2019-12-23T13%3A10%3A16Z&sp=r" alt="japuc.2011040104.m06" style="max-width: 100%;" /></a></span></span> is the step length and <span class="xmlReaderInlineFormula"><span><a href="https://igiprodst.blob.core.windows.net:443/source-content/9781613505403_47850/japuc.2011040104.m07.png?sv=2015-12-11&sr=c&sig=6PQj5nFGehu%2FknTdLCcYj%2FFIhl5SpdW4rTA8YHZHL8E%3D&se=2019-12-23T13%3A10%3A16Z&sp=r" target="_blank"><img src="https://igiprodst.blob.core.windows.net:443/source-content/9781613505403_47850/japuc.2011040104.m07.png?sv=2015-12-11&sr=c&sig=6PQj5nFGehu%2FknTdLCcYj%2FFIhl5SpdW4rTA8YHZHL8E%3D&se=2019-12-23T13%3A10%3A16Z&sp=r" alt="japuc.2011040104.m07" style="max-width: 100%;" /></a></span></span> is the normalized image grey in the <i>k</i>th iteration, respectively.</p></div></div></span><div id="article-list"><h2 class="margin-top">
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