中文摘要：

把传感器和数据采矿技术基于电的抵抗断层摄影术(ERT ) ，一个新 voidage 测量方法为空气水被建议二阶段的流动。在这个工作使用的数据采矿技术是和特征抽取方法的一个最少的广场支持向量机器(LS-SVM ) 算法，并且三个特征抽取方法被测试：主要部件分析(PCA ) ，部分最少的广场(请) 并且独立部件分析(集成通信适配器) 。在实际 voidage 测量进程，流动模式第一从 ERT 传感器获得的传导力值直接被识别。然后，适当 voidage 测量模型根据流动模式鉴定结果被选择。最后， voidage 是计算的。试验性的结果证明建议方法能有效地测量 voidage，并且测量精确性和速度是令人满意的。与常规 voidage 测量方法相比基于 ERT，建议方法不需要任何图象重建进程，它因此有好即时性能的优点。由于流动模式鉴定的介绍， voidage 测量上的流动模式的影响被克服。而且，这被表明 LS-SVM 方法与请，特征抽取在测试方法之中介绍最好的测量表演。

英文摘要：

Based on an electrical resistance tomography（ERT） sensor and the data mining technology,a new voidage measurement method is proposed for air-water two-phase flow.The data mining technology used in this work is a least squares support vector machine（LS-SVM） algorithm together with the feature extraction method,and three feature extraction methods are tested：principal component analysis（PCA）,partial least squares（PLS） and independent component analysis（ICA）.In the practical voidage measurement process,the flow pattern is firstly identified directly from the conductance values obtained by the ERT sensor.Then,the appropriate voidage measurement model is selected according to the flow pattern identification result.Finally,the voidage is calculated.Experimental results show that the proposed method can measure the voidage effectively,and the measurement accuracy and speed are satisfactory.Compared with the conventional voidage measurement methods based on ERT,the proposed method doesn＇t need any image reconstruction process,so it has the advantage of good real-time performance.Due to the introduction of flow pattern identification,the influence of flow pattern on the voidage measurement is overcome.Besides,it is demonstrated that the LS-SVM method with PLS feature extraction presents the best measurement performance among the tested methods.