C217 セル型ニューラルットワークによる3次元PTV

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タイトル別名
  • 3-D Particle Tracking Velocimetry by Using Cellular Neural Network
  • セルガタ ニューラル ネットワーク ニ ヨル 3ジゲン PTV

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A cellular neural network (CNN) algorithm is introduced in the particle pairing process of the 3-D particle tracking velocimetry. Unlike the Hopfield neural network, each neuron is connected only to its neighbors and thereby the time required for learning is drastically reduced with respect to its counterpart. In order to establish a single step profile of 3-D velocity, this neural network is used three times: two time of stereoscopic particle pairing and one more time of time-differential particle pairing. In the stereoscopic particle pairing, the network uses object functions aiming at minimization of the sum of the epipolar-line normal distances. In contrast, the time-differential particle pairing uses more abject functions, indicating minimization of the sum of the Euclid distances between the particles as well as smoothness of the velocity variance over the measurement field and rigidity of particle distribution patterns,

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