Gas–liquid two-phase flow abounds in industrial processes and facilities. Identification of its flow pattern plays an essential role in the field of multiphase flow measurement. A bluff body was introduced in this s...Gas–liquid two-phase flow abounds in industrial processes and facilities. Identification of its flow pattern plays an essential role in the field of multiphase flow measurement. A bluff body was introduced in this study to recognize gas–liquid flow patterns by inducing fluid oscillation that enlarged differences between each flow pattern. Experiments with air–water mixtures were carried out in horizontal pipelines at ambient temperature and atmospheric pressure. Differential pressure signals from the bluff-body wake were obtained in bubble, bubble/plug transitional, plug, slug, and annular flows. Utilizing the adaptive ensemble empirical mode decomposition method and the Hilbert transform, the time–frequency entropy S of the differential pressure signals was obtained. By combining S and other flow parameters, such as the volumetric void fraction β, the dryness x, the ratio of density φ and the modified fluid coefficient ψ, a new flow pattern map was constructed which adopted S（1–x）φ and （1–β）ψ as the vertical and horizontal coordinates, respectively. The overall rate of classification of the map was verified to be 92.9% by the experimental data. It provides an effective and simple solution to the gas–liquid flow pattern identification problems.展开更多
图像深度信息获取是机器视觉领域的活跃研究课题之一。将图像深度估计问题归结为模式识别问题,以单目图像深度为模式类,在多尺度下从图像块中提取绝对和相对深度特征,并选择表征上下文关系的DRF(Discriminative Random Field)方法来表...图像深度信息获取是机器视觉领域的活跃研究课题之一。将图像深度估计问题归结为模式识别问题,以单目图像深度为模式类,在多尺度下从图像块中提取绝对和相对深度特征,并选择表征上下文关系的DRF(Discriminative Random Field)方法来表述某图像块的深度和其邻域深度之间的关系,从而构建起基于DRF-MAP(Maximum a posteriori)的单目图像深度估计模型。通过实验,得到了一类单目图像对应的深度图像,从而证明了单目图像深度估计模型对应的改进算法的有效性。展开更多
基金Project(51576213)supported by the National Natural Science Foundation of ChinaProject(2015RS4015)supported by the Hunan Scientific Program,ChinaProject(2016zzts323)supported by the Innovation Project of Central South University,China
文摘Gas–liquid two-phase flow abounds in industrial processes and facilities. Identification of its flow pattern plays an essential role in the field of multiphase flow measurement. A bluff body was introduced in this study to recognize gas–liquid flow patterns by inducing fluid oscillation that enlarged differences between each flow pattern. Experiments with air–water mixtures were carried out in horizontal pipelines at ambient temperature and atmospheric pressure. Differential pressure signals from the bluff-body wake were obtained in bubble, bubble/plug transitional, plug, slug, and annular flows. Utilizing the adaptive ensemble empirical mode decomposition method and the Hilbert transform, the time–frequency entropy S of the differential pressure signals was obtained. By combining S and other flow parameters, such as the volumetric void fraction β, the dryness x, the ratio of density φ and the modified fluid coefficient ψ, a new flow pattern map was constructed which adopted S（1–x）φ and （1–β）ψ as the vertical and horizontal coordinates, respectively. The overall rate of classification of the map was verified to be 92.9% by the experimental data. It provides an effective and simple solution to the gas–liquid flow pattern identification problems.
文摘图像深度信息获取是机器视觉领域的活跃研究课题之一。将图像深度估计问题归结为模式识别问题,以单目图像深度为模式类,在多尺度下从图像块中提取绝对和相对深度特征,并选择表征上下文关系的DRF(Discriminative Random Field)方法来表述某图像块的深度和其邻域深度之间的关系,从而构建起基于DRF-MAP(Maximum a posteriori)的单目图像深度估计模型。通过实验,得到了一类单目图像对应的深度图像,从而证明了单目图像深度估计模型对应的改进算法的有效性。
