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Various substances that possess liquid states include drinking water, various types of fuel, pharmaceuticals, and chemicals, which are indispensable in our daily lives. There are numerous real-world applications for liquid content detection in transparent containers, for example, service robots, pouring robots, security checks, industrial observation systems, etc. However, the majority of the existing methods either concentrate on transparent container detection or liquid height estimation; the former provides very limited information for more advanced computer vision tasks, whereas the latter is too demanding to generalize to open-world applications. In this paper, we propose a dataset for detecting liquid content in transparent containers (LCDTC), which presents an innovative task involving transparent container detection and liquid content estimation. The primary objective of this task is to obtain more information beyond the location of the container by additionally providing certain liquid content information which is easy to achieve with computer vision methods in various open-world applications. This task has potential applications in service robots, waste classification, security checks, and so on. The presented LCDTC dataset comprises 5916 images that have been extensively annotated through axis-aligned bounding boxes. We develop two baseline detectors, termed LCD-YOLOF and LCD-YOLOX, for the proposed dataset, based on two identity-preserved human posture detectors, i.e., IPH-YOLOF and IPH-YOLOX. By releasing LCDTC, we intend to stimulate more future works into the detection of liquid content in transparent containers and bring more focus to this challenging task.

The liquid-solid fluidization bed is an effective method for removing hard ions from water. However, it is widely believed that the flow in the liquid-solid fluidization bed is homogeneous, which limits the transfer rates of heat, mass, momentum, and mixing. In this study, the results of the computational fluid dynamics (CFD) method showed significant heterogeneous particle–fluid patterns in the liquid-solid fluidization bed. On the other hand, simulations of the hydrodynamics behavior in the liquid-solid fluidized bed were first performed using different solid particle sizes, then particle classification, velocity distribution, and the vortical structures in the liquid-solid fluidized bed were assessed. In addition, a new model was proposed in this study to predict the flow behavior of the fluid-particle system used. The obtained results demonstrated the presence of the heterogeneous flow regime in the liquid-solid fluidized bed. The developed model for the onset of heterogeneous fluidization behavior revealed reasonable prediction results. Therefore, this model can be applied in future related studies on the hydrodynamics of the liquid-solid fluidized bed.

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