Four-Dimensional Fuzzy Relation Investigation in Turbulence Kinetic Energy Distribution, Surface Cluster Modeling

Video tracking for high-similarity drug tablets based on reflective lightness intensity and fuzzy recognition system

Video tracking of drug tablet exerts important influences on the efficiency and reliability of its mass production; this topic also becomes a difficult and targeted focus for pharmaceutical production monitory in the past several years due to the high similarity and random distribution of those objectives to be searched for. By measuring the reflective lightness intensity of illumination lightness on tablet surface, reflective lightness intensity matrix was established and demonstrated in the form of grey image, presenting its shape topology and topography details in return. On this basis, a series of mathematical properties for describing reflective lightness intensity images were proposed, thereafter a set of fuzzy recognition system and its identification rules can be employed to classify those moving tablets with inputted image properties, which facilitates the determination of their instantaneous coordinate positions on given image frame accordingly. By repeating identical operations on the next frame, the real-time motions of tablet objectives were traced successfully. Orthogonal tracking experiment and performance comparisons verified the accuracy and reliability of this new method in pharmaceutical industry. With original suggestions concerning imaging arrangements, tablet descriptions and video tracking, this article provides reliable references and new research ideas for tablet tracking performance in high-yielding production domain.

Image tracking for the high similarity drug tablets based on light intensity reflective energy and artificial neural network

It is obvious that tablet image tracking exerts a notable influence on the efficiency and reliability of high-speed drug mass production, and, simultaneously, it also emerges as a big difficult problem and targeted focus during production monitoring in recent years, due to the high similarity shape and random position distribution of those objectives to be searched for. For the purpose of tracking tablets accurately in random distribution, through using surface fitting approach and transitional vector determination, the calibrated surface of light intensity reflective energy can be established, describing the shape topology and topography details of objective tablet. On this basis, the mathematical properties of these established surfaces have been proposed, and thereafter artificial neural network (ANN) has been employed for classifying those moving targeted tablets by recognizing their different surface properties; therefore, the instantaneous coordinate positions of those drug tablets on one image frame can then be determined. By repeating identical pattern recognition on the next image frame, the real-time movements of objective tablet templates were successfully tracked in sequence. This paper provides reliable references and new research ideas for the real-time objective tracking in the case of drug production practices.