Introducing the Visual Imaging Feature to the Text Analysis: High Efficient Soft Computing Models with Bayesian Network

摘要

In today’s industrial production process, in order to keep the product quality unchanged or improve and keep the production operation in a continuous and stable state, the real-time monitoring of process variables of product quality becomes more and more important. Although the development of measurement technology makes the previously unmeasurable variables become measurable, some key quality variables are still difficult to meet the requirements of real-time measurement due to the bad measurement environment, high cost of instruments and the time lag caused by assay. Therefore, there is an urgent need for a processor to predict the reliability of complex and changeable state information, keep the reliable and useful state information, and realize the reliability measurement of state information, so as to provide people with targeted guidance and help. The information content in digital image is divided into perceptual content and semantic content. Perceptual content includes color, shape, texture, frequency, material and time-domain change; semantic content includes target, event and its relationship. Text is a special target which contains rich semantic information. Text analysis is the key clue to describe and understand the content of scene. Therefore, this paper uses visual imaging technology and text analysis to build an efficient soft computing model based on Bayesian network.