A learning system for adjustment processes based on human sensory perceptions

Abstract

Creating, designing and adjusting products are essential decision processes underlying creative industries, such as painting, perfume, food and beverage industries. These processes require the participation and continuous supervision of professionals with highlydeveloped expert sensory abilities. Training of these experts is very complex due to the difficulty of transmitting intuitive knowledge obtained from perception. A new methodology for capturing this sensory expert knowledge that relies on a machine learning tool, previously trained with 'state-action' type patterns, jointly with an actions generator module, is proposed in this work. The method is based on a closed loop architecture together with the decomposition of complex sensory knowledge into basic elements capable of being handled by standard machine learning systems. A real case application to color-adjustment in the automotive paint manufacturing industry is presented showing the potential benefits of the method.

Geometric Spatial Ability as an Element of Cognitive Learning Process

“…That is why the principle of continuity is very important to form the content of educational material. In the case when a student has not to study technical drawing or has poorly learned basic geometry, continuity of education material is being broken, and as a result, there are problems in learning geometric and graphic disciplines (Ruiz et al, 2018;Shen et al, 2018;Rutherford et al, 2018;Ionova et al, 2018).…”