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A mathematical model is established for self-twist spinning process by applying laws of fluid mechanics (i.e. mass conservation and momentum conservation). The motion curve of the convergent point was determined and the convergence point was decided by densities and velocities of two fed-in strands and the resultant self-twist yarn, independent of their mechanical properties. The experimental verification proves that the motion trajectory of the convergence point reaches a better agreement with the theoretical trajectory. The results provide general way to study the self-twist yarn spinning process and optimize the process parameters.

In this study, a methodology integrating a sketch design recognition approach with an interactive genetic algorithm is proposed to help laypersons get clothes reflecting their preferences. The sketch design recognition approach consists of a composite description model, a sketch recognition method and a database. First, a composite description model based on the knowledge of fashion design is developed to describe the characteristics of a skirt. Second, a sketch recognition method is used to help laypersons get satisfactory clothes. Third, a database in constructed to record general elements of skirts. Moreover, an interactive genetic algorithm (IGA) is used to accelerate the sketch recognition process. The experimental results demonstrate that the proposed method can 'recognize' users' preferred styles efficiently. The subjective evaluation has shown that the system can help general consumers without any fashion design or sketch training to create their own designs.

“A Canadian company required information on a process in Switzerland. Since data were too complicated to obtain by mail a vice‐president of the company was assigned to make a trip. The company librarian hearing of this situation accidentally made a search and produced the information from the company's own files.”