People are paying greater attention to health. To maintain a good health status and obtain food fast, customers may go to healthy fast-food chain restaurants such as Subway more often than before in China and Taiwan. Healthy fast-food chain restaurants come with a healthy spin, seeking to differentiate themselves from other fast-food restaurants. This paper combined the refined Kano model and the quality function deployment (QFD) method. The refined Kano model was used to understand how customers perceive service attributes developed based on DINESERV measurements. QFD was employed to describe the relationships among the critical service attributes and corresponding improvements as well as to identify the priority for these improvements. The analysis results revealed that providing limited offers (due to periods, seasons, and regions) should be at the top of their improvement list, followed by staff suggestions for ingredients, and a temperature display to enhance the image of fresh ingredients. Other improvement actions include providing regular launches of new flavors/products, designing new and attractive slogans, and providing restaurant apps.

We make use of embedded BASIC Stamp 2 (BS2) microchip controller to design with Hopfield neural network (HNN) as the foundation of sample training, which applies for a soldering platform of mechanical vision and accomplishes PCB soldering positioning technology. The system is verified by Matlab and Simulink to set up the simulation of PCB image soldering positioning. The experiment is proved to improve the traditional low efficiency of PCB soldering technology, to achieve the feasibility of PCB image positioning and to promote the soldering quality.