Color filter (CF) and touch panel (or touch sensor (TS)) are important components for optoelectronic materials and component manufacturing. Due to the cut-throat world of market in the manufacturing, the processes of color filters are similar to touch sensors. The case invested in the production of touch panels in 2009. After a long period of quality improvement, the problem of contamination pollution still accounts for ~30% of the total variation. In addition to the external problem, there is also the fail of communication caused by dirt or peeling. Therefore, the case was established to improve the dirt defect by setting up Lean Six Sigma project, and the project goal was to reduce the proportion to 0.18%. After three months of improvement and three months of continuous observation, the abnormal proportion of pollution decreased from 0.35% of the overall average defect loss to 0.13% (the improvement rate reached 63%). It is estimated that the entire product can generate 3 million (USD) of performance for the case in one year. It is also possible to increase the customer’s satisfaction and the company’s technical competitiveness by improving yield and achieving the continuous improvement of objectives.

Since the outbreak of the novel coronavirus disease 2019 (COVID-19), the epidemic has gradually slowed down in various countries and people’s lives have gradually returned to normal. To monitor the spread of the epidemic, studies discussing the design of related healthcare information systems have been increasing recently. However, these studies might not consider the aspect of user-centric design when developing healthcare information systems. This study examined these innovative technology applications and rapidly built prototype systems for smart healthcare through a systematic literature review and a study of patient innovation. The design guidelines for the Smart Healthcare System (SHS) were then compiled through an expert review process. This will provide a reference for future research and similar healthcare information system development.

In recent years, how to improve the performance of smart factories and reduce the cost of operation has been the focus of industry attention. This study proposes a new type of location-based service (LBS) to improve the accuracy of location information delivered by self-propelled robots. Traditional localization algorithms based on signal strength cannot produce accurate localization results because of the multipath effect. This study proposes a localization algorithm that combines the Kalman filter (KF) and the adaptive-network-based fuzzy inference system (ANFIS). Specifically, the KF was adopted to eliminate noise during the signal transmission process. Through the learning of the ANFIS, the environment parameter suitable for the target was generated, to overcome the deficiency of traditional localization algorithms that cannot obtain real signal strength. In this study, an experiment was conducted in a real environment to compare the proposed localization algorithm with other commonly used algorithms. The experimental results show that the proposed localization algorithm produces minimal errors and stable localization results.