Fu Jen Catholic University

About: Fu Jen Catholic University is a education organization based out in Taipei, Taiwan. It is known for research contribution in the topics: Population & Medicine. The organization has 6842 authors who have published 9512 publications receiving 171005 citations. The organization is also known as: FJU & Fu Jen.

Location Aware Information Agent over WAP

Abstract: Information agents on mobile computing environment should adaptively provide information based on the changing location of mobile users. The agent has to obtain user’s location dynamically, and respond him the information or service depending upon his geographical position. We propose a novel framework for location-aware information agent that operates on the Wireless Application Protocol (WAP) environment. There is a locating module that can obtain user’s location by GSM cellular network. A topology-based data model is devised to represent geographical information. Search algorithm can find location-dependent results from the topology graph. Search results are organized as dynamic documents, which are Wireless Markup Language (WML) cards and decks. Finally our framework is implemented with Ericsson WAP simulation environment.

Identification of process disturbance using SPC/EPC and neural networks

Abstract: Since solely using statistical process control (SPC) and engineering process control (EPC) cannot optimally control the manufacturing process, lots of studies have been devoted to the integrated use of SPC and EPC. The majority of these studies have reported that the integrated approach has better performance than that by using only SPC or EPC. Almost all these studies have assumed that the assignable causes of process disturbance can be identified and removed by SPC techniques. However, these techniques are typically time-consuming and thus make the search hard to implement in practice. In this paper, the EPC and neural network scheme were integrated in identifying the assignable causes of the underlying disturbance. For finding the appropriate setup of the networks' parameters, such as the number of hidden nodes and the suitable input variables, the all-possible-regression selection procedure is applied. For comparison, two SPC charts, Shewhart and cumulative sum (Cusum) charts were also developed for the same data sets. As the results reveal, the proposed approaches outperform the other methods and the shift of disturbance can be identified successfully.