Chung Yuan Christian University

About: Chung Yuan Christian University is a education organization based out in Taoyuan City, Taiwan. It is known for research contribution in the topics: Membrane & Fuzzy logic. The organization has 9819 authors who have published 11623 publications receiving 213139 citations. The organization is also known as: Tiong-gôan-tāi-ha̍k & CYCU.

A Study on the Luminescence and Energy Transfer of Single-Phase and Color-Tunable KCaY(PO4)2:Eu2+,Mn2+ Phosphor for Application in White-Light LEDs

Abstract: Novel single-phased white light-emitting KCaY(PO4)2:Eu2+,Mn2+ phosphors for light-emitting diode (LED) applications were synthesized by conventional solid-state reaction. The emission hue could be controlled by tuning the Eu2+/Mn2+ ratio via the energy transfer; the the emission hue of KCaY(PO4)2:Eu2+,Mn2+ varied from blue (0.1853, 0.2627) to white-light (0.3350, 0.3203) and eventually to purple (0.3919, 0.2867). The mechanism of energy transfer from a sensitizer Eu2+ to an activator Mn2+ in KCaY(PO4)2:Eu2+,Mn2+ phosphors was demonstrated to be an electric dipole–quadrupole interaction. Combining a NUV 405-nm chip and a white-emitting KCaY(PO4)2:1%Eu2+,4%Mn2+ phosphor produced a white-light NUV LED, demonstrating CIE chromaticity coordinates of (0.314, 0.329) and a color temperature of 6507 K.

Wireless Health Care Service System for Elderly With Dementia

Abstract: The purpose of this paper is to integrate the technologies of radio frequency identification, global positioning system, global system for mobile communications, and geographic information system (GIS) to construct a stray prevention system for elderly persons suffering from dementia without interfering with their activities of daily livings. We also aim to improve the passive and manpowered way of searching the missing patient with the help of the information technology. Our system provides four monitoring schemes, including indoor residence monitoring, outdoor activity area monitoring, emergency rescue, and remote monitoring modes, and we have developed a service platform to implement these monitoring schemes. The platform consists of a web service server, a database server, a message controller server, and a health-GIS (H-GIS) server. Family members or volunteer workers can identify the real-time positions of missing elderly using mobile phone, PDA, Notebook PC, and various mobile devices through the service platform. System performance and reliability is analyzed. Experiments performed on four different time slots, from three locations, through three mobile telecommunication companies show that the overall transaction time is 34 s and the average deviation of the geographical location is about 8 m. A questionnaire surveyed by 11 users show that eight users are satisfied with the system stability and 10 users would like to carry the locating device themselves, or recommend it to their family members

The refined Kano's model and its application

Abstract: Kano's model of five categories of quality attributes – attractive, one-dimensional, must-be, indifferent, and reverse quality – is widely used by industries and researchers. However, the model has a deficiency that prevents firms from precisely evaluating the influences of quality attributes. The weakness is a failure to take account of the degree of importance accorded to certain quality elements by customers. Kano's model can be refined. The present study adds the importance of quality attributes and, in so doing, the refined model divides Kano's first four categories into eight categories – highly attractive and less attractive, high value-added and low value-added, critical and necessary, and potential and care-free. Based on the refined model, firms can obtain a more accurate understanding of the quality attributes from the customer's perspective, and can thus make more precise quality decisions. In addition to modifying Kano's model, the present study also develops an importance–satisfacti...

Using wavelet transform and fuzzy neural network for VPC detection from the holter ECG

Abstract: A novel method for detecting ventricular premature contraction (VPC) from the Holter system is proposed using wavelet transform (WT) and fuzzy neural network (FNN). The basic ideal and major advantage of this method is to reuse information that is used during QRS detection, a necessary step for most ECG classification algorithm, for VPC detection. To reduce the influence of different artifacts, the filter bank property of quadratic spline WT is explored. The QRS duration in scale three and the area under the QRS complex in scale four are selected as the characteristic features. It is found that the R wave amplitude has a marked influence on the computation of proposed characteristic features. Thus, it is necessary to normalize these features. This normalization process can reduce the effect of alternating R wave amplitude and achieve reliable VPC detection. After normalization and excluding the left bundle branch block beats, the accuracies for VPC classification using FNN is 99.79%. Features that are extracted using quadratic spline wavelet were used successfully by previous investigators for QRS detection. In this study, using the same wavelet, it is demonstrated that the proposed feature extraction method from different WT scales can effectively eliminate the influence of high and low-frequency noise and achieve reliable VPC classification. The two primary advantages of using same wavelet for QRS detection and VPC classification are less computation and less complexity during actual implementation.