In this note we propose a fuzzy diagnosis of headache. The method is based on the relations between symptoms and diseases. For this purpose, we suggest a new diagnosis measure using the occurrence information of patient's symptoms and develop an improved interview chart with fuzzy degrees assigned according to the relation among symptoms and three labels of headache. The proposed method is illustrated by two examples.

Wireless power transfer (WPT) can be classified into magnetic induction, magnetic resonance, and RF radiation types, of which the magnetic resonance WPT system is especially attracting attention due to its high potential for development. The magnetic resonance system using a specific resonance frequency is applicable to small mobile devices and in-body wireless charging modules because it enables the implementation of single-input multiple-output (SIMO), where the transmitter transmits power to multiple receivers, and the miniaturization of receiving coil. The most important consideration of the magnetic resonance WPT is the optimization of the power transfer distance and efficiency, which requires a precise design and the analysis of the transmission coil. Ferrite-embedded LTCC inductors are more advantageous for WPT applications than coil inductors because of their low cost, batch manufacturing and durability. A coil with the substate size of 10.0×12.0×0.7mm3 was manufactured using the ferrite-embedded LTCC technology to miniaturize the receiver coil. The sum of power transferred from transmitter sized of 80×60mm2 to two receivers is approximately 32%, which indicates a high potential for use in small terminals or in-body modules.

The fuzzy set framework can be utilized in several different approaches to modeling the diagnostic process. In this paper, we introduce two main relations between symptoms and diseases where the relations are described by intuitionistic fuzzy set data. Also, we suggest four measures for medical diagnosis. We are dealing with the preliminary diagnosis from the information of interview chart. We quantify the qualitative information based on the interview chart by dual scaling. Prototype of fuzzy diagnostic sets and the linear regression methods are established with these quantified data. These methods can be used to classify new patient's tone of diseases with certain degrees of belief and its concerned symptoms.