This paper describes a new approach to extract affiliations of researchers from the Web. In the context of Semantic Web and information retrieval, there have been many studies on social network mining and utilization. In such systems, it is important to obtain personal metadata. In this paper, a novel algorithm using a search engine and machine learning is proposed to output the affiliation for a given researcher. First, given a researcher name, we query to a search engine with a combination of the name and candidate affiliations. Using the hit counts of a search engine, we measure the strength of co-occurrences, and provide the most possible affiliations. Then, web pages including the researcher's CV or profile are sought in order to confirm the affiliations. We evaluate our system on 60 researchers and show effectiveness of the algorithm as well as the scope and limitations.
We focus on blogs' information propagation, that is part of information propagation on the real world. Our objective is to make it clear how the size of the information propagation is distributed and what determines this distribution. This is because the size distribution make an important role in the information propagation. To do so, we search for a proper mathematical model by comparing a result of information propagation distribution on the blogs observed on a real network with ones derived from a mathematical model. The number of entries propagated by cascaded trackbacks is used as the size of information propagation. A percolation model on a complex network is adopted for the mathematical model. As a result, the information propagation appeared on the blogs is similar to the percolation model on a scale-free network.
This paper is concerned with robust output feedback stabilization of uncertain discrete-time Takagi-Sugeno fuzzy systems with immeasurable premise variables. When we consider Takagi-Sugeno fuzzy systems, the selection of premise variables plays an important role. The premise variables are usually given, and so the output is selected as the premise variable. In this case, however, a fuzzy system description is limited. If the premise variable is the state of the system, then a fuzzy system describes a wide class of nonlinear systems. In this case, a controller design based on parallel distributed compensation is impossible because the controller depends on immeasurable premise variables. In this paper, we consider the robust output feedback stabilization problem for uncertain fuzzy systems where the premise variable is the state, which is not measurable. We formulate such a problem as a robust stabilization problem of uncertain linear systems. Some numerical examples illustrate our theory.
In this paper, we propose the method of predicting patient's abdominal thickness required to select radiographic factors by fuzzy reasoning using triangular-type fuzzy function. In X-ray photography of the radiation domain in medicine, radiographic factors (tube kV, tube mA, and exposure time) have big influence on contrast and granularity of X-ray photograph, and patient's abdominal thickness by the difference in a frame has big influence on the selecting radiographic factors. There are a method of the measured abdominal thickness and a method of using Automatic Exposure Control (AEC) in selecting the method for radiographic factors by a change of patient's abdominal thickness. The method of the measured patient's abdominal thickness is not being correctly performed with the advent of the digital technology. Then, we do fuzzy reasoning from the information on the patient's frame (height, weight) of Obesity Index and Frame Index, and examine whether patient's abdominal thickness can be predicted from the correlation of the fuzzy reasoning value and the measured patient's abdominal thickness. As the result, patient's abdominal thickness (Frontal) was well correlated with Body Mass Index (kg/m2.0 : fuzzy reasoning value) and its abdominal thickness (Lateral) was well correlated with 1.5 Power Index (kg/m1.5 : fuzzy reasoning value) . We show that predicting of patient's abdominal thickness required for X-ray photography is possible by the primary regression equation.