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Study on Sensor Networks Correlated with Human’s Thermal Sensation
Kazuyuki Kojima 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
In this paper, the prediction method of system behavior containing human’s sensation using unspecified sensors on a sensor network is described. This paper shows how to associate the values of unspecified sensors with the value to predict is shown. Here, how to form the dynamically changing neural network to predict the system behavior is presented. As an example case, human’s thermal sensation is predicted by using unspecified temperature sensors, humidity sensors, and globe temperature sensors. We set a lot of sensors and the subject to the experimental room, and conduct the experiment for associating sensors and human’s thermal sensation. Finally, an experimental result is shown, unspecified sensors are chosen properly and used in order to predict the desired value.
Prediction of Individual Thermal Sensation Using Unspecified Sensors in Sensor Networks
Kazuyuki Kojima 제어로봇시스템학회 2008 제어로봇시스템학회 국제학술대회 논문집 Vol.2008 No.10
This paper describes a prediction method for predicting human thermal sensation by using unspecified sensors over an unstable sensor network. First, a dynamically changing neural network is utilized for predicting the thermal sensation. The neural network is formed dynamically and trained by considering the strength of the correlation between the sensor readings and the thermal sensations of subjects. The neural network is modified when the difference between its estimation and the actual values increases. Next, in order to perform experiments with actual subjects, we built a sensor network in an indoor environment. For two weeks, we regularly measured certain values, such as the temperature in the environment, and investigated the thermal sensation of the subjects once every fifteen minutes while they were in this environment. Then, using our method, the thermal sensation and the thermal values were associated with each other, after which a dynamical neural network which estimates each thermal sensation was built automatically.