Shotaro Miwa

Distributed Multi-task Learning for Sensor Network

A sensor in a sensor network is expected to be able to make prediction or decision utilizing the models learned from the data observed on this sensor. However, in the early stage of using a sensor, there may be not a lot of data available to train the model for this sensor. A solution is to leverage the observation data from other sensors which have similar conditions and models with the given sensor. We thus propose a novel distributed multi-task learning approach which incorporates neighborhood relations among sensors to learn multiple models simultaneously in which each sensor corresponds to one task. It may be not cheap for each sensor to transfer the observation data from other sensors; broadcasting the observation data of a sensor in the entire network is not satisfied for the reason of privacy protection; each sensor is expected to make real-time prediction independently from neighbor sensors. Therefore, this approach shares the model parameters as regularization terms in the objective function by assuming that neighbor sensors have similar model parameters. We conduct the experiments on two real datasets by predicting the temperature with the regression. They verify that our approach is effective, especially when the bias of an independent model which does not utilize the data from other sensors is high such as when there is not plenty of training data available.

Robust Face Recognition System Using a Reliability Feedback

In the real world there are a variety of lighting conditions, and there exist many directional lights as well as ambient lights. These directional lights cause partial dark and bright regions on faces. Even if auto exposure mode of cameras is used, those uneven pixel intensities are left, and in some cases saturated pixels and black pixels appear. In this paper we propose robust face recognition system using a reliability feedback. The system evaluates the reliability of the input face image using prior distributions of each recognition feature, and if the reliability of the image is not enough for face recognition, it capture multiple images by changing exposure parameters of cameras based on the analysis of saturated pixels and black pixels. As a result the system can cumulates similarity scores of enough amounts of reliable recognition features from multiple face images. By evaluating the system in an office environment, we can achieve three times better EER than the system only with auto exposure control.

A learning environment for maintenance of power equipment using virtual reality

This paper deals with a learning environment for maintenance of power equipment using Virtual Reality. First of all, the insights of cognitive science and the analysis of maintenance expertise are discussed from the viewpoint of understanding support system. Then design philosophy of the learning environment is proposed based on the analysis. The prototype system is designed and implemented using both EWS (Engineering Workstation) and GWS(Graphic Workstation). This prototype system is applied to the maintenance of the Gas Insulated Substation, which is one of power equipment, and its performance is evaluated through demonstration.