The utilization of real-world data is gaining momentum. However, when applying Cox regression model to real data containing time-dependent covariates, we are almost always forced to deal with incomplete data. We propose using Gaussian process regression technique to complete the missing values necessary to estimate the parameters of Cox regression model. Multiple imputation framework is also used to consider the uncertainty of imputed values. Utilizing the information of auxiliary variables are also discussed. Results of simulation study suggest that the combination of Gaussian process regression and multiple imputation method provides better estimates than standard method. The proposed method is simple and easy to implement, and the results are highly convincing.

This paper introduces the performance of a power leveling system with a 3.0-MJ, 3315-r/min flywheel energy storage. In terms of cost reduction, this system uses low cost ball bearings and general purpose induction motor. Therefore, such a system configurations occurs large loss during standby mode. In order to overcome this problem, low loss design algorithm that focuses on the mechanical loss is applied to the design of the flywheel. As a result, the flywheel loss in the steady state consists of bearing loss of 28.3%, the copper loss of 22.5% for the induction motor. Moreover, charging and discharging efficiency are measured to evaluate the prototype flywheel system. From the analysis, it is confirmed that the charging efficiency is 75.4% and the discharging efficiency is 77.2%.

In this paper, a novel method to synthesize a desired trajectory and sensory feedback control laws for robots based on the statistical characteristics of direct teaching data by a human is proposed. This work was motivated by a poor performance of an origami-folding robot developed by the authors. Since the robot simply replayed a given trajectory without sensory feedback control, it often failed in folding due to the fluctuation of origami paper behaviors.

For the purpose of realizing the functions of the human hand on a mechanical system, fundamental understanding of the human hand is necessary through intensive observations of manipulation tasks by humans. As an example of skilled manipulation tasks, we target origami folding.We first analyzed the difficulty of origami manipulation and observed how the human folds the origami. We then designed a robotic mechanism that can make an origami work, the Tadpole, in the same way as the human does based on the observation of the origami folding by the human. Although the appearance of the developed robotic hand is dissimilar to that of the human hand, the robot succeeded in performing the origami task, the Tadpole.