Recently, Virtual Reality (VR) technology is becoming widespread. In particular, techniques of hand tracking has enabled us to reflect movements of fingers in the real space upon the VR environments without wearing any special devices. Along with that, methods for freehand text input, especially flick input which is widely used in mobile devices, has been studied. However, the flick input in VR environments has a problem of poor input accuracy. In this study, we propose a method for improving the flick input interface using hand tracking in input accuracy and operability. Since one of the previous studies suggests that some users occasionally fail to notice the visual feedback, we introduce three types of feedback, namely visual, auditory and tactile feedback, to the system so that the user can easily notice it. Furthermore, we focus on the fact that failure of hand tracking often make input accuracy and operability worse, and propose a novel key layout suitable for flick input in VR environments that prevents the failure of hand tracking. Experimental results show that three types of feedback introduced in this study improve subjective evaluation of the user, and the proposed layout is effective in improving both input accuracy and subjective evaluation of the user.

FRIT and VRFT are widely applied as representatives of Data-driven control, but the actual closed-loop response cannot be predicted. Recently, Data-driven prediction methods including V-Tiger that predict the actual closed-loop response have been proposed, but assume that there is no noise in one-shot experimental data. System identification attempts to separate the one-shot experimental data into dynamic response and noise. For the purpose of noise reduction in the data, there is no problem even if the model order is large. Therefore, we focuse on the Correlation analysis and High-order ARX identification, which are less related to the model order among the identification methods. The Correlation analysis estimates impulse response of stable plant. The waveform of the impulse response is mainly in the interval from the start of the response to its decay. This paper proposes (1) Estimate the impulse response by the Correlation analysis to concentrate the waveform in the interval of the initial response, (2) Cut out the interval and perform High-order ARX identification, and (3) Apply the output response of the identified model to Data-driven control especially V-Tiger as the experimental data with noise removed.

In this paper, we accelerated a reinforcement learning algorithm for port-Hamiltonian systems using a natural gradient method. The proposed algorithm consists of an actor-critic structure wherein the actor generates a control input according to a policy and learns the policy using a temporal difference (TD) error, and the critic computes the TD error and learns a state-value function. Furthermore, the reinforcement learning algorithm for port-Hamiltonian systems has two types of the policy parameters which the proposed algorithm learns using the natural gradient method. Additionally, the proposed method was applied to the problem of swing-up control for an inverted pendulum through numerical simulation. The simulation result showed that the learning speed of the proposed method was higher than that of the existing method.

In the field of production scheduling, there have been many reports of scheduling techniques based on evaluation indices from the manager's viewpoints, such as improving production efficiency and meeting deadlines. On the other hand, as companies are increasing their efforts to reform the way they work every year, attention is also being paid to scheduling that takes into account the perspective of the workers, such as minimizing excessive overtime. Uncertainty in the processing time of workers is thought to be one the cause of this overtime. In this paper, we propose an optimization technique for a scheduling problem in the machining processes where overtime is allowed and there is uncertainty about the processing time by human operators. In the proposed model, we regard a human operator as a processing machine as well as a machine tool, and show that overtime can be formulated as a stochastic programming model by considering it as a recourse in two-stage stochastic programming. Furthermore, by considering the probability distribution of the processing time that appears in this stochastic programming model as a discrete distribution, it is formulated as a mixed integer programming model, which enables optimization by mathematical programming. A hybrid solution method combining the simulated annealing and the mathematical programming is used from the viewpoint of reducing the amount of computation. The effectiveness of the proposed model is demonstrated through several numerical experiments on actual cases in textile processing.

This report proposes the algorithm to estimate the position information of the object in rotational motion. From the experimental results, it is confirmed that the radius of gyration can be estimated within the error rate of -15 to +18%.