Exoskeleton robots need to always actively assist the user’s movements otherwise robot just becomes a heavy load for the user. However, estimating diversified movement intentions in a user’s daily life is not easy and no algorithm so far has achieved that level of estimation. In this study, we rather focus on estimating and assisting a limited number of selected movements by using an EMG-based movement classification and a newly developed lightweight exoskeleton robot. Our lightweight knee exoskeleton is composed of a carbon fiber frame and highly backdrivable joint driven by a pneumatic artificial muscle. Thus, our robot does not interfere with the user’s motions even when the actuator is not activated. As the classification method, we adopted a positive-unlabeled (PU) classifier. Since precisely labeling all the selected data from large-scale daily movements is not practical, we assumed that only part of the selected data was labeled and used a PU classifier that can handle the unlabeled data. To validate our approach, we conducted experiments with five healthy subjects to selectively assist sit-to-stand movements from four possible daily motions. We compared our approach with two classification methods that assume fully labeled data. The results showed that all subject’s movements were properly assisted.

In order to clarify the growth mechanism of Y123 film in the MOD process using TFA, TEM observation of the quenched samples during the growth, and measurement of the growth rate under several different conditions were carried out. TEM observation showed that the Y 2 Cu 2 O 5 , BaF 2 and CuO are converted into Y123 by release of HF due to supplying H 2 O at the reaction interface. The MOD TFA process using the multi coating method was applied to form thicker Y123 films on LaAlO 3 substrates, and the growth mechanism for the Y123 crystallization was investigated. In order to evaluate the growth rate, the electrical resistance of the precursor films during the crystallization was measured by the DC 4 probe method. It was observed that the growth thickness of the Y123 linearly increases with increasing annealing time. This result suggests that the growth rate is not limited by the HF diffusion in the precursor film. According to the gas flow rate dependence of the growth rate, it was suggested that the growth of Y123 films in this process might be limited by both the HF diffusion in the boundary layer and the interface kinetics. Then, the growth model, which includes the two limiting systems, was proposed. This model reveals a basic idea of the mechanism to determine the steady state growth rate.

Model experiments were carried out to demonstrate the effectiveness of two blasting methods for achieving fracture plane control. The methods used are the guide hole method utilizing a circular hole with notches and a charge holder method with two wedge-shaped air cavities. In these experiments, dynamic strains and the crack propagation velocity were measured by using strain gauges and velocity gauges, respectively. The dynamic behavior of cracks propagating along the control fracture plane was clarified by high-speed videography. The experimental results show that the two methods are effective in controlling the orientation of the fracture plane and in driving the controlled cracks to greater distances.

Japan's largest platform for academic e-journals: J-STAGE is a full text database for reviewed academic papers published by Japanese societies

Remote video monitoring over networks inevitably introduces a certain degree of communication latency. Although numerous studies have been conducted to reduce latency in network systems, achieving 'zero-latency' is fundamentally impossible for video monitoring. To address this issue, we investigate a practical method to compensate for latency in video monitoring using video prediction techniques. We apply the lightweight PredNet to predict future frames, and their image qualities are evaluated through quantitative image quality metrics and subjective assessment. The evaluation results suggest that for simple movements of the robot arm, the prediction time to generate future frames can tolerate up to 333 ms. The video prediction method is integrated into a remote monitoring system, and its processing time is also evaluated. We define the object-to-display latency for video monitoring and explore the potential for realizing a zero-latency remote video monitoring system. The evaluation, involving simultaneous capture of the robot arm's movement and the display of the remote monitoring system, confirms the feasibility of compensating for the object-to-display latency of several hundred milliseconds by using video prediction. Experimental results demonstrate that our approach can function as a new compensation method for communication latency.