Katsufumi Inoue

Hiroyoshi Nagahama, Katsufumi Inoue, Masayoshi Todorokihara et al.

Predictive maintenance of rotating machinery increasingly relies on vibration signals, yet most learning-based approaches either discard phase during spectral feature extraction or use raw time-waveforms without explicitly leveraging phase information. This paper introduces two phase-aware preprocessing strategies to address random phase variations in multi-axis vibration data: (1) three-axis independent phase adjustment that aligns each axis individually to zero phase (2) single-axis reference phase adjustment that preserves inter-axis relationships by applying uniform time shifts. Using a newly constructed rotor dataset acquired with a synchronized three-axis sensor, we evaluate six deep learning architectures under a two-stage learning framework. Results demonstrate architecture-independent improvements: the three-axis independent method achieves consistent gains (+2.7\% for Transformer), while the single-axis reference approach delivers superior performance with up to 96.2\% accuracy (+5.4\%) by preserving spatial phase relationships. These findings establish both phase alignment strategies as practical and scalable enhancements for predictive maintenance systems.

Mizuki Maruyama, Shrey Singh, Katsufumi Inoue et al.

Word-level sign language recognition (WSLR) has attracted attention because it is expected to overcome the communication barrier between people with speech impairment and those who can hear. In the WSLR problem, a method designed for action recognition has achieved the state-of-the-art accuracy. Indeed, it sounds reasonable for an action recognition method to perform well on WSLR because sign language is regarded as an action. However, a careful evaluation of the tasks reveals that the tasks of action recognition and WSLR are inherently different. Hence, in this paper, we propose a novel WSLR method that takes into account information specifically useful for the WSLR problem. We realize it as a multi-stream neural network (MSNN), which consist of three streams: 1) base stream, 2) local image stream, and 3) skeleton stream. Each stream is designed to handle different types of information. The base stream deals with quick and detailed movements of the hands and body, the local image stream focuses on handshapes and facial expressions, and the skeleton stream captures the relative positions of the body and both hands. This approach allows us to combine various types of data for more comprehensive gesture analysis. Experimental results on the WLASL and MS-ASL datasets show the effectiveness of the proposed method; it achieved an improvement of approximately 10\%--15\% in Top-1 accuracy when compared with conventional methods.