Research on the three-dimensional virtual space “metaverse” is being actively conducted. The metaverse allows communication between distant “people” through voice and/or gestures. Applying the metaverse to distant “objects” is expected to bring industrial innovation. The industrial metaverse can be implemented in remote measurement systems. In previous research, we successfully controlled a measurement instrument using voice control via the metaverse. However, appropriate automatic speech recognition methods for metaverse measurement instruments have not been explored. In this study, we investigate the performance of representative automatic speech recognition methods that use deep neural networks, including Google Speech-to-Text and Faster-Whisper, an end-to-end generative pretrained transformer. In the case of dedicated control commands with the prefix, high performance was achieved with an average word error rate of 2%. Based on the results, we developed a measurement system and successfully demonstrated that it is possible to control measurement instruments in remote sites via the metaverse.

Resistive voltage dividers (RVDs) have a wide range of use in volage measurements. It is due to their simple structures and applications under both DC and AC conditions. We have been developing a highly accurate and wideband RVD and its error measurement method from industrial frequencies to MHz ranges. In this paper, complex errors could be measured by the method up to 100 kHz. To confirm that the result is valid, a new validation method has been proposed where accurate equivalent circuit parameters can be measured, and a valid complex error can be determined. Also, we discussed the problem that avoids an accuracy degradation of the parameters through a comparison between the measured error result and the calculated result by the proposed validation method.

We are developing a smart bed system with the aim of improving the quality of nursing care. Here we report on a system that estimates behavior of target persons from the sounds they make in bed, which is one of the main elements of this system. When estimating behavior from sounds, not only the spectral information but also the location and movement of the sound source are important information. For this reason, we devised a method to generate a two-dimensional distribution of sound sources, and we performed long short term memory deep learning using a sound source image sequence and a mel spectrogram as multimodal input and evaluated the behavior estimation performance. As a result, we obtained generally good estimation results, but it became clear that there were issues with the accuracy of identifying the location of scratching.

In this paper, we propose a new fully digital phase noise measurement method in which the oscillation signal of the oscillator under test is sampled directly by analog-to-digital converters and the phase noise spectral density is calculated by digital numerical calculation. In precision phase noise measurement, it was necessary to prepare a reference oscillator in addition to the oscillator to be measured. This is because the phase noise measurement required comparison with the reference phase. Furthermore, the fully digital phase noise measurement, four analog-to-digital converters were required to reduce the phase noise of the analog-to-digital converter itself and the clock source for driving. We have reviewed the configuration of a fully digital phase noise measurement system and demonstrated that it is possible to measure phase noise with fewer components. As a result, our method requires only two analog-to-digital converters and does not require a reference oscillator. As an application, if four analog-to-digital converters are used as before, simultaneous measurement of the phase noise of two oscillators and simultaneous measurement of the signal of the source oscillator and the synthesized signal of the frequency synthesizer can be performed.

In this paper, we propose a method to further improve the frequency measurement accuracy of the phase-detection QCM, which is superior to conventional QCMs in terms of time resolution and frequency resolution. Since the measurement accuracy of the phase-detection QCM is limited by the ENOB of the AD converter used, we have shown that it is possible to improve the ENOB by parallelizing multiple AD converters, and thus actually improve the frequency measurement accuracy.

We investigated both maximum pressure and pressure distribution by strikes of lightning impulse discharge using pressure measurement film attached to ground electrode. As a result, we found that pressure distribution due to discharge was almost circular with locally distributed high pressure points. Maximum pressure due to discharge was 5.9 MPa, 9.2 MPa and 8.5 MPa at applied voltages of -600 kV, -700 kV and -800 kV, respectively.

This paper investigates the charge accumulation behavior of proton irradiated fluorinated insulating materials by applying a DC voltage to the materials and measuring the space charge distribution. In addition, the electronic energy levels were calculated using the quantum chemical calculation, and the external circuit current density was measured to examine the charge accumulation behavior. The results showed that proton irradiation lowers the charge injection barrier and changes the conductivity distribution in the sample. The charge accumulation distribution was changed by those phenomena. Furthermore, it was found that changes in charge accumulation characteristics caused by proton irradiation continue for a long time.

A PIC simulation was performed on a configuration in which a toroidal magnetic field remains near the magnetic axis like the configuration formed by counter-helicity merging spheromaks. It was revealed that when the radius of the region with the residual toroidal magnetic field falls below the ion gyroradius, a force is generated on the electrons that moves them away from the magnetic axis, causing the toroidal magnetic field to decay and relax to a Field-Reversed Configuration (FRC).