Electronics and Communications in Japan最新文献

Recently, optically pumped magnetometers (OPMs), which use the spin polarization of alkali metal atoms to measure magnetic fields, have attracted much attention. However, in order to realize high-sensitivity biomagnetic field measurements using OPMs, it is necessary to reduce environmental magnetic noise and system noise. In this study, we investigate the effectiveness of the noise reduction of OPM signals when principal component analysis, independent component analysis, and empirical mode decomposition are used for signal processing.

This paper analyzes the stability of a grid-following inverter by the complex vector control in αβ domain and by the PLL synchronization control in dq domain. It is known that the frequency transfer function of the PLL may affect mutual interactions between grid and line impedances. This paper analyzes the frequency characteristics of the inverter based on the mixed-domain control for stability by the impedance method. Considering frequency coupling effects by the PLL control, it is found that the passivity of the inverter is violated and it may become unstable for a weak grid. This result is validated by analytical derivation, simulation, and experiment.

We have developed a novel nondestructive inspection technique for infrastructures using spintronics technologies. Since the tunnel magneto-resistance (TMR) effect has become dramatically more sensitive in recent years, TMR sensors can be applied to the highly sensitive nondestructive inspection named magnetic hammering test (MHT). The proposed MHT technique is based on the principle of detecting slight fluctuations in the spatial magnetic field caused by the vibration of steel materials. Since the fluctuations in the magnetic field occur with the natural vibration frequency of the steel materials, their condition can be detected from the change of natural frequency using highly sensitive TMR sensors. In this work, we have demonstrated that the size of steel plates was determined with high accuracy using the MHT technique.

The recent development of wireless communication technology has increased the risk of electronic equipment malfunctions and information leaks. To address this issue, we developed and evaluated a lightweight and flexible electromagnetic shielding material that can be applied to wearable devices by combining carbon nanotubes and elastomer materials.

This research developed a complex capacitance sensor using a quartz oscillator for non-invasively imaging of a rebars position in reinforced concrete structures. The use of different electrode structures and a floating electrode located on the backside allowed for control of the sensing depth. This technology is expected to realize a simpler maintenance and management process for infrastructure quality.

This paper proposes a new event-triggered robust control system for robot manipulators, based on two event-triggered mechanisms (ETMs), which lead to intermittent communication not only for the sensor-to-controller channel but also for the controller-to-actuator channel. The proposed control system enjoys the advantage of requiring fewer resources of data communication and less signal updating of the control actuators. The control performance of the proposed control system is analyzed based on the Lyapunov approach. Furthermore, the absence of the Zeno behavior in the triggering sequence is proved rigorously. Finally, experiments are carried out on a two-link robot manipulator system to support the theoretical results.

We aim at the engineering applications of reservoir computing using hardware chaotic neural networks, including associative memory recall. The reservoir layer used in reservoir computing is networked and constructed using pulse-type hardware chaos neuron models (P-HCNMs). The structure of the reservoir layer is simple, which is advantageous for hardware implementation. By inducing chaos in the reservoir layer, it is possible to use the “chaotic edge” where the reservoir reaches its highest efficiency. It has also been reported that incorporating self-correction within the reservoir layer increases the efficiency of the task. In this paper, we constructed a hardware small-world neural network using a synaptic model with spike timing-dependent synaptic plasticity (STDP) and a gap junction model. As a result, it is clarified that all cell body models with synaptic model connections show chaotic firing by simulation at the same time, and that the STDP model enables learning while keeping the chaotic phenomena. In addition, comparison with the firing of cell body models coupled only with synaptic models suggested that the gap junction model works significantly in inducing chaos in neural networks.

We developed an assist robot that changes its assist force in real time according to the lumbar load estimated from the load information on the hand measured using a hand sensor device and the posture information. Furthermore, using the developed assist robot, the effect of the load-following control on muscle fatigue was verified. The load was set at 6 and 1 kgf, and the amount of muscle activity in the lumbar region was measured using a muscle potential sensor during continuous flexion–extension exercises. The central frequency of the power spectrum was calculated as muscle fatigue, and its time trend was obtained. For comparison, similar experiments were also conducted without an assistive robot and with an existing assistive robot. Consequently, when load-following control was used, muscle fatigue was reduced compared with existing assist robots in which the assist force was excessive in relation to the load. This shows the importance of load-following control that is necessary to control the assist force according to the load when the assist robot is worn during work in which the load changes in a complex manner.

Train localization is an essential technology for effective train control. Currently, train localization primarily relies on using track circuits and balises, which are placed along the track to provide precise location information. However, balises have to be placed at intervals of a few kilometers. This increases maintenance costs and makes them vulnerable to being damaged by ice blocks falling from moving trains. Therefore, in this study, we propose a method for absolute train localization based on structure detection and identification using a 1D light detection and ranging (LiDAR) sensor to reduce the number of balises. Structure identification is achieved using scan matching. In the experiments using a car, the proposed method achieved an identification success rate of over 90%. We also considered the effect of raindrops by filtering the measurement data. By testing and analyzing the identification results, we successfully reduced all cases of misidentification.

It is imperative to develop the simulation of vacuum arcs as a tool to aid electrode design in vacuum circuit breakers. Although the development of electromagnetic thermo-fluid simulations for vacuum arcs has been reported, most of them do not take cathode sheath phenomena into account and have not been able to reproduce vacuum arc phenomena, especially cathode spots. As the first step, the cathode sheath voltage, cathode surface electric field, and temperature and field (T-F) electron emission current were analyzed numerically on the basis of the space charge in the cathode sheath. In this study, the cathode sheath was assumed to exist when the charge density induced on the cathode surface is positive, and the temperature and density of vacuum arc plasma and cathode temperature, which are physical quantities obtained by electromagnetic thermo-fluid simulation, were used as parameters of the analysis. As a result, the cathode sheath voltage, electric field, and electron emission current density can be calculated from the vacuum arc plasma temperature, density, and cathode temperature. Numerical results show that the electron emission current density has a dominant effect on the presence or absence of the cathode sheath.