We propose a method of estimation on the basis of model-based simulations by taking into consideration the lifestyles of individual family segments to manage energy over a wide area. We adopted a user model that effectively classified families during simulations that took into account the lifestyles of families. There is a risk of power shortages during hours of peak power demand over wide areas in Japan. Therefore, we focused on power supply leveling over a wide area by using electric vehicles in the home sector as storage batteries as one countermeasure to avoid power shortages. We applied model-based simulation in this study with a user model to evaluate the possibility and efficacy of power supply leveling by shifting peak power demand with electric vehicles in the home sector. We defined models of all actors that were related to power supply leveling in these model-based simulations. We defined four user models for typical family segments and predicted the possibility and efficacy of power supply leveling until 2050, by using the most effective family segment to compare possibilities and efficacies due to differences between family segments.

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Artifact reduction from electroencephalographic (EEG) signals is an important process in the numerical analysis of brain activities. In general, independent component analysis (ICA) is employed for artifact reduction from multichannel EEG devices. On the other hand, single-channel EEG devices have recently become attractive because of their usability for measurement and their portability. However, it is ill-defined problem to design a numerical approach for eye-blink artifact reduction from single-channel EEG signals. In this paper, we therefore propose a new artifact reduction method based on 2-step nonnegative matrix factorization (NMF) for single-channel EEG signals. In an experiment, we conducted 2-step NMF to reject eye-blink artifacts using single-channel EEG signals recorded at Fp1. We also applied ICA to multichannel EEG signals and compared the results with those obtained by the proposed method. The experimental results show a relatively high signal-to-noise ratio (SNR) between the signals reconstructed using the proposed method and those obtained by ICA. Moreover, we confirm the coefficient of correlation of over 99% for estimating the recorded EEG signals by the proposed method.

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The maximum-flow neural network(MF-NN) is a novel neural network model for the maximum flow problem. From the max-flow min-cut theorem, it is known that the maximum flow problem and the minimum cut problem are dual problems. This indicates that MF-NN is applicable to the minimum cut algorithm. In this paper, we propose a novel minimum cut solution using MF-NN in directed and undirected graphs. Furthermore, since the proposed method is intended to circuit implementation based on nonlinear circuit theory, it has considerable potential for speeding up computation time.

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Voltage and current of electricity are presented by complex functions. Since product of voltage and current represents power, power is presented by complex function. Real part of power presents energy, and imaginary part of power presents reactive power. Eigen oscillation is caused by reactive power. LC ladder circuit has eigen oscillations. This paper shows that a circuit obtained by a parallel connection of two LC ladder circuits also has eigen oscillations.

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