Although previous studies have shown that sleep can inspire insight, it is still unclear whether meditation can promote insight. Meditation differs from other types of passive rest such as relaxation and sleep because it requires full consciousness and mindfulness of targets such as one's breathing. Forty-eight university students without meditation experience were recruited to learn a simple meditation technique. They were given a list of 10 insight problems to solve (the pre-test session). In this study, we focused on the unsolved problems and examined if they could be successfully solved after a 20 min rest interval with or without meditation. Results showed that relative to the control group that listened to Chinese or English words and made a language judgment, the groups who learned meditation successfully solved significantly more failed problems from the pre-test session, providing direct evidence for the role of meditation in promoting insight. Further analysis showed that maintaining a mindful and alert state during meditation (raising a hand to report every 10 deep breaths compared to every 100 deep breaths) resulted in more insight regarding the failed items from the pre-test session. This implies that it was watchfulness in meditation, rather than relaxation, that actually contributed to insight. Consistently, in the meditation session or control task, the percentage of alpha waves-a brain index of mental relaxation-was negatively correlated with insight. These results suggest a meditation-based insight-promoting mechanism different from that involved in passive rest such as relaxation and sleep.
An estimation of the power battery state of charge (SOC) is related to the energy management, the battery cycle life and the use cost of electric vehicles. When a lithium-ion power battery is used in an electric vehicle, the SOC displays a very strong time-dependent nonlinearity under the influence of random factors, such as the working conditions and the environment. Hence, research on estimating the SOC of a power battery for an electric vehicle is of great theoretical significance and application value. In this paper, according to the dynamic response of the power battery terminal voltage during a discharging process, the second-order RC circuit is first used as the equivalent model of the power battery. Subsequently, on the basis of this model, the least squares method (LS) with a forgetting factor and the adaptive unscented Kalman filter (AUKF) algorithm are used jointly in the estimation of the power battery SOC. Simulation experiments show that the joint estimation algorithm proposed in this paper has higher precision and convergence of the initial value error than a single AUKF algorithm.
Abstract:Cell balancing plays an important role in preserving the life of series-connected battery packs; without a suitable balancing system, the individual cell voltages will differ over time, and the battery pack capacity will decrease quickly. This paper presents a novel power inductor-based bidirectional lossless equalization circuit. This circuit consists of several balancing sub-circuits, which allow the dynamic adjustment of the equalization path and equalization threshold. The simulation and experiment results demonstrate that the proposed circuit, which features a simple control method, fast balancing, and a large equalization current, exhibits outstanding equalization performance.
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