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Wearable Energy harvester offers clean and continuous power for wearable sensors or devices, which can play an important role in the health monitoring, motion track and so on. In this study, we investigated a small electromagnetic resonance wearable kinetic energy harvester. A permanent magnet (PM) which was tied with two springs is forming a 3-degree-of-freedom (3-DoF) vibrator and is put in a box. Ferrofluid was adopted which is adsorbed at the pole of PM and makes the PM away from the surface of the box which decreased the friction significantly. Coils are placed on the outside surface and the electric energy is generated when the PM is vibration. It can be used to harvest kinetic energy of human and offer continuous power. The effect of ferrofluid was simulated and analyzed which indicated that the ferrofluid can keep the PM contactless even under 10 times gravity acceleration. A prototype was developed and tested under different loading conditions. Resistance load experiments results indicated that the proposed harvester can generate 0.75mW average power when walking and 1.4mW when running. An energy storage circuit which can transfer the generated alternating power to 5V direct current was developed to store the electrical power into capacitor. Energy storage experiments results indicated that the average storage power when walking and running are 20.8µW and 35.2µW, respectively. The developed harvester can be placed on the shoe and used to offer continuous

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Numerical simulation of the debris-flow process is commonly based on the shallow water equations. However, as a two-phase anisotropic mixture, debris flows display complex rheological behavior, making it difficult to model or to simulate these using standard approaches. In this paper, an improved cellular automaton (CA) model is developed for simulating the extent of debris-flow run-out. The CA model consists of three essential components: cellular space, lattice relation, and transition function. A two-dimensional rectangular cellular space is generated from mesh grid in the digital terrain model data, and the Moore neighborhood type is selected as the lattice relation. We also use a transition function based on a Monte Carlo iteration algorithm to automatically search the flow direction and flow routine. Specifically, this new transition function combines the topography function and persistence function (due to the flow inertia), and is advanced in its ability to avoid certain illogical lateral spreading due to abrupt changes in topography. In addition, in contrast to previous studies, in the present work, we regressed the persistence function from a well-documented flume experiment, rather than using a manipulated constant value as described in earlier empirical studies. Our results show that the debris-flow persistence function is closely related to the channel slope. It approximates the law of cosines at a steep slope and Gamma law at a gentle slope. To illustrate the performance of the improved CA model, we selected the 2010 Yohutagawa debris-flow event in Japan as a case study. Our results show that the simulated deposition perimeter pattern and run-out distance are in high accordance with the data from in situ investigation.