Several indexes, such as the heat index, wet-bulb globe temperature, and the universal thermal climate index, are used to estimate the risk of seasonal heat illness. These indexes correspond to the heat load of an individual in identical environmental conditions for a prolonged period of time. In daily life, the environment changes with time, and different individuals are vulnerable to heat-related illness to different degrees. An appropriate health risk assessment covering 90% of the population would facilitate an effective response to increased rates of heat illness for major summer sport events and the elderly in daily life. In this paper, a fast computation for simulating temperature elevation and sweating is implemented using weather forecast data. In particular, a bioheat equation considering thermoregulatory responses is solved in the time domain using anatomical human body models including young adults, the elderly, and children. To accelerate simulation, the computational code is vectorized and parallelized, and subsequently implemented on an SX-ACE supercomputer. The computational results are validated in typical cases of young adults, children, and the elderly. The computational time for estimating the body temperature elevation and water loss for 3 h based on the forecasted temperature, humidity, and solar radiation was 8 min for a total of nine human models that cover an estimated 90% of the population. This demonstrates the effectiveness of the proposed system for pre-emptive health risk management. To improve public awareness, a web-based risk management application has been developed and used, since 2017 in Japan.
This paper considers a design problem of adaptive gain robust model-following/tracking controllers for a class of uncertain systems with multiple unknown dead-zone inputs via piecewise Lyapunov functions. The parameters for dead-zone characteristics are assumed to be unknown, and an adaptive dead-zone inverse method is applied so as to reduce the effect for dead-zone non-linearities. Moreover, for the purpose of reducing the effects of matched and mismatched uncertainties, compensation inputs are introduced. The proposed adaptive gain robust model-following/tracking controller can achieve that the tracking error asymptotically converges to zero. In this paper, by using piecewise Lyapunov functions, we show sufficient conditions for the existence of the proposed adaptive gain robust model-following/tracking controller. Finally, an example is given to demonstrate the effectiveness of the proposed controller design method.
