Hiromichi Akimoto

Wing in surface effect ship with canard configuration

Wing in surface effect ship (WISES) is a high-speed ship which supports its weight with an aerodynamic wing in very low altitude flight. The ship utilises the enhanced performance of wing in proximity to the water, according to the same mechanism as ground effect. The authors propose a canard-type WISES as a suitable concept for commercialisation. This concept has many advantages for conventional airplane-like configurations, especially for taking off and alighting on water. This paper describes the canard concept, its self-propulsion model tests and the preliminary designs for actual ships.

Production risk management system with demand probability distribution

As market globalization has changed the nature of their business, the types of products are multiplied by customizing in order to seize each market segment. Moreover, model lifecycle have been shortened by releasing new models for stimulating customers. That makes it difficult to control the proper timing and volume of the product supply. Manufactures face the overstock risks and stock shortage risks. Each product needs its customized supply control to keep profitability. This study proposes a reproduction decision support system that measures demand risks through a sales forecasting method. This system gives manufactures the proper volume and timing guidance for daily reproduction of products. The system is applied to the case of a Japanese publisher. A three-month operational test of the system proved it able to provide the optimized supply volume options based on manufacturers decision strategies. The ratio of surplus stock decreased from 40% to 34% as a result.

Capacity fade model of Lithium-ion batteries for Practical use

Lithium-ion (Li-ion) batteries are essential components for electric vehicles and smart grid systems that will become increasingly widespread in the future. However, the complexity of the capacity fade mechanism of Li-ion batteries has been a technical barrier to their practical use. This study proposes a capacity fade model composed of three factors: capacity loss, increase in internal resistance, and diffusion effect. The model derives sufficient information from battery data and conditions of use for evaluating and predicting capacity fade. The battery data includes discharge curves and electrode materials. The conditions of use are elements correlated to the three factors listed above and include the number of cycles and storage time. This paper discusses the availability of our proposed model by applying it to Li-ion battery data obtained from reference papers.