Naoki Tamura

A wireless agent for autonomous file synchronization in a mobile environment

This paper describes a wireless agent that contributes to the realization of pervasive computing. More concretely, we present a method that manages the file synchronization in a mobile environment. Our wireless agent resides at a mobile client, and automatically completes the file synchronization with the serve while users move through communication service areas. It has the following features: First, to deal with the instability unavoidable in a highly mobile communication, the connection time is predicted based on the average and standard deviation of RF signal strength to determine the optimal timing for attempting connection. Secondly, based on the predicted connection time and due times of files, it determines the file for synchronization in an optimal manner. Thirdly, by maintaining the metadata, it guarantees the consistent synchronization even under several sudden disconnections. We show the experimental results to demonstrate the usefulness and effectiveness of our wireless agent.

Computational fluid dynamics in the design of M-V launch vehicle

In the design of M-V launch vehicle, computational fluid dynamics (CFD) has been used frequently in order to obtain design data, prediction models, and flowfield images. Numbers of simulations have been carried out dealing with various kinds of external and internal flows. Their objectives are analyzed and categorized, brief description is given for the solvers, and plenty of examples are shown. Discussions are made on the current status of CFD as a design tool based on the analyses made above. The rapid growth of computational power in recent years and much effort of CFD researchers have made CFD reliable and efficient enough for its use as the bases for design decisions. It is concluded that CFD has become an indispensable tool for the design of the launch vehicle such as M-V.

Application of Artificial Intelligence to Operation Control of Blast Furnace

Abstract This paper describes about the application of artificial intelligence to the operation control of blast furnace. This operation control system consists of three parts, that is, the forecasting system for the blast furnace heat level, the action guidance system for controlling the blast furnace heat level, and the learning system for forecasting. The forecasting system has two prediction models. One is the rule - based model useful for the short term heat level variation and the other is the statistical quantitative model for the long one. Applying the fuzzy inference theory to these models, a new forecasting system has been developed available for both the short and long term hea t level variation. The action guidance system is an expert system which calculates the operations conditions quantitatively according to the result of the forecasting system. The learning system estimates the actual heat level quantitatively using fuzzy inference . After comparing this result with the forecasted one, this system can calculate the accuracy of the forecasting system, and update the parameters of the system. Applying this system to the actual operation, the failure ratio of the forecasting system proved to be less than 5%, and the heat level control operation could be taken more accurately than before . As a result, the furnace heat level has been stabilized and furnace fuel ratio has been improved to a higher level. Furthermore, the changes in fuel ratio have resulted in decrease hot metal Si content.