This paper proposes a restoration method for the pneumatic pressure signal distorted due to dynamics of a cavity-mounted pressure sensing system, in order to obtain the “correct” or true pressure waveform to be sensed directly at the measuring point. Restoration is performed in the time domain by computing the output waveform of the “inverse transfer function” for the sensing system whose input is the actual, distorted signal from the system. For a relatively short (or thin) hole, restoration is to evaluate the left-hand side of a second order nonlinear differential equation itself. The restored pressure signal form has excellent agreement with the directly measured original one over a wide range of signal frequencies and amplitudes, or, despite overshoot and delay to a step-like change of the pressure.

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This study proposes an essential issue to construct a driver model for a railway in order to keep a safe and integral society throughout the world. As of now, no study has been made on the transfer function of train drivers and brake systems as to safety of a railway. This paper describes the stability and safety of railway vehicles controlled by train drivers. The driver model used the model as proposed by Iguchi et al., and used the transfer function that consisted of the proportion, differentiation and the integrated operation. This study describes the frequency characteristics: transfer function from a braking command to error between the reference velocity and real velocity. Moreover, overrun distances against driver model parameters are shown. According to the simulation results, an integral gain of the transfer function of the driver model is important in the brake and vehicle dynamics.

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The purpose of this study was to develop an air actuated robot finger having a tactile sense similar to a human finger. The result was an artificial finger which simulated the sensitivity function system of a human by combining the appropriate sensors and a neural network system. A sensory evaluation of humans by the SD method and multivariable data factor analysis was performed in order to clarify the human tactile sensitivity mechanism. The results were used to decide the type of sensors needed and were used as teaching models for the neural network. After the trainings, the artificial finger reached a level that could distinguish various materials like as a human, and an evaluation of its precision was carried out using the generalized functional results of the neural network.

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