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This study aimed to discover an easy and precise prediction model for the acoustic properties of nanofiber nonwoven fabrics. For this purpose, a prediction model focusing on the two dominant parameters in the Limp frame model—bulk density and flow resistivity—was suggested. The propagation constant and characteristic impedance was generated from the effective density and effective volume modulus generated by the predictive model and treated as a one-dimensional transfer matrix. The sound absorption coefficient was then estimated using the transfer matrix approach. The trend of the normal Incident sound absorption coefficient measured and the sound absorption coefficient obtained from the predictive model were consistent. Thus, it is suggested that the predictive model for the proposed nanofiber nonwoven composite sheet is valid.

We propose a system in which users can intuitively instruct the robot gripper’s positions and attitudes simply by tracing the object’s grasp part surface with one stroke (one drag) of the laser beam. The proposed system makes use of the “real world clicker (RWC)” we have developed earlier, a system capable of obtaining with high accuracy the three-dimensional coordinate values of laser spots on a real object by mouse-operating the time-of-flight (TOF) laser sensor installed on the pan-tilt actuator. The grasping point is specified as the centroid of the grasp part’s plane region by the laser drag trajectory. The gripper attitude is specified by selecting the left and right drag modes that correspond to the PC mouse’s left and right click buttons. By doing so, we realize a grasping instruction interface where users can take into account various physical conditions for the objects, environments, and grippers. We experimentally evaluated the proposed system by measuring the grasping instruction time of multiple test subjects for various daily use items.