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In this study, we have developed a non-destructive inspection system for detecting external wall tile delamination by incorporating a pulse water jet impingement force into a suspended apparatus. The delamination inspection using a pulse water jet offers flexibility in adapting to variations in the distance between the inspection device and the inspection surface, a challenge inherent to the suspended apparatus. Furthermore, the delamination inspection using a pulse water jet exhibits exceptional safety and cost-effectiveness. Using this inspection system, we conducted a delamination detection test on tiles with delaminated mortar and were able to detect it. The inspection speed of this apparatus is approximately 10 centimeters per second in horizontal movement, allowing for an inspection rate of roughly 18 square meters per hour for tiles with a 50-millimeter spacing. This inspection speed is comparable to that achievable by human operators. Moreover, the system can perform inspections at heights of up to 30 meters above ground level. This inspection system has the potential to address the issue of labor shortage by facilitating automation.

When learning to play a musical instrument, it is important to improve the quality of self-practice. Many systems have been developed to assist practice. Some practice assistance systems use special sensors (pressure, flow, and motion sensors) to acquire the control parameters of the musical instrument, and provide specific guidance. However, it is difficult to acquire the control parameters of wind instruments (e.g., saxophone or flute) such as flow and angle between the player and the musical instrument, since it is not possible to place sensors into the mouth. In this paper, we propose a sensorless control parameter estimation system based on the recorded sound of a wind instrument using only machine learning. In the machine learning framework, many training samples that have both sound and correct labels are required. Therefore, we generated training samples using a robotic performer. This has two advantages: (1) it is easy to obtain many training samples with exhaustive control parameters, and (2) we can use the correct labels as the given control parameters of the robot. In addition to the samples generated by the robot, some human performance data were also used for training to construct an estimation model that enhanced the feature differences between robot and human performance. Finally, a flute control parameter estimation system was developed, and its estimation accuracy for eight novice flute players was evaluated using the Spearman’s rank correlation coefficient. The experimental results showed that the proposed system was able to estimate human control parameters with high accuracy.