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In the traditional Japanese (Kampo) medicine, diagnosis is based on the observation of facial color and expression. Disease states that are diagnosable from facial characteristics include blood stagnation, blood deficiency, and yin deficiency. However, these diagnoses are subjective and require lots of experience. In this paper, we aimed to quantify facial diagnosis by creating a system to evaluate the severity of disease states using feature values obtained from facial images. A Kampo physician evaluated the facial images and rated them from 1 to 5 according to severity of disease states. We verified the accuracy of this system using the mean squared error calculated from the difference between the physician evaluation scores and the system estimates. The mean squared error was close to zero, indicating that the system has high accuracy. The selection of feature values using this system corresponded with those facial regions used by the Kampo physician in diagnosing the disease states.

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Robotic welding has been the biggest use in many industry. It is important to control weld robots without weld spatter for small size target. To prevent flying weld spatter, the precursory phenomenon of weld spatter must be detected, and power source of robotic welding must be controlled to reduce before weld spatter occurs. This study aimed to propose a control system to prevent the occurrence of flying weld spatter during laser or gas tungsten arc (GTA) welding process in real-time. Because the strategy involves detecting the precursory phenomenon at high-speed, we simplified the recognition system as much as possible. The system consists of a lighting system to illuminate the weld pool, a high-speed camera and an FPGA board. The control algorithm of this system used image data information for a histogram. We analyzed the images from the high-speed camera during welding. Our analysis found that, just before the weld spatter is generated, very bright pixels temporarily and abnormally increased as the precursory phenomenon. According to the proposed system, it is possible to prevent the occurrence of weld spattering in real time during the welding process. This integration of visual feedback in a robotic welding system enhances the quality of the weld work.

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