Maintenance of power distribution facilities is a significant subject in the power supplies. Fault caused by deterioration in power distribution facilities may damage the entire power distribution system. However, current methods of diagnosing power distribution facilities have been manually diagnosed by the human inspector, resulting in continuous pole accidents. In order to improve the existing diagnostic methods, a thermal image analysis model is proposed in this work. Using a thermal image technique in diagnosis field is emerging in the various engineering field due to its non-contact, safe, and highly reliable energy detection technology. Deep learning object detection algorithms are trained with thermal images of a power distribution facility in order to automatically analyze its irregular energy status, hereby efficiently preventing fault of the system. The detected object is diagnosed through a thermal intensity area analysis. The proposed model in this work resulted 82% of accuracy of detecting an actual distribution system by analyzing more than 16,000 images of its thermal images.

BCI is called BMI (brain-machine interface) sometimes. It is the interface which controls things with human consciousness. In this paper, we look into several of the headset protocol, and we proposed unified protocol with medical standard MFER (Medical Waveform Format Encording Rules) through the analysis of the protocol. And to prove the excellence of the proposed protocol as a case study, we demonstrate embedded control car so as to; (1) prevent overlapped investment on new BCI input interface development, and (2) help to create BCI-related functional game easily, (3) realize variety services through the each equipment compatibility.