Abstract: A cotter pin with a separation preventing means and a method of manufacturing the same are proposed. The method includes a material cutting step, a heating step of heating a preform resulting from cutting, a first shaping step of forming a dome-shaped head at each of opposite ends of the heated preform, a cutting step of forming segmented shaped bodies, a second shaping step of forming a side groove, a third shaping step of forming a lower groove in a lower end of each of the segmented shaped bodies, a through-hole forming step, and a separation preventing means coupling step. Therefore, the present invention has a technical feature that maximizes work efficiency and productivity and provides economic savings by integrating the separation preventing means and the cotter pin, and by simultaneously making manufacturing two cotter pins in a single process cycle.

Abstract: The present invention relates to an on-off lever of a disconnecting switch for a power distribution line that can connect or disconnect lines through easy and safe manipulation of the on-off lever. According to an embodiment of the present invention, the on-off lever of the disconnecting switch for the power distribution line, the on-off lever having: a lever body mounted to an end part of a movable member; a shaft hole formed on a first portion of a middle part of the lever body; a locking step provided on a lower part of the lever body such that the locking step is locked to a locking end; a guide hole formed on a second portion of the middle part of the lever body; and a manipulation means provided on an upper part of the lever body.

Abstract: Disclosed is a method of detecting anomalies suspected of an attack based on time series statistics according to the present invention. The method of detecting anomalies suspected of an attack according to the present invention includes the steps of: collecting log data and traffic data in real-time and extracting at least one piece of preset traffic feature information from the collected log data and traffic data; and training through a time series analysis-based normal traffic training model using the extracted traffic feature information, and detecting abnormal network traffic according to a result of the training.