A Study on the Designing of a Laboratory Accident Cognition Model Using Smart Sensor Based Decision Tree.

There are diverse risk factor in the laboratories of universities and research institutions according to research purposes, accidents caused by this are highly likely to accompany secondary damages such as fire, explosion. Presently, sensors are utilized to manage safety. They are only capable of supporting the decision making process, but have yet to able to cognize and process accidents on its own. For this reason, the study researches a model capable of cognizing, analyzing of accident autonomously and informing users, based on smart sensors. To this end, designs a system that utilizes smart sensor and Arduino to collect factors such as temperature, transmits them to the main server, and save. Then, in order to cognize accidents, use the decision tree algorithm at the main server to find the threshold value indicating accident occurrence. By doing this, presents a model designing capable of preventing secondary damage expansion and reducing losses.