부산대학교 도서관

A source localization of a chemical gas dispersion is important to understand the situation and facilitate effective responses to incidents. The source point can be simply identified on a map assuming an infinite number of chemical sensors in every region. However, it is limited to manufacture and install a number of sensors due to the several conditions. Consequently, when dispersion begins in a region without sensors, tracking the movement of chemical gas cloud and pinpointing its origin is challenging since obtaining information during the early stages of an incident is difficult. Therefore, this study proposes a recursive tracking model based on a transformer for the source localization of a chemical gas dispersion. This machine learning model recursively tracks the past spatial dispersion distribution using sequential frames from a chemical concentration map. When N sequential image frames $(C_{1}, C_{2}, \ldots, C_{N})$ are input, the model outputs a single image frame $(C_{0})$ , which represents the image frame prior to the input time sequence. Thus, the model predicts the origin by successively predicting past image frames. The gas concentration data for this study were sourced from a gas dispersion model based on Fick’s law. For each scenario, 15000 datasets were generated to train and test the model, each dataset comprising 15 image frames that describe various aspects of gas dispersion. The performance of source localization was assessed based on accuracy. The model was validated using 1500 test data obtained from the model based on Fick’s law and 100 test data obtained from the Nuclear Biological Chemical Reporting and Modeling System (NBC_RAMS).