An interpolation method, which estimates unknown values with constrained information, is based on mathematical calculations. In this study, we addressed interpolation from an image-based perspective and expanded the use of image inpainting to estimate values at unknown points. When chemical gas is dispersed through a chemical attack or terrorism, it is possible to determine the concentration of the gas at each location by utilizing the deployed sensors. By interpolating the concentrations, we can obtain the contours of gas concentration. Accurately distinguishing the contours of a contaminated region from a map enables the optimal response to minimize damage. However, areas with an insufficient number of sensors have less accurate contours than other areas. In order to achieve more accurate contour data, an image inpainting-based method is proposed to enhance reliability by erasing and reconstructing low-accuracy areas in the contour. Partial convolution is used as the machine learning approach for image-inpainting, with the modified loss function for optimization. In order to train the model, we developed a gas diffusion simulation model and generated a gas concentration contour dataset comprising 100,000 contour images. The results of the model were compared to those of Kriging interpolation, one of the conventional spatial interpolation methods, finally demonstrating 13.21% higher accuracy. This suggests that interpolation from an image-based perspective can achieve higher accuracy than numerical interpolation on well-trained data. The proposed method was validated using gas concentration contour data from the verified gas dispersion modeling software Nuclear Biological Chemical Reporting And Modeling System (NBC_RAMS), which was developed by the Agency for Defense Development, South Korea.

The critical role of a remote chemical sensing using a Fourier Transform Infrared (FT-IR) spectrometer has been emphasized for detecting lethal chemicals in the atmosphere. To enhance standoff detection capabilities, acquiring adequate gas spectral data is crucial for training and optimizing detection algorithms across diverse outdoor scenarios. However, the collection of outdoor infrared spectra with a number of conditions is constrained owing to uncontrolled weather factors including a temperature and humidity, leading to impaired reliability of the data. In addressing outdoor data acquisition challenges, we introduced a data augmentation method using a conditional CycleGAN. This technique utilizes spectral data obtained exclusively under controlled laboratory conditions. The proposed deep generative model takes as input the background spectrum, which is concatenated with two critical attributes: the temperature difference between the target substance and the background, and pathlength concentration. Subsequently, the model computes a brightness temperature spectrum for a gas against a specific background, employing SF ₆ as the target chemical gas. The validity of the generated data was assessed using two detection algorithms: the Pearson Correlation Coefficient and Adaptive Subspace Detector. In addition, the accuracy performance of detectors trained with the augmented dataset was compared and evaluated against those trained with the pure dataset. The results demonstrated that the model can simulate gas spectra onto unseen background spectra and enhance the chemical sensing database, and it can contribute to data augmentation for improving the performance of chemical gas detection systems.

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 ,…, 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, 15,000 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 1,500 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).