Due to the concentration effect, there is a major challenge for the electronic nose system to identify different odor samples with multiple concentrations. The development of artificial intelligence provides new...

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Exposure to air pollutants, such as PM2.5 and ozone, has a serious adverse effect on health, with more than 4 million deaths, including early deaths. Air pollution in ports is caused by exhaust gases from various elements, including ships, and to reduce this, the International Maritime Organization (IMO) is also making efforts to reduce air pollution by regulating the sulfur content of fuel used by ships. Nevertheless, there is a lack of measures to identify and minimize the effects of air pollution. The Community Multiscale Air Quality (CMAQ) model is the most used to understand the effects of air pollution. In this paper, we propose a hybrid model combining the CMAQ model and RNN-LSTM, an artificial neural network model. Since the RNN-LSTM model has very good predictive performance, combining these two models can improve the spatial distribution prediction performance of a large area at a relatively low cost. In fact, as a result of prediction using the hybrid model, it was found that IOA improved by 0.235~0.317 and RMSE decreased by 4.82~8.50 μg/m3 compared to the case of using only CMAQ. This means that when PM2.5 is predicted using the hybrid model, the accuracy of the spatial distribution of PM2.5 can be improved. In the future, if real-time prediction is performed using the hybrid model, the accuracy of the calculation of exposure to air pollutants can be increased, which can help evaluate the impact on health. Ultimately, it is expected to help reduce the damage caused by air pollution through accurate predictions of air pollution.

In this paper, we propose a method for detecting semantic segments in a live TV program using closed captions and visual features. Since the unseen closed captions and video frames become available as time passes in live broadcasting, we first divide the currently available video into two groups by computing the segmentation costs, which are the linear combination of the textual and visual segmentation costs. Then, we discover the segment boundaries by computing the stabilities of the segmentations with additional data feed. Experimental results show that the proposed method outperforms the previous work on both precision and recall while processing live TV programs in real-time.

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