This paper presents a novel method for generation of synthetic images of obstacles on and near rail tracks over long-range distances. The main goal is to augment the dataset for autonomous obstacle detection (OD) in railways, by inclusion of synthetic images that reflect the specific need for long-range OD in rail transport. The presented method includes a novel deep learning (DL)-based rail track detection that enables context- and scale-aware obstacle-level data augmentation. The augmented dataset is used for retraining of a state-of-the-art CNN for object detection. The evaluation results demonstrate significant improvement of detection of distant objects by augmentation of training dataset with synthetic images.
Classical discrete mathematics could not model complex and uncertain systems effectively. These discrete mathematical equations can be replaced by a method of fuzzy logic systems wherein the operational laws are expressed in linguistic terms. Fuzzy logic means it is the form of knowledge acquisition to be suitable for notions that cannot be defined as precisely as discrete mathematics would do. But it depends upon their context, knowledge acquisition to enable the computerized devices to think like humans. The Matlab was employed to develop the operation model of the Vaigai reservoir which is in South India. Fuzzy logic control was successfully found to be a logical way for mapping between input and output variables of the operation of a reservoir. The objective of the study was to capture the experience gained over past years of operation of the Vaigai system so that the experiential knowledge was made available to the operator for assisting him inefficient management of the reservoir. The data mining helped to develop an improved database for heuristic models such as the non-crisp model for deriving an effective monthly operational policy for Vaigai reservoir.
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