The decomposition of the deep learning machine for specialized datasets for time minimizing of spatial information processing

Abstract

For effective military management, the creation of complexes of automation tools, spatial information processing systems, primarily consolidated, is a priority task in the conditions of constant growth of data and requirements for their collection, transmission, storage, processing and use.
 The problem of consolidated processing of spatial information is related to the diversity of sources, the diversity of data formats, the diversity of acquisition and use, the diversity of processing etc. All that imply an extremely complex organizational and technical structure, a kind of ‘system of systems’.
 Deep Learning Machine (DLM) ensure the high accuracy of prediction. But such DLM should be matched for the military conditions of the usage where time restrictions and space lack are present.
 So, the effort to create a dataset for the maximum accuracy of the Deep Learning Machine rests on computing resources (in common research it is possible to overcome but in military sphere it isn’t). In military applied tasks, the criterion of time and overcoming uncertainties due to confrontation are critical. This allows us to put forward the hypothesis that it is impossible to achieve absolute accuracy in deep learning machine. Therefore, for variable tactical situations, it is advisable to create specialized datasets and achieve maximum efficiency from each iteration step (or their combination) using the decomposition method of the consolidated spatial information processing system. It is analysed the methods of scientific and technical solutions in the deep learning machine and the method of systematizing data types in existing tactical situations. In the end the detection and recognition system with a deep learning machine and a set of specialized datasets is proposed in the paper.
 The volume of each specialized dataset at the level of 103 enables ultra-high speed of information processing processes and allows a person to set up such a system of consolidated processing of spatial information without excessive organizational and time demands. The dataset itself is revealed.
 This principle of forming a dataset, or their sets, allows obtaining high-accurate and high -fast detection and recognition systems.