Vol. 4, №5, 2019
Murynin A. B., Trekin A. N., Ignatiev V. Yu., Kulchenkova V. G., Rakova K. O. Approach to enhancement of spatial resolution of the rigid objects satellite imagery // Machine Learning and Data Analysis, 2019, 4(5):296-308. doi:10.21469/22233792.4.5.01 The approach is proposed allows to increase the spatial resolution of space images of rigid objects using vector information about the geometric properties of these objects. Enhancement of the resolution of a low-quality image is carried out using a probabilistic approach using the most optimal parameters obtained by minimizing the difference between the reference image and the result of the method's work on the test data set. The results of the study of the spatial resolution enhancement on different types of the underlying surface at different scales are presented.
Rogozin A. B. Accelerated Nesterov Method for Decentralized Distributed Optimization on Time-Varying graphs // Machine Learning and Data Analysis, 2019, 4(5):309-315. doi:10.21469/22233792.4.5.02 The paper is focused on first-order methods in case when the aim function changes from one iteration to another. This problem is motivated by distributed optimization on networks which can periodically change because of technical malfunctions such as a loss of connection between two nodes. The main results of the paper include theoretical guarantees for linear convergence of distributed gradient descent and distributed Nesterov accelerated method on strongly convex smooth objective functions under the assumption that the network has a finite number of changes.
Dulin S. K., Yakushev D. A. Development of methods for filtering laser reflection points (mathematical morphological filtering, progressive filtering, segmentation) for the identification of technogenic objects // Machine Learning and Data Analysis, 2019, 4(5):316-323. doi:10.21469/22233792.4.5.03 A large amount of data obtained by laser scanning, imply the presence of effective data processing technology. The authors have developed a two-stage technology of spatial data processing (point cloud), obtained as a result of mobile laser scanning and tied to a high-precision coordinate network, which ensures the construction of the 3D model in accordance with the established norms. It can also be applied to the results of high-resolution photogrammetric photography. The technology includes methods for rapid determination of the railway track, algorithms for the construction of individual elements of the infrastructure (support of a contact network), algorithms to determine the profile of the railway, as well as methods of processing another elements of the infrastructure (facilities and devices of the railway, automatics and telemechanic and telecommunications station facilities).
Dulin S. K., Yakushev D. A. Implementation of methods of geoinformation description of technogenic objects of railway transport in the experimental software and hardware complex, providing geoinformation support for the management of the transportation process // Machine Learning and Data Analysis, 2019, 4(5):324-329. doi:10.21469/22233792.4.5.04 One of the most important results of the project was the creation of a digital railway model, which is a formalized mathematical and semantic description of the geometric characteristics and spatial position of the railway track and other object of infrastructure, obtained as result the processing of geodetic measurements in high-precision coordinate space. A digital railway model is actively used to form the optimal design position of the railway track in a single coordinate space and, accordingly, it is possible to accurately compare the design position of the track with the actual data before and after repair. The fundamental point here is the fact of the exact coordinate reference of all measurements with each other, but there are nuances. A central repository of digital railway models is the integrated railway infrastructure spatial data system, which also has the tools to compare design and actual data loaded into the system or converted by the system in the form of a digital railway model.