Earth remote sensing data is used to solve many practical problems, for example, for digitizing satellite images. This problem can be divided into three stages, in this case let it be the classification of objects, th...
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The region of Southern Baikal belongs to the territories with high seismic activity. However, the issues of complex assessment of the influence of the lithosphere model on the dynamic parameters of the rock ground vib...
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Nowadays, a provision of the computational process fault-tolerance in Grid is a relevant issue. In the paper, we address a fault-tolerance improvement in solving large-scale scientific and applied problems that are im...
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Nowadays, a provision of the computational process fault-tolerance in Grid is a relevant issue. In the paper, we address a fault-tolerance improvement in solving large-scale scientific and applied problems that are implemented through modular programming in heterogeneous distributed computing environments. We describe a computational process by an abstract program (problem-solving scheme) that correlates to a workflow. The problem-solving scheme specifies modules (applied software) and their relations with each other. This paper proposes a new multi-agent algorithm for re-allocating Grid-resources when the computational process fails. The algorithm execution involves forming a residual problem-solving scheme using methods of the abstract program specialization and reallocating its modules between agents that represent computational resources. In comparison to the known algorithms for the same purpose, the proposed algorithm implements an adaptive multi-scenario solving this issue and therefore increases a degree of computational process fault-tolerance. Extensive modeling and practical experiments demonstrate the practicability of the proposed algorithm.
The German-Russian Astroparticle Data Life Cycle Initiative is an international project launched in 2018. The Initiative aims to develop technologies that provide a unified approach to data management, as well as to d...
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The paper addresses the relevant issue of ensuring the reliability of solving large scientific and applied problems in computing environments that integrate Grid and cloud computing. The main reliability parameter is ...
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The paper is devoted to discovering new features of diagonal Latin squares of small order. We present an algorithm, based on a special kind of transformations, that constructs a canonical form of a given diagonal Lati...
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The paper provides a general view on the original logical inference based approach to dealing with discrete event systems as subject to supervisory controltheory. The approach suggests a representation of discrete ev...
The paper provides a general view on the original logical inference based approach to dealing with discrete event systems as subject to supervisory controltheory. The approach suggests a representation of discrete event system as a positively constructed formula to imply automated logical inference in the calculus of positively constructed formulas. During the inference, languages of the discrete event system are generated and analyzed. The minimally restricting supervisor for uncontrollable specification may be also designed. A nonblocking supervisor design is illustrated with a simplified model of autonomous underwater vehicle operational modes switching.
Nowadays methods and software for extracting tables from document images and portable documents (PDF) continue to be actively developed. One of the promising approaches to this task is the usage of fine-tuned object d...
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ISBN:
(数字)9789532330991
ISBN:
(纸本)9781728153391
Nowadays methods and software for extracting tables from document images and portable documents (PDF) continue to be actively developed. One of the promising approaches to this task is the usage of fine-tuned object detection models. However, this approach involves many manipulations with data preparation and training process configuration. This paper proposes an automated workflow for fine-tuning deep neural network models for the table detection in document images. It enables us to automate two sub-tasks: (i) preparing a training dataset in the PascalVOC format with image transformation and augmentation; (ii) training a table detection model by using the well-known Faster R-CNN architecture. Implementation of the workflow design simplifies the use of the approach proposed by decreasing the number of required manipulations.
This paper deals with the problem of optimal packing a given number of equal spheres into different closed sets. We consider the problem both in three-dimensional Euclidean and non-Euclidean spaces. The special algori...
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