The topicality of spatial clustering of emergency situations locations is substantiated. The adapted STING clustering algorithm is presented. The work has a description of a choice of a database for storing clustering results based on comparative analysis of alternative storages. An algorithm for data preparation for following visualization is offered
Knowledge management is a combination of the processes which manage creation, extraction, processing, utilization, and distribution of knowledge in a certain domain. The knowledge management system is a complex of the procedures which realize these processes. Ontologies are widely used today for the knowledge representation both in Russia and abroad. A part of any system interacting with user is a possibility to personify the information and knowledge flow between the system and the user. A method of ontology-oriented clustering for grouping knowledge management system users based on their preferences is proposed. Such grouping makes it possible to reveal common preferences of user groups and to adapt the information and knowledge flow based on these preferences.
The article presents the application of a statistical analysis algorithm for multi-temporal multispectral aerial photography data to identify areas of historical anthropogenic impact on the natural environment. The investigated site is located on the outskirts of the urban-type village of Znamenka (Znamensky District, Tambov Region) in a forest-steppe zone with typical chernozem soils, where arable lands were located in the second half of the 19th - early 20th centuries. Grown vegetation as a result of secondary succession in abandoned areas can be a sign for identifying traces of historical anthropogenic impact. Distinctive signs of such vegetation from the surrounding natural environment are its type, age and growth density. Thus, the problem of detecting the boundaries of anthropogenic impact on multispectral images is reduced to the problem of vegetation classification. The initial data were the results of multi-temporal multispectral imaging in green (Green), red (Red), edge of red (RedEdge) and near-infrared (NIR) spectral ranges. The first stage of the algorithm is the calculation of the Haralick texture features on multispectral images, the second stage – reduction in the number of features by the principal component analysis, the third stage – the segmentation of images based on the obtained features by the k-means method. The effectiveness of the proposed algorithm is shown by comparing the segmentation results with the reference data of historical cartographic materials. The study of multi-temporal multispectral images makes it possible to more fully characterize and take into account the dynamics of phytomass growth in different periods of the growing season. Therefore, the obtained segmentation result reflects not only the configuration of areas of an anthropogenic transformed natural environment, but also the features of overgrowth of abandoned arable land.
This paper focuses on capturing the meaning of Natural Language Understanding (NLU) text features to detect the duplicate unsupervised features. The NLU features are compared with lexical approaches to prove the suitable classification technique. The transfer-learning approach is utilized to train the extraction of features on the Semantic Textual Similarity (STS) task. All features are evaluated with two types of datasets that belong to Bosch bug and Wikipedia article reports. This study aims to structure the recent research efforts by comparing NLU concepts for featuring semantics of text and applying it to IR. The main contribution of this paper is a comparative study of semantic similarity measurements. The experimental results demonstrate the Term Frequency–Inverse Document Frequency (TF-IDF) feature results on both datasets with reasonable vocabulary size. It indicates that the Bidirectional Long Short Term Memory (BiLSTM) can learn the structure of a sentence to improve the classification.
The paper proposes a method for fusioning multi-angle images implementing the algorithm for quasi-optimal clustering of pixels to the original images of the land surface. The original multi-angle images formed by the onboard equipment of multi-positional location systems are docked into a single composite image and, using a high-speed algorithm for quasi-optimal pixel clustering, are reduced to several colors while maintaining characteristic boundaries. A feature of the algorithm of quasi-optimal pixel clustering is the generation of a series of partitions with gradually increasing detail due to a variable number of clusters. This feature allows you to choose an appropriate partition of a pair of docked images from the generated series. The search for reference points of the isolated contours is performed on a pair of images from the selected partition of the docked image. A functional transformation is determined for these points. And after it has been applied to the original images, the degree of correlation of the fused image is estimated. Both the position of the reference points of the contour and the desired functional transformation itself are refined until the evaluation of the fusion quality is acceptable. The type of functional transformation is selected according to the images reduced in color, which later is applied to the original images. This process is repeated for clustered images with greater detail in the event that the assessment of the fusion quality is not acceptable. The purpose of present study is to develop a method that allows synthesizing fused image of the land surface from heteromorphic and heterogeneous images. The paper presents the following features of the fusing method. The first feature is the processing of a single composite image from a pair of docked source images by the pixel clustering algorithm, what makes it possible to isolate the same areas in its different parts in a similar way. The second feature consists in determining the functional transformation by the isolated reference points of the contour on the processed pair of clustered images, which is later applied to the original images to combine them. The paper presents the results on the synthesis of a fused image both from homogeneous (optical) images and from heterogeneous (radar and optical) images. A distinctive feature of the developed method is to improve the quality of synthesis, increase the accuracy and information content of the final fused image of the land surface.
In this article the task of determining the current position of pneumatic actuators is considered. The solution to the given task is achieved by using a technical vision system that allows to apply the fuzzy clustering method to determine in real time the center coordinates and the displacement position of a color label located on the mechatronic complex actuators. The objective of this work is to improve the accuracy of the moving actuator’s of mechatronic complex by improving the accuracy of the color label recognition. The intellectualization of process of the color shade recognition is based on fuzzy clustering. First, a fuzzy model is built, that allows depending on the input parameters of the color intensity for each of the RGB channels and the color tone component, to select a certain color in the image. After that, the color image is binarized and noise is suppressed. The authors used two defuzzification models during simulation a fuzzy system: one is based on the center of gravity method (CoG) and the other is based on the method of area ratio (MAR). The model is implemented based on the method of area ratio and allows to remove the dead zones that are present in the center of gravity model. The method of area ratio determines the location of the color label in the image frame. Subsequently, when the actuator is moved longitudinally, the vision system determines the location of the color label in the new frame. The color label position offset between the source and target images allows to determine the moved distance of the color label. In order to study how noise affects recognition accuracy, the following digital filters were used: median, Gaussian, matrix and binomial. Analysis of the accuracy of these filters showed that the best result was obtained when using a Gaussian filter. The estimation was based on the signal-to-noise coefficient. The mathematical models of fuzzy clustering of color label recognition were simulated in the Matlab/Simulink environment. Experimental studies of technical vision system performance with the proposed fuzzy clustering model were carried out on a pneumatic mechatronic complex that performs processing, moving and storing of details. During the experiments, a color label was placed on the cylinder, after which the cylinder moved along the guides in the longitudinal direction. During the movement, video recording and image recognition were performed. To determine the accuracy of color label recognition, the PSNR and RMSE coefficients were calculated which were equal 38.21 and 3.14, respectively. The accuracy of determining the displacement based on the developed model for recognizing color labels was equal 99.7%. The defuzzifier speed has increased to 590 ns.
. The analysis of networks of a diverse nature, which are citation networks, social networks or information and communication networks, includes the study of topological properties that allow one to assess the relationships between network nodes and evaluate various characteristics, such as the density and diameter of the network, related subgroups of nodes, etc. For this, the network is represented as a graph – a set of vertices and edges between them. One of the most important tasks of network analysis is to estimate the significance of a node (or in terms of graph theory – a vertex). For this, various measures of centrality have been developed, which make it possible to assess the degree of significance of the nodes of the network graph in the structure of the network under consideration.
The existing variety of measures of centrality gives rise to the problem of choosing the one that most fully describes the significance and centrality of the node.
The relevance of the work is due to the need to analyze the centrality measures to determine the significance of vertices, which is one of the main tasks of studying networks (graphs) in practical applications.
The study made it possible, using the principal component method, to identify collinear measures of centrality, which can be further excluded both to reduce the computational complexity of calculations, which is especially important for networks that include a large number of nodes, and to increase the reliability of the interpretation of the results obtained when evaluating the significance node within the analyzed network in solving practical problems.
In the course of the study, the patterns of representation of various measures of centrality in the space of principal components were revealed, which allow them to be classified in terms of the proximity of the images of network nodes formed in the space determined by the measures of centrality used.
The article considers main methods to use intelligent techniques and algorithms, synthesized on their basis, as well as examinesdata presentation of network monitoring for IT security risk management of secure multiservice networks. The mathematical model of intelligent data presentation is developed and examined for IT security risk investigation and assessment.
The paper describes the original algorithm of a heterogeneous data clustering is based on complex application of a set of measures of distances and clustering methods and multi-stage clustering. In the algorithm we use ranging of attributes the object on their importance for group and a choice of an optimum attributes set, ensemble approach to get the final clustering solution. The algorithm is realized in MixDC (Mixed Data Clustering) software system. The technique and results of the solution of a real problem of a medical data clustering in software system are described.
The main goal of this paper is to create algorithm of synonyms thesaurus generation. Modern search engines use such thesauri for query expansion. Such approach allows to return not only documents containing words from query, but also ones containing their synonyms or semantically similar terms. Semi-automatic method of named entity recognizer training was developed as a part of this work. Semi-automatic method of extracted entities validation is also given.
The article is devoted to development of a complex speaker model for using at the text-independent speaker identification. The complex speaker model is based on gaussian mixture method. The model is formed by preliminary segmented speech signal, where each segment matches to certain broad phonetic class. Method of speaker models structuring is proposed. Speaker models are structured as a tree, which allows to identify speaker without running a full search on the set of models. Researches have shown the division of the acoustic space of speaker's voice on the set of classes that represent some phonetic events, increases the efficiency of voice identification and the proposed structuring method of models accelerates the search operation.
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