Industry 4.0 marks a shift toward fully automated digital production, where intelligent systems manage processes in realtime and interact continuously with their environment. Central to this evolution is robotic technology, which enhances productivity and precision in manufacturing. A key aspect of this advanced production model is human-robot interaction, where operators and robots work together on complex tasks. Ensuring safe collaboration between humans and robots is a primary objective. This paper proposes a method for human gesture recognition based on multi-sensor data fusion. By incorporating data from multiple sensors, we achieve a more complete and robust representation of gestures. Our approach involves an algorithm that classifies human movements in real-time using visual data. The process consists of several steps: data preprocessing, feature extraction, data integration, and gesture classification. By employing machine learning and deep learning techniques for feature extraction and analysis, we aim to achieve high accuracy in recognizing gestures.
Innovative design, construction, and operating companies actively introduce building and structure information modeling (BIM) technologies into their production activities. Introducing these technologies makes it possible to improve the quality of design and construction and effectively optimize and reduce costs at the stages of construction and subsequent operation of a capital construction project. The article describes the problem of automating the operation processes of buildings and structures using information modeling technologies (BIM technologies). Several digital technological platforms for the operation of capital construction projects are considered, including solving the problem of monitoring the technical condition of buildings and structures.
Raster masks (binary, object-by-object, or energy fields) of areas of interest are formed by identifying certain informative features in images. To ensure transparency in presenting these features, raster data is vectorized to obtain information in a readable form. Vectorization involves primary and secondary transformations. For two-dimensional images, the immediate change consists of getting points, sections, and polygons from pixels and raster areas of the “substrate”, and the secondary transformation consists of a series of morphological operations on it. A mathematical model of labyrinths (two-dimensional rectilinear), mathematical characteristics of labyrinths and a model of generation of labyrinths with given characteristics are presented. The types of labyrinths from the point of view of satellite monitoring and some examples of artificial and natural origin are given. The “labyrinth effect” for pedestrians and transport is noted, which arises during the restructuring of the territory using remote sensing.
The article proposes a technology for operating an automatic space monitoring system for the presence of waste disposal sites. The technology is based on the ideas of stochastic geometry, as well as geometric probability and covariograms. The paper proposes an algorithm based on the trace transform using discrete orthogonal transforms to minimize the feature space. The problem of developing a trace matrix and selecting informative features using the stochastic geometry method for finding waste disposal sites from high-resolution satellite images is studied using the orthogonal transformations apparatus. The proposed methodology is tested using space images depicting waste disposal sites.
The article analyzes the tourism business in conditions of increased risk of emergencies of natural and artificial nature and the involvement of medical and preventive measures to maintain the population's health (flora, fauna) in critical situations. Exploring the Earth from space - remotely using Earth remote sensing (ERS) methods - objectively monitors the dynamics of changes in the resource potential of the tourism component of the country's modern economy. The purpose of the work is to use technologies for remote sensing of the Earth in recreational areas with subsequent socio-economic analysis to reduce the resource potential in the tourism business in conditions of deteriorating ecology of the territories, using the decoding of space images from the perspective of the synthesis of orthogonal systems with predetermined properties (calculation speed, order of transformation, etc.). The work uses the technique of discrete orthogonal transformations.
The article presents methodological approaches to assessing the expected socio-economic effect of reducing the resource potential of the tourism business in conditions of increased possibility of natural and artificial emergencies. The research aims to use Earth remote sensing technologies in recreational areas with subsequent socio-economic analysis to reduce the resource potential in the tourism business in the context of the deteriorating ecology of the territories. The work uses regression analysis, methods for deciphering satellite images using various techniques, mathematical modelling and the formation of forecasts using regression models. A general methodology is proposed for constructing a geo-ecological model using information generated from data from space, Earth remote sensing technologies, and monitoring territories for waste sites with a socio-economic justification for tourism business in recreational areas. A mathematical model for the formation of tourist flows using remote sensing technologies has been developed, as well as a methodology for forecasting tourist flows using PLS and PLS-LM methods.
The paper proposes the conceptual foundations of robotization of serial engineering equipment designed for emergency rescue and other urgent work. The relevance of this approach to extreme robotics is caused by the frequent failure of prototypes of robotic tools and the lack of sufficient funds for their repair, which ultimately does not allow the full use of the existing fleet of automatic devices for fire and rescue and other urgent work. The system of a mobile remotely controlled complex for robotizing serial equipment intended for emergency rescue and other critical work is considered. The developed system is based on optical methods of control and recognition of objects.
From the point of view of satellite monitoring, construction objects include fixed objects of artificial origin (buildings and structures for various purposes), created from building materials and lying directly on the earth's surface. Someone can conditionally divide the life cycle of a construction object into initial, main and final stages. From the point of view of satellite, ground, aerial photography it is possible to distinguish building objects at the initial, main and final stages from each other. In this case, not all initial stages are exposed to the images, and additional conditions are required to distinguish the final stages in the images (the same applies to the main stages). We can produce the current stage of the life cycle of a building object for various reasons. The paper considers some features of deciphering construction objects at the initial and final stages of their life cycle, primarily in an emergency and abandoned state. Relevant types of construction objects are identified, the structure of decoding signs is determined, and decoding areas are established, we derive the signs themselves for different construction objects. An experiment on the detection of abandoned construction sites on several databases is given.
Remote sensing is an objective monitoring of the dynamics of changes in the resource potential of the tourism component of the country's modern economy. This paper proposes the information storage model for creating a global space monitoring system for the presence of municipal solid waste objects with elements of economic analysis and recreation of the health of potential tourists and the ecology of recreational areas around the world. The proposed model uses methods for decoding remote sensing images by fractal-percolation image analysis and elements of convolutional neural networks. The purpose of the work is to design a model of a global automatic monitoring system for waste disposal facilities, including industrial ones, using Earth remote sensing technologies in recreational areas, followed by an economic analysis to reduce the resource potential in the tourism business in the face of deteriorating ecology of the studied areas.
We proposed an approach for estimating the shape and geometric parameters of the observed objects from a perspective image based on typed elements, perspective geometry methods and convolutional neural networks. The proposed method uses the assumption that the object under study is rigid. A method is proposed for restoring a 3-D model of an observed object from one perspective image using reference objects and typed elements. Semantic segmentation of typed elements allows to set the photometric parameters of the coordinate system attached to the points on the image. According to the calculated photometric parameters and segmentation of the observed object in the image, its parameters and a 3-D model are estimated. The developed method is applicable for calculating 3-D models from a single perspective image in the vicinity of a road (both road and railway) infrastructure, where there are a large number of typed elements.
We describe the digital technological platform SODIS Building CM for visual control of construction using a BIM model. It can track the work schedule and control the processes of delivery and acceptance of construction and installation works. BIM model elements can be associated with various tasks, documents, equipment, and processes. Elements of the BIM model can automatically change color depending on the state of the object. Staining changes depending on the percentage of completion and the backlog from the base schedule of work. Using a 3D model of an object allows control the amount of work performed on structural elements and all engineering systems. The article also discusses the capabilities of the SODIS Building FM platform, which allows you to get, instead of a pile of operational documentation, which is extremely inconvenient to use, an almost ideal system for safe operation, where the search for the necessary information takes a matter of seconds. This is especially important in the event of an emergency, when there is an acute shortage of time to make effective management decisions.
The paper describes an approach to restoring a three-dimensional model of rigid objects from a single satellite image based on informative classes identified from the results of machine learning, which include railway rails and poles, roofs and walls of buildings, shadows of poles and buildings, and others. The proposed algorithms take into account various conditions for the presence of certain classes in the image, identified by the results of machine learning, as well as the conditions for the absence of metadata on the spatial resolution and spatial orientation of the shooting and the Sun (shooting angle, scanning azimuth, etc.).
Remote sensing of the Earth allows to receive medium information, a high spatial resolution from space vehicles, and to conduct hyperspectral measurements. This study presents a remote sensing application using time-series Landsat satellite images to monitor the solid waste disposal site (WDS). In this work, neural network image processing methods are used as part of the proposed algorithm for computer modeling of the fractal-percolation process of the underlying surface filtrate in remote sensing imaging. Investigations related to the analysis of the filtrate and decryption of space images are carried out using the apparatus of discrete orthogonal transformations and two convolutional neural networks. The first neural network detects a waste disposal facility, the second works to localize the area of the leachate identified based on the first network. The results obtained can serve as a basis for developing a methodology for assessing the effectiveness of measures to neutralize the underlying surface of waste disposal sites from leachate and its seepage into the soil using remote sensing technologies. This technique can become the object of further research on developing a medical-prophylactic expert system at the territorial level for the detection and neutralization of unauthorized waste disposal sites based on medium and high-resolution space images. As a result, the proposed method demonstrates good accuracy in detection the solid waste disposal site on real satellite images.
Remote sensing of the Earth allows to receive medium information, a high spatial resolution from space vehicles, and to conduct hyperspectral measurements. This study presents a remote sensing application using time-series Landsat satellite images to monitor the solid waste disposal site (WDS). The article proposes algorithms for working with spatial information, namely the transformation (convolution) of these manifolds into a one-dimensional sample. Recursive quasi-continuous sweeps are used for which the following conditions are satisfied: 1) preservation of the topological proximity of the original and expanded spaces, 2) preservation of correlations between the elements of the original and transformed spaces. An automated system is proposed for detecting and investigating waste objects based on the concept of fractal sets and convolutional neural networks. The first neural network detects WDS, the second works to localize the waste objects. This technique can become the object of further research on developing a medical-prophylactic expert system at the territorial level to detect and neutralize unauthorized waste disposal sites based on medium and highresolution space images. As a result, the proposed method demonstrates good accuracy in detecting the solid waste disposal site on real satellite images.
Pollution of the Arctic territories with garbage dumps provides the general warming in the northern latitudes and cooling in the southern latitudes of the Earth. This article examines the state of the cryosphere of the studied territories and the impact on the constituent elements of solid domestic and industrial waste. The necessary information of medium, high spatial resolution for further study can be obtained using technologies for remote sensing of the Earth from spacecraft with hyperspectral measurements. We propose a method for detecting leachate elements in unauthorized dumpsites in the Arctic using space vehicles. This task is relevant for the implementation of geo-ecological monitoring of the Arctic territories covered with snow. An algorithm for finding the creation of leachate under the influence of solid household and industrial waste has been developed. The article examines the consequences of climate change on forming the biomedical component of the process under consideration. We present a comparison of the proposed processing algorithm on the space images of the Arctic and subarctic territories of the Russian Federation.
The article explores the detection of unauthorized landfills based on remote sensing data. We have developed a mathematical approach that translates ordinary images to the 2-D manifold or 3-D manifolds in stereo images. At the same time, the posed task when developing algorithms is the transformation (convolution) of these manifolds into a one-dimensional sample. The developed method corresponds to the following two conditions: 1) preservation of the topological proximity of the elements of the original and expanded spaces, 2) preservation of correlations between the elements of the original and transformed spaces. The basic idea that underlies the mathematical support of the developed automated system is the concept of fractal sets. The concept of continuous orthogonal transformations, including the Fibonacci transform, is used as a mathematical basis for determining anomalous signal structures in the surrounding background. The problem of monitoring and decoding space images from the point of view of the synthesis of orthogonal systems with predetermined properties (speed of calculations, order of transformation, etc.) is presented. Examples of processing by the proposed algorithm are presented on the satellite images of the Moscow Region territories of the Russian Federation.
Remote sensing of the Earth allows receiving medium, high spatial resolution, and hyperspectral measurements from spacecraft. This study presents a remote sensing application using time-series satellite images to monitor solid waste disposal facilities (WDF) of northern and high mountainous areas with landfills. This study develops a mathematical model describing the physicochemical process of changing the natural state of the snowy regions of the Earth. This technique is directly related to the landfill's physicochemical and biological processes, which can be assessed using space monitoring data. This is relevant for the implementation of geological monitoring of territories covered by snow cover, particularly for the regions of the North Caucasus. It also studies the consequences of climate change on forming the biomedical component of the process under study. The paper proposes a methodology and a mathematical model of the impact of solid domestic and industrial waste on the snow cover using remote sensing technologies. The work uses the methods of fractal-percolation, chemical, biological, and regression analysis. The algorithm results are shown on the example of satellite images of the mountainous territories of the North Caucasus.
Remote sensing of the Earth allows receiving medium, high spatial resolution, and hyperspectral measurements from spacecraft. This study presents a remote sensing application of using time-series satellite images for monitoring solid waste disposal facilities (WDF). We proposed a method for satellite image processing using the percolation for physicochemical analysis of soil cover of industrial waste facilities. This work aims to study different methods for assessing percolation parameters from space images. The article discusses ways of fractal-percolation, chemical, and regression analysis. The proposed algorithm results are shown on the example of the solid household and the industrial waste landfill. The received results can serve as the basis for developing a methodology for assessing the effectiveness of measures to neutralize the underlying surface of the WDF against the filtrate and seep it into the soil using remote sensing technologies of Earth.
This study presents a remote sensing application of using time-series satellite images for monitoring the solid waste disposal facilities (WDF). Solid waste management and monitoring is a critical issue for the metropolitan authorities of developed and developing countries. This is due to the appearance of natural, unauthorized garbage dumps that negatively affect the ecological and epidemiological state of the environment. It is advisable to solve this problem remotely, using remote sensing technologies, having high-and medium-resolution information from spacecraft. We propose a method of filtrate analysis and space images (SI) decryption are carried out with the use of a DOT apparatus and, in particular, with the use of Viner filtering. In this work, Winer filtering is used, as part of the proposed algorithm of computer simulation of the fractal-percolation process of filtrate of the underlying surface of WDF, the filtering threshold is determined, as well as studies for correctness on Tikhonov are carried out. The experiment is carried out on the example of a SI with a WDF image. An extension of the feature space is also modeled using stochastic geometry. The results obtained can serve as a basis for the development of a methodology for assessing the effectiveness of measures to neutralize the underlying surface of the WDF from the filtrate and leak it into the soil using remote sensing of the Earth technologies. This methodology can be the subject of further research on the development of a medical and preventive expert system at the territorial level for the detection and neutralization of unauthorized WDFs on medium and high-resolution space images.
Remote sensing of the Earth allows receiving information of medium, high spatial resolution, and hyperspectral measurements from spacecraft. The paper studies the percolation processes of the underlying surface of the objects of distribution of household and industrial waste. An algorithm for constructing a percolation threshold is proposed. The method of labeling clusters is used. A mathematical model of the percolation process has been developed. This technique is directly related to the physicochemical processes of the landfill, which can be estimated from space monitoring data. The purpose of the work is to develop and propose a methodology for assessing the percolation parameters of landfills from space images. The results of the proposed algorithm are shown on the example of the Salaryevo solid household and the industrial waste landfill (Leninsky district of the Moscow region).
Remote sensing of the Earth allows to receive information of medium, a high spatial resolution from space vehicles and to conduct hyperspectral measurements. We propose a new approach of construction of 3D-models of the Earth's surface in urban environments using remote sensing technologies. The main idea is the use of the algebra of geometric objects (addition, subtraction, multiplication, a division of objects and grids, multiplication of an object by a factor, raising an object to power, factoring, etc.). These operations automate the process of multidimensional modeling. We have developed programs that automate the process of geometric programming of scenes, in particular, scenes on the surface of the Earth (for example, trees, buildings, roads, and their arrays). In the Matlab software environment, a script library has been developed in the field of processing geometric objects, including such categories as the generation of urban objects; algebraic operations; texturing, marking and painting; work with objects in dynamics; work with random objects; differentiation and integration of objects and others. Examples of programs for automating the construction of 3D models of residential arrays based on the simplest scenarios (using algebraic operations of multiplying an object by a coefficient, a coefficient by a coefficient, an object by an object, an object by a grid of points and a line of points) are given.
While constructing a mathematical model of the space observation system for information processing of the data of monitoring the territories on the presence of the waste disposal sites (WDS), we have a stochastic process with a fractal structure. The physical processes of the WDS are formed under the influence of thermal, chemical, etc. factors. There are different methods and approaches for describing the scattering and radiating surfaces of the objects that make up solid household and industrial waste using single-scale and multi-scale correlation radii. With an increase in the order of multi-scale, the multi-scale of the correlation radius also increases, and this, accordingly, complicates the construction of a mathematical model of a space image. The transition to the fractal presentation of the image allows solving the problem of the space images processing. The paper proposes some algorithm for processing the aerospace images using a field of fractal dimension. The theoretical and experimental studies are being held with respect to improving the results of aerospace image classification results while monitoring for the WDS presence. An assessment of the size of the “window” and the magnitude of the “jump” on the parameters of the fractal dimensions is also made. The purpose of the work is to describe the system synergistic approach of multi-scale selection, which allows overcoming the problems of the image processing. This is due to incomplete knowledge of signals, non-stationarity, non- Markov, noise singularity based on preliminary information about the spatial scales of the detected signals. While working with low-contrast images, the usual signal processing technique (contour-texture, spectral methods) does not adequate. There is a need to apply the theory of fractals in the study of processes occurring on the surface of the WDS through remote sensing. An experiment is carried out using the example of fractals, an image is decoded. The features of fractals are considered.
Remote sensing of the Earth allows to receive information of medium, a high spatial resolution from space vehicles and to conduct hyperspectral measurements. In many cases, waste disposal sites (WDS) is not legal. So, it is very important to use the system for automatic detection such places using satellite images. In this paper, a model of the automated space monitoring system for the presence of waste disposal sites is developed. The proposed system includes the following blocks: a database of WDS; a subsystem for detecting unauthorized WDS; a subsystem for monitoring the design, operation and reclamation rules for existing WDS; a subsystem for estimating the parameters of the WDS and their impact on the environment; a subsystem of satellite monitoring. We propose a method of detecting high-rise buildings landfills, such as municipal dumps and solid waste, according to a radar image (the height of the ground level). For system design, we use the apparatus of discrete orthogonal transformations. The impact of the WDS influence on agricultural crops is analyzed, based on the data of the Earth remote space sensing based on the orthogonal transform. The purpose of the work is the modeling of an automated space monitoring system for the presence of waste disposal facilities using regularization method in the problem of filtering of space images from the noise stored in the archives. As a result, the proposed method demonstrates good accuracy in detection the solid waste disposal site on real satellite images.
In this paper, Haar's generalized wavelet functions are applied to the problem of ecological monitoring by the method of remote sensing of the Earth. We study generalized Haar wavelet series and suggest the use of Tikhonov's regularization method for investigating them for correctness. In the solution of this problem, an important role is played by classes of functions that were introduced and described in detail by I.M. Sobol for studying multidimensional quadrature formulas and it contains functions with rapidly convergent series of wavelet Haar. A theorem on the stability and uniform convergence of the regularized summation function of the generalized wavelet-Haar series of a function from this class with approximate coefficients is proved. The article also examines the problem of using orthogonal transformations in Earth remote sensing technologies for environmental monitoring. Remote sensing of the Earth allows to receive from spacecrafts information of medium, high spatial resolution and to conduct hyperspectral measurements. Spacecrafts have tens or hundreds of spectral channels. To process the images, the device of discrete orthogonal transforms, and namely, wavelet transforms, was used. The aim of the work is to apply the regularization method in one of the problems associated with remote sensing of the Earth and subsequently to process the satellite images through discrete orthogonal transformations, in particular, generalized Haar wavelet transforms. General methods of research. In this paper, Tikhonov's regularization method, the elements of mathematical analysis, the theory of discrete orthogonal transformations, and methods for decoding of satellite images are used. Scientific novelty. The task of processing of archival satellite snapshots (images), in particular, signal filtering, was investigated from the point of view of an incorrectly posed problem. The regularization parameters for discrete orthogonal transformations were determined.
The paper proposes a method for fuzzy interactive enhancement of objects identification in the image which allows identifying hidden or no defined details and objects in the images. The application of the method and its difference from other image enhancement techniques are shown. The paper presents the algorithm and describes the basic processing procedures (sampling, scaling, convolution, contrast). The main processing parameters (increasing and reduction of dimensions, convolutions, brightness, and thresholds contrast) are demonstrated. The results from the applied algorithm are explained on an example related to landfill Kutchino in the Moscow region, on the satellite images with low and high spatial resolution.
This study presents a remote sensing application of using time series Landsat satellite images for monitoring the solid waste disposal site (WDS). We propose a method of detecting high-rise buildings landfills, such as municipal dumps and solid waste, according to a radar image (the height of the ground level). For disposal site detection a variety steps of image processing used (calculation image average level of the earth's surface; filtering thresholds spectral brightness coefficients, the size of the connected components, the nature of reducing the level of height with the distance of the maximum level). The spatial geometric features of waste disposal facilities are analytically expressed by linear and radial characteristics from other objects of the earth surface. As a result, the proposed method demonstrates good accuracy in detection the solid waste disposal site on real satellite images.
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