Complex Object Structures Research Articles

Application of Fractal Dimension for Cardiac Arrhythmias Classification

Fractal analysis is crucial for understanding complex, irregular patterns found in nature, finance, and various scientific fields. It helps to reveal self-similarity, where structures repeat at different scales, providing insights into chaotic systems like weather patterns, stock markets, and biological growth. By applying fractal analysis, researchers can model phenomena that traditional geometric methods cannot easily describe, enabling better predictions and deeper comprehension of dynamic systems. The Fractals are a fascinating mathematical tool for modeling the roughness of nature and understanding structure of such complex objects. They are considered a tool for understanding the world. In general, fractal objects are characterized by the fractal dimension. The application of fractal geometry to the analysis of ECG time series data is examined in this paper. A method based on the assessment of the Fractal Dimension (FD) of ECG recordings is suggested for the identification of cardiac diseases. In this work, and in order to exploit the fractal dimension to analyze fractal signals, the notion of fractal dimension is defined by presenting methods for calculating this dimension such as Higuchi algorithm, Katz method, regularization, box-counting etc… Each of them has its own advantages and disadvantages. This study has shown that the electrocardiogram (ECG) is a fractal signal. This allows to classify heartbeats founded on the concept of fractals. The main aim is to develop a digital technique to analyze ECG signals in order to make an accurate diagnosis of cardiovascular diseases.

Image scanning microscopy using radially polarized beam with polarization conversion

Image scanning microscopy (ISM), which combines the confocal scanning microscopy (CSM) with fast wide-field CCD detection, has been demonstrated in the fluorescent imaging, achieving both high resolution and signal to noise ratio (SNR). In this paper, we introduce radially polarized beam to ISM for non-fluorescent imaging. Radially polarized beam is applied to obtain a sharper illumination point spread function (PSF), along with a polarization converter to enhance detection PSF. It is found that the lateral resolution of ISM using radially polarized beam with polarization conversion (RPC-ISM) is improved to 0.28λ by a factor of 20%, as compared to traditional CSM. Further, images of complex object structures are simulated based on the complete model of scanning microscopy. Simulation results indicate an enhanced transverse resolution in RPC-ISM, compared with conventional ISM using circularly polarized beam. This technique can be used for label-free imaging such as semiconductor inspection, nano-lithography and so on.

ERT, GPR, and magnetic surveying: the case study of Khayrabadtepa settlement (Southern Uzbekistan)

The use of geophysical methods has become an integral part of the work at all stages of archaeological research. Geophysics contribute to the efficient and rapid detection of buried objects. One of the effective methods for mapping archaeological sites is the magnetic survey that reveals anomalies associated with the residual magnetization of such objects. To study the deep structure of complex objects in conditions of variable relief, the method of electrical tomography (ERT) has been well recommended. Ground Penetrating Radar (GPR) measurements delineate buried structures in soil strata. Geophysical work was carried out within the boundaries of the settlement of Khayrabadtepa, which is a monument of the Kushan period in the territory of Northern Bactria. The settlement is located 1 km southwest of the city of Angor, 30 km northwest of the city of Termez.

Phase compensation algorithm based on image segmentation in dual-wavelength holographic microscopy.

In order to solve the problem of phase compensation errors in the traditional 2π phase compensation method caused by a rough surface and complex structure of objects in dual-wavelength digital holographic microscopy, a phase compensation algorithm based on image segmentation was proposed. First, the phase less than zero in the phase obtained by an equivalent wavelength is compensated for by adding 2π initially. Then the phase after the initial compensation is binarized, and the small connected areas in the binarized graph are removed, so as to obtain a new binarized graph. Finally, according to the two binarized graphs, the phase of the object after the initial 2π phase compensation is recompensated for in different regions, so as to obtain the continuous phase distribution of the object. Based on the dual-wavelength digital holographic microscopy experimental system with an adjustable equivalent wavelength, the proposed algorithm is used to perform three-dimensional imaging of the channel of the microfluidic chip. The experimental results show that the proposed method can effectively obtain the continuous real phase of the object when the structure of the object is known, so as to obtain a more accurate and reliable three-dimensional topography of the object. The above results provide a new idea for the high-quality three-dimensional imaging of the microfluidic system.

Terahertz Scanless Hypertemporal Imaging

AbstractSince its first demonstration in 1995, terahertz time‐domain imaging has attracted an increasingly growing interest for its ability to reveal spectral fingerprints of materials and probe changes in refractive index and absorption, as well as detect the inner structure of complex objects via time‐of‐flight measurements. Practically, however, its widespread use has been hampered by the very long acquisition time typically required to spatially raster‐scan the object, and for each spatial point, record the field in time via a delay line. Here, this fundamental bottleneck is addressed by implementing a scanless single‐pixel imaging scheme, which sets the path for an unprecedented reduction of both system complexity and acquisition time. By properly exploiting natural wave diffraction, time‐to‐space encoding applied to terahertz point detection allows for an almost instantaneous capture of the terahertz waveforms, while multidimensional images are reconstructed via a computational approach. The scheme is a promising solution for the development of next‐generation fast and compact terahertz imagers perfectly suitable for high‐repetition‐rate laser sources.

Decoupling inequalities with exponential constants

Decoupling inequalities disentangle complex dependence structures of random objects so that they can be analyzed by means of standard tools from the theory of independent random variables. We study decoupling inequalities for vector-valued homogeneous polynomials evaluated at random variables. We focus on providing geometric conditions ensuring decoupling inequalities with good constants depending only exponentially on the degree of the polynomial. Assuming the Banach space has finite cotype we achieve this for classical decoupling inequalities that compare the polynomials with their associated multilinear operators. Under stronger geometric assumptions on the involved Banach spaces, we also obtain decoupling inequalities between random polynomials and fully independent random sums of their coefficients. Finally, we present decoupling inequalities where in the multilinear operator just two independent copies of the random vector are involved.

Проектирование цифровых образовательных ресурсов для осуществления информационно-цифровой подготовки магистров

The article considers the use of computer modeling technologies for visualization of learning elements of engineering disciplines in order to improve understanding of the structure and functioning of complex technical objects and technological systems. The principles of digital didactics and their influence on the formation of the content of educational resources are analyzed. An education blog is presented as a modern tool for information and digital training of vocational education teachers. The scientific novelty of the work lies in the fact that the use of computer modeling technologies as meta-technologies for organizing information and digital training of modern masters allows us to take a fresh look at the process of digitalization of education and at the design of its content. The practical significance of the study lies in the fact that inclusion of computer modeling technologies in educational practice allows not only to improve its quality, but also to prepare a graduate to enter the innovative digital system of an educational organization.

A Weakly Supervised Graph Deep Learning Framework for Point Cloud Registration

The point cloud registration is important and necessary for the applications of changing detection, deformation monitoring, and so on, which is also challenging due to the vast clustered points, irregular, and complex structures of spatial objects, and quality effects of the labeled corresponding points. Aiming at this problem, we design an end-to-end 3-D graph deep learning framework of point cloud registration, which can simultaneously learn the detector (graph attention expression) and the descriptor (graph deep feature) for point cloud registration in a weakly supervised way, so that the learned detector and descriptor promote each other in the process of model optimization. Then, the detector is used to automatically extract the keypoints, and the descriptor describes the deep feature of each keypoint. In the framework, we innovatively propose a new module (named MLP_GCN), which fuses multilayer perceptron (MLP) and graph convolutional network (GCN). The MLP_GCN module is further integrated into the detector branch and descriptor branch to fully express the detector and descriptor of the point cloud. In the training process of the framework, we rotate and translate the point cloud randomly to form the training data in a weakly supervised way, which can save plenty of manually labeling time of corresponding points. In the experiments, our method can achieve better results of point cloud registration in comparison with other methods, which verifies the advantages of the proposed method.

Local and Global Structure for Urban ALS Point Cloud Semantic Segmentation With Ground-Aware Attention

Interpretation of airborne laser scanning (ALS) point clouds plays a notable role in geoinformation production. As a critical step for interpretation, accurate semantic segmentation can considerably broaden various applications of ALS data. However, most existing methods cannot provide precise annotations and high robustness due to occlusions, varied point densities, and complex and incomplete object structures. Therefore, we developed a semantic segmentation framework focusing on ALS point clouds. The framework comprises contextual feature extraction from a local neighborhood, scene-aware global information representation, and a ground-aware attention module. To verify its effectiveness, comprehensive experiments were conducted on three airborne light detection and ranging (LiDAR) datasets: DublinCity, Dayton Annotated LiDAR Earth Scan (DALES), and DFC2019 datasets. The experimental results demonstrate that the proposed method achieves better segmentation performance than that some advanced methods. For the DublinCity dataset, our model&#x2019;s overall accuracy (OA) can be improved to 67.5&#x0025; with an average F₁ (Avg<inline-formula> <tex-math notation="LaTeX">$F_{1}$ </tex-math></inline-formula>) of 37.6&#x0025;. For the DALES dataset, our method achieved an OA of 96.5&#x0025; and a mean intersection over union (mIoU) of 77.6&#x0025;. Our method also achieves a more accurate result on the DFC2019 dataset than that obtained using other models with an OA of 94.8&#x0025; and an AvgF₁ of 81.4&#x0025;.

ASSESSMENT OF SOCIO-ECONOMIC DEVELOPMENT OF REGIONS POTENTIAL

The article describes the results of a multidimensional comparative analysis of the socio-economic potential of the regions of Ukraine, both the strongest and weakest ones. The concept of research based on categories such as change and structure is used. The changes occurring in the structure of complex objects and systems are studied. These changes can affect the quantitative results, and thus increase or decrease certain characteristics of the object, as well as the qualitative aspect, i.e. the creation of new properties of the object. The process of socio-economic development is characterized by a lack of uniformity in space. Due to different conditions and changes in internal and external factors, development processes do not occur with the same intensity in each area. Thus, based on research, it is determined that in Ukraine there is uneven regional development, which is due to the high differentiation of economic activities and heterogeneity of the economic structure of the territories. All this justifies the need to improve the measures and levers of state regulation of regional development and create effective mechanisms to reduce its disparity. The results of the analysis of strengths and weaknesses of the region according to the proposed set of 26 indicators, grouped in the following modules: economic policy, labor and taxation, family and social policy, stimulating activity, are represented. Thus, the heterogeneity of regions is observed both in the development of a particular region and in comparison, with other regions of the country. Such inequality significantly affects the functioning of the state, the structure and efficiency of the economy and socio-economic policy. This leads to an increase in the number of depressed regions and the growth of interregional contradictions. Taxonomic analysis of spatial differentiation of the level of socio-economic development in Ukraine is used. Gaps in the socio-economic level of development in the regions were found for assessment based on multidimensional analysis. The analysis gives an accurate and deep idea of the socio-economic development of the regions. Keywords: socio-economic potential, regional development, economic development, spatial differentiation, heterogeneity of regional development, innovations.

The analysis of Titan’s physical surface using multifractal geometry methods

Titan makes up 95% of the mass of all 82 satellites of Saturn. Titan’s diameter is 5152 km, which means that it is larger than the Moon by 50%, and it is also significantly larger than Mercury. On the satellite, a subsurface ocean is possible, the theory of the presence of which has already been advanced earlier by some scientists. It is located under a layer of ice and consists of 10% ammonia, which is a natural antifreeze for it and does not allow the ocean to freeze. On the one hand, the ocean contains a huge amount of salt, which makes the likelihood of life in it hardly possible. But on the other hand, since chemical processes constantly occur on Titan, forming molecules of complex hydrocarbon substances, this can lead to the emergence of the simplest forms of life. There are limitations on the probabilistic and statistical approaches, since not every process and not every result (form and structure of the system) is probabilistic in nature. In contrast to this, fractal analysis allows one to study the structure of complex objects, taking into account their qualitative specifics, for example, the relationship between the structure and the processes of its formation. When constructing a harmonic model of Titan, the method of decomposition of topographic information into spherical functions was used. As a result, based on the harmonic analysis of the Cassini mission data, a topographic model of Titan was created. In the final form, the model describing Titan’s surface includes the expansion of the height parameter depending on the spherical coordinates into a slowly converging regression series of spherical harmonics. For modeling surface details of the surface on a scale of 1 degree requires analysis of the (180 + 1)2 harmonic expansion coefficients. An over determined topographic information system was solved to meet the regression modelling conditions. In this case, a number of qualitative stochastic data, such as external measures, were used together with the standard postulation of the harmonic system of the Titan model. As a result of a sampling of self-similar regions (with close values of the self-similarity coefficients) on the surface of Titan, coinciding with the SRGB parameter (characterizes the color fractal dimension), the elements of the satellite’s surface were determined, which with a high degree of probability were evolutionarily formed under the action of the same selenochemical processes.

Применение активной стратегии актуализации потенциальных свойств человеко-машинных комплексов в процессах их проектирования и эксплуатации

The analysis of the literature, reflecting the problem of potentiality in creating complex systems, including human-machine complexes (HMC) is carried out. On the basis of the generalized data, potentiality is defined as a fundamental property of objective reality, an integral part of the integrity structure of complex objects, which in the process of their functioning manifests itself in the form of engineering control situations unforeseen by the developers.&#x0D; A strategy for solving the problem of revealing the potential properties of HMC is considered from the viewpoint of comparing two approaches of “passive” and “active” strategies.&#x0D; A “passive” strategy means that the situations that arise unforeseen by the developers are taken into account by them, usually in the future when improving the technique. This approach does not consider the specifics of manifesting the systemic properties of the HMC and is incorrect from the methodological positions of modern systemic studies that assert the equivalence of subject-object relations in HMC.&#x0D; An “active” strategy for solving the problem under study includes a targeted search, disclosure and actualization of the potential properties of an object not only at the stages of its design and creation, but also at the stages of the exploitation in the joint activity of all professionals, namely developers, operators, engineering psychologists. This approach is based on forecasting and studying the potential properties of an exploited object on the basis of organizing a systematic and controlled cognitive process and studying the dynamics of changes in its operational characteristics under conditions of external factors that go beyond normal situations.&#x0D; It is concluded that to assess the characteristics of the HMC and determine the rational organization of its structures, it is necessary to develop an “active” strategy that allows using any type of information, including accurate data obtained on the basis of the deterministic methods of analysis, and inaccurate data obtained on the basis of an analysis of intuition, experience, considering all specialists’ values of judgments and figurative guesses, which will contribute to disclosing the potential of the created technical objects in the process of ergonomic support for their design and operation.

GraNet: Global relation-aware attentional network for semantic segmentation of ALS point clouds

Semantic labeling is an essential but challenging task when interpreting point clouds of 3D scenes. As a core step for scene interpretation, semantic labeling is the task of annotating every point in the point cloud with a label of semantic meaning, which plays a significant role in plenty of point cloud related applications. For airborne laser scanning (ALS) point clouds, precise annotations can considerably broaden its use in various applications. However, accurate and efficient semantic labeling is still a challenging task, due to the sensor noise, complex object structures, incomplete data, and uneven point densities. In this work, we propose a novel neural network focusing on semantic labeling of ALS point clouds, which investigates the importance of long-range spatial and channel-wise relations and is termed as global relation-aware attentional network (GraNet). GraNet first learns local geometric description and local dependencies using a local spatial discrepancy attention convolution module (LoSDA). In LoSDA, the orientation information, spatial distribution, and elevation information are fully considered by stacking several local spatial geometric learning modules and the local dependencies are learned by using an attention pooling module. Then, a global relation-aware attention module (GRA), consisting of a spatial relation-aware attention module (SRA) and a channel relation-aware attention module (CRA), is presented to further learn attentions from the structural information of a global scope from the relations and enhance high-level features with the long-range dependencies. The aforementioned two important modules are aggregated in the multi-scale network architecture to further consider scale changes in large urban areas. We conducted comprehensive experiments on three ALS point cloud datasets to evaluate the performance of our proposed framework. The results show that our method can achieve higher classification accuracy compared with other commonly used advanced classification methods. For the ISPRS benchmark dataset, our method improves the overall accuracy (OA) to 84.5 % and the average F1 measure (AvgF1) to 73.6 %, which outperforms other baselines. Besides, experiments were conducted using a new ALS point cloud dataset covering highly dense urban areas and a newly published large-scale dataset.

CONSTRUCTION OF NEURAL NETWORKS IN ONLINE ENVIRONMENTS WHEN STUDYING THE CONTENT LINE "MODELING AND FORMALIZATION" IN THE LESSONS OF INFORMATICS AT SCHOOL

Modeling as a universal approach to the study of the structure and behavior of complex objects is used in many academic disciplines, as it is a general didactic means of acquiring knowledge. A promising direction in the development of mathematical modeling is modeling the behavior of intelligent systems, which is being improved due to the introduction of new information technologies, telecommunication systems and the massive use of computer technology. Modeling takes on the functionality of a powerful and effective tool for researching a variety of intelligent systems from various fields of human activity. One of the most popular methods for studying the functioning of intelligent systems are machine learning methods for processing large amounts of data, in particular, the apparatus of neural networks. The article discusses the features of constructing the simplest neural networks by using the Teachable Machine online system and online compilers of programming languages in the classroom on computer modeling at school. Detailed examples of building neural network models in Python and C ++ programming languages are given.

A method for designing the logical structure of a distributed telecommunication environment

The article presents a methodology for the development of information exchange tools based on the apparatus of hierarchical Petri nets, characterized by the possibility of simultaneous events and allows solving the problem of correctness analysis and verification of protocols. Protocol objects and access points are represented on the basis of the Petri nets apparatus: the simplest object of the reception/transmission protocol, the queue object, a single object and a trivial object. It describes the construction of complex structures of objects and operations on them, the management of timeouts based on the timer object, the specification of protocol objects, the specification of protocols and levels of logical structure. The proposed method is relevant for the analysis of complex communication systems of large size, which are mobile ad-hoc networks with a large number of nodes, characterized by topology instability due to unstable communication channel characteristics and high nodes mobility, and especially flying ad-hoc networks of aircraft (FANET) and flying sensor networks (FSN).

Cartilage Segmentation in High-Resolution 3D Micro-CT Images via Uncertainty-Guided Self-training with Very Sparse Annotation.

Craniofacial syndromes often involve skeletal defects of the head. Studying the development of the chondrocranium (the part of the endoskeleton that protects the brain and other sense organs) is crucial to understanding genotype-phenotype relationships and early detection of skeletal malformation. Our goal is to segment craniofacial cartilages in 3D micro-CT images of embryonic mice stained with phosphotungstic acid. However, due to high image resolution, complex object structures, and low contrast, delineating fine-grained structures in these images is very challenging, even manually. Specifically, only experts can differentiate cartilages, and it is unrealistic to manually label whole volumes for deep learning model training. We propose a new framework to progressively segment cartilages in high-resolution 3D micro-CT images using extremely sparse annotation (e.g., annotating only a few selected slices in a volume). Our model consists of a lightweight fully convolutional network (FCN) to accelerate the training speed and generate pseudo labels (PLs) for unlabeled slices. Meanwhile, we take into account the reliability of PLs using a bootstrap ensemble based uncertainty quantification method. Further, our framework gradually learns from the PLs with the guidance of the uncertainty estimation via self-training. Experiments show that our method achieves high segmentation accuracy compared to prior arts and obtains performance gains by iterative self-training.

Minimizing information security risks based on security threat modeling

The well-elaborated models of information security threats (IST) allow for the optimal development of a protection plan. This plan will be based on current threats and will consider effective countermeasures that increase the level of information security (IS). The threat model is described using different automation methodologies of this process. It is also possible to visualize the structure of complex objects and processes them from the required angle and with sufficient granularity. Constructing various threat implementations as trees or attack graphs (AG) is one of the relevant directions in assessing the level of IS. The creation of a software application for automation and formalization of assessing the information security process of IS assets, and the localization of bottlenecks in the IS protection, is dealt with in the article. A distinctive feature of the application is the use of the Federal Service for Technology and Export Control of Russia (FSTEC of Russia) threat data bank for modeling the attack tree. The developed software application allows you to reduce time, simplify the process of assessing the security of an IS, and also visualizes the threat modeling process. The scope of the developed software product may be small and medium-sized businesses, as well as state-owned enterprises.

Economic Optimization of the Modular Structure of Complex Objects

In the design of complex objects, modular principles are of great interest. That approach has both negative and positive consequences. It clearly affects the cost of acquiring the required stock of equipment and, in particular, the spare parts necessary for its maintenance. Optimization of the modular structure of industrial equipment may best be approached in economic terms. Smart information systems for the support of management decisions prove very useful. A structure is proposed for such a system. Examples of such optimization in the aviation industry are presented, and practical experience is noted.

ISPEED: a Scalable and Distributed In-Memory Based Spatial Query System for Large and Structurally Complex 3D Data.

The recent technological advancement in digital pathology has enabled 3D tissue-based investigation of human diseases at extremely high resolutions. Discovering and verifying spatial patterns among massive 3D micro-anatomic biological objects such as blood vessels and cells derived from 3D pathology image volumes plays a pivotal role in understanding diseases. However, the exponential increase of available 3D data and the complex structures of biological objects make it extremely difficult to support spatial queries due to high I/O, communication and computational cost for 3D spatial queries. In this demonstration, we present our scalable in-memory based spatial query system iSPEED for large-scale 3D data with complex structures. Low latency is managed by storing in memory with progressive compression including successive levels of detail on object level. On the other hand, low computational cost is achieved by pre-generation of global spatial indexes in memory and additional on-demand generation of indexing at run-time. Furthermore, iSPEED applies structural indexing on complex structured objects in multiple query types to gain performance advantage. During query processing, the memory footprint of iSPEED is minimal due to its indexing structure and progressive decompression on-demand. We demonstrate iSPEED query capability with three representative queries: 3D spatial joins, nearest neighbor and spatial proximity estimation on multiple datasets using a web based RESTful interface. Users can furthermore explore the input data structure, manage and adjust query pipeline parameters on the interface.PVLDB Reference Format:Hoang Vo, Yanhui Liang, Jun Kong, and Fusheng Wang. iSPEED: a Scalable and Distributed In-Memory Based Spatial Query System for Large and Structurally Complex 3D Data.

Numerical Modelling of Structures with Uncertainties

Abstract The nature of environmental interactions, as well as large dimensions and complex structure of marine offshore objects, make designing, building and operation of these objects a great challenge. This is the reason why a vast majority of investment cases of this type include structural analysis, performed using scaled laboratory models and complemented by extended computer simulations. The present paper focuses on FEM modelling of the offshore wind turbine supporting structure. Then problem is studied using the modal analysis, sensitivity analysis, as well as the design of experiment (DOE) and response surface model (RSM) methods. The results of modal analysis based simulations were used for assessing the quality of the FEM model against the data measured during the experimental modal analysis of the scaled laboratory model for different support conditions. The sensitivity analysis, in turn, has provided opportunities for assessing the effect of individual FEM model parameters on the dynamic response of the examined supporting structure. The DOE and RSM methods allowed to determine the effect of model parameter changes on the supporting structure response.