Coordinated Research Articles

Izbrana esejistika Matjaža Kmecla

The characteristics of Matjaž Kmecl’s essay writing are clearly visible in all his key texts. First, the structural role of the story or the meaning of the example must be emphasized, then the sensibility, the synthesis of subjectivity and objectivity, and vitalism. The third is the conceptual starting point, which works along the following lines: contact with nature and the resulting faith in the natural order of things flourishes and is the condition and goal of the good. This view is simple and effective when it rejects hypocrisy, reveals and overcomes the hyperbolized sense of smallness of a nation of some two million people, evokes individuality, overcomes loneliness and self-destructiveness. Kmecl notes the consequences of fatigue and overload when he places literary-historical explanations in temporal and spatial coordinates or in the case of accurate reflections of individuals and events. If I add to this Kmecl’s understanding and consideration of social and socio-historical parallels, it is possible to present a round picture of the mental foundations on which individual texts are created.

Exact solutions to the oberbeck–boussinesq equations for convective stokes flows

To study large-scale convective flows (fluid motion in a thin layer), it is possible, for initial studies, to consider the Stokes approximation when integrating the Oberbeck–Boussinesq equation. The convective derivative in the momentum transfer equations and in the heat conduction equation is in this case assumed to be identically equal to zero. The paper discusses several approaches to constructing exact solutions for slow (creeping) flows of a non-uniformly heated fluid. Formulas for three-dimensional flows in the Lin–Sidorov–Aristov class are given for steady flows. The hydrodynamic fields are described by polynomials. Exact solutions are given for the velocity field nonlinearly depending on two spatial coordinates (longitudinal, or horizontal) with coefficients of nonlinear forms, which depend on the third coordinate. The study shows how it is possible to automate the computation of unknown coefficients for the formation of hydrodynamic fields (velocities and temperatures).

MoReVis: A Visual Summary for Spatiotemporal Moving Regions.

Spatial and temporal interactions are central and fundamental in many activities in our world. A common problem faced when visualizing this type of data is how to provide an overview that helps users navigate efficiently. Traditional approaches use coordinated views or 3D metaphors like the Space-time cube to tackle this problem. However, they suffer from overplotting and often lack spatial context, hindering data exploration. More recent techniques, such as MotionRugs, propose compact temporal summaries based on 1D projection. While powerful, these techniques do not support the situation for which the spatial extent of the objects and their intersections is relevant, such as the analysis of surveillance videos or tracking weather storms. In this article, we propose MoReVis, a visual overview of spatiotemporal data that considers the objects' spatial extent and strives to show spatial interactions among these objects by displaying spatial intersections. Like previous techniques, our method involves projecting the spatial coordinates to 1D to produce compact summaries. However, our solution's core consists of performing a layout optimization step that sets the size and positions of the visual marks on the summary to resemble the actual values on the original space. We also provide multiple interactive mechanisms to make interpreting the results more straightforward for the user. We perform an extensive experimental evaluation and usage scenarios. Moreover, we evaluated the usefulness of MoReVis in a study with 9 participants. The results point out the effectiveness and suitability of our method in representing different datasets compared to traditional techniques.

IMJENSE: Scan-Specific Implicit Representation for Joint Coil Sensitivity and Image Estimation in Parallel MRI.

Parallel imaging is a commonly used technique to accelerate magnetic resonance imaging (MRI) data acquisition. Mathematically, parallel MRI reconstruction can be formulated as an inverse problem relating the sparsely sampled k-space measurements to the desired MRI image. Despite the success of many existing reconstruction algorithms, it remains a challenge to reliably reconstruct a high-quality image from highly reduced k-space measurements. Recently, implicit neural representation has emerged as a powerful paradigm to exploit the internal information and the physics of partially acquired data to generate the desired object. In this study, we introduced IMJENSE, a scan-specific implicit neural representation-based method for improving parallel MRI reconstruction. Specifically, the underlying MRI image and coil sensitivities were modeled as continuous functions of spatial coordinates, parameterized by neural networks and polynomials, respectively. The weights in the networks and coefficients in the polynomials were simultaneously learned directly from sparsely acquired k-space measurements, without fully sampled ground truth data for training. Benefiting from the powerful continuous representation and joint estimation of the MRI image and coil sensitivities, IMJENSE outperforms conventional image or k-space domain reconstruction algorithms. With extremely limited calibration data, IMJENSE is more stable than supervised calibrationless and calibration-based deep-learning methods. Results show that IMJENSE robustly reconstructs the images acquired at 5× and 6× accelerations with only 4 or 8 calibration lines in 2D Cartesian acquisitions, corresponding to 22.0% and 19.5% undersampling rates. The high-quality results and scanning specificity make the proposed method hold the potential for further accelerating the data acquisition of parallel MRI.

TranSR-NeRF: Super-resolution neural radiance field for reconstruction and rendering of weak and repetitive texture of aviation damaged functional surface

In order to reconstruct and render the weak and repetitive texture of the damaged functional surface of aviation, an improved neural radiance field, named TranSR-NeRF, is proposed. In this paper, a data acquisition system was designed and built. The acquired images generated initial point clouds through TransMVSNet. Meanwhile, after extracting features from the images through the improved SE-ConvNeXt network, the extracted features were aligned and fused with the initial point cloud to generate high-quality neural point cloud. After ray-tracing and sampling of the neural point cloud, the ResMLP neural network designed in this paper was used to regress the volume density and radiance under a given viewing angle, which introduced spatial coordinate and relative positional encoding. The reconstruction and rendering of arbitrary-scale super-resolution of damaged functional surface is realized. In this paper, the influence of illumination conditions and background environment on the model performance is also studied through experiments, and the comparison and ablation experiments for the improved methods proposed in this paper is conducted. The experimental results show that the improved model has good effect. Finally, the application experiment of object detection task is carried out, and the experimental results show that the model has good practicability.

Discovering Logo Identification through Machine Learning

Logo detection is the process of identifying and localizing logos within images or videos, involving recognizing specific brand logos and determining their spatial coordinates within the visual content. This task is essential for various applications such as brand recognition, copyright protection, and contextual advertising across fields like social media monitoring, autonomous driving, and market research. Logo detection aids in tracking and analyzing advertisements, making it crucial in marketing and media industries. In this paper, the authors have studied and described the process of Logo detection along with its types and also have discovered the techniques used in various researches that have dealt with Logo detection. This study further paves way for a developing a better logo detection technique by analyzing the use of existing techniques. Key Words: Logo, Detection, Deep Learning, Machine Learning.

Spatially confined coordination platform in covalent organic framework for selective uranium adsorption from aqueous solutions

The use of covalent organic frameworks (COFs) to remove uranium from nuclear wastewater, especially capture uranium by well-defined nanopores and customizable groups, is highly desired. The development of an ideal binding site with optimal balance between adsorption capacity and selectivity has remained an attractive challenge. Herein, a staggered stacking self-assembly approach was adapted to control the pore size and confine space of dioxin-linked COF-316 with modified functional groups to achieve higher capture efficiency. EXAFS analysis suggested that COF-316-COOH and COF-316-AO both have high affinities toward U(VI) owing to inner-sphere surface complexation by two or three atoms, DFT calculations further revealed that the uranyl cation was coordinated by = NOH or –COOH groups from two adjacent COF layers. The synergistic coordination in multi-confined interlaminar position provides an ideal spatial coordination platform for uranium, and the merits of capacity, selectivity and stability underscore the potential application of COF-316-AO for efficient uranium removal.

Peculiarities of Firing Vessels of the Beloyar and Kalinkina Cultures on the Barsova Gora in the Surgut Ob River Region (according to termal analysis)

The article presents the results of a of the features of firing hand-made pottery of the Beloyar and Kalinkinа cultures, collected during excavations from eight settlements of the first half of the Early Iron Age in the Barsova Gora tract near the western outskirts of the city of Surgut. In the course of production, the vessels of both cultures were subjected to heat treatment of a low degree of intensity. With the exception of a few containers, they were fired under approximately the same conditions. Among the Beloyar ceramics, the complex from the fortified settlement Barsov gorodok I/3 stands out. It presents three groups of pots and jars, differing in the degree of firing intensity: intensive, medium and least. It was established that the difference in the intensity of firing ceramics was due not to temperature fluctuations in the fires and hearths, but to differences in the skills of the craftsmen. These differences could manifest themselves in the temperature and / or duration of firing, the time spent by containers in fires and hearths, the design features of heat engineering devices, etc. In terms of the intensity of firing, a group of vessels from the settlement Barsova Gora III/20, made of highly sanded clays, stands out in the collection of dishes from the Kalinkina culture. On the clay component preservation diagram, the values for such samples are located closer to its center. This is especially characteristic of vessel 32, which was fired more intensively than all the other items of this group. The corresponding characteristics of the three vessels from the settlement Barsova Gora III/12 on the same diagram are at the maximum distance from the origin of coordinates. This also distinguishes them from the rest of the pottery from this site and testifies to less intense firing. It is not yet possible to reconstruct specific types and design features of devices for firing ceramics used by potters of the Beloyar and Kalinkina cultures. One can only assume that the dishes were fired in bonfires and hearths.

The materiality key: how work on empirical data can improve analytical models and theoretical frameworks for multimodal discourse analysis

This article is a critical reflection on the way the notion of materiality informed the project and the development of The Kinesemiotic Body project carried out by a UK and German research teams and of the model of analysis it adopted, the Functional Grammar of Dance. It starts with an excursus of some of the most interesting developments in other discipline that turned to the investigation of materiality as an epistemological perspective, and it shows how the same type of focus has impacted on multimodal discourse analysis focusing on movement-based communication. The overarching theme that characterises this multidisciplinary attention to materiality is its anchoring function to the temporal and spatial coordinates in which social phenomena are contextualised, which is taken as the fundamental condition for shaping our perception and understanding of the world in all areas of experience and knowledge. A more specific example of how the notion of materiality impacted on the development of movement-based discourse analysis will be provided by an example of analysis of rich movement data captured live from professional dancers from the English National Ballet.

Any-Stereo: Arbitrary Scale Disparity Estimation for Iterative Stereo Matching

Due to unaffordable computational costs, the regularized disparity in iterative stereo matching is typically maintained at a lower resolution than the input. To regress the full resolution disparity, most stereo methods resort to convolutions to decode a fixed-scale output. However, they are inadequate for recovering vital high-frequency information lost during downsampling, limiting their performance on full-resolution prediction. In this paper, we introduce AnyStereo, an accurate and efficient disparity upsampling module with implicit neural representation for the iterative stereo pipeline. By modeling the disparity as a continuous representation over 2D spatial coordinates, subtle details can emerge from the latent space at arbitrary resolution. To further complement the missing information and details in the latent code, we propose two strategies: intra-scale similarity unfolding and cross-scale feature alignment. The former unfolds the neighbor relationships, while the latter introduces the context in high-resolution feature maps. The proposed AnyStereo can seamlessly replace the upsampling module in most iterative stereo models, improving their ability to capture fine details and generate arbitrary-scale disparities even with fewer parameters. With our method, the iterative stereo pipeline establishes a new state-of-the-art performance. The code is available at https://github.com/Zhaohuai-L/Any-Stereo.

Abstract 4298: Predicting tumor evolution from digital histology using AI

Abstract Diverse clinical presentations of clear cell renal cell carcinoma (ccRCC) confound clinical decision making, leading to over and undertreatment. Clonal evolution of ccRCC proceeds through distinct trajectories characterised by differing levels of genomic intratumoral heterogeneity (gITH) and chromosomal complexity (weighted genomic instability index, wGII). However, accurate evaluation of these indices requires multiregional profiling of fresh tumour; cost prohibitive and logistically challenging in the clinical setting. Clinical histopathology workflows routinely capture multiple tumour areas enabling the use artificial intelligence (AI) to predict tumour evolutionary features directly from clinical grade H&E whole slide image (WSIs). ccRCC displays profound genetic and histological ITH but the link between these entities remains unclear. We leverage the TRACERx Renal cohort, comprising 1485 WSIs from 81 tumours to predict WGII and gITH and to gain insights into the relationship between genetic and histological ITH. Critically, each WSI is associated with a wGII and gITH label derived from a closely linked fresh tumour sample. For both prediction tasks, we extracted meaningful features for each WSI using self-supervised representation learning “MoCo”. Since high wGII confers poor prognosis we focussed on predicting binary stratification label of high wGII or low wGII (relative to the cohort median). First we predicted wGII as a continuous variable using a supervised multiple instance learning regression model trained on the MoCo features, and then classified the predicted wGII into “high” or “low” achieving 0.80 AUROC. To predict gITH we postulated that the degree of gITH would correlate with histological ITH. Using an unsupervised clustering of refined MoCo features we defined 24 histological clusters. The number of computationally derived histological clusters within a single tumour positively correlated with gITH (pearson’s 0.56). We used the number of clusters to classify WSIs into prognostic binary groups of high or low gITH (relative to the cohort median) achieving an AUROC of 0.80. To understand the biological relationship between histological and genetic ITH we pathologically characterised the histological clusters: a pathologist annotated WSIs with tumour architecture and cytomorphology. Image tiles were associated with the annotations using spatial coordinates, illuminating phenotypic traits of different evolutionary trajectories and providing an interpretability framework for our AI pipelines. Since the tumour evolutionary course dictates disease progression tempo, applying evolutionary classification in clinic can fundamentally improve patient care. Here, for the first time, we provide a framework to translate fundamental evolutionary principles underpinning tumour biology and clinical progression into a prognostic computational pathology biomarker possible to clinically implement. Citation Format: Charlotte E. Spencer, Axel Camara, Auriane Riou, Lewis Au, Jose I. Lopez, Zayd Tippu, Charles Maussion, Kenneth Ho, Amy Strange, Emma Nye, Veronique Birault, Lydwine Van-praet, Kim Edmonds, Eleanor Carlyle, Steve Hazell, Sarah Rudman, James Larkin, Samra Turajlic. Predicting tumor evolution from digital histology using AI [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4298.

Inter-plane feedback coordinates cell morphogenesis and maintains 3D tissue organization in the Drosophila pupal retina.

How complex organs coordinate cellular morphogenetic events to achieve three-dimensional (3D) form is a central question in development. The question is uniquely tractable in the late Drosophila pupal retina, where cells maintain stereotyped contacts as they elaborate the specialized cytoskeletal structures that pattern the apical, basal and longitudinal planes of the epithelium. In this study, we combined cell type-specific genetic manipulation of the cytoskeletal regulator Abelson (Abl) with 3D imaging to explore how the distinct cellular morphogenetic programs of photoreceptors and interommatidial pigment cells (IOPCs) organize tissue pattern to support retinal integrity. Our experiments show that photoreceptor and IOPC terminal differentiation is unexpectedly interdependent, connected by an intercellular feedback mechanism that coordinates and promotes morphogenetic change across orthogonal tissue planes to ensure correct 3D retinal pattern. We propose that genetic regulation of specialized cellular differentiation programs combined with inter-plane mechanical feedback confers spatial coordination to achieve robust 3D tissue morphogenesis.

Research on sector scanning imaging technology of EMAT for through-hole defects in aluminium plates

ABSTRACT To overcome the drawbacks of traditional electromagnetic acoustic transducers (EMATs) that depend on mechanical scanning, adding to both cost and complexity, this study introduces a sector scanning imaging method utilizing Electromagnetic Ultrasonic Shear Vertical (SV) waves. A simulation model for EMAT was developed, optimizing through variable frequency and unidirectional line focusing coils. The simulation revealed that a novel composite magnetic structure significantly enhanced the magnetic flux density to 1.318 T, a 159.44% increase over conventional magnets, leading to a 102.08% rise in ultrasonic signal intensity and a marked improvement in the signal-to-noise ratio. By converting echo signal time data into spatial coordinates, the technology enables high-resolution defect imaging. The variable frequency-EMAT (VF-EMAT) technique accurately identified defects, with minor discrepancies from actual defect centers, showcasing its precision. The unidirectional line focusing-EMAT (ULF-EMAT) method further improved defect detection accuracy and shape restoration. However, the technique’s reliance on the main-pulse limits its ability to detect near-surface defects, suggesting an avenue for future research.

Dynamic Morphological Growth Trajectory of Crouzon Population: A Geometric Morphometric Analysis.

This study is to visualize the morphological growth trajectory of the Crouzon population and provide a more comprehensive and perceptual understanding of pathologic aggregation in terms of maxillofacial and soft tissue surfaces. Twenty-two patients with Crouzon syndrome were included in this retrospective surgery. Preoperative computed tomography was segmented into maxillofacial and soft tissue surface morphologies. Fifty-two anatomical landmarks and a standardized template were used to extract the morphological phenotypes and transform them into spatial coordinates. Geometric morphometrics (GM) were applied to visualize the growth trajectory and estimate the morphological variation of the Crouzon population. Cephalometric analysis was conducted to validate the growth trajectory in a clinical aspect. It was found that both maxillofacial and soft tissue surface morphologies were strongly covaried with age. Morphological development of the Crouzon population was characterized by (1) maxillary retrusion and clockwise rotation, (2) mandibular counterclockwise rotation, (3) facial width narrowing and aggregated concave face, and (4) sunken nasal bone. Pathologic maxillary retrusion continuously existed from infancy to adulthood, and rapid aggravation took place at 3 to 6 years old. In conclusion, this study is the first to visualize the dynamic growth trajectories on both maxillofacial and soft tissue surface morphologies. More attention and monitoring of breathing, snoring, intelligence, and global development should be cast on Crouzon patients between 3 and 6 years old in family care. If any functional abnormalities occur during this period, professional consultations and evaluations should be conducted timely to avoid pathologic aggravation. Consistency between GMs and cephalometry validates the reliability of GM potentials in the clinical field, allowing a promising and revolutionary methodology for dynamically and qualitatively analyzing pathologic changes in some rare congenital diseases.

State primary special standard of the unit of length GET 199-2024: ensuring the uniformity of measurements of high-precision total stations and their analogues in the mode of three-dimensional measurements

The metrological support of optical-electronic means of measuring spatial coordinates is described. To meet modern consumer requirements for the accuracy of measurements of spatial coordinates with electronic tacheometers and their analogues (laser scanners, absolute trackers), the State primary special standard of the unit of length GET 199-2024 includes technical means for reproducing, storing and transmitting the unit of length in three-dimensional measurement mode. A reference complex of three-dimensional measurements (coordinate measurements, coordinate increments) has been developed and studied. The means and methods for transmitting a unit of length are presented, as well as the metrological characteristics of the GET 199-2024 in the mode of measuring coordinate increments (length). The sources of non-excluded systematic error and the standard deviation of the measurement result are determined. As a result of the research performed, it became possible to reproduce a unit of length in the mode of measuring coordinate increments in the range of 0–60 m with a standard deviation of no more than 25 μm for 10 independent measurements and a non-excluded systematic error (with a confidence probability of 0.99) of 19 μm. The functionality of the GET 199-2024 will allow solving current problems of metrological support for high-precision electronic total stations and their analogues in three-dimensional measurement mode.

Summer solstice orchestrates the subcontinental-scale synchrony of mast seeding.

High interannual variation in seed production in perennial plants can be synchronized at subcontinental scales with wide consequences for ecosystem functioning, but how such synchrony is generated is unclear1-3. We investigated the factors contributing to masting synchrony in European beech (Fagus sylvatica), which extends to a geographic range of 2,000 km. Maximizing masting synchrony via spatial weather coordination, known as the Moran effect, requires a simultaneous response to weather conditions across distant populations. A celestial cue that occurs simultaneously across the entire hemisphere is the longest day (the summer solstice). We show that European beech abruptly opens its temperature-sensing window on the solstice, and hence widely separated populations all start responding to weather signals in the same week. This celestial 'starting gun' generates ecological events with high spatial synchrony across the continent.

Demonstrating the relevance of spatial-functional statistical analysis in marine ecological studies: The case of environmental variations in micronektonic layers

In this study, we conducted an analysis of a multifrequency acoustics dataset acquired from scientific echosounders in the West African water. Our objective was to explore the spatial arrangement of marine organism aggregations. We investigated various attributes of these intricate biological entities, such as thickness, relative density, and depth, in relation to their surroundings. These environmental conditions were represented at a fine scale using a towed multiparameter system. This study is closely intertwined with two key domains: Fisheries acoustics techniques and functional data analysis. Fisheries acoustics techniques facilitate the collection of high-resolution spatial and temporal data concerning marine organisms at various depths and spatial scales, all without causing any disturbance. On the other hand, spatial-functional data analysis is a statistical approach for examining data characterised by functional attributes distributed across a spatial domain. This analysis encompasses dimension reduction techniques, as well as supervised and unsupervised methods, which take into consideration spatial dependencies within extensive datasets.We began by applying multivariate statistical techniques and subsequently employed Functional Data Analysis (FDA). In the modeling section, we introduced the spatial dimension with the spatial coordinates as covariates in the General Additive Model (GAM) and Functional Generalized Spectral Additive Model (FGSAM) models, aiming to underscore its relevance in those contexts. In an exploratory phase, Multivariate Functional Principal Component Analysis provided detailed insights into the variations of parameters at different depths, a capability not offered by traditional Principal Component Analysis. When it came to regression tasks, we explored the interactions between descriptors of Sound Scattering Layers and key environmental variables, both with and without considering spatial dimensions. Our findings revealed significant distinctions between northern and southern Sound Scattering Layers, as well as between coastal and high-sea regions. The use of the spatial locations enhanced the performance of GAM and FGSAM, particularly in the case of salinity, reflecting the influence of water mixing and seawater temperature. The multifaceted effects of environmental variations on Sound Scattering Layers underscore the importance of spatial-functional statistical analysis in ecological studies involving complex, spatially functional objects. Beyond the scope of this specific case study, the application of functional data analysis shows promise for a wide array of ecological studies dealing with extensive spatial datasets.

Extrinsic calibration for large FOV based on inverse depth parameterized bundle adjustment

To overcome the reliance on high-precision calibration plates in camera calibration, this paper proposes an extrinsic calibration method suitable for three-dimensional digital image correlation (3D-DIC) in large field of view (FOV). The method combines image feature algorithms with DIC techniques to extract matching point pairs (MPPs) from the left and right images of binocular cameras. These MPPs are then homogenized within the FOV. Next, initial values of the extrinsic parameters are solved based on epipolar constraint theory. Finally, the calibration parameters are nonlinearly optimized using the bundle adjustment method. To achieve stable and reliable numerical optimization in large FOV applications, the calibration control points (CCPs)’ spatial coordinates are represented using inverse depth parameterization. In scenarios where there might be a lack of sufficient CCPs, speckle patterns are artificially introduced to supplement the scene features. However, there is a lack of reliable experimental basis on how to add CCPs within the FOV. Therefore, through simulations, the factors affecting calibration accuracy are analyzed to guide the actual calibration process. The proposed method’s reliability and accuracy in large FOV 3D measurement are verified through experiments.

Magnetized effects of double diffusion model on mixed convective Casson nanofluid subject to generalized perspective of Fourier and Fick’s laws

Understanding the flow behavior of non-Newtonian fluids from an industrial standpoint is crucial. Many industrial and technical activities, such as the extrusion of polymer sheets, the manufacturing of paper, and the development of photographic films, require non-Newtonian fluids. Heat and mass transport have various manufacturing uses. However, classical heat and mass transfer theories (Fourier and Fick laws) cannot anticipate thermal and solute relaxation time occurrences. The purpose of this investigation is to apply the modified Ohm law to the heat and mass transportation systems, which are established by generalized Fourier and Fick’s equations, respectively. A three-dimensional Darcy–Forchheimer flow through a porous medium integrating Hall and ion slip effects is studied for a non-Newtonian fluid known as a “Casson nanofluid” with mixed convection across a stretched surface. To investigate heat transfer augmentation, the modified Buongiorno model for nanofluids is used. It covers practical nanofluid properties as well as the mechanics of random motion and thermo-migration in nanoparticles. These groups of Partial Differential Equations (PDEs) that represent the mathematical model are combined with the proper similarity transformations to create an ordinary differential equations system, which is then resolved using the power of the Lobatto IIIA method. Examples of numerical and graphical data are given to show how various physical constraints affect the variation for velocities, temperatures, mass transfer, dimensionless shear stress, as well as Nusselt and Sherwood numbers. It turns out that lowering the Casson fluid parameters’ values reduces the velocity in the spatial coordinates (x, y). A rise in the Hall parameter's values ultimately leads to an improvement in the fluid. This paper sheds light on useful applications including power generation, conservation of energy, friction elimination, and nanofluidics. Nonetheless, the work highlights an important point: by carefully adjusting the Casson parameter, thermophoresis parameter, and Brownian motion parameter, the flow of a Casson fluid, including nanoparticles, may be controlled.

Synthesis of the Spatial Arrangement of Magnetic Field Sensors for Active Magnetic Field Shielding Systems of Overhead Power Lines

The purpose of the work is to develop a synthesis method for the spatial and angular arrangement of magnetic field sensors in order to ensure maximum efficiency of a robust system for active shielding of the magnetic field generated by overhead power lines, and to reduce the sensitivity of the synthesized system to initial uncertainties in changes in the level of the initial magnetic field, as well as system parameters while working. To achieve this goal, the spatial location coordinates and angular position of all magnetic field sensors, controller parameters, and gain vector for the compensating windings are determined. A synthesis of the spatial location and angular position of magnetic field sensors is created to solve a minimax vector optimization problem in which the vector objective function is calculated based on the Biot-Savart’s law. Solution of the minimax vector optimization problem, calculated on the basis of optimization algorithms for a multi-swarm of particles from Pareto-optimal solutions, taking into account the parameters of binary relations. Significant results obtained on the basis of the developed synthesis method are the effectiveness of a robust active shielding system for magnetic fields generated, as a consequence, by power lines with a synthesized spatial arrangement and angular position of magnetic field sensors obtained in the process of theoretical and experimental research. The significance of the results lies in the fact that practical recommendations are given for the reasonable choice of spatial location and angular position of magnetic field sensors, the spatial arrangement of shielding windings of a robust magnetic field shielding system for various characteristics of power lines.