Models Of Objects Research Articles

Modern approach to computer modeling of functional 3d objects in the professional training of future engineers and vocational education teachers

This article examines the features of computer modeling of functional 3D objects using the SolidWorks software suite, which is an integral component in the training of future engineers and vocational education teachers. The SolidWorks software suite provides tools not only for creating three-dimensional models but also for analyzing the mechanical properties of parts and visualizing the technical condition of objects. This enables future engineers and vocational education teachers to better understand the principles of designing and analyzing complex part structures. A step-by-step algorithm for computer modeling has been developed, from creating a basic sketch to completing a three-dimensional model of a functional object. It has been established that the computer modeling process allows for the selection of appropriate tools and commands for creating geometric transformations of objects and setting parameters. This ensures high accuracy in constructing a computer model according to its geometric parameters. It has been established that the modern approach to computer modeling in the educational process demonstrates the implementation of innovative technological solutions to improve the quality of training in higher education institutions. It has been determined that the proposed approach will not only help future engineers and vocational education teachers develop practical skills but also contribute to a deeper understanding of engineering concepts.

Role of [formula omitted] gravity with class one space-time in constructing new spherically symmetric stellar solutions

This paper aims to investigate the possibility of generating exact solutions for appropriate anisotropic spherically symmetric systems in F(Q,T) gravity where Q and T are non-metricity and the trace of the energy-momentum tensor respectively. These solutions involve embedding a spherically symmetric static metric into a five-dimensional pseudo-Euclidean space. To solve Einstein's field equations and ensure that the solution is free of center singularities, a physically plausible selection of the metric coefficient grr is used. With the help of the Karmarkar condition, we compute the gtt component of the metric tensor using the metric coefficient grr. At the boundary of the compact star, we match interior spacetime with the exterior spacetime to find the values of unknown constants. To make the solution match the measured mass and radius, we have tuned up the solution for compact star PSRJ1614-220. The behavior of the solution has been thoroughly examined for the same star. By examining the necessary physical characteristics, such as energy conditions, causality condition, hydrostatic equilibrium, pressure-density ratio, Herera Cracking criterion, etc., the physical acceptability of the model in the context of F(Q,T) has been investigated. It is observed that the present solution allows viable modeling of stellar objects in F(Q,T) gravity.

Digital mapping and surface modeling of historical objects using modern visualization techniques

Digital mapping and surface modeling of historical objects using modern visualization techniques

An Efficient Dense Reconstruction Algorithm from LiDAR and Monocular Camera

Dense reconstruction have been studied for decades in the fields of computer vision and robotics, in which LiDAR and camera are widely used. However, vision-based methods are sensitive to illumination variation and lack direct depth, and LiDAR-based methods are limited by sparse LiDAR measurement and lacking color and texture information. In this paper, we propose a novel 3D reconstruction algorithm based on LiDAR and a monocular camera, which realizes dense reconstruction. In the algorithm, a LiDAR odometry is used to get accurate poses and poses calculated by the odometry module are used in the calculation of depth maps and fusion of depth maps, and then mesh and texture mapping are implemented. In addition, a semantic segmentation network and a depth completion network are used to obtain dense and accurate depth maps. The concept of symmetry is utilized to generate 3D models of objects or scenes; that is, the reconstruction and camera imaging of these objects or scenes are symmetrical. Experimental results on public dataset show that the proposed algorithm achieves higher accuracy, efficiency and completeness than existing methods.

Modeling of the interaction of stressful components agricultural production

There is a completely natural, organic nonlinear relationship between crop production and animal husbandry, which are crucial components of agricultural production in Ukraine. Timely and appropriate management of these subsystems contributes to the stable growth of the country’s agriculture with minimal risk. This approach requires improvements in terms of the digitalization of the economy, particularly in the context of applying computer modeling to the nonlinear processes of interaction between these components. The article develops a modern methodology for computer modeling of an economic management object—a thorough study of a nonlinear dynamic model of the VolterraLotka class, featuring a component that characterizes the increase in livestock profitability due to feed supply. The model takes into account various influencing factors, allowing for the assessment of the efficiency of the interaction between crop production and animal husbandry at different stages of agricultural production. The study examines the behavior of the dynamic model in the vicinity of the leverage point, analyzing the modeled trend dependencies and statistical components: in the vicinity of the equilibrium point, there are oscillatory movements with a certain defined period, and the solutions of the mathematical model, i.e., integral curves, over time reflect the seasonal nature of the interaction between crop production and animal husbandry. The behavior of virtual scenarios—integral curves and phase portraits based on the mathematical model of nonlinear interaction of agricultural production components—wasstudied, showing the location of the equilibrium point, extreme points, and other critical points for the respective curves in a specific coordinate system. A transition to a dimensionless dynamic model and an economic interpretation of its implicit solution were indicated. Additionally, the impact of external economic factors on the stability of the system was analyzed, allowing for better forecasting of potential risks and the preparation of appropriate strategies to minimize them. For the first time, an approximate numerical estimate of the economic risk measure of the evolution of the interaction and interplay of agricultural subsystems over time was carried out in the study of the nonlinear dynamic model of the economic management object. The obtained results can be used to improve management strategies in the agricultural sector, particularly to enhance the stability of production processes and reduce economic risks in the context of market instability and climate change. This research provides new opportunities for effective planning and the implementation of innovations in Ukraine’s agricultural sector.

Совершенствование методов создания картографических анимаций

Modeling and presentation of objects’ and phenomena dynamics in geographic space is one of the tasks in cartography. Currently, due to the active society digitalization and scientific and technological progress, many different tools, technologies and opportunities have appeared that can be used for creating maps that would reflect dynamics of processes occurred in the mapped territory. Multimedia cartography and, in particular, animation cartography is relatively new direction in cartography developed based on such new technologies. The animation cartography considers general characteristics, concepts, principles, technological schemes for creating, designing and presenting animated maps. Currently, most of the animated maps require using a variety of specialized software, like geographic information systems (GIS), vector and raster graphic editors, video effects editing software, etc. Consequently, it is important to study functionality of the popular desktop GIS applications in view of the implementation of a full cycle of animated maps preparation, without parallel or subsequent use of any additional software. Within the framework of our study, we have solved a number of tasks, including generalizing the theoretical and methodological foundations and identifying the main types of cartographic animations; analyzing existing methods for cartographic animating of objects; analyzing the functionality of the QGIS application ensuring creation of cartographic animations of objects. As a result of the study, methods and their algorithm implementations have been developed to create cartographic animations of objects in the QGIS environment; descriptions of the developed algorithms in UML (Unified Modeling Language) have been prepared; and the role of the developed methods in the knowledge system of cartography and geoinformatics has been analyzed and determined.

Technological aspects of 3D conceptual modeling of hydrogeological objects and processes: A case-study of Stoilensky GOK

Technological aspects of 3D conceptual modeling of hydrogeological objects and processes: A case-study of Stoilensky GOK

Estimation of Longwave Radiation Intensity Emitted from Urban Obstacles in Each Direction Using Drone-Based Photogrammetry

Longwave radiation is a crucial factor affecting human thermal comfort and thermal stress, especially in outdoor spaces in summer, owing to the vast effect of longwave radiation emitted from high-heated asphalt roads, building walls, and automobiles. Although controlling the longwave radiation environment to improve thermal comfort in summer is crucial, the prediction of the longwave radiation environment is frequently conducted only at the assessment stage of the final proposal because of the high computational cost of radiation calculations and unsteady heat balance analysis considering multiple reflections. This is a significant constraint for the design of urban and architectural environments. A previous study proposed a method to rapidly estimate the longwave radiation environment based on a point-by-point method with longwave radiation intensity distributions of the heat sources. To use this method, 3D models of the geographical objects in urban areas, such as buildings and trees, must be accurately generated, and these models should have information on the longwave radiation emitted in each direction from each object. However, no specific examples of a 3D model and longwave radiation intensity distribution have been presented. In this study, a 3D modeling method for geographical objects in urban areas with longwave radiation information based on drones and photogrammetric techniques was utilized. Moreover, a 3D model of a small-scale building was generated. A longwave radiation intensity distribution was produced for the building. Based on the distribution data, the directional characteristics of longwave radiation were discussed, and the availability of the proposed method was assessed.

3D stochastic simulation of a deep-water turbidite system: An example from the Los Molles Formation, southern region of the Neuquén Basin, Argentina

A recurring challenge in geological modeling is bridging the gap between different scales. For example, difficulties arise when connecting outcrop data through production to exploration scale models. An object-based stochastic simulation was performed using outcrop data from the Arroyo La Jardinera area in the southern region of the Neuquén Basin, Argentina. This simulation involved four depositional sequences in a vertical succession that includes turbidites and associated deep marine facies. The aims of this study are (a) to determine the best geological model consistent with field and aerial image data; (b) to validate the application of object modeling to determine facies distribution; (c) to evaluate uncertainties from models based on scarce data. The studied interval covers a transgressive (J1) to regressive succession (J21, J22, and J23) of basin plain to slope depositional settings, featuring sandy and gravelly turbidite channels, turbidite lobes and interlobes, lobe fringes, and muddy slope and basin plain. Each depositional sequence model was constructed using specific input parameters for architectural elements, with lithological proportions based on sedimentary logs. The J1 sequence includes basin plain, lobe fringe, and minor lobe deposits; J21 features turbidite lobes, fringes, and subordinate channels; J22 accommodates turbidite channels scoured into muddy slope facies; and J23 encompasses gravelly and sandy turbidite channels carved on muddy slope facies. The geostatistical modeling of outcrop data has allowed building a quantitative sedimentological model useful for understanding subsurface facies heterogeneity in both exploration (vertical) and production (horizontal) scales.

Identification of the electric motor mathematical model based on a data sample with feature engineering

The object of this study is a mathematical model of a synchronous electric motor, obtained on the basis of experimental data, which takes into account the temperature mode and uses artificial features to increase the accuracy of its operation. A characteristic feature of this work is that the model takes into account the temperature mode as a component of the technical-operational state of the object. The resulting mathematical model could make it possible to synthesize an optimal automatic control system in terms of the operational state of the object. The problem addressed was to increase the accuracy of the identified mathematical models by applying the approach of feature engineering. The results showed that the identification of mathematical models by the initial data leads to a low level of accuracy of the obtained models, namely 65–70 % for the first output channel, 80–85 % for the second, and 75–80 % for the third, fourth, and fifth output channels. Accordingly, building models with a higher threshold of accuracy requires the use of other, more significant data for identification. This paper reports a method for reformatting the original data into artificial features and provides results of their effectiveness in relation to the original channels. The resulting artificial features and the original features were used for further identification; the resulting mathematical model has on average higher accuracy thresholds, namely 82 %, 93 %, 88 %, 85 % for the corresponding output channels. The results prove the effectiveness of applying the principle of feature engineering since the accuracy of the resulting model is 5–10 % higher compared to the baseline. The scope of practical application of the results includes the synthesis of automatic control systems based on mathematical models of control objects obtained as a result of identification.

The Sonora Substellar Atmosphere Models. III. Diamondback: Atmospheric Properties, Spectra, and Evolution for Warm Cloudy Substellar Objects

We present a new grid of cloudy atmosphere and evolution models for substellar objects. These models include the effect of refractory cloud species, including silicate clouds, on the spectra and evolution. We include effective temperatures from 900 to 2400 K and surface gravities from log g = 3.5 to 5.5, appropriate for a broad range of objects with masses between 1 and 84 M J. Model pressure–temperature structures are calculated assuming radiative–convective and chemical equilibrium. We consider the effect of both clouds and metallicity on the atmospheric structure, resulting spectra, and thermal evolution of substellar worlds. We parameterize clouds using the A. S. Ackerman & M. S. Marley cloud model, including cloud parameter f sed values from 1 to 8; we include three metallicities (−0.5, 0.0, and +0.5). Refractory clouds and metallicity both alter the evolution of substellar objects, changing the inferred temperature at a given age by up to 100–200 K. For solar-metallicity evolution models including clouds in warm objects, we find a hydrogen-burning minimum mass of 70.2 M J, close to empirical measurements; we find a deuterium-burning minimum mass of 12.05 M J (50% of initial D burned). We compare to the observed photometry of brown dwarfs, finding broad agreement with the measured photometry. We publish the spectra, evolution, and other data products online with open access on Zenodo (doi:10.5281/zenodo.12735103).

Neural radiance fields as a complementary method to photogrammetry for forensic 3D documentation: Initial comparative insights

ObjectivesPhotogrammetry is widely used in forensic practice to create 3D models of crime scenes, bodies, living individuals, and objects. However, it has limitations in accurately capturing transparent, reflective, and low-texture surfaces, which can hinder forensic investigations. Neural Radiance Fields (NeRFs), a recently developed method, offer a potential solution by creating more accurate and detailed 3D models in these challenging contexts. This study aims to evaluate whether NeRFs can serve as effective alternatives to structure-from-motion (SfM) photogrammetry for recording forensic autopsies. Materials and MethodsPhotogrammetric scans were performed on a variety of forensic subjects, including a cadaver with skin discoloration and epidermal exfoliation, a metal trashcan, a vehicle, and a mock crime scene. The scans were processed using traditional photogrammetry software (Meshroom) and compared with NeRF-based visualizations generate using instant neural graphics primitives. ResultsNeRF-based models provided more lifelike and detailed visualizations than photogrammetry, particularly when documenting transparent, reflective, or featureless surfaces. NeRF demonstrated superior capability in capturing complex details that photogrammetry struggled with. ConclusionNeRF technology shows considerable promise for improving the documentation of forensic autopsies, offering enhanced visual fidelity for challenging surfaces such as transparent or reflective materials. While the method presents challenges related to editing, software compatibility, and high computational demands, its potential benefits in forensic investigations are evident and merit further exploration.

Three-Dimensional Mapping of the Rotation of Interactive Virtual Objects with Eye-Tracking Data.

We present a method for real-time recording of human interaction with three-dimensional (3D) virtual objects. The approach consists of associating rotation data of the manipulated object with behavioral measures, such as eye tracking, to make better inferences about the underlying cognitive processes. The task consists of displaying two identical models of the same 3D object (a molecule), presented on a computer screen: a rotating, interactive object (iObj) and a static, target object (tObj). Participants must rotate iObj using the mouse until they consider its orientation to be identical to that of tObj. The computer tracks all interaction data in real time. The participant's gaze data are also recorded using an eye tracker. The measurement frequency is 10 Hz on the computer and 60 Hz on the eye tracker. The orientation data of iObj with respect to tObj are recorded in rotation quaternions. The gaze data are synchronized to the orientation of iObj and referenced using this same system. This method enables us to obtain the following visualizations of the human interaction process with iObj and tObj: (1) angular disparity synchronized with other time-dependent data; (2) 3D rotation trajectory inside what we decided to call a "ball of rotations"; (3) 3D fixation heatmap. All steps of the protocol have used free software, such as GNU Octave and Jmol, and all scripts are available as supplementary material. With this approach, we can conduct detailed quantitative studies of the task-solving process involving mental or physical rotations, rather than only the outcome reached. It is possible to measure precisely how important each part of the 3D models is for the participant in solving tasks, and thus relate the models to relevant variables such as the characteristics of the objects, cognitive abilities of the individuals, and the characteristics of human-machine interface.

Ghost Removal from Forward-Scan Sonar Views near the Sea Surface for Image Enhancement and 3-D Object Modeling

Underwater sonar is the primary remote sensing and imaging modality within turbid environments with poor visibility. The two-dimensional (2-D) images of a target near the air–sea interface (or resting on a hard seabed), acquired by forward-scan sonar (FSS), are generally corrupted by the ghost and sometimes mirror components, formed by the multipath propagation of transmitted acoustic beams. In the processing of the 2-D FSS views to generate an accurate three-dimensional (3-D) object model, the corrupted regions have to be discarded. The sonar tilt angle and distance from the sea surface are two important parameters for the accurate localization of the ghost and mirror components. We propose a unified optimization technique for improving both the measurements of these two parameters from inexpensive sensors and the accuracy of a 3-D object model using 2-D FSS images at known poses. The solution is obtained by the recursive updating of sonar parameters and 3-D object model. Utilizing the 3-D object model, we can enhance the original images and generate synthetic views for arbitrary sonar poses. We demonstrate the performance of our method in experiments with the synthetic and real images of three targets: two dominantly convex coral rocks and a highly concave toy wood table.

THE ROLE OF GEOSPATIAL ARTIFICIAL INTELLIGENCE (GEOAI) IN SMART BUILT ENVIRONMENT MAPPING: AUTOMATIC OBJECT DETECTION OF RASTER TOPOGRAPHIC MAPS IN MALAYSIA

Smart built environment mapping is integrating Geospatial Artificial Intelligence (GeoAI) to enable advanced analysis, pattern recognition, and decision-making processes. This shift in understanding, planning, designing, and managing the built environment is paving the way for a smarter, more sustainable future. This commentary explores the current role of AI in enhancing technology use within the geospatial field, focusing specifically on the application of GeoAI in mapping the built environment. Additionally, the paper presents a selection of case studies related to the implementation of AI in developing automatic vectorization, particularly for geospatial mapping in built environments. This research demonstrates the effectiveness of using Convolutional Neural Network (CNN) models for sorting objects in scanned, old topographic maps of the built environment. The findings of this study are valuable for making informed decisions, devising effective strategies, and identifying opportunities for further research and exploration within the dynamic field of GeoAI in smart built environment mapping and applications.

Microphysical Prescriptions for Parameterized Water Cloud Formation on Ultra-cool Substellar Objects

Water must condense into ice clouds in the coldest brown dwarfs and exoplanets. When they form, these icy clouds change the emergent spectra, temperature structure, and albedo of the substellar atmosphere. The properties of clouds are governed by complex microphysics but these complexities are often not captured by the simpler parameterized cloud models used in climate models or retrieval models. Here, we combine microphysical cloud modeling and 1D climate modeling to incorporate insights from microphysical models into a self-consistent, parameterized cloud model. Using the 1D Community Aerosol and Radiation Model for Atmospheres (CARMA), we generate microphysical water clouds and compare their properties with those from the widely used EddySed cloud model for a grid of Y dwarfs. We find that the mass of water condensate in our CARMA water clouds is significantly limited by available condensation nuclei; in models without additional seed particles for clouds added, the atmosphere becomes supersaturated. We incorporate water latent heat release in the convective and radiative parts of the atmosphere and find no significant impact on water-ice cloud formation for typical gas giant compositions. Our analysis reveals the CARMA cloud profiles have a gradual decrease in opacity of approximately 4% per bar below the cloud base. Incorporating this gradual cloud base falloff and a variable f sed parameter allows spectra generated from the parameterized Eddysed model to better match those of the microphysical CARMA model. This work provides recommendations for efficiently generating microphysically informed water clouds for future models of cold substellar objects with H/He atmospheres.

Метод опорних моделей синтезу інтелектуальних систем ідентифікації нелінійних динамічних об’єктів

The paper is devoted to resolving the contradiction between the accuracy of modeling nonlinear dynamics and the speed of model construction under conditions of limited computing resources. The aim of the work is to reduce the time for building models of nonlinear dynamic objects with continuous characteristics in the form of neural networks while ensuring a given modeling accuracy. This goal is achieved by developing a new method for synthesizing intelligent systems based on the superposition of pre-trained (support) models in the form of neural networks that reflect the basic properties of the object. The scientific novelty of the work lies in the development of a method for identifying nonlinear dynamic objects in the form of neural networks with time delays based on a set of pre-trained neural network models that reflect the basic properties of the subject area. In contrast to the traditional approach to pre-training, the developed method allows building models of lower complexity and with shorter training time while ensuring the required accuracy. To determine the initial parameters of the model, expressions based on the superposition of reference models in the form of neural networks are proposed. The practical utility of the work lies in the development of an algorithm for the method of reference models for training neural networks with time delays, which can significantly reduce the training time of neural networks without losing model accuracy

A Flexible Hierarchical Framework for Implicit 3D Characterization of Bionic Devices.

In practical applications, integrating three-dimensional models of bionic devices with simulation systems can predict their behavior and performance under various operating conditions, providing a basis for subsequent engineering optimization and improvements. This study proposes a framework for characterizing three-dimensional models of objects, focusing on extracting 3D structures and generating high-quality 3D models. The core concept involves obtaining the density output of the model from multiple images to enable adaptive boundary surface detection. The framework employs a hierarchical octree structure to partition the 3D space based on surface and geometric complexity. This approach includes recursive encoding and decoding of the octree structure and surface geometry, ultimately leading to the reconstruction of the 3D model. The framework has been validated through a series of experiments, yielding positive results.

Automating construction of road digital twin geometry using context and location aware segmentation

Geometric Digital Twins (GDT) represent a critical advancement in road management, yet their practical implementation encounters a substantial obstacle due to development costs outweighing the expected benefits. This paper addresses this challenge and introduces an automated solution for creating 3D geometric foundation models for road digital twins. The proposed approach utilises point clouds to generate meshed, coloured, and semantically labelled models of road objects. The proposed solution incorporates context- and location-aware segmentation, followed by a 3D representation step via meshing. Experiments showed that the solution achieves a 91.7% mean intersection over union segmentation on road furniture in the Digital Roads dataset and surpasses the current leader on the KITTI360 dataset by +16.93%. As a result, the fully automatic method enables scalable and affordable geometry digital twinning for roads.

Method of reference models for synthesis of intellectual systems of nonlinear dynamic objects identification

The paper is devoted to resolving the contradiction between the accuracy of modeling nonlinear dynamic objects and the speed of models building under conditions of limited computing resources. The purpose of the work is to reduce the time for building models of nonlinear dynamic objects with continuous characteristics while ensuring a given modeling accuracy. This goal is achieved by further developing the method of synthesing intelligent systems based on the superposition of pre-trained reference models in the form of neural networks reflecting the basic properties of the object. The scientific novelty of the work novelty consists in the development of a method for identifying nonlinear dynamic objects in the form of neural networks with time delays based on a set of pre-trained neural network models that reflect the basic properties of the subject area. In contrast to the traditional approach based on pre-trained neural networks the developed method allows building models of lower complexity and with shorter training time while ensuring the required accuracy. To determine the initial parameters of the model, expressions based on the superposition of reference models in the form of neural networks are proposed. The practical usefullness of the work consists in the development of an algorithm for the method of reference models for training neural networks with time delays in the tasks of identifying nonlinear dynamic objects with continuous characteristics, which can significantly reduce the training time of neural networks without losing the accuracy of the model. The value of the study lies in determining the area of effective use of the proposed method, namely, in the availability of a sufficient amount of qualitative data for the building of reference models. Insufficient data or poor data quality can significantly reduce the accuracy of reference models and, as a result, significantly reduce the training time of the target model.