Models Of Objects Research Articles

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.

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.

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.

Method for assessing the durability of structures under stationary and non-stationary random loading using variational mode decomposition (VMD)

In the era of digital transformation, most devices are equipped with a variety of sensors, which provide information to be used in developing intelligent models for predicting the fatigue life. The development of digital models of real objects for fatigue life estimation faces the lack of algorithms for performing such an estimation. The problem arises in the adaptive and automatic analysis of signals from real sensors installed on the object and their subsequent processing to estimate the fatigue life of the object. We propose a new method for assessing the fatigue life of structures based on application of the adaptive method of variational mode decomposition to signals gained from sensors, including non-stationary ones. Variational mode decomposition involves decomposition of an initial complex random process into simpler random processes (modes) that are stationary and narrowband. An original method of summarizing damage resulted from the action of the modes obtained as a result of the decomposition is used. The developed method can be used as an algorithmic support for the generation of digital doubles of the residual lifetimes of natural products. The proposed method is compared with the «rainflow» method in the time domain. Different realizations of random stationary and non-stationary signals with different bandwidths are compared numerically. In addition, the traditional frequency methods of Dirlik and Benasciutti have been compared with the «rainflow» method. The analysis showed good results for the proposed method, i.e., the error was smaller compared to traditional frequency methods, especially for non-stationary processes.

Exploling the Scientific Model for Constructing a Platform for the International Communication of PingJiang Opera

Purpose: Considerable changes have occurred in many areas of science and technology as a result of the current information technologies' quick development and use. Specifically, new theories in mathematics have surfaced; they may be used to construct and refine current mathematical models of different processes and objects. Method: Regulatory and permitting decision-makers are worried about the ambiguity surrounding the possible environmental consequences arising from the introduction of these innovative devices in coastal and riverine ecosystems as research and testing of maritime energy devices progress. The Triton Initiative conducts studies and makes suggestions for environmental monitoring techniques and technology to give industry stakeholders with the information required to approve maritime energy system testing. Results: The efficient distribution of research findings is crucial in enhancing data accessibility for stakeholders who might use the information to make informed policy choices. Nevertheless, there are few frameworks available for carrying out scientific communications related to marine energy projects. Using the Triton Initiative's pilot scientific communication program as a case study, we discuss methods, resources, and strategies for creating a science communication framework for marine energy projects or other related fields of research in this article. Conclusion: This article uses the historic Pingjiang Road in Suzhou, China, as a case study to investigate how, in the era of globalization and digital media, urban spaces that have been revitalized for historic and cultural consumption may demonstrate the publicness or potential capacity for publicity.

Multi-granularity spatiotemporal object modelling of waterborne traffic elements

The electronic navigational charts are crucial carriers for representing the multi-source heterogeneous data of Waterborne Traffic Elements (WTEs). However, their layer-based modelling method has some shortcomings in expressing the multi-granularity features, complex relationships, and dynamic evolution of elements. This paper proposes an objectification modelling method for WTEs based on the concept of multi-granularity spatiotemporal object modelling. A classification system for waterborne traffic objects is developed based on the relevance of behavior to elements; combining characteristics of waterborne traffic, a data model for waterborne traffic objects is constructed from eight aspects: spatiotemporal reference, spatiotemporal position, spatial form, basic information, attributes, behavioral ability, structure, and associative relationships. An object extraction function is also established, extracting object attributes and relationships between objects according to different element classes. Taking the Jiashan section of the Hangzhou-Shanghai Line in Zhejiang Province as the experimental subject, the multi-granularity spatiotemporal characteristics, dynamic evolution, and relationship expression of channel class objects are tested. The experimental results show that the proposed method provides the theoretical basis and data organization mode for the multi-granularity expression of WTEs.

3D Scene Creation and Rendering via Rough Meshes: A Lighting Transfer Avenue.

This paper studies how to flexibly integrate reconstructed 3D models into practical 3D modeling pipelines such as 3D scene creation and rendering. Due to the technical difficulty, one can only obtain rough 3D models (R3DMs) for most real objects using existing 3D reconstruction techniques. As a result, physically-based rendering (PBR) would render low-quality images or videos for scenes that are constructed by R3DMs. One promising solution would be representing real-world objects as Neural Fields such as NeRFs, which are able to generate photo-realistic renderings of an object under desired viewpoints. However, a drawback is that the synthesized views through Neural Fields Rendering (NFR) cannot reflect the simulated lighting details on R3DMs in PBR pipelines, especially when object interactions in the 3D scene creation cause local shadows. To solve this dilemma, we propose a lighting transfer network (LighTNet) to bridge NFR and PBR, such that they can benefit from each other. LighTNet reasons about a simplified image composition model, remedies the uneven surface issue caused by R3DMs, and is empowered by several perceptual-motivated constraints and a new Lab angle loss which enhances the contrast between lighting strength and colors. Comparisons demonstrate that LighTNet is superior in synthesizing impressive lighting, and is promising in pushing NFR further in practical 3D modeling workflows.

ARCHAEOLOGICAL RESEARCH IN 2023 ON THE TERRITORY OF THE DOMINICAN MONASTERY IN VINNYTSIA

Archaeological researches of 2023 on the territory of the site of architecture and urban planning «Dominican Monastery» in Vinnytsia was carried out by the Archaeological Expedition of the Vinnytsia Regional Museum of Local Lore as part of the «Program of Archaeological Research on the Territory of Sites which are part of the Vinnytsia City Territorial Community for 2022—2024» approved by the Vinnytsia City Council. The main task for 2023 was to open a preserved excavation area (excavation area 1) and to complete it. This excavation area was established and explored in 2013—2018 by the Archaeological Expedition of the Institute of Archaeology of the National Academy of Sciences of Ukraine, headed by Dr. Larysa Vynogrodska. In the Excavation area 1 several objects in 2023 were discovered: building 1 — the remains of the foundations of a cult (?) building from the late 17th — the first half of the 18th century and constructive ditches of the building (the name and number according to the numbering of Dr. L. Vynogrodskaya); building 2 — the remains of the foundations of the building, constructed in the late 19th — the first half of the 20th century; building 3 is a deepened structure, explored in 2013—2014 (the name and number according to the numbering made by Dr. L. Vynogrodska); building 4 — a brick crypt (?) of the late 17th — early 18th centuries; object 1 — deepened with a step (partially explored) — late 16th — 17th centuries; object 2 is a burial contained the remains of five persons (burials 3—7), burials 5 and 6 are dated ca. late 17th — early 18th century; pits 1, 3 — objects of the 20th century (pit 2 is the object explored in 2013—2014); burial 1 — an animal skeleton in anatomical order which was discovered in the 17th—19th century layer (sector B6—B7 in the excavation area); burial 2 — a human burial approximately dated to the 18th century. Photofixation, processing and drawing of the material have been done, orthophoto plans and 3D modeling of objects in excavation area 1 were made based on the results of the archaeological expedition of the Vinnytsia Regional Museum of Local Lore in 2023. In addition to archaeological research, all known written sources of the 17th century about the territory of the Dominican monastery, its founders and clergy were widely involved. These sources were processed by researcher of Vinnytsia Museum Victoria Kolesnyk. Due to her sophisticated research in 2023 it was possible to attribute the architectural remains in excavation area-1 as the stone foundations of the Dominican monastery during its first phase of its existence. Probably, the discovered remains are traces of the renovation of the monastery building from the late 17th — the first half of the 18th century and existed until the time of the second phase of construction, which began in the second half of the 18th century. The result of the second phase of construction is the raising of a complex of buildings which are in use for now and as a complex with a status of National Importance, named the «Dominican Monastery».