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Accepted papers
Image Processing Methods for the Problem of Plasma Control of the Bow Shock Wave
O.V. Kravchenko, T.A. Lapushkina, E.V. Reshetova, O.A. Azarova
Accepted: 2026-08-25
Abstract
This paper presents an image processing procedure for experimental and numerical study of problems of plasma high-speed flow control. The experiments were conducted using a shock tube setup with an aerodynamic model placed in a high-speed flow. The impact zone was organized at the leading edge of the body by creating a local plasma gas discharge region. Flow visualization was performed using a pulsed laser schlieren system, which allows obtaining images of flow dynamics under the influence of discharge plasma. To extract quantitative data from the visualizations, an image processing pipeline was developed, including pre-processing, selection of the region of interest, and an adaptive thresholding setup for extracting information about the geometry of the plasma discharge luminosity region. This made it possible to perform numerical modeling of the two-dimensional Navier-Stokes equations for a viscous heat-conducting gas with a real source geometry. The plasma discharge was modeled as a localized energy deposition zone. For high-precision reconstruction of the flow field, a complex-conservative difference scheme of the second order on a shifted grid was used. The calculated position of the front of the bow shock wave (BSW) on the symmetry axis showed agreement with its position on the experimental schlieren image, confirming the qualitative adequacy of the numerical model. Quantitative comparison of the energy input the difference between the experimental and calculated values of the discharge power did not exceed 1%. The results showed that the presented image processing procedure opens up an approach to high accuracy prediction of the dynamics of high-speed flows.
Deep-Learning-Based Workflow for Cavern Segmentation and Derived Profile Construction from Core Photos
D.O. Makienko, G.A. Kossov, V.V. Abashkin, I.A. Seleznev, I.V. Safonov
Accepted: 2026-08-24
Abstract
To estimate hydrocarbon reserves and plan production, geologists construct reservoir models that describe lithofacies variations with depth. In carbonate rocks, caverns provide important information about storage capacity and fluid flow potential. Photographs of core samples allow analysis of surface-visible caverns. Segmentation of cavern-like objects in core photographs enables profile construction to identify depth intervals where lithofacies change, reflecting variations in sedimentation conditions and corresponding reservoir properties. Rock heterogeneity, varying imaging conditions, and non-target structures such as fractures and artifacts complicate cavern analysis from photographs. This paper proposes a deep-learning-based workflow for segmentation of cavern-like objects for visu-alization of the cavernosity profile intended to support lithofacies delineation. The work-flow includes semi-automatic annotation preparation for training a deep learning segmen-tation model. Model training relies on augmented images to account for varying bright-ness distributions. The segmentation results enable visualization of the depth-dependent profile that qualitatively reflects the distribution of cavern-like objects and may support description and delineation of lithofacies.
Visualizing Attractor Basin Structures via the Method
of Inverse Discrete-Dissipative Projections: A Case Study
of the Lorenz System
G. Chernykh, A. Zhukova
Accepted: 2026-08-24
Abstract
This article advances a numerical method for visualizing the structure of attractor basins in dynamical systems defined by ordinary differential equations. Herein, “structure” denotes any manner of inhomogeneity within the phase space, construed through the lens of a priori defined measurable characteristics. The method's foundation lies in discretizing the original mathematical model and monitoring the concomitant reduction of discrete phase volumes as the attractor is approached. The procedure starts from an initial condition, which is subsequently ascribed a numerical signature encapsulating the rate at which the phase volume that starts from that initial condition shrinks. These conditions are selected on a two-dimensional surface that forms a bounded region of a cross-section of the phase space. The visualization is generated by mapping the derived numerical signatures onto a prescribed color palette and projecting them onto the chosen domain. Experimental validation, conducted using the Lorenz system for both regular dynamics and parameters inducing a strange attractor, demonstrates the method's efficacy, particularly in revealing subtle structures that emerge in systems subjected to discretization.
Identifying Territorial Changes from Multi-Temporal Aerospace Images using Multilayer Neural Networks
D.V. Stepinin, P.I. Kuzin
Accepted: 2026-07-06
Abstract
One element of the digital map updating process is the creation and updating of objects by identifying terrain changes using high-resolution aerospace images acquired at different times. Currently, the primary method for detecting changes in high-resolution aerospace images is direct visual analysis of multi-temporal images performed by an operator-decipherer of a ground-based complex for processing imagery data, which generally reduces the efficiency of digital map preparation. The use of advanced technologies for automatic change detection will improve the efficiency of digital map preparation, including using diverse remote sensing data (radar surveys, laser scanning). Methods based on artificial intelligence technologies, in particular, deep learning methods for neural networks, demonstrate significant "stability", which is due to the formation of the most informative features of significant changes during the neural network training process. The proposed ResNet-50 neural network and the algorithm for reverse processing of input images will ensure a lower probability of type I and type II errors, resulting in a higher Jaccard index and higher boundary localization accuracy.
Application of the Finite Element Method to the Analysis of Adaptive Transformations of the Jaw Apparatus of Fish
Yu.P. Tolmacheva, T.E. Pomoynitskaya, A.I. Tolmachev, A.V. Chmatkova, S.A. Zaides
Accepted: 2026-06-24
Abstract
This paper presents the potential of modern visualization and numerical analysis ap-proaches using the finite element method to study adaptive skeletal deformations in lower vertebrates. The functional analysis of the visceral skeleton of fish was based on the virtual reconstruction of dental CT data into finite element models using engineering design and modeling software (Femap, Nastran, Patran). Visualization of the stress-strain state of ana-tomical structures revealed that their transformation occurs at sites of maximum bone stress concentration and is a consequence of changes in force load during the development of the species' feeding strategy. Overall, the approach used facilitates a more comprehensive analysis of the functional foundations of skeletal structure and adaptive transformations for a given morphology of a particular species.
Mapping the Research Landscape of Depression Detection Using Machine Learning: Trends and Visual Insights
Ginto Chirayath, K. Premamalini, Jeena Joseph, Jobi Babu, F. Vincent Rajasekar
Accepted: 2026-03-27
Abstract
The application of Machine Learning (ML) in detecting depression has seen exponential growth with advancements in artificial intelligence, natural language processing, and biomedical signal analysis. The paper presents a systematic bibliometric study on research on detecting depression with a basis in ML from Scopus-indexed literature between 2008 and 2025. The database with 1,407 documents from 745 sources gives a 26.02% growth rate annually, which reflects growing academic interest in AI-based mental health diagnostics. The study utilizes CiteSpace, VOSviewer, and Biblioshiny in analyzing research trends, leading contributors, networks of collaboration, and thematic evolutions. Journal articles (708), followed by conference papers (644), are predominant in the field, which reflects its dynamic, multidisciplinary character. The research areas are primarily on deep learning, natural language processing, social media-based diagnosis, and detection methods with a basis in EEG. The leading authors, Li Y, and Zhang X, contribute from Chinese institutes predominantly. The leading collaborating nations are the United States, China, and India. Co-citation as well as coupling between bibliographics in research discloses a high level of incorporation between AI with clinical psychiatry, neurology, as well as digital-based healthcare interventions. The trend from traditional classification methods towards models with a basis in transformer models in form of BERT as well as GPT is observed. The thematic mapping discloses a new emergence in terms of mobile-based healthcare application as well as AI-based suicide prediction. The research highlights rapid development in AI in mental healthcare with far-reaching impacts on early detection, distant-based care, as well as ethics in AI development. The research in the future should be directed towards enhancing model interpretation, resolving data privacy issues, as well as improving AI-based mental healthcare in terms of global application.
Color and Material Matching Space Effect Optimization in Soft Decoration Design Based on Computer Image Processing Algorithm
Shan Dan, Wanchun Wang, Qinwei Xu
Accepted: 2025-12-13
Abstract
This study introduces an intelligent optimization method based on computer vision to solve the problem that color and material matching in soft decoration design is highly subjective and lacks quantitative standards. ConvNeXt (Convolutional Next Network) is used to extract the color-material features of the interior space, and a GMM (Gaussian Mixture Model) color database is constructed to quantify the coordination of different color distributions. The PSO (Particle Swarm Optimization) algorithm is used for multi-objective optimization to ensure the balance and visual beauty of color matching. The design scheme is realistically rendered in the Unity platform to simulate the visual effects in the actual environment, and CHI (Color Harmony Index), ECS (Emotional Color Similarity) and MCI (Material Conflict Index) are used to quantitatively evaluate the design effect. The results show that the proposed method is superior to traditional manual design and feature extraction combined with manual design in terms of color harmony, consistency of emotional expression and material adaptability. The average CHI of the proposed design method reaches 0.92, the average ECS is 0.93, and the average MCI is reduced to 0.12, which significantly optimizes the visual coordination and overall aesthetic effect of the soft decoration scheme. The design method in this paper can improve efficiency while ensuring the aesthetics of the design, and provides a feasible quantitative optimization strategy for intelligent soft decoration design.
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