Object Model Research Articles

A novel method for estrous cycle staging using supervised object detection

The estrous cycle regulates reproductive events and hormone changes in female mammals and is analogous to the menstrual cycle in humans. Monitoring this cycle is necessary as it serves as a biomarker for overall health and is crucial for interpreting study results. The estrous cycle comprises four stages influenced by fluctuating levels of hormones, mainly estradiol and progesterone. Tracking the cycle traditionally relies on vaginal cytology, which categorizes stages based on three epithelial cell concentrations. However, this method has limitations, including time-consuming training and variability among researchers. This study assesses the feasibility and reliability of two previous image classification models as well as introducing an alternative method of machine learning to address the challenges posed by manual vaginal cytology and image classification. An object detection-based machine learning model, Object Detection Estrous Staging (ODES), was employed to identify cell types throughout the estrous cycle in mice. A dataset of 730 vaginal cytology images with four different stains was used, with 335 images for training, 45 for validation, and 350 for testing. A novel, accurate set of rules for classification was derived by analyzing training images. ODES achieved an average accuracy of 80% in classifying cycle stages, comparable to human accuracy (66%) and previous image classification models (41–79%). The efficiency of ODES, processing 100 test images in just 2.67 minutes, makes it a valuable tool for large-scale neuropsychiatric studies involving female rodents and also encourages the integration of this variable into neurological and psychiatric research. These results demonstrate that ODES offers a fast, reliable, and accessible method for estrous cycle monitoring, potentially improving how researchers approach sex-based variables in neuropsychiatric studies.

Fast frequency and phase synchronization of high-stability oscillators with 1 PPS signal from satellite navigation systems

Abstract In this paper, we propose a novel algorithm for fast frequency and phase synchronization of high-stability oscillators synchronized with 1 PPS signal from satellite navigation systems. The algorithm uses a model of a control object in the space of state variables and controls the frequency of an oscillator operating in a phase-locked loop. A new element is the introduction to the theoretical analysis and the design process, the time of entering synchronization. Currently, the literature lacks theoretical analysis and design methodology that considers the impact of the synchronization time on the choice of the steering algorithm and its parameters. All the data needed to determine the numerical values of the model were found experimentally for three different classes of control objects. Short synchronization times, a detailed description of the design methodology, and the use of values measured in the real system distinguish the proposed algorithm from the solutions described in the literature. The effect of optimization was achieved thanks to the algorithm’s two-stage operation. In the first stage, the algorithm aims to minimize the phase error quickly. The best solution for this stage is Sliding Mode Control (SMC). In the second stage, the algorithm strives to maximize the control quality, understood as minimizing the values of Maximum Time Interval Error (MTIE) and Time Deviation (TDEV). The Model Predictive Control (MPC) and Linear-Quadratic Regulator (LQR) optimal control algorithms were used at this stage. The paper also investigated the influence of the tuning parameters of these algorithms (weights as a function of cost) on the long-term behavior of the control system.

Manipulable Semantic Components: a Computational Representation of Data Visualization Scenes.

Various data visualization applications such as reverse engineering and interactive authoring require a vocabulary that describes the structure of visualization scenes and the procedure to manipulate them. A few scene abstractions have been proposed, but they are restricted to specific applications for a limited set of visualization types. A unified and expressive model of data visualization scenes for different applications has been missing. To fill this gap, we present Manipulable Semantic Components (MSC), a computational representation of data visualization scenes, to support applications in scene understanding and augmentation. MSC consists of two parts: a unified object model describing the structure of a visualization scene in terms of semantic components, and a set of operations to generate and modify the scene components. We demonstrate the benefits of MSC in three applications: visualization authoring, visualization deconstruction and reuse, and animation specification.

Синтез нейрорегулятора для системы, содержащей существенно нелинейный блок

In some cases, in order to improve the quality indicators of the transients of the automatic control system, it is necessary to take into account in the model of the control object various mechanisms that drive the control object itself (DC motor, wheels, amplifier-converting devices). When calculating controllers by analytical methods, difficulties arise due to the presence of various kinds of irregularities in such systems, including "significant" ones ("backlash", "friction", etc.). In this case, the solution of this issue may be related to the use of artificial neural networks as part of the simulator. This paper shows the application of a neurofeedback scheme using a neuro-emulator and a neurocontroller. This allows you to form a training sample and train a neural network controller in the operating modes of the system that are beyond the control capabilities of a nonlinear model of the control object using a controller calculated by an analytical method. This scheme is considered as an addition to the algorithm of synthesis of neural network controllers with a deterministic way of choosing the architecture and weighting coefficients of a neural network using a scheme of imitating neural control. An example is given of improving the qualitative characteristics of the transients of a system by means of fine-tuning a neurocontroller for a nonlinear system "inverse pendulum on a movable base", taking into account the presence in the system of an inertial link containing a significant non-linearity of the "backlash" type. The purpose of the control was indicated, i.e. stabilization of the inverted pendulum in a vertical position and moving the mobile base to a set value. To achieve these goals, a neurocontrol scheme is used, which contains two neural networks: a neurocontroller (performs the function of forming a control effect on an object) and a neuroemulator (performs the function of simulating a model of the control object and is necessary to calculate the error back pass and adjust the weighting coefficients of the neurocontroller). As a result, it is possible to obtain an automatic control system capable of controlling the specified object.

Solving the problem of technological object identification

Abstract. Problem. The identification task is formulated as follows: based on the results of observations of the input and output variables of a system, a model, i.e. a formalized representation of this system, should be built that is optimal in some sense. This shows the connection between the identification task and the above general scheme of establishing regularities based on the results of observations. The identification task is based on modern control theory. To solve it, modern computers are used. The latter, with their high speed and virtually unlimited memory capacity, create the prerequisites for receiving, transmitting and processing large arrays of observations that are necessary for building adequate models of real objects. Goal. Studying the possibilities of solving the problem of identifying technological objects using MATLAB+Simulink. Job tasks: to consider the tasks of identifying technological objects of management; to choose an identification method; identify the electric motor model. Methodology. The research problems should be solved by methods of analysis and synthesis of automatic control systems by the method of least squares. Results. In the work, experimental data on the dependence of the engine idling speed on the excitation current were obtained and solved the identification problem using the MATLAB+SIMULINK package. Originality. Based on the analysis of existing solutions, it was found that the works did not consider the calculation of absolute and relative errors of the results of identifying the object and the model developed using the least squares method. Thus, there is a need to develop a system for identifying a model of a technological object made by the least squares method. To solve the identification problem, an electric motor is chosen as the object of study. Practical value. The paper examines the methods used to identify complex objects. The comparative analysis showed that the most acceptable method is the least squares method, due to simple mathematical calculations in comparison with the other methods. To illustrate the application of this method, two tasks were solved: identification of the model of an electric motor; identification of the model of an alarm system. The studies have shown that model identification can be performed using the MATLAB+Simulink package. The research results obtained on the model differ from the experimental data by no more than 4 %

Memory Leaks in Spa: Prevention, Detection, and Remediation Methods

This article addresses the issue of memory leaks in modern single-page applications (SPAs). By investigating the causes of leaks associated with dynamic content updates, active interaction with the document object model (DOM) interface, and asynchronous operations, developers gain insights into avoiding the excessive accumulation of unused objects in memory. The article discusses methods for preventing and addressing leaks, including the use of weak references, component state management, and optimizing asynchronous requests. It also emphasizes the importance of using monitoring tools, such as Chrome DevTools, and integrating automated testing into the continuous integration (CI) and continuous delivery (CD) process. The article offers a comprehensive approach for efficient memory management and preventing performance issues in SPA applications.

Structured Model for Hydrothermal System Operation and Optimization

This paper presents an analysis for optimizing hydrothermal system operation within the Brazilian energy sector.In the last years, researchers at the Laboratory of Bio-Inspired Technologies and Solutions from Federal University of ABC (LabBITS - UFABC) have been implementing a platform for accessing the information of this Brazilian electric power generation system from a SQL (Structured Query Language) database, regularly updated from the ONS’s provided package of files. The database is accessed by the laboratory researchers via a REST (Representational State Transfer) API (Application Programming Interface) which is used to supply the parameters of the energy system models used in the energy operation planning calculations.In 2024, utilizing Java object models for hydroelectric power plants, a structured approach was developed, organizing plants into power plant systems to capture the interdependencies between flow data and storage of water in reservoirs throughout a cascade and across time.The simulation technique of genetic algorithms was then applied to test the structure for optimal operating schedules. Different populations of solutions were tested, considering the maximization of energy generation.

A Preliminary Study: Gaussian Splatting Technique in Generating a 3D Model of Textureless Object

Abstract The traditional static method faces limitations and challenges in reconstructing a 3D model of a real object with a textureless surface, in various environmental lighting conditions. Gaussian Splatting is approached as a superior method to reduce the processing time and draw back those limitations. To assess the visualization and accuracy, a project was designed to create a 3D model of a wooden board and a metal cube under different lighting scenarios using Gaussian Splat Creator & Viewer web-based software. The results show that the model can obviously represent the object’s color, shape, and shadow spot. The additional light source can increase the brightness, and draw different impacts on the models to recognize the fine features on the surface. Furthermore, the translucent effect was exposed on all models, except the wooden board without an additional light source. The models have RMSE less than 6 cm and are categorized as the Level of Detail 4. The highest accuracy was achieved by performing the adjusting scale option, even though a rescaling factor is not defined. Meanwhile, defining the rescale factor and neglecting the scale adjustment process simultaneously, can decrease the accuracy. Last, the 3D reconstruction process of 300 to 400 images takes around 30 to 45 minutes, and the storage size is 1 to 3 Megabytes only.

CONTROL MODEL OF THE NATURAL GAS PURIFICATION PROCESS

The equipment used in the processes of cleaning and drying natural gas for transportation and consumption and the methods applied for drying gas were comparatively analyzed, it was noted that the absorption method is more widespread among these methods due to its simplicity and efficiency, it was shown that drying gas should be carried out immediately after its cleaning from mechanical and other impurities so that during the transportation and use of gas, there are no blockages in pipelines, malfunctions in measuring devices and other gas equipment. Drying gas prevents the formation of hydrates in pipes due to condensation during transportation and increases the heat-generating capacity of gas by burning it well. The characteristics of the liquid absorbent used to dry gas in the absorption process - the absorbent, being regenerated and returned to the gas drying process were investigated, and the parameters and quality indicators affecting the technological process were determined for optimal control of the gas drying technological process at normal temperature and pressure for that process, as well as the output parameters of the gas drying unit. It was noted that although the pneumatic control system is considered morally obsolete, some elements and devices of this system, in particular, pneumatic actuators, are distinguished by their reliability in mining and field conditions. Therefore, it is advisable to use them in modern control systems with controllers. For this purpose, the structure of the general control system of the process, the information model of the control object was built, the input and output parameters, the limiting conditions for reflecting the exciting and controlling effects in the structure of the mathematical model were determined, and the limit ranges containing the minimum and maximum values of each parameter were determined. The control problem can also be formulated and solved as a problem of stabilizing productivity or another parameter. An optimal control model of the gas drying process was built, and the optimization criterion was selected. In the problem of optimizing the technological process of gas drying, the maximum productivity of the plant was adopted as the main criterion, and the humidity of the gas was adopted as the limiting condition. Several recommendations are given for solving this problem. Keywords: gas drying, absorbent, control system, optimal control, model.

General isotropic charged fluid spheres within the matter coupling gravity formalism

In this paper, we proposed a new mathematical model of charged isotropic compact relativistic object in the f(R,T) gravity. In the f(R,T) framework, the gravitational action involves the trace of the energy–momentum tensor T and the Ricci scalar R. We choose a specific f(R,T) framework such that f(R,T)=R+2χT, in which χ represents the coupling parameter, which is responsible for measuring the deviation from the standard Einstein’s general theory of relativity (GR). A short overview of the modified f(R,T) gravity theory is presented and the field equations are formulated in this novel modified gravity. It is shown that for a specific limit of the coupling parameter, the standard GR can be restored from the considered f(R,T) model. We modeled, mathematically, a specific charged compact star XTEJ1739−217 (M=1.51M⨀, R=10.9 km), within the f(R,T) extended gravity theory framework by taking benefit from the well studied Heintzmann IIa ansatz [H. Heintzmann, Z. Physik 228, 489–493 (1969)]. To examine the reliability and physical plausibility of Our model, different physical characteristics such as the energy density and pressure, electric field, energy conditions, stability analysis via Herrara cracking technique and the adiabatic index, equilibrium conditions under different forces, mass–radius relationship, compactness and surface red-shift are studied carefully, which are essential for confirming the model’s physical feasibility. The mathematically established results are more accurately represented by graphical illustrations for the various chosen values of the coupling parameter χ. In this study, we also compared our findings with the standard GR results and the observational facts, and we inferred that for nonzero values of the coupling parameter, χ, our results in f(R,T) framework are closely related to the observational facts in comparison with the standard GR. We conclude that our presented model is well consistent with all the requirements and viably modeled the compact star XTEJ1739−217.

THE VISION AND IMPLEMENTATION OF INTELLIGENT SECURITY SYSTEM

The implementation of artificial intelligence in all areas of information technology promises significant productivity improvements, particularly in security systems. In the context of modern threats and a high level of unpredictability, security systems are an environment where the use of intelligent technologies can provide maximum efficiency. Intelligent security systems not only respond to incidents but also predict their development, enabling proactive actions and risk minimization. Such systems represent a new approach to security, focusing on situational awareness and real-time data processing. This article presents approaches to the implementation of an intelligent security system capable of detecting and analyzing situations, using the collected information to make operational decisions. The core of the system is based on the General Formal Ontology, which allows for the creation of a unified model of concepts and objects, ensuring data consistency and interpretability. This approach simplifies the integration of the system with other components and expands its application capabilities. The initial version of the intelligent security system includes two key modules: a front-end for user interaction and a back-end that handles data processing and system management. These components perform essential functions such as access control and surveillance, allowing for effective management of security processes in real-time. A vital part of the system is the knowledge base, which contains scenarios of typical incidents and algorithms for handling them, enhancing the responsiveness and reducing the number of false alerts. The system provides not only monitoring but also analytics, optimizing risk management processes. The implementation of such solutions opens new perspectives for the integration of AI into security systems, enhancing their efficiency. The proposed architecture allows for the development and scaling of the system in accordance with new requirements and challenges faced by modern infrastructures. This research represents a significant step towards the creation of next-generation security solutions, focused on high performance, reliability, and situational awareness.

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.

An object detection-based model for automated screening of stem-cells senescence during drug screening

Deep learning-based cell senescence detection is crucial for accurate quantitative analysis of senescence assessment. However, senescent cells are small in size and have little differences in appearance and shape in different states, which leads to insensitivity problems such as missed and false detection. In addition, complex intelligent models are not conducive to clinical application. Therefore, to solve the above problems, we proposed a Faster Region Convolutional Neural Network (Faster R-CNN) detection model with Swin Transformer (Swin-T) and group normalization (GN), called STGF R-CNN, for the detection of different senescent cells to achieve quantification assessment of induced pluripotent stem cell-derived mesenchymal stem cells (iP-MSCs) senescence. Specifically, to enhance the representation learning ability of the network, Swin-T with a hierarchical structure was constructed. It utilizes a local window attention mechanism to capture features of different scales and levels. In addition, the GN strategy is adopted to achieve a lightweight model. To verify the effectiveness of the STGF R-CNN, a cell senescence dataset, the iP-MSCs dataset, was constructed, and a series of experiments were conducted. Experiment results show that it has the advantage of high senescent detection accuracy, mean Average Precision (mAP) is 0.835, Params is 46.06M, and FLOPs is 95.62G, which significantly reduces senescent assessment time from 12 h to less than 1 s. The STGF R-CNN has advantages over existing cell senescence detection methods, providing potential for anti-senescent drug screening. Our code is available at https://github.com/RY-97/STGF-R-CNN.

A Hierarchical Spatiotemporal Data Model Based on Knowledge Graphs for Representation and Modeling of Dynamic Landslide Scenes

Represention and modeling the dynamic landslide scenes is essential for gaining a comprehensive understanding and managing them effectively. Existing models, which focus on a single scale make it difficult to fully express the complex, multi-scale spatiotemporal process within landslide scenes. To address these issues, we proposed a hierarchical spatiotemporal data model, named as HSDM, to enhance the representation for geographic scenes. Specifically, we introduced a spatiotemporal object model that integrates both their structural and process information of objects. Furthermore, we extended the process definition to capture complex spatiotemporal processes. We sorted out the relationships used in HSDM and defined four types of spatiotemporal correlation relations to represent the connections between spatiotemporal objects. Meanwhile, we constructed a three-level graph model of geographic scenes based on these concepts and relationships. Finally, we achieved representation and modeling of a dynamic landslide scene in Heifangtai using HSDM and implemented complex querying and reasoning with Neo4j’s Cypher language. The experimental results demonstrate our model’s capabilities in modeling and reasoning about complex multi-scale information and spatio-temporal processes with landslide scenes. Our work contributes to landslide knowledge representation, inventory and dynamic simulation.

Methodology of a Comprehensive Approach to the Analysis, Structuring, and Aggregation of Data Based on the Requirements of Resource-Intensive Applications

The work presents a methodology for in-depth data description for resource-intensive applied tasks, particularly in the field of materials science. It consists of three basic stages. In the first stage, quantitative and semantic requirements are formalized, and working datasets are defined, which can be classified as reference, structured, instrumental, or experimental-computational, depending on the source. These datasets are stored in a specialized research area for further investigation. The second stage involves the analysis and hierarchical structuring of the accumulated information content. This allows for the refinement and comparison of data, storage of different sets, and the application of various processing tools. In the third stage, the data is aggregated and represented as objects in the domain area according to the database schema of the application support system. The steps outlined in the proposed methodology enable full utilization of the specialized research area of informational content for building object models of application scenarios, structuring the domain data representation schema, and forming event-information forms. The practical application of this methodology in the examined subject area clarified the requirements, criteria, and filters for information search, data description, parameter cataloging, and their verification when solving a number of practical tasks. The software implementation of this approach could be oriented towards a web platform for local and cloud technologies, allowing remote collaborative access to content requirements and solution catalogs with role-based access for different categories of researchers and users.

Solving the Control Synthesis Problem Through Supervised Machine Learning of Symbolic Regression

This paper considers the control synthesis problem and its solution using symbolic regression. Symbolic regression methods, which were previously called genetic programming methods, allow one to use a computer to find not only the parameters of a given regression function but also its structure. Unlike other works on solving the control synthesis problem using symbolic regression, the novelty of this paper is that for the first time this work employs a training dataset to address the problem of general control synthesis. Initially, the optimal control problem is solved from each point in a given set of initial states, resulting in a collection of control functions expressed as functions of time. A reference model is then integrated into the control object model, which generates optimal motion trajectories using the derived optimal control functions. The control synthesis problem is framed as an approximation task for all optimal trajectories, where the control function is sought as a function of the deviation of the object from the specified terminal state. The optimization criterion for solving the synthesis problem is the accuracy of the object’s movement along the optimal trajectory. The paper includes an example of solving the control synthesis problem for a mobile robot using a supervised machine learning method. A relatively new method of symbolic regression, the method of variational complete binary genetic programming, is studied and proposed for the solution of the control synthesis problem.

A viable model of self-gravitating object in the 5D Einstein Gauss-Bonnet Gravity

A viable model of self-gravitating object in the 5D Einstein Gauss-Bonnet Gravity

Anisotropic gravastar as horizonless regular black hole spacetime and its images illuminated by thin accretion disk

A connection between regular black holes and horizonless ultracompact objects was proposed in Carballo-Rubio et al. (JHEP 08:046, 2023, arXiv:2211.05817 [gr-qc]). In this paper, we construct a model of a horizonless compact object, specifically an anisotropic gravastar with continuous pressure, that corresponds to regular black hole spacetime in the appropriate limit. The construction begins by modeling an equation of state that satisfies the anisotropic gravastar conditions and transitions to the de Sitter (p=-ϵ) upon horizon formation. The spacetime structure is similar to the Quantum Horizonless Compact Object (QHCO) described in Chen and Yokokura (Phys Rev D 109:104058, 2024, arXiv:2403.09388 [gr-qc]). Within this model, we also generate images of the corresponding objects surrounded by a thin accretion disk. The resulting images reveal that assuming that the emitting matter exists only outside the object, the inner light ring structure closely resembles that of the horizonless configuration of a regular black hole and the QHCO, yet it exhibits a distinct light ring structure compared to the thin-shell gravastar model. However, the opposite occurs when emitting matter is taken into account inside the object.

NeuralFeels with neural fields: Visuotactile perception for in-hand manipulation.

To achieve human-level dexterity, robots must infer spatial awareness from multimodal sensing to reason over contact interactions. During in-hand manipulation of novel objects, such spatial awareness involves estimating the object's pose and shape. The status quo for in-hand perception primarily uses vision and is restricted to tracking a priori known objects. Moreover, visual occlusion of objects in hand is imminent during manipulation, preventing current systems from pushing beyond tasks without occlusion. We combined vision and touch sensing on a multifingered hand to estimate an object's pose and shape during in-hand manipulation. Our method, NeuralFeels, encodes object geometry by learning a neural field online and jointly tracks it by optimizing a pose graph problem. We studied multimodal in-hand perception in simulation and the real world, interacting with different objects via a proprioception-driven policy. Our experiments showed final reconstruction F scores of 81% and average pose drifts of 4.7 millimeters, which was further reduced to 2.3 millimeters with known object models. In addition, we observed that, under heavy visual occlusion, we could achieve improvements in tracking up to 94% compared with vision-only methods. Our results demonstrate that touch, at the very least, refines and, at the very best, disambiguates visual estimates during in-hand manipulation. We release our evaluation dataset of 70 experiments, FeelSight, as a step toward benchmarking in this domain. Our neural representation driven by multimodal sensing can serve as a perception backbone toward advancing robot dexterity.

AN APPROACH OF DOM RECORD MANAGEMENT COMPONENT UI CREATION

This paper presents an approach of Document Object Model database record management component user interface (UI) creation. Mainstream of UI development approaches concentrates in few directions, omitting holistic view on the fundamental database operations. The proposed solution uses method lay on of standard clauses adopted by the ECMA Script edition sixth and above. The program logic for the UI component was built with the intention - The existing functionality of such UI component to be extended and complemented.