Object Model Research Articles

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

AbstractA 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 ($$p=-\epsilon $$ 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.

Pioneering Museum Data Transformation: Unifying Legacy Systems and Enhancing Data Integrity

In the digital era, museums confront the challenge of modernising legacy data systems to align with current standards. Part of the RECODE (Rethinking Collections Data Ecosystems) Programme (Dupont et al. 2022) examines the complex transition from disparate departmental object models to a unified system, reflecting the broader museum community's struggle with data standardisation. The case study centres on consolidating five distinct data models accumulated over two decades, each tailored to different departmental needs under various constraints. The data mapping process identifies and aligns similar fields across models, facilitating the integration of analogous data points and exposing redundancies and unique practices embedded over years. Major challenges include data complexity, diversity, and quality issues (cleaning, standardizing, and deduplicating), compounded by the massive scale of the data: currently, the Natural History Museum London (NHM) has 262,831,590 records across 8,175 fields, and a total volume of 12 TB of data, which define both the ongoing data modelling work and our future data migration. A key innovation in our process is the immediate visibility of data issues that became apparent upon migrating to Amazon Web Services (AWS). AWS serves as the staging environment for our transition to the new NHM Collections Management System (CMS) and offers an unprecedented platform for directly addressing these challenges. It enables us to query all our data—across all departments and fields—in just seconds. This capability provides a comprehensive view that was previously unattainable. The core of this presentation is sharing "lessons learned" from navigating the intricacies of an ongoing CMS transition within a museum. The endeavor to untangle and unify diverse data models is a common challenge (IEEE Big Data Governance and Metadata Management Industry Connections Activity 2020,Wu et al. 2022, Wu et al. 2021,Little et al. 2022,Woodburn et al. 2022) highlighting the importance of community engagement and knowledge exchange. Our findings underscore the necessity of cross-departmental collaboration and the benefits of a data-driven data modelling approach. By sharing our journey and the developed comprehensive object model (Collier and Woodburn 2022), we aim to contribute valuable insights to museums undergoing similar transformations. The RECODE Programme sheds light on the practical aspects of CMS modernisation through the analytics and knowledge derived from over two decades of collections data and data use.

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.

AI+AR based Framework for Guided Visual Diagnosis of Equipment

Automated solutions for effective support services, such as failure diagnosis and repair, are crucial to keep customer satisfaction and loyalty. However, providing consistent, high quality, and timely support is a difficult task. In practice, customer support usually requires technicians to perform onsite diagnosis, but service quality is often adversely affected by limited expert technicians, high turnover, and minimal automated tools. To address these challenges, we present a novel solution framework for aiding technicians in performing visual equipment diagnosis. We envision a workflow where the technician reports a failure and prompts the system to automatically generate a diagnostic plan that includes parts, areas of interest, and necessary tasks. The plan is used to guide the technician with augmented reality (AR), while a perception module analyzes and tracks the technician’s actions to recommend next steps. Our framework consists of three components: planning, tracking, and guiding. The planning component automates the creation of a diagnostic plan by querying a knowledge graph (KG). We propose to leverage Large Language Models (LLMs) for the construction of the KG to accelerate the extraction process of parts, tasks, and relations from manuals. The tracking component enhances 3D detections by using perception sensors with a 2D nested object detection model. Finally, the guiding component reduces process complexity for technicians by combining 2D models and AR interactions. To validate the framework, we performed multiple studies to:1) determine an effective prompt method for the LLM to construct the KG; 2) demonstrate benefits of our 2D nested object model combined with AR model.

Codebase release 4.0 for RIVET

The Rivet toolkit is the primary mechanism for phenomenological preservation of collider-physics measurements, containing both a computational core and API for analysis implementation, and a large collection of more than a thousand preserved analyses. In this note we summarise the main changes in the new Rivet 4 major release series. These include a major generalisation and more semantically coherent model for histograms and related data objects, a thorough clean-up of inelegant and legacy observable-computation tools, and new systems for extended analysis-data, incorporation of preserved machine-learning models, and serialization for high-performance computing applications. Where these changes introduce backward-incompatible interface changes, existing analyses have been updated and indications are given on how to update new analysis routines and workflows.

Robust independent validation of experiment and theory: Rivet version 4 release note

The Rivet toolkit is the primary mechanism for phenomenological preservation of collider-physics measurements, containing both a computational core and API for analysis implementation, and a large collection of more than a thousand preserved analyses. In this note we summarise the main changes in the new Rivet 4 major release series. These include a major generalisation and more semantically coherent model for histograms and related data objects, a thorough clean-up of inelegant and legacy observable-computation tools, and new systems for extended analysis-data, incorporation of preserved machine-learning models, and serialization for high-performance computing applications. Where these changes introduce backward-incompatible interface changes, existing analyses have been updated and indications are given on how to update new analysis routines and workflows.

A new multiple-arc model of the resonant Kuiper belt objects – plutinos

Abstract To incorporate the gravitational influence of Kuiper belt objects (KBOs) in planetary ephemerides, uniform-ring models are commonly employed. In this paper, for representing the KBO population residing in Neptune’s 2:3 mean motion resonance (MMR), known as the Plutinos, we introduce a three-arc model by considering their resonant characteristics. Each ‘arc’ refers to a segment of the uniform ring and comprises an appropriate number of point masses. Then the total perturbation of Plutinos is numerically measured by the change in the Sun-Neptune distance (ΔdSN). We conduct a comprehensive investigation to take into account various azimuthal and radial distributions associated with the resonant amplitudes (A) and eccentricities (e) of Plutinos, respectively. The results show that over a 100-year period: (1) at the smallest e = 0.05, the Sun-Neptune distance change ΔdSN caused by Plutinos decreases significantly as A reduces. It can deviate from the value of ΔdSN obtained in the ring model by approximately 100 km; (2) as e increases in the medium range of 0.1-0.2, the difference in ΔdSN between the arc and ring models becomes increasingly significant; (3) at the largest e ≳ 0.25, ΔdSN can approach zero regardless of A, and the arc and ring models exhibit a substantial difference in ΔdSN, reaching up to 170 km. Then the applicability of our three-arc model is further verified by comparing it to the perturbations induced by observed Plutinos on the positions of both Neptune and Saturn. Moreover, the concept of the multiple-arc model, designed for Plutinos, can be easily extended to other MMRs densely populated by small bodies.

Complexity data science: A spin-off from digital twins.

Digital twins offer a new and exciting framework that has recently attracted significant interest in fields such as oncology, immunology, and cardiology. The basic idea of a digital twin is to combine simulation and learning to create a virtual model of a physical object. In this paper, we explore how the concept of digital twins can be generalized into a broader, overarching field. From a theoretical standpoint, this generalization is achieved by recognizing that the duality of a digital twin fundamentally connects complexity science with data science, leading to the emergence of complexity data science as a synthesis of the two. We examine the broader implications of this field, including its historical roots, challenges, and opportunities.

Research on Deep Learning Detection Model for Pedestrian Objects in Complex Scenes Based on Improved YOLOv7.

Pedestrian detection is very important for the environment perception and safety action of intelligent robots and autonomous driving, and is the key to ensuring the safe action of intelligent robots and auto assisted driving. In response to the characteristics of pedestrian objects occupying a small image area, diverse poses, complex scenes and severe occlusion, this paper proposes an improved pedestrian object detection method based on the YOLOv7 model, which adopts the Convolutional Block Attention Module (CBAM) attention mechanism and Deformable ConvNets v2 (DCNv2) in the two Efficient Layer Aggregation Network (ELAN) modules of the backbone feature extraction network. In addition, the detection head is replaced with a Dynamic Head (DyHead) detector head with an attention mechanism; unnecessary background information around the pedestrian object is also effectively excluded, making the model learn more concentrated feature representations. Compared with the original model, the log-average miss rate of the improved YOLOv7 model is significantly reduced in both the Citypersons dataset and the INRIA dataset. The improved YOLOv7 model proposed in this paper achieved good performance improvement in different pedestrian detection problems. The research in this paper has important reference significance for pedestrian detection in complex scenes such as small, occluded and overlapping objects.

A two-minute burst of highly polarized radio emission originating from low Galactic latitude

ABSTRACT Several sources of repeating coherent bursts of radio emission with periods of many minutes have now been reported in the literature. These ‘ultralong period’ (ULP) sources have no clear multiwavelength counterparts and challenge canonical pulsar emission models, leading to debate regarding their nature. In this work, we report the discovery of a bright, highly polarized burst of radio emission at low Galactic latitude as part of a wide-field survey for transient and variable radio sources. ASKAP J175534.9$-$252749.1 does not appear to repeat, with only a single intense two-minute $\sim$200-mJy burst detected from 60 h of observations. The burst morphology and polarization properties are comparable to those of classical pulsars but the duration is more than one hundred times longer, analogous to ULPs. Combined with the existing ULP population, this suggests that these sources have a strong Galactic latitude dependence and hints at an unexplored population of transient and variable radio sources in the thin disc of the Milky Way. The resemblance of this burst with both ULPs and pulsars calls for a unified coherent emission model for objects with spin periods from milliseconds to tens of minutes. However, whether or not these are all neutron stars or have the same underlying power source remains open for debate.

Efficient object recognition under cluttered scenes via descriptor-based matching and single point voting

This paper addresses the problem of recognizing multiple objects and multiple instances from point clouds. Whereas existing methods utilize descriptors on 3D fields or pointwise voting to achieve this task, our framework takes advantage of both descriptor-based and voting-based schemes to realize more robust and efficient prediction. Specifically, we propose a novel and robust descriptor called an orientation-enhanced fast point feature histogram (OE-FPFH) to describe points in both the object model and scene, and further to build the correspondence set. The OE-FPFH integrates an orientation vector through mining the geometric tensor of the local structure of a surface point, which is more representative than the original FPFH descriptor. To improve voting efficiency, we devise a novel single-point voting mechanism (SPVM), which constructs a unique local reference frame (LRF) on a single point using the orientation vector. The SPVM takes as input the corresponding point set and can generate a pose candidate for each correspondence. The process is realized by matching LRFs from two corresponding points. All pose candidates are subsequently divided into clusters and aggregated using the K-means clustering algorithm to deduce the poses for different objects or instances in the scene. Experiments on three challenging datasets demonstrate that our method is effective, efficient, and robust to occlusions and multiple instances.

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.

Synthesis of a four-point model of an airplane

The modeling of echo signals from distributed radar objects taking into account the noise of their angular coordinates is considered. Relationships are presented that allow the transition from a multipoint model of an arbitrary object containing tens, hundreds and even thousands of emitting points to a model composed of four emitting points located at the vertices of a square. As an example, we synthesized an airplane model containing only four points. It is obtained on the basis of a multipoint model composed of 56 points. Using numerical modeling methods, it has been shown that angular noise generated by multi-point and four-point aircraft models have identical correlation functions and similar parameters of the probability density distribution of angular coordinate noise. The obtained result is also confirmed by semi-natural modeling methods using a matrix simulator.

SODD-YOLOv8: an insulator defect detection algorithm based on feature enhancement and variable row convolution

Abstract To address the detection challenges in defective images of insulators in transmission lines, including tiny object size, significant scale variations, a wide variety of defects, and complex background interference. In this study, an improved insulator defect detection algorithm is proposed, based on the YOLOv8s framework and combining feature enhancement and deformable convolution techniques. Firstly, to address the image feature distortion problem caused by aerial photography, a deformable convolutional feature extraction module (DCFEM) is introduced, which is designed to enhance the model’s ability to adapt to the local geometric deformation, so as to effectively recover the distorted feature information in the image. Moreover, to enhance the detection ability of the model for small objects, a small object feature enhancement module is designed, which adopts an efficient multi-scale attention mechanism, and aims to enhance the feature extraction ability of small objects, improve the sensitivity to small-size defects, and improve the detection accuracy. Eventually, to optimize the computational efficiency of the model, the average pooling-sparse convolution-batch normalization (BN) module is proposed. This module combines average pooling, sparse convolution and BN techniques to achieve a lightweight model while maintaining a high level of feature extraction capability. Experimental results on the China power line insulator dataset show that the improved model achieves a 4.3 percentage point improvement in the mAP metric compared to YOLOv8s, and the number of parameters in the model is reduced by 10%. The proposed scheme not only improves the accuracy and efficiency of defect detection, but also reduces the demand for computational resources, thus providing a more reliable and efficient solution for insulator defect detection in practical applications.

Semantic Enrichment of Non-Graphical Data of a BIM Model of a Public Building from the Perspective of the Facility Manager

Building information modeling (BIM) is undeniably the most important trend in the digitization of the construction sector in recent years. BIM models currently being built are extremely geometrically rich, that is, they are modeled at a high level of detail in terms of geometry. Thanks to object-oriented programming paradigms, BIM models include high-level relationships to ensure interactions between objects, rapid view generation, and documentation. However, these models are not always equally rich in non-graphical data. This is true for parameters at the library object level, with which building object models are saturated, but also at the project, site, building, or floor levels according to the structure of the interoperable industry foundation classes (IFC) format. The current state of knowledge also lacks a clear methodology for inputting such data. For this reason, experimental work was undertaken on semantic enrichment in non-graphical data of a public building (a public kindergarten, Secemin, Poland), which has its BIM model at a high level of geometric detail but is poor in non-graphical data. As a result of the research and development work, all levels of the IFC structure were saturated with non-graphical data and validated, and the possibilities of their use were shown from the perspective of the facility manager. Documentation from the manager was used to achieve this goal, and selected analyses and simulations were performed on the enriched model. This article contributes to the discussion on semantic enrichment from CAD3D to BIM by presenting a detailed process for entering non-graphical data into a BIM model. The presented data entry method can be used by both modelers and facility managers. Thus, this paper fills an important research gap related to semantic enrichment in non-graphical data at different levels of the IFC structure.

Real-Time Tracking Target System Based on Kernelized Correlation Filter in Complicated Areas.

The achievement of rapid and reliable image object tracking has long been crucial and challenging for the advancement of image-guided technology. This study investigates real-time object tracking by offering an image target based on nuclear correlation tracking and detection methods to address the challenge of real-time target tracking in complicated environments. In the tracking process, the nuclear-related tracking algorithm can effectively balance the tracking performance and running speed. However, the target tracking process also faces challenges such as model drift, the inability to handle target scale transformation, and target length. In order to propose a solution, this work is organized around the following main points: this study dedicates its first part to the research on kernelized correlation filters (KCFs), encompassing model training, object identification, and a dense sampling strategy based on a circulant matrix. This work developed a scale pyramid searching approach to address the shortcoming that a KCF cannot forecast the target scale. The tracker was expanded in two stages: the first stage output the target's two-dimensional coordinate location, and the second stage created the scale pyramid to identify the optimal target scale. Experiments show that this approach is capable of resolving the target size variation problem. The second part improved the KCF in two ways to meet the demands of a long-term object tracking task. This article introduces the initial object model, which effectively suppresses model drift. Secondly, an object detection module is implemented, and if the tracking module fails, the algorithm is redirected to the object detection module. The target detection module utilizes two detectors, a variance classifier and a KCF. Finally, this work includes trials on object tracking experiments and subsequent analysis of the results. Initially, this research provides a tracking algorithm assessment system, including an assessment methodology and the collection of test videos, which helped us to determine that the suggested technique outperforms the KCF tracking method. Additionally, the implementation of an evaluation system allows for an objective comparison of the proposed algorithm with other prominent tracking methods. We found that the suggested method outperforms others in terms of its accuracy and resilience.

Advanced Model with a Trajectory Tracking Stabilisation System and Feasible Solution of the Optimal Control Problem

In this study, we consider the extended optimal control problem and search for a control function in the class of feasible functions for a real control object. Unlike the classical optimal control problem, the control function should depend on the state, not time. Therefore, the control synthesis problem for the initial-state domain should be solved, instead of the optimal control problem with one initial state. Alternatively, an optimal trajectory motion stabilisation system may be constructed. Both approaches—control and trajectory motion stabilisation system syntheses—cannot be applied to real-time control, as the task is too complex. The minimum threshold of quality criteria is searched for in the space of mathematical expression codes. Among other problems, the search space is difficult to define and the gradient is hard to determine. Therefore, the advanced control object model is used to obtain a feasible control function. The advanced model is firstly obtained before solving the optimal control problem and it already includes a trajectory motion stabilisation system; in particular, this stabilisation system is synthesised in advance at the control system design stage. When the optimal control problem appears, it is solved in real time in the classical statement, and a control function is searched for as a function of time. The advanced control object model also uses the reference model to generate the optimal trajectory. The search for the optimal control function is performed in real time and considers the synthesised stabilisation system of motion along a determined trajectory. Machine learning control via symbolic regression, namely, the network operator method, is used to directly solve the control synthesis problem. An example solution of the optimal control problem, with an advanced model moving in the environment with obstacles for a group of two mobile robots, is presented. The obtained solution is a control function for a reference model that generates a trajectory from a class of trajectories stabilised with the object’s control system.