Object Model Research Articles

Development of a Software Module for Studying Historical and Cultural Heritage Objects Using Non-Invasive Research Data

This work proposes the development of a software module for studying historical and cultural heritage objects using remote and non-invasive research data. The module aims to integrate modern technologies such as image processing, data analysis, and visualization to provide access to heritage information for researchers, conservators, and the general public. Utilizing non-invasive data, such as geophysical surveys, enables the collection of information about heritage objects without causing damage. The module facilitates the analysis and visualization of this data as well as the creation of interactive object models, thereby promoting further research, preservation, and popularization of cultural heritage. The module consists of three blocks: defining areas for ground-based research using interferograms; constructing a comprehensive 3D model based on ground and underground research data; and refining the boundaries of historical-cultural heritage objects and establishing protective zones. The program is developed in the object-oriented programming language VisualBasic with additional modules. This developed module could become a significant tool for studying and preserving historical and cultural objects in the modern world.

Application of Interactive Object Model in Sports Teaching

Traditional physical education is just repeated training without quantitative teaching and training data results. Multimedia information analysis and retrieval technology can make repeated research on the content of competition or training, and increase the quantitative analysis of training indicators. Therefore, this paper proposes an interactive compressed domain key video object model to realize the design and production of physical education video, so as to quantify the effect of physical education. This paper discusses the interactive design and application of PE teaching video from three aspects: static, dynamic, and semantic. The interactive object model proposed in this paper can formulate corresponding strategies for different key sports video objects. The distortion rate of the complete key video object is 2.3%, which is 0.09% lower than the distortion rate of the Kim2002 scheme. From a usage perspective, through the scheme proposed in this article, coaches can obtain teaching videos with simple initial extreme values on an interactive interface, and the distortion rate of the video object is lower than that of traditional schemes.

Using the Multiple-Sensor-Based Frost Observation System (MFOS) for Image Object Analysis and Model Prediction Evaluation in an Orchard

Accurate frost observations are crucial for developing and validating frost prediction models. In 2022, the multi-sensor-based automatic frost observation system (MFOS), including an RGB camera, a thermal infrared camera, a leaf wetness sensor (LWS), LED lighting, and three glass plates, was developed to replace the naked-eye observation of frost. The MFOS, herein installed and operated in an apple orchard, provides temporally high-resolution frost observations that show the onset, end, duration, persistence, and discontinuity of frost more clearly than conventional naked-eye observations. This study introduces recent additions to the MFOS and presents the results of its application to frost weather analysis and forecast evaluation in an orchard in South Korea. The NCAM’s Weather Research and Forecasting (WRF) model was employed as a weather forecast model. The main findings of this study are as follows: (1) The newly added image-based object detection capabilities of the MFOS helped with the extraction and quantitative comparison of surface temperature data for apples, leaves, and the LWS. (2) The resolution matching of the RGB and thermal infrared images was made successful by resizing the images, matching them according to horizontal movement, and conducting apple-centered averaging. (3) When applied to evaluate the frost-point predictions of the numerical weather model at one-hour intervals, the results showed that the MFOS could be used as a much more objective tool to verify the accuracy and characteristics of frost predictions compared to the naked-eye view. (4) Higher-resolution and realistic land-cover and vegetation representation are necessary to improve frost forecasts using numerical grid models based on land–atmosphere physics.

Cooperative Image Orientation with Dynamic Objects

Using images to supplement classical navigation solutions purely based on global navigation satellite systems (GNSSs) has the potential to overcome problems in densely built-up areas. These approaches usually assume a static environment; however, this assumption is not necessarily valid in urban areas. Therefore, many approaches delete information stemming from moving objects in a first processing step, but this results in information being lost. In this paper, we present an approach that detects and models so-called dynamic objects based on image sequences and includes these object models into a bundle adjustment. We distinguish dynamic objects that provide information about their position to others (cooperating objects) and those that do not (non-cooperating objects). Dynamic objects that observe the environment with the help of sensors in order to determine their position are called observing objects. In the experiments discussed here, the observing object is equipped with a stereo camera and a GNSS receiver. We show that cooperating objects can have a positive effect on the exterior orientation of the observing object after the bundle adjustment, both in terms of precision and accuracy. However, we found that introducing non-cooperating objects did not result in further improvements, probably because in our case the photogrammetric block was already stable without them due to the large number and good distribution of static tie points.

SHOWMe: Robust object-agnostic hand-object 3D reconstruction from RGB video

In this paper, we tackle the problem of detailed hand-object 3D reconstruction from monocular video with unknown objects, for applications where the required accuracy and level of detail is important, e.g. object hand-over in human–robot collaboration, or manipulation and contact point analysis. While the recent literature on this topic is promising, the accuracy and generalization abilities of existing methods are still lacking. This is due to several limitations, such as the assumption of known object class or model for a small number of instances, or over-reliance on off-the-shelf keypoint and structure-from-motion methods for object-relative viewpoint estimation, prone to complete failure with previously unobserved, poorly textured objects or hand-object occlusions. To address previous method shortcomings, we present a 2-stage pipeline superseding state-of-the-art (SotA) performance on several metrics. First, we robustly retrieve viewpoints relying on a learned pairwise camera pose estimator trainable with a low data regime, followed by a globalized Shonan pose averaging. Second, we simultaneously estimate detailed 3D hand-object shapes and refine camera poses using a differential renderer-based optimizer. To better assess the out-of-distribution abilities of existing methods, and to showcase our methodological contributions, we introduce the new SHOWMe benchmark dataset with 96 sequences annotated with poses, millimetric textured 3D shape scans, and parametric hand models, introducing new object and hand diversity. Remarkably, we show that our method is able to reconstruct 100% of these sequences as opposed to SotA Structure-from-Motion (SfM) or hand-keypoint-based pipelines, and obtains reconstructions of equivalent or better precision when existing methods do succeed in providing a result. We hope these contributions lead to further research under harder input assumptions. The dataset can be downloaded at https://download.europe.naverlabs.com/showme.

КОМПЛЕКСНАЯ ИНФОЛОГИЧЕСКАЯ МОДЕЛЬ ОБЪЕКТОВ ЗАЩИТЫ КОНФИДЕНЦИАЛЬНОЙ ИНФОРМАЦИИ

The problem of harmonization of approaches to the definition of information security objects is becoming increasingly relevant due to the growth of information arrays, the complexity of information technologies, technical and cryptographic means of information protection, and the increasing number of computer attacks, including on critical information infrastructure. Historically, the concept of objects of protection in Russia is associated with the requirements of state regulators, so it has different interpretations. The purpose of the study is to develop an infological model of information security objects. The goal of infological modeling may be to provide the most natural ways for a person to collect and present information about objects of protection for its consideration when creating protection systems and storing in a database. The author based on the analysis of the requirements of the main regulators of the Federal service according to the technical and export control of Russia and the Federal security service of Russia and the modeling method, an infological model of information security objects has been developed. This model is most consistent with the concept of object-oriented design, which is the basis for the development of complex software systems, the development of technology for designing databases of information security objects based on the ER-model. The entity-relationship model (ER-model) has several basic concepts, from which more complex objects are built according to predefined rules.

ESE-YOLOv8: A Novel Object Detection Algorithm for Safety Belt Detection during Working at Heights.

To address the challenges associated with supervising workers who wear safety belts while working at heights, this study proposes a solution involving the utilization of an object detection model to replace manual supervision. A novel object detection model, named ESE-YOLOv8, is introduced. The integration of the Efficient Multi-Scale Attention (EMA) mechanism within this model enhances information entropy through cross-channel interaction and encodes spatial information into the channels, thereby enabling the model to obtain rich and significant information during feature extraction. By employing GSConv to reconstruct the neck into a slim-neck configuration, the computational load of the neck is reduced without the loss of information entropy, allowing the attention mechanism to function more effectively, thereby improving accuracy. During the model training phase, a regression loss function named the Efficient Intersection over Union (EIoU) is employed to further refine the model's object localization capabilities. Experimental results demonstrate that the ESE-YOLOv8 model achieves an average precision of 92.7% at an IoU threshold of 50% and an average precision of 75.7% within the IoU threshold range of 50% to 95%. These results surpass the performance of the baseline model, the widely utilized YOLOv5 and demonstrate competitiveness among state-of-the-art models. Ablation experiments further confirm the effectiveness of the model's enhancements.

Augmented Reality Learning Media Application In Computer Networking Courses

In computer network learning, there is still little use of media which has an impact on students' understanding of device material and computer network topology. Augmented Reality (AR) based learning media can answer these problems by providing dynamic visualization and interactive simulations. The research objective is that AR applications can be used to help visualize abstract concepts for understanding and structure of an object model. The development method used is MDLC (Multimedia Development Life Cycle) which consists of six stages, namely concept, design, material collecting, assembly, testing, and distribution. The results of the AR application research show that the value of the learning media application in terms of material is declared valid at 0.85 and in terms of design it is declared valid at 0.86. The AR application was also stated to be very practical, this can be seen from the responses of lecturers and students with the practicality of the learning media application being 87% as seen from ease, motivation, attractiveness, and usefulness. From the results of this research, the AR learning media application is very practical to apply to students, especially in computer networking courses.

Object Registration Techniques For 3D Particle Tracking

Image based 3D particle tracking is currently the most widely used technique for volumetric velocity measurements. Inspecting the flow-field around an object is however, hampered by the latter, obstructing the view across it. In this study, the problem of measurement limitations due to the above is addressed. The present work builds upon the recent proposal from Wieneke and Rockstroh (2024), whereby the information of the occluded lines of sight can be incorporated into the particle tracking algorithm. The approach, however, necessitates of methods that accurately evaluate the shape and position of the object within the measurement domain. Methods of object marking and the following 3D registration of a digital object model (CAD) are discussed. For the latter, the Iterative Closest Point (ICP) registration algorithm is adopted. The accuracy of object registration is evaluated by means of experiments, where marking approaches that include physical and optically projected markers are discussed and compared. Three objects with growing level of geometrical complexity are considered: a cube, a truncated wing and a scaled model of a sport cyclist. The registered CAD representations of the physical objects are included in aerodynamic experiments, and the flow field is measured by means of large-scale particle tracking using helium filled soap bubbles. Three operating regimes are studied and compared: monolithic, partitioned and object-aware (OA) monolithic. The results indicate that object registration enables a correct reconstruction of particle tracers and strongly reduces the domain clipping typical of the monolithic approach. Furthermore, the dynamical use of all views in the OA monolithic method offers clear benefits compared to the partitioned approach, namely a lower occurrence of ghost particles. Finally, the combined visualization of the object and the surrounding flow pattern offers means of insightful data inspection and interpretation, along with posing a basis for PIV data assimilation at the fluid-solid interface.

Information control model of distributed energy resources

Currently, there is a constant growth of distributed energy resources, it became necessary to integrate them into the power grid. However, the integration process must consider not only the control parameters of the distributed energy resource but also the parallel operation of several such resources, the parameters of the power system as a whole. At the highest level of control, it is also necessary to consider the economic indicators formed by the energy market. Special devices – controlled inverters – can be effectively used for integration. The most difficult task is choosing a control system for controlled inverters. There are many different control systems such as Proportional-Integral, Deadbeat controllers, etc. The purpose of this paper is to build an information model for controlling distributed power resources. The current control method was used to control active and reactive power. The advantage of current control is resistance to variations in the parameters of the inverter and the external grid, excellent dynamic performance, and higher control accuracy. The proposed approach for building an automatic control system using the Internal model control strategy and taking into account restrictions on the control variable. The advantages of a system with an Internal model control controller compared to a conventional Proportional-Integral controller are the use of a powerful control method based on the object model, the ability to influence the roughness of the system with a separate setting parameter, increased speed with almost complete absence of over-regulation, simplified setup procedure. Such a system is proposed for use in facilities with strict requirements to minimize over-regulation and the speed of working out disturbances and tasks. The PSCAD software app was used to model the work results.

Feature-Driven Next View Planning for Cutting Path Generation in Robotic Metal Scrap Recycling

Metal recycling in scrapyards, where workers cut decommissioned structures using gas torches, is labor-intensive, difficult, and dangerous. As global metal scrap recycling demands are rising, robotics and automation technologies could play a significant role to address this demand. However, the unstructured nature of the scrap cutting problem—due to highly variable object shapes and environments—poses significant challenges to integrate robotic solutions. We propose a novel collaborative workflow for robotic metal cutting that combines worker expertise with robot autonomy. In this workflow, the skilled worker studies the scene, determines an appropriate cutting reference, and marks it on the object with spray paint. The robot, then, autonomously explores the surface of the object for identifying and reconstructing the drawn reference, converts it to a cutting trajectory, and finally executes the cut. This paper focuses on the surface exploration and cutting reference reconstruction tasks, which require appropriate next view planning (NVP) algorithms. We devise three NVP algorithms enabling the robot to explore and extract desired features from the scene, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , the drawn reference, without requiring any <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> object model. Contrasting with global or feature-agnostic NVP algorithms, our approaches guide the robot via desired local features to increase the efficiency of the exploration. We evaluate our NVP algorithms against six categories of objects both in simulation and in physical experiments. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is motivated by the need of extracting a desired cutting reference determined and drawn on the object by scrap yard workers. From the robot’s perspective, it must explore and reconstruct the drawing, starting from an unknown scene containing an unknown object featuring an unknown drawing. We assume that an RGB-D camera is attached to the tool-tip of the robot, and the color of the drawn path is significantly different from the object’s color. The goal of the robotic system is to explore the object surface to uncover the drawn path entirely without colliding with the object. The exploration algorithm must overcome complex object shapes and must be fast enough for practical use in scrap yards. This means conventional exploration (active vision) techniques are insufficient since they focus on exploring the entirety of the object, which is unnecessary for our task and is time-consuming. Our methods exploit the drawing information to guide the exploration for quickly determining a suitable viewpoint, which results in an efficient extraction of the entire cutting reference, without needing to explore the entire object’s surface. Our algorithms are robust against adversarial features such as discontinuous, non-smooth, or self-occluded object surfaces. Our feature-driven strategies are not limited to robotic scrap cutting as they are applicable to any viewpoint planning problem requiring high performance while extracting the local features in the scene.

Сравнительный анализ вариантов построения алгоритмов управления температурой в химическом реакторе

In conditions of increased demand for the quality and efficiency of control systems for objects of various nature, for example, chemical reactors, it is advisable to apply the principle of state vector control, and not only the principle of output coordinate control. Application of modern methods of synthesis of control algorithms encounters difficulties associated with the multidimensionality and nonlinearity of objects, the parametric uncertainty of models and noncomplete measurability of the state vector. To synthesize the temperature control algorithms in a chemical reactor, the method of analytical design of aggregated regulators and the method of modal control based on a state controller have been used. The problem of noncomplete measurability of the state vector has been solved by decomposition, reduction of the object model. The authors have synthesized two nonlinear algorithms of temperature control using the method of the analytical design of aggregated regulators and two algorithms based on state controller using a reduced linearized object model. The properties of invariance, covariance, and robustness of the control system with various algorithms are investigated by modeling the control system using the initial nonlinear model of the object. The best quality indicators of transient control processes are demonstrated by a nonlinear algorithm with an integral component synthesized by the method of analytical design of aggregated regulators.

YADE - An extensible framework for the interactive simulation of multiscale, multiphase, and multiphysics particulate systems

This contribution presents the key elements of YADE, an extensible open-source framework for dynamic simulations. During the past 19 years, YADE has evolved from “Yet Another Dynamic Engine” to a versatile multiscale and multiphysics solver, counting a large, active, and growing community of users and developers. The computationally intense parts of the source code are written in C++, using flexible object models that allow for easy implementation of new features. The source code is wrapped in Python, equipping the software with an interactive kernel used for rapid and concise scene construction, simulation control, post-processing, and debugging. The project, including documentation and examples, is hosted on https://yade-dem.org, while the source code is freely available on GitLab. Over the last decade, YADE has expanded in terms of capabilities thanks to the contribution of many developers from different fields of expertise, including soil and rock mechanics, chemical engineering, physics, bulk material handling, and mineral processing. The rapid growth of YADE can be attributed to (1) the careful and robust design of the framework core, (2) a continuous integration pipeline with fully embedded thorough tests which are executed upon each merge request, ensuring stable compilation for various operating systems, and (3) user-friendliness, facilitated by the Python interface, detailed documentation, and rigorous user support. In this paper, we review the main features of YADE, highlighting its versatility in terms of applications, its flexibility in terms of code development, as well as recent improvements in terms of computational efficiency. Program summaryProgram Title: YADE - Yet Another Dynamic EngineCPC Library link to program files:https://doi.org/10.17632/n4f5fw97rd.1Developer's repository link:https://gitlab.com/yade-dev/trunkLicensing provisions: GNU General Public License 2Programming language: C++, PythonNature of problem: Numerical simulation of many-particle systems requires accurate models for particle-to-particle interactions, efficient contact detection between objects of various shapes, and robust time integration. In addition, the flow of fluids, thermal effects, as well as other coupled problems in the presence of particles are found in many fundamental and practical applications and they need dedicated computational tools. YADE provides a computational framework to perform such simulations using the discrete element method and multiple extensions of it.Solution method:YADE simulates particulate systems using the Discrete Element Method (DEM) in a flexible platform combining C++ and Python. It provides a large variety of shape and interaction models, an explicit time-integration scheme, and many post-processing tools. YADE features dedicated solvers for both fluid and heat fluxes at the pore scale, and it supports couplings with third-party software such as Escript (finite element method) and OpenFOAM (computational fluid dynamics).

Contrastive Prediction and Estimation of Deformable Objects based on Improved Resnet

Because the dynamic model of deformable linear object is complex, the learning based on visual model is difficult, and the feature information extraction is insufficient. Therefore, we propose a joint visual representation model using contrast learning of optimized encoder. We start with the encoder, add the residual structure to the encoder, optimize the extraction and compression of its feature information, and control its parameters to 3 million. In this way, we can not only obtain excellent feature information, but also have good efficiency. In the rope experiment, we collect information from the simulated environment without manual marking, extract features through the encoder and transmit them to the downstream task. Experiments show that the evaluation of our model at 135 ° and 45 ° is improved by about 50%.

Digital technologies for remote control and monitoring of construction works

Introduction. The construction industry in the Russian Federation has good potential for digitalization. In spite of numerous unfavorable factors, a significant number of the most progressive construction companies adopt new technologies in their work, recognizing their high potential and effectiveness. Therefore, the legalization of information modeling technologies, which has been initiated at the governmental level, has a great chance of success. Digital transformation constitutes a necessary step for the sustainable development of the economy and construction industry.Aim. To identify the advantages of using modern digital technologies in construction in order to further implement them in construction projects for automating routine processes and minimizing errors.Materials and methods. The study provides a meta-analysis of 18 domestic and foreign studies (VAK or SCOPUS publications) on the application of digital technologies in construction, as well as an effectiveness assessment of the studied technologies.Results. The advantages of unmanned aerial vehicles and mapping drones are undeniable, as well as the difficulties in using them in urban environments, especially in the centers of million cities. The main advantages of using drone technologies in construction include reception of detailed data from construction sites and their comparison with design documentation; objective monitoring of construction dynamics; a reduction in the number of occupational health and safety violations; improved quality of site monitoring and round-the-clock access to hard-to-reach areas; assessment of the scope of work performed; prompt response to deviations from design documentation and timely corrective actions; work with the BIM (Building Information Model) and digital model of the current object with the possibility of export to convenient CAD programs. The main disadvantages of using drone technology in construction are as follows: the maximum wind resistance of 15 m/s; the data obtained during rain or snowfall may be incorrect; the impossibility of automatic imaging within the project under construction.Conclusions. The Pix4D Crane Camera is currently the safest solution for capturing aerial images, with subsequent conversion to 2D and 3D.

Digital reconstruction of substation equipment and facility layout via LiDAR point cloud registration

Generating as-built Building information models (BIMs) is promising in power substation construction projects because they can reflect the actual conditions of facilities. However, traditional manual-designed BIMs are different from real-world scenarios due to reality gaps. In this paper, we present a new method of reconstructing the layout of power equipment and facilities in substations using LIDAR point clouds. The proposed method extracts electric equipment and facilities via object segmentation and model retrieval. In particular, we investigate PFH, FPFH and SHOT descriptors for the 3D-SIFT keypoints in the 3D shape retrieval of complex electric equipment and facilities. After the best-match model is retrieved from a model library, the layout of typical electric equipment and facilities is reconstructed by aligning the model to the scene point cloud via point cloud registration. Experimental results validate the effectiveness of the proposed method. The proposed method enhances the efficiency of generating 3D models of power substations.

STATNet: One-stage coal-gangue detector based on deep learning algorithm for real industrial application

Coal-gangue object detection has attracted substantial attention because it is the core of realizing vision-based intelligent and green coal separation. However, most existing studies have been focused on laboratory datasets and prioritized model lightweight. This makes the coal-gangue object detection challenging to adapt to the complex and harsh scenes of real production environments. Therefore, our project collected and labeled image datasets of coal and gangue under real production conditions from a coal preparation plant. We then designed a one-stage object model, named STATNet, following the “backbone-neck-head” architecture with the aim of enhancing the detection accuracy under industrial coal preparation scenarios. The proposed model utilizes Swin Transformer as backbone module to extract multi-scale features, improved path augmentation feature pyramid network (iPAFPN) as neck module to enrich feature fusion, and task-aligned head (TAH) as head module to mitigate conflicts and misalignments between classification and localization tasks. Experimental results on a real-world industrial dataset demonstrate that the proposed STATNet model achieves an impressive AP50 of 89.27 %, significantly surpassing several state-of-the-art baseline models by 2.02 % to 5.58 %. Additionally, it exhibits stronger robustness in resisting image corruption and perturbation. These findings demonstrate its promising prospects in practical coal and gangue separation applications.

Zero‐Shot 3D Pose Estimation of Unseen Object by Two‐step RGB-D Fusion

3D Object pose estimation is a critical task in many real-world applications, e.g., robotic manipulation and augmented reality. Most existing methods focus on estimating the object instances or categories which have been seen in the training phase. However, it is imperative to estimate the pose of unseen objects without re-training the network in real world. Therefore, we proposed a 3D pose estimation method for unseen objects without re-training. Specifically, given the CAD model of the unseen object, a set of template RGB-D images (RGB images and depth images) is rendered at different viewpoints. Then a feature embedding network, named PoseFusion, is designed to extract the scene feature. In this network, RGB-D images are utilized to extract the texture feature and geometric feature, respectively. Afterwards, a cross-modality alignment module is proposed to eliminate the noise in single modality. The aligned texture feature and aligned geometric feature are fused through a geometry guided fusion module. Thus, by PoseFusion, the template RGB-D images generated from the CAD model are abstracted into a set of template scene features, and the query scene features are also embedded from the captured RGB-D images from the unseen object. Finally, the query scene features are matched with the template scene features by calculating the masked local similarity. Then the identity and pose of unseen object are determined by the most similar template. Experiments on LINEMOD and T-LESS datasets demonstrate that our method outperforms other methods and generalizes better to unseen objects. Extensive ablation studies are performed to verify the effectiveness of the PoseFusion.

Increase the operational reliability of the electric drive of the weaving machine

The main purpose of this research work is to analyze malfunctions, power consumption, engine overheating and vibrations based on the loadings of electrical circuits through artificial neural networks. The reliability of artificial intelligence systems was proven on the basis of a model-based system depending on the task, and the obtained values were experimentally compared in the electrical operation of existing equipment in general industrial enterprises. An imitation model of the real object was developed. A concept of increasing productivity was set up to identify malfunctions, in contrast to the existing annular method. The article developed an algorithm for increasing the operational reliability of the electrical operation of the weaving machine on the basis of integrated indicators of excitation to determine the probability of failure of electrical operation. The article proposes the possibility of directly processing the diagnostic energy parameters through artificial neural networks. An experimental combination of signals resulted in a model based on input power and torque, and was based on an asynchronous motorized electrical circuit. It has been proven that intellectual reliability can be increased by 3-5% compared to operational reliability in traditional methods.

Model for motivating learners with personalized learning objects in a hypermedia adaptive learning system

&lt;span lang="EN-US"&gt;A number of weaknesses were demonstrated in the E-learning platforms during the Covid-19 pandemic despite the efforts invested. This has negatively influenced learners' motivation and consequently their performance. With the proliferation of technology and the revolution of information and communication technologies (ICT), learning objects have become new epitomes widely used, accessible, and implemented with educational resources and technological support. The integration of learning objects into E-learning has enhanced educational progress, but during critical periods, it is crucial to ensure pedagogical continuity and learner motivation. Based on this observation, we will propose architecture of a personalized learning object model in the context of an adaptive hypermedia learning system (AHS). The objective of our model is to increase the motivation factor which is a determining element in the success of E-learning, our model aims to improve the performance of the learners in order to avoid the abounding of learning and to promote the attendance of the learners. This will be useful later for any design or development of learning objects in hypermedia learning systems that are adaptive to the needs of the learners and in line with their preferences and profiles throughout the learning process offered by the system. &lt;/span&gt;