Object Model Research Articles

Comparison of Ingestion of Different Size Hard and Soft Bodies Into a Representative Fan Assembly Model

Abstract Foreign matter ingestion into a jet engine is a significant hazard to the safety of aircraft. While soft body ingestions (i.e., bird or ice ingestions) have been extensively researched, the threat posed by uncrewed aircraft systems (UASs) is a more recent concern due to their recent rise in popularity and has not been thoroughly studied. To better understand the damage caused by UAS ingestions, it is crucial to examine the resulting ingestion damage in comparison to birds of similar mass. This analysis is an essential initial step in determining how previous knowledge regarding soft body ingestions can relate to this new threat. To properly analyze these ingestions, development of a model that accurately represents a fan assembly is essential. This model should include a fan, as well as representative boundary conditions for the ingestion, such as blade retention systems, nose cone, casing, and shaft. Items that are analyzed during the ingestion include the overall damage to the fan blades, average and peak forces imparted on the retention systems, impact loads with the casing, and transient loads due to impact on the shaft. The foreign object models used were experimentally validated at their nominal sizes to increase confidence in the results. Comparison of the effects of ingestion of UAS and birds of difference mass into the representative fan model are presented and discussed to gain a better understanding of the differences between soft and hard body ingestion.

E-CARGO-based dynamic weight offload strategy with resource contention mitigation for edge networks

With the widespread use of Mobile Edge Computing (MEC) in smart manufacturing systems in Industrial Internet of Things (IIoT) and 5G networks, determining how to efficiently offload computing tasks has become a hot research area. The Role-Based Collaboration (RBC) Environments-Classes, Agents, Roles, Groups, and Objects (E-CARGO) model is introduced to comprehensively manage MEC servers and user computation tasks in edge network environments, thereby improving the effectiveness and performance of task offloading in smart manufacturing systems. To begin with, latency and energy consumption are important indicators for evaluating the offloading effect. A pre-allocation algorithm based on user latency tolerance is proposed to dynamically adjust the latency-energy consumption weighting factor to optimize system resource allocation for real-time adjustment of offloading decisions. Second, the Group Role Assignment of Agent Role Conflicts (GRACAR) model based on E-CARGO is extended, along with a dynamic weighting of the GRACAR (GRACAR-DW) model and formal modeling. By introducing resource contention constraints, the resource contention caused by excessive task data offloading to the same MEC server is proactively mitigated. Finally, a Gurobi solution based on Mixed-Integer Linear Programming (MILP) is developed to help validate and synthesize the proposed model. Simulation results show that the strategy considerably enhances the MEC system's overall performance in terms of latency and energy consumption while also providing new ideas and technological support for offloading decisions in edge networks.

Proving Correctness of Parallel Implementations of Transition System Models

This article addresses the long-standing problem of program correctness for programs that describe systems of parallel executing processes. We propose a new method for proving correctness of parallel implementations of high-level models expressed as transition systems. The implementation language underlying the method is based on the concurrency model of actors and active objects. The method defines program correctness in terms of a simulation relation between the transition system that specifies the program semantics of the parallel program and the transition system that is described by the correctness specification. The simulation relation itself abstracts from the fine-grained interleaving of parallel processes by exploiting a global confluence property of the concurrency model of the implementation language considered in this article. As a proof of concept, we apply our method to the correctness of a parallel simulator of multicore memory systems.

Multi-granularity spatiotemporal object modelling of waterborne traffic elements

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

Monocular Pose and Shape Reconstruction of Vehicles in UAV imagery using a Multi-task CNN

Estimating the pose and shape of vehicles from aerial images is an important, yet challenging task. While there are many existing approaches that use stereo images from street-level perspectives to reconstruct objects in 3D, the majority of aerial configurations used for purposes like traffic surveillance are limited to monocular images. Addressing this challenge, a Convolutional Neural Network-based method is presented in this paper, which jointly performs detection, pose, type and 3D shape estimation for vehicles observed in monocular UAV imagery. For this purpose, a robust 3D object model is used following the concept of an Active Shape Model. In addition, different variants of loss functions for learning 3D shape estimation are presented, focusing on the height component, which is particularly challenging to estimate from monocular near-nadir images. We also introduce a UAV-based dataset to evaluate our model in addition to an augmented version of the publicly available Hessigheim benchmark dataset. Our method yields promising results in pose and shape estimation: utilising images with a ground sampling distance (GSD) of 3 cm, it achieves median errors of up to 4 cm in position and 3° in orientation. Additionally, it achieves root mean square (RMS) errors of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm 6$$\\end{document} cm in planimetry and \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm 18$$\\end{document} cm in height for keypoints defining the car shape.

Rapid automatic multiple moving objects detection method based on feature extraction from images with non-sidereal tracking

ABSTRACT Optically observing and monitoring moving objects, both natural and artificial, is important to human space security. Non-sidereal tracking can improve the system’s limiting magnitude for moving objects, which benefits the surveillance. However, images with non-sidereal tracking include complex background, as well as objects with different brightness and moving mode, posing a significant challenge for accurate multi-object detection in such images, especially in wide field-of-view telescope images. To achieve a higher detection precision in a higher speed, we proposed a novel object detection method, which combines the source feature extraction and the neural network. First, our method extracts object features from optical images such as centroid, shape, and flux. Then, it conducts a naive labelling based on those features to distinguish moving objects from stars. After balancing the labelled data, we employ it to train a neural network aimed at creating a classification model for point-like and streak-like objects. Ultimately, based on the neural network model’s classification outcomes, moving objects whose motion modes consistent with the tracked objects are detected via track association, while objects with different motion modes are detected using morphological statistics. The validation, based on the space objects images captured in target tracking mode with the 1-m telescope at Nanshan, Xinjiang Astronomical Observatory, demonstrates that our method achieves 94.72 per cent detection accuracy with merely 5.02 per cent false alarm rate, and a processing time of 0.66 s per frame. Consequently, our method can rapidly and accurately detect objects with different motion modes from wide-field images with non-sidereal tracking.

Refined Prior Guided Category-Level 6D Pose Estimation and Its Application on Robotic Grasping

Estimating the 6D pose and size of objects is crucial in the task of visual grasping for robotic arms. Most current algorithms still require the 3D CAD model of the target object to match with the detected points, and they are unable to predict the object’s size, which significantly limits the generalizability of these methods. In this paper, we introduce category priors and extract high-dimensional abstract features from both the observed point cloud and the prior to predict the deformation matrix of the reconstructed point cloud and the dense correspondence between the reconstructed and observed point clouds. Furthermore, we propose a staged geometric correction and dense correspondence refinement mechanism to enhance the accuracy of regression. In addition, a novel lightweight attention module is introduced to further integrate the extracted features and identify potential correlations between the observed point cloud and the category prior. Ultimately, the object’s translation, rotation, and size are obtained by mapping the reconstructed point cloud to a normalized canonical coordinate system. Through extensive experiments, we demonstrate that our algorithm outperforms existing methods in terms of performance and accuracy on commonly used benchmarks for this type of problem. Additionally, we implement the algorithm in robotic arm-grasping simulations, further validating its effectiveness.

Визуальная интерпретация парадигмы управления организационной системой c разнородными производственными ресурсами

The object of the study is developing a formalized mechanism for managing an organizational system that implements complex production activities, including different types of industrial production, and applies the corresponding heterogeneous resources. The subject of the article is an important intermediate stage of this development, which is an abstract interpretation of the optimal management paradigm of limited resources of such an organizational system, proposed earlier by the authors. The interpretation will allow reasonably forming a strict mathematical model of objects and control processes. The management mechanism in any subject area, providing for adopting management decisions by people authorized to do so, must have an ergonomic development and appropriate information support for these decisions. The so-called result information plays the role of such support, that is the information on the characteristics of the current and planned states of the management object in the problem being solved which is the productivity of the organizational system resources. It seems advisable to carry out such ergonomic and information development of the designed problem-oriented management mechanism in the form of a visual interpretation of its substantive concept. The prototype of such interpretation tools is graphic images used in modelling and designing technical systems, developed in this work in the field of management in organizational systems with heterogeneous resources. The article provides examples of their application, demonstrating the possibility of visual representation of the current state of the organizational system and the set of its possible controlled states.

Improved region proposal network for enhanced few-shot object detection

Despite significant success of deep learning in object detection tasks, the standard training of deep neural networks requires access to a substantial quantity of annotated images across all classes. Data annotation is an arduous and time-consuming endeavor, particularly when dealing with infrequent objects. Few-shot object detection (FSOD) methods have emerged as a solution to the limitations of classic object detection approaches based on deep learning. FSOD methods demonstrate remarkable performance by achieving robust object detection using a significantly smaller amount of training data. A challenge for FSOD is that instances from novel classes that do not belong to the fixed set of training classes appear in the background and the base model may pick them up as potential objects. These objects behave similarly to label noise because they are classified as one of the training dataset classes, leading to FSOD performance degradation. We develop a semi-supervised algorithm to detect and then utilize these unlabeled novel objects as positive samples during the FSOD training stage to improve FSOD performance. Specifically, we develop a hierarchical ternary classification region proposal network (HTRPN) to localize the potential unlabeled novel objects and assign them new objectness labels to distinguish these objects from the base training dataset classes. Our improved hierarchical sampling strategy for the region proposal network (RPN) also boosts the perception ability of the object detection model for large objects. We test our approach and COCO and PASCAL VOC baselines that are commonly used in FSOD literature. Our experimental results indicate that our method is effective and outperforms the existing state-of-the-art (SOTA) FSOD methods. Our implementation is provided as a supplement to support reproducibility of the results https://github.com/zshanggu/HTRPN.11Early partial results of this work is presented in the 2023 ICCV Workshop on Visual Continual Learning (Shangguan and Rostami, 2023).

Identifying sulphurous water discharge from legacy oil and gas wells using spectral band analysis of aerial and satellite imagery

Legacy oil and gas wells are a significant source of hydrogen sulphide and methane gas release to the atmosphere. Unanticipated occurrences of gas release in urbanized areas can pose human health risks. Several high hydrogen sulphide-emitting wells have been identified in Norfolk County, Ontario, Canada, based on the remote detection of sulphurous water discharging to the surface along the wellbore. Although oil and gas well records report well status and location, community reports of noxious hydrogen sulphide odours suggest potentially compromised well seals and inaccuracies in their documented location. Given the broad-scale nature of this issue and limitations in site access, a remote-sensing based methodology utilizing satellite and high-resolution aerial photography could support identification of undocumented sulphurous water leaks associated with compromised oil and gas wells and subsequent characterization of hydrological and ecohydrological impacts over time. This study presents a multifaceted approach to identifying sulphurous leaking wells utilizing complimentary remote sensing imagery and image analysis tools within ArcGIS. Southwestern Ontario Orthophotography Project air photos and Sentinel-2 satellite imagery were determined to be the most applicable sources of imagery based on their respective advantages in resolution and revisit time. The application of a normalized difference vegetation index showed that major well leaks could exhibit signs of vegetative scarcity, while minor well leaks may have only a limited impact on vegetation. A band combination utilizing the green and near infrared bands was created to enhance the detection of sulphurous water leaks. Utilizing the identified band combination, a pair of potentially undiscovered leaks were located west of a fluvial river channel. Continued research should attempt to employ an automated approach for discerning additional sulphurous water leaks in the region or at different points in time. Possible techniques may involve the deep learning object and change detection models incorporated in ArcGIS.

Unity and ROS as a Digital and Communication Layer for Digital Twin Application: Case Study of Robotic Arm in a Smart Manufacturing Cell.

A digital twin (DT) is a virtual/digital model of any physical object (physical twin), interconnected through data exchange. In the context of Industry 4.0, DTs are integral to intelligent automation driving innovation at scale by providing significant improvements in precision, flexibility, and real-time responsiveness. A critical challenge in developing DTs is achieving a model that reflects real-time conditions with precision and flexibility. This paper focuses on evaluating latency and accuracy, key metrics for assessing the efficacy of a DT, which often hinder scalability and adaptability in robotic applications. This article presents a comprehensive framework for developing DTs using Unity and Robot Operating System (ROS) as the main layers of digitalization and communication. The MoveIt package was used for motion planning and execution for the robotic arm, showcasing the framework's versatility independent of proprietary constraints. Leveraging the versatility and open-source nature of these tools, the framework ensures interoperability, adaptability, and scalability, crucial for modern smart manufacturing applications. Our approach was validated by conducting extensive accuracy and latency tests. We measured latency by timestamping messages exchanged between the physical and digital twin, achieving a latency of 77.67 ms. Accuracy was assessed by comparing the joint positions of the DT and the physical robotic arm over multiple cycles, resulting in an accuracy rate of 99.99%. The results highlight the potential of DTs in enhancing operational efficiency and decision-making in manufacturing environments.

Model-agnostic explainable artificial intelligence for object detection in image data

In recent years, deep neural networks have been widely used for building high-performance Artificial Intelligence (AI) systems for computer vision applications. Object detection is a fundamental task in computer vision, which has been greatly progressed through developing large and intricate AI models. However, the lack of transparency is a big challenge that may not allow the widespread adoption of these models. Explainable artificial intelligence is a field of research where methods are developed to help users understand the behavior, decision logics, and vulnerabilities of AI systems. Previously, few explanation methods were developed for object detection based on random masking. However, random masks may raise some issues regarding the actual importance of pixels within an image. In this paper, we design and implement a black-box explanation method named Black-box Object Detection Explanation by Masking (BODEM) through adopting a hierarchical random masking approach for object detection systems. We propose a hierarchical random masking framework in which coarse-grained masks are used in lower levels to find salient regions within an image, and fine-grained mask are used to refine the salient regions in higher levels. Experimentations on various object detection datasets and models showed that BODEM can effectively explain the behavior of object detectors. Moreover, our method outperformed Detector Randomized Input Sampling for Explanation (D-RISE) and Local Interpretable Model-agnostic Explanations (LIME) with respect to different quantitative measures of explanation effectiveness. The experimental results demonstrate that BODEM can be an effective method for explaining and validating object detection systems in black-box testing scenarios.

Experimental Comparison of Two 6D Pose Estimation Algorithms in Robotic Fruit-Picking Tasks

This paper presents an experimental comparison between two existing methods representative of two categories of 6D pose estimation algorithms nowadays commonly used in the robotics community. The first category includes purely deep learning methods, while the second one includes hybrid approaches combining learning pipelines and geometric reasoning. The hybrid method considered in this paper is a pipeline of an instance-level deep neural network based on RGB data only and a geometric pose refinement algorithm based on the availability of the depth map and the CAD model of the target object. Such a method can handle objects whose dimensions differ from those of the CAD. The pure learning method considered in this comparison is DenseFusion, a consolidated state-of-the-art pose estimation algorithm selected because it uses the same input data, namely, RGB image and depth map. The comparison is carried out by testing the success rate of fresh food pick-and-place operations. The fruit-picking scenario has been selected for the comparison because it is challenging due to the high variability of object instances in appearance and dimensions. The experiments carried out with apples and limes show that the hybrid method outperforms the pure learning one in terms of accuracy, thus allowing the pick-and-place operation of fruits with a higher success rate. An extensive discussion is also presented to help the robotics community select the category of 6D pose estimation algorithms most suitable to the specific application.

Augmented Reality for Interactive, Innovative and Fun Science Learning: Systematic Literature Review

The development of the learning process by utilizing current technological developments needs to adapt to the character of 21st-century learning. The fact that 21st-century learning is still not implemented optimally and well enough in schools has encouraged several studies that integrate Augmented Reality technology into science learning. AR Learning Media can visualize science learning concepts for understanding and structure of an object model, making AR a more effective, innovative, and fun media. Where the research objective is to examine Augmented Reality for Interactive, Innovative, and Fun Science Learning: Systematic Literature Review. The review was conducted based on state-of-the-art methods using the preferred reporting items for reviews and meta-analyses (PRISMA) guidelines. The results of this research explain that there are 3 types of Augmented Reality Methods, namely image recognition, Markerless Augmented Reality, Projection Augmented Reality, and Superimposition Augmented Reality; There are 4 things to use Augmented Reality, namely the use of augmented reality (AR) as a learning medium, the use of augmented reality (AR) to train skills, Abstract Thinking Skills, Creative Thinking Skills, Interactive, Innovative and fun. Some fields use AR, namely: entertainment, military training, robotics and telerobotics, consumer design, and medicine.

Assessment of Vulnerabilities in Student Records Web-Based Systems for Public and Private Higher Learning Institutions in Tanzania

In spite that HLIs in Tanzania use web-based systems for managing, storing and processing of HLIs information and data such as website contents, academic results and financial records. The HLIs web-based system have been compromised by attackers due to presence of vulnerabilities. The main objective of this study is to assess the vulnerabilities of Students Records Web-based Systems (SRWBS) for private and public Higher Learning Institutions (HLIs) in Tanzania using black-box testing methodology by employing two automatic vulnerability scanners namely OWASP ZAP (Open Webs Application Security Project Zed Attack Proxy; open-source tool) and Acunetix (proprietary tool). This study assesses the vulnerability of SRWBS for 3 private HLIs and 5 public HLIs in Tanzania. The results reveal the total of 29 vulnerabilities which include but are not limited to Broken Authentication and Session Management, Broken Access Control, Security Misconfiguration, Sensitive Data Exposure, Vulnerable JS (Java Script) Libraries, CSRF (Cros Site Request Forgery), Using Components with Known Vulnerabilities, XSS (Cross Site Script), DOM (Document Object Model) based XSS and Reflected XSS. SRWBS of public HLIs were found more vulnerable by average 44.2% than the SRWBS of private HLIs which were vulnerable by average of 37%. Based on these results, this study provides some recommendations for mitigating vulnerabilities and improving the security of SRWBS for private and public HLIs in Tanzania.

Motion Planning for Dynamic Three-Dimensional Manipulation for Unknown Flexible Linear Object

Generally, deformable objects have large and nonlinear deformations. Because of these characteristics, recognition and estimation of their movement are difficult. Many studies have been conducted aimed at manipulating deformable objects at will. However, they have been focused on situations wherein a rope’s properties are already known from prior experiments. In our previous work, we proposed a motion planning algorithm to manipulate unknown ropes using a robot arm. Our approach considered three steps: motion generation, manipulation, and parameter estimation. By repeating these three steps, a parameterized flexible linear object model that can express the actual rope movements was estimated, and manipulation was realized. However, our previous work was limited to 2D space manipulation. In this paper, we extend our previously proposed method to address casting manipulation in a 3D space. Casting manipulation involves targeting the flexible linear object tips at the desired object. While our previous studies focused solely on two-dimensional manipulation, this work examines the applicability of the same approach in 3D space. Moreover, 3D manipulation using an unknown flexible linear object has never been reported for dynamic manipulation with flexible linear objects. In this work, we show that our proposed method can be used for 3D manipulation.

Intelligent optimal control of furnace temperature for the municipal solid waste incineration process using multi-loop controller and particle swarm optimization

Furnace temperature (FT) is a key variable in municipal solid waste incineration (MSWI) processes, influenced by many manipulated variables and directly impacting pollutant concentrations in exhaust gas. Domain experts cannot achieve the optimal FT setpoint value under manual control, leading to abnormal pollutant emission concentrations. To address this, we propose an intelligent optimal control framework for FT aiming to minimize pollutant emission concentration. First, the FT controlled object model is established using the Tikhonov regularization-least regression decision tree (TR-LRDT) algorithm. Then, based on the experience of domain experts, a multi-loop controller is developed using an improved single neuron adaptive PID (ISNA-PID) algorithm to stabilize FT. Next, after establishing NOx and CO2 indicator models, the particle swarm optimization (PSO) algorithm is employed to determine the FT setpoint value in terms of minimum pollutant emission concentration. Finally, the FT intelligent optimal control framework is verified. Experimental results indicate that the optimal FT setpoint value can reduce NOx and CO2 emission concentrations by 19.93 % and 6.99 %, respectively.

Detection Strategies for COM, WMI, and ALPC-Based Multi-Process Malware.

Behavioral malware detection is based on attributing malicious actions to processes. Malicious processes may try to hide by changing the behavior of other benign processes to achieve their goals. We showcase how Component Object Model (COM) and Windows Management Instrumentation (WMI) can be used to create such spoofing attacks. We discuss the internals of COM and WMI and Asynchronous Local Procedure Call (ALPC). We present multiple functional monitoring techniques to identify the spoofing and discuss the strong and weak points of each technique. We create a robust process monitoring system that can correctly identify the source of malicious actions spoofed via COM, WMI and ALPC with a low performance impact. Finally, we discuss how malicious actors use COM, WMI and ALPC by examining real-world malware detected by our monitoring system.

Polygonal Approximation Learning for Convex Object Segmentation in Biomedical Images With Bounding Box Supervision.

As a common and critical medical image analysis task, deep learning based biomedical image segmentation is hindered by the dependence on costly fine-grained annotations. To alleviate this data dependence, in this article, a novel approach, called Polygonal Approximation Learning (PAL), is proposed for convex object instance segmentation with only bounding-box supervision. The key idea behind PAL is that the detection model for convex objects already contains the necessary information for segmenting them since their convex hulls, which can be generated approximately by the intersection of bounding boxes, are equivalent to the masks representing the objects. To extract the essential information from the detection model, a repeated detection approach is employed on biomedical images where various rotation angles are applied and a dice loss with the projection of the rotated detection results is utilized as a supervised signal in training our segmentation model. In biomedical imaging tasks involving convex objects, such as nuclei instance segmentation, PAL outperforms the known models (e.g., BoxInst) that rely solely on box supervision. Furthermore, PAL achieves comparable performance with mask-supervised models including Mask R-CNN and Cascade Mask R-CNN. Interestingly, PAL also demonstrates remarkable performance on non-convex object instance segmentation tasks, for example, surgical instrument and organ instance segmentation.

A New High-Precision and Lightweight Detection Model for Illegal Construction Objects Based on Deep Learning

A New High-Precision and Lightweight Detection Model for Illegal Construction Objects Based on Deep Learning