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UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

Computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

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Exploring Children’s Strategies in Response to Robot’s Advice During a Group Task with iCub and Nao 16th

Exploring Children’s Strategies in Response to Robot’s A...

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16th International Conference on Social Robotics, ICSR + AI 2024

作者： Pusceddu, Giulia Sangineto, Mariapia Cocchella, Francesca Bogliolo, Michela Belgiovine, Giulia Lastrico, Linda Casadio, Maura Rea, Francesco Gena, Cristina Sciutti, Alessandra Robotics Brain and Cognitive Science Department Italian Institute of Technology Genoa Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Department of Computer Science University of Turin Turin Italy Cognitive Architecture for Collaborative Technologies Unit Italian Institute of Technology Genoa Italy Scuola di Robotica Genoa Italy

ISBN: (纸本)9789819635184

This study investigates children’s trust in two humanoid robots, Nao and iCub, through a cooperative game designed to elicit spontaneous behaviors and group dynamics. We investigate whether participants change their choices after hearing advice from a robot, considering rounds where the advice is correct or incorrect, with the robot apologizing for its mistakes. Results reveal that, in most cases, participants prefer not to change their moves after the robot’s advice. Additionally, after the robots made mistakes, participants are less likely to actively follow their advice and more likely to reject it. No significant differences in strategies among the iCub and Nao samples are evidenced. Furthermore, we examine the influence of group dynamics, identifying "Proactive Players" who consistently initiated moves. A significant association between the strategies of this player and their teammates is found, suggesting potential influence. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Deep Reinforcement Learning-Based Framework for Enhancing Cybersecurity

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第3期19卷 170-190页

作者： Al-Nawashi, Malek M. Al-Hazaimeh, Obaida M. Tahat, Nedal M. Gharaibeh, Nasr Abu-Ain, Waleed A. Abu-Ain, Tarik Al-Huson University College Al-Balqa Applied University Irbid Jordan Faculty of Information Technology Department of Information Security and Cybersecurity Philadelphia University Amman Jordan Faculty of Science The Hashemite University Zarqa Jordan College of Science and Computer Engineering Taibah University Yanbu Saudi Arabia College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

The detection of cyberattacks has been increasingly emphasized in recent years, focusing on both infrastructure and people. Conventional security measures such as intrusion detection, firewalls, and encryption are insufficient in protecting cyber systems against growing and changing threats. In order to address this problem, scholars have explored reinforcement learning (i.e., RL) as a potential solution for intricate cybersecurity decision-making difficulties. Nevertheless, the use of RL faces several obstacles, including dynamic attack scenarios, insufficient training data, and the challenge of replicating real-world complexities. This study presents a novel framework that uses deep reinforcement learning (i.e., DRL) to simulate harmful cyberattacks and improve cybersecurity. This study presents an agent-based framework that is capable of ongoing learning and adaptation in a dynamic network security environment. The agent determines the optimal course of action by considering the current state of the network and the rewards it receives for its decisions. The CIC-IDS-2018 database, constructed using Python 3.7 programming, was used. The conducted studies yielded outstanding results, with a detection accuracy of 98.82% achieved for the CIC-IDS-2018 database in cyber-attack classification. © 2025 by the authors of this article. Published under CC-BY.

关键词： Cybersecurity

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Vision-based control of supernumerary robotic limbs for grasping tasks 38

Vision-based control of supernumerary robotic limbs for gras...

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Tian, Linfeng Wang, Hai Zeng, Chao Pang, Muye Chen, Junming Luo, Jing School of Automation Wuhan University of Technology Wuhan China University of Hamburg Department of Informatics Hamburg Germany School of Electrical and Information Engineering Changsha University of Science and Technology Changsha China

ISBN: (纸本)9798350303636

Accuracy is a crucial factor when robots interact with humans in certain industrial tasks, such as grasping tasks. Therefore, the main goal of this paper is to propose a method to improve the accuracy of supernumerary robotic limbs (SRL) for assisting humans to complete tasks. First, the kinematics of the supernumerary robotic limbs is analyzed, and the kinematics of the six degrees of freedom are analyzed to lay the theoretical foundation for the subsequent experiments. Using Kinect as an image collection device, the YOLOv5 method without candidate regions is trained with a training set to recognize object information. Additionally, a Generative Residual Convolutional Neural Network (GRCNN) method is used to predict the parameters such as position and angle of the grasping task. After coordinate transformation, location of crawl is obtained through MoveIt for trajectory planning, and then controlling the mechanical claw of SRL to perform the grasping task. Simulation results verify the effectiveness of the proposed approach. © 2023 IEEE.

关键词： Gazebo simulation platform Generative Residual Convolutional Neural Network human-robot cooperation kinematics supernumerary robotic limbs

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A Social Community Sensor for Natural Disaster Monitoring in Indonesia Using Hybrid 2D CNN LSTM 23

A Social Community Sensor for Natural Disaster Monitoring in...

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8th International Conference on Sustainable Information engineering and technology, SIET 2023

作者： Faisal, Mohammad Reza Nugrahadi, Dodon Turianto Budiman, Irwan Muliadi Delimayanti, Mera Kartika Prastya, Septyan Eka Khairie, Muhammad Lambung Mangkurat University Computer Science Department Indonesia Politeknik Negeri Jakarta Computer and Informatics Engineering Department Indonesia Sari Mulia University Department of Information Technology Indonesia Computer Science Department Universitas Mangkurat University Indonesia

ISBN: (纸本)9798400708503

One of the most notable advantages of utilizing social media is its ability to aid communication during natural disasters, specifically in regards to disseminating information. Social media users serve as community monitors by collecting public messages from a multitude of social media platforms. The implementation of artificial intelligence can facilitate the automatic recognition of messages concerning natural disasters. Deep learning-based artificial intelligence models for text classification, such as Convolutional Neural Network (CNN) and Long Short-Term Memory networks (LSTM), each possess their own unique strengths and weaknesses. Nevertheless, the utilization of word padding techniques presents a further challenge to accurately classify texts, as its implementation may negatively impact classification performance. In this study, feature extraction based on word embedding and word padding techniques were employed, utilizing a maximum number of words to be processed by a hybrid 2D CNN LSTM model. The results indicate the highest level of accuracy in cases of floods, with an 81.27% accuracy rate, forest fires with an 86.14% accuracy rate, and earthquakes with an 80.16% accuracy rate. This outcome represents a significant advancement over the accuracy attained by the classification model constructed with feature extraction based on word embedding and word padding based on mean and using solely 2D CNN. © 2023 ACM.

关键词： Long short-term memory

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Implementing a Robust and Scalable Detect-and-Avoid Algorithm for UAV Navigation in ROS 15

Implementing a Robust and Scalable Detect-and-Avoid Algorith...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Meimetis, Dimitrios Daramouskas, Ioannis Lappas, Vaios Perikos, Isidoros Kapoulas, Vaggelis Paraskevas, Michael Nkua Department of Aerospace Science & Technology Athens Greece Computer Technology Institute and Press 'Diophantus' Patras Greece UoP Applied Mechanics Lab Patras Greece UoP Computer Engineering and Informatics Department Patras Greece Computer Technology Institute and Press 'Diophantus' University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350368833

Unmanned aerial vehicles (UAVs) have garnered significant attention from the research community during the last decade, due to their diverse capabilities and potential applications. One of the most critical functions that drones must execute efficiently is navigation in real-world environments. This paper presents a decentralized approach for enabling unmanned aerial vehicles (UAVs) to navigate safely in unknown environments and avoid obstacles. Leveraging the Optimal Reciprocal Collision Avoidance (ORCA) algorithm, implemented in the Robot Operating System (ROS), our method facilitates conflict detection and resolution in 2D environments. Through simulations using ROS, Gazebo, and Iris drones, we validate the effectiveness of our approach in scenarios with initial trajectory conflicts. Our work addresses the pressing need for UAVs to autonomously plan and execute safe flights, laying the groundwork for enhanced UAV capabilities in various real-world applications. The simulation results demonstrate the efficiency and robustness of our approach. © 2024 IEEE.

关键词： Drones

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Pose Estimation of Spacecraft Using Dual Transformers and Efficient Bayesian Hyperparameter Optimization

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International Journal of Advanced Computer science and Applications 2025年第4期16卷 629-644页

作者： Raja, N. Kannaiya Ramesh, Janjhyam Venkata Naga El-Ebiary, Yousef A. Baker Muniyandy, Elangovan Reddy, N. Konda Kumar, Vanipenta Ravi Devarasetty, Prasad School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal466114 India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun248002 India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Engineering Mathematics K L University Greenfields Guntur Dist Andhra Pradesh Vaddeswaram522302 India Department of Mathematics Annamacharya University New Boyanapalli Rajampet India Department of Computer Science and Engineering DVR & Dr HS MIC College of Technology Andhra Pradesh Kanchikacherla India

Spacecraft pose estimation is an essential contribution to facilitating central space mission activities like autonomous navigation, rendezvous, docking, and on-orbit servicing. Nonetheless, methods like Convolutional Neural Networks (CNNs), Simultaneous Localization and Mapping (SLAM), and Particle Filtering suffer significant drawbacks when implemented in space. Such techniques tend to have high computational complexity, low domain generalization capacity for varied or unknown conditions (domain generalization problem), and accuracy loss with noise from the space environment causes such as fluctuating lighting, sensor limitations, and background interference. In order to overcome these challenges, this study suggests a new solution through the combination of a Dual-Channel Transformer Network with Bayesian Optimization methods. The innovation is at the center with the utilization of EfficientNet, augmented with squeeze-and-excitation attention modules, to extract feature-rich representations without sacrificing computational efficiency. The dual-channel architecture dissects satellite pose estimation into two dedicated streams—translational data prediction and orientation estimation via quaternion-based activation functions for rotational precision. Activation maps are transformed into transformer-compatible sequences via 1×1 convolutions, allowing successful learning in the transformer's encoder-decoder system. To maximize model performance, Bayesian Optimization with Gaussian Process Regression and the Upper Confidence Bound (UCB) acquisition function makes the optimal hyperparameter selection with fewer queries, conserving time and resources. This entire framework, used here in Python and verified with the SLAB Satellite Pose Estimation Challenge dataset, had an outstanding Mean IOU of 0.9610, reflecting higher accuracy compared to standard models. In total, this research sets a new standard for spacecraft pose estimation, by marrying the versatility of deep le

关键词： Convolutional neural networks

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Insights into acoustic beams in nonlinear, weakly dispersive and dissipative media

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Optical and Quantum Electronics 2025年第5期57卷 1-18页

作者： Khater, Mostafa M. A. Wang, Heng Alfalqi, Suleman H. Vokhmintsev, Aleksander Owyed, Saud School of Information Engineering Yango University Fuzhou350015 China Engineering School of Digital Technologies Yugra State University Khanty-Mansiysk628012 Russia Department of Basic Science The Higher Institute for Engineering & Technology Al-Obour Cairo10587 Egypt School of Medical Informatics and Engineering Xuzhou Medical University Jiangsu Province Xuzhou221004 China Department of Mathematics Faculty of Science and Arts in Mahayil Asir King Khalid University Abha Saudi Arabia Institute of Information Technology Chelyabinsk State University Chelyabinsk Russia Department of Mathematics College of Sciences University of Bisha Bisha61922 Saudi Arabia

This study examines the (2+1)–dimensional dissipative Zabolotskaya–Khokhlov equation, a fundamental nonlinear wave model with broad applications in diverse physical domains, such as acoustic wave propagation, nonlinear optics, and fluid dynamics. This equation exhibits profound connections with other nonlinear evolution equations, particularly in describing wave interactions in dispersive and dissipative media, highlighting its significance in characterizing complex wave phenomena. The principal objective of this work is to derive both closed-form and numerical solutions to gain deeper insight into the equation’s underlying dynamics. To this end, we employ the Khater II and the modified Kudryashov approaches-two systematic mathematical techniques for obtaining explicit solutions. Additionally, He’s variational iteration method is implemented as a numerical scheme to evaluate the reliability and precision of the derived exact solutions. A comparative assessment of the analytical and numerical results underscores the accuracy of the obtained solutions and the effectiveness of the employed methods in handling highly nonlinear wave systems. This investigation provides novel perspectives on dissipative wave dynamics and illustrates the advantages of integrating exact and numerical methodologies. The findings hold substantial implications for applications in fluid mechanics, nonlinear optics, and acoustic wave theory, fostering a more comprehensive understanding of nonlinear dissipative structures. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Nonlinear optics

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Modelling and control of a multi-product pipeline transportation process

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International Journal of Information technology (Singapore) 2025年 1-11页

作者： Jiménez-Cabas, Javier Piñeres-Espitia, Gabriel Rodelo-Serrano, Esteban Pamucar, Dragan Ramirez-Leon, Hermes Butt, Shariq Aziz Naz, Sumera Simic, Vladimir Computer Science and Electronic Department Universidad de La Costa Barranquilla Colombia Departamento de Ingeniería Mecánica Universidad Simón Bolívar Barranquilla Venezuela Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Department of Industrial Engineering & Management Yuan Ze University Taoyuan Taiwan Department of Mechanics and Mathematics Western Caspian University Baku Azerbaijan Faculty of Transport and Traffic Engineering University of Belgrade Vojvode Stepe 305 Belgrade 11010 Serbia Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 South Korea School of Systems and Technology Department of Computer Science University of Management and Technology Lahore Pakistan Department of Mathematics Division of Science and Technology University of Education Lahore Pakistan

One of the most critical processes in the petroleum industry is transporting crude oil and its derivatives. Usually, it is done, due to easiness and economic aspects, through pipelines;several products, such as gasoline, kerosene, naphtha, and liquefied petroleum gas, among others, are moved in batch processes to both distribution and storage centers or transport ships. Since these transport systems transport different products, many works have focused on developing optimization strategies for scheduling these pipelines. That is, strategies that seek to optimize the use of the pipeline by establishing at what time points certain products must be transported. These strategies would be located at the planning level in the automation pyramid. In this work, on the other hand, different control strategies are proposed for a pipeline, but at the operation level. These strategies would be located at the control level, below the planning strategies in the automation pyramid. A pipeline of three pipe sections is modeled to develop the proposed control strategies, and the mathematical model is validated with actual data. The results show that, first, the proposed model approximates the actual behavior of the pipeline quite well and, second, that the combination of PID and feedforward control is an excellent alternative to implement control strategies for the operation of liquid transportation systems through pipelines. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： control strategies liquid transportation modeling of system

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Lightning Statistics in Hungary between 2002 and 2022 37

Lightning Statistics in Hungary between 2002 and 2022

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37th International Conference on Lightning Protection, ICLP 2024

作者： Tóth, Zoltán Simon, Csilla Department of Electric Power Engineering Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary Institute of Digital Health Science Faculty of Public Services Semmelweis University Budapest Hungary Department of Meteorology Faculty of Science Eötvös Loránd University Budapest Hungary

ISBN: (纸本)9783800764174

Climate change is universally acknowledged as a global phenomenon with the potential to alter local climates significantly. However, contrary to the commonly perceived uniform warming and drying trend, local climates exhibit diverse changes. This article aims to explore the specific regional climatic shifts that have occurred in Hungary over the past 20 years. Utilising data from the Hungarian Meteorological Service, we employed both qualitative and quantitative methods to dissect the changes experienced across different Hungarian regions. Our findings reveal a complex tapestry of climatic alterations, with some areas undergoing transformations that defy the expectation of increased warmth and aridity. The study provides a detailed account of these regional changes, offering insights into the varied manifestations of climate change locally. Furthermore, it discusses the potential future scenarios for the climate of Hungary, laying a foundation for region-specific adaptation strategies. This article contributes to the broader understanding of how global climate dynamics can lead to a spectrum of local changes, some of which may be counterintuitive to prevailing climate change narratives. © 2024 IEEE, © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Miocene

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