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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Lee, Hyeonmin Kim, Dongchan Beom, Donggyu Byun, Gyurin Vo, Van-Vi Choo, Hyunseung Department of AI Systems Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Convergence Research Institute Sungkyunkwan University Suwon16419 Korea Republic of Department of Computer Science and Engineering Sungkyunkwan University Suwon16419 Korea Republic of

ISBN: (纸本)9798331507817

This study proposes a hybrid deep learning approach to address the complexity and dynamic characteristics of modern network environments. The research integrates Graph Neural Networks (GNNs) and Convolutional Long and Short-Term Memory (ConvLSTM) networks to predict future network traffic and detect cyberattacks. The model was trained and evaluated on the CICIDS 2018 dataset which includes various types of normal and malicious traffic. Grid-based representations were created from the dataset. The attack type of each timestamp were visualized using distinct colors to the grid images. These images are sued as input of ConvLSTM model. Protocol and destination port are modeled as nodes in a graph, with volume of data flow represented as weighted edges. This graph-based representation is designed to capture unique traffic patterns of specific services and enhance the analysis of spatial and temporal features. Experimental results show a 13% increase in macro F1 score and a 4% increase in weighted F1 score, highlighting the effectiveness of the proposed method. This research highlights its potential to enable proactive responses through the prediction of potential cyberattack occurrences. © 2025 IEEE.

关键词： Convolutional Long Short-Term Memory Cyberattack Detection Deep Learning Graph Neural Networks Network Traffic Prediction

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Acceleration-level coordinated control of a dual-arm free-floating space robot under hard joint constraints

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Advances in Space Research 2025年第11期75卷 8122-8139页

作者： Xue, Zhihui Liu, Jinguo Zhang, Xin Zhou, Hao Li, Yangmin State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Kowloon999077 Hong Kong School of Electrical and Mechanical Engineering University of Portsmouth PortsmouthPO1 3DJ United Kingdom

Dual-arm space robots are becoming increasingly crucial in on-orbit servicing missions. The online motion control of a dual-arm redundant space robot with hard joint constraints presents significant challenges due to its free-floating base. In this article, we proposed an acceleration-level coordinated control framework to solve hard joint constraints and dynamic singularity. The proposed framework combines a task scheduling strategy to achieve saturation in the null space, so that different tasks can achieve trajectory tracking with minimal task scaling under given joint hard constraints. The framework can achieve efficient online control by sequentially abandoning the use of joints that exceed the joint constraint and reintroducing these commands at their saturation level by projecting them into the appropriate null space. Finally, we verify the tracking performance of the algorithm through numerical simulation and experimental research. By comparing the proposed algorithm with the quadratic programming and pseudo-inverse methods, the algorithm has shown excellent online motion control performance, with a computation time of approximately 1 ms within a single sampling period. © 2025 COSPAR

关键词： Quadratic programming

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Deep Learning Model for Analyzing EEG Signal Analysis

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IEEE Access 2025年 13卷 91034-91045页

作者： Gupta, Varun Kumar, Vivek Prince Singh, Saurabh Lee, Young-Seok Ra, In-Ho Department of Electronics and Communication National Institute of Technology Sikkim India National Institute of Technology Department of Electrical and Electronics Engineering Sikkim India Dongguk University Department of Industrial & Systems Engineering Seoul04620 Korea Republic of Woosong University Department of AI and Bigdata Daejeon34606 Korea Republic of Kunsan National University School of Computer & Software Gunsan Korea Republic of Kunsan National University School of Software Gunsan54150 Korea Republic of

To analyze the physiological information within the acquired EEG signal is very cumbersome due to the possibility of several factors, viz. noise and artifacts, complexity of brain dynamics, and inter-subject variability. To address these issues, this paper compares a U-shaped encoder-decoder network (UNET) and Bat-based UNET signal analysis (BUSA) techniques to classify depression rates in the Electroencephalogram (EEG) datasets. The main objective of including these two techniques is to reveal their effectiveness. It comprises pre-processing, feature extraction, feature selection, and classification stages. The framework excels at noise reduction during pre-processing, enhancing dataset integrity. Feature extraction leverages band power and correlation dimension to extract crucial features. Furthermore, feature selection optimizes classification accuracy by refining the fitness function of bats in the classification layer. The performance of UNET and BUSA are compared based on the following performance evaluating parameters viz. accuracy (Acc), Area Under the Curve (AUC), precision (P), and recall (R) (or sensitivity (Se)). The results indicated that the BUSA technique outperforms the UNET technique. © 2013 IEEE.

关键词： Electroencephalography

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XMeta-OS: A Conceptual Framework for Unified Resource Management in Distributed Edge-Cloud Environments 3rd

XMeta-OS: A Conceptual Framework for Unified Resource Manag...

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3rd International Conference on Advances in Computing Research, ACR 2025

作者： Zacharioudakis, Eleftherios Kakoulli, Elena Evripidou, Salomi Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos Pafos Cyprus National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (纸本)9783031876462

This paper introduces XMeta-OS, a meta-operating system specially designed with Linux as its foundation to unify and optimize resource management for the distributed edge-cloud when dynamic use of GPU resources is possible. This approach offer a new GPU abstraction layer and dynamic edge-cloud allocation model that offer enhanced flexibility and resource utilization over existing systems like Kubernetes and OpenStack. By using likely sensitive data, the model applied cloud-pushed deployment model. XMeta-OS manages computer resources in real-time. Therefore, fulfilling high performance various needs. It allocates CPU and GPU in real-time to workloads requiring massive parallel processing, such as machine learning, scientific simulations, real-time data analytics, and transportation tasks. Fabrication in the laboratory of an edge-cloud environment can help to assess the scalability, fault tolerance and efficiency of the system. It showcases the overall architectural design of XMeta-OS. Changes to the kernel and security solutions for safe and effective distribution are suggested. This article presents the design of the XMeta-OS. The future of XMeta-OS entails usage and testing with which we see latency, energy, computation and other such measurements in labs. XMeta-OS is a solution to cost performance, emerging application, large-scale edge-cloud deployments, and distributed GPU-enabled infrastructures. Meta-Operating System in Edge-Cloud Continuum for dynamic resource management of GPU. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cluster computing

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CPDZ: A Credibility-Aware and Privacy-Preserving Data Collection Scheme with Zero-Trust in Next-Generation Crowdsensing Networks

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2183-2199页

作者： Tang, Jianheng Fan, Kejia Yang, Shihao Liu, Anfeng Xiong, Neal N. Song, Houbing Herbert Leung, Victor C. M. Central South University School of Computer Science and Engineering Changsha410083 China Peking University School of Computer Science Beijing100871 China Sul Ross State University Department of Computer Mathematical and Physical Sciences AlpineTX79830 United States Department of Information Systems BaltimoreMD21250 United States Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Next-Generation Crowdsensing Networks (NGCNs) have become increasingly critical for smart cities, where data privacy and quality are pivotal concerns. Traditional trust mechanisms in crowdsensing mainly rely on static trust models, which are insufficient for dynamic security requirements. Zero-Trust security represents a promising opportunity, yet coming with notable challenges in NGCNs, including Unknown Workers Online Recruitment (UWOR), Information Elicitation Without Verification (IEWV), Privacy Preserving Data Evaluation (PPDE), and Dynamic Trust Abrupt Shift (DTAS). To address these challenges, we propose a Credibility-aware and Privacy-preserving Data collection scheme with Zero-trust (CPDZ) for secure and quality data collection in NGCNs. First, our CPDZ scheme encompasses a quality worker recruitment strategy with combinatorial multi-armed bandit models, utilizing Thompson Sampling for the secure and efficient resolution of the UWOR. Second, an active dispatching scheme for unmanned aerial vehicles is crafted to collect data as a gold standard to assist in overcoming the IEWV challenge. Third, as for the PPDE challenge, we propose a lightweight privacy-preserving scheme for dependable truth discovery and secure trust verification. Fourth, the DTAS challenge is managed by a dual verification scheme that integrates short-term and long-term trust assessments, ensuring stability and adaptability of the zero-trust security in our CPDZ scheme. Experiments confirm the superiority of our CPDZ scheme, showing a 12.5% increase in recruitment revenue and a 57.8% reduction in relative error compared to existing approaches. © 1983-2012 IEEE.

关键词： Differential privacy

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A Semantic Mapping Framework for Service Robots 25

A Semantic Mapping Framework for Service Robots

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Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing

作者： Lucyanno Frota Rui P. Rocha Dept. of Electrical and Computer Engineering University of Coimbra Coimbra Portugal Institute of Systems and Robotics University of Coimbra Coimbra Portugal

ISBN: (纸本)9798400706295

Service robots are undergoing a massification process similar to what happened with personal computers and cell phones a few decades ago. Their ubiquitous coexistence and interaction with humans requires that their representation models of the workspace go beyond metric information used for safe navigation. They are also required to assign semantic meaning to objects and places, i.e. to build semantic maps, in order to understand scenes and engage in human-like interactions. This paper proposes the Semantic MAPping (SMAP) framework to provide a service robot operating in human populated environments with a semantic mapping layer on top of a metric SLAM layer. SMAP is modular, expandable, and efficient enough to run locally on the robot. It has been implemented in Robot Operating System 2 (ROS2) using modular Docker containers. Preliminary experiments with a Pioneer 3-DX mobile robot having a system on module Nvidia Jetson AGX Xavier demonstrated its potential for future service robotics applications.

关键词： semantic mapping

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Tolerant Tracking Control Protocol for PMSM Based on Policy Iteration Algorithm and Fault Compensation 16th

Tolerant Tracking Control Protocol for PMSM Based on Polic...

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16th International Conference on Social robotics, ICSR + InnoBiz 2024

作者： Yan, Shuya Li, Xiaocong Qian, Huaming Ma, Jun Al Mamun, Abdullah College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin150001 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Guangzhou China Information Science and Technology Eastern Institute of Technology Ningbo China

ISBN: (纸本)9789819611508

In robotics applications, ensuring reliable performance in the presence of actuator faults is essential for maintaining system safety and reliability. This paper presents a tolerant tracking control method for permanent magnet synchronous motors (PMSMs) based on adaptive dynamic programming and fault compensation. The method simultaneously considers tracking accuracy and energy consumption through a policy iteration algorithm. In the optimality analysis of the algorithm, more relaxed conditions are provided to demonstrate that the performance function can converge to a near-optimal value within a finite number of iterations. In practical implementation, an actor-critic network is used to approximate the performance function and control protocol, alongside a fault detection mechanism based on an expanded time horizon, which achieves fault detection from arbitrary initial values. The effectiveness of the proposed algorithm is verified using a high-fidelity PMSM model in Simulink. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Permanent magnets

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Onboard Perception-Assisted High Fidelity Simulation Framework for Autonomous Planetary Soft-Landing

Onboard Perception-Assisted High Fidelity Simulation Framewo...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Alibekov, Ulugbek Banerjee, Avijit Satpute, Sumeet Gajanan Nikolakopoulos, George ERASMUS MUNDUS JOINT MASTER in Intelligent Field Robotic Systems Faculty of Informatics Eötvös Loránd University Budapest Hungary Robotics and AI research group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This article presents an onboard perception-assisted high-fidelity simulation framework for autonomous planetary soft-landing, enabling visual information processing tightly integrated with advanced onboard guidance system in a realistic simulation environments. Utilizing the open source Unity game engine, a physics-based simulation toolbox integrated with the Robotic Operating System (ROS2), we effectively emulate the motion of spacecraft approaching planetary terrain. The simulation environment features a realistic 3D terrain model and a lander spacecraft equipped with onboard camera to effectively capture detailed local terrain images. The simulation engine accurately captures the spacecraft’s 6-degrees of freedom motion, seamlessly integrating space craft orientation with the associated camera feed while incorporating various illumination conditions using a light source representing the Sun. To demonstrate the efficacy of the framework, we implement a recently developed attitude-constrained minimum jerk guidance algorithm to emulate spacecraft motion. Concurrently, the visual feed from the onboard RGB camera is processed through a gradient extraction-based perception system, providing the visual odometer. This system identifies provably safe landing sites, effectively avoiding potential risks of landing on uneven terrain. © 2025, American institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Planetary landers

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Toward a Low-Cost Perception System in Autonomous Vehicles: A Spectrum Learning Approach

arXiv

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arXiv 2025年

作者： Alsakabi, Mohammed Erickson, Aidan Dolan, John M. Tonguz, Ozan K. Department of Electrical and Computer Engineering College of Engineering The Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA United States

We present a cost-effective new approach for generating denser depth maps for Autonomous Driving (AD) and Autonomous Vehicles (AVs) by integrating the images obtained from deep neural network (DNN) 4D radar detectors with conventional camera RGB images. Our approach introduces a novel pixel positional encoding algorithm inspired by Bartlett’s spatial spectrum estimation technique. This algorithm transforms both radar depth maps and RGB images into a unified pixel image subspace called the Spatial Spectrum, facilitating effective learning based on their similarities and differences. Our method effectively leverages high-resolution camera images to train radar depth map generative models, addressing the limitations of conventional radar detectors in complex vehicular environments, thus sharpening the radar output. We develop spectrum estimation algorithms tailored for radar depth maps and RGB images, a comprehensive training framework for data-driven generative models, and a camera-radar deployment scheme for AV operation. Our results demonstrate that our approach also outperforms the state-of-the-art (SOTA) by 27.95% in terms of Unidirectional Chamfer Distance (UCD). © 2025, CC BY-NC-ND.

关键词： Radar imaging

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