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IEEE Transactions on Artificial Intelligence 2023年第6期4卷 1514-1525页

作者： Nath, Krishanu Bera, Manas Kumar Jagannathan, Sarangapani National Institute of Technology Department of Electronics and Instrumentation Engineering Silchar788010 India Missouri University of Science and Technology Department of Electrical and Computer Engineering RollaMO65409 United States

In this article, an event-based neuroadaptive robust tracking controller for a perturbed and networked differential drive mobile robot (DMR) is designed with concurrent learning. A radial basis function neural network (RBFNN), which approximates an unknown perturbation, is used to design an adaptive sliding mode controller. The RBFNN weights and sliding mode controller parameters are estimated online using an adaptive tuning law to ensure performance with reduced chattering. To improve the convergence of RBFNN weight estimation error, a concurrent learning-based adaptive law is derived, which uses measured online and recorded data. Furthermore, a suitable triggering condition is designed to achieve a reduced number of control computations while minimizing network resources without sacrificing the stability of the sampled data closed-loop control system. A finite sampling frequency is guaranteed for the designed triggering condition by establishing a positive lower bound on the inter-event execution time, which is equivalent to the Zeno-free behavior of the system. Finally, the proposed event-based neuroadaptive robust controller is implemented on a practical system (Q-bot 2e) to show the effectiveness of the proposed design. © 2020 IEEE.

关键词： Mobile robots

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Traffic Sign Categorization Using YOLO Algorithm: Leveraging Real-Time Object Detection for Improved Road Safety 9

Traffic Sign Categorization Using YOLO Algorithm: Leveraging...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Santha Sheela, K. Jason Kingsly, R. Bhalaji, C.K. Selvalakshmi, C.B. Radhika, A. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Velammal college of engineering and Technology Dept of Electrical and Electronics Engineering Madurai India

ISBN: (纸本)9798350377972

Traffic signs, and navigation provide drivers with crucial information about speed limits, hazards and are essential for maintaining road safety. Detecting these signs and classifying in real-time plays a vital for autonomous driving systems, as timely information can prevent accidents and ensure compliance with traffic regulations. This paper presents a traffic sign detection system based on the YOLOv8 algorithm, known for its high-speed object detection capabilities, addressing challenges such as occlusion, small object detection, and adverse lighting conditions. The system is trained using a dataset of traffic signs sourced from Roboflow, capturing various types of traffic signs under different environmental conditions, such as lighting, weather, and occlusion scenarios. By using advanced feature extraction techniques, we have visualize the critical features that proposed for the model's classification decisions. The results confirm the efficacy and real-time applications in autonomous vehicles and smart traffic management systems of YOLOv8, indicating the intelligence potential for widespread adoption in the field of vehicles transportation systems. © 2024 IEEE.

关键词： Advanced traffic management systems

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Weibull probability distribution based on four years wind speed data using nine numerical methods

Weibull probability distribution based on four years wind sp...

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International Conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Badawi, Ahmed Ali, Hassan Ismail, Nasser A. Zyoud, Alhareth Yusoff, Siti Hajar Al-Rimawi, Ashraf Ghanem, Wasel College of Engineering Technology University of Doha for Science and Technology Doha Qatar Department of Electrical and Computer Engineering Birzeit University Birzeit Ramallah Palestine Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9781032803722

Wind energy potential can be estimated in a specific area using the Probability density function (PDF). PDF is used for the mean wind speed data on daily basis. This paper represents the analysis of the climatology of the mean wind speed data for Askalan city on the Mediterranean coastal plain of Palestine. A selection of the PDF had been implemented using nine numerical analysis methods to represent the cumulative distribution function and Weibull probability distribution. The two main parameters of the Weibull shape factor are k = 1.7848 and the scale factor parameter c = 4.3642 ms-1. The study assessment finds out the characteristics of wind energy conversion and results confirm that it can produce energy using small-scale wind turbines. Matlab software was used to implement the numerical methods and extract the wind characteristics. © 2025 The Author(s).

关键词： Windmill

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GEOMETRY-AWARE PROJECTIVE MAPPING FOR UNBOUNDED NEURAL RADIANCE FIELDS 12

GEOMETRY-AWARE PROJECTIVE MAPPING FOR UNBOUNDED NEURAL RADIA...

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12th International Conference on Learning Representations, ICLR 2024

作者： Lee, Junoh Jung, Hyunjun Park, Jin-Hwi Bae, Inhwan Jeon, Hae-Gon School of Electrical Engineering and Computer Science Gwangju Korea Republic of AI Graduate School Gwangju Institute of Science and Technology Gwangju Korea Republic of

Estimating neural radiance fields (NeRFs) is able to generate novel views of a scene from known imagery. Recent approaches have afforded dramatic progress on small bounded regions of the scene. For an unbounded scene where cameras point in any direction and contents exist at any distance, certain mapping functions are used to represent it within a bounded space, yet they either work in object-centric scenes or focus on objects close to the camera. The goal of this paper is to understand how to design a proper mapping function that considers per-scene optimization, which remains unexplored. We first present a geometric understanding of existing mapping functions that express the relation between the bounded and unbounded scenes. Here, we exploit a stereographic projection method to explain failures of the mapping functions, where input ray samples are too sparse to account for scene geometry in unbounded regions. To overcome the failures, we propose a novel mapping function based on a p-norm distance, allowing to adaptively sample the rays by adjusting the p-value according to scene geometry, even in unbounded regions. To take the advantage of our mapping function, we also introduce a new ray parameterization to properly allocate ray samples in the geometry of unbounded regions. Through the incorporation of both the novel mapping function and the ray parameterization within existing NeRF frameworks, our method achieves state-ofthe-art novel view synthesis results on a variety of challenging datasets. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Geometry

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MAPPO based Single-hop Task Offloading with Dynamic Vehicle Prediction in RSU-assisted IoV 10

MAPPO based Single-hop Task Offloading with Dynamic Vehicle ...

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10th IEEE Smart World Congress, SWC 2024

作者： Zhao, Wei Yang, Dongling Weng, Tangjie Xu, Xinwei Shao, Xun Liu, Zhi Anhui University of Technology School of Computer Science and Technology China Anhui University of Technology School of Management Science and Engineering China Toyohashi University of Technology Department of Electrical and Electronic Information Engineering Japan The University of Electro-Communications Department of Computer and Network Engineering Japan

ISBN: (纸本)9798331520861

In the realm of Internet of Vehicles (IoV), Roadside Units (RSUs) play a vital role due to their advanced sensing and computing capabilities. They support Mobile Edge Computing (MEC) by enabling real-time data processing and reducing latency and energy consumption. Given the large volume of data and the limited computing capacity of RSUs, tasks are often offloaded to other nodes, such as the cloud, other RSUs, or vehicles. However, due to the environment dynamic caused by vehicle mobility, the set of neighboring nodes for each RSU frequently changes, and variations in neighboring nodes are not perceived timely. In such cases, RSUs need to predict changes in neighboring nodes to ensure effective task offloading. Moreover, since each RSU can only perceive the surrounding environment, it is difficult for a single node to make optimal offloading decisions for each task independently. To address these issues, this paper proposes a distributed task offloading scheme that incorporates vehicle trajectory prediction. First, each RSU uses the vehicle trajectories predicted by the Gated Recurrent Unit (GRU) model to determine neighboring vehicles and update the set of neighboring nodes. Then, to enable each RSU to select a suitable computing node and minimize computation delay and energy consumption for the task, we adopt a distributed approach where each RSU independently makes offloading decisions based on local observations. To achieve this goal, we establish a 01 mathematical model and transform it into a Markov Decision Process (MDP). Subsequently, we propose a solution based on Multi-Agent Proximal Policy Optimization (MAPPO). Through extensive simulation experiments, we demonstrate that the scheme effectively reduces both long-term average processing delay and energy consumption. © 2024 IEEE.

关键词： MAPPO neighboring vehicle prediction RSU task offloading

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Robust Monocular Camera-Based Localization in Industrial Environments

Robust Monocular Camera-Based Localization in Industrial Env...

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Won, Yungu Choi, Yein Ryu, Hwangryol Moon, Youngmin Kwon, Kihoon Hwang, Sung Soo Handong Global University Dept. of Computer Science and Electrical Engineering Pohang Korea Republic of Research Institute of Industrial Science & Technology Smart Engineering Research Laboratory Pohang Korea Republic of School of Computer Science and Electrical Engineering Handong Global University Pohang Korea Republic of

ISBN: (纸本)9798331530839

This paper proposes a camera-based system for robust localization of workers or robots in large-scale factories and industrial sites. The system is designed with the convenience and efficiency of workers in mind, providing stable tracking capabilities. To achieve this, a monocular camera is attached to a safety helmet, enabling localization without restricting the worker's range of motion. Additionally, by combining Visual Simultaneous Localization and Mapping and Visual Place Recognition, the system tracks the location of workers or robots while minimizing tracking loss, ensuring consistent localization. Our methodology has enhanced the accuracy and reliability of position estimation by approximately 46.0%. By accurately identifying the location of workers or robots, the system aims to prevent accidents and ensure swift responses in emergency situations. © 2024 IEEE.

关键词： Industrial robots

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Classifying wireless IOT ICU traffic with machine learning models 19

Classifying wireless IOT ICU traffic with machine learning m...

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19th International Conference on Future Networks and Communications, FNC 2024 / 21st International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2024 / 14th International Conference on Sustainable Energy Information technology, SEIT 2024

作者： Sibai, Fadi N. Sibai, Ahmad Electrical & Computer Engineering Dept. Gulf University for Science and Technology Mishref Kuwait Computer Science & Engineering Dept. University of South Florida TampaFL33620 United States

In this work, we classified the wireless internet of things (IoT) traffic of the IoT health intensive care unit (IHI) dataset which belongs to three general classes: patient monitoring, environment monitoring, and network attack. We trained and tested 7 machine learning (ML) models with Orange 3 including kNN, Decision Tree (tree), SVM, Random Forest (RF), Neural Network (NN), Gradient Boosting (GB), and AdaBoost (AB). With the original dataset, 5 ML models performed perfect classification. After pruning the dataset columns by keeping the features with the highest correlations with the label in the dataset, good classifications were obtained with only 4 TCP/IP features by the Gradient Boosting, kNN, and RF models with MSEs in the range 0.008-0.011, and R2s in the range 0.978-0.984. With only 6 MQTT features, Gradient Boosting, RF, Tree, and NN were the top classifiers with MSEs in the range 0.073-0.074, and R2s in the range 0.856-0.859. This work demonstrates the effectiveness of guiding the feature pruning process by the values of the correlation coefficients in order to minimize the long training times of ML models while achieving good accuracies. © 2024 The Authors. Published by Elsevier B.V.

关键词： correlation coefficient feature pruning Internet of things IOT health intensive care unit dataset machine learning network security

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Dzyaloshinskii-Moriya-type spin-spin interaction from mixed-parity superconductivity

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Physical Review B 2025年第6期111卷 L060504-L060504页

作者： Jabir Ali Ouassou Takehito Yokoyama Jacob Linder Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences NO-5528 Haugesund Norway Department of Physics Tokyo Institute of Technology Meguro Tokyo 152-8551 Japan Center for Quantum Spintronics Department of Physics Norwegian University of Science and Technology NO-7491 Trondheim Norway

Interacting impurity spins adsorbed on surfaces have been suggested as basic components for applications in quantum computation and spintronics. Such spins usually prefer a parallel or antiparallel configuration, but weakly noncollinear alignments are possible due to the Dzyaloshinskii-Moriya interaction (DMI) that arises in the presence of relativistic spin-orbit coupling. Here, we show that an effective Dzyaloshinskii-Moriya-type interaction (DMTI) can emerge purely from superconducting correlations without any spin-orbit interaction. We give an analytical proof and provide a numerical study which shows that DMTI arises in mixed-parity superconductors solely from the superconducting pairing. Moreover, we show that the same effect can be realized in Josephson junctions between s-wave and p-wave superconductors, where a phase bias toggles the DMTI entirely on and off. These results enable a way to engineer spin textures using superconducting order.

关键词： Shear waves

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Deploying On-Device AIGC Inference Services in 6G via Optimal MEC-Device Offloading

IEEE Networking Letters

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IEEE Networking Letters 2024年第4期6卷 232-236页

作者： Zhou, Changshi Liu, Weiqi Han, Tao Ansari, Nirwan New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States Auburn University at Montgomery Department of Computer Science and Computer Information Systems MontgomeryAL36117 United States

From AI-assisted art creation to large language model (LLM)-powered ChatGPT, AI-generated contents and services are becoming a transforming force. It calls for the telecom industry to embrace the prospects of AIGC services and face the unique challenges posed by incorporating generative model services into the AI-native 6G wireless network paradigm. We propose enabling AIGC inference services on mobile devices by optimizing MEC-device computing offloading, through which AIGC task latency is minimized by reinforcement learning based policy agent in a computing resource constrained and bandwidth limited wireless environment. Simulation results are presented to demonstrate the performance advantage. © 2019 IEEE.

关键词： Reinforcement learning

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Coherence analysis of the observed delayed-choice quantum eraser using coherent photons 2

Coherence analysis of the observed delayed-choice quantum er...

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Quantum Sensing, Imaging, and Precision Metrology II 2024

作者： Ham, Byoung S. School of Electrical Engineering and Computer Science Gwangju Institute Science and Technology 123 Chumdanwagi-ro Buk-gu Gwangju61005 Korea Republic of

ISBN: (纸本)9781510670846

Quantum eraser is known as one of the mysterious quantum features, where the action of the delayed choice retrospectively determines the photon’s nature. Over the last decades, various photon characteristics have been used to demonstrate the quantum eraser, especially for violating the cause-effect relation. In this manuscript, recent observations of the quantum eraser based on a Mach-Zehnder interferometer (MZI) using an attenuated laser are speculated as the origin of the quantum mystery, where the delayed choice is conducted for the MZI output photons via polarization projection measurements. For this, the wave nature of a photon is taken for the present pure coherence optics-based analysis. By the definition of group velocity-based information, the violation of the cause-effect relation in the quantum eraser should be related to the ensemble coherence of measured photons, but not to individual photon’s coherence, where the ensemble coherence is dramatically decreased as the spectral bandwidth of the photon ensemble increases. © 2024 SPIE. All rights reserved.

关键词： Photons

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