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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Yang, Bowen Yu, Yao Li, Jianqi Xu, Tongze Deng, Der-Jiunn Chin, Hui-Hsin Li, Yonghui Northeastern University School of Computer Science and Engineering Shenyang China National Changhua University of Education Department of Computer Science and Information Engineering China Overseas Chinese University Department of Information Technology China University of Sydney School of Electrical and Information Engineering Sydney Australia Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China

ISBN: (纸本)9798350345384

The recently proposed concept of Age of Information (AoI) measures the freshness of the sensor data sampled by remote Internet of Things (IoT) devices, which is an important indicator for the smart city. Unmanned Aerial Vehicles (UAV) communication can significantly reduce the sensor data's AoI due to its high maneuverability. However, the UAV's high energy consumption remains a challenging issue. In this paper, we propose a Reconfigurable Intelligent Surface (RIS) assisted UAV data collection framework to minimize UAV energy consumption. By adjusting the phase of the RIS, the UAV can collect more sensor data simultaneously. A RIS phase alignment method is introduced to reduce the complexity of RIS phase solving. The IoT devices are clustered to discretize UAV trajectory. The optimal clustering scheme to maximize the AoI suppression efficiency is found by adjusting the RIS phase alignment points continuously. Therefore, the AoI requirement can be achieved with a shorter UAV flight distance. Finally, the UAV trajectory is solved by the Deep Q network (DQN) algorithm to guarantee the long-term AoI requirement. Simulation results validate the effectiveness of the proposed method. © 2023 IEEE.

关键词： Maneuverability

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Adaptive Deep Reinforcement Learning-based Resource Management for Complex Decision Making in Industry Internet of Things Applications 4

Adaptive Deep Reinforcement Learning-based Resource Manageme...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Karne, Niharika Ramagundam, Chandra Shekar Patnala, Ranga Rao Ramagundam, Shashishekhar Tadiboina, Sai Nitisha Sr. Principal Systems Engineer in C&RS Platform Services University of South Alabama Department of Electrical Engineering Masters in Electrical Engineering Amtrak DT United States Software Engineer in Cloud Platform Services City of Philadelphia Pennsylvania United States Vice President Neuberger Berman United States Software Development & Engineering in Content Discovery & AI Troy University Department of Computer Science Masters in Computer Science AL Comcast Montgomery Campus United States Department of Information Technology GEICO - Chevy Chase Chevy ChaseMD20815 United States

An independent industrial system may transform into a connected network through the assistance of Industrial Internet of Things (IIoT). The deployed sensors in the IIoT maintain surveillance of the industrial machinery and equipment. As a result, safety and reliability emerge as the primary concerns in IIoT. This presents a variety of well-known and increasing issues related to the industrial system. The IIoT devices are exposed to a wide range of malware, threats, and assaults. To prevent the IIoT devices from malware effects, effective protection plans must be implemented. But adequate security mechanisms are not be incorporated in IIoT devices with limited resources. It is essential to ensure the accuracy and dependability of information gathered by IIoT devices. Decisions taken with incomplete or inaccurate data might be devastating. To overcome these difficulties deep learning with reinforcement learning for complex decision-making in industry applications is developed in this research work. In this developed model, an Adaptive Deep Reinforcement learning (ADRL)-based resource management is performed to reduce the operation cost associated with IIoT deployments. Energy efficiency is essential in IIoT ecosystem, particularly for the devices that run on batteries. Through dynamic resource allocation based on workload needs and energy limits, ADRL-based resource management optimizes the usage of energy. The reliability of the designed model is enhanced by fine-tuning the parameters from DRL using the Ship Rescue Optimization (SRO) algorithm. Thus, ADRL-based resource management systems make real-time decisions based on current environmental conditions and system requirements. This helps the IIoT systems to react quickly to change demands and optimize resource allocation. Finally, the experimental analysis is performed to find the success rate of the developed resource management system via various metrics. Throughout the validation, the statistical analysis of the

关键词： Deep reinforcement learning

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The Positive Impact of Artificial Intelligence on Education 1

The Positive Impact of Artificial Intelligence on Education

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1st International Conference on Global Aeronautical engineering and Satellite Technology, GAST 2024

作者： Gourari, Abdelali El Raoufi, Mustapha Skouri, Mohammed Ben Braim, Abdelghani Ait Ezzini, Mustapha Lahsen, Yassine Ait Achki, Samira Cadi Ayyad University Laboratory of Physics High Energy and Astrophysics Faculty of Science Semlalia Marrakech Morocco Cadi Ayyad University Laboratory of Materials Energy and Environment Faculty of Sciences Semlalia Marrakech Morocco Cadi Ayyad University Faculty of Sciences Semlalia Fluid Mechanics and Energetic Laboratory Marrakesh Morocco Cadi Ayyad University Laboratory of Computer Systems Engineering Faculty of Sciences Semlalia Marrakech Morocco Fstg Computer Science Department Marrakech Morocco

ISBN: (纸本)9798350371598

Those interested in artificial intelligence technologies, especially supervised and unsupervised learning in education, know they need considerable data for well-modeled training and high-quality accuracy. However, data access is not easy as it drains time and effort. So, in this paper, we will talk about the impact of artificial intelligence on education in general and how it will affect teachers' role in the future. We will also create an algorithm that will integrate several algorithms sequentially that will add value to this paper and contribute significantly to solving most of the problems that exist in education. Since the reinforcement learning (RL) algorithm is based on trial and error during training, it stores all the good events done in a considerable buffer. Once the RL model is ready, we will collect enough data to be used to train one of the algorithms mentioned earlier (supervised learning and unsupervised learning). © 2024 IEEE.

关键词： Reinforcement learning

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Linear Active Disturbance Rejection Control with a Fractional-Order Integral Action

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computers, Materials & Continua 2022年第11期73卷 3057-3079页

作者： Maâmar Bettayeb Rachid Mansouri Ubaid M.Al-Saggaf Abdulrahman U.Alsaggaf Mohammed Moinuddin Electrical Engineering Department University of SharjahUnited Arab Emirates L2CSP Laboratory University Mouloud Mammeri of Tizi-OuzouAlgeria Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddahSaudi Arabia Electricaland Computer Engineering Department King Abdulaziz UniversityJeddahSaudi Arabia

Linear active disturbance rejection control(LADRC)is a powerful control structure thanks to its performance in uncertainties,internal and external disturbances estimation and *** extended state observer(ESO)based controller is the key to the LADRC *** this article,the LADRC scheme combined with a fractional-order integral action(FOILADRC)is proposed to improve the robustness of the standard *** the robust closed-loop Bode’s ideal transfer function(BITF),an appropriate pole placement method is proposed to design the set-point tracking controller of the FOI-LADRC *** simulations and experimental results on a cart-pendulum system will illustrate the effectiveness of the proposed FOI-LADRC scheme for the disturbance rejection,the set-point tracking and the improved *** illustrate the LADRC control schemes and to verify the performance of the proposed FOI-LADRC,compared to the standard LADRC and IOI-LADRC structures,two tests will be carried ***,simulation tests on an academic example will be presented to show the effect of the different parameters of the control law on the performance of the closed-loop ***,these three control structures are implemented on an experimental test bench,the cart-pendulum system,to show their efficiency and to show the superiority of the proposed method compared to the two other structures.

关键词： Fractional calculus active disturbance rejection control pole placement cart-pendulum system robust control

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A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements 63

A Multi-Player Potential Game Approach for Sensor Network Lo...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Xu, Gehui Chen, Guanpu Fidan, Baris Hong, Yiguang Qi, Hongsheng Parisini, Thomas Johansson, Karl H. Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden University of Waterloo Department of Mechanical and Mechatronics Engineering WaterlooONN2L 3G1 Canada Tongji University Department of Control Science and Engineering shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China University of Chinese Academy of Sciences School of Mathematical Sciences Beijing China Aalborg University Department of Electronic Systems Denmark Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom University of Trieste Department of Engineering and Architecture Italy

ISBN: (纸本)9798350316339

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multiplayer non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies. © 2024 IEEE.

关键词： Nash equilibrium

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Capacity Maximization for V2I-VLC System with Angular Diversity Receiver 3

Capacity Maximization for V2I-VLC System with Angular Divers...

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3rd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2023

作者： Yahia, Selma Meraihi, Yassine Cherif, Amar Ramdane Eldeeb, Hossien B. Muhaidat, Sami Ho, Tu Dac University of m'Hamed Bougara Boumerdes List Laboratory Boumerdes35000 Algeria University of Versailles St-Quentin-en-Yvelines Lisv Laboratory Velizy78140 France Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates UiT Arctic University of Norway Faculty of Engineering Science and Technology Department of Electrical Engineering Norway

ISBN: (纸本)9798350333732

This paper investigates the effectiveness of angle diversity technology-based vehicle-to-infrastructure (V2I) visible light communication (VLC) system designed for a multi-lane road scenario. Cars use their headlights to transmit signals to a traffic light pole, which is equipped with an angle diversity receiver (ADR) comprising three photodetectors (PD) arranged in different directions to improve signal reception from various directions. We utilize an advanced ray tracing channel modeling approach and investigate the impact of the number of PDs and the elevation angles on the received power. Additionally, we conduct a comprehensive performance analysis in terms of capacity, considering different car positions along the road. The results demonstrate that the V2I-VLC system with ADR achieves a channel capacity of over 2.5 Mb/s at a transmission distance of 50 meters, highlighting its potential to enhance V2I-VLC connectivity. © 2023 IEEE.

关键词： Visible light communication

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The Goldilocks Framework: Towards Selecting the Optimal Approach to Conducting AI Projects 22

The Goldilocks Framework: Towards Selecting the Optimal Appr...

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1st International Conference on AI engineering - Software engineering for AI, CAIN 2022

作者： Dzhusupova, Rimman Bosch, Jan Olsson, Helena Holmstrom Control & Safety Systems McDermott Dept. of Electrical & Instrumentation The Hague Netherlands Chalmers University of Technology Department of Computer Science and Engineering Gothenburg Sweden Malmö University Department of Computer Science and Media Technology Malmö Sweden

ISBN: (纸本)9781450392754

Artificial intelligence is increasingly becoming important to businesses since many companies have realized the benefits of applying Machine Learning (ML) and Deep Learning (DL) into their operations. Nevertheless, ML/DL technologies' industrial development and deployment examples are still rare and generally confined within a small cluster of large international companies who are struggling to apply ML more broadly and deploy their use cases at a large scale. Meanwhile, current AI market has started offering various solutions and services. Thus, organizations must understand how to acquire AI technology based on their business strategy and available resources. This paper discusses the industrial experience of developing and deploying ML/DL use cases to support organizations in their transformation towards AI. We identify how various factors, like cost, schedule, and intellectual property, can be affected by the choice of approach towards ML/DL project development and deployment within large international engineering corporations. As a research result, we present a framework that covers the trade-offs between those various factors and can support engineering companies to choose the best approach based on their long-term business strategies and, therefore, would help to accomplish their ML/DL project deployment successfully. © 2022 ACM.

关键词： Commerce

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First wetland mapping at 10-m spatial resolution in South America using multi-source and multi-feature remote sensing data

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science China Earth sciences 2024年第10期67卷 3252-3269页

作者： Weiwei SUN Gang YANG Yuling HUANG Dehua MAO Ke HUANG Lin ZHU Xiangchao MENG Tian FENG Chao CHEN Yong GE Department of Geography and Spatial Information Techniques Ningbo UniversityNingbo 315211China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences&Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Natural Resources and Planning Bureau Shanghai Pudong New DistrictShanghai 200100China Key Laboratory of Wetland Ecology and Environment Northeast Institute of Geography and AgroecologyChinese Academy of SciencesChangchun 130102China Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China School of Geography Science and Geomatics Engineering Suzhou University of Science and TechnologySuzhou 215009China

Wetland degradation has been accelerating in recent years globally. Accurate information on the geographic distribution and categories of wetlands is essential for their conservation and management. Despite being the world′s fourth largest continent, South America has limited research on wetland mapping, and there is currently no available map that provides comprehensive information on wetland distribution and categories in the region. To address this issue, we used Sentinel-1, Sentinel-2 and SRTM data, developed a sample collection method and a wetland mapping method with a collection of multi-source features such as optical features, polarization features and shape features for South American wetlands. We produced a 10-m resolution wetland map based on the Google Earth Engine(GEE) platform. Our Level-1 wetland cover map accurately captured six wetland sub-categories with an overall accuracy of 96.24% and a kappa coefficient of 0.8649, while our Level-2 water cover map included five sub-categories with an overall accuracy of 97.23% and a kappa coefficient of0.9368. The results show that the total area of existing wetlands in South America is approximately 1,737,000 km~2, which is6.8% of the total land area. Among the ten wetland categories, shallow sea had the largest area(960,527.4 km~2), while aquaculture ponds had the smallest area 1513.6 km~2. Swamp had the second largest area(306,240.1 km~2). Brazil, Argentina,Venezuela, Bolivia, and Colombia were found to have the largest wetland areas, with Brazil and Colombia having the most diverse wetland categories. This product can serve as baseline data for subsequent monitoring, management, and conservation of South American wetlands.

关键词： Wetland mapping Google Earth Engine Sentinel imagery South America

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Texture Analysis of Breast US Images Using Morphological Transforms, Hausdorff Dimension and Bagging Ensemble Method 32

Texture Analysis of Breast US Images Using Morphological Tra...

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32nd Mediterranean Conference on Control and Automation, MED 2024

作者： Tabacaru, Gigi Moldovanu, Simona Barbu, Marian 'Dunarea de Jos' University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Automatic Control and Electrical Engineering Romania 'Dunarea de Jos' University of Galati Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Romania Dunarea de Jos University of Galati The Modelling & Simulation Laboratory Galati800008 Romania

ISBN: (纸本)9798350395440

In this work, an attempt is made for the first time to use the measurement pattern generated by morphological transformation quantified by Hausdorff fractal dimension (HFD) and classified with ensemble learning based on bagging. The proposed work uses three morphological transformations for image preprocessing: hit-and-miss transform (HMT), white (WHT), and black top-hat (BHT). The pattern texture of US breast images is described by extracting the HFD from the regions of interest (ROI) after the ultrasound (US) images have been preprocessed. The main objective of this study was achieved by comparatively analyzing the classification performance of features using the Random Forest (RF), Extra Trees (ET) classifier, and bagging ensemble method based on XGBoot classifier. In presented study, the XGBoost classifier and BHT image processing method give an accuracy of 89.8% in a binary classification, benign versus malignant breast cancer. © 2024 IEEE.

关键词： Fractal dimension

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Energy Efficient Probabilistic Semantic Communication over SAGIN

Energy Efficient Probabilistic Semantic Communication over S...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhao, Zhouxiang Yang, Zhaohui Chen, Mingzhe Gan, Xu Huang, Chongwen Xu, Wei Zhang, Zhaoyang Zhejiang University College of Information Science and Electronic Engineering Hangzhou China Hangzhou China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL33146 United States Southeast University National Mobile Communications Research Laboratory Nanjing China

ISBN: (纸本)9798350351255

In this paper, the energy efficiency maximization problem in space-air-ground integrated network (SAGIN)-enabled probabilistic semantic communication (PSC) is investigated. In the considered model, a satellite needs to transmit data to multiple ground terminals (GTs) via an unmanned aerial vehicle (UAV) acting as a relay. During transmission, the satellite and the UAV can use PSC technique to compress the transmitted data, while the GTs can automatically recover the original data. In the considered PSC system, shared probability graphs serve as a common knowledge base among the transceivers, allowing for resource-saving communication at the expense of increased computation resource. Therefore, it is important to study the trade-off between communication and computation to achieve optimal energy efficiency. The joint communication and computation problem is formulated as an optimization problem aiming to minimize the total communication and computation energy consumption of the network under latency, semantic compression ratio, and UAV location constraints. To solve this non-convex problem, we propose an alternating algorithm. Numerical results show the effectiveness of the proposed algorithm. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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