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Journal of Network Intelligence 2023年第4期8卷 1150-1163页

作者： Yang, Li Tan, Liang Zhang, Fuquan Department of information engineering Sichuan Vocational and Technical College of Communications Chengdu611130 China Thai-Chinese International College Assumption University of Thailand Bangkok100027 Thailand College of Computer and Control Engineering Minjiang University Fuzhou350108 China

Track planning technology, as a key technology for UAV mission planning, has always been a major research hotspot in the field of UAVs. In real-life applications, due to the complex and constrained UAV flight environment, how to establish an accurate environment model and how to select an efficient planning algorithm become the elements to solve the trajectory planning problem. In this work, after studying existing trajectory planning algorithms, a method using a combination of improved A* algorithm and differential evolution algorithm is proposed for 3D trajectory planning in complex environments. Firstly, the mathematical modelling of the UAV track planning related problems, including the UAV model, manoeuvrability constraints and equivalent digital maps, is carried out. Secondly, an improved sparse A* algorithm is proposed for solving the winding path problem of the sparse A* algorithm, which reduces the winding path of the trajectory by extracting path feature points and quadratic optimisation. Finally, the improved sparse A* algorithm is combined with the differential evolutionary algorithm and applied to the UAV real-time trajectory planning, where the improved sparse A* algorithm is used for the UAV global trajectory planning and the differential evolutionary algorithm is applied to the emergent threat avoidance, i.e. local trajectory planning. The results of MATLAB simulations show that the proposed method can solve the patharound problem of the sparse A* algorithm and can be flexible in complex environments to achieve dynamic trajectory planning, thus effectively guaranteeing the flight safety of UAVs in practical environments. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Optimizing Maximum Profit for Electric Vehicle Users in a Bipolar DC Distribution Grid with Probabilistic EV Load Demand and V2G Integration 12

Optimizing Maximum Profit for Electric Vehicle Users in a Bi...

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12th International Electrical engineering Congress, iEECON 2024

作者： Sakulphaisan, Guntinan Son-In, Sirirat Junhuathon, Nitikorn Pathumwan Institute Of Technology Industrial Technology And Innovation Management Faculty Of Science And Technology Bangkok Thailand Bansomdejchaopraya Rajabhat University Electrical Engineering And Automation Control Systems Faculty Of Engineering And Industrial Technology Bangkok Thailand Rajamangala University Of Technology Thanyaburi Faculty Of Engineering Department Of Electrical Engineering Pathumthani Thailand

ISBN: (纸本)9798350383591

This article delves into the synergistic potential of integrating Bipolar DC distribution grids, Probabilistic EV load demand forecasting, and Vehicle-to-Grid (V2G) technology. It offers a blueprint for electric vehicle (EV) users to smartly improve their financial results. Through the amalgamation of these components, individuals who possess electric vehicles have the potential to mitigate their energy expenditures while concurrently generating additional income. The study reveals that the integration of EV into the distribution system can lead to a substantial increase in daily energy loss, reaching up to 25.91%. However, when V2G technology is employed within the distribution grid, it has the remarkable capability to mitigate this increase in daily energy loss significantly, achieving a reduction of up to 64.12%. © 2024 IEEE.

关键词： Integration

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Efficient Prompting for LLM-Based Generative Internet of Things

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IEEE Internet of Things Journal 2025年第1期12卷 778-791页

作者： Xiao, Bin Kantarci, Burak Kang, Jiawen Niyato, Dusit Guizani, Mohsen University of Ottawa School of Electrical Engineering and Computer Science OttawaONK1N 6N5 Canada Guangdong University of Technology School of Automation Guangzhou510006 China Nanyang Technological University School of Computer Science and Engineering Jurong West Singapore Singapore Mohamed bin Zayed University of Artificial Intelligence Machine Learning Department Abu Dhabi United Arab Emirates

Large language models (LLMs) have demonstrated remarkable capacities on various tasks, and integrating the capacities of LLMs into the Internet of Things (IoT) applications has drawn much research attention recently. Due to security concerns, many institutions avoid accessing state-of-the-art commercial LLM services, requiring the deployment and utilization of open-source LLMs in a local network setting. However, open-source LLMs usually have more limitations regarding their performance, such as their arithmetic calculation and reasoning capacities, and practical systems of applying LLMs to IoT have yet to be well-explored. Therefore, we propose an LLM-based Generative IoT (GIoT) system deployed in the local network setting in this study. To alleviate the limitations of LLMs and provide service with competitive performance, we apply prompt engineering methods to enhance the capacities of the open-source LLMs, design a Prompt Management Module and a Postprocessing Module to manage the tailored prompts for different tasks and process the results generated by the LLMs. To demonstrate the effectiveness of the proposed system, we discuss a challenging table question answering (Table-QA) task as a case study of the proposed system, as tabular data is usually more challenging than plaintext because of their complex structures, heterogeneous data types and sometimes huge sizes. We conduct comprehensive experiments on the two popular Table-QA data sets, and the results show that our proposal can achieve competitive performance compared with state-of-the-art LLMs, demonstrating that the proposed LLM-based GIoT system can provide competitive performance with tailored prompting methods and is easily extensible to new tasks without training. © 2014 IEEE.

关键词： Generative adversarial networks

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An adaptive observer-based SOC estimation method for lithium-ion batteries against sensor measurement uncertainties 38

An adaptive observer-based SOC estimation method for lithium...

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

作者： Xu, Xiaoyi Luo, Hao Chow, Mo-Yuen Huang, Cong-Sheng Jiang, Yuchen Harbin Institute of Technology Department of Control Science and Engineering Harbin China North Carolina State University Department of Electrical and Computer Engineering RaleighNC United States

ISBN: (纸本)9798350303636

Accurate estimation of battery state of charge (SOC) is critical for efficient and safe battery applications. The measurement uncertainties of sensors, including measurement noises and sensor bias will affect the estimation accuracy inevitably. Therefore, quantifying the relationship between the sensor measurement uncertainties and the SOC estimation errors and seeking better SOC estimation methods has been a hot topic of research. In this paper, model errors and SOC estimation errors under measurement noise and sensor bias are derived. The resulting analyses can be used to assess the robustness of the SOC estimates. In addition, an adaptive observer-based SOC estimation method for lithium-ion batteries is proposed to cope with the measurement uncertainty. Simulation experiments demonstrate the effectiveness of the proposed method. © 2023 IEEE.

关键词： adaptive observer lithium-ion battery sensor bias sensor noises State-of-Charge (SOC) estimation

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The Immense Impact of Reverse Edges on Large Hierarchical Networks

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engineering 2024年第5期36卷 240-249页

作者： Haosen Cao Bin-Bin Hu Xiaoyu Mo Duxin Chen Jianxi Gao Ye Yuan Guanrong Chen Tamás Vicsek Xiaohong Guan Hai-Tao Zhang MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Intelligent Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Jiangsu Key Laboratory of Networked Collective Intelligence School of MathematicsSoutheast UniversityNanjing 210096China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA Department of Electronic Engineering City University of Hong KongHong Kong 999077China Department of Biological Physics Eötvös UniversityBudapest 1117Hungary MOE Key Laboratory for Intelligent Networks and Network Security School of Automation Science and EngineeringXi’an Jiaotong UniversityXi’an 710049China

Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so *** structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes,such as lagging birds’howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social *** study reveals that,for most large-scale real hierarchical networks,the majority of the reverse edges do not affect the synchronization process of the entire network;the synchronization process is influenced only by a small part of these reverse edges along specific *** surprisingly,a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%.The effect of such edges depends not on the network size but only on the average in-degree of the involved *** overwhelming majority of active reverse edges turn out to have some kind of“bunching”effect on the information flows of hierarchical networks,which slows down synchronization *** finding refines the current understanding of the role of reverse edges in many natural,social,and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these *** study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

关键词： Synchronizability Large hierarchical networks Reverse edges Information flows Complex networks

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Experimental study of hardness and corrosion properties of AZ91 magnesium alloy after electroless nano composite coating

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Materials Today: Proceedings 2022年 62卷 2926-2929页

作者： Sayyad, Dr Farook Sayyad, Dr Shabnam Ansari, Dr. Md. Imran Mechanical Engineering Department. Dr. D. Y. Patil School of Engineering Pune India Associate Professor Computer Engineering Department. AISSMS College of Engineering Pune India Robotics & Automation Department. Bharati Vidyappeth Pune India

Magnesium and its numerous alloys have excellent rigidity, low density, and electromagnetic protection properties, making them ideal for usage in numerous industrial components. Magnesium alloys have a significant disadvantage in terms of corrosion and hardness, which restricts their practical uses. One of the most recent ways for improving the corrosion and hardness of Mg alloys is electro-less nano-coating. The experimental research of corrosion and hardness of AZ91 Mg Alloy due to nano composite coating procedures of ENi-B-TiO2is examined in this study. It would be useful to conduct a thorough examination, including experimental analysis, into the possibilities of increasing the deposit efficiency and characteristics of composite deposits by designing a suitable bath composition and operating circumstances. It has been discovered that as the concentration of titania particles increases, so does the surface hardness of coatings. The optimal concentration of second-phase (titania) particles and bath agitation are also examined in order to get superior surface properties following nano coating. Following this nano coating, the materials may be employed for a variety of materials engineering applications. © 2022

关键词： Surface properties

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Velocity-Free Adaptive Neural-Fuzzy Predefined-Time Attitude control for Spacecraft

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IEEE Transactions on Aerospace and Electronic Systems 2025年第3期61卷 6354-6372页

作者： Liu, Kang Wang, Yu Li, Yu Zhang, Yu Wen, Chih-Yung Hong Kong Polytechnic University Department of Aeronautical and Aviation Engineering Hong Kong Hong Kong Hohai University College of Artificial Intelligence and Automation Changzhou213000 China University of Sheffield Department of Computer Science SheffieldS10 2TN United Kingdom

The high-performance control of the spacecraft attitude is significant for successfully executing diverse tasks. To realize this goal, a velocity-free adaptive neural-fuzzy predefined-time attitude controller is presented for the spacecraft with uncertain inertia, exogenous disturbances, and input saturation. Firstly, an improved predefined-time stable system is established, featuring an adjustable convergence time to enhance the flexibility of the controller design. Utilizing the robust approximation ability of the neural-fuzzy network, a state observer and a nonsingular sliding mode controller are developed to achieve accurate state measurements, improve strong robustness, and eliminate singularity issues. Subsequently, a modified anti-saturation method is designed via the Gaussian function and auxiliary compensation system to resolve the input saturation problem. Based on the Lyapunov theorem, the predefined-time stability of the whole system is confirmed. Finally, through comparative simulations and numerical analysis, it can be concluded that: 1) the system state converges within a predefined time related to only a single parameter, and the actual convergence time is adjustable;and 2) compared to existing control schemes, the proposed control scheme demonstrates superior anti-disturbance ability, avoids potential singularities, achieves faster convergence, and eliminates input saturation. © 1965-2011 IEEE.

关键词： Attitude control

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Study of the Relationship Between Temperature and Efficiency in Photovoltaic Systems

Renewable Energy and Power Quality Journal

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Renewable Energy and Power Quality Journal 2023年 21卷 560-564页

作者： Salazar, Andres O. Fonseca, Diego A. de M. Ribeiro, Caio M. L. Xavier, Vinicius M. Lopes, Gabriela M. S. Maciel, Kalynne G. da S. Department Computer Engineering and Automation Federal University of Rio Grande do Norte Natal Brazil School of Science and Technology Federal University of Rio Grande do Norte Natal Brazil Department of Electrical Engineering Federal University of Rio Grande do Norte Natal Brazil

Photovoltaic solar energy has been growing due to the demand for diversification of the global energy grid and the concern about replacing fossil fuels with renewable energy. There is a great potential for this form of energy generation due to high levels of solar irradiation. However, the efficiency of commercial panels is still low and is negatively affected by temperature rises on the panel. To mitigate this effect, several cooling techniques have been previously proposed. This work focuses on studying the relation between the temperature of solar panels and the energy produced by them. It also uses thermoelectric cells acting as coolers for the system bringing down the panel temperature to increase its efficiency. The main goal is to build a plant consisting of a solar panel attached to one or more thermoelectric cells and connected to a data acquisition system. This setup will be used to evaluate the improvement in performance obtained through this strategy. The experiments will be carried out at the Petroleum Measurement Evaluation Laboratory-LAMP, and the results will be compared with mathematical and simulation models present in the literature. © 2023, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： Fossil fuels

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Fall Detection using NAO Robot Pose Estimation in RoboCup SPL Matches 9

Fall Detection using NAO Robot Pose Estimation in RoboCup SP...

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9th Italian Workshop on Artificial Intelligence and Robotics, AIRO 2022

作者： Zampino, Cristian Biancospino, Flavio Brienza, Michele Laus, Francesco Di Stefano, Gianluca Romano, Rocchina Pennisi, Andrea Suriani, Vincenzo Bloisi, Domenico Daniele Department of Mathematics Computer Science and Economics University of Basilicata Potenza Italy Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

RoboCup is an International robotics initiative whose aim is to promote robotics and AI research. RoboCup's long-term goal is to create a fully autonomous humanoid robot team capable of competing and winning a soccer game against the human World champion team, in compliance with the official rules of FIFA, by 2050. In this paper, we describe a two-step method for action recognition. In the first step, we extract the pose of the robots using a pose detector trained on a novel dataset for pose estimation called UNIBAS NAO Pose Dataset, which is a contribution of this work. In the second step, a Spatial-Temporal Graph Convolutional Network is used for modeling the gameplay, with particular regard to fall-down detection. Experimental results show the effectiveness of our approach in detecting falls for humanoid robots. © 2022 Copyright for this paper by its authors.

关键词： Sports

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Machine learning applied to property prediction of metal additive manufacturing products with textural features extraction

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International Journal of Advanced Manufacturing Technology 2024年第1-2期132卷 83-98页

作者： Chang, Lien-Kai Chen, Ri-Sheng Tsai, Mi-Ching Lee, Rong-Mao Lin, Ching-Chih Huang, Jhih-Cheng Chang, Tsung-Wei Horng, Ming-Huwi Electrical Motor Technology Research Center National Cheng Kung University Tainan700 Taiwan Department of Mechanical Engineering National Cheng Kung University Tainan700 Taiwan Department of Intelligent Automation Engineering National Chin-Yi University of Technology Taichung411 Taiwan Department of Computer Science and Information Engineering National Cheng Kung University Tainan700 Taiwan Academy of Innovative Semiconductor and Sustainable Manufacturing National Cheng Kung University Tainan700 Taiwan

Laser powder bed fusion (LPBF) is one of the common metal additive manufacturing technologies, which has been increasingly applied across various industries, including healthcare, manufacturing, and aerospace, owing to its advantages in customization and faster prototyping. However, acquiring accurate product properties necessitates repetitive and time-consuming measurements, which risk damaging the product. Thus, there is a pressing need to develop an automated method for predicting product properties. In this study, to forecast these properties, we documented details related to metal additive manufacturing products, encompassing both the process parameters and textural features. These features were extracted from layer-by-layer images using the gray-level co-occurrence matrix (GLCM). Subsequently, we employed machine learning (ML) models, such as support vector regression (SVR), XGBoost, and LightGBM, to predict product properties and compare their performance. The experimental results reveal stronger correlations between process parameters and texture features of three-dimensional co-occurrence matrices of the product images, compared to two-dimensional ones. Additionally, the models exhibit high predictive accuracy, especially XGBoost, and LightGBM, with R2 scores approaching 0.9 for all properties. These findings highlight the superiority and feasibility of the proposed approach. Moreover, this proposed approach holds promise in accurately predicting diverse product properties, meeting the demands of multiple application contexts. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： 3D printing

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