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IEEE Conference on Global Communications (GLOBECOM)

作者： Guangyuan Liu Hongyang Du Dusit Niyato Jiawen Kang Zehui Xiong Boon Hee Soong Energy Research Institute@NTU Interdisciplinary Graduate Program School of Computer Science and Engineering Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Electronic Engineering Nanyang Technological University Singapore

The popularity of Metaverse as an entertainment, social, and work platform has led to a great need for seamless avatar integration in the virtual world. In Metaverse, avatars must be updated and rendered to reflect users' behaviour. Achieving real-time synchronization between the virtual bilocation and the user is complex, placing high demands on the Metaverse Service Provider (MSP)'s rendering resource allocation scheme. To tackle this issue, we propose a semantic communication framework that leverages contest theory to model the interactions between users and MSPs and determine optimal resource allocation for each user. To reduce the consumption of network resources in wireless transmission, we use the semantic communication technique to reduce the amount of data to be transmitted. Under our simulation settings, the encoded semantic data only contains 51 bytes of skeleton coordinates instead of the image size of 8.243 megabytes. Moreover, we implement Deep Q-Network to optimize reward settings for maximum performance and efficient resource allocation. With the optimal reward setting, users are incentivized to select their respective suitable uploading frequency, reducing down-sampling loss due to rendering resource constraints by 66.076% compared with the traditional average distribution method. The framework provides a novel solution to resource allocation for avatar association in VR environments, ensuring a smooth and immersive experience for all users.

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AImotion Challenge Results: a Framework for AirSim Autonomous Vehicles and Motion Replication

AImotion Challenge Results: a Framework for AirSim Autonomou...

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Computers and automation (CompAuto), International Conference on

作者： Bruno J. Souza Lucas C. de Assis Dominik Rößle Roberto Z. Freire Daniel Cremers Torsten Schön Munir Georges Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Curitiba Brazil Control and Automation Eng. Department Pontifical Catholic University of Parana (PUCPR) Curitiba Brazil Technische Hochschule Ingolstadt AImotion Bavaria Institute Ingolstadt Germany Department of Informatics Technical University of Munich Munich Germany

ISBN: (纸本)9781665481953

The use of simulation environments is becoming more significant in the development of autonomous cars, as it allows for the simulation of high-risk situations while also being less expensive. In this paper, we presented a framework that allows the creation of an autonomous vehicle in the AirSim simulation environment and then transporting the simulated movements to the TurtleBot. To maintain the car in the correct direction, computer vision techniques such as object detection and lane detection were assumed. The vehicle's speed and steering are both determined by Proportional-Integral-Derivative (PID) controllers. A virtual personal assistant was developed employing natural language processing to allow the user to interact with the environment, providing movement instructions related to the vehicle's direction. Additionally, the conversion of the simulator movements for a robot was implemented to test the proposed system in a practical experiment. A comparison between the real position of the robot and the position of the vehicle in the simulated environment was considered in this study to evaluate the performance of the algorithms.

关键词： Computer vision Lane detection Robot kinematics Atmospheric modeling Computational modeling Virtual environments Natural language processing

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Formability of Third Generation Advanced High Strength Steel: Experimental and Theoretical Approach

SSRN

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SSRN 2024年

作者： Butuc, Marilena C. Vincze, Gabriela Santos, Rafael Pereira, António Santos, Abel D. Amaral, Rui L. Barlat, Frederic Centre for Mechanical Technology and Automation Department of Mechanical Engineering University of Aveiro Portugal LASI - Intelligent Systems Associate Laboratory Guimarães Portugal Graduate Program in Metallurgical Engineering Universidade Federal Fluminense Rio de Janeiro Volta Redonda Brazil Faculty of Engineering University of Porto R. Dr. Roberto Frias s/n Porto4200-465 Portugal INEGI – Institute of Science and Innovation in Mechanical and Industrial Engineering Portugal Graduate Institute of Ferrous Technology Pohang University of Science and Technology Korea Republic of

The aim of this paper is to evaluate, predict and analyze the plastic properties and formability of third-generation advanced high-strength steel (3genAHSS), with a specific focus on USS CR980XG3™️ AHSS as the reference material. Forming limit diagrams were determined, utilizing a range of experimental methods, including Nakajima tests, Erichsen tests, uniaxial tensile tests, and the Bulge test. A pivotal aspect is the application of an advanced sheet metal forming limit model based on Marciniak-Kuczynski analysis to simulate localized necking onset. Various constitutive equations, such as von Mises, Hill 48, and Yld2000-2d yield conditions, were used in simulation, alongside strain hardening laws like Swift and Voce. Comparative analyses were conducted to assess the accuracy of different models in predicting forming limits, with a particular emphasis on the influence of strain rate sensitivity. The study underscores the significant impact of the experimental method on forming limit values, highlighting that Yld2000-2d and the Swift hardening law with strain rate sensitivity provide optimal reproducibility of experimental data. Furthermore, the research emphasizes the necessity of considering strain rate sensitivity for accurate numerical simulations, stressing the effectiveness of the Marciniak-Kuczynski model when combined with proper constitutive equations in predicting forming limits for USS CR980XG3™️ AHSS. © 2024, The Authors. All rights reserved.

关键词： Strain hardening

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Semantic Communications for Artificial Intelligence Generated Content (AIGC) Toward Effective Content Creation

arXiv

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arXiv 2023年

作者： Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In Shen, Xuemin The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo Canada

Artificial Intelligence Generated Content (AIGC) Services have significant potential in digital content creation. The distinctive abilities of AIGC, such as content generation based on minimal input, hold huge potential, especially when integrating with semantic communication (SemCom). In this paper, a novel comprehensive conceptual model for the integration of AIGC and SemCom is developed. Particularly, a content generation level is introduced on top of the semantic level that provides a clear outline of how AIGC and SemCom interact with each other to produce meaningful and effective content. Moreover, a novel framework that employs AIGC technology is proposed as an encoder and decoder for semantic information, considering the joint optimization of semantic extraction and evaluation metrics tailored to AIGC services. The framework can adapt to different types of content generated, the required quality and the semantic information utilized. By employing a Deep Q Network (DQN), a case study is presented that provides useful insights into the feasibility of the optimization problem and its convergence characteristics. © 2023, CC BY.

关键词： Wireless networks

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Enabling AI-Generated Content (AIGC) Services in Wireless Edge Networks

arXiv

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arXiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Shen, Xuemin Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Artificial Intelligence-Generated Content (AIGC) refers to the use of AI to automate the information creation process while fulfilling the personalized requirements of users. However, due to the instability of AIGC models, e.g., the stochastic nature of diffusion models, the quality and accuracy of the generated content can vary significantly. In wireless edge networks, the transmission of incorrectly generated content may unnecessarily consume network resources. Thus, a dynamic AIGC service provider (ASP) selection scheme is required to enable users to connect to the most suited ASP, improving the users’ satisfaction and quality of generated content. In this article, we first review the AIGC techniques and their applications in wireless networks. We then present the AIGC-as-a-service (AaaS) concept and discuss the challenges in deploying AaaS at the edge networks. Yet, it is essential to have performance metrics to evaluate the accuracy of AIGC services. Thus, we introduce several image-based perceived quality evaluation metrics. Then, we propose a general and effective model to illustrate the relationship between computational resources and user-perceived quality evaluation metrics. To achieve efficient AaaS and maximize the quality of generated content in wireless edge networks, we propose a deep reinforcement learning-enabled algorithm for the optimal ASP selection. Simulation results show that the proposed algorithm can provide a higher quality of generated content to users and achieve fewer crashed tasks by comparing with four benchmarks, i.e., overloading-avoidance, random, round-robin policies, and the upper-bound schemes. © 2023, CC BY.

关键词： Reinforcement learning

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An ultra thin,bright,and sensitive interactive tactile display based on organic mechanoluminescence for dual-mode handwriting identification

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InfoMat 2024年第6期6卷 76-87页

作者： Tingting Hou Wenlang Li Haoyu Wang Yuantian Zheng Chaojie Chen Haoran Zhang Kai Chen Huilin Xie Xin Li Shaoshuai He Siwei Zhang Dengfeng Peng Cheng Yang Jacky W.Y.Lam Ben Zhong Tang Yunlong Zi Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong Kongthe People's Republic of China Department of Chemistry Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction and Institute for Advanced StudyThe Hong Kong University of Science and TechnologyHong Kongthe People's Republic of China Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhenthe People's Republic of China Thrust of Sustainable Energy and Environment The Hong Kong University of Science and Technology(Guangzhou)Guangzhouthe People's Republic of China The Hong Kong University of Science and Technology Interdisciplinary Program Office Hong Kong SARthe People's Republic of China Institute of Materials Research Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhenthe People's Republic of China School of Science and Engineering Shenzhen Institute of Aggregate Science and TechnologyThe Chinese University of Hong KongShenzhenthe People's Republic of China HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Shenzhenthe People's Republic of China Guangzhou HKUST Fok Ying Tung Research Institute Guangzhouthe People's Republic of China

Visible light-based human–machine interactive media is capable of transmitting electrical readouts to machines and providing intuitive feedback to users ***,many inorganic mechanoluminescent(ML)materials-based interactive media,typically ZnS-loaded phosphors(ZLPs),have been successfully ***,organic ML materials-based solutions were rarely exploited despite their huge merits of strong structural modification,abundant luminescence property,low cost,easy preparation,and so ***,we propose a novel interactive tactile display(ITD)based on organic ML materials(Cz-A6-dye)and triboelectric nanogenerator,with ultra-brightness(130%enhancement)and ultra-low threshold pressure(57%reduction)as compared to *** proposed ITD achieves the conversion of weak mechanical stimuli into visible light and electrical signals simultaneously,without extra power ***,the relationship between the luminous performance of organic ML materials and mechanical force is quantified,benefiting from the uniform ML layer *** by convolutional neural networks,the high-accuracy recognition(97.1%)for handwriting and identity of users is realized at the same ***,the ITD has great potential for intelligent wearable electronics and classified military applications.

关键词： interactive media organic mechanoluminescence triboelectric nanogenerators

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Accelerating the Artificial Bee Colony Algorithm by Hardware Parallel Implementations

Accelerating the Artificial Bee Colony Algorithm by Hardware...

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IEEE Latin American Symposium on Circuits and systems

作者： Daniel M. Mufioz Carlos H. Llanos Leandro dos S. Coelho Mauricio Ayala-Rincon Department of Mechanical Engineering Automation and Control Group/GRACO University of Brasilia Industrial and Systems Engineering Graduate Program LAS/PPGEPS Pontifical Catholic University of Parana Department of Computer Science and Mathematics University of Brasilia

ISBN: (纸本)9781467312073

Artificial bee colony (ABC) is an optimization algorithm inspired on the intelligent behavior of honey bee swarms. It is suitable to be applied when mathematical techniques are impractical or provide suboptimal solutions. As a population-based algorithm, the ABC suffers on large execution times specifically for embedded optimization problems with computational limitations. For that we propose a hardware parallel architecture of the opposition-based ABC algorithm (HPOABC) that facilitates the implementation in Field programmable Gate Arrays (FPGAs). Numerical simulations using four well-known benchmark problems demonstrate that the opposition-based approach allows the algorithm to improve its functionality, preserving the swarm diversity. Additionally, synthesis results point outs that the HPOABC architecture is effectively mapped in hardware and is suitable for embedded applications.

关键词： Global optimization Swarm intelligence Artificial bee colony FPGAs Floating-point arithmetic

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Experimental Analyses of the Combustion Instability Parameters in an Ammonia/Methane Fuelled Low Eddy Premix Burner for Gas Turbine Power Generation

SSRN

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SSRN 2022年

作者： Ilbas, Mustafa Kumuk, Osman Alabas, Bugrahan Department of Energy Systems Engineering Faculty of Technology Gazi University Teknikokullar Ankara Turkey Graduate School of Natural and Applied Science Gazi University Teknikokullar Ankara Turkey Unmanned Aerial Vehicle Technology and Operatorship Program Electronic and Automation Department Iskenderun Vocational School of Higher Education Iskenderun Technical University Hatay Turkey Department of Aeronautical Engineering Faculty of Aeronautics and Astronautics Erciyes University Kayseri Turkey

The main factor that directs scientists to work on alternative energy sources is the environmental damage caused by the waste gases generated by the use of fossil fuels. Since ammonia is a carbonless substance and contains hydrogen atoms per unit volume, it can be used as an alternative fuel. In this study, experimental analyzes were carried out to examine the combustion instability properties of methane-ammonia mixed fuels for a low eddy and premix burner. The aim of this study is to determine the effect of ammonia addition on combustion instability with different compelling acoustic frequencies, dynamic pressure amplitude and light emission intensity. In addition, the effects of ammonia addition to methane fuel gas on temperature and emissions were also investigated. Pure methane and three different methane-ammonia mixtures (by volume 100% CH4, 90% CH 4 - 10 NH3, 80% CH4 - 20% NH3 and 70% CH4-30 NH3) as fuel at 0.7 equivalents and with three different acoustics have been used for analyzes. The burner power was determined as 5 kW and the excitation frequencies of the combustion chamber were determined as 95, 160 and 300 Hz. In this study, as the ammonia addition increased, there was an increase in the flame instability values up to 30% NH3, especially at the 95 Hz frequency, where the highest distortion effect occurred. It was observed that there was no significant change in the combustion performance with the addition of ammonia to methane fuel. The predicted NOX emission levels were found to increase significantly due to nitrogen bound to ammonia. However, it is seen that the NOx emission levels in the exhaust measurements are not very high (Maximum 560 ppm). Therefore, it can be concluded that ammonia supplemented methane fuels have significant potential as an alternative fuel. © 2022, The Authors. All rights reserved.

关键词： Ammonia

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Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services

TechRxiv

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TechRxiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Huang, Huawei Mao, Shiwen School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore School of Software Engineering Sun Yat-Sen University Zhuhai China Department of Electrical and Computer Engineering Auburn University Auburn United States

As Metaverse emerges as the next-generation Internet paradigm, the ability to efficiently generate content is paramount. AI-Generated Content (AIGC) emerges as a key solution, yet the resource-intensive nature of large Generative AI (GAI) models presents challenges. To address this issue, we introduce an AIGC-as-a-Service (AaaS) architecture, which deploys AIGC models in wireless edge networks to ensure broad AIGC services accessibility for Metaverse users. Nonetheless, an important aspect of providing personalized user experiences requires carefully selecting AIGC Service Providers (ASPs) capable of effectively executing user tasks, which is complicated by environmental uncertainty and variability. Addressing this gap in current research, we introduce the AI-Generated Optimal Decision (AGOD) algorithm, a diffusion model-based approach for generating the optimal ASP selection decisions. Integrating AGOD with Deep Reinforcement Learning (DRL), we develop the Deep Diffusion Soft Actor-Critic (D2SAC) algorithm, enhancing the efficiency and effectiveness of ASP selection. Our comprehensive experiments demonstrate that D2SAC outperforms seven leading DRL algorithms. Furthermore, the proposed AGOD algorithm has the potential for extension to various optimization problems in wireless networks, positioning it as a promising approach for future research on AIGC-driven services. The implementation of our proposed method is available at: https://***/Lizonghang/AGOD. © 2023, CC BY.

关键词： Reinforcement learning

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Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services

arXiv

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arXiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Huang, Huawei Mao, Shiwen The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Software Engineering Sun Yat-Sen University Zhuhai China The Department of Electrical and Computer Engineering Auburn University Auburn United States

关键词： Reinforcement learning

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