Students often struggle with basic programming tasks after their first programming course. Adaptive tutoring systems can support students’ practice by generating tasks, providing feedback, and evaluating students’ p...
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Predicting traffic flow is important for improving safety and efficiency in IoV systems. Techniques such as traditional models, deep learning, hybrid approaches, graph-based methods, optimization, edge computing, and ...
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This paper explores the application of game theoretic approaches to load balancing across edge nodes within distributed computingsystems. Focusing on a non-cooperative game model, we aim to enhance system efficiency ...
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This research focuses on the evolving field of hybrid computing, which combines traditional computing architectures with advanced computational paradigms like quantum computing, neuromorphic computing, or cloud-edge c...
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ISBN:
(纸本)9798331515911
This research focuses on the evolving field of hybrid computing, which combines traditional computing architectures with advanced computational paradigms like quantum computing, neuromorphic computing, or cloud-edge computing. Hybrid computing involves integrating multiple computing models or systems to leverage their complementary strengths. For example, combining classical computing with quantum or neuromorphic approaches to solve complex problems more efficiently. The goal is to enhance computational power, increase efficiency, and tackle problems that are difficult for traditional computing models alone. Emerging technologies, including quantum computing, machine learning, and AI, are driving the need for hybrid systems. These technologies enable the handling of more complex computations that classical systems struggle with, such as optimization, simulation, and cryptography. The integration of cloud and edge computing is also central to hybrid computing, enabling decentralized computation and processing closer to data sources. Combining different computing paradigms requires seamless communication and integration between systems, which is a significant technical challenge. Main challenging areas are scalability, software and hardware compatibility, energy efficiency and security. There is a need for standard protocols and frameworks to enable smoother integration of various computingsystems. New algorithms that can take advantage of hybrid architectures need to be developed. Depend on applications significant advancements in hardware, including quantum processors, neuromorphic chips, and integrated hybrid processors required. The rise of hybrid computing will also bring ethical concerns, such as the impact on jobs, privacy, and inequality in access to advanced technologies. The study concludes by emphasizing the transformative potential of hybrid computing while acknowledging the technical, economic, and societal hurdles that need to be overcome. It highlights
With the development of artificial intelligence (AI) and machine learning (ML) technologies, CAD systems have evolved from simple drawing tools to complex design and analysis platforms. The system is now able to lever...
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Visible Light Communication (VLC) is a growing trend in future communication technology. In VLC systems, Free Space Optics (FSO) is used as the transmission medium. These VLC FSO systems offer solutions to congestion ...
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This study proposes an enhanced wireless 6G communications architecture for context-aware systems to enable adaptive u-learning environments for music education. A centralised data processing centre at edge nodes anal...
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This study proposes an enhanced wireless 6G communications architecture for context-aware systems to enable adaptive u-learning environments for music education. A centralised data processing centre at edge nodes analyses user behaviours and network conditions to enable coordinated control across the core network, transport network, and radio access network, which fully exploits the feature of edge intelligence. The architecture supports self-consistent capabilities within each network function entity and flexible multi-level couplings between entities based on real-time user needs. For radio resource management, an AI-driven intelligentcontroller is introduced to enable intelligent and automated management of wireless resources. Experiments compared learning effectiveness between groups with and without the proposed enhanced 6G context-aware capabilities in an adaptive u-learning music learning environment. Results demonstrated significantly improved task completion times and learning accuracy with the 6G-enhanced context-aware system in adaptive u-learning environments for music education via edge intelligence.
In recent years, affine formation control in multi-agent systems has garnered significant attention as an advanced strategy. This paper investigates the maneuver control problem of leader-follower multi-agent systems ...
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The complexity of current optimization methods for HVAC systems is increasing, resulting in relatively lower computational efficiency, particularly in more complex systems. This difficulty makes real-time optimization...
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The complexity of current optimization methods for HVAC systems is increasing, resulting in relatively lower computational efficiency, particularly in more complex systems. This difficulty makes real-time optimization and control challenging in practice. Therefore, there is an urgent need to simultaneously improve both system energy efficiency and computational efficiency to enhance system robustness. Present optimization methods predominantly emphasize enhancing system energy efficiency, often overlooking computational efficiency. Consequently, these methods become infeasible or unstable when implemented in practical systems. In our research, a multi-agent-based collaborative optimization method is proposed to solve the global optimization problem of complex HVAC systems. Under the multi-agent framework, the global optimization problem is decomposed into multiple sub-optimization problems considering the interaction characteristics among components, thus reducing the complexity of the global optimization problem in HVAC systems. The proposed AH-AFSA algorithm supports the solution of optimization problems containing hybrid decision variables (continuous and discrete variables) and can directly search for optimal discrete variables in the binary space. This feature is suitable for searching the optimal ON/OFF sequence and setpoints simultaneously during the global optimization process. The results demonstrate that the proposed method can save 18.9 % of electricity consumption with an average computing time of 12.2 s for each operating condition, saving about 54 % of the time cost compared to centralized methods. The methodology used in our research holds significant theoretical and practical value for enhancing the computational efficiency and productivity of optimization methods in complex HVAC systems.
作者：
Soma, Arun KumarPark University
Department of Information Systems & Business Analytics 8700 NW River Park Dr ParkvilleMO64152 United States
The Aether sensor network is a system of sensors that are used to monitor the environment and detect changes in the atmosphere. It is a system of sensors that are used to monitor the environment and detect changes in ...
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