To minimize the impact of new energy access on operational control of the distribution network, a coordinated control strategy for battery energy storage systems in a distribution network containing new energy is prop...
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Based on native-AI, the 6G wireless network has the ability to handle higher-level and time-sensitive tasks, which leads to difficulties in efficient resource allocation. In this paper, the deep reinforcement learning...
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ISBN:
(纸本)9798350333077
Based on native-AI, the 6G wireless network has the ability to handle higher-level and time-sensitive tasks, which leads to difficulties in efficient resource allocation. In this paper, the deep reinforcement learning is used to optimize system energy consumption and service quality by training joint allocation model of communication resources and computing resources. Firstly, a two-tier heterogeneous network of control base stations and data base stations is modeled considering computing power. Then, an intelligent resource allocation strategy is proposed that base stations coordinate communication and computing resources. Finally, DDPG, COMA, and MAPPO are conducted in a native-AI simulation platform for the task. The results show that native-AI architecture is benefit for auto-optimized network, multi-agent has better global control than single-agent, and MAPPO has superior trade-off in energy consumption and service quality. Compared with DDPG, MAPPO reduces average service delay and system energy consumption by 15.6% and 43.8% respectively.
The rapid development of the semiconductor industry represents a global challenge to meet the needs of Science, Technology, and Innovation in the 21st century. Advancements in semiconductors optimize computer technolo...
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ISBN:
(纸本)9798350311907
The rapid development of the semiconductor industry represents a global challenge to meet the needs of Science, Technology, and Innovation in the 21st century. Advancements in semiconductors optimize computer technology with innovations, among others, in nanotechnology, quantum technology, cognitive computing, Cyber-Physical Human Systems, Artificial Intelligence, and Robotics. The rapid development of semiconductors demands an imperative acceleration of training and the development of a highly skilled workforce. There is a gap between the knowledge and skills of students in the current education system and the fast development of new knowledge. To accelerate comprehensive training in the semiconductor workforce we must start at an early stage, this paper proposes that STEM education must be pedagogically grounded in the advances of cognitive neuroscience and learning. By understanding how the brain functions and how it learns, the process can be enhanced, the education of an Integral Engineer needs to be addressed by forming professionals equipped with adaptability, complex problem-solving, assertive communication, emotional intelligence, interdisciplinary teamwork skills, systems, analytical and critical thinking. This paper proposes curricular transformation processes, ethical formation, pedagogical foundations of interdisciplinary research laboratories, enhanced by an international network of remote laboratories, and a TRUE system of cultural transformation.
Millibots, miniature robotic platforms, have emerged as pivotal tools in various domains, ranging from medical interventions to environmental monitoring. However, their diminutive size presents formidable challenges i...
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The proceedings contain 40 papers. The topics discussed include: intelligent power source selection for solar energy optimization;human-centric considerations in deploying biometric modalities: a multi-modal approach ...
ISBN:
(纸本)9798350387902
The proceedings contain 40 papers. The topics discussed include: intelligent power source selection for solar energy optimization;human-centric considerations in deploying biometric modalities: a multi-modal approach for public services application;deepcai-v3: improved brain tumor classification from noisy brain MR images using convolutional autoencoder and inception-V3 architecture;analyzing the regularization technique in automatic speech recognition system using Sepedi-English code-switched data;a machine-learning based IoT smart home system to detect and reduce urban insecurity in Uganda: a case of Kampala metropolitan area;optimized coordinated resource allocation and power control for wireless communication networks;a parking assistant system for residential applications;and deep learning-based colorectal cancer image segmentation and classification: a concise bibliometric analysis.
This paper presents an innovative prototype device designed to improve vehicular traffic control by integrating fuzzy logic and computer vision. The system uses image processing techniques such as the YOLO (You Only L...
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In light of enriching data format and computing methodology, the distributed controls system's expansion its functionality to stream computing makes it possible for steam data well expressed play an important role...
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As mobile augmented reality (MAR) becomes integral to interacting with digital twins and the Metaverse, it demands stringent requirements in latency, computational efficiency, and energy conservation. Traditional meth...
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ISBN:
(纸本)9798350377675;9798350377682
As mobile augmented reality (MAR) becomes integral to interacting with digital twins and the Metaverse, it demands stringent requirements in latency, computational efficiency, and energy conservation. Traditional methods leveraging edge, cloud computing, and 5G networks encounter considerable challenges, notably communication latency with visual data, which compromises the quality of experience (QoE). This paper presents an innovative semantic communication framework tailored for enhancing MAR applications. By adapting the Lewis signaling game, we train two agents to develop an efficient communication protocol, allowing the exchange of complex visual information via compact, meaningful messages. Unlike existing semantic communication models that emphasize data reconstruction, our approach prioritizes task completion, employing a discrete protocol optimized for referential tasks. To align with real-world scenarios, channel uncertainty is integrated into our training process. Experimental validations reveal that our method surpasses traditional semantic communications in referential task performance significantly, showcasing its potential for MAR applications while acknowledging the challenges of implementation. This work not only proposes a solution to a pressing problem in MAR but also opens avenues for future research in task-oriented communication.
This paper focuses on developing a comprehensive system for optimised climate control in a simulated greenhouse setting with complex environment modelling, communication-enabled agents, and hyperparameter optimisation...
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The proceedings contain 74 papers. The topics discussed include: a weighted slope one collaborative filtering algorithm for improved user relevance;research on the application of machine learning-based artificial inte...
ISBN:
(纸本)9798350383805
The proceedings contain 74 papers. The topics discussed include: a weighted slope one collaborative filtering algorithm for improved user relevance;research on the application of machine learning-based artificial intelligence algorithms in recommendation systems;deep learning-based joint extraction model for cyber security entities and events;a dynamic perception algorithm for security in scale intelligent job scenarios based on machine learning;research on edge computing in single-light monitoring system for navigational aids lighting;and comparison and analysis of accuracy of various machine learning algorithms in ancient glass classification.
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