An intrusion is any deliberate attempt to get through a network39;s defenses and make it less available, less private, or all three. Intrusions include anything from threats to break the rules for acceptable use to ...
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Bike Fitting is a professional service aimed at helping riders personalize and adjust their bicycles to achieve synchronization between the vehicle and the rider, in order to achieve the most comfortable, efficient, p...
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Personalized skincare depends critically on the classification of skin types since different skin types - normal, oily, and dry - need different treatment plans to preserve beauty and health. Recommendation of suitabl...
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In this paper, a dynamic planning and intelligent decision-making model of power grid project reserve based on multi-source data and deep learning is proposed to meet the new challenges of power grid project reserve a...
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This study explores the application of deep reinforcement learning algorithms in the field of air traffic, particularly focusing on intelligent trajectory planning for multiple aircraft. By using the K-means clusterin...
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In the rapidly evolving world of wireless cellular network, optimizing key parameters like data throughput and latency is of critical importance for ensuring high quality communication services. The proposed presents ...
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The proceedings contain 354 papers. The topics discussed include: quantifying effectiveness of governmental agriculture policies using RNN-transformer based sentiment analysis;artificial intelligence to enhance the se...
ISBN:
(纸本)9798331527549
The proceedings contain 354 papers. The topics discussed include: quantifying effectiveness of governmental agriculture policies using RNN-transformer based sentiment analysis;artificial intelligence to enhance the security of internet of things: a comprehensive review;dual-plate verification system for vehicle identification using IoT and embedded systems;eco-friendly next-gen air conditioning with IoT-based embedded monitoring and control for reduced energy consumption;investigation on attack detection in IoT networks: a study and analysis of the existing machine learning and deep learning techniques;monitoring and leak detection of hazardous gas using internet of things;and navigating SAP ERP implementation: identifying success drivers and pitfalls.
With the increasing complexity of the power system, the traditional centralized scheduling strategy has been unable to meet the demand for efficient management and stable operation of the system, and the multi-agent c...
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ISBN:
(纸本)9798350375145;9798350375138
With the increasing complexity of the power system, the traditional centralized scheduling strategy has been unable to meet the demand for efficient management and stable operation of the system, and the multi-agent collaborative regulation technology has gradually become a key means of power system management. In this paper, a coordinated scheduling strategy for multi-agent system in power distribution network based on deep reinforcement learning method is proposed. First, each subject such as distributed power sources, energy storage devices and flexible loads is taken as an intelligent agent in an active distribution grid, and the real-time state information of different intelligent agents in the grid, including voltage, current, power, etc., is obtained. And then, based on the real-time state information, the expected cumulative rewards of taking load management actions under a given smart grid state are evaluated by a neural network representation of the Q-value function, in which a single intelligent agent obtains the optimal energy allocation strategy by maximizing the Q-value function. This paper also constructs a Maldivian network communication model, based on which information exchange between multiple intelligent agents is realized. Finally, a deep deterministic policy gradient algorithm is used to decide the energy allocation of the multi-agent system, and the effectiveness of the proposed method is verified.
Digital Twins represent virtual replicas of real-world objects, processes, or systems, enabling continuous real-time monitoring, dynamic adaptation, and predictive analytics. Their integration has become central in in...
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Edge systems are undergoing a groundbreaking computing evolution to support artificial intelligence, deep learning, and complex computational algorithms. Using cloud servers to perform deep learning model inference po...
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ISBN:
(纸本)9798350350494;9798350350500
Edge systems are undergoing a groundbreaking computing evolution to support artificial intelligence, deep learning, and complex computational algorithms. Using cloud servers to perform deep learning model inference poses challenges such as response delays, increased communication costs, and data privacy concerns. Therefore, significant efforts have been made to push the processing of deep learning models to edge systems, which has led to the creation of edge intelligence as the intersection of learning and edge computing. learning models, especially deep convolutional neural networks, have made significant achievements in machine vision, which provide high accuracy and predictability by spending computing power and memory. If these models are optimized and deployed on edge systems, there will be a revolution in the applications of edge systems in real time. In this paper, by using optimization techniques such as quantization, weight pruning, and weight clustering, the possibility of deploying a typical convolutional neural network model on edge systems that have limited computing resources and memory is investigated. The results show that by using a collaborative algorithm, despite the slight decrease in the accuracy of the model, it is possible to achieve a small-sized model that can even be deployed on microcontrollers.
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