The design of digital technology product management system based on distributed architecture is the focus of this article. This article analyzes the importance and development trends of digital technology product life...
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The importance of integrating distributed Generation (DG) into power system networks has increased in recent years due to rising electricity demand and limited production capacity. DG provides various solutions to var...
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Aggregate computing is a macro-level approach for programming collective intelligence and self-organisation in distributedsystems. In this paradigm, system behaviour unfolds as a combination of a system-wide program,...
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ISBN:
(数字)9781665471374
ISBN:
(纸本)9781665471374
Aggregate computing is a macro-level approach for programming collective intelligence and self-organisation in distributedsystems. In this paradigm, system behaviour unfolds as a combination of a system-wide program, functionally manipulating distributed data structures called computational fields, and a distributed protocol where devices work at asynchronous rounds comprising sense-compute-interact steps. Interestingly, there exists a large amount of flexibility in how aggregate systems could actually execute while preserving the desired functionality. The ideal place for making choices about execution is the aggregate computing platform (or middleware), which can be engineered with the goal of promoting efficiency and other non-functional goals. In this work, we explore the possibility of applying Reinforcement Learning at the platform level in order to optimise aspects of a collective computation while achieving coherent functional goals. This idea is substantiated through synthetic experiments of data propagation and collection, where we show how Q-Learning could reduce the power consumption of aggregate computations.
Providing personalized services for electricity users with different needs has become an important part of deepening the reform of the electricity sales side market system, and achieving efficient and accurate electri...
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Cognitive sensor Networks are sophisticated systems that use adaptive mechanisms, fuzzy logic, fuzzy clustering algorithms, and other cutting-edge technologies to improve environmental monitoring through flexibility, ...
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Image captioning is a challenging task in artificial intelligence that involves generating descriptive captions for images automatically. In this project, we propose a novel approach leveraging advanced technologies s...
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As sports video collections continue to expand at a rapid pace, there is a need for advanced classification methods that can accurately and efficiently categorize and manage the massive amount of content available. Th...
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Locating the unknown sensors with a high degree of precision in wireless sensor networks is a vital issue. The localization method suggested by various researchers focused only on the mobility of an anchor (or) beacon...
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This paper focuses on the fully distributed event-triggered linear multi-agents issue with network delay and external disturbance. Based on the designed observer, slide mode control theory is introduced to deal with t...
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This article establishes a chance constrained programming model with the overall efficiency of compensating capacitors and distributed generations as the goal, and optimizes the configuration of distributed generation...
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