Real-time monitoring of kefir-facilitated milk fermentation using microbial potentiometric sensors

Hristovski, Kiril; Burge, Scott R.; Boscovic, Dragan; Burge, Russell G.; Babanovska-Milenkovska, Frosina

doi:10.1016/j.jece.2022.107491

Cited by 5 publications

(1 citation statement)

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“…Han et al presents the design and implementation of a cloud computingbased electricity demand response system for large users, which takes advantage of the elasticity, scalability, and low cost of www.ijacsa.thesai.org cloud computing to build a distributed electricity demand response platform, realizing real-time monitoring, analysis, and response to the electricity demand of large users, and providing data support and intelligent services for the scheduling and optimization of the power system [19]. A method for analyzing and identifying the electricity consumption behavior of large users based on the fusion of multi-source data is proposed, which utilizes multi-source data such as the electricity consumption data, electricity consumption contract, and electricity consumption equipment of large users, and provides an effective means for the supervision and service of the electricity consumption of large users [20]. It realizes the dynamic prediction of the electricity consumption cost of large users, and provides a reference basis for the decision-making and optimization of electricity consumption of large users [21].…”

Section: Literature Reviewmentioning

confidence: 99%

Cloud-Enabled Real-Time Monitoring and Alert System for Primary Network Resource Scheduling and Large-Scale Users

Zhang,

Shu,

et al. 2024

ijacsa

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This paper innovatively combines cloud computing with Bayesian networks, aiming to provide an efficient and realtime prediction and scheduling platform for power main network scheduling and large-scale user monitoring. The core of the research lies in the development of a set of novel intelligent scheduling algorithms, which integrates multi-objective optimization theory and deep reinforcement learning technology to achieve dynamic and optimal allocation of power grid resources in the cloud environment. By constructing a comprehensive evaluation system, this study verifies the advancement of the proposed model in multiple dimensions: not only does it make breakthroughs in the in-depth parsing and accurate prediction of electric power data, but it also significantly improves the prediction accuracy of the main grid load changes, tariff dynamic adjustments, grid security posture, and power consumption patterns of large users. The empirical study shows that compared with the existing methods, the model proposed in this study effectively reduces energy consumption and operation costs while improving prediction accuracy and dispatching efficiency, demonstrating its significant innovative value and practical significance in the field of intelligent grid management. The innovation of this paper lies in the development of a composite prediction model that integrates the powerful classification and prediction capabilities of Bayesian networks and the efficient learning mechanism of deep reinforcement learning in complex decision-making scenarios.

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Section: Literature Reviewmentioning

confidence: 99%