In this letter, we propose a market-based bi-level conic optimal energy flow (OEF) model of integrated electricity and natural gas systems (IENGSs). Conic alternating current optimal power flow (ACOPF) is formulated i...
详细信息
In this letter, we propose a market-based bi-level conic optimal energy flow (OEF) model of integrated electricity and natural gas systems (IENGSs). Conic alternating current optimal power flow (ACOPF) is formulated in the upper-level model, and the generation cost of natural gas fired generation units (NGFGUs) is calculated based on natural gas locational marginal prices (NG-LMPs). The market clearing process of natural gas system is modeled in the lower-level model. The bi-level model is then transferred into a mixed-integer second-order cone programming (MISOCP) problem. Simulation results demonstrate the effectiveness of the proposed conic OEF model.
An on-chip FIRE resonator, consisting of an angled laser array monolithically integrated with a slab waveguide propagation region, is experimentally demonstrated for coherent beam combining with the increased supermod...
详细信息
We demonstrate nanophotonic enhancement of the intrinsic spontaneous emission of X-ray-driven scintillation. By designing a 16-layers nanophotonic scintillator structure, we present the first observation of Purcell-en...
详细信息
The importance of data and its transmission rate are increasing as the world is moving towards online services every day. Thus, providing data security is becoming of utmost importance. This paper proposes a secure da...
详细信息
Due to the important part of batteries in industrial systems, its safety analysis has causes widespread attention from researchers, and its effective maintenance decision-making is needed. Data-driven state-of-health ...
详细信息
This study investigates the use of Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) to improve power utilization in green data centers. The study examines each mo...
详细信息
In the realm of flower-rich Bangladesh, the presence of these blossoms enriches our everyday experiences, whether encountered during leisurely strolls, along railway tracks, or within our gardens. However, the beauty ...
详细信息
This exploration paper investigates the operation of machine literacy in terms of real-time temperature vaticination in electronic systems. Temperature monitoring and control are pivotal for icing the trustability, pe...
详细信息
Machine learning has been adopted for efficient cooperative spectrum sensing. However, it incurs an additional security risk due to attacks leveraging adversarial machine learning to create malicious spectrum sensing ...
详细信息
ISBN:
(数字)9798350383508
ISBN:
(纸本)9798350383515
Machine learning has been adopted for efficient cooperative spectrum sensing. However, it incurs an additional security risk due to attacks leveraging adversarial machine learning to create malicious spectrum sensing values to deceive the fusion center, called adversarial spectrum attacks. In this paper, we propose an efficient framework for detecting adversarial spectrum attacks. Our design leverages the concept of the distance to the decision boundary (DDB) observed at the fusion center and compares the training and testing DDB distributions to identify adversarial spectrum attacks. We create a computationally efficient way to compute the DDB for machine learning based spectrum sensing systems. Experimental results based on realistic spectrum data show that our method, under typical settings, achieves a high detection rate of up to 99% and maintains a low false alarm rate of less than 1%. In addition, our method to compute the DDB based on spectrum data achieves 54%–64% improvements in computational efficiency over existing distance calculation methods. The proposed DDB-based detection framework offers a practical and efficient solution for identifying malicious sensing values created by adversarial spectrum attacks.
This paper reviews recent developments in learning-based adaptive optimal output regulation that aims to solve the problem of adaptive and optimal asymptotic tracking with disturbance *** proposed framework aims to br...
详细信息
This paper reviews recent developments in learning-based adaptive optimal output regulation that aims to solve the problem of adaptive and optimal asymptotic tracking with disturbance *** proposed framework aims to bring together two separate topics—output regulation and adaptive dynamic programming—that have been under extensive investigation due to their broad applications in modern control *** this framework,one can solve optimal output regulation problems of linear,partially linear,nonlinear,and multi-agent systems in a data-driven *** will also review some practical applications based on this framework,such as semi-autonomous vehicles,connected and autonomous vehicles,and nonlinear oscillators.
暂无评论