Persistent Link: https://ieeexplore.ieee.org/servlet/opac?punumber=5 ...

Abstract: In this article, an integral reinforcement learning (IRL) method is developed for dynamic event-triggered nonzero-sum (NZS) games to achieve the Nash equilibrium of unmanned surface vehicles ...

Abstract: Accurate arrival time picking of microseismic events is crucial for understanding subsurface processes and assessing associated risks. However, the low signal-to-noise ratio (SNR) inherent ...

Persistent Link: https://ieeexplore.ieee.org/servlet/opac?punumber=34 ...

Abstract: This paper presents the first discrete-time distributed algorithm to track the tightest ellipsoids that outer approximates the global dynamic intersection of ellipsoids. Given an undirected ...

Abstract: Next Point-of-interest recommendation involves modeling user interactions with Point-of-interests (PoIs) to analyze user behavior patterns and suggest future scenarios. Data sparsity ...

Abstract: In the field of multi-intersection signal control, Reinforcement Learning (RL) has demonstrated significant technical benefits in terms of optimization speed, stability, and scalability.

Abstract: Spectrum cartography (SC) aims to construct a global radio-frequency (RF) map across multiple domains, e.g., space, frequency and time, from sparse sensor samples. Recent state-of-the-art SC ...

Based on the author's vast industry experience and collaborative works with other industries, Control of Electric Machine Drive Systems is packed with tested, implemented, and verified ideas that ...

Thoroughly revised and expanded, this second edition of the popular reference addresses the many important advances that have taken place in the field since the publication of the first edition and ...

In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically ...

Persistent Link: https://ieeexplore.ieee.org/servlet/opac?punumber=6731005 ...