News

IEEE15h
Adaptive Variable Length Sliding Window Network Coding for Low Latency Transmission in FANETs via Deep Reinforcement Learning - IEEE Xplore
Future flying ad-hoc networks (FANETs) need to address issues related to delay and channel interference while ensuring high data transmission accuracy. In this letter, we propose a proximal policy ...
IEEE1d
Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning - IEEE Xplore
Dynamic graph augmentation is used to improve the performance of dynamic GNNs. Most methods assume temporal locality, meaning that recent edges are more influential than earlier edges. However, for ...
IEEE2d
Adaptive Iterative Learning Algorithm of Strict-feedback Systems with Initial State Error - IEEE Xplore
This paper presents an adaptive iterative learning control algorithm for a class of strict-feedback systems, which can achieve complete tracking with initial st ...
IEEE3d
Adversarial Style-Irrelevant Feature Learning With Refined Soft Pseudo Labels for Domain-Adaptive Vehicle Re-Identification - IEEE Xplore
Domain-adaptive vehicle re-identification is a challenging task that aims to transfer the knowledge from a labeled source domain to an unlabeled target domain for effective vehicle re-identification.
IEEE4d
Adaptive Learning Systems: Revolutionizing Higher Education through AI-Driven Curricula - IEEE Xplore
In the rapidly evolving landscape of higher education, traditional pedagogical methods often fall short in meeting the diverse needs and learning styles of students. This paper advocates for a ...
IEEE7d
Graph Attention Network-Based Unified TCAD Modeling Enabling Fast Design Technology Co-Optimization Through Transfer Learning - IEEE Xplore
An innovative framework that leverages artificial intelligence (AI) and graph representation for semiconductor device encoding in TCAD device simulation is proposed. A graph-based universal encoding ...
IEEE7d
Smart City: Application of Multi-agent Reinforcement Learning Systems in Adaptive Traffic Management - IEEE Xplore
Traffic congestion on urban road networks has increased substantially during the last decade, characterized by slower speeds, longer travel times, increased vehicular queuing, and increased pollution.
IEEE8d
Reinforcement Learning and Adaptive Optimal Control for Continuous-Time Nonlinear Systems: A Value Iteration Approach - IEEE Xplore
This article studies the adaptive optimal control problem for continuous-time nonlinear systems described by differential equations. A key strategy is to exploit the value iteration (VI) method ...
IEEE9d
Adaptive Federated Learning on Non-IID Data With Resource Constraint - IEEE Xplore
Federated learning (FL) has been widely recognized as a promising approach by enabling individual end-devices to cooperatively train a global model without exposing their own data. One of the key ...
IEEE10d
View-Adaptive Graph Neural Network for Action Recognition
Skeleton-based recognition of human actions has received attention in recent years because of the popularity of 3-D acquisition sensors. Existing studies use 3-D skeleton data from video clips ...
IEEE11d
Self-Adaptation Graph Attention Network via Meta-Learning for Machinery Fault Diagnosis With Few Labeled Data - IEEE Xplore
Effective application of fault diagnosis models requires that new fault types can be recognized rapidly after they occur few times, even only one time. To this end, a self-adaptation graph attention ...
IEEE14d
Reinforcement-Learning-Based Fuzzy Adaptive Finite-Time Optimal Resilient Control for Large-Scale Nonlinear Systems Under False Data Injection Attacks - IEEE Xplore
In this article, the reinforcement learning (RL)-based finite-time adaptive optimal resilient control issue is studied for uncertain large-scale nonlinear systems under unknown sensor false data ...