Class-incremental learning allows a model to continuously acquire new classes from sequentially arriving data while preserving its ability to recognize previously learned ones, which is essential for improving adaptability and supporting long-term ev...
Real-time semantic segmentation models offer an excellent balance between accuracy and inference speed. However, deploying these models in dynamic real-world environments often requires the ability to learn novel classes incrementally without retrain...
In class-incremental learning (CIL), a model must learn new classes while retaining previous knowledge without storing all historical data. Generative replay mitigates catastrophic forgetting by synthesizing old class samples, but conventional pipeli...
Fixed offline prognostic models can lose accuracy when sensor-data distribution changes after model development. This paper presents a validation-selected incremental-learning framework for time-before-failure (TBF) regression in three simulated unma...
Continual learning of large language models requires incorporating new knowledge while limiting catastrophic forgetting; however, many low-rank adaptation (LoRA)-based methods retain task-specific modules or separate learning spaces that grow with th...
Due to the unknown motion model and the complexity of the environment, the problem of target tracking for autonomous underwater vehicles (AUVs) became one of the major difficulties in model-based controllers. Therefore, the target tracking task of AU...
Autonomous learning of robotic skills seems to be more natural and more practical than engineered skills, analogous to the learning process of human individuals. Policy gradient methods are a type of reinforcement learning technique which have great...
Background: Continual learning (CL) is increasingly important for developing adaptive clinical AI models; however, its application to histopathology remains challenging due to privacy constraints, expanding diagnostic categories, and staining variabi...
A novel model-free hierarchical reinforcement learning (HRL)–based Linear Quadratic Regulator (LQR) control framework with adaptive weight selection is proposed to address the reliance of conventional LQR methods on accurate system models and m...
We learn compatible destination and owner views in bounded, transcript-free records. Structural source/type/version addressing selects the first relation; authoritative, requested-attribute filtering reduces ordinary candidates to a mean of 3.28. Lea...
Path planning for robots based on reinforcement learning encounters challenges in integrating semantic information about environments into the training process. In unseen or complex environmental information, agents often perform sub-optimally and re...
Piano fingering estimation remains a complex problem due to the combinatorial nature of hand movements and no best solution for any situation. A recent model-free reinforcement learning framework for piano fingering modeled each monophonic piece as a...
Due to the strong representation ability and capability of learning from data measurements, deep reinforcement learning has emerged as a powerful control method, especially for nonlinear systems, such as the aero-engine control system. In this paper,...
Continual learning (CL), also known as lifelong learning, is an emerging research topic that has been attracting increasing interest in the field of machine learning. With human activity recognition (HAR) playing a key role in enabling numerous real-...
This paper investigates the problem of multi-robot formation control strategies in environments with obstacles based on deep reinforcement learning methods. To solve the problem of value function overestimation in the deep deterministic policy gradie...
Deep learning (DL)-based multi-user physical layer authentication (PLA) in the Industrial Internet of Things (IIoT) requires frequent updates as new users join. Class incremental learning (CIL) addresses this challenge, but existing generative replay...
Atmospheric turbulence severely limits the stability and reliability of free-space optical (FSO) uplinks by inducing wavefront distortions and random intensity fluctuations. This study investigates the use of reinforcement learning (RL) with beacon-b...
The path planning problem of nuclear environment robots refers to finding a collision-free path under the constraints of path length and an accumulated radiation dose. To solve this problem, the Improved Dueling Deep Double Q Network algorithm (ID3QN...
Class-incremental three-dimensional point cloud semantic segmentation requires models to learn newly introduced categories while preserving previously acquired knowledge without storing historical point clouds. This setting is challenged by represent...
In the proton exchange membrane fuel cell (PEMFC) system, the flow of air and hydrogen is the main factor influencing the output characteristics of PEMFC, and there is a coordination problem between their flow controls. Thus, the integrated controlle...
This paper addresses the challenge of offloading resource-intensive image segmentation tasks and allocating computing resources within the Internet of Vehicles (IoV) using edge-based AI. To overcome the limitations of onboard computing in smart vehic...
In the rapidly evolving landscape of cybersecurity, model extraction attacks pose a significant challenge, undermining the integrity of machine learning models by enabling adversaries to replicate proprietary algorithms without direct access. This pa...
Ancillary services rely on operating reserves to support an uninterrupted electricity supply that meets demand. One of the hidden reserves of the grid is in thermostatically controlled loads. To efficiently exploit these reserves, a new realization o...
Cooperative multi-agent reinforcement learning (MARL) is hard when rewards are sparse. This is an evaluation study built around a negative result. We study CHARM (Cooperative Hindsight-Augmented Role-conditioned Memory), a four-part memory layer that...
Cooperative multi-UAV path planning under dynamic and adversarial conditions demands simultaneous satisfaction of safety, efficiency, and coordination constraints, yet existing swarm-intelligence and RL–swarm hybrids rely on deterministic switc...