Research on Improving UAV Obstacle Avoidance in Vision-Constrained Environments Based on Nonexpert Policy Reinforcement Learning In recent years, unmanned aerial vehicles (UAVs) have gained widespread application in civilian fields such as package delivery, risk assessment, and emergency rescue, owing to their superior maneuverability and versatili...

Adaptive Hypersphere Neural Network Classifier: Overview of ASNN Research Introduction and Research Background In recent years, with the development of artificial intelligence and deep learning, neural networks (NNs) have been widely applied to classification tasks. The essence of these tasks lies in establishing decision boundaries through neural ...

Research on the Application of Knowledge Probabilization in Ensemble Distillation Academic Background: Significance of the Research and Problem Statement In recent years, deep neural networks (DNNs) have found broad applications in safety-critical fields such as autonomous driving, medical diagnosis, and climate prediction due to their outstanding ...

Efficient Implementation of RNN Activation Functions: Breakthroughs in CORDIC Algorithms and FPGA Hardware Acceleration Background and Research Significance In recent years, with the rapid advancement of deep learning technologies, Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) networks, have demonstrated powerful capa...

A New Approach to Enhance Few-Shot Semantic Segmentation: Prior-Driven Edge Feature Enhancement Network In the field of artificial intelligence, semantic segmentation is a core technology in computer vision that aims to assign semantic category labels to every pixel in an image. However, traditional semantic segmentation methods rely on large amoun...

Frontier Research in Partial Multi-Label Learning: A New Method Based on Label-Specific Feature Corrections In recent years, partial multi-label learning (PML) has become a hot research topic in the field of machine learning. With the rise of crowdsourcing platforms, the cost of data annotation has dropped significantly, but the quality of annotati...

A New Breakthrough in Robotic Grasp Pose Estimation — MetaCoorNet Network Academic Background and Research Challenges Robotic grasping is a fundamental challenge in the field of robotics, centered on enabling robots to interact with their environment to perform object picking and manipulation tasks. Despite the immense potential applications of aut...

Understanding Why Label Distribution Learning Exhibits Better Generalization in Classification Background Introduction In the fields of artificial intelligence and machine learning, classification problems have always been a central focus for researchers. With the continuous development of multi-label learning (MLL) and single-label learning (SLL),...

Monolithic 3D IGZO-RRAM-SRAM Compute-in-Memory Architecture: A Breakthrough in Improving Neural Network Computation Efficiency Background and Research Motivation As neural networks (NNs) continue to find applications in artificial intelligence, traditional computing architectures struggle to meet their needs for energy efficiency, speed, and densit...

Research on Cooperative Output Regulation of Heterogeneous Directed Multi-Agent Systems: A Fully Distributed Model-Free Reinforcement Learning Framework Background In recent years, the study of distributed control and optimization has demonstrated broad application prospects in smart transportation, smart grids, distributed energy systems, and othe...