A5084165979- Maritime Navigation and Safety
- Maritime Ports and Logistics
- Robotic Path Planning Algorithms
- Radiomics and Machine Learning in Medical Imaging
- Colorectal Cancer Screening and Detection
- Target Tracking and Data Fusion in Sensor Networks
- Advanced Measurement and Detection Methods
- Gastric Cancer Management and Outcomes
- Advanced Algorithms and Applications
- AI in cancer detection
- Advanced Neural Network Applications
Taiyuan Normal University
2024
Beihang University
2002
In dense anchorage areas, the challenge of navigation for Unmanned Surface Vehicles (USVs) is particularly pronounced, especially regarding path safety and economy. A Risk-Aware Path Optimization Algorithm (RAPO) proposed to enhance efficiency USV navigating in areas. The algorithm incorporates risk assessment based on A* generate an optimized employs a Dual-Phase Smoothing Strategy ensure smoothness. First, area spatially separated using Voronoi polygon, RAPO includes grid function, derived...
Accurate segmentation of medical images is vital for disease detection and treatment. Convolutional Neural Networks (CNN) Transformer models are widely used in image due to their exceptional capabilities recognition segmentation. However, CNNs often lack an understanding the global context may lose spatial details target, while Transformers struggle with local information processing, leading reduced geometric detail target. To address these issues, this research presents a Global-Local...
Gastric cancer is a leading cause of cancer-related deaths globally. As mortality rates continue to rise, predicting survival using multimodal data-including histopathological images, genomic data, and clinical information-has become increasingly crucial. However, extracting effective predictive features from this complex data has posed challenges for analysis due the high dimensionality heterogeneity histopathology images data. Furthermore, existing methods often lack sufficient interaction...
In this paper, a new multi-resolution multiple-model algorithm based on model mixing is proposed. The combines the advantages of with that interacting multiple algorithm, thus it outperforms both algorithms.