A Prototype-Empowered Kernel-Varying Convolutional Model for Imbalanced Sea State Estimation in IoT-Enabled Autonomous Ship

Keyphrases

• Internet of Things 100%
• Convolution Model 100%
• Sea State Estimation 100%
• Autonomous Ships 100%
• Data Preprocessing 50%
• Feature Extractor 50%
• Convolution Feature 50%
• Sea State 50%
• Distance-based Classifier 50%
• Preprocessing Module 50%
• Parallel Convolution 50%
• Distance Metric 25%
• Main Components 25%
• Data-driven Methods 25%
• Model-based Approach 25%
• Motion Data 25%
• Data-driven Approach 25%
• Distinctive Features 25%
• Data Quality 25%
• Classification Task 25%
• Physical Model 25%
• Sensor Noise 25%
• Marine Conditions 25%
• Prototype-based 25%
• Multivariate Time Series Classification 25%
• Convolutional Structure 25%
• Nearest Prototype 25%
• Wave Radar 25%
• UEA Archive 25%
• Efficient Navigation 25%
• Real-time Capability 25%
• Wave Buoy 25%
• Ship Dynamics 25%
• Ship Motions 25%
• Data Imbalance Problem 25%
• Navigation Safety 25%

Computer Science

• Internet-Of-Things 100%
• State Estimation 100%
• Data Preprocessing 50%
• Preprocessing Module 50%
• Convolution 50%
• Distance Metric 25%
• Traditional Method 25%
• Driven Approach 25%
• Classification Task 25%
• Main Component 25%
• Generalizability 25%
• Distinctive Feature 25%
• Multivariate Time Series 25%
• Physical Model 25%

Engineering

• Internet-Of-Things 100%
• State Estimation 100%
• Sea State 100%
• Extractor 33%
• Data Preprocessing 33%
• Observables 16%
• Metrics 16%
• Main Component 16%
• Quality Data 16%
• Physical Model 16%
• Classification Task 16%
• Sensor Noise 16%
• Motion Data 16%
• Sea Condition 16%
• Ship Motion 16%