Cheng et al., 2020 - Google Patents
A novel densely connected convolutional neural network for sea-state estimation using ship motion dataCheng et al., 2020
View PDF- Document ID
- 1927710280735988638
- Author
- Cheng X
- Li G
- Ellefsen A
- Chen S
- Hildre H
- Zhang H
- Publication year
- Publication venue
- IEEE Transactions on Instrumentation and Measurement
External Links
Snippet
The sea-state estimation is a fundamental problem in the development of autonomous ships. Traditional methods such as wave buoy, satellites, and wave radars are limited by locations, clouds, and costs, respectively. Model-based methods are prone to incorrect estimations …
- 230000001537 neural 0 title abstract description 15
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/08—Learning methods
- G06N3/082—Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6261—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation partitioning the feature space
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6228—Selecting the most significant subset of features
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6288—Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion
- G06K9/629—Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion of extracted features
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/04—Architectures, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/68—Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/64—Methods or arrangements for recognition using electronic means using simultaneous comparisons or correlations of the image signals with a plurality of references, e.g. resistor matrix
- G06K9/66—Methods or arrangements for recognition using electronic means using simultaneous comparisons or correlations of the image signals with a plurality of references, e.g. resistor matrix references adjustable by an adaptive method, e.g. learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
- G06K9/4604—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Cheng et al. | A novel densely connected convolutional neural network for sea-state estimation using ship motion data | |
Liu et al. | STMGCN: Mobile edge computing-empowered vessel trajectory prediction using spatio-temporal multigraph convolutional network | |
Kowalska et al. | Maritime anomaly detection using Gaussian process active learning | |
Yin et al. | A real-time sequential ship roll prediction scheme based on adaptive sliding data window | |
Zhao et al. | k-GCN-LSTM: A k-hop Graph Convolutional Network and Long–Short-Term Memory for ship speed prediction | |
CN109766830A (en) | A kind of ship seakeeping system and method based on artificial intelligence image procossing | |
Song et al. | Segmentation of sidescan sonar imagery using markov random fields and extreme learning machine | |
Han et al. | A deep learning approach to detect and isolate thruster failures for dynamically positioned vessels using motion data | |
Xiao et al. | Bidirectional data-driven trajectory prediction for intelligent maritime traffic | |
Li et al. | Spatially self-paced convolutional networks for change detection in heterogeneous images | |
Zhang et al. | Loitering behavior detection and classification of vessel movements based on trajectory shape and Convolutional Neural Networks | |
Li et al. | A novel robustness-enhancing adversarial defense approach to ai-powered sea state estimation for autonomous marine vessels | |
Chungath et al. | Transfer learning and few-shot learning based deep neural network models for underwater sonar image classification with a few samples | |
Han et al. | Directional wave spectrum estimation with ship motion responses using adversarial networks | |
Chen et al. | Unsupervised segmentation in real-world images via spelke object inference | |
Smith et al. | Maritime abnormality detection using Gaussian processes | |
Kim et al. | Scalable learning with a structural recurrent neural network for short-term traffic prediction | |
Cheng et al. | Data-driven modeling for transferable sea state estimation between marine systems | |
Vo et al. | Accurate bounding box prediction for single-shot object detection | |
Beckler et al. | Multilabel classification of heterogeneous underwater soundscapes with Bayesian deep learning | |
Gebhardt et al. | Hunting for naval mines with deep neural networks | |
Poudel et al. | Black-box adversarial attacks on network-wide multi-step traffic state prediction models | |
Cheng et al. | SAFENESS: A semi-supervised transfer learning approach for sea state estimation using ship motion data | |
Liu et al. | Research on ship anomaly detection algorithm based on Transformer-GSA encoder | |
Farahnakian et al. | Abnormal behaviour detection by using machine learning-based approaches in the marine environment: A literature survey |