Passive Ship Localization in a Shallow Water Using Pre-trained Deep Learning Networks
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
In: Berlin, Germany : Deutsche Gesellschaft für Akustik, 2019
- Event
-
Veröffentlichung
- (where)
-
Aachen
- (who)
-
Universitätsbibliothek der RWTH Aachen
- (when)
-
2019
- Creator
-
Wang, Dezhi
Zhang, Lilun
Bao, Changchun
Ma, Shuqing
Wang, Yongxian
- DOI
-
10.18154/RWTH-CONV-238947
- URN
-
urn:nbn:de:101:1-2020073014482495743492
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
25.03.2025, 1:45 PM CET
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Wang, Dezhi
- Zhang, Lilun
- Bao, Changchun
- Ma, Shuqing
- Wang, Yongxian
- Universitätsbibliothek der RWTH Aachen
Time of origin
- 2019
Other Objects (12)
Ship waves in shallow water
Analyzing pre-trained and fine-tuned language models
A Rankine source method for ship ship interaction and shallow water problems
Comparability, evaluation and benchmarking of large pre-trained language models
Robustness of Pre-trained Language Models for Natural Language Understanding
Optimal ship forms for minimum total resistance in shallow water
Optimal ship forms for minimum total resistance in shallow water
Sentiment Analysis in Nepali Tweets: Leveraging TransformerBased Pre-trained Models
On Simulation-based Ship Maneuvering Prediction in Deep and Shallow Water
The POLAR framework: Polar opposites enable interpretability of pre-trained word embeddings
Named Entity Recognition for Nepali Text Using Pre-Trained BERT-Based Model
Pre-Trained Driving in Localized Surroundings with Semantic Radar Information and Machine Learning
Ship waves in shallow water
Analyzing pre-trained and fine-tuned language models
A Rankine source method for ship ship interaction and shallow water problems
Comparability, evaluation and benchmarking of large pre-trained language models
Robustness of Pre-trained Language Models for Natural Language Understanding
Optimal ship forms for minimum total resistance in shallow water
Optimal ship forms for minimum total resistance in shallow water
Sentiment Analysis in Nepali Tweets: Leveraging TransformerBased Pre-trained Models
On Simulation-based Ship Maneuvering Prediction in Deep and Shallow Water
The POLAR framework: Polar opposites enable interpretability of pre-trained word embeddings
Named Entity Recognition for Nepali Text Using Pre-Trained BERT-Based Model
Pre-Trained Driving in Localized Surroundings with Semantic Radar Information and Machine Learning
Ship waves in shallow water
Analyzing pre-trained and fine-tuned language models
A Rankine source method for ship ship interaction and shallow water problems
Comparability, evaluation and benchmarking of large pre-trained language models
Robustness of Pre-trained Language Models for Natural Language Understanding
Optimal ship forms for minimum total resistance in shallow water
Optimal ship forms for minimum total resistance in shallow water
Sentiment Analysis in Nepali Tweets: Leveraging TransformerBased Pre-trained Models
On Simulation-based Ship Maneuvering Prediction in Deep and Shallow Water
The POLAR framework: Polar opposites enable interpretability of pre-trained word embeddings
Named Entity Recognition for Nepali Text Using Pre-Trained BERT-Based Model