Results 221 to 230 of about 12,636 (256)

Underwater acoustic targets recognition algorithm based on NMF

2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), 2020
To solve the problem of low recognition rate of underwater targets for the reason of their large feature discreteness within class and high feature overlapping between classes, the underwater targets recognition algorithm based on NMF universal dictionary model (UDM) is proposed, in which, the UDM is established using the existing underwater acoustic ...
Xiaoqing Zheng, Zhichao Shan, Xianzhong Liu, Liang Yu   +3 more
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Intelligent Recognition of Underwater Acoustic Target Noise on Underwater Glider Platform

2018 Chinese Automation Congress (CAC), 2018
The underwater acoustic target detection system based on the underwater glider platform requires the platform itself to have the ability of target automatic tracking, identification and evaluation, but the traditional methods of underwater target noise identification have strong human-computer interaction characteristics.
Zhang Shao-Kang, Tian De-Yan, Wang Chao, Zhang Xiao-Chuan   +3 more
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Underwater acoustic target recognition using graph convolutional neural networks

The Journal of the Acoustical Society of America, 2018
Motivated by recent progress in signal processing on graphs and convolutional neural networks, we have developed an underwater acoustic target recognition system based on graph convolutional neural networks. We evaluate our framework by application to various real-world datasets and validate its effectiveness.
Razi Sabara, Sergio Jesus
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Multi-scale spectral feature extraction for underwater acoustic target recognition

Measurement, 2020
Abstract In this work, we proposed a multi-scale spectral (MSS) feature set for underwater acoustic target recognition problem, whose main difficulty lies in the fact that the acoustic signal is often submerged by intense environmental noise. With explicit physical meaning, the proposed MSS feature set fits better with traditional machine learning ...
Junjun Jiang, Tuo Shi, Min Huang, Zhongzhe Xiao   +3 more
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An Underwater Acoustic Target Recognition Method Based on Transfer Learning

2024 9th International Conference on Electronic Technology and Information Science (ICETIS)
 Underwater acoustic target recognition(UATR)is a challenging task Due to the high cost of sampling data, it is difficult to build a large-scale dataset.In this study,a new method based on transfer learning with a VGG16 model(Transfer-VGG16) is developed in which three-dimensional(3-D)data is used as the input.Use the dataset obtained in real scenarios
Xiaozhuo Yang, Huapeng Yu, Hanmin Sheng, Wenlong Zeng, Qingyuan He, Junyang Tu   +5 more
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Underwater Acoustic Target Recognition Based on ReLU Gated Recurrent Unit

2020 6th International Conference on Robotics and Artificial Intelligence, 2020
In general, the traditional acoustic models'(e. g. Gaussian mixture model, GMM) for underwater acoustic target recognition (UATR) performance in sequential data has so far been disappointing. In contrast, Recurrent Neural Network (RNN) is a powerful tool for sequential data.
Xiaodong Sun, Xiaohan Yin, YaGuang Yin, Peishun Liu, Liang Wang, Ruichun Tang   +5 more
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CA_MobileNetV2 for Underwater Acoustic Target Recognition

2023 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), 2023
Gao Tian, Yao Haiyang, Wang Haiyan, Wu Fan, Chen Xiao   +4 more
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Target classification and recognition using underwater acoustic signals

2020
ABSTRACT TARGET CLASSIFICATION AND RECOGNITION USING UNDERWATER ACOUSTIC SIGNALS YA?CI, Tayfun M. S., Department of Computer Engineering Supervisor: Assoc. Prof. Dr. Ahmet COŞAR July 2005, 116 pages Nowadays, fulfillment of the tactical operations in secrecy has great importance for especially subsurface and surface warfare platforms as a result of ...
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Domain-adaptive meta learning for underwater acoustic target recognition

The Journal of the Acoustical Society of America
This study provides a unique opportunity for advancements in underwater acoustic target recognition (UATR). The ShipsEar dataset, comprised of underwater acoustic recordings captured by hydrophones across eight distinct locations, encapsulates diverse environmental conditions and vessel sound characteristics.
Junho Bae, Wooyoung Hong, Youngmin Choo
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Underwater Acoustic Target Recognition with Fusion Feature

2021
Pengyuan Qi, Jianguo Sun, Yunfei Long, Liguo Zhang, null Tianye   +4 more
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