OCEANS 2016 - Shanghai最新文献_第10页

Evaluation of shipboard wave estimation techniques through model-scale experiments 通过模型尺度实验评价船载波浪估计技术

OCEANS 2016 - Shanghai Pub Date : 2016-04-10 DOI: 10.1109/OCEANSAP.2016.7485701

U. Nielsen, R. Galeazzi, Astrid H. Brodtkorb

引用次数: 19

ZooplanktoNet: Deep convolutional network for zooplankton classification 浮游动物网:用于浮游动物分类的深度卷积网络

OCEANS 2016 - Shanghai Pub Date : 2016-04-10 DOI: 10.1109/OCEANSAP.2016.7485680

Jialun Dai, Ruchen Wang, Haiyong Zheng, Guangrong Ji, Xiaoyan Qiao

{"title":"ZooplanktoNet: Deep convolutional network for zooplankton classification","authors":"Jialun Dai, Ruchen Wang, Haiyong Zheng, Guangrong Ji, Xiaoyan Qiao","doi":"10.1109/OCEANSAP.2016.7485680","DOIUrl":"https://doi.org/10.1109/OCEANSAP.2016.7485680","url":null,"abstract":"Zooplankton are quite significant to the ocean ecosystem for stabilizing balance of the ecosystem and keeping the earth running normally. Considering the significance of zooplantkon, research about zooplankton has caught more and more attentions. And zooplankton recognition has shown great potential for science studies and mearsuring applications. However, manual recognition on zooplankton is labour-intensive and time-consuming, and requires professional knowledge and experiences, which can not scale to large-scale studies. Deep learning approach has achieved remarkable performance in a number of object recognition benchmarks, often achieveing the current best performance on detection or classification tasks and the method demonstrates very promising and plausible results in many applications. In this paper, we explore a deep learning architecture: ZooplanktoNet to classify zoolankton automatically and effectively. The deep network is characterized by capturing more general and representative features than previous predefined feature extraction algorithms in challenging classification. Also, we incorporate some data augmentation to aim at reducing the overfitting for lacking of zooplankton images. And we decide the zooplankton class according to the highest score in the final predictions of ZooplanktoNet. Experimental results demonstrate that ZooplanktoNet can solve the problem effectively with accuracy of 93.7% in zooplankton classification.","PeriodicalId":382688,"journal":{"name":"OCEANS 2016 - Shanghai","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130343045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 62

Multi features combination for automated zooplankton classification 多功能组合，实现浮游动物自动分类

OCEANS 2016 - Shanghai Pub Date : 2016-04-10 DOI: 10.1109/OCEANSAP.2016.7485675

Ruchen Wang, Jialun Dai, Haiyong Zheng, Guangrong Ji, Xiaoyan Qiao

{"title":"Multi features combination for automated zooplankton classification","authors":"Ruchen Wang, Jialun Dai, Haiyong Zheng, Guangrong Ji, Xiaoyan Qiao","doi":"10.1109/OCEANSAP.2016.7485675","DOIUrl":"https://doi.org/10.1109/OCEANSAP.2016.7485675","url":null,"abstract":"Zooplankton are the key components of marine food webs. The abundance of it influences the ocean ecological balance. To efficiently monitor species richness of zooplankton and protect marine environment, marine biologists and computer vision experts started to research automated zooplankton classification system with computer vision technologies. Most current research focuses on achieving high classification accuracy. In this paper, we propose a new system based on multi features combination to enhance the zooplankton classification performance. In our system, the geometric and grayscale features, Local Binary Patterns features, and Inner-distance Shape Context features are extracted as low-level features. According to the properties of machine learning algorithms, an appropriate algorithm is chosen to generate middle-level features by processing all kinds of low-level features. After that, we concatenate middle-level features and apply Support Vector Machine to get the final classifier. By combining different types of features, the system we proposed can capture richer biomorphic information than those with a few features. And the experimental results also show that our system achieves better classification performance.","PeriodicalId":382688,"journal":{"name":"OCEANS 2016 - Shanghai","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115329597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Underwater moving source detection based on bearing estimation variance 基于方位估计方差的水下运动源检测

OCEANS 2016 - Shanghai Pub Date : 2016-04-10 DOI: 10.1109/OCEANSAP.2016.7485556

Shao-hua Chen

引用次数: 0

Vibration and radiation from underwater structure in shallow water 浅水水下结构的振动与辐射

OCEANS 2016 - Shanghai Pub Date : 2016-04-10 DOI: 10.1109/OCEANSAP.2016.7485682

Wei Li, Y. Chai, Qifan Zhang, Renchao Ji

{"title":"Vibration and radiation from underwater structure in shallow water","authors":"Wei Li, Y. Chai, Qifan Zhang, Renchao Ji","doi":"10.1109/OCEANSAP.2016.7485682","DOIUrl":"https://doi.org/10.1109/OCEANSAP.2016.7485682","url":null,"abstract":"Prediction of vibration and radiation from large underwater structures is one of the most important research contents in engineering. The cylindrical shell structure is most common structure in engineering, and it is the main component of submarine at the same time. Shells with structural similarity have similar properties due to the similarity principle. Free vibration of the simple cylindrical shell in shallow waters with proper structure mesh and flow field gird is firstly carried out with the usage of finite element method (FEM). The comparison between the FEM result, analytical solution and experimental results gets a satisfactory agreement, indicating the accuracy of FEM approach when used in the fluid-structure interaction problem. A stiffened cylindrical shell structure in shallow waters, taking consideration of the surface and bottom of the waters, is also studied. The natural frequency data and radiation results provide assessment of the influences of water depth, free surface and rigid boundary occurring in the fluid-structure interaction process. The acoustic response in infinite water domain is calculated in parallel. The result shows that the impact of surface, bottom and depth in water on the structure vibration response and the acoustic radiation cannot be ignored, which could provide experimental and numerical introduction in underwater vibration and radiation prediction or measurement.","PeriodicalId":382688,"journal":{"name":"OCEANS 2016 - Shanghai","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114873914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Meshing impact on numerical simulation of marine systems performance 网格对舰船系统性能数值模拟的影响

OCEANS 2016 - Shanghai Pub Date : 2016-04-10 DOI: 10.1109/OCEANSAP.2016.7485363

Lihong Wu, Yiping Li, Huichen Zhang, Xisheng Feng

引用次数: 1

Comparison of two electromagnetic couplers in an inductive power transfer system for Autonomous Underwater Vehicle docking application 两种电磁耦合器在感应动力传输系统中水下机器人对接应用的比较

OCEANS 2016 - Shanghai Pub Date : 2016-04-10 DOI: 10.1109/OCEANSAP.2016.7485443

Wen Hai-bing, Zhang Kehan, Yan Zhengchao, Song Bao-wei

引用次数: 7

On the sign of the waveguide invariant 在波导不变量的符号上

OCEANS 2016 - Shanghai Pub Date : 2016-04-10 DOI: 10.1109/OCEANSAP.2016.7485368

D. Rouseff, L. Zurk

引用次数: 3

Fading characteristics of ship-to-land propagation channel at 5.2 GHz 5.2 GHz舰地传播信道的衰落特性

OCEANS 2016 - Shanghai Pub Date : 2016-04-10 DOI: 10.1109/OCEANSAP.2016.7485529

Wei Wang, R. Raulefs, T. Jost

引用次数: 10

A modular designed dynamic positioning experiment system and its application on a ship model 一种模块化设计的动力定位实验系统及其在船舶模型上的应用

OCEANS 2016 - Shanghai Pub Date : 2016-04-10 DOI: 10.1109/OCEANSAP.2016.7485681

Jian Wang, Jingyang Liu, H. Yi

引用次数: 0