2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO)最新文献_第10页

Ocean-Atmosphere Observations in Philippine Sea by Moored Buoy 菲律宾海系泊浮标海洋大气观测

2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO) Pub Date : 2018-05-28 DOI: 10.1109/OCEANSKOBE.2018.8558886

A. Nagano, I. Ueki, T. Hasegawa, K. Ando

{"title":"Ocean-Atmosphere Observations in Philippine Sea by Moored Buoy","authors":"A. Nagano, I. Ueki, T. Hasegawa, K. Ando","doi":"10.1109/OCEANSKOBE.2018.8558886","DOIUrl":"https://doi.org/10.1109/OCEANSKOBE.2018.8558886","url":null,"abstract":"Offequatorial extension of equatorial buoy arrays such as Tropical Atmosphere and Ocean/Triangle Trans-Ocean Buoy Network (TAO/TRITON) buoy array is required to monitor global and regional climates. On December 3, 2016, Japan Agency for Marine-Earth Science and Technology (JAMSTEC) deployed a moored buoy (Ph buoy) at 13°N, 137° E in the Philippine Sea and are measuring temperature, salinity, and dissolved oxygen concentration from the sea surface to 300 m and atmospheric parameters. The Philippine Sea is located in the northern edge of the western Pacific warm pool, where atmospheric and oceanic disturbances on timescales from days to decades such as typhoons, cold surges, seasonal march of the East Asian monsoon, Madden-Julian Oscillation, El Niño and Southern Oscillation, and Pacific Decadal Oscillation occur. Global and regional climate changes possibly increase the frequency of occurrences of extreme weather events. Sensors installed on the buoy tower observed tropical depressions in the winter of 2016/2017. In addition, the development and annihilation of the barrier layer, which exists between the bases of the isothermal and mixed layers, were observed by the underwater sensors. The data collected at this mooring site serve to researches on extreme weather events.","PeriodicalId":441405,"journal":{"name":"2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121617779","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: 2

Underwater Channel Sounder (UCS) for Characterizing Radio Propagation in Seawater 水下信道测深仪(UCS)用于表征海水中的无线电传播

2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO) Pub Date : 2018-05-28 DOI: 10.1109/OCEANSKOBE.2018.8559459

K. Takizawa, T. Matsuda, F. Kojima, Ryotaro Suga, H. Yoshida

引用次数: 5

Automated Interpretation of Seafloor Visual Maps Obtained Using Underwater Robots 使用水下机器人获得的海底视觉地图的自动解释

2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO) Pub Date : 2018-05-28 DOI: 10.1109/OCEANSKOBE.2018.8559247

Jin Wei Lim, A. Prügel-Bennett, B. Thornton

{"title":"Automated Interpretation of Seafloor Visual Maps Obtained Using Underwater Robots","authors":"Jin Wei Lim, A. Prügel-Bennett, B. Thornton","doi":"10.1109/OCEANSKOBE.2018.8559247","DOIUrl":"https://doi.org/10.1109/OCEANSKOBE.2018.8559247","url":null,"abstract":"Scientific surveys using underwater robots can recover a huge volume of seafloor imagery. For mapping applications, these images can be packaged into vast, seamless and georeferenced seafloor visual reconstructions in a routine way, however interpreting this data to extract useful quantitative information typically relies on the manual effort of expert human annotators. This process is often slow and is a bottleneck in the flow of information. This work explores the feasibility of using Machine Learning tools, specifically Convolutional Neural Networks (CNNs) to at least partially automate the annotation process. A CNN was constructed to identify Shinkaia Crosnieri galetheid crabs and Bathymodiolus mussels, which are two distinct megabenthic taxa found in vast numbers in hydrothermally active regions of the seafloor. The CNN was trained with varying numbers of annotated data, where each annotation consisted of a small region surrounding a positive label at the centre of each individual within a seamless seafloor image reconstruction. The performance was assessed using an independent set of annotated data, taken from a separate reconstruction located approximately 500 m away. While the results show that the trained network can be used to classify new datasets at well characterized levels of uncertainty, the performance was found to vary between the different taxa and with a control dataset that showed only unpopulated regions of the seafloor. The analysis suggests that the number of training examples required to achieve a given level of accuracy is subject dependent, and this should be considered by humans when devising annotation strategies that make best use of their efforts to leverage the advantages offered by CNNs.","PeriodicalId":441405,"journal":{"name":"2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127249332","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: 0

A Sparse Bayesian Learning Based Joint Channel and Impulsive Noise Estimation Algorithm for Underwater Acoustic OFDM Systems 基于稀疏贝叶斯学习的水声OFDM联合信道和脉冲噪声估计算法

2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO) Pub Date : 2018-05-28 DOI: 10.1109/OCEANSKOBE.2018.8559054

Shuche Wang, Zhiqiang He, K. Niu, Peng Chen, Y. Rong

引用次数: 7

Underwater Acoustic Communication Using Multiple-Input Multiple-Output Doppler-Resilient Orthogonal Signal Division Multiplexing 基于多输入多输出多普勒弹性正交信号分复用的水声通信

2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO) Pub Date : 2018-05-28 DOI: 10.1109/OCEANSKOBE.2018.8559404

T. Ebihara, G. Leus, H. Ogasawara

引用次数: 6

Wideband Signals for Phase Differencing Sonar Systems 相位差声纳系统的宽带信号

2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO) Pub Date : 2018-05-28 DOI: 10.1109/OCEANSKOBE.2018.8559146

Jitendra S. Sewada, C. Ioana, M. Geen, J. Mars

引用次数: 1

Shallow Water Sound Speed Estimation with Neural Networks-Based Nonlinear Regression of Space-Time Variability 基于神经网络时空变异性非线性回归的浅水声速估计

2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO) Pub Date : 2018-05-28 DOI: 10.1109/OCEANSKOBE.2018.8559072

E. Zheldak, V. Petukhov, Kiseon Kim

{"title":"Shallow Water Sound Speed Estimation with Neural Networks-Based Nonlinear Regression of Space-Time Variability","authors":"E. Zheldak, V. Petukhov, Kiseon Kim","doi":"10.1109/OCEANSKOBE.2018.8559072","DOIUrl":"https://doi.org/10.1109/OCEANSKOBE.2018.8559072","url":null,"abstract":"Traditional way to estimate propagation losses in a region with no actual measures is to use oceanographic climatologies, built from archived data. Usually such statistical models have 0.25°-1° resolution. While it is enough for large-scale ocean acoustic simulation, higher-resolution climatology reflects regional ocean state better. With increasing of resolution, size of the models also increases, which makes it difficult to use them in small autonomous underwater systems, such as underwater sensor networks nodes, where space and power resources are limited. To minimize the size of a model the artificial neural network regression is proposed. To check applicability of method, shallow water area near Jeju island (East China Sea) was choosen. Set of neural networks was trained on data from World Ocean Database 2013. To estimate the error of sound speed profile reconstruction data from SAVEX15 shallow water acoustic experiment was used. Although the RMS error of prediction was high, vertical gradients of sound speed profile was reconstructed with good accuracy, which was shown using propagation loss calculations.","PeriodicalId":441405,"journal":{"name":"2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO)","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128651540","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

An Arrival-Structure-Dependent Formula to Calculate Source Depth in Deep Water 基于到达结构的深水震源深度计算公式

2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO) Pub Date : 2018-05-28 DOI: 10.1109/OCEANSKOBE.2018.8559266

Rui Duan, Kunde Yang

引用次数: 0

Hydrodynamic and Flow Noise Analysis of Floating Underwater Anchored Platform 浮式水下锚定平台水动力与流动噪声分析

2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO) Pub Date : 2018-05-28 DOI: 10.1109/OCEANSKOBE.2018.8559399

Zhen Wang, Yi Zheng, Qun Yang, Yue Zhang, Jin-yan Du

引用次数: 0

Clustering Sound Speed Profiles in East China Sea 东海声速聚集剖面

2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO) Pub Date : 2018-05-28 DOI: 10.1109/OCEANSKOBE.2018.8559172

Y. Liu, Cheng Chen

引用次数: 1