Pub Date : 2019-10-19DOI: 10.1109/OCEANSE.2019.8867176
C. Gilbert, J. Browell, D. McMillan
Access forecasting for offshore wind farm operations is concerned with the prediction of conditions during transfer of personnel between offshore structures and vessels. Currently dispatch/scheduling decisions are typically made on the basis of single-valued forecasts of significant wave height from a numerical weather prediction model. The aim of this study is to move beyond the significant wave height metric using a data-driven methodology to estimate vessel motion during transfer. This is because turbine access is constrained by the behaviour of crew transfer vessels and the transition piece in the local wave climate. Using generalised additive models for location, scale, and shape, we map the relationship between measured vessel heave motion and measured wave conditions in terms of significant wave height, peak wave period, and peak wave direction. This is explored via a case study where measurements are collected via vessel telemetry and an on-site wave buoy during the construction phase of an east coast offshore wind farm in the UK. Different model formulations are explored and the best performing trained model, in terms of the Akaike Information Criterion, is defined. Operationally, this model is driven by temporal scenario forecasts of the input wave buoy measurements to estimate the vessel motion during transfer up to 5 days ahead.
{"title":"A Data-driven Vessel Motion Model for Offshore Access Forecasting","authors":"C. Gilbert, J. Browell, D. McMillan","doi":"10.1109/OCEANSE.2019.8867176","DOIUrl":"https://doi.org/10.1109/OCEANSE.2019.8867176","url":null,"abstract":"Access forecasting for offshore wind farm operations is concerned with the prediction of conditions during transfer of personnel between offshore structures and vessels. Currently dispatch/scheduling decisions are typically made on the basis of single-valued forecasts of significant wave height from a numerical weather prediction model. The aim of this study is to move beyond the significant wave height metric using a data-driven methodology to estimate vessel motion during transfer. This is because turbine access is constrained by the behaviour of crew transfer vessels and the transition piece in the local wave climate. Using generalised additive models for location, scale, and shape, we map the relationship between measured vessel heave motion and measured wave conditions in terms of significant wave height, peak wave period, and peak wave direction. This is explored via a case study where measurements are collected via vessel telemetry and an on-site wave buoy during the construction phase of an east coast offshore wind farm in the UK. Different model formulations are explored and the best performing trained model, in terms of the Akaike Information Criterion, is defined. Operationally, this model is driven by temporal scenario forecasts of the input wave buoy measurements to estimate the vessel motion during transfer up to 5 days ahead.","PeriodicalId":375793,"journal":{"name":"OCEANS 2019 - Marseille","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122671698","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}
Pub Date : 2019-10-14DOI: 10.1109/OCEANSE.2019.8867045
L. Perera, Karen V. Czachorowski
Data driven networks applicable for shipping industrial applications to create decentralized system intelligence are considered in this study. Such system intelligence can facilitate to improve the respective operational efficiency in local (i.e. vessel operations) and global (i.e. logistics operations) scales in shipping as the main advantage. The main features of these data driven networks are summarized in the first part of this study. Two applications of digital models and blockchain technologies are discussed and compared with their features to illustrate their similarities and differences in the second part of this study. A digital model represents a vector based mathematical structure derived from ship performance and navigation data sets and has categorized as a low-level information model. It is also believed that the respective data sets from industrial IoT (internet of things) should go through such low-level models to improve their quality. These data driven networks can be used to quantify ship performance and navigation conditions, where the outcome can also be used to improve vessel energy efficiency and reduce engine emissions in a local scale. A blockchain represents a decentralized, distributed and digital ledger system in a public domain and can handle and record transactions executed by many users. That has categorized as a high-level information model due the high quality data sets from industrial processes that these networks are handling. Such data driven networks can be used to formulate various logistics operations in shipping and optimize their operational conditions in a global scale. The outcomes of these data driven networks can be used to improve operational efficiency and reduce the respective costs in the shipping industry.
{"title":"Decentralized System Intelligence in Data Driven Networks for Shipping Industrial Applications: Digital Models to Blockchain Technologies","authors":"L. Perera, Karen V. Czachorowski","doi":"10.1109/OCEANSE.2019.8867045","DOIUrl":"https://doi.org/10.1109/OCEANSE.2019.8867045","url":null,"abstract":"Data driven networks applicable for shipping industrial applications to create decentralized system intelligence are considered in this study. Such system intelligence can facilitate to improve the respective operational efficiency in local (i.e. vessel operations) and global (i.e. logistics operations) scales in shipping as the main advantage. The main features of these data driven networks are summarized in the first part of this study. Two applications of digital models and blockchain technologies are discussed and compared with their features to illustrate their similarities and differences in the second part of this study. A digital model represents a vector based mathematical structure derived from ship performance and navigation data sets and has categorized as a low-level information model. It is also believed that the respective data sets from industrial IoT (internet of things) should go through such low-level models to improve their quality. These data driven networks can be used to quantify ship performance and navigation conditions, where the outcome can also be used to improve vessel energy efficiency and reduce engine emissions in a local scale. A blockchain represents a decentralized, distributed and digital ledger system in a public domain and can handle and record transactions executed by many users. That has categorized as a high-level information model due the high quality data sets from industrial processes that these networks are handling. Such data driven networks can be used to formulate various logistics operations in shipping and optimize their operational conditions in a global scale. The outcomes of these data driven networks can be used to improve operational efficiency and reduce the respective costs in the shipping industry.","PeriodicalId":375793,"journal":{"name":"OCEANS 2019 - Marseille","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123773564","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}
Pub Date : 2019-06-19DOI: 10.1109/OCEANSE.2019.8867438
Meungsuk Lee, Jason Kim, Son-cheol Yu
Object detection and classification in the water enhances not only the application of the autonomous underwater vehicle(AUV) but also localization of the AUV. Object detection and classification using sonar images are challenging problems due to low resolution and low signal-to-noise ratio. In this paper, we propose shape classification method using multi-view sonar images for AUV. To train multi-view of sonar images, we used network which is connected in parallel with convolutional neural network(CNN). We used Alex-net for the basic CNN model. The extracted features by the CNN are collected through the pooling layer and connected to the fully connected layer to classify the shape. To overcome the lack of training data, sonar simulator was used to generate data set. As a result, 6 shape are well classified and also shows possibility for the recognition of the real sonar images acquired in water tank.
{"title":"Robust 3D Shape Classification Method using Simulated Multi View Sonar Images and Convolutional Nueral Network","authors":"Meungsuk Lee, Jason Kim, Son-cheol Yu","doi":"10.1109/OCEANSE.2019.8867438","DOIUrl":"https://doi.org/10.1109/OCEANSE.2019.8867438","url":null,"abstract":"Object detection and classification in the water enhances not only the application of the autonomous underwater vehicle(AUV) but also localization of the AUV. Object detection and classification using sonar images are challenging problems due to low resolution and low signal-to-noise ratio. In this paper, we propose shape classification method using multi-view sonar images for AUV. To train multi-view of sonar images, we used network which is connected in parallel with convolutional neural network(CNN). We used Alex-net for the basic CNN model. The extracted features by the CNN are collected through the pooling layer and connected to the fully connected layer to classify the shape. To overcome the lack of training data, sonar simulator was used to generate data set. As a result, 6 shape are well classified and also shows possibility for the recognition of the real sonar images acquired in water tank.","PeriodicalId":375793,"journal":{"name":"OCEANS 2019 - Marseille","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121704062","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}
Pub Date : 2019-06-18DOI: 10.1109/OCEANSE.2019.8867531
Junwoo Jang, Jinwhan Kim
Bathymetric navigation enables the long-term operation of autonomous underwater vehicles by reducing navigation drift errors with no need for GPS position fixes. In the case that a bathymetric map is not available, the simultaneous localization and mapping (SLAM) algorithm is required, but this increases computational complexity and memory requirement. Panel-based bathymetric SLAM could considerably reduce the computational burden. However, it may suffers from incorrect update when the vehicle does not belong to the updated panel. This study proposes a new update method, called weighted grid partitioning, which considers the probability distribution of a vehicle's location, and is more effective in terms of the map accuracy, computational burden, and memory usage compared to standard update methods. The feasibility of the proposed algorithm is verified through simulations.
{"title":"Weighted Grid Partitioning for Panel-Based Bathymetric SLAM","authors":"Junwoo Jang, Jinwhan Kim","doi":"10.1109/OCEANSE.2019.8867531","DOIUrl":"https://doi.org/10.1109/OCEANSE.2019.8867531","url":null,"abstract":"Bathymetric navigation enables the long-term operation of autonomous underwater vehicles by reducing navigation drift errors with no need for GPS position fixes. In the case that a bathymetric map is not available, the simultaneous localization and mapping (SLAM) algorithm is required, but this increases computational complexity and memory requirement. Panel-based bathymetric SLAM could considerably reduce the computational burden. However, it may suffers from incorrect update when the vehicle does not belong to the updated panel. This study proposes a new update method, called weighted grid partitioning, which considers the probability distribution of a vehicle's location, and is more effective in terms of the map accuracy, computational burden, and memory usage compared to standard update methods. The feasibility of the proposed algorithm is verified through simulations.","PeriodicalId":375793,"journal":{"name":"OCEANS 2019 - Marseille","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128849954","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}
Pub Date : 2019-06-17DOI: 10.1109/OCEANSE.2019.8867095
Marie Lamouret, Arnaud Abadie, C. Viala, P. Boissery, N. Thirion-Moreau
The modern multi-beam echo sounders (MBES) are advanced instrumentation for active underwater acoustic surveys that can be boarded on oceanic vessels as well on light crafts. Although their versatility allows scientists to perform various environmental studies, their potential is seldom fully exploited. A single data acquisition cruise is not only able to display the seabed backscatter, but also provide an estimation of the fish activities from an underwater site thanks to water column imagery. This work is aiming at developing some (automatic) signal processing techniques to detect, analyse and classify objects observed in the water column with a focus on fish activities to provide fish accumulation and classification but also some comparative analyses along with the seafloor classification.
{"title":"Measuring fish activities as additional environmental data during a hydrographic survey with a multi-beam echo sounder","authors":"Marie Lamouret, Arnaud Abadie, C. Viala, P. Boissery, N. Thirion-Moreau","doi":"10.1109/OCEANSE.2019.8867095","DOIUrl":"https://doi.org/10.1109/OCEANSE.2019.8867095","url":null,"abstract":"The modern multi-beam echo sounders (MBES) are advanced instrumentation for active underwater acoustic surveys that can be boarded on oceanic vessels as well on light crafts. Although their versatility allows scientists to perform various environmental studies, their potential is seldom fully exploited. A single data acquisition cruise is not only able to display the seabed backscatter, but also provide an estimation of the fish activities from an underwater site thanks to water column imagery. This work is aiming at developing some (automatic) signal processing techniques to detect, analyse and classify objects observed in the water column with a focus on fish activities to provide fish accumulation and classification but also some comparative analyses along with the seafloor classification.","PeriodicalId":375793,"journal":{"name":"OCEANS 2019 - Marseille","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123047995","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}
Pub Date : 2019-06-17DOI: 10.1109/OCEANSE.2019.8867341
C. Gervaise, J. Lossent, L. Di Iorio, L. Béguery, Romain Tricarico, P. Boissery, Cathy-Anna Valentini- Poirier
In response to concerns about the impact of manmade noise on marine ecosystems, research and regulatory communities are currently collecting in situ measurements of oceanic noise levels and developing models that map the levels of underwater noise at large scales, forecasting the impact of shipping noise on marine fauna. The objectives of in situ measurements are twofold. First, they provide necessary inputs, i.e. the acoustic signatures of individual ships to feed the models. Second, they are used to calibrate the model and adjust its parameters. The usefulness of the data collected depends on the duration of acquisition and measurement diversity (e.g., shipping density, water depth). Gliders are ideal candidates to collect noise level data across oceanic basins and over long time periods. Here we show results from a SeaExplorer glider equipped with a high quality acoustics payload travelling for 30 days along a 1000km-long transect of the Western French Mediterranean Sea. The trajectory of the glider was chosen to sample the highest and lowest shipping densities. We here report on:–the statistical distribution of oceanic noise levels in the bandwidths assessed by the European Marine Framework Strategy Directive,–the anthropogenic contribution of shipping to the global noise budget and the acoustic footprint of main shipping lanes,–comparisons of the lowest Mediterranean ambient noise levels to the ones of a pristine area with regard to shipping noise,–comparisons between long term coastal fixed mooring measurements ( 3 continuous points) and glider measurements and assessment of the pro and cons of each method (fixed mooring and glider).
{"title":"Mapping underwater noise with a SeaExplorer glider at a basin level: Feedback from the first 1000km-long acoustics exploration of the Western French Mediterranean Sea","authors":"C. Gervaise, J. Lossent, L. Di Iorio, L. Béguery, Romain Tricarico, P. Boissery, Cathy-Anna Valentini- Poirier","doi":"10.1109/OCEANSE.2019.8867341","DOIUrl":"https://doi.org/10.1109/OCEANSE.2019.8867341","url":null,"abstract":"In response to concerns about the impact of manmade noise on marine ecosystems, research and regulatory communities are currently collecting in situ measurements of oceanic noise levels and developing models that map the levels of underwater noise at large scales, forecasting the impact of shipping noise on marine fauna. The objectives of in situ measurements are twofold. First, they provide necessary inputs, i.e. the acoustic signatures of individual ships to feed the models. Second, they are used to calibrate the model and adjust its parameters. The usefulness of the data collected depends on the duration of acquisition and measurement diversity (e.g., shipping density, water depth). Gliders are ideal candidates to collect noise level data across oceanic basins and over long time periods. Here we show results from a SeaExplorer glider equipped with a high quality acoustics payload travelling for 30 days along a 1000km-long transect of the Western French Mediterranean Sea. The trajectory of the glider was chosen to sample the highest and lowest shipping densities. We here report on:–the statistical distribution of oceanic noise levels in the bandwidths assessed by the European Marine Framework Strategy Directive,–the anthropogenic contribution of shipping to the global noise budget and the acoustic footprint of main shipping lanes,–comparisons of the lowest Mediterranean ambient noise levels to the ones of a pristine area with regard to shipping noise,–comparisons between long term coastal fixed mooring measurements ( 3 continuous points) and glider measurements and assessment of the pro and cons of each method (fixed mooring and glider).","PeriodicalId":375793,"journal":{"name":"OCEANS 2019 - Marseille","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116584185","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}
Pub Date : 2019-06-17DOI: 10.1109/OCEANSE.2019.8867417
Martin Zurowietz, Daniel Langenkämper, T. Nattkemper
Digital imaging is gaining more and more attention in marine environmental monitoring and exploration. Nowadays, mobile platforms such as autonomous underwater vehicles (AUVs) are equipped with high-resolution cameras that can collect gigabytes of digital images in a single dive. To extract quantitative and qualitative information from the accumulating image collections, annotation tools such as BIIGLE 2.0 have been proposed recently and have been established in the data analysis workflow. These tools run as web applications on a central server and can be accessed worldwide via the Internet. However, marine science and engineering are naturally associated with a high degree of mobility and, in some cases, limited resources or Internet access. Here we present a new application architecture for BIIGLE 2.0, which is particularly suitable for offshore deployment on a variety of platforms such as a server, workstation, laptop or even a small single-board computer such as a Raspberry Pi. We refer to the application architecture in combination with a mobile hardware platform as "BIIGLE2Go", which addresses the need for more flexibility and mobility in image annotation. We present and evaluate a first prototype for BIIGLE2Go, which runs as a mobile annotation system on a low-cost Raspberry Pi 3B.
{"title":"BIIGLE2Go—A scalable image annotation system for easy deployment on cruises","authors":"Martin Zurowietz, Daniel Langenkämper, T. Nattkemper","doi":"10.1109/OCEANSE.2019.8867417","DOIUrl":"https://doi.org/10.1109/OCEANSE.2019.8867417","url":null,"abstract":"Digital imaging is gaining more and more attention in marine environmental monitoring and exploration. Nowadays, mobile platforms such as autonomous underwater vehicles (AUVs) are equipped with high-resolution cameras that can collect gigabytes of digital images in a single dive. To extract quantitative and qualitative information from the accumulating image collections, annotation tools such as BIIGLE 2.0 have been proposed recently and have been established in the data analysis workflow. These tools run as web applications on a central server and can be accessed worldwide via the Internet. However, marine science and engineering are naturally associated with a high degree of mobility and, in some cases, limited resources or Internet access. Here we present a new application architecture for BIIGLE 2.0, which is particularly suitable for offshore deployment on a variety of platforms such as a server, workstation, laptop or even a small single-board computer such as a Raspberry Pi. We refer to the application architecture in combination with a mobile hardware platform as \"BIIGLE2Go\", which addresses the need for more flexibility and mobility in image annotation. We present and evaluate a first prototype for BIIGLE2Go, which runs as a mobile annotation system on a low-cost Raspberry Pi 3B.","PeriodicalId":375793,"journal":{"name":"OCEANS 2019 - Marseille","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122565347","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}
Pub Date : 2019-06-17DOI: 10.1109/OCEANSE.2019.8867091
V. Meyer, C. Audoly
In the context of the increase of human activities in coastal areas, awareness of the impact on marine mammals has risen. Following the Maritime Strategy Framework Directive of the European Union in 2008, actions have been undertaken to achieve good environmental status of the European seas. For instance, one of the topics is the reduction of the underwater noise, which is mainly due to commercial traffic. In order to set guidelines and propose solutions to reduce the radiated noise from commercial ships, there is a need to define a procedure to measure the underwater sound from ships. In 2016, the ISO committee on underwater acoustics has published a standard describing the experimental procedure to measure the underwater sound from ships, under the reference ISO17208-1. This procedure is intended for deep water environments, i.e. for environments with a minimum depth of 150 m or 1.5 times the overall ship length. A second part, not published yet, has been written to correct the measured data from the reflection of the acoustic waves on the sea surface, effect known as the Lloyd’s mirror effect. In some maritime areas, it can be difficult to find trial zones with sufficient water depths and the measurements can only be done in shallow waters. It is well known that in a shallow water environment, it is difficult to assess the level of a sound source, because of the multiple reflections of the acoustic waves on the bottom and on the sea surface. The aim of this study is to understand which parameters influence the sound measured by a hydrophone array in such a configuration, assuming that the source level is known. Knowing the environment, the sound level measured from each hydrophone on the array is calculated using the open source underwater propagation toolbox AcTUP. The levels are then corrected by the distance and quadratically summed over all the hydrophones, according to the procedure described in ISO17208-1 for deep water measurements. At low frequencies, the radiation of the source is similar to a dipole because of the Lloyd’s mirror effect. At high frequencies, the third-octave bands level tends to a constant number with respect to frequency. It can be shown that the constant value depends on the ratio of water depth to distance to source, and to the sea floor properties. The influence of different parameters is successively investigated: number of hydrophones, sea bottom properties, speed of sound profile. Based on the simulations, empirical formulas are put forward in order to correct the effect of the shallow water environment.
{"title":"Influence of the measurement configuration for the assessment of underwater noise radiated from ships in shallow water","authors":"V. Meyer, C. Audoly","doi":"10.1109/OCEANSE.2019.8867091","DOIUrl":"https://doi.org/10.1109/OCEANSE.2019.8867091","url":null,"abstract":"In the context of the increase of human activities in coastal areas, awareness of the impact on marine mammals has risen. Following the Maritime Strategy Framework Directive of the European Union in 2008, actions have been undertaken to achieve good environmental status of the European seas. For instance, one of the topics is the reduction of the underwater noise, which is mainly due to commercial traffic. In order to set guidelines and propose solutions to reduce the radiated noise from commercial ships, there is a need to define a procedure to measure the underwater sound from ships. In 2016, the ISO committee on underwater acoustics has published a standard describing the experimental procedure to measure the underwater sound from ships, under the reference ISO17208-1. This procedure is intended for deep water environments, i.e. for environments with a minimum depth of 150 m or 1.5 times the overall ship length. A second part, not published yet, has been written to correct the measured data from the reflection of the acoustic waves on the sea surface, effect known as the Lloyd’s mirror effect. In some maritime areas, it can be difficult to find trial zones with sufficient water depths and the measurements can only be done in shallow waters. It is well known that in a shallow water environment, it is difficult to assess the level of a sound source, because of the multiple reflections of the acoustic waves on the bottom and on the sea surface. The aim of this study is to understand which parameters influence the sound measured by a hydrophone array in such a configuration, assuming that the source level is known. Knowing the environment, the sound level measured from each hydrophone on the array is calculated using the open source underwater propagation toolbox AcTUP. The levels are then corrected by the distance and quadratically summed over all the hydrophones, according to the procedure described in ISO17208-1 for deep water measurements. At low frequencies, the radiation of the source is similar to a dipole because of the Lloyd’s mirror effect. At high frequencies, the third-octave bands level tends to a constant number with respect to frequency. It can be shown that the constant value depends on the ratio of water depth to distance to source, and to the sea floor properties. The influence of different parameters is successively investigated: number of hydrophones, sea bottom properties, speed of sound profile. Based on the simulations, empirical formulas are put forward in order to correct the effect of the shallow water environment.","PeriodicalId":375793,"journal":{"name":"OCEANS 2019 - Marseille","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129491834","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}
Pub Date : 2019-06-17DOI: 10.1109/OCEANSE.2019.8867366
N. Yoder, Victoria L. Preston, A. Michel
Water quality monitoring is a critical task for safeguarding human health, understanding ecosystem balance, and informing regulatory policy in waterway use and maintenance. Direct bottle sampling is the standard for most water quality analysis, however it is limited in both space and time resolution by virtue of ex situ analysis. This inspires the need for in situ observation systems. Unmanned mobile platforms provide the capability for real-time response and spatial coverage. Current platforms for water quality monitoring tend to be expensive to build and maintain, or are large and difficult to deploy. Since even basic water measurements (e.g., temperature, pH) provide useful information about the health of an environment, we leverage the use of open-source low-cost probes on a small unmanned platform. The Platform for Environmental Sensing Technology (PEST), is a first prototype towards a persistent low-cost unmanned water quality monitoring solution for shallow, narrow, and difficult environments that is suitable for deployment by non-robotics experts.
{"title":"The PEST: Platform for Environmental Sensing Technology","authors":"N. Yoder, Victoria L. Preston, A. Michel","doi":"10.1109/OCEANSE.2019.8867366","DOIUrl":"https://doi.org/10.1109/OCEANSE.2019.8867366","url":null,"abstract":"Water quality monitoring is a critical task for safeguarding human health, understanding ecosystem balance, and informing regulatory policy in waterway use and maintenance. Direct bottle sampling is the standard for most water quality analysis, however it is limited in both space and time resolution by virtue of ex situ analysis. This inspires the need for in situ observation systems. Unmanned mobile platforms provide the capability for real-time response and spatial coverage. Current platforms for water quality monitoring tend to be expensive to build and maintain, or are large and difficult to deploy. Since even basic water measurements (e.g., temperature, pH) provide useful information about the health of an environment, we leverage the use of open-source low-cost probes on a small unmanned platform. The Platform for Environmental Sensing Technology (PEST), is a first prototype towards a persistent low-cost unmanned water quality monitoring solution for shallow, narrow, and difficult environments that is suitable for deployment by non-robotics experts.","PeriodicalId":375793,"journal":{"name":"OCEANS 2019 - Marseille","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130570534","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}
Pub Date : 2019-06-17DOI: 10.1109/OCEANSE.2019.8867085
Yue Li, Xiaochuan Ma, Yu Liu, Lei Wang, Xuan Li, Dongyu Yuan
Underwater acoustic pulse interception is an important task for underwater signal processing system, including the detection and identification of unknown acoustic pulses. An acoustic pulse interception method based on deep learning is proposed. The interception system consists of a pulse detection network and a DOA estimation network. The pulse detection neural network is used to achieve multi-pulse detection and bounding box inference on the spectrogram. The phase component of the short-time Fourier transform coefficients in the time-frequency bounding box is extracted. Then the DOA estimation network learns the phase feature to figure out the direction of arrival of each detected pulse by regression. Finally, the number of sources and their DOA estimates could be obtained through such operations as outlier removal and data fusion. Simulation results show that this method is able to achieve reliable pulse detection, source number estimation and high precision DOA estimation in underwater acoustic environment.
{"title":"Application of Deep Object Detection in Underwater Acoustic Pulse Interception","authors":"Yue Li, Xiaochuan Ma, Yu Liu, Lei Wang, Xuan Li, Dongyu Yuan","doi":"10.1109/OCEANSE.2019.8867085","DOIUrl":"https://doi.org/10.1109/OCEANSE.2019.8867085","url":null,"abstract":"Underwater acoustic pulse interception is an important task for underwater signal processing system, including the detection and identification of unknown acoustic pulses. An acoustic pulse interception method based on deep learning is proposed. The interception system consists of a pulse detection network and a DOA estimation network. The pulse detection neural network is used to achieve multi-pulse detection and bounding box inference on the spectrogram. The phase component of the short-time Fourier transform coefficients in the time-frequency bounding box is extracted. Then the DOA estimation network learns the phase feature to figure out the direction of arrival of each detected pulse by regression. Finally, the number of sources and their DOA estimates could be obtained through such operations as outlier removal and data fusion. Simulation results show that this method is able to achieve reliable pulse detection, source number estimation and high precision DOA estimation in underwater acoustic environment.","PeriodicalId":375793,"journal":{"name":"OCEANS 2019 - Marseille","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116371380","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}
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