scholarly journals Augmenting biologging with supervised machine learning to studyin situbehavior of the medusaChrysaora fuscescens

2019 ◽  
Author(s):  
Clara Fannjiang ◽  
T. Aran Mooney ◽  
Seth Cones ◽  
David Mann ◽  
K. Alex Shorter ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Laboratory Data ◽  
Supervised Machine Learning ◽  
Motion Sensors ◽  
Fine Scale ◽  
Video Footage ◽  
Research Findings ◽  
Summary Statement

AbstractZooplankton occupy critical roles in marine ecosystems, yet their fine-scale behavior remains poorly understood due to the difficulty of studying individualsin situ. Here we combine biologging with supervised machine learning (ML) to demonstrate a pipeline for studyingin situbehavior of larger zooplankton such as jellyfish. We deployed the ITAG, a biologging package with high-resolution motion sensors designed for soft-bodied invertebrates, on 8Chrysaora fuscescensin Monterey Bay, using the tether method for retrieval. Using simultaneous video footage of the tagged jellyfish, we develop ML methods to 1) identify periods of tag data corrupted by the tether method, which may have compromised prior research findings, and 2) classify jellyfish behaviors. Our tools yield characterizations of fine-scale jellyfish activity and orientation over long durations, and provide evidence that developing behavioral classifiers onin siturather than laboratory data is essential.Summary StatementHigh-resolution motion sensors paired with supervised machine learning can be used to infer fine-scalein situbehavior of zooplankton for long durations.

scholarly journals Identification of abnormal tribological regimes using a microphone and semi-supervised machine-learning algorithm

Friction ◽  
2021 ◽  
Author(s):  
Vigneashwara Pandiyan ◽  
Josef Prost ◽  
Georg Vorlaufer ◽  
Markus Varga ◽  
Kilian Wasmer
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Industrial Applications ◽  
Supervised Machine Learning ◽  
Normal Operation ◽  
Promising Alternative ◽  
Wear And Tear ◽  
Validation Procedure ◽  
And Performance

AbstractFunctional surfaces in relative contact and motion are prone to wear and tear, resulting in loss of efficiency and performance of the workpieces/machines. Wear occurs in the form of adhesion, abrasion, scuffing, galling, and scoring between contacts. However, the rate of the wear phenomenon depends primarily on the physical properties and the surrounding environment. Monitoring the integrity of surfaces by offline inspections leads to significant wasted machine time. A potential alternate option to offline inspection currently practiced in industries is the analysis of sensors signatures capable of capturing the wear state and correlating it with the wear phenomenon, followed by in situ classification using a state-of-the-art machine learning (ML) algorithm. Though this technique is better than offline inspection, it possesses inherent disadvantages for training the ML models. Ideally, supervised training of ML models requires the datasets considered for the classification to be of equal weightage to avoid biasing. The collection of such a dataset is very cumbersome and expensive in practice, as in real industrial applications, the malfunction period is minimal compared to normal operation. Furthermore, classification models would not classify new wear phenomena from the normal regime if they are unfamiliar. As a promising alternative, in this work, we propose a methodology able to differentiate the abnormal regimes, i.e., wear phenomenon regimes, from the normal regime. This is carried out by familiarizing the ML algorithms only with the distribution of the acoustic emission (AE) signals captured using a microphone related to the normal regime. As a result, the ML algorithms would be able to detect whether some overlaps exist with the learnt distributions when a new, unseen signal arrives. To achieve this goal, a generative convolutional neural network (CNN) architecture based on variational auto encoder (VAE) is built and trained. During the validation procedure of the proposed CNN architectures, we were capable of identifying acoustics signals corresponding to the normal and abnormal wear regime with an accuracy of 97% and 80%. Hence, our approach shows very promising results for in situ and real-time condition monitoring or even wear prediction in tribological applications.

scholarly journals Coupling physics and biogeochemistry thanks to high resolution observations of the phytoplankton community structure in the North-Western Mediterranean Sea

2017 ◽  
Author(s):  
Pierre Marrec ◽  
Andrea M. Doglioli ◽  
Gérald Grégori ◽  
Mathilde Dugenne ◽  
Alice Della Penna ◽  
...  
Keyword(s):  
High Resolution ◽  
Surface Waters ◽  
Phytoplankton Community ◽  
Western Mediterranean ◽  
Fine Scale ◽  
Phytoplankton Community Structure ◽  
The North ◽  
Cold Core ◽  
North Western

Abstract. Fine-scale physical structures and ocean dynamics strongly influence and regulate biogeochemical and ecological processes. These processes are particularly challenging to describe and understand because of their ephemeral nature. The OSCAHR (Observing Submesoscale Coupling At High Resolution) campaign has been conducted in fall 2015 in which, a fine-scale structure in the North Western Mediterranean Ligurian subbasin was pre-identified using both satellite and numerical modeling data. Along the ship track, various variables were measured at the surface (temperature, salinity, chlorophyll-a and nutrients concentrations) with ADCP current velocity. We also deployed a new model of CytoSense automated flow cytometer (AFCM) optimized for small and dim cells, for near real-time characterization of surface phytoplankton community structure of surface waters with a spatial resolution of few km and a hourly temporal resolution. For the first time with this type of AFCM we were able to resolve Prochlorococcus and Synechococcus picocyanobacteria. The vertical physical dynamics and biogeochemical properties of the studied area were investigated by continuous high resolution CTD profiles thanks to a moving vessel profiler (MVP) during the vessel underway associated to a 1-m vertical resolution pumping system deployed during fixed stations. The observed fine-scale feature presented a cyclonic structure with a relatively cold core surrounded by warmer waters. Surface waters were totally depleted in nitrate and phosphate. In addition to the doming of the isopycnals by the cyclonic circulation, an intense wind event induced Ekman pumping. The upwelled subsurface cold nutrient-rich water fertilized surface waters, characterized by an increase in Chl-a concentration. Prochlorococcus, pico- and nano-eukaryotes were more abundant in cold core waters while Synechococcus dominated in warm boundary waters. Nanoeukaryote were the main contributors (> 50 %) in terms of pigment content (FLR) and biomass. Biological observations based on the mean cell's red fluorescence recorded by AFCM combined with physical properties of surface waters suggest a distinct origin for two warm boundary waters. Finally, the application of a matrix growth population model based on high-frequency AFCM measurements in warm boundary surface waters provides estimates of in-situ growth rate and apparent net primary production for Prochlorococcus (μ = 0.21 d−1, NPP = 0.11 mgC m−3 d−1) and Synechococcus (μ = 0.72 d−1, NPP = 2.68 mgC m−3 d−1), which corroborate their opposite surface distribution pattern. The innovative adaptive strategy applied during OSCAHR with a combination of several multidisciplinary and complementary approaches involving high-resolution in-situ observations and sampling, remote-sensing and model simulations provided a deeper understanding of the marine biogeochemical dynamics through the first trophic levels.

scholarly journals Portraying Gradients of Structural Complexity in Coral Reefs Using Fine-Scale Depth Profiles

2021 ◽  
Vol 8 ◽  
Author(s):  
Lauriane Ribas-Deulofeu ◽  
Pierre-Alexandre Château ◽  
Vianney Denis ◽  
Chaolun Allen Chen
Keyword(s):  
High Resolution ◽  
Surface Profile ◽  
Structural Complexity ◽  
Mean Square ◽  
Fine Scale ◽  
Coarse Scale ◽  
Predator Prey ◽  
Depth Profiles ◽  
Multiple Levels

Structural complexity is an important feature to understand reef resilience abilities, through its role in mediating predator-prey interactions, regulating competition, and promoting recruitment. Most of the current methods used to measure reef structural complexity fail to quantify the contributions of fine and coarse scales of rugosity simultaneously, while other methods require heavy data computation. In this study, we propose estimating reef structural complexity based on high-resolution depth profiles to quantify the contributions of both fine and coarse rugosities. We adapted the root mean square of the deviation from the assessed surface profile (Rq) with polynomials. The efficiency of the proposed method was tested on nine theoretical cases and 50 in situ transects from South Taiwan, and compared to both the chain method and the visual rugosity index commonly employed to characterize reef structural complexity. The Rq indices proposed as rugosity estimators in this study consider multiple levels of reef rugosity, which the chain method and the visual rugosity index fail to apprehend. Furthermore, relationships were found between Rq scores and specific functional groups in the benthic community. Indeed, the fine scale rugosity of the South Taiwan reefs mainly comes from biotic components such as hard corals, while their coarse scale rugosity is essentially provided by the topographic variations that reflect the geological context of the reefs. This approach allows identifying the component of the rugosity that could be managed and which could, ultimately, improve strategies designed for conservation.

Coupling machine learning with high resolution satellite imagery to estimate spatiotemporal changes of salinity in water bodies

2020 ◽  
Author(s):  
Majid Bayati ◽  
Mohammad Danesh-Yazdi
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Water Quality ◽  
Artificial Neural Networks ◽  
High Resolution ◽  
Satellite Imagery ◽  
Water Bodies ◽  
Wide Range ◽  
High Resolution Satellite Imagery

<p>The spatiotemporal dynamics of salinity in hypersaline lakes is strongly dependent on the rate of water flow feeding the lake, evaporation rate, and the phenomena of precipitation and dissolution. Although in-situ observations are most reliable in quantifying water quality variables, the spatiotemporal distribution of such data are typically limited or cannot be readily extrapolated for long-term projections. Alternatively, remotely-sensed imagery has facilitated less expensive and stronger ability to estimate water quality over a wide range of spatiotemporal resolutions. This study introduces a machine learning model that leverages in-situ measurements and high-resolution satellite imagery to estimate the salinity concentration in water bodies. To this end, 123 points were sampled in April and July of 2019 across the Lake Urmia surface covering the wide range of salinity fluctuations. Among the artificial neural networks, ANFIS, and linear regression tools examined to determine the relationship between salinity and surface reflectance, artificial neural networks yielded the best accuracy evidenced by R<sup>2</sup> = 0.94 and RMSE = 6.8%. The results show that the seasonal change of salinity is linearly correlated with the volume of water feeding the lake, witnessing that dilution imposes a stronger control on the salinity than bed salt dissolution. The impact of disturbance in the lake circulation due to the causeway is also evident from the sharp changes of salinity around the bridge piers near spring when the mixing of fresh and hypersaline water from the southern and northern parts, respectively, takes place. The results of this study prove the promising potential of machine learning tools fed multi-spectral satellite information to map other water quality metrics than salinity as well.</p>

Artificial intelligence for discrimination of sediment facies based on high-resolution elemental and colour data from coastal sediments of the East Frisian Wadden Sea, Germany

2020 ◽  
Author(s):  
An-Sheng Lee ◽  
Dirk Enters ◽  
Sofia Ya Hsuan Liou ◽  
Bernd Zolitschka
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Wadden Sea ◽  
Environmental Variability ◽  
Conventional Approach ◽  
Coastal Sediments ◽  
Supervised Machine Learning ◽  
Chemical Information ◽  
Xrf Core Scanner ◽  
Physical And Chemical

<p>Sediment facies provide vital information for the reconstruction of past environmental variability. Due to rising interest for paleoclimate data, sediment surveys are continually growing in importance as well as the amount of sediments to be discriminated into different facies. The conventional approach is to macroscopically determine sediment structure and colour and combine them with physical and chemical information - a time-consuming task heavily relying on the experience of the scientist in charge. Today, rapidly generated and high-resolution multiproxy sediment parameters are readily available from down-core scanning techniques and provide qualitative or even quantitative physical and chemical sediment properties. In 2016, an interdisciplinary research project WASA (Wadden Sea Archive) was launched to investigate palaeo-landscapes and environments of the Wadden Sea. The project has recovered 92 up to 5 m long sediment cores from the tidal flats, channels and off-shore around the island of Norderney (East Frisian Wadden Sea, Germany). Their facies were described by the conventional approach into glacioflucial sands, moraine, peat, tidal deposits, shoreface sediments, etc. In this study, those sediments were scanned by a micro X-ray fluorescence (µ-XRF) core scanner to obtain high-resolution records of multi-elemental data (2000 µm) and optical images (47 µm). Here we propose a supervised machine-learning application for the discrimination of sediment facies using these scanning data. Thus, the invested time and the potential bias common for the conventional approach can be reduced considerably. We expect that our approach will contribute to developing a more comprehensive and time-efficient automatic sediment facies discrimination.</p><p>Keywords: the Wadden Sea, µ-XRF core scanning, machine-learning, sediment facies discrimination</p>

scholarly journals In-situ identification of material batches using machine learning for machining operations

2020 ◽  
Author(s):  
Benjamin Lutz ◽  
Dominik Kisskalt ◽  
Andreas Mayr ◽  
Daniel Regulin ◽  
Matteo Pantano ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Machine Learning Model ◽  
Multiple Classification ◽  
Subtractive Manufacturing ◽  
Smart Service ◽  
Machining Operations

AbstractIn subtractive manufacturing, differences in machinability among batches of the same material can be observed. Ignoring these deviations can potentially reduce product quality and increase manufacturing costs. To consider the influence of the material batch in process optimization models, the batch needs to be efficiently identified. Thus, a smart service is proposed for in-situ material batch identification. This service is driven by a supervised machine learning model, which analyzes the signals of the machine’s control, especially torque data, for batch classification. The proposed approach is validated by cutting experiments with five different batches of the same specified material at various cutting conditions. Using this data, multiple classification models are trained and optimized. It is shown that the investigated batches can be correctly identified with close to 90% prediction accuracy using machine learning. Out of all the investigated algorithms, the best results are achieved using a Support Vector Machine with 89.0% prediction accuracy for individual batches and 98.9% while combining batches of similar machinability.

scholarly journals ON THE DEVELOPMENT OF A NOVEL APPROACH FOR IDENTIFYING PERENNIAL DRAINAGE IN SOUTHERN BRAZIL: A STUDY CASE INTEGRATING SENTINEL-2 AND HIGH-RESOLUTION DIGITAL ELEVATION MODELS WITH MACHINE LEARNING TECHNIQUES

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 161-168
Author(s):  
A. Montibeller ◽  
M. Vilela ◽  
F. Hino ◽  
P. Mallmann ◽  
M. Nadas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Surface Water ◽  
High Resolution ◽  
Digital Elevation Models ◽  
Machine Learning Techniques ◽  
Water Runoff ◽  
Learning Techniques ◽  
Digital Elevation ◽  
Sentinel 2

Abstract. Riparian vegetation plays a key role in maintaining water quality and preserving the ecosystems along riverine systems, as they prevent soil erosion, retain water by increased infiltration, and act as a buffer zone between rivers and their surroundings. Within urban spaces, these areas have also an important role in preventing illegal occupation in areas of hydrologic risk, such as in floodplains. The goal of this research is to propose a framework for identifying areas of permanent protection associated with perennial drainage, utilizing satellite imagery and digital elevation models (DEM) in association with machine learning techniques. The specific objectives include the development of a decision tree to retrieve perennial drainage over high resolution, 1-meter DEM’s, and the development of digital image processing workflow to retrieve surface water bodies from Sentinel-2 imagery. In-situ information on perennial and ephemeral conditions of streams and rivers were obtained to validate our results, that happened in the first trimester of 2020. We propose a minimum of 7 days without precipitation prior to in-situ validation, for more accurate assessment of streamflow conditions, in order to minimize impacts of surface water runoff in flow regime. The proposed method will benefit decision makers by providing them with reliable information on drainage network and their buffer zones, as well as yield detailed mapping of the areas of permanent protection that are key to urban planning and management.

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