scholarly journals Synchronized high-resolution bed-level change and biophysical data from 10 marsh–mudflat sites in northwestern Europe

2021 ◽  
Vol 13 (2) ◽  
pp. 405-416
Author(s):  
Zhan Hu ◽  
Pim W. J. M. Willemsen ◽  
Bas W. Borsje ◽  
Chen Wang ◽  
Heng Wang ◽  
...  
Keyword(s):  
Ecosystem Services ◽  
High Resolution ◽  
Model Development ◽  
Sensor Data ◽  
Storm Events ◽  
Sediment Grain Size ◽  
Multiple Sensors ◽  
Level Change ◽  
Tidal Current Velocity ◽  
Developed Surface

Abstract. Tidal flats provide valuable ecosystem services such as flood protection and carbon sequestration. Erosion and accretion processes govern the ecogeomorphic evolution of intertidal ecosystems (marshes and bare flats) and, hence, substantially affect their valuable ecosystem services. To understand the intertidal ecosystem development, high-frequency bed-level change data are thus needed. However, such datasets are scarce due to the lack of suitable methods that do not involve excessive labour and/or costly instruments. By applying newly developed surface elevation dynamics (SED) sensors, we obtained unique high-resolution daily bed-level change datasets in the period 2013–2017 from 10 marsh–mudflat sites situated in the Netherlands, Belgium, and the United Kingdom in contrasting physical and biological settings. At each site, multiple sensors were deployed for 9–20 months to ensure sufficient spatial and temporal coverage of highly variable bed-level change processes. The bed-level change data are provided with synchronized hydrodynamic data, i.e. water level, wave height, tidal current velocity, medium sediment grain size (D50), and chlorophyll a level at four sites. This dataset has revealed diverse spatial morphodynamics patterns over daily to seasonal scales, which are valuable to theoretical and model development. On the daily scale, this dataset is particularly instructive, as it includes a number of storm events, the response to which can be detected in the bed-level change observations. Such data are rare but useful to study tidal flat response to highly energetic conditions. The dataset is available from 4TU.ResearchData (https://doi.org/10.4121/12693254.v4; Hu et al., 2020), which is expected to expand with additional SED sensor data from ongoing and planned surveys.

scholarly journals High resolution bed level change and synchronized biophysical data from 10 tidal flats in northwestern Europe

2020 ◽  
Author(s):  
Zhan Hu ◽  
Pim W. J. M. Willemsen ◽  
Bas W. Borsje ◽  
Chen Wang ◽  
Heng Wang ◽  
...  
Keyword(s):  
Ecosystem Services ◽  
High Resolution ◽  
Organic Matter Content ◽  
Tidal Flats ◽  
Matter Content ◽  
Sensor Data ◽  
Storm Events ◽  
Level Change ◽  
Tidal Current Velocity ◽  
Developed Surface

Abstract. Tidal flats provide valuable ecosystem services such as flood protection and carbon sequestration. Erosion and accretion processes govern the eco-geomorphic evolution of intertidal ecosystems (marshes and bare flats), and hence substantially affect their valuable ecosystem services. To understand the intertidal ecosystem development, high-frequency bed-level change data are thus needed. However, such datasets are scarce due to the lack of suitable methods that do not involve excessive labour and/or instrument cost. By applying newly-developed Surface Elevation Dynamics sensors (SED-sensors), we obtained unique high-resolution daily bed-level change data sets in the period 2013–2017 from 10 salt marsh sites situated in the Netherlands, Belgium and Britain in contrasting physical and biological settings. At each site, multiple sensors were deployed for 9–20 months to ensure sufficient spatial and temporal coverage of highly variable bed level change processes. The bed level change data are provided with synchronized hydrodynamic data, i.e. water level, wave height, tidal current velocity, and medium grain size (D50) as well as (for some sites) chlorophyll-a level and organic matter content of the surface sediment. This dataset has revealed diverse spatial morphodynamic patterns over daily to seasonal scales, which are valuable to theoretical and model development. On the daily scale, this dataset is particularly instructive as it includes a number of storm events, the response to which can be detected in the bed level change observations. Such data are rare but useful to study tidal flat response to highly energetic conditions. The dataset is available from the 4TU.Centre for Research Data (https://doi.org/10.4121/uuid:4830dbc2-84b8-46f9-99a3- 90f01ab5b923, Hu et al., 2020), which is expected to expand with additional SED-sensor data from ongoing and planned surveys.

scholarly journals Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion

2021 ◽  
Vol 4 (1) ◽  
pp. 3
Author(s):  
Parag Narkhede ◽  
Rahee Walambe ◽  
Shruti Mandaokar ◽  
Pulkit Chandel ◽  
Ketan Kotecha ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Network Architecture ◽  
Cost Effective ◽  
Sensor Data ◽  
Dense Layer ◽  
Gaseous Emissions ◽  
Multiple Sensors ◽  
Single Output ◽  
Novel Approach ◽  
Single Sensor

With the rapid industrialization and technological advancements, innovative engineering technologies which are cost effective, faster and easier to implement are essential. One such area of concern is the rising number of accidents happening due to gas leaks at coal mines, chemical industries, home appliances etc. In this paper we propose a novel approach to detect and identify the gaseous emissions using the multimodal AI fusion techniques. Most of the gases and their fumes are colorless, odorless, and tasteless, thereby challenging our normal human senses. Sensing based on a single sensor may not be accurate, and sensor fusion is essential for robust and reliable detection in several real-world applications. We manually collected 6400 gas samples (1600 samples per class for four classes) using two specific sensors: the 7-semiconductor gas sensors array, and a thermal camera. The early fusion method of multimodal AI, is applied The network architecture consists of a feature extraction module for individual modality, which is then fused using a merged layer followed by a dense layer, which provides a single output for identifying the gas. We obtained the testing accuracy of 96% (for fused model) as opposed to individual model accuracies of 82% (based on Gas Sensor data using LSTM) and 93% (based on thermal images data using CNN model). Results demonstrate that the fusion of multiple sensors and modalities outperforms the outcome of a single sensor.

Multisensor Degradation Data Fusion and Remaining Life Prediction

2017 ◽  
Author(s):  
Changxi Wang ◽  
E. A. Elsayed ◽  
Kang Li ◽  
Javier Cabrera
Keyword(s):  
Data Fusion ◽  
Prediction Accuracy ◽  
Life Prediction ◽  
Clustering Algorithm ◽  
Partial Information ◽  
Degradation Process ◽  
Sensor Data ◽  
Multiple Sensors ◽  
Degradation Data ◽  
Degradation Monitoring

Multiple sensors are commonly used for degradation monitoring. Since different sensors may be sensitive at different stages of the degradation process and each sensor data contain only partial information of the degraded unit, data fusion approaches that integrate degradation data from multiple sensors can effectively improve degradation modeling and life prediction accuracy. We present a non-parametric approach that assigns weights to each sensor based on dynamic clustering of the sensors observations. A case study that involves a fatigue-crack-growth dataset is implemented in order evaluate the prognostic performance of the unit. Results show that the fused path obtained with the proposed approach outperforms any individual sensor data and other paths obtained with an adaptive threshold clustering algorithm in terms of life prediction accuracy.

Vulnerability and Importance of Arctic Wetlands as large-scale nature-based solutions for Sustainability in a Changing Climate

2021 ◽  
Author(s):  
Elisie Kåresdotter ◽  
Zahra Kalantari
Keyword(s):  
Ecosystem Services ◽  
High Resolution ◽  
Large Scale ◽  
Natural Capital ◽  
Environmental Benefits ◽  
Indigenous Populations ◽  
The Arctic ◽  
Current Status ◽  
Human Beings ◽  
Arctic Wetlands

<p>Wetlands as large-scale nature-based solutions (NBS) provide multiple ecosystem services of local, regional, and global importance. Knowledge concerning location and vulnerability of wetlands, specifically in the Arctic, is vital to understand and assess the current status and future potential changes in the Arctic. Using available high-resolution wetland databases together with datasets on soil wetness and soil types, we created the first high-resolution map with full coverage of Arctic wetlands. Arctic wetlands' vulnerability is assessed for the years 2050, 2075, and 2100 by utilizing datasets of permafrost extent and projected mean annual average temperature from HadGEM2-ES climate model outputs for three change scenarios (RCP2.6, 4.5, and 8.5). With approximately 25% of Arctic landmass covered with wetlands and 99% being in permafrost areas, Arctic wetlands are highly vulnerable to changes in all scenarios, apart from RCP2.6 where wetlands remain largely stable. Climate change threatens Arctic wetlands and can impact wetland functions and services. These changes can adversely affect the multiple services this sort of NBS can provide in terms of great social, economic, and environmental benefits to human beings. Consequently, negative changes in Arctic wetland ecosystems can escalate land-use conflicts resulting from natural capital exploitation when new areas become more accessible for use. Limiting changes to Arctic wetlands can help maintain their ecosystem services and limit societal challenges arising from thawing permafrost wetlands, especially for indigenous populations dependent on their ecosystem services. This study highlights areas subject to changes and provides useful information to better plan for a sustainable and social-ecological resilient Arctic.</p><p>Keywords: Arctic wetlands, permafrost thaw, regime shift vulnerability, climate projection</p>

Dynamic Model Development of Wind Turbine Drivetrains by Using Sensor Measurements

2021 ◽  
Author(s):  
Diederik van Binsbergen ◽  
Amir R. Nejad ◽  
Jan Helsen
Keyword(s):  
Wind Turbine ◽  
Angular Displacement ◽  
Model Development ◽  
Rotational Frequency ◽  
Sensor Data ◽  
Gear Mesh ◽  
Modal Properties ◽  
Vibration Sensors ◽  
Displacement Sensors ◽  
Simulation Based

Abstract This paper aims to analyze the feasibility of establishing a dynamic drivetrain model from condition monitoring measurements. In this study SCADA data and further sensor data is analyzed from a 1.5MW wind turbine, provided by the National Renewable Energy Laboratory. A multibody model of the drivetrain is made and simulation based sensors are placed on bearings to look at the possibility to obtain geometrical and modal properties from simulation based vibration sensors. Results show that the axial proxy sensor did not provide any usable system information due to its application purpose. SCADA data did not meet the Nyquist frequency and cannot be used to determine geometrical or modal properties. Strain gauges on the shaft can provide the shaft rotational frequency, while torque and angular displacement sensors can provide the torsional eigenfrequency of the system. Simulation based vibration sensors are able to capture gear mesh frequencies, harmonics, sideband frequencies and shaft rotational frequencies.

scholarly journals Comparison of delayless digital filtering algorithms and their application to multi-sensor signal processing

2018 ◽  
Vol 41 (8) ◽  
pp. 2338-2351 ◽  
Author(s):  
Anna Swider ◽  
Eilif Pedersen
Keyword(s):  
Signal Processing ◽  
Fourier Transformation ◽  
Filter Design ◽  
Real Life ◽  
Digital Filtering ◽  
Data Preprocessing ◽  
Sensor Data ◽  
Discrete Fourier Transformation ◽  
Filtering Algorithms ◽  
Multiple Sensors

In the phase of industry digitalization, data are collected from many sensors and signal processing techniques play a crucial role. Data preprocessing is a fundamental step in the analysis of measurements, and a first step before applying machine learning. To reduce the influence of distortions from signals, selective digital filtering is applied to minimize or remove unwanted components. Standard software and hardware digital filtering algorithms introduce a delay, which has to be compensated for to avoid destroying signal associations. The delay from filtering becomes more crucial when the analysis involves measurements from multiple sensors, therefore in this paper we provide an overview and comparison of existing digital filtering methods with an application based on real-life marine examples. In addition, the design of special-purpose filters is a complex process and for preprocessing data from many sources, the application of digital filtering in the time domain can have a high numerical cost. For this reason we describe discrete Fourier transformation digital filtering as a tool for efficient sensor data preprocessing, which does not introduce a time delay and has low numerical cost. The discrete Fourier transformation digital filtering has a simpler implementation and does not require expert-level filter design knowledge, which is beneficial for practitioners from various disciplines. Finally, we exemplify and show the application of the methods on real signals from marine systems.

scholarly journals Sensor Fusion for Recognition of Activities of Daily Living

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4029 ◽  
Author(s):  
Jiaxuan Wu ◽  
Yunfei Feng ◽  
Peng Sun
Keyword(s):  
Time Series ◽  
Sensor Fusion ◽  
Daily Living ◽  
Single Point ◽  
Recognition System ◽  
Sensor Data ◽  
Multiple Sensors ◽  
Life Patterns ◽  
Functional Capabilities ◽  
Correction Technique

Activity of daily living (ADL) is a significant predictor of the independence and functional capabilities of an individual. Measurements of ADLs help to indicate one’s health status and capabilities of quality living. Recently, the most common ways to capture ADL data are far from automation, including a costly 24/7 observation by a designated caregiver, self-reporting by the user laboriously, or filling out a written ADL survey. Fortunately, ubiquitous sensors exist in our surroundings and on electronic devices in the Internet of Things (IoT) era. We proposed the ADL Recognition System that utilizes the sensor data from a single point of contact, such as smartphones, and conducts time-series sensor fusion processing. Raw data is collected from the ADL Recorder App constantly running on a user’s smartphone with multiple embedded sensors, including the microphone, Wi-Fi scan module, heading orientation of the device, light proximity, step detector, accelerometer, gyroscope, magnetometer, etc. Key technologies in this research cover audio processing, Wi-Fi indoor positioning, proximity sensing localization, and time-series sensor data fusion. By merging the information of multiple sensors, with a time-series error correction technique, the ADL Recognition System is able to accurately profile a person’s ADLs and discover his life patterns. This paper is particularly concerned with the care for the older adults who live independently.

scholarly journals Mobilisation or dilution? Nitrate response of karst springs to high rainfall events

2014 ◽  
Vol 18 (11) ◽  
pp. 4423-4435 ◽  
Author(s):  
M. Huebsch ◽  
O. Fenton ◽  
B. Horan ◽  
D. Hennessy ◽  
K. G. Richards ◽  
...  
Keyword(s):  
High Resolution ◽  
Nitrate Contamination ◽  
Karst Aquifers ◽  
The European Union ◽  
Storm Events ◽  
Discharge Data ◽  
Rainfall Events ◽  
Nitrate Concentrations ◽  
Nitrate Response ◽  
Karst Systems

Abstract. Nitrate (NO3−) contamination of groundwater associated with agronomic activity is of major concern in many countries. Where agriculture, thin free draining soils and karst aquifers coincide, groundwater is highly vulnerable to nitrate contamination. As residence times and denitrification potential in such systems are typically low, nitrate can discharge to surface waters unabated. However, such systems also react quickest to agricultural management changes that aim to improve water quality. In response to storm events, nitrate concentrations can alter significantly, i.e. rapidly decreasing or increasing concentrations. The current study examines the response of a specific karst spring situated on a grassland farm in South Ireland to rainfall events utilising high-resolution nitrate and discharge data together with on-farm borehole groundwater fluctuation data. Specifically, the objectives of the study are to formulate a scientific hypothesis of possible scenarios relating to nitrate responses during storm events, and to verify this hypothesis using additional case studies from the literature. This elucidates the controlling key factors that lead to mobilisation and/or dilution of nitrate concentrations during storm events. These were land use, hydrological condition and karstification, which in combination can lead to differential responses of mobilised and/or diluted nitrate concentrations. Furthermore, the results indicate that nitrate response in karst is strongly dependent on nutrient source, whether mobilisation and/or dilution occur and on the pathway taken. This will have consequences for the delivery of nitrate to a surface water receptor. The current study improves our understanding of nitrate responses in karst systems and therefore can guide environmental modellers, policy makers and drinking water managers with respect to the regulations of the European Union (EU) Water Framework Directive (WFD). In future, more research should focus on the high-resolution monitoring of karst aquifers to capture the high variability of hydrochemical processes, which occur at time intervals of hours to days.

scholarly journals DOC sources and DOC transport pathways in a small headwater catchment as revealed by carbon isotope fluctuation during storm events

2014 ◽  
Vol 11 (11) ◽  
pp. 3043-3056 ◽  
Author(s):  
T. Lambert ◽  
A.-C. Pierson-Wickmann ◽  
G. Gruau ◽  
A. Jaffrezic ◽  
P. Petitjean ◽  
...  
Keyword(s):  
High Resolution ◽  
High Flow ◽  
Published Data ◽  
Storm Events ◽  
Transport Pathways ◽  
Relative Contribution ◽  
Temporal And Spatial ◽  
High Flow Period ◽  
Doc Flux ◽  
Headwater Catchment

Abstract. Monitoring the isotopic composition (δ13CDOC) of dissolved organic carbon (DOC) during flood events can be helpful for locating DOC sources in catchments and quantifying their relative contribution to stream DOC flux. High-resolution (< hourly basis) δ13CDOC data were obtained during six successive storm events occurring during the high-flow period in a small headwater catchment in western France. Intra-storm δ13CDOC values exhibit a marked temporal variability, with some storms showing large variations (> 2 ‰), and others yielding a very restricted range of values (< 1 ‰). Comparison of these results with previously published data shows that the range of intra-storm δ13CDOC values closely reflects the temporal and spatial variation in δ13CDOC observed in the riparian soils of this catchment during the same period. Using δ13CDOC data in conjunction with hydrometric monitoring and an end-member mixing approach (EMMA), we show that (i) > 80% of the stream DOC flux flows through the most superficial soil horizons of the riparian domain and (ii) the riparian soil DOC flux is comprised of DOC coming ultimately from both riparian and upland domains. Based on its δ13C fingerprint, we find that the upland DOC contribution decreases from ca.~30% of the stream DOC flux at the beginning of the high-flow period to < 10% later in this period. Overall, upland domains contribute significantly to stream DOC export, but act as a size-limited reservoir, whereas soils in the wetland domains act as a near-infinite reservoir. Through this study, we show that δ13CDOC provides a powerful tool for tracing DOC sources and DOC transport mechanisms in headwater catchments, having a high-resolution assessment of temporal and spatial variability.

scholarly journals The effects of the big storm events in the first half of 2015 on the radiation belts observed by EPT/PROBA-V

2016 ◽  
Vol 34 (1) ◽  
pp. 75-84 ◽  
Author(s):  
V. Pierrard ◽  
G. Lopez Rosson
Keyword(s):  
Charged Particle ◽  
High Resolution ◽  
Geomagnetic Storm ◽  
Energetic Particle ◽  
High Energy ◽  
Radiation Belts ◽  
Radiation Environment ◽  
Storm Event ◽  
Storm Events ◽  
Particle Radiation

Abstract. With the energetic particle telescope (EPT) performing with direct electron and proton discrimination on board the ESA satellite PROBA-V, we analyze the high-resolution measurements of the charged particle radiation environment at an altitude of 820 km for the year 2015. On 17 March 2015, a big geomagnetic storm event injected unusual fluxes up to low radial distances in the radiation belts. EPT electron measurements show a deep dropout at L > 4 starting during the main phase of the storm, associated to the penetration of high energy fluxes at L < 2 completely filling the slot region. After 10 days, the formation of a new slot around L = 2.8 for electrons of 500–600 keV separates the outer belt from the belt extending at other longitudes than the South Atlantic Anomaly. Two other major events appeared in January and June 2015, again with injections of electrons in the inner belt, contrary to what was observed in 2013 and 2014. These observations open many perspectives to better understand the source and loss mechanisms, and particularly concerning the formation of three belts.

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