Reference Materials and Intercomparison Samples Available from the Environmental Monitoring Systems Laboratory — Las Vegas

2008 ◽  
pp. 360-360-10
Author(s):  
EJ Kantor ◽  
PR Laska
Keyword(s):  
Environmental Monitoring ◽  
Reference Materials ◽  
Las Vegas ◽  
Monitoring Systems

Using Machine Learning for Dependable Outlier Detection in Environmental Monitoring Systems

2021 ◽  
Vol 5 (3) ◽  
pp. 1-30
Author(s):  
Gonçalo Jesus ◽  
António Casimiro ◽  
Anabela Oliveira
Keyword(s):  
Machine Learning ◽  
Environmental Monitoring ◽  
Data Quality ◽  
Outlier Detection ◽  
Prediction Models ◽  
Sensor Data ◽  
Natural Phenomenon ◽  
Monitoring Systems ◽  
Data Errors ◽  
Redundant Data

Sensor platforms used in environmental monitoring applications are often subject to harsh environmental conditions while monitoring complex phenomena. Therefore, designing dependable monitoring systems is challenging given the external disturbances affecting sensor measurements. Even the apparently simple task of outlier detection in sensor data becomes a hard problem, amplified by the difficulty in distinguishing true data errors due to sensor faults from deviations due to natural phenomenon, which look like data errors. Existing solutions for runtime outlier detection typically assume that the physical processes can be accurately modeled, or that outliers consist in large deviations that are easily detected and filtered by appropriate thresholds. Other solutions assume that it is possible to deploy multiple sensors providing redundant data to support voting-based techniques. In this article, we propose a new methodology for dependable runtime detection of outliers in environmental monitoring systems, aiming to increase data quality by treating them. We propose the use of machine learning techniques to model each sensor behavior, exploiting the existence of correlated data provided by other related sensors. Using these models, along with knowledge of processed past measurements, it is possible to obtain accurate estimations of the observed environment parameters and build failure detectors that use these estimations. When a failure is detected, these estimations also allow one to correct the erroneous measurements and hence improve the overall data quality. Our methodology not only allows one to distinguish truly abnormal measurements from deviations due to complex natural phenomena, but also allows the quantification of each measurement quality, which is relevant from a dependability perspective. We apply the methodology to real datasets from a complex aquatic monitoring system, measuring temperature and salinity parameters, through which we illustrate the process for building the machine learning prediction models using a technique based on Artificial Neural Networks, denoted ANNODE ( ANN Outlier Detection ). From this application, we also observe the effectiveness of our ANNODE approach for accurate outlier detection in harsh environments. Then we validate these positive results by comparing ANNODE with state-of-the-art solutions for outlier detection. The results show that ANNODE improves existing solutions regarding accuracy of outlier detection.

scholarly journals MULTI SENSOR AND PLATFORMS SETUPS FOR VARIOUS AIRBORNE APPLICATIONS

2016 ◽  
Vol XLI-B1 ◽  
pp. 211-215
Author(s):  
G. Kemper ◽  
R. Vasel
Keyword(s):  
High Resolution ◽  
Environmental Monitoring ◽  
Monitoring Systems ◽  
Sensor Systems ◽  
Monitoring Applications ◽  
Pros And Cons

To combine various sensors to get a system for specific use became popular within the last 10 years. Metric mid format cameras meanwhile reach the 100 MPix and entered the mapping market to compete with the big format sensors. Beside that also other sensors as SLR Cameras provide high resolution and enter the aerial surveying market for orthophoto production or monitoring applications. Flexibility, purchase-costs, size and weight are common aspects to design multi-sensor systems. Some sensors are useful for mapping while others are part of environmental monitoring systems. Beside classical surveying aircrafts also UL Airplanes, Para/Trikes or UAVs make use of multi sensor systems. Many of them are customer specific while other already are frequently used in the market. This paper aims to show some setup, their application, what are the results and what are the pros and cons of them are.

Elastomeric Risers in the Offshore Wind Industry

2020 ◽  
Vol 54 (6) ◽  
pp. 77-83
Author(s):  
David G. Aubrey ◽  
Jennifer Wehof ◽  
Stephen O'Malley ◽  
Rajai Aghabi
Keyword(s):  
Environmental Monitoring ◽  
Fiber Optic ◽  
Power Transmission ◽  
Autonomous Underwater Vehicle ◽  
Data Transfer ◽  
Benthic Communities ◽  
Underwater Vehicle ◽  
Offshore Wind ◽  
Monitoring Systems ◽  
Fishing Activity

AbstractFloating LiDAR systems (FLS) and other moored environmental monitoring systems are used extensively for wind and environmental assessments in offshore wind projects. In addition, wave energy converters (WECs) are being evaluated for more extensive use in coastal and deeper waters, most of which also require anchoring to the seabed. Since these systems must be moored, heavy anchors and typically heavy chain are used to secure the mooring and measurement/WEC buoy to the seabed. Disadvantages of present mooring technology include 1) damage to the seabed and benthic communities in vicinity of the mooring, as chain sweeps over the sea bottom; 2) an unnecessarily large watch circle at the water's surface; 3) slightly increased likelihood of marine mammal entanglement; 4) mooring damage from nearby fishing activity; and 5) likelihood of mooring failure due to self-entanglement within the mooring itself. This study presents an alternative mooring using mechanically compliant, elastomeric hoses to connect the buoyed system to the bottom anchor. Modeling the two mooring types with a typical buoy used in wind resource assessments shows a significant decrease in anchor drag area and surface watch circle with the use of the elastomeric hose versus the traditional chain and polyethylene line mooring. The hose also is equipped with copper conductors and/or fiber-optic conductors, providing power and data transmission between the bottom and the surface. For WEC solutions, the elastomeric hose provides similar benefits as for FLS and environmental monitoring systems, with the added advantage of being able to transmit power to the seafloor for distribution. For one WEC application, we have developed an elastomeric solution containing not only larger copper conductors to enable power transmission but also fiber-optic conductors to permit data transfer from a garage mounted on the bottom (servicing an autonomous underwater vehicle [AUV] or unmanned underwater vehicle [UUV], for instance) to the surface buoy for onward transmission to shore.

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