Towards long term data quality in a large scale biometrics experiment

2010 ◽  
Author(s):  
Hoang Bui ◽  
Diane Wright ◽  
Clarence Helm ◽  
Rachel Witty ◽  
Patrick Flynn ◽  
...  
Keyword(s):  
Data Quality ◽  
Large Scale ◽  
Term Data

scholarly journals Exploiting the RSSI Long-Term Data of a WSN for the RF Channel Modeling in EPS Environments

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3076
Author(s):  
Roddy A. R. Antayhua ◽  
Maicon D. Pereira ◽  
Nestor C. Fernandes ◽  
Fernando Rangel de Sousa
Keyword(s):  
Performance Optimization ◽  
Electromagnetic Interference ◽  
Large Scale ◽  
Power Level ◽  
Hydroelectric Power Plant ◽  
Channel Modeling ◽  
Electric Power System ◽  
Hydroelectric Power ◽  
Term Data

In this paper, we propose a methodology to use the received signal strength indicator (RSSI) available by the protocol stack of an installed Wireless Sensor Network (WSN) at an electric-power-system environment (EPS) as a tool for obtaining the characteristic of its communication channel. Thereby, it is possible to optimize the settings and configuration of the network after its deployment, which is usually run empirically without any previous knowledge of the channel. A study case of a hydroelectric power plant is presented, where measurements recorded over a two-month period were analyzed and treated to obtain the large-scale characteristics of the radiofrequency channel at 2.4 GHz. In addition, we showed that instantaneous RSSI data can also be used to detect specific issues in the network, such as repetitive patterns in the transmitted power level of the nodes, and information about its environment, such as the presence of external sources of electromagnetic interference. As a result, we demonstrate the practical use of the RSSI long-term data generated by the WSN for its own performance optimization and the detection of particular events in an EPS or any similar industrial environment.

scholarly journals Variability of seasonal and annual rainfall in the River Nile riparian countries and possible linkages to ocean–atmosphere interactions

2015 ◽  
Vol 47 (1) ◽  
pp. 171-184 ◽  
Author(s):  
Charles Onyutha
Keyword(s):  
Large Scale ◽  
Equatorial Region ◽  
Annual Rainfall ◽  
River Nile ◽  
Short Term ◽  
Ocean Atmosphere ◽  
Data Points ◽  
The Difference ◽  
Term Data

Variability analyses for the rainfall over the Nile Basin have been confined mostly to sub-basins and the annual mean of the hydroclimatic variable based on observed short-term data from a few meteorological stations. In this paper, long-term country-wide rainfall over the period 1901–2011 was used to assess variability in the seasonal and annual rainfall volumes in all the River Nile countries in Africa. Temporal variability was determined through temporal aggregation of series rescaled nonparametrically in terms of the difference between the exceedance and non-exceedance counts of data points such that the long-term average (taken as the reference) was zero. The co-occurrence of the variability of rainfall with those of the large-scale ocean–atmosphere interactions was analyzed. Between 2000 and 2012, while the rainfall in the equatorial region was increasing, that for the countries in the northern part of the River Nile was below the reference. Generally, the variability in the rainfall of the countries in the equatorial (northern) part of the River Nile was found to be significantly linked to occurrences in the Indian and Atlantic (Pacific and Atlantic) Oceans. Significant linkages to Niño 4 regarding the variability of both the seasonal and annual rainfall of some countries were also evident.

Critical Review and Appraisal of Traditional and New Procedures for the Quantification of Creep Fracture Behavior Using 1Cr–1Mo–0.25V Steel

2009 ◽  
Vol 131 (2) ◽  
Author(s):  
M. Evans
Keyword(s):  
Large Scale ◽  
Rupture Life ◽  
Design Data ◽  
Data Set ◽  
New Approach ◽  
Creep Fracture ◽  
Scale Test ◽  
New Procedures ◽  
Term Data

The approaches traditionally used to quantify creep and creep fracture are critically assessed and reviewed in relation to a new approach proposed by Wilshire and Scharning. The characteristics, limitations, and predictive accuracies of these models are illustrated by reference to information openly available for the bainitic 1Cr–1Mo–0.25V steel. When applied to this comprehensive long-term data set, the estimated 100,000–300,000 h strength obtained from the older so called traditional methods varied considerably. Further, the isothermal predictions from these models became very unstable beyond 100,000 h. In contrast, normalizing the applied stress through an appropriate ultimate tensile strength value not only reduced the melt to melt scatter in rupture life, but also the 100,000 h strengths determined from this model for this large scale test program are predicted very accurately by extrapolation of creep life measurements lasting less than 5000 h. The approach therefore offers the potential for reducing the scale and cost of current procedures for acquisition of long-term engineering design data.

scholarly journals MODIS and VIIRS Calibration and Characterization in Support of Producing Long-Term High-Quality Data Products

2020 ◽  
Vol 12 (19) ◽  
pp. 3167
Author(s):  
Xiaoxiong Xiong ◽  
Amit Angal ◽  
Tiejun Chang ◽  
Kwofu Chiang ◽  
Ning Lei ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Quality ◽  
Infrared Imaging ◽  
Quality Data ◽  
Earth Remote Sensing ◽  
Research Activities ◽  
The Earth ◽  
Data Products ◽  
Term Data

Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) have successfully operated since their launches in 1999 and 2002, respectively, and generated various data products to support the Earth remote sensing disciplines and users worldwide for their research activities and applications, including studies of the Earth system, and its changes over time and geographic regions. The MODIS data have also significantly contributed to the continuity of multi-decadal satellite data records and led to major advances in the Earth remote sensing field. The long-term data records from MODIS observations have been and will continue to be extended by the Visible Infrared Imaging Radiometer Suite (VIIRS) instruments, currently operated aboard the Suomi-National Polar-Orbiting Partnership (NPP) and NOAA-20 satellites. The data quality of satellite instruments strongly depends on their calibration accuracy and stability. In order to help scientists and users gain a better understanding of MODIS and VIIRS data quality, this paper provides an overview of their on-orbit calibration methodologies, approaches, and results derived from instrument on-board calibrators and lunar observations, as well as select Earth view targets. What is also discussed is the calibration consistency between MODIS and VIIRS and its potential impact on producing multi-sensor long-term data records. As illustrated, the overall performance of both MODIS and VIIRS continues to meet their design requirements.

Initiating & Managing Long-Term Data with Amateur Scientists

2017 ◽  
Vol 79 (1) ◽  
pp. 28-34
Author(s):  
Will H. Ryan ◽  
Elise S. Gornish ◽  
Lynn Christenson ◽  
Stacey Halpern ◽  
Sandra Henderson ◽  
...  
Keyword(s):  
Data Collection ◽  
Large Scale ◽  
Data Availability ◽  
Outreach Program ◽  
Interest In Science ◽  
Large Scale Data ◽  
Funding Agencies ◽  
Scale Data ◽  
Term Data

The value of long-term data (generally >10 years) in ecology is well known. Funding agencies clearly see the value in these data and have supported a limited number of projects to this end. However, individual researchers often see the challenges of long-term data collection as insurmountable. We propose that long-term data collection can be practical as part of any teaching or outreach program, and we provide guidance on how long-term projects can fit into a teaching and research schedule. While our primary audience is college faculty, our message is appropriate for anyone interested in establishing long-term studies. The benefits of adopting these kinds of projects include experience for students, encouraging public interest in science, increased publication potential for researchers, and increased large-scale data availability, leading to a better understanding of ecological phenomena.

scholarly journals Predict and Measure Air Quality Monitoring System Using Machine Learning

2021 ◽  
Vol 12 (2) ◽  
pp. 2562-2571
Author(s):  
D.Saravanan , Et. al.
Keyword(s):  
Air Quality ◽  
Large Scale ◽  
Quality Monitoring ◽  
Suitable Model ◽  
Air Quality Monitoring ◽  
Related Data ◽  
Multiple Tasks ◽  
Standard Regularization ◽  
Term Data

This article looks at how artificial intelligence can help expect the hourly consolidation of air toxinSulphur ozone, element matter (PM2.5), and Sulphur dioxide. As one of the most excellently procedures, AI can efficiently prepare a model on a large amount of data by using large-scale streamlining computations. Even thoughseveral works use AI to predict air quality, most of the earlier studies are limited to long-term data and easilyinstruct regular relapse designs (direct or nonlinear) to expect the hourly air pollution focus. This paper suggestsadvanced analysis to simulate the hourly environmental change focus based on previous days' weather-related data by calculating the expectation for more than 24 hours as an execute multiple tasks learning (MTL) issue. This allows us to choose a suitable model with a variety of regularization strategies. We suggest a useful regularization that maintains the assumption patterns of concurrent hours to be nearby to each other, and we evaluate it to a few common MTL expect completion such as normal Frobenius standard regularization, normal atomicregularization, and '2,1-standard regularization. Our tests revealed that the suggested boundary declining concepts and constant hour-related regularizations outperform open product relapse models and regularizations in terms of execution.

scholarly journals MODIS and VIIRS Calibration History and Future Outlook

2020 ◽  
Vol 12 (16) ◽  
pp. 2523
Author(s):  
Xiaoxiong Xiong ◽  
James J. Butler
Keyword(s):  
Data Quality ◽  
Sensor Calibration ◽  
High Quality ◽  
Future Outlook ◽  
Ground Testing ◽  
And Performance ◽  
Earth’S System ◽  
Term Data ◽  
Quality Measurements

The MODIS is a key instrument for NASA’s EOS program, currently operated onboard the Terra and Aqua spacecraft launched in 1999 and 2002, respectively. The VIIRS is a MODIS follow-on instrument for the JPSS program. Adding to the ones operated onboard the S-NPP and NOAA-20 satellites launched in 2011 and 2017, respectively, three nearly identical VIIRS instruments will also be launched. This will enable the data records from MODIS and VIIRS to be extended beyond 2040. In addition to various applications and scientific studies of the Earth’s system, long-term data records from MODIS and VIIRS observations will greatly benefit the space-based climate observing system. This is attributed to the high-quality measurements and extensive calibration efforts, from pre-launch to post-launch. This paper provides an overview of MODIS and VIIRS calibration history and approaches applied to establish and maintain sensor calibration traceability and accuracy. It illustrates calibration and performance issues through different phases of the mission using examples derived from ground testing equipment, on-board calibrators, and other calibration targets. Moreover, discussed in this paper are outstanding challenges and future efforts to maintain and improve sensor calibration stability and long-term data quality, and to better support the space-based climate observing system.

scholarly journals Visualization and normalization of drift effect across batches in metabolome-wide association studies

2020 ◽  
Author(s):  
Nasim Bararpour ◽  
Federica Gilardi ◽  
Cristian Carmeli ◽  
Jonathan Sidibe ◽  
Julijana Ivanisevic ◽  
...  
Keyword(s):  
Large Scale ◽  
Biomarker Discovery ◽  
Association Studies ◽  
Model Performance ◽  
Analytical Signal ◽  
Biologically Relevant ◽  
Signal Drift ◽  
Best Fitting ◽  
Term Data

AbstractAs a powerful phenotyping technology, metabolomics provides new opportunities in biomarker discovery through metabolome-wide association studies (MWAS) and identification of metabolites having regulatory effect in various biological processes. While MS-based metabolomics assays are endowed with high-throughput and sensitivity, large-scale MWAS are doomed to long-term data acquisition generating an overtime-analytical signal drift that can hinder the uncovering of true biologically relevant changes.We developed “dbnorm”, a package in R environment, which allows visualization and removal of signal heterogeneity from large metabolomics datasets. “dbnorm” integrates advanced statistical tools to inspect dataset structure, at both macroscopic (sample batch) and microscopic (metabolic features) scales. To compare model performance on data correction, “dbnorm” assigns a score, which allows the straightforward identification of the best fitting model for each dataset. Herein, we show how “dbnorm” efficiently removes signal drift among batches to capture the true biological heterogeneity of data in two large-scale metabolomics studies.

AQUACOSM-plus: an International Network for Experimental Mesocosm Studies Supporting Experimental Studies of Aquatic-Terrestrial Coupling

2020 ◽  
Author(s):  
Jens C Nejstgaard ◽  
Stella Berger ◽  
Katharina Makower ◽  
Iordanis Magiopoulos
Keyword(s):  
Large Scale ◽  
Experimental Studies ◽  
Terrestrial Ecosystems ◽  
Biodiversity Loss ◽  
Aquatic Systems ◽  
Terrestrial Environment ◽  
Wide Range ◽  
Research Facilities ◽  
Term Data

<p>Although processes in aquatic systems are closely connected to the terrestrial environment, these environments are often studied separately. We argue that for a better understanding of both aquatic and terrestrial ecosystems a combination of long-term data from connected environments, coupled with experimental ecosystem-scale experiments, have a greater potential for successful model testing and development of predictive concepts, than using only long-term data (without experiments) from separate systems. This talk will present the new EU-funded RI-project<strong> AQUACOSM-plus</strong> (www.aquacosm.eu, 2020-2024) that offers access to >50 research facilities across the EU and is linked to world-wide cooperation through the <strong>MESOCOSM.EU</strong> portal, a virtual network of >100 research facilities. Both networks include mesocosm facilities in all aquatic systems, including rivers, ponds, lakes, estuaries and marine systems – offering unique opportunities to conduct ecosystem-scale experimental studies of relevance to aquatic-terrestrial coupling. This network of research facilities can be used for large-scale process-based studies to test models based on trend or response observations from long-term-data, in order to understand underlying mechanisms of ecosystem functioning relating to the present global Grand Challenges (climate change, biodiversity loss, eutrophication, emerging pollutants, etc.). Interested parties are also welcome to suggest other uses of these research facilities, such as conducting ecosystem solution-based experiments to enable effective management in aquatic ecosystems. The network will fund access to >10.000 days for a wide range of external users, including scientists, students, industry and developers, from the whole world.</p>

Sign in / Sign up

Export Citation Format

Share Document