scholarly journals A monitoring data set for evaluating QoS-aware service-based systems

2012 ◽  
Author(s):  
Philipp Leitner ◽  
Waldemar Hummer ◽  
Schahram Dustdar
Keyword(s):  
Monitoring Data ◽  
Data Set

scholarly journals Weekly water quality monitoring data for the River Thames (UK) and its major tributaries (2009–2013): The Thames Initiative research platform

2018 ◽  
Author(s):  
Michael J. Bowes ◽  
Linda K. Armstrong ◽  
Sarah A. Harman ◽  
Heather D. Wickham ◽  
Peter M. Scarlett ◽  
...  
Keyword(s):  
Water Quality ◽  
River Flow ◽  
Sewage Treatment ◽  
Rapid Change ◽  
Water Quality Monitoring ◽  
Environmental Data ◽  
Monitoring Data ◽  
Data Set ◽  
River Thames ◽  
Study Sites

Abstract. The River Thames and 15 of its major tributaries have been monitored at weekly intervals since March 2009. Monitored determinands include major nutrient fractions, anions, cations, metals, pH, alkalinity and chlorophyll a., and linked to mean daily river flows at each site. This catchment-wide biogeochemical monitoring platform captures changes in the water quality of the Thames basin during a period of rapid change, related to increasing pressures (due to a rapidly growing human population, increasing water demand and climate change) and improvements in sewage treatment processes and agricultural practises. The platform provides the research community with a valuable data and modelling resource for furthering our understanding of pollution sources and dynamics, and interactions between water quality and aquatic ecology. Comparing Thames Initiative data with previous (non-continuous) monitoring data sets from many common study sites, dating back to 1997, has shown that there have been major reductions is phosphorus concentrations at most sites, occurring at low river flow, and these are principally due to reduced loadings from sewage treatment works. This ongoing monitoring programme will provide the vital underpinning environmental data required to best manage this vital drinking water resource, which is key for the sustainability of the city of London and the wider UK economy. The Thames Initiative data set is freely available from the Centre for Ecology & Hydrology's Environmental Information Data Centre at doi:10.5285/e4c300b1-8bc3-4df2-b23a-e72e67eef2fd.

scholarly journals On the Use of Air Quality Monitoring Networks for the Evaluation of Nitrogen Oxide Emission Inventories

2008 ◽  
Vol 2 (1) ◽  
pp. 232-248 ◽  
Author(s):  
I.B. Konovalov ◽  
M. Beekmann
Keyword(s):  
Air Quality ◽  
Nitrogen Oxide ◽  
Quality Monitoring ◽  
Monitoring Data ◽  
Emission Inventories ◽  
Air Quality Monitoring ◽  
Data Set ◽  
Rural Background ◽  
Study Results ◽  
Different Types

The usefulness of ground based air quality monitoring data for diagnostics of uncertainties in gridded emission inventories is examined. A general probabilistic procedure for comparison of levels of uncertainties in different emission datasets is developed. It implies the evaluation of the agreement between modeling results obtained with these emission datasets and corresponding measurements. This procedure is applied to the evaluation of different datasets for European gridded nitrogen oxide (NOx) emissions by using the AirBase monitoring data and the CHIMERE chemistry-transport model. Numerical experiments are performed for two different types of spatial distributions of emission uncertainties and five different types of monitors. The results are also generalized for various levels of uncertainties in simulated and measured data. It is found, in particular, that most informative, from the point of view of diagnostics of NOx emission uncertainties, are the measurements of NO2 at rural background sites and measurements of ozone at suburban sites situated in the vicinity of intensive sources of emissions. A more precise conclusion regarding the relative accuracy of two emission datasets can be drawn with a larger number of monitors in a network and a higher accuracy of the model and measurements. For example, with a network of 50 rural background NO2 monitors, the probability of choosing the more certain emission data set is more than 90 percent, if differences in uncertainty of two sets are more than 50 percent. Practical recommendations for designing or evolving surface measurement networks, in light of the study results, are given.

Comparison on outlier detection methods using measured data from a long span tied-arch bridge

2021 ◽  
Author(s):  
Peng Ni ◽  
Haili Jiang ◽  
Wurong Fu ◽  
Ye Xia ◽  
Limin Sun
Keyword(s):  
Outlier Detection ◽  
Measured Data ◽  
Monitoring Data ◽  
Detection Methods ◽  
Data Set ◽  
Rule Based ◽  
Long Span ◽  
The Mean ◽  
Popular Rule ◽  
Tied Arch Bridge

<p>As the demand for the detections of outliers in the structural health monitoring data-set increases, numerous approaches are presented for it. However, the characteristics of the existing methods dealing with different kinds of measured data are not yet clear enough for practical use. Therefore, this paper conducts a comparative study of several popular rule-based methods based on monitoring data of an arch-tied bridge in China. For measured data, outliers are not known in advance. In this way, this study evaluates and compares the detection performances rely on two indicators: the quantity of the detected outliers and the extreme value of the outliers deviating from the mean of the data. Conclusions on the features and applicable situations of involved methods are given. Additionally, combining the results of different methods proves to be beneficial. Finally, a software incorporating the research results is developed for outlier detection.</p>

Pres­sure lesion monitoring –  data set validation after second pilot data col­lection

2018 ◽  
Vol 81/114 (Suppl 1) ◽  
pp. S6-S12
Author(s):  
Andrea Pokorná ◽  
Jan Mužík ◽  
Petra Búřilová ◽  
Simona Saibertová ◽  
Lucie Kubátová ◽  
...  
Keyword(s):  
Monitoring Data ◽  
Data Set ◽  
Pilot Data

Environmetric approaches to estimate pollution impacts on a coastal area by sediment and river water studies

2002 ◽  
Vol 46 (8) ◽  
pp. 45-52 ◽  
Author(s):  
G. Mihailov ◽  
V. Simeonov ◽  
N. Nikolov ◽  
G. Mirinchev
Keyword(s):  
Water Quality ◽  
Regression Analysis ◽  
River Water ◽  
Hot Spot ◽  
Linear Regression Analysis ◽  
Oil Refinery ◽  
Monitoring Data ◽  
Latent Factors ◽  
Multivariate Statistical ◽  
Data Set

This paper represents an effort to demonstrate the opportunities of some environmetric methods like regression analysis, cluster analysis and principal components analysis. Their role for data modeling is stressed and the basic theoretical principles are given. The application of the multivariate statistical methods is illustrated by two major examples: Assessment of metal pollution based on multivariate statistical modeling of “hot spot” sediments from the Black Sea; and a trend study of Kamchia River water quality. In the first part of the study the environmetric approach makes it possible to separate three zones of the marine environment with different levels of pollution (Bourgas gulf, Varna gulf and lake buffer zone). Further, the extraction of four latent factors offers a specific interpretation of the possible pollution sources and separates the natural factors from the anthropogenic ones, the latter originating from contamination by chemical and steel-works and an oil refinery. In the second part of the study nine sampling sites along Kamchia River were considered as sources for water quality monitoring data. Trends for all parameters are calculated by the use of linear regression analysis and special attention is paid to a specific coastal site. Then five latent factors were extracted from the monitoring data set in order to gain information about some structural characteristics of the set.

scholarly journals An Electro-Mechanical Actuator Motor Voltage Estimation Method with a Feature-Aided Kalman Filter

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4190 ◽  
Author(s):  
Yujie Zhang ◽  
Liansheng Liu ◽  
Yu Peng ◽  
Datong Liu
Keyword(s):  
Kalman Filter ◽  
Electromagnetic Interference ◽  
Estimation Method ◽  
State Equation ◽  
Monitoring Data ◽  
Published Data ◽  
Data Set ◽  
State Data ◽  
Current Voltage ◽  
Current Monitoring

Electro-Mechanical Actuators (EMA) have attracted growing attention with their increasing incorporation in More Electric Aircraft. The performance degradation assessment of EMA needs to be studied, in which EMA motor voltage is an essential parameter, to ensure its reliability and safety of EMA. However, deviation exists between motor voltage monitoring data and real motor voltage due to electromagnetic interference. To reduce the deviation, EMA motor voltage estimation generally requires an accurate voltage state equation which is difficult to obtain due to the complexity of EMA. To address this problem, a Feature-aided Kalman Filter (FAKF) method is proposed, in which the state equation is substituted by a physical model of current and voltage. Consequently, voltage state data can be obtained through current monitoring data and a current–voltage model. Furthermore, voltage estimation can be implemented by utilizing voltage state data and voltage monitoring data. To validate the effectiveness of the FAKF-based estimation method, experiments have been conducted based on the published data set from NASA’s Flyable Electro-Mechanical Actuator (FLEA) test stand. The experiment results demonstrate that the proposed method has good performance in EMA motor voltage estimation.

scholarly journals Weekly water quality monitoring data for the River Thames (UK) and its major tributaries (2009–2013): the Thames Initiative research platform

2018 ◽  
Vol 10 (3) ◽  
pp. 1637-1653 ◽  
Author(s):  
Michael J. Bowes ◽  
Linda K. Armstrong ◽  
Sarah A. Harman ◽  
Heather D. Wickham ◽  
David J. E. Nicholls ◽  
...  
Keyword(s):  
Water Quality ◽  
River Flow ◽  
Sewage Treatment ◽  
Rapid Change ◽  
Agricultural Practices ◽  
Water Quality Monitoring ◽  
Environmental Data ◽  
Monitoring Data ◽  
Data Set ◽  
River Thames

Abstract. The River Thames and 15 of its major tributaries have been monitored at weekly intervals since March 2009. Monitored determinands include major nutrient fractions, anions, cations, metals, pH, alkalinity, and chlorophyll a and are linked to mean daily river flows at each site. This catchment-wide biogeochemical monitoring platform captures changes in the water quality of the Thames basin during a period of rapid change, related to increasing pressures (due to a rapidly growing human population, increasing water demand and climate change) and improvements in sewage treatment processes and agricultural practices. The platform provides the research community with a valuable data and modelling resource for furthering our understanding of pollution sources and dynamics, as well as interactions between water quality and aquatic ecology. Combining Thames Initiative data with previous (non-continuous) monitoring data sets from many common study sites, dating back to 1997, has shown that there have been major reductions in phosphorus concentrations at most sites, occurring at low river flow, and these are principally due to reduced loadings from sewage treatment works (STWs). This ongoing monitoring programme will provide the vital underpinning environmental data required to best manage this vital drinking water resource, which is key for the sustainability of the city of London and the wider UK economy. The Thames Initiative data set is freely available from the Centre for Ecology and Hydrology's (CEH) Environmental Information Data Centre at https://doi.org/10.5285/e4c300b1-8bc3-4df2-b23a-e72e67eef2fd.

scholarly journals Evaluation and interpretation of regional and site-specific hydrochemical data bases for water quality assessment

1999 ◽  
Vol 3 (4) ◽  
pp. 565-580 ◽  
Author(s):  
M. G. Hutchins ◽  
B. Reynolds ◽  
B. Smith ◽  
G. N. Wiggans ◽  
T. R. Lister
Keyword(s):  
Land Use ◽  
Temporal Variability ◽  
Stream Water ◽  
Monitoring Data ◽  
Spatial And Temporal Variability ◽  
Stream Chemistry ◽  
Stream Order ◽  
Time Step ◽  
Data Set ◽  
Site Specific

Abstract. The spatial distribution of stream water composition, as determined by the Geochemical Baseline Survey of the Environment (G-BASE) conducted by the British Geological Survey (BGS) can be successfully related under baseflow conditions to bedrock geochemistry. Further consideration of results in conjunction with site-specific monitoring data enables factors controlling both spatial and temporal variability in major element composition to be highlighted and allows the value of the survey to be enhanced. Hence, chemical data (i) from streams located on Lower Silurian (Llandovery) bedrock at 1 km2 resolution collected as part of the G-BASE survey of Wales and the West Midlands and (ii) from catchment monitoring studies located in upland mid-Wales (conducted by Institute of Terrestrial Ecology), have been considered together as an example. Classification of the spatial survey data set in terms of potentially controlling factors was carried out so as to illustrate the level of explanation they could give in terms of observed spatial chemical variability. It was therefore hypothesised that on a geological lithostratigraphic series of limited geochemical contrast, altitude and land-use factors provide better explanation of this variability than others such as lithology at sampling site and stream order. At an individual site, temporal variability was also found to be of considerable significance and, at a monthly time-step, is explicable in terms of factors such as antecedent conditions and seasonality. Data suggest that the degree of this variability may show some relationship with stream order and land-use. Monitoring data from the region also reveal that relationships between stream chemistry and land-use may prove to be strong not only at base flow but also in storm flow conditions. In a wider context, predictions of the sensitivity of stream water to acidification based on classifications of soil and geology are successful on a regional scale. However, the study undertaken here has shown that use of such classification schemes on a catchment scale results in considerable uncertainty associated with prediction. Uncertainties are due to the large degree of variability in stream chemistry encountered both spatially within geological units and temporally at individual sampling sites.

scholarly journals COSMOGRAIL

2020 ◽  
Vol 640 ◽  
pp. A105 ◽  
Author(s):  
M. Millon ◽  
F. Courbin ◽  
V. Bonvin ◽  
E. Paic ◽  
G. Meylan ◽  
...  
Keyword(s):  
Time Delay ◽  
Time Delays ◽  
Light Curves ◽  
Monitoring Data ◽  
Data Set ◽  
Delay Measurement ◽  
Leonhard Euler ◽  
The Difference ◽  
Quasar Variability ◽  
First Time

We present the results of 15 years of monitoring lensed quasars, which was conducted by the COSMOGRAIL programme at the Leonhard Euler 1.2 m Swiss Telescope. The decade-long light curves of 23 lensed systems are presented for the first time. We complement our data set with other monitoring data available in the literature to measure the time delays in 18 systems, among which nine reach a relative precision better than 15% for at least one time delay. To achieve this, we developed an automated version of the curve-shifting toolbox PyCS to ensure robust estimation of the time delay in the presence of microlensing, while accounting for the errors due to the imperfect representation of microlensing. We also re-analysed the previously published time delays of RX J1131−1231 and HE 0435−1223, by adding six and two new seasons of monitoring, respectively, and confirming the previous time-delay measurements. When the time delay measurement is possible, we corrected the light curves of the lensed images from their time delay and present the difference curves to highlight the microlensing signal contained in the data. To date, this is the largest sample of decade-long lens monitoring data, which is useful to measure H0 and the size of quasar accretion discs with microlensing as well as to study quasar variability.

Secondary Organic Carbon Estimations with Hourly Monitoring Data and the OC/EC Primary Ratio Method

2013 ◽  
Vol 295-298 ◽  
pp. 849-853
Author(s):  
Mei Fang Lu ◽  
Mei Chuan Huang ◽  
Chiau Yi Wen ◽  
Yi Hui Wu ◽  
Jim Jui Min Lin
Keyword(s):  
Organic Carbon ◽  
Pollution Source ◽  
Photochemical Reactions ◽  
Monitoring Data ◽  
Ratio Method ◽  
High Temporal Resolution ◽  
Data Set ◽  
Daily Data ◽  
Hourly Data ◽  
Secondary Organic Carbon

This study examined the hourly monitoring data from 2006 to 2009 collected by the Aerosol Supersite of the Environmental Protection Administration of Taiwan. The OC/EC primary ratio method has been applied to estimate the content of secondary organic carbon (SOC). Results of this study indicated that the monthly concentrations of PM2.5, OC, and EC all remained low in summer but went up in winter. Possible factors were climate-related and influences from continental high pressure systems. The content (24–36%) of SOC in summer was significantly higher than in other seasons, indicating that a great formation of organic carbon in summer. When considering the hourly trend, apparent peaks can be consistently observed in the morning, which may be due to an increase of mobile pollution source and photochemical reactions. (OC/EC)min ratio values were calculated based on both hourly and daily concentrations of OC and EC, then annual values (2006~2009) were 0.20~1.11 and 0.68~2.72 for hourly and daily data base respectively. Content of SOC in PM2.5 and OC were estimated to be 16~23 % and 75~93 % based on (OC/EC)min ratio from hourly data set, and were 11~18 % and 42~77 % based on (OC/EC)min ratio from daily data set. Results from this study, as well as those from other studies, demonstrated that the OC/EC ratio is dependent upon the sampling method as well as the method of analysis. Furthermore, the daily OC/EC ratio may change, and significant variations may be found even within 24 hours. Taken together, when conducting estimation of SOC, it is important to eliminate the consideration on background concentrations but to take a good advantage of the high temporal resolution of hourly monitoring data in order to estimate SOC using a corrective approach.

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