scholarly journals Data Errors in Results

Keyword(s):  
2021 ◽  
Vol 5 (3) ◽  
pp. 1-30
Author(s):  
Gonçalo Jesus ◽  
António Casimiro ◽  
Anabela Oliveira

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.


2002 ◽  
Vol 2 (1/2) ◽  
pp. 3-14 ◽  
Author(s):  
F. Ardizzone ◽  
M. Cardinali ◽  
A. Carrara ◽  
F. Guzzetti ◽  
P. Reichenbach

Abstract. Identification and mapping of landslide deposits are an intrinsically difficult and subjective operation that requires a great effort to minimise the inherent uncertainty. For the Staffora Basin, which extends for almost 300 km2 in the northern Apennines, three landslide inventory maps were independently produced by three groups of geomorphologists. In comparing each map with the others, large positional discrepancies arise (in the range of 55–65%). When all three maps are overlain, the locational mismatch of landslide deposit polygons increases to over 80%. To assess the impact of these errors on predictive models of landslide hazard, for the study area discriminant models were built up from the same set of geological-geomorphological factors as predictors, and the occurrence of landslide deposits within each terrain-unit, derived from each inventory map, as dependent variable. The comparison of these models demonstrates that statistical modelling greatly minimises the impact of input data errors which remain, however, a major limitation on the reliability of landslide hazard maps.


JAMA ◽  
2017 ◽  
Vol 317 (23) ◽  
pp. 2453
Keyword(s):  

Author(s):  
C. W. Groetsch ◽  
Martin Hanke

Abstract A simple numerical method for some one-dimensional inverse problems of model identification type arising in nonlinear heat transfer is discussed. The essence of the method is to express the nonlinearity in terms of an integro-differential operator, the values of which are approximated by a linear spline technique. The inverse problems are mildly ill-posed and therefore call for regularization when data errors are present. A general technique for stabilization of unbounded operators may be applied to regularize the process and a specific regularization technique is illustrated on a model problem.


2018 ◽  
Vol 7 (2) ◽  
pp. 36-39
Author(s):  
Galuh Lukitasari ◽  
Aad Hariyadi ◽  
Ridho Hendra Yoga Perdana

One of the communication technologies that can be used for monitoring current usage is communication technology via electric grids or what is called Power Line Communication (PLC). The advantages of this technology are that the electricity network is already distributed in each building so there is no need for new installations, it is economical and affordable in terms of economics, and is more practical and flexible in its use. In this study, a current usage monitoring system using PLC is proposed which will be applied in every class in the AI ??building of Malang State Polytechnic. The sensor that will be used in measuring the current value is the current sensor SCT-013. By using the KQ330 PLC module as a PLC transmission modem. The implementation of PLC which is applied in each class in the AI ??building has a BER (bit error rate) value of 0.012 in the experiment of sending 1000 data with an error of 12 times at the farthest distance of approximately 15 meters. And have a BER value of 0 or have no error in sending data at the closest distance of approximately 2-5 meters. In this study, the process of sending and receiving data using Power Line Communication allows it to be done at a distance that is not too far away to avoid excessive data errors.


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