Anomaly Detection in Streaming Sensor Data

2012 ◽  
pp. 910-928
Author(s):  
Alec Pawling ◽  
Ping Yan ◽  
Julián Candia ◽  
Tim Schoenharl ◽  
Greg Madey
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Anomaly Detection ◽  
Emergency Response ◽  
Support System ◽  
Cell Phone ◽  
Scientific Work ◽  
Streaming Data ◽  
Sensor Data ◽  
A Cell

This chapter considers a cell phone network as a set of automatically deployed sensors that records movement and interaction patterns of the population. The authors discuss methods for detecting anomalies in the streaming data produced by the cell phone network. The authors motivate this discussion by describing the Wireless Phone Based Emergency Response (WIPER) system, a proof-of-concept decision support system for emergency response managers. This chapter also discusses some of the scientific work enabled by this type of sensor data and the related privacy issues. The authors describe scientific studies that use the cell phone data set and steps we have taken to ensure the security of the data. The authors also describe the overall decision support system and discuss three methods of anomaly detection that they have applied to the data.

Anomaly Detection in Streaming Sensor Data

2010 ◽  
pp. 99-117 ◽  
Author(s):  
Alec Pawling ◽  
Ping Yan ◽  
Julián Candia ◽  
Tim Schoenharl ◽  
Greg Madey
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Anomaly Detection ◽  
Emergency Response ◽  
Support System ◽  
Cell Phone ◽  
Scientific Work ◽  
Streaming Data ◽  
Sensor Data ◽  
A Cell

This chapter considers a cell phone network as a set of automatically deployed sensors that records movement and interaction patterns of the population. The authors discuss methods for detecting anomalies in the streaming data produced by the cell phone network. The authors motivate this discussion by describing the Wireless Phone Based Emergency Response (WIPER) system, a proof-of-concept decision support system for emergency response managers. This chapter also discusses some of the scientific work enabled by this type of sensor data and the related privacy issues. The authors describe scientific studies that use the cell phone data set and steps we have taken to ensure the security of the data. The authors also describe the overall decision support system and discuss three methods of anomaly detection that they have applied to the data.

scholarly journals Transforming Unmanned Aerial Vehicle (UAV) and Multispectral Sensor into a Practical Decision Support System for Precision Nitrogen Management in Corn

2020 ◽  
Vol 12 (10) ◽  
pp. 1597
Author(s):  
Laura J. Thompson ◽  
Laila A. Puntel
Keyword(s):  
Decision Support ◽  
High Resolution ◽  
Decision Support System ◽  
Support System ◽  
Critical Issue ◽  
N Fertilization ◽  
Sensor Data ◽  
N Application ◽  
High Resolution Imagery ◽  
N Management

Determining the optimal nitrogen (N) rate in corn remains a critical issue, mainly due to unaccounted spatial (e.g., soil properties) and temporal (e.g., weather) variability. Unmanned aerial vehicles (UAVs) equipped with multispectral sensors may provide opportunities to improve N management by the timely informing of spatially variable, in-season N applications. Here, we developed a practical decision support system (DSS) to translate spatial field characteristics and normalized difference red edge (NDRE) values into an in-season N application recommendation. On-farm strip-trials were established at three sites over two years to compare farmer’s traditional N management to a split-application N management guided by our UAV sensor-based DSS. The proposed systems increased nitrogen use efficiency 18.3 ± 6.1 kg grain kg N−1 by reducing N rates by 31 ± 6.3 kg N ha−1 with no yield differences compared to the farmers’ traditional management. We identify five avenues for further improvement of the proposed DSS: definition of the initial base N rate, estimation of inputs for sensor algorithms, management zone delineation, high-resolution image normalization approach, and the threshold for triggering N application. Two virtual reference (VR) methods were compared with the high N (HN) reference strip method for normalizing high-resolution sensor data. The VR methods resulted in significantly lower sufficiency index values than those generated by the HN reference, resulting in N fertilization recommendations that were 31.4 ± 10.3 kg ha−1 higher than the HN reference N fertilization recommendation. The use of small HN reference blocks in contrasting management zones may be more appropriate to translate field-scale, high-resolution imagery into in-season N recommendations. In view of a growing interest in using UAVs in commercial fields and the need to improve crop NUE, further work is needed to refine approaches for translating imagery into in-season N recommendations.

Machine-learning based wind turbine operating state detection and diagnosis

2020 ◽  
Author(s):  
Angela Meyer
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Lifetime Cost ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Wind Park ◽  
State Boundary ◽  
Detection And Diagnosis

<p>The operation cost for wind parks make up a major fraction of the park’s overall lifetime cost. To facilitate an optimal wind park operation and maintenance, we present a decision support system that automatically scans the stream of telemetry sensor data generated from the turbines. By learning decision boundaries and normal reference operating states using machine learning algorithms, the decision support system can detect anomalous operating behaviour in individual wind turbines and diagnose the involved turbine sub-systems. Operating personal can be alerted if a normal operating state boundary is exceeded. We demonstrate the successful detection and diagnosis of anomalous power production for a case study of a German onshore wind park for turbines of 3 MW rated power.</p>

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