scholarly journals A hybrid data-driven BSDF model to predict light transmission through complex fenestration systems including high incident directions

2017 ◽  
Vol 4 (3-4) ◽  
pp. 79-89
Author(s):  
Marek Krehel ◽  
Lars O. Grobe ◽  
Stephen Wittkopf
Keyword(s):  
Light Transmission ◽  
Data Driven ◽  
Hybrid Data

Hybrid-Data Approach for Estimating Trip Purposes

2021 ◽  
pp. 036119812110184
Author(s):  
Xiaoling Luo ◽  
Adrian Cottam ◽  
Yao-Jan Wu ◽  
Yangsheng Jiang
Keyword(s):  
Transportation Systems ◽  
Data Driven ◽  
Trip Purpose ◽  
Additional Information ◽  
Point Of Interest ◽  
Average Accuracy ◽  
Hybrid Data ◽  
Added Benefit ◽  
Data Source ◽  
High Level

Trip purpose information plays a significant role in transportation systems. Existing trip purpose information is traditionally collected through human observation. This manual process requires many personnel and a large amount of resources. Because of this high cost, automated trip purpose estimation is more attractive from a data-driven perspective, as it could improve the efficiency of processes and save time. Therefore, a hybrid-data approach using taxi operations data and point-of-interest (POI) data to estimate trip purposes was developed in this research. POI data, an emerging data source, was incorporated because it provides a wealth of additional information for trip purpose estimation. POI data, an open dataset, has the added benefit of being readily accessible from online platforms. Several techniques were developed and compared to incorporate this POI data into the hybrid-data approach to achieve a high level of accuracy. To evaluate the performance of the approach, data from Chengdu, China, were used. The results show that the incorporation of POI information increases the average accuracy of trip purpose estimation by 28% compared with trip purpose estimation not using the POI data. These results indicate that the additional trip attributes provided by POI data can increase the accuracy of trip purpose estimation.

scholarly journals Developing a Hybrid Data-Driven, Mechanistic Virtual Flow Meter - a Case Study

2020 ◽  
Vol 53 (2) ◽  
pp. 11692-11697
Author(s):  
M. Hotvedt ◽  
B. Grimstad ◽  
L. Imsland
Keyword(s):  
Data Driven ◽  
Flow Meter ◽  
Hybrid Data

scholarly journals ProCAVIAR: Hybrid Data-Driven and Probabilistic Knowledge-Based Activity Recognition

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 146876-146886
Author(s):  
Claudio Bettini ◽  
Gabriele Civitarese ◽  
Davide Giancane ◽  
Riccardo Presotto
Keyword(s):  
Activity Recognition ◽  
Data Driven ◽  
Knowledge Based ◽  
Probabilistic Knowledge ◽  
Hybrid Data

Extracting useful signals from flawed sensor data: Developing hybrid data-driven approaches with physical factors

2020 ◽  
Vol 185 ◽  
pp. 116282
Author(s):  
Cheng Yang ◽  
Glen T. Daigger ◽  
Evangelia Belia ◽  
Branko Kerkez
Keyword(s):  
Data Driven ◽  
Sensor Data ◽  
Physical Factors ◽  
Hybrid Data

Hybrid Data-Driven Modelling for Inverse Control of Hydraulic Excavators

2021 ◽  
Author(s):  
Jonas Weigand ◽  
Julian Raible ◽  
Nico Zantopp ◽  
Ozan Demir ◽  
Adrian Trachte ◽  
...  
Keyword(s):  
Data Driven ◽  
Inverse Control ◽  
Hydraulic Excavators ◽  
Hybrid Data

scholarly journals Hybrid Data-Driven and Physics-Based Modeling for Gas Turbine Prescriptive Analytics

2020 ◽  
Vol 5 (4) ◽  
pp. 29
Author(s):  
Sergei Belov ◽  
Sergei Nikolaev ◽  
Ighor Uzhinsky
Keyword(s):  
Gas Turbine ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Flame Tube ◽  
Learning Techniques ◽  
Prescriptive Analytics ◽  
Hybrid Data ◽  
Data Driven Modeling ◽  
Engine Power

This paper presents a methodology for predictive and prescriptive analytics of a gas turbine. The methodology is based on a combination of physics-based and data-driven modeling using machine learning techniques. Combining these approaches results in a set of reliable, fast, and continuously updating models for prescriptive analytics. The methodology is demonstrated with a case study of a jet-engine power plant preventive maintenance and diagnosis of its flame tube. The developed approach allows not just to analyze and predict some problems in the combustion chamber, but also to identify a particular flame tube to be repaired or replaced and plan maintenance actions in advance.

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