scholarly journals A New Data-Driven Turbulence Model Framework for Unsteady Flows Applied to Wall-Jet and Wall-Wake Flows

2019 ◽  
Author(s):  
Chitrarth Lav ◽  
Jimmy Philip ◽  
Richard D. Sandberg
Keyword(s):  
Machine Learning ◽  
Mass Flow ◽  
Vortex Shedding ◽  
Learning Algorithm ◽  
Data Driven ◽  
Wall Jet ◽  
Machine Learning Algorithm ◽  
Length Scales ◽  
Wake Flows ◽  
The Impact

Abstract The unsteady flow prediction for turbomachinery applications relies heavily on unsteady RANS (URANS). For flows that exhibit vortex shedding, such as the wall-jet/wake flows considered in this study, URANS is unable to predict the correct momentum mixing with sufficient accuracy. We suggest a novel framework to improve that prediction, whereby the deterministic scales associated with vortex shedding are resolved while the stochastic scales of pure turbulence are modelled. The framework first separates the stochastic from the deterministic length scales and then develops a bespoke turbulence closure for the stochastic scales using a data-driven machine-learning algorithm. The novelty of the method lies in the use of machine-learning to develop closures tailored to URANS calculations. For the walljet/wake flow, three different mass flow ratios (0.86, 1.07 and 1.26) have been considered and a high-fidelity dataset of the idealised geometry is utilised for the sake of model development. This study serves as an a priori analysis, where the closures obtained from the machine-learning algorithm are evaluated before their implementation in URANS. The analysis looks at the impact of using all length scales versus the stochastic scales for closure development, and the impact of the extent of the spatial domain for developing the closure. It is found that a two-layer approach, using bespoke trained models for the near wall and the jet/wake regions, produce the best results. Finally, the generalisability of the developed closures is also evaluated by applying a given closure developed using a particular mass flow ratio to the other cases.

scholarly journals Analyzing the Impact of Climate Factors on GNSS-Derived Displacements by Combining the Extended Helmert Transformation and XGboost Machine Learning Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Hanlin Liu ◽  
Linqiang Yang ◽  
Linchao Li
Keyword(s):  
Machine Learning ◽  
Puerto Rico ◽  
Reference Frame ◽  
Learning Algorithm ◽  
Virgin Islands ◽  
Machine Learning Algorithm ◽  
Climate Factors ◽  
Helmert Transformation ◽  
The Impact

A variety of climate factors influence the precision of the long-term Global Navigation Satellite System (GNSS) monitoring data. To precisely analyze the effect of different climate factors on long-term GNSS monitoring records, this study combines the extended seven-parameter Helmert transformation and a machine learning algorithm named Extreme Gradient boosting (XGboost) to establish a hybrid model. We established a local-scale reference frame called stable Puerto Rico and Virgin Islands reference frame of 2019 (PRVI19) using ten continuously operating long-term GNSS sites located in the rigid portion of the Puerto Rico and Virgin Islands (PRVI) microplate. The stability of PRVI19 is approximately 0.4 mm/year and 0.5 mm/year in the horizontal and vertical directions, respectively. The stable reference frame PRVI19 can avoid the risk of bias due to long-term plate motions when studying localized ground deformation. Furthermore, we applied the XGBoost algorithm to the postprocessed long-term GNSS records and daily climate data to train the model. We quantitatively evaluated the importance of various daily climate factors on the GNSS time series. The results show that wind is the most influential factor with a unit-less index of 0.013. Notably, we used the model with climate and GNSS records to predict the GNSS-derived displacements. The results show that the predicted displacements have a slightly lower root mean square error compared to the fitted results using spline method (prediction: 0.22 versus fitted: 0.31). It indicates that the proposed model considering the climate records has the appropriate predict results for long-term GNSS monitoring.

Impact of Encoding of High Cardinality Categorical Data to Solve Prediction Problems

2020 ◽  
Vol 17 (9) ◽  
pp. 4197-4201
Author(s):  
Heena Gupta ◽  
V. Asha
Keyword(s):  
Machine Learning ◽  
Categorical Data ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Prediction Problem ◽  
Encoding Scheme ◽  
The Impact ◽  
Prediction Problems

The prediction problem in any domain is very important to assess the prices and preferences among people. This issue varies for different kinds of data. Data may be nominal or ordinal, it may involve more categories or less. For any category to be considered by a machine learning algorithm, it needs to be encoded before any other operation can be further performed. There are various encoding schemes available like label encoding, count encoding and one hot encoding. This paper aims to understand the impact of various encoding schemes and the accuracy among the prediction problems of high cardinality categorical data. The paper also proposes an encoding scheme based on curated strings. The domain chosen for this purpose is predicting doctors’ fees in various cities having different profiles and qualification.

Input Recognition

2018 ◽  
Author(s):  
Otmar Hilliges
Keyword(s):  
Machine Learning ◽  
Signal Analysis ◽  
Learning Algorithm ◽  
Data Driven ◽  
Complex Nature ◽  
Machine Learning Algorithm ◽  
Challenging Problem ◽  
User Input ◽  
Linear Dynamic ◽  
The Core

Sensing of user input lies at the core of HCI research. Deciding which input mechanisms to use and how to implement them such that they work in a way that is easy to use, robust to various environmental factors and accurate in reconstruction of the users intent is a tremendously challenging problem. The main difficulties stem from the complex nature of human behavior which is highly non-linear, dynamic and context dependent and can often only be observed partially. Due to these complexities, research has turned its attention to data-driven techniques in order to build sophisticated and robust input recognition mechanisms. In this chapter we discuss the most important aspects that constitute data-driven signal analysis approaches. The aim is to provide the reader with an overall understanding of the process irrespective of the exact choice of sensor or machine learning algorithm.

scholarly journals A novel machine learning algorithm has the potential to reduce by 1/3 the quantity of ILR episodes needing review

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
A Rosier ◽  
E Crespin ◽  
A Lazarus ◽  
G Laurent ◽  
A Menet ◽  
...  
Keyword(s):  
Machine Learning ◽  
False Positive ◽  
Learning Algorithm ◽  
False Positive Rate ◽  
Machine Learning Algorithm ◽  
The Novel ◽  
Funding Sources ◽  
High Workload ◽  
Positive Rate ◽  
The Impact

Abstract Background Implantable Loop Recorders (ILRs) are increasingly used and generate a high workload for timely adjudication of ECG recordings. In particular, the excessive false positive rate leads to a significant review burden. Purpose A novel machine learning algorithm was developed to reclassify ILR episodes in order to decrease by 80% the False Positive rate while maintaining 99% sensitivity. This study aims to evaluate the impact of this algorithm to reduce the number of abnormal episodes reported in Medtronic ILRs. Methods Among 20 European centers, all Medtronic ILR patients were enrolled during the 2nd semester of 2020. Using a remote monitoring platform, every ILR transmitted episode was collected and anonymised. For every ILR detected episode with a transmitted ECG, the new algorithm reclassified it applying the same labels as the ILR (asystole, brady, AT/AF, VT, artifact, normal). We measured the number of episodes identified as false positive and reclassified as normal by the algorithm, and their proportion among all episodes. Results In 370 patients, ILRs recorded 3755 episodes including 305 patient-triggered and 629 with no ECG transmitted. 2821 episodes were analyzed by the novel algorithm, which reclassified 1227 episodes as normal rhythm. These reclassified episodes accounted for 43% of analyzed episodes and 32.6% of all episodes recorded. Conclusion A novel machine learning algorithm significantly reduces the quantity of episodes flagged as abnormal and typically reviewed by healthcare professionals. FUNDunding Acknowledgement Type of funding sources: None. Figure 1. ILR episodes analysis

scholarly journals A Data-Driven Prediction Method for an Early Warning of Coccidiosis in Intensive Livestock Systems: A Preliminary Study

Animals ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 747
Author(s):  
Federica Borgonovo ◽  
Valentina Ferrante ◽  
Guido Grilli ◽  
Riccardo Pascuzzo ◽  
Simone Vantini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Warning ◽  
Learning Algorithm ◽  
Data Driven ◽  
Economic Losses ◽  
Parasitic Infections ◽  
Machine Learning Algorithm ◽  
Automatic Data ◽  
Intensive Farming ◽  
Poultry Farms

Coccidiosis is still one of the major parasitic infections in poultry. It is caused by protozoa of the genus Eimeria, which cause concrete economic losses due to malabsorption, bad feed conversion rate, reduced weight gain, and increased mortality. The greatest damage is registered in commercial poultry farms because birds are reared together in large numbers and high densities. Unfortunately, these enteric pathologies are not preventable, and their diagnosis is only available when the disease is full-blown. For these reasons, the preventive use of anticoccidials—some of these with antimicrobial action—is a common practice in intensive farming, and this type of management leads to the release of drugs in the environment which contributes to the phenomenon of antibiotic resistance. Due to the high relevance of this issue, the early detection of any health problem is of great importance to improve animal welfare in intensive farming. Three prototypes, previously calibrated and adjusted, were developed and tested in three different experimental poultry farms in order to evaluate whether the system was able to identify the coccidia infection in intensive poultry farms early. For this purpose, a data-driven machine learning algorithm was built, and specific critical values of volatile organic compounds (VOCs) were found to be associated with abnormal levels of oocystis count at an early stage of the disease. This result supports the feasibility of building an automatic data-driven machine learning algorithm for an early warning of coccidiosis.

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