Data-Driven Analytics: A Novel Approach to Performance Diagnosis Using SpatioTemporal Analysis in a Giant Field Offshore Abu Dhabi

2018 ◽  
Author(s):  
Mohamed Mehdi El Faidouzi ◽  
Djamel Eddine Ouzzane
Keyword(s):  
Spatiotemporal Analysis ◽  
Data Driven ◽  
Abu Dhabi ◽  
Performance Diagnosis ◽  
Novel Approach

scholarly journals High-Fidelity Simulated Players for Interactive Narrative Planning

2018 ◽  
Author(s):  
Pengcheng Wang ◽  
Jonathan Rowe ◽  
Wookhee Min ◽  
Bradford Mott ◽  
James Lester
Keyword(s):  
State Of The Art ◽  
Data Driven ◽  
High Fidelity ◽  
Interactive Narrative ◽  
Interaction Data ◽  
Convolutional Networks ◽  
Novel Approach ◽  
Adaptation Policies ◽  
Narrative Planning ◽  
Prior State

Interactive narrative planning offers significant potential for creating adaptive gameplay experiences. While data-driven techniques have been devised that utilize player interaction data to induce policies for interactive narrative planners, they require enormously large gameplay datasets. A promising approach to addressing this challenge is creating simulated players whose behaviors closely approximate those of human players. In this paper, we propose a novel approach to generating high-fidelity simulated players based on deep recurrent highway networks and deep convolutional networks. Empirical results demonstrate that the proposed models significantly outperform the prior state-of-the-art in generating high-fidelity simulated player models that accurately imitate human players’ narrative interactions. Using the high-fidelity simulated player models, we show the advantage of more exploratory reinforcement learning methods for deriving generalizable narrative adaptation policies.

scholarly journals Integrated mechanistic and data-driven modelling for multivariate analysis of signalling pathways

2006 ◽  
Vol 3 (9) ◽  
pp. 515-526 ◽  
Author(s):  
Fei Hua ◽  
Sampsa Hautaniemi ◽  
Rayka Yokoo ◽  
Douglas A Lauffenburger
Keyword(s):  
Decision Tree ◽  
Mathematical Models ◽  
Large Scale ◽  
Therapeutic Interventions ◽  
Data Driven ◽  
Signalling Pathways ◽  
Apoptotic Pathway ◽  
Tree Analysis ◽  
Modelling Framework ◽  
Novel Approach

Mathematical models of highly interconnected and multivariate signalling networks provide useful tools to understand these complex systems. However, effective approaches to extracting multivariate regulation information from these models are still lacking. In this study, we propose a data-driven modelling framework to analyse large-scale multivariate datasets generated from mathematical models. We used an ordinary differential equation based model for the Fas apoptotic pathway as an example. The first step in our approach was to cluster simulation outputs generated from models with varied protein initial concentrations. Subsequently, decision tree analysis was applied, in which we used protein concentrations to predict the simulation outcomes. Our results suggest that no single subset of proteins can determine the pathway behaviour. Instead, different subsets of proteins with different concentrations ranges can be important. We also used the resulting decision tree to identify the minimal number of perturbations needed to change pathway behaviours. In conclusion, our framework provides a novel approach to understand the multivariate dependencies among molecules in complex networks, and can potentially be used to identify combinatorial targets for therapeutic interventions.

scholarly journals A Novel Approach to Data Driven Pandemic Recovery: The Pandemic Recovery Acceleration Model

2020 ◽  
Author(s):  
Jeffrey P Gold ◽  
Christopher Wichman ◽  
Kenneth Bayles ◽  
Ali S Khan ◽  
Christopher Kratochvil ◽  
...  
Keyword(s):  
Public Health ◽  
Health Care ◽  
Real Time ◽  
Local Community ◽  
The State ◽  
Data Driven ◽  
Real Time Analysis ◽  
Novel Approach ◽  
Acceleration Model ◽  
Care Systems

A data driven approach to guide the global, regional and local pandemic recovery planning is key to the safety, efficacy and sustainability of all pandemic recovery efforts. The Pandemic Recovery Acceleration Model (PRAM) analytic tool was developed and implemented state wide in Nebraska to allow health officials, public officials, industry leaders and community leaders to capture a real time snapshot of how the COVID-19 pandemic is affecting their local community, a region or the state and use this novel lens to aid in making key mitigation and recovery decisions. This is done by using six commonly available metrics that are monitored daily across the state describing the pandemic impact: number of new cases, percent positive tests, deaths, occupied hospital beds, occupied intensive care beds and utilized ventilators, all directly related to confirmed COVID-19 patients. Nebraska is separated into six Health Care Coalitions based on geography, public health and medical care systems. The PRAM aggregates the data for each of these geographic regions based on disease prevalence acceleration and health care resource utilization acceleration, producing real time analysis of the acceleration of change for each metric individually and also combined into a single weighted index, the PRAM Recovery Index. These indices are then shared daily with the state leadership, coalition leaders and public health directors and also tracked over time, aiding in real time regional and statewide decisions of resource allocation and the extent of use of comprehensive non-pharmacologic interventions.

scholarly journals Reaction Wheels Fault Isolation Onboard 3-Axis Controlled Satel-lite using Enhanced Random Forest with Multidomain Features

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Afshin Rahimi ◽  
Mofiyinoluwa O. Folami
Keyword(s):  
Machine Learning ◽  
Fault Isolation ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Superior Performance ◽  
Alternative Methods ◽  
Model Parameters ◽  
High Fidelity ◽  
Novel Approach ◽  
Learning Techniques

As the number of satellite launches increases each year, it is only natural that an interest in the safety and monitoring of these systems would increase as well. However, as a system becomes more complex, generating a high-fidelity model that accurately describes the system becomes complicated. Therefore, imploring a data-driven method can provide to be more beneficial for such applications. This research proposes a novel approach for data-driven machine learning techniques on the detection and isolation of nonlinear systems, with a case-study for an in-orbit closed loop-controlled satellite with reaction wheels as actuators. High-fidelity models of the 3-axis controlled satellite are employed to generate data for both nominal and faulty conditions of the reaction wheels. The generated simulation data is used as input for the isolation method, after which the data is pre-processed through feature extraction from a temporal, statistical, and spectral domain. The pre-processed features are then fed into various machine learning classifiers. Isolation results are validated with cross-validation, and model parameters are tuned using hyperparameter optimization. To validate the robustness of the proposed method, it is tested on three characterized datasets and three reaction wheel configurations, including standard four-wheel, three-orthogonal, and pyramid. The results prove superior performance isolation accuracy for the system under study compared to previous studies using alternative methods (Rahimi & Saadat, 2019, 2020).

scholarly journals Degradation detection for internal gear pumps using pressure reduction time maps

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Kurt Pichler ◽  
Rainer Haas ◽  
Veronika Putz ◽  
Christian Kastl
Keyword(s):  
Operating Time ◽  
Close Relation ◽  
Data Driven ◽  
Pressure Reduction ◽  
Reduction Time ◽  
Novel Approach ◽  
Gear Pumps ◽  
Data Driven Approach ◽  
Pump Pressure ◽  
Internal Gear

In this paper, a novel approach for detecting degradation in internal gear pumps is proposed. In a data-driven approach, pressure reduction time maps (PRTMs) are identified as a useful indicator for degradation detection. A PRTM measures the time for reducing the internal pump pressure from certain levels to any lower level when the pump engine is stopped and the valves are closed. The PRTM can thus be interpreted as an internal leakage indicator of the pump. For simplified evaluation, PRTMs are compressed to a single scalar indicator by computing their volume (PRTMV). When the internal leakage increases due to wear, the pressure in the pump decreases faster (implying a decreased PRTMV). The proposed approach has been developed and tested with data of real internal gear pumps with different operating times. The PRTMV shows a close relation to the operating time of the pump. Moreover, we compare PRTMV with the commonly used and well known approach of observing pressure holding speed (PHS). Especially for medium degradation, PRTMV shows better sensitivity then PHS.

Sign in / Sign up

Export Citation Format

Share Document