Forecast of chemostat dynamics using data-driven approach

2021 ◽  
Author(s):  
Benaissa Dekhici ◽  
Boumediene Benyahiya ◽  
Brahim Cherki
Keyword(s):  
Data Driven ◽  
Data Driven Approach ◽  
Using Data

Modeling the Gas Exchange Processes of a Modern Diesel Engine With an Integrated Physics-Based and Data-Driven Approach

2019 ◽  
Author(s):  
Jorge Pulpeiro Gonzalez ◽  
King Ankobea-Ansah ◽  
Elena Escuder Milian ◽  
Carrie M. Hall
Keyword(s):  
Diesel Engine ◽  
Gas Exchange ◽  
High Efficiency ◽  
Direct Injection ◽  
Data Driven ◽  
Model Framework ◽  
Variable Valve Actuation ◽  
Exchange Processes ◽  
Data Driven Approach ◽  
Using Data

Abstract The gas exchange processes of engines are becoming increasingly complex since modern engines leverage technologies including variable valve actuation, turbochargers, and exhaust gas recirculation. Control of these many devices and the underlying gas flows is essential for high efficiency engine concepts. If these processes are to be controlled and estimated using model-based techniques, accurate models are required. This work explores a model framework that leverages a data-driven model of the turbocharger along with submodels of the intercooler, intake and exhaust manifolds and engine processes to provide cylinder-specific predictions of the pressure and temperatures of the gases across the system. This model is developed and validated using data from a 2.0 liter VW turbocharged, direct-injection diesel engine and shown to provide accurate prediction of critical gas properties.

Using data-driven approach for wind power prediction: A comparative study

2016 ◽  
Vol 118 ◽  
pp. 193-203 ◽  
Author(s):  
Ehsan Taslimi Renani ◽  
Mohamad Fathi Mohamad Elias ◽  
Nasrudin Abd. Rahim
Keyword(s):  
Comparative Study ◽  
Wind Power ◽  
Data Driven ◽  
Power Prediction ◽  
Wind Power Prediction ◽  
Data Driven Approach ◽  
Using Data

A Data Driven Approach to Study the Social and Political Statuses of Urban Communities in Kunming

2020 ◽  
Vol 4 (2) ◽  
pp. 461-481
Author(s):  
Charles Chang
Keyword(s):  
Urban Communities ◽  
Computational Analysis ◽  
Critical Evaluation ◽  
Data Driven ◽  
Accurate Information ◽  
Sufficient Detail ◽  
The Social ◽  
Data Driven Approach ◽  
Using Data ◽  
Political Economic

AbstractThis article presents a data-driven approach to the study of the social and political statuses of urban communities in modern Kunming. Such information is lacking in government maps and documents. Using data from a wide variety of sources, many unconventional, I subject them to critical evaluation and computational analysis to extract information that can be used to produce a land use map of sufficient detail and accuracy to allow scholars to address and even answer questions of a socio-political, economic and, indeed, humanistic nature. My method can also be applied to other Chinese cities and to cities elsewhere that lack accurate information.

Dynamics and path tracking of continuum robotic arms using data-driven identification tools

Robotica ◽  
2021 ◽  
pp. 1-27
Author(s):  
Aida Parvaresh ◽  
S. Ali A. Moosavian
Keyword(s):  
Inverse Dynamics ◽  
Computational Cost ◽  
Experimental Tests ◽  
Path Tracking ◽  
Data Driven ◽  
Robotic Arms ◽  
Rod Theory ◽  
Data Driven Approach ◽  
Using Data ◽  
Continuum Robotic

Abstract In this paper, forward/inverse dynamics of a continuum robotic arm is developed using a data-driven approach, which could tackle uncertainties and extreme nonlinearities to obtain reliable solutions. By establishing a direct mapping between the actuator and task spaces, the unnecessary mappings of actuator-to-configuration then configuration-to-task are eliminated, to reduce extra computational cost. The proposed approach is validated through simulation (based on Cosserat rod theory) and experimental tests on RoboArm. Next, path tracking in the presence/absence of obstacles as well as load carrying maneuver are investigated. Finally, the obtained results concerning repeatability, scalability, and disturbance rejection performance of the approach are discussed.

scholarly journals Localizing sensory processing sensitivity and its subdomains within its relevant trait space: a data-driven approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Taraneh Attary ◽  
Ali Ghazizadeh
Keyword(s):  
Big Five ◽  
Sensory Processing ◽  
Data Driven ◽  
Anxiety And Depression ◽  
Sensory Threshold ◽  
Highly Sensitive ◽  
Data Driven Approach ◽  
Sensory Processing Sensitivity ◽  
Using Data ◽  
Positive Traits

AbstractSensitivity arising from enhanced processing of external and internal stimuli or sensory processing sensitivity (SPS) is known to be present in a sizable portion of the population. Yet a clear localization of SPS and its subdomains with respect to other relevant traits is currently lacking. Here, we used a data-driven approach including hierarchical clustering, t-distributed stochastic neighbor embedding (t-SNE) and graph learning to portrait SPS as measured by Highly Sensitive Person Scale (HSPS) in relation to the Big-Five Inventory (neuroticism, extraversion, openness, agreeableness, and conscientiousness) as well as to shyness, alexithymia, autism quotient, anxiety, and depression (11 total traits) using data from more than 800 participants. Analysis revealed SPS subdomains to be divided between two trait clusters with questions related to aesthetic sensitivity (AES) falling within a cluster of mainly positive traits and neighbored by openness while questions addressing ease of excitation (EOE) and low sensory threshold (LST) to be mostly contained within a cluster of negative traits and neighbored by neuroticism. A similar spread across clusters was seen for questions addressing autism consistent with it being a spectrum disorder, in contrast, alexithymia subdomains were closely fit within the negative cluster. Together, our results support the view of SPS as a distinct yet non-unitary trait and provide insights for further refinements of the current SPS concept and scales.

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