Alexandre SAVA

Associate professor (Maître de conférences)

Université de Lorraine

France

Research Interests

Explore related searches

Professors in France

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Dr. Alexandre SAVA is an Associate Professor (Maître de conférences) at Université de Lorraine, France. His research interests include decision risk assessment in manufacturing, wind turbine output prediction, and advanced detection methods in high-speed milling processes. He has published extensively on topics such as adaptive neuro-fuzzy inference systems and noise-assisted empirical mode decomposition algorithms.

Articles (9)

Year:

2024

Year:

2023

Year:

2022

Year:

2022

Year:

2021

Year:

2021

Year:

2020

Year:

2020

Year:

2020

Collaborators (3)

RACHID LAREF

Université d'Artois

FRANCE

View details

Maryam Siadat

Professeure des Universités

Université de Lorraine

FRANCE

View details

Kondo Hloindo Adjallah

Université de Lorraine

FRANCE

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Alexandre SAVA

Research Interests

Explore related searches

Contact this professor

About

Articles (9)

A Decision Risk Assessment and Alleviation Framework under Data Quality Challenges in Manufacturing

Wind turbine output power prediction and optimization based on a novel adaptive neuro-fuzzy inference system with the moving window

Incipient chatter fast and reliable detection method in high-speed milling process based on cumulative strategy

Wind power conversion system model identification using adaptive neuro-fuzzy inference systems: A case study

Influence of Sampling Frequency Ratio on Mode Mixing Alleviation Performance: A Comparative Study of Four Noise-Assisted Empirical Mode Decomposition Algorithms

Empiric Unsupervised Drifts Correction Method of Electrochemical Sensors for in Field Nitrogen Dioxide Monitoring

Fuzzy model identification based on mixture distribution analysis for bearings remaining useful life estimation using small training data set

MaxEnt feature-based reliability model method for real-time detection of early chatter in high-speed milling

Bearings degradation monitoring indicators based on discarded projected space information and piecewise linear representation

Collaborators (3)

RACHID LAREF

Maryam Siadat

Kondo Hloindo Adjallah

Make your next move.