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Ricardo A. Ramirez-Mendoza

Researcher at Monterrey Institute of Technology and Higher Education

Publications -  234
Citations -  2069

Ricardo A. Ramirez-Mendoza is an academic researcher from Monterrey Institute of Technology and Higher Education. The author has contributed to research in topics: Damper & Computer science. The author has an hindex of 15, co-authored 195 publications receiving 1139 citations. Previous affiliations of Ricardo A. Ramirez-Mendoza include University of Monterrey.

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An experimental artificial-neural-network-based modeling of magneto-rheological fluid dampers

Juan C. Tudon-Martinez, +3 more
- 01 Aug 2012 - 
TL;DR: In this paper, a static model for a magneto-rheological (MR) damper based on artificial neural networks (ANNs) is proposed, and an intensive and experimental study is presented for designing the ANN structure.
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Impact of climate change and early development of coffee rust - An overview of control strategies to preserve organic cultivars in Mexico.

Nora E. Torres Castillo, +5 more
- 10 Oct 2020 - 
TL;DR: The contribution of climate change effects in the early development of CLR in Mexico is discussed and focus is given on possible schemes and actions taken around the world as control measures to adapt the vulnerable coffee varieties to tackle this challenging issue.
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Sensors for Sustainable Smart Cities: A Review

Mauricio Adolfo Ramírez-Moreno, +15 more
- 01 Sep 2021 - 
TL;DR: An analysis of different sensors that are typically used in efforts toward creating smart cities is presented, as well as the main opportunities and challenges faced when making a transition to a smart city.
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Virtual/Remote Labs for Automation Teaching: a Cost Effective Approach

Ruben Morales-Menendez, +2 more
- 01 Jan 2019 - 
TL;DR: A hybrid proposal that combines virtual and remote laboratories approaches for teaching industrial automation courses is presented and can serve as a guide to improve teaching/learning process in experimental automation courses.
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DM: Dehghani method for modifying optimization algorithms

Mohammad Javad Dehghani, +10 more
- 01 Nov 2020 - 
TL;DR: Simulation results show that the modified optimization algorithms with Dehghani method provide more acceptable and competitive performance than the original versions in solving optimization problems.