Martinez-Villaseñor, Lourdes
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Preferred name
Martinez-Villaseñor, Lourdes
Official Name
Martínez Villaseñor, María de Lourdes
Alternative Name
lmartine
Main Affiliation
ORCID
Scopus Author ID
55521085100
Researcher ID
N-7607-2018

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Versatility of Artificial Hydrocarbon Networks for Supervised Learning

2018 , Ponce, Hiram , Martinez-Villaseñor, Lourdes

Surveys on supervised machine show that each technique has strengths and weaknesses that make each of them more suitable for a particular domain or learning task. No technique is capable to tackle every supervised learning task, and it is difficult to comply with all possible desirable features of each particular domain. However, it is important that a new technique comply with the most requirements and desirable features of as many domains and learning tasks as possible. In this paper, we presented artificial hydrocarbon networks (AHN) as versatile and efficient supervised learning method. We determined the ability of AHN to solve different problem domains, with different data-sources and to learn different tasks. The analysis considered six applications in which AHN was successfully applied. © Springer Nature Switzerland AG 2018.

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A Survey on Freezing of Gait Detection and Prediction in Parkinson’s Disease

2020 , Martinez-Villaseñor, Lourdes , Ponce, Hiram , Miralles-Pechuán, Luis

Most of Parkinson’s disease (PD) patients present a set of motor and non-motor symptoms and behaviors that vary during the day and from day-to-day. In particular, freezing of gait (FOG) impairs their quality of life and increases the risk of falling. Smart technology like mobile communication and wearable sensors can be used for detection and prediction of FOG, increasing the understanding of the complex PD. There are surveys reviewing works on Parkinson and/or technologies used to manage this disease. In this review, we summarize and analyze works addressing FOG detection and prediction based on wearable sensors, vision and other devices. We aim to identify trends, challenges and opportunities in the development of FOG detection and prediction systems. © 2020, Springer Nature Switzerland AG.

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A Genetic Algorithm to Solve Power System Expansion Planning with Renewable Energy

2018 , Martinez-Villaseñor, Lourdes , Ponce, Hiram , Marmolejo Saucedo, José Antonio , Ramírez, Juan Manuel , Hernández, Agustina

In this paper, a deterministic dynamic mixed-integer programming model for solving the generation and transmission expansion-planning problem is addressed. The proposed model integrates conventional generation with renewable energy sources and it is based on a centralized planned transmission expansion. Due a growing demand over time, it is necessary to generate expansion plans that can meet the future requirements of energy systems. Nowadays, in most systems a public entity develops both the short and long of electricity-grid expansion planning and mainly deterministic methods are employed. In this study, an heuristic optimization approach based on genetic algorithms is presented. Numerical results show the performance of the proposed algorithm. © 2018, Springer Nature Switzerland AG.

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