Multi-Objective Genetic Algorithm for Optimizing an ELM-Based Driver Distraction Detection System

Multi-Objective Genetic Algorithm for Optimizing an ELM-Based Driver Distraction Detection System

Egileak:
Javier Echanobe;Koldo Basterretxea;Inés del Campo;Victoria Martínez;Naiara Vidal
Non argitaratua:
IEEE Transactions on Intelligent Transportation Systems
Liburukia:
(Early Access Article)
Hasierako orria - Amaierako orria:
11946 - 11959
ISBN/ISSN:
1524-9050
DOI:
10.1109/TITS.2021.3108851

"An eco-driving approach for ride comfort improvement"

An eco-driving approach for ride comfort improvement

Egileak:
O. Mata-Carballeira, I. del Campo, E. Asua
Urtea:
2022
Non argitaratua:
IET INTELLIGENT TRANSPORT SYSTEMS
Liburukia:
16(2)
Hasierako orria - Amaierako orria:
186 - 205

Argitalpenak

Computational Intelligence Techniques for maximum energy efficiency of an internal combuestion engine and a steam turbine of a cogeneration process

Egileak:
Sandra Seijo Fernández, I. del Campo, J. Echanobe, J. García-Sedano, E. Suso, E. Arbizu
Urtea:
2014
Aldizkaria:
International Journal of Energy Environment Engineering
Liburukia:
5
Hasierako orria - Amaierako orria:
1 - 10
Deskribapena:

In this paper, the optimization of a real Combined Heat and Power (CHP) plant and a slurry drying process is proposed. Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference Systems (ANFISs) are used to generate predictive models of the process. A dataset collected over a one-year period, with variables for the whole plant, is used to generate the predictive models. First, data mining techniques are used to obtain a representative dataset for the process as well as the input and target parameters for each model. Subsequently, models are used to optimize the plant performance in order to maximize the effective electrical efficiency of the process. For this purpose, 12 input parameters are selected as decision variables, i.e., variables which can change their values to optimize the plant. Plant performance optimization is a multi-objective problem with three goals: to maximize electrical production, minimize fuel consumption and maximize the amount of heat used in the slurry process. The optimization algorithm calculates the values of the decision variables for each time-step using Gradient Descent Methods (GDM). The simulation results show that optimization using a multi-objective function increases the CHP plant's effective electrical efficiency by around 3% on average.