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All Outputs (25)

XOR Binary Gravitational Search Algorithm with Repository: Industry 4.0 Applications (2020)
Journal Article
Ahmadieh Khanesar, M., Bansal, R., Martínez-Arellano, G., & Branson, D. (2020). XOR Binary Gravitational Search Algorithm with Repository: Industry 4.0 Applications. Applied Sciences, 10(8), Article 6451. https://doi.org/10.3390/app10186451

Industry 4.0 is the fourth generation of industry which will theoretically revolutionize manufacturing methods through the integration of machine learning and artificial intelligence approaches on the factory floor to obtain robustness and sped-up pr... Read More about XOR Binary Gravitational Search Algorithm with Repository: Industry 4.0 Applications.

Tool Wear Classification using Time Series Imaging and Deep Learning (2019)
Journal Article
Martínez-Arellano, G., Terrazas, G., & Ratchev, S. (2019). Tool Wear Classification using Time Series Imaging and Deep Learning. International Journal of Advanced Manufacturing Technology, 104(9-12), 3647–3662. https://doi.org/10.1007/s00170-019-04090-6

Tool Condition Monitoring (TCM) has become essential to achieve high quality machining as well as cost-effective production. Identification of the cutting tool state during machining before it reaches its failure stage is critical. This paper present... Read More about Tool Wear Classification using Time Series Imaging and Deep Learning.

Towards an active learning approach to tool condition monitoring with Bayesian deep learning (2019)
Conference Proceeding
Martinez Arellano, G., & Ratchev, S. (2019). Towards an active learning approach to tool condition monitoring with Bayesian deep learning

With the current advances in the Internet of Things (IoT), smart sensors and Artificial Intelligence (AI), a new generation of condition monitoring solutions for smart manufacturing is starting to emerge. Computer Numerical Control (CNC) machines can... Read More about Towards an active learning approach to tool condition monitoring with Bayesian deep learning.

Creating AI Characters for Fighting Games Using Genetic Programming (2016)
Journal Article
Martinez-Arellano, G., Cant, R., & Woods, D. (2017). Creating AI Characters for Fighting Games Using Genetic Programming. IEEE Transactions on Computational Intelligence and AI in Games, 9(4), 423-434. https://doi.org/10.1109/tciaig.2016.2642158

This paper proposes a character generation approach for the M.U.G.E.N. fighting game that can create engaging AI characters using a computationally cheap process without the intervention of the expert developer. The approach uses a genetic programmin... Read More about Creating AI Characters for Fighting Games Using Genetic Programming.