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El espacio arquitectónico
(Hospitalidad ESDAI, 2003)
Vázquez del Mercado, Cristina Amieva
;
Campus Ciudad de México
Man by nature carries out his activities in spaces limited and designed by himself, however it would appear the importance to the users is forgotten until something does not function. A proyect is an element which must support itself in its structure and be supported by the people who inhabit it. This architectonic space must be the result of a detailed analysis based on the geography, history and psychology of the user and it must have an absolute communion with the environment and the construction; and naturally with its inhabitants. It is important to respect space, in order to be able to rescue the functional and dynamic sense it has, and to recognize it as responsible from its history and its time; if it does not give satisfaction to the senses it cannot be called architecture.
6 158
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Differences in Defect Distribution Across Scan Strategies in Electron Beam AM Ti-6Al-4V : The fraction and size of pores present in EBM Ti-6Al-4V specimens varies depending on the melting strategy used, whether linear raster melting or point melting
(2021)
Quintana, María José
;
O’Donnell, Katie
;
Kenney, Matthew J.
;
Collins, Peter C.
In recent years, additive manufacturing (AM) has begun to displace traditional manufacturing techniques for specific applications. Notable benefits of AM include reduced times from design to product, an improved buy-tofly ratio, lower waste, and the ability to produce complex geometries[1,2]. An additional benefit of additive manufacturing is the variety of manufacturing processes that span across heat source (e.g., laser, electron beam, plasma), input material type (e.g., powder, wire), atmosphere, and the number of axes of control among others[2-4]. This variability in processing route means that a process can be identified and optimized for a class of products or parts. Despite these various advantages, one of the primary drawbacks of AM processes is porosity within builds, which ultimately reduces the ability of a part to withstand tensile stresses and can lead to premature failure[4-6]. Copyright 2021 ASM International.
Scopus© Citations 3 24 1
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Special issue on Mexican International Conference on Artificial Intelligence, MICAI 2014 and 2015
(2017)
González-Mendoza, Miguel
;
Ponce, Hiram
;
Martinez-Villaseñor, Lourdes
This special issue of the journal Soft Computing offers extended versions of some of the best-awarded, high-reviewed and invited papers presented on the 13th Mexican International Conference on Artificial Intelligence, MICAI 2014, held in Tuxtla Gutiérrez, Chiapas, Mexico, on November 16–22, 2014, under the organization of the Mexican Society for Artificial Intelligence (SMIA) in cooperation with the Instituto Tecnológico de Tuxtla Gutiérrez and Universidad Autónoma de Chiapas, and on the 14th Mexican International Conference on Artificial Intelligence, MICAI 2015, held in Cuernavaca, Morelos, Mexico, on October 25–31, 2015, under the organization of the SMIA in cooperation with the Instituto de Investigaciones Eléctricas. ©2017 Soft Computing, Springer Verlag.
30 1
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Development of Fast and Reliable Nature-Inspired Computing for Supervised Learning in High-Dimensional Data
(2019)
Ponce, Hiram
;
Souza, Paulo
;
González Mora, José Guillermo
Machine learning and data mining tasks in big data involve different nature of inputs that typically exhibit high dimensionality, e.g. more than 1,000 features, far from current acceptable scales computing in one machine. In many different domains, data have highly nonlinear representations that nature-inspired models can easily capture, outperforming simple models. But, the usage of these approaches in high-dimensional data are computationally costly. Recently, artificial hydrocarbon networks (AHN)—a supervised learning method inspired on organic chemical structures and mechanisms—have shown improvements in predictive power and interpretability in contrast with other well-known machine learning models, such as neural networks and random forests. However, AHN are very time-consuming that are not able to deal with big data until now. In this chapter, we present a fast and reliable nature-inspired training method for AHN, so they can handle high-dimensional data. This training method comprises a population-based meta-heuristic optimization with defined both individual encoding and objective function related to the AHN-model, and it is also implemented in parallel-computing. After benchmark performing of population-based optimization methods, grey wolf optimization (GWO) was selected. Our results demonstrate that the proposed hybrid GWO-based training method for AHN runs more than 1400x faster in high-dimensional data, without loss of predictability, yielding a fast and reliable nature-inspired machine learning model. We also present a use case in assisted living monitoring, i.e. human fall classification comprising 1,269 features from sensor signals and video recordings, with this proposed training algorithm to show its implementation and performance. We anticipate our new training algorithm to be useful in many applications like medical engineering, robotics, finance, aerospace, and others, in which big data is essential. © Springer Nature Switzerland AG 2020.
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