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Your search keyword '"Chairez I"' showing total 219 results
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219 results on '"Chairez I"'

9. Robust Control of a Master-Slave Manipulator Under Restricted Task-Space

Author

Cruz-Ortiz, D., Chairez, I., Poznyak, A., Ceccarelli, Marco, Series Editor, Hernandez, Alfonso, Editorial Board Member, Huang, Tian, Editorial Board Member, Takeda, Yukio, Editorial Board Member, Corves, Burkhard, Editorial Board Member, Agrawal, Sunil, Editorial Board Member, Hernandez, Eusebio E., editor, Keshtkar, Sajjad, editor, and Valdez, S. Ivvan, editor

Published
2020
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43. Hybrid forecasting modelling of cost and time entities for planning and optimizing projects in the die-cast aluminium industry.

Author

Muñoz-Ibañez, C., Chairez, I., Jimenez-Martinez, M., Molina, A., and Alfaro-Ponce, M.

Subjects
*ALUMINUM industry, *COST estimates, *SCHEDULING, *ECONOMIC impact, *DIE-casting, *FORECASTING, *ALUMINUM alloys
Abstract

The techniques employed to manage an industrial project are based on tools that aim to achieve the objectives set by an organization. Most of these techniques consider the development of operative and predictive models. The difficulty in developing project planning models relies on estimating large sets of parameters and the need to include model sections of poorly identifiable, that increase costs and time. This work develops a hybrid forecasting model for all the phases that make up die-casting projects through a series of parameters and sub-models that contemplate the particularities of each case, thereby achieving greater precision in the forecast. The model identifies the cost and time factors that affect project planning, specifically in the die-casting industry, and intends to predict their future behaviour when certain initially given conditions are modified. To estimate the parameters of the hybrid model, several factors in the processes were considered that interact in this industry, such as primary matter costs and activities associated to the process. The considered processes that have a substantial economic impact on the implementation of the project were selected. The criteria for this selection considered identifying the relevant parts of the design and manufacturing in the diecasting industry. Process factors such as the Cost of aluminium and its related activities, whose processes will be grouped into cost and time entities to build a set of metrics that allow better control over them. Finally, the proposed model is based on analytical, parametric, and analog methods that achieve accuracy greater than 85 % in predicting the time and Cost of the process. [ABSTRACT FROM AUTHOR]

Published
2023
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45. Deep Learning Adapted to Differential Neural Networks Used as Pattern Classification of Electrophysiological Signals.

Author

Llorente-Vidrio, D., Ballesteros, M., Salgado, I., and Chairez, I.

Subjects
DEEP learning, SIGNAL classification, ARTIFICIAL neural networks, BIOLOGICAL classification, BIOLOGICAL neural networks
Abstract

This manuscript presents the design of a deep differential neural network (DDNN) for pattern classification. First, we proposed a DDNN topology with three layers, whose learning laws are derived from a Lyapunov analysis, justifying local asymptotic convergence of the classification error and the weights of the DDNN. Then, an extension to include an arbitrary number of hidden layers in the DDNN is analyzed. The learning laws for this general form of the DDNN offer a contribution to the deep learning framework for signal classification with biological nature and dynamic structures. The DDNN is used to classify electroencephalographic signals from volunteers that perform an identification graphical test. The classification results show exponential growth in the signal classification accuracy from 82 percent with one layer to 100 percent with three hidden layers. Working with DDNN instead of static deep neural networks (SDNN) represents a set of advantages, such as processing time and training period reduction up to almost 100 times, and the increment of the classification accuracy while working with less hidden layers than working with SDNN, which are highly dependent on their topology and the number of neurons in each layer. The DDNN employed fewer neurons due to the induced feedback characteristic. [ABSTRACT FROM AUTHOR]

Published
2022
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