José-Luis Roca-González

Lessons for Technological Innovation Analysis. A Case of Study Based on McLuhan Tetrad Applied to Laser Cleaning Machines

One of the main purposes for a Project Management course under the Industrial Engineering grade is to lead the students by early applying their multidisciplinary competences to a professional framework, in order to develop their active profile to encourage them to always look for the best available techniques to brings organizations up to date. This paper summarizes how the McLuhan Tetrad help the students to handle the laser cleaning technique as a case of study.

Technological Immersion in Industrial Engineering for a Project Management Course to Develop Dual Use Technology

Technological Immersion (TI) is defined as an approach process to current industrial activity. It is focused on improving the competitive standards of the production activity by analyzing innovation processes, which were regulated by any standard that summarizes most of the international and relevant knowledge on Innovation Management Processes (for Spain, this is the regulation UNE166000). It allows using education on Project Management as a basic foundation to develop dual use technologies for security and defense purposes and for any other industry framework as well. The aim of this case study is to compile, under a TI scope, the experiences acquired by the students of the fourth course at the University Centre of Defense at the Spanish Air Force Academy, when studying the last generation of films that may have high strategic interest for defense as well as for many industrial applications. The TI was performed through a workshop about UNE166000 (Spanish Regulation), McLuhan Tetrad and other lessons on Innovation Management, the main purpose being to teach the students how to draft a professional report about the technological and knowledge transfer opportunities that these films may generate in order to develop new industrial advantages on dual use industry.

Exploiting Diversity of Neural Network Ensembles based on Extreme Learning Machine

Extreme learning machine (ELM) is an emergent method for train- ing single hidden layer feedforward neural networks (SLFNs) with extremely fast training speed, easy implementation and good generalization performance. This work presents effective ensemble procedures for combining ELMs by exploiting di- versity. A large number of ELMs are initially trained in three different scenarios: the original feature input space, the obtained feature subset by forward selection and different random subsets of features. The best combination of ELMs is con- structed according to an exact ranking of the trained models and the useless net- works are discarded. The experimental results on several regression problems show that robust ensemble approaches that exploit diversity can effectively improve the performance compared with the standard ELM algorithm and other recent ELM extensions.