Development and Applications of Self-learning Simulation in Finite Element Analysis

Numerical analysis such as the finite element analysis (FEA) have been widely used to solve many engineering problems. Constitutive modelling is an important component of any numerical analysis and is used to describe the material behaviour. The accuracy and reliability of numerical analysis is greatly reliant on the constitutive model that is integrated in the finite element code. In recent years, data mining techniques such as artificial neural network (ANN), genetic programming (GP) and evolutionary polynomial regression (EPR) have been employed as alternative approach to the conventional constitutive modelling. In particular, EPR offers great advantages over other data mining techniques. However, these techniques require a large database to learn and extract the material behaviour. On the other hand, the link between laboratory or field tests and numerical analysis is still weak and more investigation is needed to improve the way that they matched each other. Training a data mining technique within the self-learning simulation framework is currently considered as one of the solutions that can be utilised to accurately represent the actual material behaviour. In this thesis an EPR based machine learning technique is utilised in the heart of the self-learning framework with an automation process which is coded in MATLAB environment. The methodology is applied to simulate different material behaviour in a number of structural and geotechnical applications. Two training strategies are used to train the EPR in the developed framework, total stress-strain and incremental stress-strain strategies. The results show that integrating EPR based models in the framework allows to learn the material response during the self-learning process and provide accurate predictions to the actual behaviour. Moreover, for the first time, the behaviour of a complex material, frozen soil, is modelled based on the EPR approach. The results of the EPR model predictions are compared with the actual data and it is shown that the proposed model can capture and reproduce the behaviour of the frozen soil with a very high accuracy. The developed EPR based self-learning methodology presents a unified approach to material modelling that can also help the user to gain a deeper insight into the behaviour of the materials. The methodology is generic and can be extended to modelling different engineering materials

User habitation in keystroke dynamics based authentication

Most computer systems use usernames and passwords for authentication and access control. For long, password security has been framed as a tradeoff between user experience and password security. Trading off one for the other appears to be an inevitable dilemma for single password based security applications. As a new biometric for authenticating access, keystroke dynamics offers great promises in hardening the password mechanism. Our research first investigate the keystroke dynamics based password security by conducting an incremental study on user\u27s habituation process for keystroke dynamics analysis using two distinct types of passwords. The study shows that (1) long and complex passwords are more efficient to be employed in keystroke dynamics systems; and (2) there is a habituation and acclimation process before the user obtains a stable keystroke pattern and the system collects enough training data. Then, based on our findings, we propose a two passwords mechanism that attempts to strike the right balance over user experience and password security by adopting a conventional easy-to-memorize password followed by a long-and-complex phrase for keystroke dynamics verification. Analysis and experimental studies successfully demonstrate the effectiveness of our proposed approach

Coopetition and innovation. Lessons from worker cooperatives in the Spanish machine tool industry

This is an electronic version of the accepted paper in Journal of Business & Industrial Marketing[EN] Purpose – This paper aims to investigate how the implementation of the inter-cooperation principle among Spanish machine-tool cooperatives helps them to coopete–collaborate with competitors, in their innovation and internationalization processes and achieve collaborative advantages. Design/methodology/approach – The paper uses a multi-case approach based on interviews with 15 CEOs and research and development (R&D) managers, representing 14 Spanish machine tool firms and institutions. Eight of these organizations are worker-cooperatives.. Findings – Worker -cooperatives achieve advantages on innovation and internationalization via inter-cooperation (shared R&D units, joint sales offices, joint after-sale services, knowledge exchange and relocation of key R&D technicians and managers). Several mutual bonds and ties among cooperatives help to overcome the risk of opportunistic behaviour and knowledge leakage associated to coopetition. The obtained results give some clues explaining to what extent and under which conditions coopetitive strategies of cooperatives are transferable to other types of ownership arrangements across sectors. Practical implications – Firms seeking cooperation with competitors in their R&D and internationalization processes can learn from the coopetitive arrangements analyzed in the paper. Social implications – Findings can be valuable for sectoral associations and public bodies trying to promote coopetition and alliances between competitors as a means to benefit from collaborative advantages. Originality/value – Focusing on an “ideal type” of co-operation -cooperative organisationsand having access to primary sources, the paper shows to what extent (and how) strong coopetitive structures and processes foster innovation and internationalization

A new approach to modeling the behavior of frozen soils

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordIn this paper a new approach is presented for modeling the behavior of frozen soils. A data-mining technique, Evolutionary Polynomial Regression (EPR), is used for modeling the thermo-mechanical behavior of frozen soils including the effects of confining pressure, strain rate and temperature. EPR enables to create explicit and well-structured equations representing the mechanical and thermal behavior of frozen soil using experimental data. A comprehensive set of triaxial tests were carried out on samples of a frozen soil and the data were used for training and verification of the EPR model. The developed EPR model was also used to simulate the entire stress-strain curve of triaxial tests, the data for which were not used during the training of the EPR model. The results of the EPR model predictions were compared with the actual data and it was shown that the proposed methodology can extract and reproduce the behavior of the frozen soil with a very high accuracy. It was also shown that the EPR model is able to accurately generalize the predictions to unseen cases. A sensitivity analysis revealed that the model developed from raw experimental data is able to extract and effectively represent the underlying mechanics of the behavior of frozen soils. The proposed methodology presents a unified approach to modeling of materials that can also help the user gain a deeper insight into the behavior of the materials. The main advantages of the proposed technique in modeling the complex behavior of frozen soil have been highlighted