Tampere University of Technology

About: Tampere University of Technology is a based out in . It is known for research contribution in the topics: Laser & Context (language use). The organization has 6802 authors who have published 19787 publications receiving 431793 citations. The organization is also known as: Tampereen teknillinen yliopisto.

Quantitative prediction of residual stress and hardness in case-hardened steel based on the Barkhausen noise measurement

Abstract: The aim of this study is to predict residual stress and hardness of a case-hardened steel samples based on the Barkhausen noise measurements. A data-based approach for building a prediction model proposed in the paper consists of feature generation, feature selection and model identification and validation steps. Features are selected with a simple forward-selection algorithm. A multivariable linear regression models are used in predictions. Throughout the selection and identification procedures a cross-validation is used to guarantee that the results are realistic and hold also for future predictions. The obtained prediction models are validated with an external validation data set. Prediction accuracy of the prediction models is good showing that the proposed modelling scheme can be applied to prediction of material properties.

Conceptualizing Big Social Data

Abstract: The popularity of social media and computer-mediated communication has resulted in high-volume and highly semantic data about digital social interactions. This constantly accumulating data has been termed as Big Social Data or Social Big Data, and various visions about how to utilize that have been presented. However, as relatively new concepts, there are no solid and commonly agreed definitions of them. We argue that the emerging research field around these concepts would benefit from understanding about the very substance of the concept and the different viewpoints to it. With our review of earlier research, we highlight various perspectives to this multi-disciplinary field and point out conceptual gaps, the diversity of perspectives and lack of consensus in what Big Social Data means. Based on detailed analysis of related work and earlier conceptualizations, we propose a synthesized definition of the term, as well as outline the types of data that Big Social Data covers. With this, we aim to foster future research activities around this intriguing, yet untapped type of Big Data.