Search results for "driven"

The TurningPoint application as a transformational learning tool in educational processes

2016

The educational convergence project driven by the European Higher Education Area (EHEA) has created new teaching methods and evaluation processes in university education systems. This new pedagogical approach generates an unquestionable opportunity for different segments of the university community see ICTs as an essential tool of the procedure of educational content development, which facilitate the teaching-learning process. This article presents the TurningPoint tool, which focuses on promoting teacher-student interactions, facilitating participation and ongoing assessment. This tool has advantages over other existing applications because of its variety of utilities and potential use. Mo…

A data-driven methodology to support pump performance analysis and energy efficiency optimization in Waste Water Treatment Plants

2017

Abstract Studies and publications from the past ten years demonstrate that generally the energy efficiency of Waste Water Treatment Plants (WWTPs) is unsatisfactory. In this domain, efficient pump energy management can generate economic and environmental benefits. Although the availability of on-line sensors can provide high-frequency information about pump systems, at best, energy assessment is carried out a few times a year using aggregated data. Consequently, pump inefficiencies are normally detected late and the comprehension of pump system dynamics is often not satisfactory. In this paper, a data-driven methodology to support the daily energy decision-making is presented. This innovati…

Data-driven design of robust fault detection system for wind turbines

2014

Abstract In this paper, a robust data-driven fault detection approach is proposed with application to a wind turbine benchmark. The main challenges of the wind turbine fault detection lie in its nonlinearity, unknown disturbances as well as significant measurement noise. To overcome these difficulties, a data-driven fault detection scheme is proposed with robust residual generators directly constructed from available process data. A performance index and an optimization criterion are proposed to achieve the robustness of the residual signals related to the disturbances. For the residual evaluation, a proper evaluation approach as well as a suitable decision logic is given to make a correct …

How Circular Dichroism in Time- and Angle-Resolved Photoemission Can Be Used to Spectroscopically Detect Transient Topological States in Graphene

2020

Pumping graphene with circularly polarized light is the archetype of light-tailoring topological bands. Realizing the induced Floquet-Chern-insulator state and demonstrating clear experimental evidence for its topological nature has been a challenge, and it has become clear that scattering effects play a crucial role. We tackle this gap between theory and experiment by employing microscopic quantum kinetic calculations including realistic electron-electron and electron-phonon scattering. Our theory provides a direct link to the build up of the Floquet-Chern-insulator state in light-driven graphene and its detection in time- and angle-resolved photoemission spectroscopy (ARPES). This approac…

No Entrepreneurship without Opportunity: The Intersection of Return Migration Research and Entrepreneurship Literature

2019

Abstract The article proposes a theoretical discussion at the crossroads of the return migration scholarship with the entrepreneurship research. Its main goal is to build an analytical framework in which entrepreneurial experiences of international return migrants are conceptualized. The fertile theoretical legacy within the study of entrepreneurship along with an idealized view of the positive effects of migration constitute essential premises for understanding the biased outputs of the empirical studies of entrepreneurship upon return to the origin country. Firstly, the article draws on the main lines of theorising opportunities within the Weberian and Schumpeterian theoretical traditions…

Learning Processes in the Control Theory

1994

Agent's actions as a classification criteria for the state space in a learning from rewards system

2008

We focus in this paper on the problem of learning an autonomous agent's policy when the state space is very large and the set of actions available is comparatively short. To this end, we use a non-parametric decision rule (concretely, a nearest-neighbour strategy) in order to cluster the state space by means of the action that leads to a successful situation. Using an exploration strategy to avoid greedy behaviour, the agent builds clusters of positively-classified states through trial and error learning. In this paper, we implement a 3D synthetic agent which plays an 'avoid the asteroid' game that suits our assumptions. Using as the state space a feature vector space extracted from a visua…

On the role of procrastination for machine learning

1992

Feedback adaptation in web-based learning systems

2007

Feedback provided by a learning system to its users plays an important role in web-based education. This paper presents an overview of feedback studies and then concentrates on the problem of feedback adaptation in web-based learning systems. We introduce our taxonomy of feedback concept with regard to its functions, complexity, intention, time of occurrence, way of presentation, and level and way of its adaptation. We consider what can be adapted in feedback and how to facilitate feedback adaptation in web-based learning systems.

Integrating Domain Knowledge in Data-Driven Earth Observation With Process Convolutions

2022

The modelling of Earth observation data is a challenging problem, typically approached by either purely mechanistic or purely data-driven methods. Mechanistic models encode the domain knowledge and physical rules governing the system. Such models, however, need the correct specification of all interactions between variables in the problem and the appropriate parameterization is a challenge in itself. On the other hand, machine learning approaches are flexible data-driven tools, able to approximate arbitrarily complex functions, but lack interpretability and struggle when data is scarce or in extrapolation regimes. In this paper, we argue that hybrid learning schemes that combine both approa…