Eugenia Minca

Bio: Eugenia Minca is an academic researcher from University of Galați. The author has contributed to research in topics: Mobile robot & Petri net. The author has an hindex of 10, co-authored 60 publications receiving 397 citations. Previous affiliations of Eugenia Minca include École nationale supérieure de mécanique et des microtechniques.

Review of prognostic problem in condition-based maintenance

Abstract: Prognostic is nowadays recognized as a key feature in maintenance strategies as it should allow avoiding inopportune maintenance spending. Real prognostic systems are however scarce in industry. That can be explained from different aspects, on of them being the difficulty of choosing an efficient technology: many approaches to support the prognostic process exist, whose applicability is highly dependent on industrial constraints. Thus, the general purpose of the paper is to explore the way of performing failure prognostics so that manager can act consequently. Different aspects of prognostic are discussed. The prognostic process is (re)defined and an overview of prognostic metrics is given. Following that, the “prognostic approaches” are described. The whole aims at giving an overview of the prognostic area, both from the academic and industrial points of views.

Adaptive Neuro-Fuzzy Inference Systems as a Strategy for Predicting and Controling the Energy Produced from Renewable Sources

Abstract: The challenge for our paper consists in controlling the performance of the future state of a microgrid with energy produced from renewable energy sources. The added value of this proposal consists in identifying the most used criteria, related to each modeling step, able to lead us to an optimal neural network forecasting tool. In order to underline the effects of users’ decision making on the forecasting performance, in the second part of the article, two Adaptive Neuro-Fuzzy Inference System (ANFIS) models are tested and evaluated. Several scenarios are built by changing: the prediction time horizon (Scenario 1) and the shape of membership functions (Scenario 2).

Trajectory-tracking and discrete-time sliding-mode control of wheeled mobile robots

Abstract: In this paper a discrete-time sliding mode control for the trajectory tracking problem of wheeled mobile robots is presented. The wheeled mobile robot (WMR) taken into account was PowerBot. PowerBot is a mobile platform with two differential driving wheels (2DW) and two balancing caster wheels. It is an automated guided vehicle specially designed and equipped for autonomous, intelligent delivery and handling of large payloads. PowerBot is a member of MobileRobots' Pioneer family of mobile robots, which are research and development platforms that share a common architecture, foundation software and employ intelligence-based client-server robotics controls. Due to its size and high load carrying capacity PowerBot is an ideal robot for both, indoor and outdoor transportation. The algorithm has been designed in discrete-time domain in order to avoid problems caused by the discretization of continuous-time controllers. The simulation results and real time results prove the effectiveness of the proposed controller.

Tackling Climate Change with Machine Learning

Abstract: Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.