Author
Eugenia Minca
Other affiliations: École nationale supérieure de mécanique et des microtechniques
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.
Topics: Mobile robot, Petri net, Mechatronics, Fuzzy logic, Mobile manipulator
Papers published on a yearly basis
Papers
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23 Aug 2009TL;DR: The general purpose of the paper is to explore the way of performing failure prognostics so that manager can act consequently and give an overview of the prognostic area, both from the academic and industrial points of views.
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.
105 citations
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TL;DR: The added value of this proposal consists in identifying the most used criteria, related to each modeling step, able to lead to an optimal neural network forecasting tool.
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).
37 citations
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TL;DR: In this paper, a real-time assembly/disassembly line balancing and a synchronised hybrid Petri nets (SHPN) model is used to model and control an assembly line with a fixed number of workstations.
29 citations
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06 Jun 2011TL;DR: A discrete-time sliding mode control for the trajectory tracking problem of wheeled mobile robots is presented and the simulation results and real time results prove the effectiveness of the proposed controller.
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.
26 citations
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TL;DR: In this article, a model of a mechatronic assembly/disassembly line served by a robotic manipulator mounted on a mobile platform, in order to perform disassembly, is proposed.
23 citations
Cited by
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TL;DR: This paper systematically reviews the recent modeling developments for estimating the RUL and focuses on statistical data driven approaches which rely only on available past observed data and statistical models.
1,667 citations
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TL;DR: From smart grids to disaster management, high impact problems where existing gaps can be filled by ML are identified, in collaboration with other fields, to join the global effort against climate change.
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.
441 citations
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TL;DR: In this paper, a systematic review of recently developed engine performance monitoring, diagnostic and prognostic techniques is presented, which provides experts, students or novice researchers and decision-makers working in the area of gas turbine engines with the state of the art for performance-based condition monitoring.
271 citations
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TL;DR: This study has addressed several aspects of CBM approach: definition, related international standards, procedure, and techniques with the introduction of some relevant case studies that have been carried out.
236 citations
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TL;DR: Validation results suggest that the proposed technique is well adapted to predict degradation in fuel cell systems.
198 citations