University of Pitești

About: University of Pitești is a based out in . It is known for research contribution in the topics: Hybrid power & Software. The organization has 758 authors who have published 1657 publications receiving 8959 citations.

The use of NARX neural networks to predict chaotic time series

Abstract: The prediction of chaotic time series with neural networks is a traditional practical problem of dynamic systems. This paper is not intended for proposing a new model or a new methodology, but to study carefully and thoroughly several aspects of a model on which there are no enough communicated experimental data, as well as to derive conclusions that would be of interest. The recurrent neural networks (RNN) models are not only important for the forecasting of time series but also generally for the control of the dynamical system. A RNN with a sufficiently large number of neurons is a nonlinear autoregressive and moving average (NARMA) model, with "moving average" referring to the inputs. The prediction can be assimilated to identification of dynamic process. An architectural approach of RNN with embedded memory, "Nonlinear Autoregressive model process with eXogenous input" (NARX), showing promising qualities for dynamic system applications, is analyzed in this paper. The performances of the NARX model are verified for several types of chaotic or fractal time series applied as input for neural network, in relation with the number of neurons, the training algorithms and the dimensions of his embedded memory. In addition, this work has attempted to identify a way to use the classic statistical methodologies (R/S Rescaled Range analysis and Hurst exponent) to obtain new methods of improving the process efficiency of the prediction chaotic time series with NARX.

A virtual cloud computing provider for mobile devices

Abstract: Along with the explosive growth of the mobile applications and up-raising cloud computing concept, mobile cloud computing appears to be a new potential technology for mobile services. Mobile cloud computing maps cloud computing ideas on top of the mobile environment, and overcomes obstacles that deal with performance (i.e. battery life, CPU, storage, and bandwidth), environment (i.e. heterogeneity, scalability, availability) and security (i.e. reliability, privacy) discussed in mobile computing. In this position paper we are going to focus in depth on a Hadoop based framework for ad-hoc mobile cloud computing — we specifically refer to a research paper and the authors' decision to use and port Hadoop to build a Virtual Cloud Computing Provider for Mobile Devices. In the beginning, we are going to outline a short introduction and enumerate the challenges that we face implementing a framework for mobile cloud computing. We conclude this paper with a short example of other Map-Reduce based MCC (Mobile Cloud Computing) framework, which achieved better performance using its own custom implementation.

Formal Approach to the Deployment of Distributed Robotic Teams

Abstract: We present a computational framework for automatic synthesis of control and communication strategies for a robotic team from task specifications that are given as regular expressions about servicing requests in an environment. We assume that the location of the requests in the environment and the robot capacities and cooperation requirements to service the requests are known. Our approach is based on two main ideas. First, we extend recent results from formal synthesis of distributed systems to check for the distributability of the task specification and to generate local specifications, while accounting for the service and communication capabilities of the robots. Second, by using a technique that is inspired by linear temporal logic model checking, we generate individual control and communication strategies. We illustrate the method with experimental results in our robotic urban-like environment.

An empirical study of the state of the practice and acceptance of model-driven engineering in four industrial cases

Abstract: Model-Driven Engineering (MDE) has been promoted for many years as a means for handling the complexity of software development by raising the abstraction level and automating labor-intensive and error-prone tasks. However, there is little empirical evidence of the acceptance of MDE in industry which is the subject of this paper. The goal of this empirical study was to investigate the state of the practice of applying MDE and factors considered as important for its adoption. The subjects were developers of four large companies participating in a research project. The collected data came from multiple sources and covered the results of tool evaluations, interviews, and a survey. Among the factors, we found perceived usefulness, ease of use and the maturity of the tools to be important determinants for the adoption of MDE. We also discuss challenges with adopting MDE and present suggestions on how to succeed with the adoption process.