Data Mining - Concepts and Techniques.

Recently, there has been growing interest in developing unmanned aircraft systems (UAS) with advanced onboard autonomous capabilities. This paper describes the current state of the art in autonomous rotorcraft UAS (RUAS) and provides a detailed literature review of the last two decades of active research on RUAS. Three functional technology areas are identified as the core components of an autonomous RUAS. Guidance, navigation, and control (GNC) have received much attention from the research community, and have dominated the UAS literature from the nineties until now. This paper first presents the main research groups involved in the development of GNC systems for RUAS. Then it describes the development of a framework that provides standard definitions and metrics characterizing and measuring the autonomy level of a RUAS using GNC aspects. This framework is intended to facilitate the understanding and the organization of this survey paper, but it can also serve as a common reference for the UAS community. The main objective of this paper is to present a comprehensive survey of RUAS research that captures all seminal works and milestones in each GNC area, with a particular focus on practical methods and technologies that have been demonstrated in flight tests. These algorithms and systems have been classified into different categories and classes based on the autonomy level they provide and the algorithmic approach used. Finally, the paper discusses the RUAS literature in general and highlights challenges that need to be addressed in developing autonomous systems for unmanned rotorcraft. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.

Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems

Remote laboratories have been introduced during the last few decades into engineering education processes as well as integrated within e-learning frameworks offered to engineering and science students. Remote laboratories are also being used to support life-long learning and student's autonomous learning activities. In this paper, after a brief overview of state-of-the-art technologies in the development of remote laboratories and presentation of recent and interesting examples of remote laboratories in several areas related with industrial electronics education, some current trends and challenges are also identified and discussed.

Current Trends in Remote Laboratories

Requirement of green supply with higher quality has been consumers’ demand around the globe The electrical power system is expected to deliver undistorted sinusoidal rated voltage and current continuously at rated frequency to the consumers This paper presents a comprehensive review of signal processing and intelligent techniques for automatic classification of the power quality (PQ) events and an effect of noise on detection and classification of disturbances It is intended to provide a wide spectrum on the status of detection and classification of PQ disturbances as well as an effect of noise on detection and classification of PQ events to the researchers, designers and engineers working on power quality More than 150 research publications on detection and classification techniques of PQ disturbances have been critically examined, classified and listed for quick reference

A critical review of detection and classification of power quality events

Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling

https://estudogeral.sib.uc.pt/bitstream/10316/9993/1/ficheiro.pdf

Brief paper: Hand movement recognition based on biosignal analysis

In this paper a new approach for power quality (PQ) event detection and classification is proposed. This approach is based on an automatic four step algorithm. First the acquired voltage signals are represented in a 3-D space referential. Then principal component analysis is performed. In the third, features are extracted from the obtained eigenvalues of each disturbance waveforms. Finally a neuro-fuzzy based classifier automatically classifies the PQ disturbances. To show the effectiveness of the proposed method several case studies are presented. From the obtained results it is possible to confirm that the proposed approach can effectively classify different PQ disturbances.

Power quality disturbances classification using the 3-D space representation and PCA based neuro-fuzzy approach

The study of power system of relays requires some previous experience in this field. Laboratories focusing on teaching and researching the area of power-system protection have therefore been amply reported. However, these facilities require the actual presence of the students in the laboratories. To overcome this problem, a power-system-relaying remote laboratory has been developed. To implement this laboratory, a testing system of the relay operating characteristic, together with Matlab-based software, was developed. This remote laboratory enables real power-system-relaying experiments through the Internet. In fact, the user can remotely test a real relay disoperation. This system will therefore allow proficient analysis of sensitivities to relay settings and network configurations.

Distance-Learning Power-System Protection Based on Testing Protective Relays

In this paper, an adaptive neural-fuzzy walking control of an autonomous biped robot is proposed This control system uses a feed forward neural network based on nonlinear regression The general regression neural network is used to construct the base of an adaptive neuro-fuzzy system The neural network uses an iterative grid partition method for the initial structure identification of the controller parameters Comparison results are done between the proposed method and the ANFIS tool provided in the fuzzy MATLAB toolbox The robot's control system uses an inverted pendulum to balance of the gaits The effectiveness of the proposed control system is demonstrated by simulation and experimental tests

Tito G. Amaral

Papers

Brief paper: Hand movement recognition based on biosignal analysis

Power quality disturbances classification using the 3-D space representation and PCA based neuro-fuzzy approach

Distance-Learning Power-System Protection Based on Testing Protective Relays

A neural-fuzzy walking control of an autonomous biped robot

Induction motor fault detection and diagnosis using a current state space pattern recognition