An alternative node deployment scheme for WSNs

© 2001-2012 IEEE. Sensing coverage is a fundamental design problem in wireless sensor networks (WSNs). This is because there is always a possibility that the sensor nodes may function incorrectly due to a number of reasons, such as failure, power, or noise instability, which negatively influences the coverage of the WSNs. In order to address this problem, we propose a fuzzy-based self-healing coverage scheme for randomly deployed mobile sensor nodes. The proposed scheme determines the uncovered sensing areas and then select the best mobile nodes to be moved to minimize the coverage hole. In addition, it distributes the sensor nodes uniformly considering Euclidean distance and coverage redundancy among the mobile nodes. We have performed an extensive performance analysis of the proposed scheme. The results of the experiment show that the proposed scheme outperforms the existing approaches.

Predictive data mining for converged internet of things: a mobile health perspective

Mobile Health (mHealth) is now emerging with Internet of Things (IoT), Cloud and big data along with the prevalence of smart wearable devices and sensors. There is also the emergence of smart environments such as smart homes, cars, highways, cities, factories and grids. Presently, it is difficult to quickly forecast or prevent urgent health situations in real-time as health data are analyzed offline by a physician. Sensors are expected to be overloaded by demands of providing health data from IoT networks and smart environments. This paper proposes to resolve the problems by introducing an inference system so that life-threatening situations can be prevented in advance based on a short and long term health status prediction. This prediction is inferred from personal health information that is built by big data in Cloud. The inference system can also resolve the problem of data overload in sensor nodes by reducing data volume and frequency to reduce workload in sensor nodes. This paper presents a novel idea of tracking down and predicting a personal health status as well as intelligent functionality of inference in sensor nodes to interface IoT networks

Approximation of centroid end-points and switch points for replacing type reduction algorithms

Despite several years of research, type reduction (TR) operation in interval type-2 fuzzy logic system (IT2FLS) cannot perform as fast as a type-1 defuzzifier. In particular, widely used Karnik-Mendel (KM) TR algorithm is computationally much more demanding than alternative TR approaches. In this work, a data driven framework is proposed to quickly, yet accurately, estimate the output of the KM TR algorithm using simple regression models. Comprehensive simulation performed in this study shows that the centroid end-points of KM algorithm can be approximated with a mean absolute percentage error as low as 0.4%. Also, switch point prediction accuracy can be as high as 100%. In conjunction with the fact that simple regression model can be trained with data generated using exhaustive defuzzification method, this work shows the potential of proposed method to provide highly accurate, yet extremely fast, TR approximation method. Speed of the proposed method should theoretically outperform all available TR methods while keeping the uncertainty information intact in the process.

Linear approximation of Karnik-Mendel type reduction algorithm

Karnik-Mendel (KM) algorithm is the most used and researched type reduction (TR) algorithm in literature. This algorithm is iterative in nature and despite consistent long term effort, no general closed form formula has been found to replace this computationally expensive algorithm. In this research work, we demonstrate that the outcome of KM algorithm can be approximated by simple linear regression techniques. Since most of the applications will have a fixed range of inputs with small scale variations, it is possible to handle those complexities in design phase and build a fuzzy logic system (FLS) with low run time computational burden. This objective can be well served by the application of regression techniques. This work presents an overview of feasibility of regression techniques for design of data-driven type reducers while keeping the uncertainty bound in FLS intact. Simulation results demonstrates the approximation error is less than 2%. Thus our work preserve the essence of Karnik-Mendel algorithm and serves the requirement of low
computational complexities.

Effect of different initializations on EKM algorithm

As an integral part of interval type-2 fuzzy logic system (IT2FLS), type reduction (TR) plays a vital role in determining the performance of IT2FLS. Out of many type reduction algorithms, only Karnik-Mendel type TR algorithms capture the essence of interval type-2 fuzzy sets in type reduction. Enhanced Karnik-Mendel (EKM) algorithm is the most commonly used TR algorithm. In this work, we propose three new initializations for EKM algorithm. It is shown they are performing better than EKM and one of the proposed initializations significantly outperforms others. The performance gain can be upto 40% as per comprehensive simulation results demonstrated in this paper. Our findings are justified by computational time savings and iteration requirement for switch point search.

Redes neurais aplicadas em processos de usinagem da madeira

Pós-graduação em Engenharia Mecânica - FEG

Geoprocessamento e computação inteligente: possibilidades, vantagens e necessidades

Pós-graduação em Geografia - IGCE

Development of neurofuzzy architecture for solving the N-Queens problem

Neural networks are dynamic systems consisting of highly interconnected and parallel nonlinear processing elements that are shown to be extremely effective in computation. This paper presents an architecture of recurrent neural networks for solving the N-Queens problem. More specifically, a modified Hopfield network is developed and its internal parameters are explicitly computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points, which represent a solution of the considered problem. The network is shown to be completely stable and globally convergent to the solutions of the N-Queens problem. A fuzzy logic controller is also incorporated in the network to minimize convergence time. Simulation results are presented to validate the proposed approach.

Artificial neural networks applied to bandsawing process control

In the search for productivity increase, industry has invested on the development of intelligent, flexible and self-adjusting method, capable of controlling processes through the assistance of autonomous systems, independently whether they are hardware or software. Notwithstanding, simulating conventional computational techniques is rather challenging, regarding the complexity and non-linearity of the production systems. Compared to traditional models, the approach with Artificial Neural Networks (ANN) performs well as noise suppression and treatment of non-linear data. Therefore, the challenges in the wood industry justify the use of ANN as a tool for process improvement and, consequently, add value to the final product. Furthermore, Artificial Intelligence techniques such as Neuro-Fuzzy Networks (NFNs) have proven effective, since NFNs combine the ability to learn from previous examples and generalize the acquired information from the ANNs with the capacity of Fuzzy Logic to transform linguistic variables in rules.

Towards automated first-order abduction: the cut-based approach

Traditional abduction imposes as a precondition the restriction that the background information may not derive the goal data. In first-order logic such precondition is, in general, undecidable. To avoid such problem, we present a first-order cut-based abduction method, which has KE-tableaux as its underlying inference system. This inference system allows for the automation of non-analytic proofs in a tableau setting, which permits a generalization of traditional abduction that avoids the undecidable precondition problem. After demonstrating the correctness of the method, we show how this method can be dynamically iterated in a process that leads to the construction of non-analytic first-order proofs and, in some terminating cases, to refutations as well.

Sobre a aplicação de sistemas inteligentes para diagnóstico de falhas em máquinas de indução - uma visão geral

Muitas pesquisas estão sendo desenvolvidas buscando nos sistemas inteligentes soluções para diagnosticar falhas em máquinas elétricas. Estas falhas envolvem desde problemas elétricos, como curto-circuito numa das fases do estator, ate problemas mecânicos, como danos nos rolamentos. Dentre os sistemas inteligentes aplicados nesta área, destacam-se as redes neurais artificiais, os sistemas fuzzy, os algoritmos genéticos e os sistemas híbridos, como o neuro-fuzzy. Assim, o objetivo deste artigo é traçar um panorama geral sobre os trabalhos mais relevantes que se beneficiaram dos sistemas inteligentes nas diferentes etapas de análise e diagnóstico de falhas em motores elétricos, cuja principal contribuição está em disponibilizar diversos aspectos técnicos a fim de direcionar futuros trabalhos nesta área de aplicação.

Global flow analysis as a practical compilation tool

This paper addresses the issue of the practicality of global flow analysis in logic program compilation, in terms of speed of the analysis, precisión, and usefulness of the information obtained. To this end, design and implementation aspects are discussed for two practical abstract interpretation-based flow analysis systems: MA , the MCC And-parallel Analyzer and Annotator; and Ms, an experimental mode inference system developed for SB-Prolog. The paper also provides performance data obtained (rom these implementations and, as an example of an application, a study of the usefulness of the mode information obtained in reducing run-time checks in independent and-parallelism.Based on the results obtained, it is concluded that the overhead of global flow analysis is not prohibitive, while the results of analysis can be quite precise and useful.

On the practicality of global flow analysis of logic programs

This paper addresses the issue of the practicality of global flow analysis in logic program compilation, in terms of both speed and precision of analysis. It discusses design and implementation aspects of two practical abstract interpretation-based flow analysis systems: MA3, the MOO Andparallel Analyzer and Annotator; and Ms, an experimental mode inference system developed for SB-Prolog. The paper also provides performance data obtained from these implementations. Based on these results, it is concluded that the overhead of global flow analysis is not prohibitive, while the results of analysis can be quite precise and useful.

Evolving and coevolving computer go players using neuroevolution.

The Go game is ancient very complex game with simple rules which still is a challenge for the AI.This work cover some neuroevolution techniques used in reinforcement learning applied to the GO game as SANE (Symbiotic Adaptive Neuro-Evolution) and presents a variation to this method with the intention of evolving better strategies in the game. The computer Go player based in SANE is evolved againts a knowed player which creates some problem as determinism for which is proposed the co-evolution. Finally, it is introduced an algorithm to co-evolve two populations of neurons to evolve better computer Go players.

Reconocimiento de gestos basado en acelerómetros

En los últimos años, ha crecido de forma significativa el interés por la utilización de dispositivos capaces de reconocer gestos humanos. En este trabajo, se pretenden reconocer gestos manuales colocando sensores en la mano de una persona. El reconocimiento de gestos manuales puede ser implementado para diversos usos y bajo diversas plataformas: juegos (Wii), control de brazos robóticos, etc. Como primer paso, se realizará un estudio de las actuales técnicas de reconocimiento de gestos que utilizan acelerómetros como sensor de medida. En un segundo paso, se estudiará como los acelerómetros pueden utilizarse para intentar reconocer los gestos que puedan realizar una persona (mover el brazo hacia un lado, girar la mano, dibujar un cuadrado, etc.) y los problemas que de su utilización puedan derivarse. Se ha utilizado una IMU (Inertial Measurement Unit) como sensor de medida. Está compuesta por tres acelerómetros y tres giróscopos (MTi-300 de Xsens). Con las medidas que proporcionan estos sensores se realiza el cálculo de la posición y orientación de la mano, representando esta última en función de los ángulos de Euler. Un aspecto importante a destacar será el efecto de la gravedad en las medidas de las aceleraciones. A través de diversos cálculos y mediante la ayuda de los giróscopos se podrá corregir dicho efecto. Por último, se desarrollará un sistema que identifique la posición y orientación de la mano como gestos reconocidos utilizando lógica difusa. Tanto para la adquisición de las muestras, como para los cálculos de posicionamiento, se ha desarrollado un código con el programa Matlab. También, con este mismo software, se ha implementado un sistema de lógica difusa con la que se realizará el reconocimiento de los gestos, utilizando la herramienta FIS Editor. Las pruebas realizadas han consistido en la ejecución de nueve gestos por diferentes personas teniendo una tasa de reconocimiento comprendida entre el 90 % y 100 % dependiendo del gesto a identificar. ABSTRACT In recent years, it has grown significantly interest in the use of devices capable of recognizing human gestures. In this work, we aim to recognize hand gestures placing sensors on the hand of a person. The recognition of hand gestures can be implemented for different applications on different platforms: games (Wii), control of robotic arms ... As a first step, a study of current gesture recognition techniques that use accelerometers and sensor measurement is performed. In a second step, we study how accelerometers can be used to try to recognize the gestures that can make a person (moving the arm to the side, rotate the hand, draw a square, etc...) And the problems of its use can be derived. We used an IMU (Inertial Measurement Unit) as a measuring sensor. It comprises three accelerometers and three gyroscopes (Xsens MTI-300). The measures provided by these sensors to calculate the position and orientation of the hand are made, with the latter depending on the Euler angles. An important aspect to note is the effect of gravity on the measurements of the accelerations. Through various calculations and with the help of the gyroscopes can correct this effect. Finally, a system that identifies the position and orientation of the hand as recognized gestures developed using fuzzy logic. Both the acquisition of samples to calculate position, a code was developed with Matlab program. Also, with the same software, has implemented a fuzzy logic system to be held with the recognition of gestures using the FIS Editor. Tests have involved the execution of nine gestures by different people having a recognition rate between 90% and 100% depending on the gesture to identify.