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25th IEEE International Parallel and Distributed Processing Symposium, Workshops and Phd Forum, IPDPSW 2011

作者： Da Luz, Eduardo Fávero Pacheco Becceneri, José Carlos De Campos Velho, Haroldo Fraga Post-Graduation Program in Applied Computing National Institute for Space Research São José dos Campos/SP Brazil Computing and Applied Mathematics Associated Laboratory National Institute for Space Research São José dos Campos/SP Brazil

ISBN: (纸本)9780769543857

The Multiple Particle Collision Algorithm (MPCA) is a nature-inspired stochastic optimization method developed specially for high performance computational environments. Its advantages resides in the intense use of computational power provided by multiple processors in the task of search the solution space for a near optimum solution. This work presents the application of MPCA in solving two inverse problems written as optimization problems, its advantages and disadvantages are also described, so are the obtained results. © 2011 IEEE.

关键词： Inverse problems

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Climate Precipitation Prediction by Neural Network

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Journal of mathematics and System Science 2015年第5期5卷 207-213页

作者： Juliana Aparecida Anochi Haroldo Fraga de Campos Velho Applied Computing Graduate Program National Institute for Space Research (INPE) Brazil. Associated Laboratory for Computing and Applied Mathematics National Institute for Space Research (INPE).

In this work a neural network model for climate forecasting is presented. The model is built by training a neural network with available reanalysis data. In order to assess the model, the development methodology considers the use of data reduction strategies that eliminate data redundancy thus reducing the complexity of the models. The results presented in this paper considered the use of Rough Sets Theory principles in extracting relevant information from the available data to achieve the reduction of redundancy among the variables used for forecasting purposes. The paper presents results of climate prediction made with the use of the neural network based model. The results obtained in the conducted experiments show the effectiveness of the methodology, presenting estimates similar to observations.

关键词： Climate Prediction Neural Networks Rough Sets Theory

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Perception Based Associations in Time Series Data Bases

Perception Based Associations in Time Series Data Bases

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Conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： I. Z. Batyrshin L. B. Sheremetov Research Program in Applied Mathematics and Computing Mexican Petroleum Institute Mexico

The paper discusses different aspects of the development of perception-based decision making systems. These systems are based on inference procedures transforming associations extracted from the time series data bases into generalized-constraint inference rules. Different types of simple and composite perception based constraints are discussed. Various measures of association between time series in the presence of perception based constraints are considered: association rules, association rules with perception based frequencies, correlation rules, and local trend associations based on moving approximations. Finally, the methods of transformation of these associations into the inference rules that can be used in perception based reasoning are proposed

关键词： Decision making Data mining Humans Petroleum Association rules Frequency mathematics Wind forecasting Wind speed Meteorology

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Perception Based Hybrid Intelligent Systems in Petroleum Applications

Perception Based Hybrid Intelligent Systems in Petroleum App...

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Conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： L. B. Sheremetov I. Z. Batyrshin D. M. Filatov Research Program in Applied Mathematics and Computing Mexican Petroleum Institute Mexico Center for Computing Research National Polytechnic Institute Mexico

We describe the methods of processing of perception based information in hybrid intelligent systems. Several innovative techniques like a multi-set based algebra of qualitative perception-based uncertainties and perception-based data mining form the technological framework of the approach. In the paper, we discuss the algebra of strict monotonic operations and inference procedures based on perception-based evaluations of uncertainty of facts and rules. They are characterized by multi-set-based representation of evaluations of uncertainty and by multi-valued inference of conclusions in expert system rules. The proposed method is implemented in the CAPNET expert system shell. We also discuss the method of evaluation of perception-based patterns in time series data bases. The approach is illustrated by examples of diagnostics of excessive water production in petroleum wells combining both methods

关键词： Hybrid intelligent systems Petroleum Production Algebra Uncertainty Diagnostic expert systems Engines Data mining Information analysis Expert systems

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Optimization of feedforward neural network by Multiple Particle Collision Algorithm

Optimization of feedforward neural network by Multiple Parti...

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IEEE Symposium on Foundations of Computational Intelligence, FOCI

作者： Juliana A Anochi Haroldo F. de Campos Velho Applied Computing Graduate Program National Institute for Space Research SP Brazil Laboratory Computing and Applied Mathematics National Institute for Space Research SP Brazil

Optimization of neural network topology, weights and neuron activation functions for given data set and problem is not an easy task. In this article, a technique for automatic configuration of parameters topology for feedforward artificial neural networks (ANN) is presented. The determination of optimal parameters is formulated as an optimization problem, solved with the use of meta-heuristic Multiple Particle Collision Algorithm (MPCA). The self-configuring networks are applied to predict the mesoscale climate for the precipitation field. The results obtained from the neural network using the method of data reduction by the Theory of Rough Sets and the self-configuring network by MPCA were compared.

关键词： Artificial neural networks Topology Meteorology Network topology Training Atmospheric modeling Biological neural networks

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Size segregation in the Brazil nut effect

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AIP Conference Proceedings 2013年第1期1558卷 2385-2388页

作者： Aline C. Soterroni Fernando M. Ramos Applied Computing Program National Institute for Space Research São José dos Campos Brazil Laboratory of Computing and Applied Mathematics National Institute for Space Research São José dos Campos Brazil

Granular materials are ubiquitous in nature and in our daily lives, and used in many industrial processes. Depending on the physical conditions that they are subjected, granular materials may present unusual behavior, combining properties of solids, liquids or gases, and displaying interesting and diversified phenomena. In this work we numerically simulated a granular system in order to investigate the phenomena of size segregation in the Brazil Nut Effect. Our simulations indicate that the phenomenon of size segregation results from the combined effect of two different mechanisms: buoyancy and convection. Increasing the vibration amplitude, the behavior of the system becomes less periodic and more turbulent, with evidence of deterministic chaos in the dynamics of the large particle.

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Feature selection and image classification using rough sets theory

Feature selection and image classification using rough sets ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Alex Sandro Aguiar Pessoa Stephan Stephany Leila Maria Garcia Fonseca Postgraduate Program in Applied Computing National Institute for Space Research Brazil Computing and Applied Mathematics Laboratory National Institute for Space Research Brazil Image Processing Division National Institute for Space Research Brazil

Current generation of satellite imaging sensors include multispectral or even hyperspectral devices. The resulting multiple images that are acquired require new processing and analysis techniques. Image classification processing demands can be very high requiring feature/attribute selection in order to employ a minimum number of bands while keeping good classification accuracy. This work shows the use of the Rough Sets theory for multi-band image classification. This theory has a good and simple mathematical formalism and does not requires further informations such as the pertinence degree or the probability distribution in the classification process. The case study was performed with a 7-band Landsat 5 image showing the suitability of the feature selection approach and its potential to be employed in multi or hyperspectral image classification.

关键词： Decision support systems

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An Efficient Three-Factor Authenticated Key Agreement Technique Using FCM Under HC-IoT Architectures

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Computers, Materials & Continua 2022年第7期72卷 1373-1389页

作者： Chandrashekhar Meshram Agbotiname Lucky Imoize Sajjad Shaukat Jamal Parkash Tambare Adel R.Alharbi Iqtadar Hussain Department of Post Graduate Studies and Research in Mathematics Jayawanti Haksar Government Post-Graduation CollegeCollege of Chhindwara UniversityBetul460001M.P.India Department of Electrical and Electronics Engineering Faculty of EngineeringUniversity of LagosAkokaLagos100213Nigeria Department of Electrical Engineering and Information Technology Institute of Digital CommunicationRuhr University44801BochumGermany Department of Mathematics College of ScienceKing Khalid UniversityAbhaSaudi Arabia Water Resources&Applied Mathematics Research Lab Nagpur440027India College of Computing and Information Technology University of TabukTabuk71491Saudi Arabia Mathematics Program Department of MathematicsStatistics and PhysicsCollege of Arts and SciencesQatar University2713DohaQatar

The Human-Centered Internet of Things(HC-IoT)is fast becoming a hotbed of security and privacy *** users can establish a common session key through a trusted server over an open communication channel using a three-party authenticated key *** of the early authenticated key agreement systems relied on pairing,hashing,or modular exponentiation processes that are computationally intensive and *** order to address this problem,this paper offers a new three-party authenticated key agreement technique based on fractional chaotic *** new scheme uses fractional chaotic maps and supports the dynamic sensing of HC-IoT devices in the network architecture without a password *** projected security scheme utilized a hash function,which works well for the resource-limited HC-IoT *** results show that our new technique is resistant to password guessing attacks since it does not use a ***,our approach provides users with comprehensive privacy protection,ensuring that a user forgery attack causes no ***,our new technique offers better security features than the techniques currently available in the literature.

关键词： Three-party authenticated key agreement anonymity fractional chaotic maps Chebyshev polynomial password table human-centered internet of things(HC-IoT)

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A Committee of MLP with Adaptive Slope Parameter Trained by the quasi-Newton Method to Solve Problems in Hydrologic Optics

A Committee of MLP with Adaptive Slope Parameter Trained by ...

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International Joint Conference on Neural Networks

作者： Fabio Dall Cortivo Ezzat S. Chalhoub Haroldo F. Campos Velho Applied Computing Graduate Program (CAP) National Institute for Space Research (INPE) Sao Jose dos Campos Associated Laboratory for Computing and Applied Mathematics (LAC) National Institute for Space Research (INPE)

ISBN: (纸本)9781467314886

Artificial Neural Networks (ANNs) can be used to solve problems in Hydrologic Optics. A relevant problem is the estimation of the single scattering albedo and the phase function parameters, from the emitted radiation at the surface of natural waters. In this work we use a committee of ANNs of Multilayer Perceptron type to perform the estimation of the two mentioned parameters. The training of each network is formulated as a nonlinear optimization problem subject to constraints. In addition, each activation function has a distinct slope parameter, that is initially chosen by a random number generator function. This set of parameter (slopes) was included within the free variables network set in order to be adjusted to reach "optimal values", together with the weights and biases, during the network training. This procedure (slope parameters inclusion) makes each one of the activation functions to have a different slope. Each network that composes the committee was trained independently, in order to become expert for the estimation of only one of the hydrologic parameters. For the networks training, we used the quasi-Newton method that is implemented in E04UCF subroutine, in the NAG library, developed by the Numerical Algorithms Group - NAG. The use of the quasi-Newton method to train the networks together with the distinct slope parameters resulted in a network with a fast learning and excellent generalization. Once the networks were trained, they were grouped so to share the input patterns, but remained independent from one another. For the validation/generalization test we used two distinct sets. For all considered noise levels, we obtained 100% of correct answers for the first set, and above 90% of correct answers for the second set.

关键词： Artificial Neural Network Multilayer Perceptron Backpropagation Quasi-Newton Method Hydrologic Optics Phase Function Single Scattering Albedo Inverse Problems SLOPE FACTOR Newton method Slope parameter Artificial neural networks CSRP3 gene Network Optics hydrologic parameters Hydrology functional parameter Random Number Generators Activating function input mode

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Association networks in time series data mining

Association networks in time series data mining

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Conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： I. Batyrshin R. Herrera-Avelar L. Sheremetov A. Panova Research Program in Applied Mathematics and Computing Mexican Petroleum Institute Mexico Research Program in Applied Mathematics and Computing Mexican Petroleum Institute Mexicali Mexico Heating Engineering Theoretical Fundamentals Department Kazan Power Engineering Institute Kazan Heating Engineering Theoretical Fundamentals Department Kazan Power Engineering Institute Kazan Tatarstan Russia

We discuss a new method of time series data mining using moving approximation (MAP) transform and association measures based on MAP. MAP transform replaces time series values by slope values of lines approximating time series data in sliding window. An effective method of MAP transform calculation for time series with fixed time step is proposed. Based on MAP, a measure of local trend associations between time series is introduced. This measure is invariant under independent linear transformations of time series. Measure of local trend associations defines association function depending on the size of sliding window for each pare of considered time series. Based on association function, different association measures may be considered to measure local trend associations or global trend associations between time series. The methods of application of association measure to construction of association network of time series are discussed and illustrated on examples of synthetic and financial time series databases. Association networks give information about relationships between time dynamics of elements of systems given by time series databases.

关键词： Data mining Time measurement Geophysical measurements Smoothing methods Linear regression Petroleum mathematics Power engineering computing Size measurement Euclidean distance

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