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Ensemble-type numerical uncertainty information from single model integrations

JOURNAL OF COMPUTATIONAL PHYSICS 2015年 292卷 30-42页

作者： Rauser, Florian Marotzke, Jochem Korn, Peter Max Planck Inst Meteorol D-20146 Hamburg Germany

We suggest an algorithm that quantifies the discretization error of time-dependent physical quantities of interest (goals) for numerical models of geophysical fluid dynamics. The goal discretization error is estimated using a sum of weighted local discretization errors. The key feature of our algorithm is that these local discretization errors are interpreted as realizations of a random process. The random process is determined by the model and the flow state. From a class of local error random processes we select a suitable specific random process by integrating the model over a short time interval at different resolutions. The weights of the influences of the local discretization errors on the goal are modeled as goal sensitivities, which are calculated via automatic differentiation. The integration of the weighted realizations of local error random processes yields a posterior ensemble of goal approximations from a single run of the numerical model. From the posterior ensemble we derive the uncertainty information of the goal discretization error. This algorithm bypasses the requirement of detailed knowledge about the models discretization to generate numerical error estimates. The algorithm is evaluated for the spherical shallow-water equations. For two standard test cases we successfully estimate the error of regional potential energy, track its evolution, and compare it to standard ensemble techniques. The posterior ensemble shares linear-error-growth properties with ensembles of multiple model integrations when comparably perturbed. The posterior ensemble numerical error estimates are of comparable size as those of a stochastic physics ensemble. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Numerical errors learning algorithm Stochastic methods Ensemble methods Geophysical fluid dynamics Mixed finite volume/finite difference scheme Icosahedral grid Shallow water equations Automatic differentiation algorithmic differentiation

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Analysis and convergence theorem of complex quadratic form as decision boundary for data classification

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ELECTRONICS LETTERS 2015年第7期51卷 561-U150页

作者： Cordero, R. Suemitsu, W. I. Pinto, J. O. P. Univ Fed Rio de Janeiro Dept Elect Engn Rio De Janeiro Brazil Univ Fed Mato Grosso do Sul Dept Elect Engn Campo Grande Brazil

The application of the complex quadratic form as the decision boundary for complex-valued data classification is described. This function is always real when its matrix is Hermitian. Thus, a simple sign function to classify the input data is used. This matrix is obtained through an iterative learning process similar to the Rosenblatt algorithm. The concept of the Frobenius matrix norm is used to prove that the proposed learning algorithm converges if a solution exists. This approach is different from other complex-valued neural networks that use optimisation techniques or feature mapping. An artificial neuron that uses a complex quadratic form as the decision boundary is called a complex quadratic neural unit.

关键词： Hermitian matrices convergence data analysis decision making iterative methods learning (artificial intelligence) neural nets pattern classification CQNU CVNN Frobenius matrix norm Hermitian matrix Rosenblatt algorithm artificial neuron complex quadratic form analysis complex quadratic neural unit complex-valued data classification complex-valued neural networks convergence theorem decision boundary feature mapping iterative learning process learning algorithm optimisation techniques simple sign function

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A Cross-layer Approach to Online Adaptive Reliability Prediction of Transient Faults 28

A Cross-layer Approach to Online Adaptive Reliability Predic...

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2015 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFTS)

作者： Farahani, Bahar Safari, Saeed Univ Tehran Sch Elect & Comp Engn Tehran 14174 Iran

ISBN: (纸本)9781509003129

As the semiconductor industry migrates into the nanometer regime, processors become increasingly susceptible to transient faults. Such faults usually stem either from soft errors due to particle strikes or timing violations due to Process, Voltage, Temperature, and Aging (PVTA) variations. These faults can propagate from circuit-level to application-level and alter the correct execution output of the application. For generations, designers build high-level resiliency such that the details of the underlying circuit are an abstraction that could be neglected. This paper argues that in contrast to the prior work, which only take into account Architectural Vulnerability Factor (AVF) as a measure to guide fault tolerant techniques, the vulnerability of each abstraction layer of design stack from circuit going up to instruction and application layers should be considered. This paper presents a novel online cross-layer reliability prediction technique based on learning algorithms which can anticipate the susceptibility of the processor considering both lower-level and higher-level details in an adaptive fashion. According to the results, the proposed technique can predict the future reliability with 6% error on average across SPEC2000 benchmarks. Our technique by forecasting the reliability emergencies can assist proactive fault tolerant techniques to maintain the reliability constraints more efficiently in comparison to reactive strategies.

关键词： fault diagnosis fault tolerance learning (artificial intelligence) radiation hardening (electronics) semiconductor industry transient analysis AVF PVTA variation SPEC2000 benchmark abstraction layer architectural vulnerability factor cross-layer approach cross-layer reliability prediction fault tolerant technique learning algorithm online adaptive reliability prediction process voltage temperature and aging variation soft error transient fault Circuit faults Predictive models Program processors Reliability Timing Transient analysis semiconductor industry transient fault Transient analysis learning algorithms Program processors Circuit faults Fault tolerance reliability evaluation Prediction models Fault diagnosis

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Shape-based defect classification for Non Destructive Testing 2

Shape-based defect classification for Non Destructive Testin...

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2nd IEEE International Workshop on Metrology for Aerospace

作者： D'Angelo, Gianni Rampone, Salvatore Univ Sannio Dept Sci & Technol Benevento Italy

ISBN: (纸本)9781479975693

The aim of this work is to classify the aerospace structure defects detected by eddy current non-destructive testing. The proposed method is based on the assumption that the defect is bound to the reaction of the probe coil impedance during the test. Impedance plane analysis is used to extract a feature vector from the shape of the coil impedance in the complex plane, through the use of some geometric parameters. Shape recognition is tested with three different machine-learning based classifiers: decision trees, neural networks and Naive Bayes. The performance of the proposed detection system are measured in terms of accuracy, sensitivity, specificity, precision and Matthews correlation coefficient. Several experiments are performed on dataset of eddy current signal samples for aircraft structures. The obtained results demonstrate the usefulness of our approach and the competiveness against existing descriptors.

关键词： Non-destructive testing (NDT) learning algorithm signature-based classifier content-based image retrieval (CBIR) shape geometric descriptor (SGD) eddy current testing (ECT)

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Dynamic Spectrum Access with Tunable Bandwidth for Multi-standard Cognitive Radio Receivers 38

Dynamic Spectrum Access with Tunable Bandwidth for Multi-sta...

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38th International Conference on Telecommunications and Signal Processing (TSP)

作者： Darak, Sumit J. Moy, Christophe Zhang, Honggang Palicot, Jacques UEB CominLabs Rennes France Supelec Rennes France Supelec IETR Dept Signal Commun & Elect Embarquee SCEE Rennes France

ISBN: (纸本)9781479984985

This paper deals with the design of low complexity dynamic spectrum access (DSA) scheme for Cognitive Radios (CRs) to search the vacant band of tunable bandwidth, B-h. Such DSA scheme with tunable B-h is very critical for CRs considering the coexistence of multiple communication standards in wideband input signal with channel bandwidths ranging from 25 kHz to 20 MHz and users expectations of seamless integration of multiple services on single mobile terminal. The proposed scheme is designed by integration of our allpass transformation and coefficient decimation method based variable digital filter and suitably modified Upper Confidence Bound based decision making algorithm. The simulation results and complexity comparisons show that the proposed DSA scheme offers superior performance in terms of vacant band selection rate (and hence high spectrum throughput) for wide range of B-h as well as different spectrum occupancies and total gate count savings of 20-90% over existing schemes.

关键词： Cognitive radios dynamic spectrum access learning algorithm variable digital filter

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The way of inductive formation of medical diagnostic knowledge bases 7

The way of inductive formation of medical diagnostic knowled...

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IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS)

作者： Kleschev, A. S. Smagin, S. V. Inst Automat & Control Proc FEB RAS Intelligent Syst Lab Vladivostok Russia Far Eastern Fed Univ Sch Nat Sci Vladivostok Russia

ISBN: (纸本)9781509019496

The paper provides an introduction into the area of inductive formation of knowledge bases. It presents traditional definitions of main problems in this area and highlights the current topical questions including the interpretability of the results. For solving of current problems in defined area the method of inductive formation of easily interpretable medical diagnostic knowledge bases is suggested. It includes the new definitions of classification and clustering problems for dependence models with parameters and the learning algorithm (solving mentioned problems in their new definitions) developed for the practically useful and easily interpretable mathematical dependence model with parameters which is a near real-life ontology of medical diagnostics (defined by a system of logical relationships with parameters). Also it includes the software package InForMedKB (INductive FORmation of MEDical Knowledge Bases) which implements above mentioned learning algorithm. InForMedKB allows to create training sets (consisting of clinical histories from various therapeutic areas) and to use them for inductive formation of medical diagnostic knowledge bases. These knowledge bases are presented in form accepted in the medical literature and contain descriptions of diseases (from specified therapeutic areas) as well as an explanation of these knowledge bases based on descriptions of clinical histories from used training sets. The formal representation of medical knowledge bases enables their usage for intelligent systems for medical diagnostics.

关键词： data mining inductive formation knowledge base dependence model with parameters learning algorithm domain ontology medical diagnostics InForMedKB

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Behavior Selection Method of Humanoid Robots to Perform Complex Tasks 3rd

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3rd International Conference on Robot Intelligence Technology and Applications

作者： Ko, Woo-Ri Kim, Jong-Hwan Korea Adv Inst Sci & Technol Dept Elect Engn 355 Gwahangno Daejeon South Korea

ISBN: (纸本)9783319168418;9783319168401

This paper proposes a behavior selection method of humanoid robots to perform complex tasks using the degree of consideration-based mechanism of thought (DoC-MoT). The four input (context) symbols and seven target (atom behavior) symbols are defined to perform five complex tasks. The degree of consideration (DoC) for each input symbol is represented by the.-fuzzy measure and the knowledge link strengths between input and target symbols are represented by the partial evaluation values. Each target symbol is globally evaluated by the fuzzy integral of the partial evaluation values with respect to the fuzzy measure values. Then, one target symbol with the highest evaluation value is selected and activated. To make corrections to the robot's wrong behaviors, a learning process from a human's behaviors is employed to update the DoCs and the knowledge link strengths. To show the effectiveness of the proposed method, simulations are performed in a text-based simulator developed in Visual Studio 2012. The results show that the proposed method can generate human-like behaviors.

关键词： Behavior selection algorithm learning algorithm complex tasks humanoid robots human-like mechanism of thought

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Global Optimization for Energy Efficient Resource Management by Game Based Distributed learning in Internet of Things

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2015年第10期9卷 3771-3788页

作者： Ju, ChunHua Shao, Qi Zhejiang Gongshang Univ Coll Comp Sci & Informat Engn Hangzhou Zhejiang Peoples R China

This paper studies the distributed energy efficient resource management in the Internet of Things (IoT). Wireless communication networks support the IoT without limitation of distance and location, which significantly impels its development. We study the communication channel and energy management in the wireless communication network supported IoT to improve the ability of connection, communication, share and collaboration, by using the game theory and distributed learning algorithm. First, we formulate an energy efficient neighbor collaborative game model and prove that the proposed game is an exact potential game. Second, we design a distributed energy efficient channel selection learning algorithm to obtain the global optimum in a distributed manner. We prove that the proposed algorithm will asymptotically converge to the global optimum with geometric speed. Finally, we make the simulations to verify the theoretic analysis and the performance of proposed algorithm.

关键词： game theory wireless networks learning algorithm Internet of Things

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A MapReduce-based Fuzzy Associative Classifier for Big Data

A MapReduce-based Fuzzy Associative Classifier for Big Data

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Ducange, Pietro Marcelloni, Francesco Segatori, Armando ECampus Univ Fac Ingn I-22060 Novedrate Italy Univ Pisa Dip Ingn Informaz I-56122 Pisa Italy

ISBN: (纸本)9781467374286

In this paper, we propose an efficient distributed fuzzy associative classification model based on the MapReduce paradigm. The learning algorithm first mines a set of fuzzy association classification rules by employing a distributed version of a fuzzy extension of the well-known FP-Growth algorithm. Then, it prunes this set by using three purposely adapted types of pruning. We implemented the distributed fuzzy associative classifier using the Hadoop framework. We show the scalability of our approach by carrying out a number of experiments on a real-world big dataset. In particular, we evaluate the achievable speedup on a small computer cluster, highlighting that the proposed approach allows handling big datasets even with modest hardware support.

关键词： Big Data data handling data mining fuzzy set theory learning (artificial intelligence) parallel processing pattern classification Big data FP-growth algorithm Hadoop framework MapReduce-based fuzzy associative classifier distributed fuzzy associative classification model learning algorithm Association rules Computers Fuzzy sets Partitioning algorithms Training

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A Brief Survey on Fuzzy Cognitive Maps Research 11th

A Brief Survey on Fuzzy Cognitive Maps Research

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11th International Conference on Intelligent Computing (ICIC)

作者： Wang, Yajie Zhang, Weiyuan Xianyang Normal Univ Sch Math & Informat Sci Xianyang Peoples R China

ISBN: (纸本)9783319220536;9783319220529

The Fuzzy Cognitive Map (FCM) has emerged as a convenient and powerful soft modeling tool since its proposal. During the last nearly 30 years, Fuzzy Cognitive Maps have gained considerable research interests and have been applied to many areas. The advantageous modeling characteristics of FCMs encourage us to investigate the FCM structure, attempting to broaden the FCM functionality and applicability in real world. In this paper, the main representation and inference characteristics of conventional Fuzzy Cognitive Maps are investigated, and also the current state of the extensions of FCMs, learning algorithms for FCMs is introduced and summarized briefly.

关键词： Fuzzy Cognitive Map learning algorithm Intelligent computing Fuzzy logic Neural network

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