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Impact of Grain Boundary Character on Electrical Property in Polycrystalline Silicon

MRS Online Proceedings Library 2011年第1期586卷 163-168页

作者： Shu Hamada Koichi Kawahara Sadahiro Tsurekawa Tadao Watanabe Takashi Sekiguchi Laboratory of Materials Design and Interface Engineering Department of Machine Intelligence and Systems Engineering Graduate School of Engineering Tohoku University Sendai Japan Institute for Materials Research Tohoku University Sendai Japan

Grain boundaries in polycrystalline silicon are most likely to generate localized states in band gap. The localized states play a dominant role in determining the performance of solar cells by acting as traps or recombination center of carriers. In the present investigation, the scanning electron microscope - electron channeling pattern(SEM/ECP) method and SEM - electron back scattered diffraction pattern(SEM/EBSD) technique were applied to characterize the grain boundaries in p-type polycrystalline silicon with 99.999%(5N) in purity. Thereafter, temperature dependence of electrical activity of individual grain boundaries was measured by an electron beam induced current(EBIC) technique. It has been found that temperature dependence of EBIC contrast at grain boundaries can change, depending on the misorientation angle the orientation of the boundary plane. The results can be explained by the difference in the position of the localized state within the band gap on the basis of the Shockley-Read-Hall statistics. The {111} Σ3 symmetrical tilt boundary has shallow states, while high Σ boundaries have deep states. Low angle boundaries reveal high electrical activities. The EBIC contrast at low angle boundaries was found to increase with increasing misorientation angle up to 2° followed by an almost constant value. High electrical activity at low angle boundaries is probably attributed to a stress field of primary dislocations forming low angle boundaries.

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A rough set approach for planner evaluation

A rough set approach for planner evaluation

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IEEE International Conference on systems, Man and Cybernetics

作者： B.K. Sy Wang Jiaxin Pattern Analysis and Machine Intelligence Group System Design Engineering Department University of Waterloo Waterloo ONT Canada Department of Computer Science CUNY Flushing NY USA The State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing China

This paper discusses a rough set approach for evaluating solutions of scheduling problems. Algorithms for solving scheduling problems are planners and the scheduling problems are modelled as constraint satisfaction problems. Conventional approach for the analysis of algorithms often focuses on the time and representational complexities, and assumes an identical cost on all operations. The proposed rough set approach augments conventional approaches for the analysis of algorithms in two ways: 1) it permits the consideration of different costs arising from different operations; and 2) it allows one to define a new utility for a complexity analysis.

关键词： Algorithm design and analysis Processor scheduling Set theory machine intelligence Computer science Costs Search problems Educational institutions pattern analysis System analysis and design

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A hierarchical genetic algorithm for the design of beta basis function neural network

A hierarchical genetic algorithm for the design of beta basi...

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International Joint Conference on Neural Networks (IJCNN)

作者： C. Aouiti A.M. Alimi F. Karray A. Maalej Faculty of Sciences of Bizerta Universite de Carthage Tunisia Department of Electrical Engineering REGIM: Research Group on Intelligent Machines University of Sfax Sfax Tunisia Systems design Engineering Department Pattern Analysis and Machine Intelligence Laboratory University of Waterloo Waterloo ONT Canada Department of Mechanical Engineering University of Sfax ENIS Sfax Tunisia

We propose an evolutionary neural network-training algorithm for beta basis function neural networks (BBFNN). Classic training algorithms for neural networks start with a predetermined network structure. Generally the network resulting from learning applied to a predetermined architecture is either insufficient or over-complicated. This paper describes a hierarchical genetic learning model of the BBFNN. In order to examine the performance of the proposed algorithm, they were used for the approximation problems. The results obtained are very satisfactory with respect to the relative error.

关键词： Genetic algorithms Algorithm design and analysis Neural networks Artificial neural networks Biological cells Neurons Chaos machine intelligence Laboratories Convergence

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Minimum Error Entropy High-Order Extended Kalman Filter

Minimum Error Entropy High-Order Extended Kalman Filter

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Cyber-Physical Social intelligence (ICCSI), International Conference on

作者： Zhi Cheng Xiaofeng Chen Hua Li Weikai Li Dongyuan Lin Department of Mathematics Chongqing Jiaotong University Chongqing China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China College of Electronic and Information Engineering Southwest University Chongqing China

The conventional extended Kalman filter(EKF) is proposed under the criterion of minimum mean square error(MMSE) and omits high-order information. This leads to poor estimation of the EKF in non-Gaussian and strongly nonlinear systems. In this paper, for a class of nonlinear non-Gaussian systems in polynomial form, Pseudo-linearizing the system while preserving the high-order information, a new filter is proposed based on the minimum error entropy(MEE) criterion called the minimum error entropy high-order extended Kalman filter (MEE-HEKF). In the MEE-HEKF algorithm, the state estimation problem is transformed into a recursive solution problem in the form of a Kalman filter by a fixed-point iterative approach based on the MEE cost function. The proposed algorithm has better robustness under strong nonlinearity and non-Gaussian noise. Finally, the effectiveness of the new algorithm is illustrated by an example.

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AN ADAPTIVE APPROACH TO FEATURE SELECTION IN pattern RECOGNITION

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International Journal of pattern Recognition and Artificial intelligence 1990年第4期4卷 573-601页

作者： H.C. SHEN R. PILKEY Department of Systems Design Engineering Pattern Analysis and Machine Intelligence Group University of Waterloo Waterloo Ontario canada N2L 3G1 Canada Present address: Carma Industries 494 The Parkway Peterborough Ontario Canada K9J 7L9.

Feature selection is an important phase in most pattern recognition problems, especially when the space of the extracted features is very large. Feature selection methods attempt to reduce the feature space to satisfy certain objectives. We propose the concept of defining a performance potential as a measure of the effectiveness of the set of selected features. This paper begins by outlining a ranking scheme for features based on a feature’s calculated “performance potential”. The performance potential is made up of a number of performance measures: extraction time, memory requirements, variance, covariance and classification success. An adaptive scheme is proposed to process a number of initial features and arrive at the “best” subset based on their performance potential. The approach is applied to a texture analysis problem. The results of the testing of the approach point to conclusions concerning its effectiveness.

关键词： Feature selection pattern recognition Adaptive approach Performance potential

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Guest Editorial

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Intelligent Automation & Soft Computing 2000年第3期6卷 171-172页

作者： Mohamed Kamel[a] & Mohammad Jamshidi[b] [a] Pattern Analysis and Machine Intelligence Lab Department of Systems Design Engineering University of Waterloo Waterloo Ontario N2L 3G1 Canada [b] Nasa Center for Autonomous Control Engineering and Department of Electrical and Computer Engineering The University of New Mexico Albuquerque NM 87131 USA http://ace.tmm.edu

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BiAdam: Fast Adaptive Bilevel Optimization Methods

arXiv

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arXiv 2021年

作者： Huang, Feihu Li, Junyi Gao, Shangqian College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Miit Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh United States

Bilevel optimization recently has attracted increased interest in machine learning due to its many applications such as hyper-parameter optimization and meta learning. Although many bilevel methods recently have been proposed, these methods do not consider using adaptive learning rates. It is well known that adaptive learning rates can accelerate optimization algorithms. To fill this gap, in the paper, we propose a novel fast adaptive bilevel framework to solve stochastic bilevel optimization problems that the outer problem is possibly nonconvex and the inner problem is strongly convex. Our framework uses unified adaptive matrices including many types of adaptive learning rates, and can flexibly use the momentum and variance reduced techniques. In particular, we provide a useful convergence analysis framework for the bilevel optimization. Specifically, we propose a fast single-loop adaptive bilevel optimization (BiAdam) algorithm, which achieves a sample complexity of Õ(ϵ-4) for finding an ϵ-stationary solution. Meanwhile, we propose an accelerated version of BiAdam algorithm (VR-BiAdam), which reaches the best known sample complexity of Õ(ϵ-3). To the best of our knowledge, we first study the adaptive bilevel optimization methods with adaptive learning rates. Experimental results on data hyper-cleaning and hyper-representation learning tasks demonstrate the efficiency of our algorithms. © 2021, CC BY-NC-SA.

关键词： Stochastic systems

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Zeroth-order stochastic alternating direction method of multipliers for nonconvex nonsmooth optimization

arXiv

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arXiv 2019年

作者： Huang, Feihu Gao, Shangqian Chen, Songcan Huang, Heng Department of Electrical & Computer Engineering University of Pittsburgh United States College of Computer Science & Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis & Machine Intelligence China JD Finance America Corporation

Alternating direction method of multipliers (ADMM) is a popular optimization tool for the composite and constrained problems in machine learning. However, in many machine learning problems such as black-box learning and bandit feedback, ADMM could fail because the explicit gradients of these problems are difficult or even infeasible to obtain. Zeroth-order (gradient-free) methods can effectively solve these problems due to that the objective function values are only required in the optimization. Recently, though there exist a few zeroth-order ADMM methods, they build on the convexity of objective function. Clearly, these existing zeroth-order methods are limited in many applications. In the paper, thus, we propose a class of fast zeroth-order stochastic ADMM methods (i.e., ZO-SVRG-ADMM and ZO-SAGA-ADMM) for solving nonconvex problems with multiple nonsmooth penalties, based on the coordinate smoothing gradient estimator. Moreover, we prove that both the ZO-SVRGADMM and ZO-SAGA-ADMM have convergence rate of O(1/T), where T denotes the number of iterations. In particular, our methods not only reach the best convergence rate of O(1/T) for the nonconvex optimization, but also are able to effectively solve many complex machine learning problems with multiple regularized penalties and constraints. Finally, we conduct the experiments of black-box binary classification and structured adversarial attack on black-box deep neural network to validate the efficiency of our algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

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Determination of the Characteristics of Gears of Robot-Like systems by Analytical Description of their Structure

Determination of the Characteristics of Gears of Robot-Like ...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Stefan Landler Raúl Molina Blanco Michael Otto Birgit Vogel-Heuser Markus Zimmermann Karsten Stahl Technical University of Munich Germany TUM School of Engineering and Design Department of Mechanical Engineering Munich Institute of Robotics and Machine Intelligence (MIRMI) Institute of Machine Elements Gear Research Center (FZG) Institute of Automation and Information Systems (AIS) Laboratory for Product Development and Lightweight Design (LPL)

The axes of robots and robot-like systems (RLS) usually include e-motor-gearbox-arrangements for optimal connection of the elements. The characteristics of the drive system and thus also of the robot depend strongly on the gears. Different gearbox designs are available which differ in stiffness, efficiency and further properties. For an application-optimal design of RLS a uniform documentation and a comparability of gearbox concepts is a decisive factor. The application-optimal design is supported by an interdisciplinary approach between mechanical engineering and software design, guided by adequate product development methodology. The quite heterogeneous characterization of gearboxes for RLS which is currently the state of the art is a relevant obstacle in the flexible and optimal design of RLS. The paper shows the analysis of the gear structure with unified symbols for specific machine elements and contact types. The introduced method gives insight into the mechanical structure of the gearboxes. Similarities between gear types can thus be revealed. This also enables the classification of new developments in the state of the art. Moreover, the developed method for analyzing the gear structure can be used to determine the characteristics of gears. Examples for these characteristics are backlash, efficiency or stiffness. Specifically, the stiffness of gears can be synthesized by the force action of individual contacts and the individual phenomena that occur with them. The representation by individual phenomena also makes it possible to extend the calculation to include influencing parameters such as temperature that have not been sufficiently taken into account so far.

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Faster stochastic alternating direction method of multipliers for nonconvex optimization

arXiv

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arXiv 2020年

作者： Huang, Feihu Chen, Songcan Huang, Heng Department of Electrical & Computer Engineering University of Pittsburgh PA15261 United States College of Computer Science & Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China MIIT Key Laboratory of Pattern Analysis & Machine Intelligence JD Finance America Corporation United States

In this paper, we propose a faster stochastic alternating direction method of multipliers (ADMM) for nonconvex optimization by using a new stochastic path-integrated differential estimator (SPIDER), called as SPIDER-ADMM. Moreover, we prove that the SPIDER-ADMM achieves a record-breaking incremental first-order oracle (IFO) complexity of O(n + n1/2∊−1) for finding an ∊-approximate stationary point, which improves the deterministic ADMM by a factor O(n1/2), where n denotes the sample size. As one of major contribution of this paper, we provide a new theoretical analysis framework for nonconvex stochastic ADMM methods with providing the optimal IFO complexity. Based on this new analysis framework, we study the unsolved optimal IFO complexity of the existing nonconvex SVRG-ADMM and SAGA-ADMM methods, and prove they have the optimal IFO complexity of O(n + n2/3∊−1). Thus, the SPIDER-ADMM improves the existing stochastic ADMM methods by a factor of O(n1/6). Moreover, we extend SPIDER-ADMM to the online setting, and propose a faster online SPIDER-ADMM. Our theoretical analysis shows that the online SPIDER-ADMM has the IFO complexity of O(∊−3 2 ), which improves the existing best results by a factor of O(∊−2 1 ). Finally, the experimental results on benchmark datasets validate that the proposed algorithms have faster convergence rate than the existing ADMM algorithms for nonconvex optimization. Copyright © 2020, The Authors. All rights reserved.

关键词： Stochastic systems

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