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Design of a Prediction Model for Cement Rotary Kiln Using Wavelet Projection Fuzzy Inference System

CYBERNETICS AND SYSTEMS 2012年第5期43卷 369-397页

作者： Sharifi, A. Shoorehdeli, M. Aliyari Teshnehlab, M. Islamic Azad Univ Dept Comp Sci & Res Branch Tehran Iran KNT Univ Technol Mechatron Dept Tehran Iran KNT Univ Technol Dept Elect Engn Tehran Iran

In a cement factory, a rotary kiln is the most complex component and it plays a key role in the quality and quantity of the final product. This system involves complex nonlinear dynamic equations that have not been completely worked out yet. In conventional modeling procedures, a large number of the involved parameters are crossed out and an approximation model is presented instead. Therefore, the performance of the obtained model is very important and an inaccurate model may cause many problems in the design of a controller. This study presents a Takagi-Sugeno (TS)-type fuzzy system called a wavelet projection fuzzy inference system (WPFIS) in which a dimension reduction section is used at the input stage of the fuzzy system. In order to clarify the structure of the extracted features, structural learning with forgetting (SLF) based on Minkowski norms is proposed. In addition, gradient descent (GD) was used as a training algorithm. The results show that the proposed method has higher performance in comparison with conventional models. The data collected from Saveh White Cement Company were used in our simulations.

关键词： cement rotary kiln gradient descent algorithm Morlet wavelet pruning algorithm TS-type fuzzy system

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Kernelization of matrix updates, when and how?

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THEORETICAL COMPUTER SCIENCE 2014年第C期558卷 159-178页

作者： Warmuth, Manfred K. Kotlowski, Wojciech Zhou, Shuisheng Univ Calif Santa Cruz Dept Comp Sci Santa Cruz CA 95064 USA Poznan Univ Tech Inst Comp Sci Poznan Poland Xidian Univ Sch Math & Stat Xian 710071 Peoples R China

We define what it means for a learning algorithm to be kernelizable in the case when the instances are vectors, asymmetric matrices and symmetric matrices, respectively. We can characterize kernelizability in terms of an invariance of the algorithm to certain orthogonal transformations. If we assume that the algorithm's action relies on a linear prediction, then we can show that in each case, the linear parameter vector must be a certain linear combination of the instances. We give a number of examples of how to apply our methods. In particular we show how to kernelize multiplicative updates for symmetric instance matrices. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Kernelization Multiplicative updates Rotational invariance Exponentiated gradient algorithm gradient descent algorithm

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Adaptive spherical Gaussian kernel in sparse Bayesian learning framework for nonlinear regression

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第2期36卷 3982-3989页

作者： Yuan, Jin Bo, Liefeng Wang, Kesheng Yu, Tao Norwegian Univ Sci & Technol Dept Prod & Qual Engn N-7491 Trondheim Norway Shanghai Univ CIMS Shanghai 200072 Peoples R China Shanghai Univ Robot Ctr Shanghai 200072 Peoples R China Xidian Univ Inst Intelligent Informat Proc Xian 710071 Peoples R China Shandong Agr Univ Sch Mech & Elect Engn Tai An 271018 Shandong Peoples R China

Kernel based machine learning techniques have been widely used to tackle problems of function approximation and regression estimation. Relevance vector machine (RVM) has state of the art performance in sparse regression. As a popular and competent kernel function in machine learning. conventional Gaussian kernel has unified kernel width with each of basis functions, which make impliedly a basic assumption: the response is represented below certain frequency and the noise is represented above such certain frequency. However, in many case, this assumption does not hold. To overcome this limitation, a novel adaptive spherical Gaussian kernel is utilized for nonlinear regression, and the stagewise optimization algorithm for maximizing Bayesian evidence in sparse Bayesian learning framework is proposed for model selection. Extensive empirical study, on two artificial datasets and two real-world benchmark datasets. shows its effectiveness and flexibility of model on representing regression problem with higher levels of sparsity and better performance than classical RVM. The attractive ability of this approach is to automatically choose the right kernel widths locally fitting RVs from the training dataset, which could keep right level smoothing at each scale of signal. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Relevance vector machine (RVM) Gaussian kernel function Regression gradient descent algorithm Bayesian evidence

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Local brightness adaptive image colour enhancement with Wasserstein distance

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IET IMAGE PROCESSING 2015年第1期9卷 43-53页

作者： Wang, Liqian Xiao, Liang Liu, Hongyi Wei, Zhihui Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Jiangsu Prov Key LAB Spectral Imaging & Intellige Nanjing 210094 Jiangsu Peoples R China Nanjing Univ Sci & Technol Sch Sci Nanjing 210094 Jiangsu Peoples R China

Colour image enhancement is an important preprocessing phase of many image analysis tasks such as image segmentation, pattern recognition and so on. This study presents a new local brightness adaptive variational model using Wasserstein distance for colour image enhancement. Under the perceptually inspired variational framework, the proposed energy functional consists of an improved contrast energy term and a Wasserstein dispersion energy term. To better adjust image dynamic range, the authors propose a local brightness adaptive contrast energy term using the average brightness of image local patch as the local brightness indicator. To restore image true colours, a Wasserstein distance-based dispersion energy term is used to measure the statistical similarity between the original image and the enhanced image. The proposed energy functional is minimised by using a gradient descent algorithm. Two objective measures are used to quantitatively measure the enhancement quality. Experimental results demonstrate the efficiency of the proposed model for removing colour cast and haze, enhancing contrast, recovering details and equalising low key images.

关键词： image colour analysis image enhancement image segmentation gradient methods local brightness adaptive image colour enhancement Wasserstein distance image analysis image segmentation pattern recognition energy functional contrast energy Wasserstein dispersion energy image dynamic range image local patch gradient descent algorithm

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Kernel regression with sparse metric learning

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2013年第4期24卷 775-787页

作者： Huang, Rongqing Sun, Shiliang E China Normal Univ Dept Comp Sci & Technol Shanghai 200241 Peoples R China

Kernel regression is a popular non-parametric fitting technique. It aims at learning a function which estimates the targets for test inputs as precise as possible. Generally, the function value for a test input is estimated by a weighted average of the surrounding training examples. The weights are typically computed by a distance-based kernel function and they strongly depend on the distances between examples. In this paper, we first review the latest developments of sparse metric learning and kernel regression. Then a novel kernel regression method involving sparse metric learning, which is called kernel regression with sparse metric learning (KR_SML), is proposed. The sparse kernel regression model is established by enforcing a mixed (2, 1)-norm regularization over the metric matrix. It learns a Mahalanobis distance metric by a gradient descent procedure, which can simultaneously conduct dimensionality reduction and lead to good prediction results. Our work is the first to combine kernel regression with sparse metric learning. To verify the effectiveness of the proposed method, it is evaluated on 19 data sets for regression. Furthermore, the new method is also applied to solving practical problems of forecasting short-term traffic flows. In the end, we compare the proposed method with other three related kernel regression methods on all test data sets under two criterions. Experimental results show that the proposed method is much more competitive.

关键词： Kernel regression sparse metric learning mixed norm regularization gradient descent algorithm traffic flow forecasting

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Extrapolated Smoothing descent algorithm for Constrained Nonconvex and Nonsmooth Composite Problems

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Chinese Annals of Mathematics,Series B 2022年第6期43卷 1049-1070页

作者： Yunmei CHEN Hongcheng LIU Weina WANG Department of Mathematics University of FloridaGainesville 118105USA Industrial and Systems Engineering University of FloridaGainesville 118105USA Corresponding author.Department of Mathematics Hangzhou Dianzi UniversityHangzhou 310018China

In this paper,the authors propose a novel smoothing descent type algorithm with extrapolation for solving a class of constrained nonsmooth and nonconvex problems,where the nonconvex term is possibly *** algorithm adopts the proximal gradient algorithm with extrapolation and a safe-guarding policy to minimize the smoothed objective function for better practical and theoretical ***,the algorithm uses a easily checking rule to update the smoothing parameter to ensure that any accumulation point of the generated sequence is an(afne-scaled)Clarke stationary point of the original nonsmooth and nonconvex *** experimental results indicate the effectiveness of the proposed algorithm.

关键词： Constrained nonconvex and nonsmooth optimization Smooth approximation Proximal gradient algorithm with extrapolation gradient descent algorithm Image reconstruction

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Single image super-resolution via internal gradient similarity

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2016年 35卷 91-102页

作者： Xian, Yang Tian, Yingli CUNY Grad Ctr New York NY 10016 USA CUNY City Coll New York NY 10016 USA

Image super-resolution aims to reconstruct a high-resolution image from one or multiple low-resolution images which is an essential operation in a variety of applications. Due to the inherent ambiguity for super-resolution, it is a challenging task to reconstruct clear, artifacts-free edges while still preserving rich and natural textures. In this paper, we propose a novel, straightforward, and effective single image super-resolution method based on internal across-scale gradient similarity. The low-resolution gradients are first upsampled and then fed into an optimization framework to construct the final high-resolution output. The proposed approach is able to synthesize natural high-frequency texture details and maintain clean edges even under large scaling factors. Experimental results demonstrate that out method outperforms existing single image super-resolution techniques. We further evaluate the super-resolution performance when both internal statistics and external statistics are adopted. It is demonstrated that generally, internal statistics are sufficient for single image super-resolution. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Image super-resolution Image quality enhancement Patch similarity Self-redundancy Across scale gradient similarity Optimization gradient descent algorithm

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Pipelined Memristive Analog-to-Digital Converter With Self-Adaptive Weight Tuning

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IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS 2022年第4期12卷 913-921页

作者： Wang, Weihe Li, Zhiwei Fu, Xingzhi Wang, Yinan Li, Qingjiang Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410073 Peoples R China

Benefiting from area and power efficiency, memristors enable the development of neural network analog-to-digital converter (ADC) to break through the limitations of conventional ADCs. Although some memristive ADC (mADC) architectures have been proposed recently, the current research is still at an early stage, which is mainly on the simulation and requires numerous target labels to train the synapse weights. In this paper, we propose a pipelined Hopfield neural network mADC architecture and experimentally demonstrate that such mADC has the capability of self-adaptive weight tuning. The proposed training algorithm is an unsupervised method originated from the random weight change (RWC) algorithm, which is modified to reduce the complexity of error feedback circuit to make it more hardware friendly. The synapse matrix could be adapted to the 1T1R crossbar array. For an 8-bit two-stage pipelined mADC, the proposed architecture in the simulation could achieve 7.69 fJ/conv FOM, 7.90 ENOB, 0.1 LSB INL, and 0.1 LSB DNL. And the experimental performance only achieves 1.56 pJ/conv FOM, 7.59 ENOB, 0.21 LSB INL, and 0.29 LSB DNL, which is mainly limited by the comparator's switching time.

关键词： Memristor Hopfield neural network analog-to-digital converter random weight change gradient descent algorithm edge computing

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Performance Analysis of Hard Iterative Channel Estimation in Turbo Equalization

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Tsinghua Science and Technology 2006年第5期11卷 574-581页

作者：邓拥军杨知行潘长勇王军 States Key Laboratory on Microwave and Digital Communications Department of Electronic Engineering Tsinghua University

Reasonable bit error rate performance requires perfect channel state information （CSI） in traditional turbo equalization （TE）, which is hard to obtain in practice. Soft and hard iterative algorithms have been developed to address the channel estimation problem with the performance of the soft iteratwe channel estimate based on the recursive least square algorithm. This paper presents an analysis of the performance of hard iterative channel estimation （HICE） based on the least mean square algorithm. The analysis uses a cost function with the hard decision on the TE output. An iterative channel correction （ICC） algorithm based on the gradient descent algorithm is used to iteratively minimize the cost function. The simulation results agree with the theoretical lower bound for the mean square error （MSE） of the estimated channels. Simulations show that, given an imperfect CSI with an MSE below the upper bound, the linear minimum mean squared error TE （LMMSE-TE） using the ICC has only small performance degradation compared to that with a perfect CSI, while the traditional LMMSE-TE suffers from severe error floor effect even with more iterations.

关键词： iterative channel estimation turbo equalization gradient descent algorithm

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Computationally efficient visual-inertial sensor fusion for Global Positioning System-denied navigation on a small quadrotor

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ADVANCES IN MECHANICAL ENGINEERING 2016年第3期8卷 1-11页

作者： Liu, Chang Prior, Stephen D. Teacy, W. T. Luke Warner, Martin Univ Southampton Engn Ctr Excellence Aeronaut Astronaut & Computat Engn Grp Boldrewood Campus Southampton SO16 7QF Hants England

Because of the complementary nature of visual and inertial sensors, the combination of both is able to provide fast and accurate 6 degree-of-freedom state estimation, which is the fundamental requirement for robotic (especially, unmanned aerial vehicle) navigation tasks in Global Positioning System-denied environments. This article presents a computationally efficient visual-inertial fusion algorithm, by separating orientation fusion from the position fusion process. The algorithm is designed to perform 6 degree-of-freedom state estimation, based on a gyroscope, an accelerometer and a monocular visual-based simultaneous localisation and mapping algorithm measurement. It also recovers the visual scale for the monocular visual-based simultaneous localisation and mapping. In particular, the fusion algorithm treats the orientation fusion and position fusion as two separate processes, where the orientation fusion is based on a very efficient gradient descent algorithm, whereas the position fusion is based on a 13-state linear Kalman filter. The elimination of the magnetometer sensor avoids the problem of magnetic distortion, which makes it a power-on-and-go system once the accelerometer is factory calibrated. The resulting algorithm shows a significant computational reduction over the conventional extended Kalman filter, with competitive accuracy. Moreover, the separation between orientation and position fusion processes enables the algorithm to be easily implemented onto two individual hardware elements and thus allows the two fusion processes to be executed concurrently.

关键词： Visual inertial sensor fusion Kalman filter gradient descent algorithm unmanned aerial vehicle quadrotor Global Positioning System denied navigation

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