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Automatic current control of magnet cranes for steel plate yard automation

CONTROL ENGINEERING PRACTICE 1998年第10期6卷 1193-1208页

作者： Park, SY Lee, JS Choi, JY Park, BH Pohang Univ Sci & Technol Dept Elect Engn Pohang 790784 South Korea Pohang Iron & Steel Co Tech Res Lab Instrumentat & Control Res Team Pohang 790785 South Korea

This paper presents an automatic current-control method for magnet cranes for thick steel plate yard automation. In moving the steel plates from stack to stack or from stack to shipping truck, it is difficult to lift the correct number of steel plates because the dimensions of the stacked steel plates are in general different, and their other parameters are not completely known and may even vary in a nonlinear fashion. In this paper, recursive form flux equations are first derived for the thick steel plates, and a current equation is then determined for the magnet coil. Based on these equations, an adaptive current predictor is developed to exert the right amount of current on the magnet, so as to lift the correct number of steel plates. When the initial trial with this current fails, a current tuning method is introduced to adjust the current and lift the correct number of plates. The developed magnet current controller has been successfully tested on data obtained from the storage yard at the Pohang Iron & Steel Co. (POSCO). (C) 1998 Published by Elsevier Science Lid. All rights reserved.

关键词： current control adaptive current prediction current tuning gradient descent algorithm adaptive fuzzy logic

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Improved Particle Swarm Optimization-Based Artificial Neural Network for Rainfall-Runoff Modeling

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JOURNAL OF HYDROLOGIC ENGINEERING 2014年第7期19卷 1320-1329页

作者： Asadnia, Mohsen Chua, Lloyd H. C. Qin, X. S. Talei, Amin Nanyang Technol Univ DHI NTU Water & Environm Res Ctr & Educ Hub Singapore 639798 Singapore Nanyang Technol Univ Sch Civil & Environm Engn Singapore 639798 Singapore Monash Univ Sch Engn Bandar Sunway 46150 Selangor Darul Malaysia

This paper presents the application of an improved particle swarm optimization (PSO) technique for training an artificial neural network (ANN) to predict water levels for the Heshui Watershed, China. Daily values of rainfall and water levels from 1988 to 2000 were first analyzed using ANNs trained with the conjugate gradient, gradient descent, and Levenberg-Marquardt neural network (LM-NN) algorithms. The best results were obtained from the LM-NN, and these results were then compared with those from PSO-based ANNs, including the conventional PSO neural network (CPSONN) and the improved PSO neural network (IPSONN) with passive congregation. The IPSONN algorithm improves PSO convergence by using the selfish herd concept in swarm behavior. The results show that the PSO-based ANNs performed better than the LM-NN. For models run using a single parameter (rainfall) as input, the root mean square error (RMSE) of the testing data set for IPSONN was the lowest (0.152 m) compared to those for CPSONN (0.161 m) and LM-NN (0.205 m). For multiparameter (rainfall and water level) inputs, the RMSE of the testing data set for IPSONN was also the lowest (0.089 m) compared to those for CPSONN (0.105 m) and LM-NN (0.145 m). The results also indicate that the LM-NN model performed poorly in predicting the low and peak water levels in comparison to the PSO-based ANNs. Moreover, the IPSONN model was superior to CPSONN in predicting extreme water levels. Lastly, IPSONN had a quicker convergence rate compared to CPSONN. (C) 2014 American Society of Civil Engineers.

关键词： Rainfall-runoff modeling Passive particle swarm optimization Artificial neural network Levenberg-Marquardt algorithm Conjugate gradient algorithm gradient descent algorithm

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Intrinsic sample mean in the space of planar shapes

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PATTERN RECOGNITION 2016年 60卷 164-176页

作者： Flores, M. J. Gual-Arnau, X. Ibanez, M. V. Simo, A. Univ Jaume 1 Dept Psicol Basica Clin & Psicobiol Castellon de La Plana Castello Spain Univ Jaume 1 Inst New Imaging Technol Dept Matemat Castellon de La Plana Castello Spain Univ Jaume 1 Inst Math & Applicat Dept Matemat Castellon de La Plana Castello Spain

In this paper we consider the shape space as the set of smooth simple closed curves in R-2 (parameterized curves), modulo translations, rotations and scale changes. An algorithm to obtain the intrinsic average of a sample data (set of planar shape realizations), from the identification of the shape space with an infinite dimensional Grassmannian is proposed using a gradient descent type algorithm. A simulation study is carried out to check the performance of the algorithm. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Intrinsic sample mean Parameterized closed curve Shape space gradient descent algorithm

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Energy optimal motion planning of a 14-DOF biped robot on 3D terrain using a new speed function incorporating biped dynamics and terrain geometry

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ROBOTICA 2022年第2期40卷 250-278页

作者： Kumar, Jitendra Dutta, Ashish IIT Kanpur Dept Mech Engn Kanpur 208016 Uttar Pradesh India

In this paper, a new method is proposed to find a feasible energy-efficient path between an initial point and goal point on uneven terrain and then to optimally traverse the path. The path is planned by integrating the geometric features of the uneven terrain and the biped robot dynamics. This integrated information of biped dynamics and associated cost (energy) for moving toward the goal point is used to define the value of a new speed function at each point on the discretized surface of the terrain. The value is stored as a matrix called the dynamic transport cost (DTC). The path is obtained by solving the Eikonal equation numerically by fast marching method (FMM) on an orthogonal grid, by using the information stored in the DTC matrix. One step of walk on uneven terrain is characterized by 10 footstep parameters (FSPs);these FSPs represent the position of swinging foot at the starting and ending time of the walk, orientation, and state (left or right) of support foot. A walking dataset was created for different walking conditions (FSPs), which the biped robot is likely to encounter when it has to walk on the uneven terrain. The corresponding energy optimal hip and foot trajectory parameters (HFTPs) are obtained by optimization using a genetic algorithm (GA). The created walk dataset is generalized by training a feedforward neural network (NN) using the scaled conjugate gradient (SCG) algorithm. The Foot placement planner gives a sequence of foot positions and orientations along the obtained path, which is followed by the biped robot by generating real-time optimal foot and hip trajectories using the learned NN. Simulation results on different types of uneven terrains validate the proposed method.

关键词： Fast marching method Speed function Uneven terrain Dynamic transport cost matrix gradient descent algorithm Genetic algorithm Footstep parameters hip and foot trajectory parameters

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

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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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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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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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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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