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1st International Conference on Information and Communication Technology for Intelligent Systems (ICTIS)

作者： Nayak, Sasmita Choudhury, B. B. Lenka, Saroj Kumar Govt Coll Engn Dept Mech Engn Kalahandi Odisha India IGIT Dept Mech Engn Sarang Odisha India Mody Univ Dept Informat Technol Lakshmangarh India

ISBN: (纸本)9783319309330;9783319309323

Fast development of industrial robots and its utilization by the manufacturing industries for many different applications is a critical task for the selection of robots. As a consequence, the selection process of the robot becomes very much complicated for the potential users because they have an extensive set of parameters of the available robots. In this paper, gradient descent momentum optimization algorithm is used with backpropagation neural network prediction technique for the selection of industrial robots. Through this proposed technique maximum, ten parameters are directly considered as an input for the selection process of robot where as up to seven robot parameter data be used in the existing methods. The rank of the preferred industrial robot evaluates from the perfectly the best probable robot that specifies the most genuine benchmark of robot selection for the particular application using the proposed algorithm. Moreover, the performance of the algorithms for the robot selection is analyzed using Mean Square Error (MSE), R-squared error (RSE), and Root Mean Square Error (RMSE).

关键词： Industrial robot selection gradient descent algorithm Backpropagation algorithm Neural network

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Tikhonov regularization and gradient descent algorithms for tomography using first-arrival seismic traveltimes

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CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION 2015年第4期58卷 1367-1377页

作者： Cui Yan Wang Yan-Fei Chinese Acad Sci Inst Geol & Geophys Key Lab Petr Resources Res Beijing 100029 Peoples R China China Univ Petr Shengli Coll Dongying 257097 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

With the development of seismic exploration, inversion and imaging become key issues because of complex structures. It is in urgent need to build accurate near-surface velocity models. Nowadays, three primary numerical methods are developed to acquire the velocity model, i.e., stack velocity analysis, migration velocity analysis and tomography velocity analysis. For the near-surface seismic problem, the first two methods are not suitable because of insufficient fold numbers and reflections. So the tomography method has received much more attention. First-arrival traveltime seismic tomography refers to inversion of medium velocity using first-arrival seismic wave traveltimes and their ray paths. The first task is model parameterization which discretizes the stratigraphic model into many slowness units by gridding. Secondly, based on the slowness units, the ray paths are analyzed by the shortest traveltime ray tracing. Then the traveltime equation is established to solve the velocity model. This is an ill-posed inverse problem. Proper regularization technique and optimization methods are required. Therefore, a Tikhonov regularization model with constraints on feasible set was established, and a gradient descent method with modified step sizes was also developed to obtain an optimized solution. Three different theoretical models were designed to test the new algorithm. The first is a horizontal layered model: there were three horizontal layers with the velocity of 600 m.s(-1), 1200 m.s(-1) and 2000 m.s(-1) top to bottom in the real model. By random disturbance of the model, we got an initial velocity model which was far away from the real model. The inversion result shows that the new algorithm can converge to the true model quickly even with poor initial condition. This shows that the new algorithm is stable and fast in convergence. Comparison with the well-known conjugate gradient (CG) method indicates that this new algorithm requires less memory and has higher conve

关键词： First-arrival traveltime seismic tomography Regularization gradient descent algorithm Ray tracing

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Spatially constrained mixture model via energy minimization and its application to image segmentation

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JOURNAL OF ELECTRONIC IMAGING 2016年第1期25卷 1-11页

作者： Xiao, Zhiyong Yuan, Yunhao Liu, Jianjun Yang, Jinlong Jiangnan Univ Sch Internet Things Engn Key Lab Adv Proc Control Light Ind Lihu Ave Wuxi 214122 Peoples R China Inst Fresnel UMR CNRS 7249 Ave Escadrille Normandie Niemen F-13397 Marseille France

A mixture model with spatial constraint is proposed for image segmentation. This model assumes that the pixel label priors are similar if the pixels are close in geometry. An energy function is defined on the spatial space for measuring the spatial information. We also derive an energy function on the observed data space from the log-likelihood function of the standard mixture model. We estimate the model parameters by minimizing the combination of the two energy functions, using the gradient descent algorithm. Then we use the parameters to compute the posterior probability. Finally, each pixel can be assigned to a class using the maximum a posterior decision rule. Numerical experiments are presented where the proposed method and other mixture model-based methods are tested on synthetic and real-world images. These experimental results demonstrate that the proposed method achieves competitive performance compared with other spatially constrained mixture model-based methods. (C) 2016 SPIE and IS&T

关键词： energy minimization mixture model spatial information image segmentation gradient descent algorithm geometric closeness function

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Online learning of mixture experts for real-time tracking

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IET COMPUTER VISION 2016年第6期10卷 585-592页

作者： Gu, S. Ma, Z. Xie, M. Chen, Z. Univ Elect Sci & Technol China Sch Commun & Informat Engn Chengdu Peoples R China Univ Elect Sci & Technol China Sch Elect Engn Chengdu Peoples R China Mem Univ Newfoundland Fac Comp Sci St John NF Canada

Template tracking has been extensively investigated in computer vision to track objects for various applications. Tracking based on gradient descent algorithm using image gradient is one of the most popular object tracking method. However, it is difficult to define the relationship between the observed data set and the warping function due to the unobserved heterogeneity of the data set which inevitably results in poor tracking performance. This study proposes a novel method based on hierarchical mixture of expert to perform robust, real-time tracking from stationary cameras. By extending the idea of hyperplane approximation, the proposed approach establishes a hierarchical mixture of generalised linear regression model instead of a single model which reduces the non-linear error. The experiments' results show significant improvement over the traditional hyperplane approximation (HA) approach.

关键词： object tracking computer vision learning (artificial intelligence) gradient methods approximation theory regression analysis image sensors mixture models online mixture experts learning template tracking computer vision gradient descent algorithm object tracking method observed data set warping function stationary cameras hyperplane approximation generalised linear regression model

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Temporal community detection based on symmetric nonnegative matrix factorization

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INTERNATIONAL JOURNAL OF MODERN PHYSICS B 2017年第13期31卷 1750102-1750102页

作者： Jiao, Pengfei Lyu, Haodong Li, Xiaoming Yu, Wei Wang, Wenjun Tianjin Univ Sch Comp Sci & Technol Tianjin 300072 Peoples R China

To understand time-evolving networks, researchers should not only concentrate on the community structures, an essential property of complex networks, in each snapshot, but also study the internal evolution of the entire networks. Temporal communities provide insights into such mechanism, i.e., how the communities emerge, expand, shrink, merge, split and decay over time. Based on the symmetric nonnegative matrix factorization (SNMF), we present a dynamic model to detect temporal communities, which not only could find a well community structure in a given snapshot but also demands the results bear some similarity to the partition obtained from the previous snapshot. Moreover, our method can handle the situation that of the number of community changes in the networks. Also, a gradient descent algorithm is proposed to optimize the objective function of the model. Experimental results on both the synthetic and real-world networks indicate that our method outperforms the state-of-art methods for temporal community detection.

关键词： Temporal community nonnegative matrix factorization time-evolving networks gradient descent algorithm

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An Adaptive Orientation Estimation Method for Magnetic and Inertial Sensors in the Presence of Magnetic Disturbances

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SENSORS 2017年第5期17卷 1161-1161页

作者： Fan, Bingfei Li, Qingguo Wang, Chao Liu, Tao Zhejiang Univ Sch Mech Engn State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Zhejiang Peoples R China Queens Univ Dept Mech & Mat Engn Kingston ON K7L 3N6 Canada

Magnetic and inertial sensors have been widely used to estimate the orientation of human segments due to their low cost, compact size and light weight. However, the accuracy of the estimated orientation is easily affected by external factors, especially when the sensor is used in an environment with magnetic disturbances. In this paper, we propose an adaptive method to improve the accuracy of orientation estimations in the presence of magnetic disturbances. The method is based on existing gradient descent algorithms, and it is performed prior to sensor fusion algorithms. The proposed method includes stationary state detection and magnetic disturbance severity determination. The stationary state detection makes this method immune to magnetic disturbances in stationary state, while the magnetic disturbance severity determination helps to determine the credibility of magnetometer data under dynamic conditions, so as to mitigate the negative effect of the magnetic disturbances. The proposed method was validated through experiments performed on a customized three-axis instrumented gimbal with known orientations. The error of the proposed method and the original gradient descent algorithms were calculated and compared. Experimental results demonstrate that in stationary state, the proposed method is completely immune to magnetic disturbances, and in dynamic conditions, the error caused by magnetic disturbance is reduced by 51.2% compared with original MIMU gradient descent algorithm.

关键词： inertial and magnetic sensors orientation estimation magnetic disturbances instrumented gimbal 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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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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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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Unmixing Of Hyperpectral Image Using Nonlinear Parametric Model 1

Unmixing Of Hyperpectral Image Using Nonlinear Parametric Mo...

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IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology (RTEICT)

作者： Navinkumar, Thacker Unnatiben Parmar, S. K. GCET EC Dept Anand Gujarat India

ISBN: (纸本)9781509007745

Hyperspectral image contains hundreds of bands so it is spectrally overloaded and contains extent information to differentiate spectrally unique material. Hyperspectral data generally used to identify the presence of material in scene. Almost all the hyperspectral cameras have spatial resolution limit (> 5m per pixel) due to that each pixel can be a mixture of several materials. The process ofunmixing is to unmixone of these mixed pixels. There are two models available to approximate mixing, (i) Linear Mixing Model (LMM) (ii) Nonlinear Mixing Model (NMM). Over a time, various approaches have been devised to address LMM and it's unmixing. In LMM, macrospectral mixtures are assumed. Nonlinear model comes under consideration due to microscopic mixing scale. In this paper, Generalized bilinear model is used which is nonlinear parametric model to get mixed data. Its accuracy depends on parametric form and parameter value chosen. It comes under convex optimization problem, so it can be solved using any optimization technique. gradient descent algorithm (GDA) is employed to solve this optimization problem. Advantage of GDA over other unmixing techniques is that it transforms nonlinear model into linear one. To improve unmixing result, it is indeed advisable to consider spatial correlation among abundances. A novel approach has been introduced in this paper which considers 2nd order neighborhood correlation between abundances. Using our approach one can achieve better segmentation.

关键词： hyperspectral image linear mixing model nonlinear mixing model unmixing generalized bilinear model gradient descent algorithm spatial correlation

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