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检索条件"主题词=Projection Algorithms"

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The pathway for agent and robot enemy in strategy game with high efficiency model

The pathway for agent and robot enemy in strategy game with ...

引用

International IEEE Conference on Intelligent Systems, IS

作者： Khammapun Khantanapoka Suwanna Rasmequan Department of Informatics Burapha University Chonburi Thailand

Path finding is importance for various areas such as robotic, game and transportation. It has a significant effect to the prosperity of economic increasingly. This research proposes three essential improvements. First, the position of each agent (player or robot) will be transferred to multilayer scene using Perspective projection Method (PPM). It is able to send begin position and destination position to difference layer in the online game. These positions will be compressed at the server site before sending to revert at PC with projection algorithm. Second, we propose Depth Direction A*(DDA*) algorithm which is a newly improve method. It uses linear graph theory together with A* classic Algorithm. DDA* will help increase efficiency in avoiding hard obstacle of the scene. With this method, an agent can move more naturally than previous method. The method will reduce the number of expanding child nodes as compare to A* classic algorithm certainly. In addition, the agent takes less movement time than previous algorithm. This research emphasizes on a more reality environment of multi-layer together with multi-terrain types. Third, we propose technique to separate out different route by generate sub-begin point and sub-destination point in each part between the begin point and the destination point. With this technique, the search procedures will be adjusted automatically according to the heterogeneous environment. The experimental result shows that our method is able to decrease expansion of child nodes between 22.12 - 70.67% in a single layer environment. This depends upon the property of terrain types which will affect the movement speed obviously.

关键词： Robots Layout Clustering algorithms Informatics Road transportation Environmental economics Nonhomogeneous media projection algorithms Graph theory Artificial intelligence

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Adaptive proportional-integral speed controller for direct torque control of induction motor using neural networks

Adaptive proportional-integral speed controller for direct t...

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International Symposium on Computer Communication Control and Automation (3CA)

作者： Shun-Yuan Wang Chwan-Lu Tseng Chaur-Yang Chang Chih-Chun Yeh Jen-Hsiang Chou Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9781424455652;9781424455669

The design and implementation of an adaptive neural network based proportional-integral controller (NNPIC) were carried out on the direct torque control (DTC) system. The proposed controller consists of neural network and projection algorithm, in order to tune its parameters adaptively online under environmental variations. The experimental results proved that the performance of the proposed controller was quite better than the conventional PI controller by the criterion, root-mean-square-error (RMSE).

关键词： Torque control Programmable control Adaptive control Pi control Proportional control Induction motors Neural networks Adaptive systems projection algorithms Root mean square

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Time-varying neural networks based indirect adaptive ILC for discrete-time varying nonlinear systems

Time-varying neural networks based indirect adaptive ILC for...

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Chinese Control Conference (CCC)

作者： Weili Yan Mingxuan Sun College of Information Engineering Zhejiang University of Technology Hangzhou 310023 China

By using the iterative learning projection algorithm with dead-zone for training time-varying weights, a time-varying neural networks (TVNNs) based indirect adaptive iterative learning control (I-AILC) scheme is presented for a class of uncertain discrete-time varying nonlinear systems with unknown control gain sign. The control singularity has been overcome through a modification of the control gain estimation which can be bounded away from zero. The proposed TVNNs-based I-AILC doesn't require the strict initial resetting condition and the reference trajectory can vary along the iteration axis. Theoretical analysis proves the boundedness of all signals of the closed-loop system and convergence of the tracking error to a bounded region. The numerical results presented verify effectiveness of the proposed method.

关键词： Iterative algorithm Time varying systems Artificial neural networks Nonlinear systems Adaptive systems Convergence projection algorithms

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Locality Preserving projections Algorithm Based on Improved Iterative SelfOrganize Data Analysis

Locality Preserving Projections Algorithm Based on Improved ...

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Chinese Conference on Pattern Recognition (CCPR)

作者： Shu-liang Sun Shou-jue Wang Department of Electronics and Information Engineering University of Tongji Shanghai China Laboratory of Artificial Neural Networks Institute of Semiconductors Chinese Academy and Sciences Beijing China

The new locally preserving projections algorithm is proposed in this paper which is based on Bayesian criteria and adapted improved iterative self-organize data analysis. The experiment shows that the new algorithm can put forward the optimum number of dimensions and be more available than principle component analysis. That is because it takes into account the relation the number of between dimensions and classification. The new algorithm not only preserves the structure of original data and eliminates the correlation and redundancy of high dimension vectors.

关键词： Principal component analysis Electronic mail Data analysis Iterative algorithm Classification algorithms Algorithm design and analysis projection algorithms

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Satellite Illumination Algorithm Applied to Panel Multi-Axis Rotation

Satellite Illumination Algorithm Applied to Panel Multi-Axis...

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International Conference on Information Engineering and Computer Science (ICIECS)

作者： Ling Li Bo Cai Jing Hu Biao He Yuan Long Dengyi Zhang School of Computer Wuhan University of China Wuhan Hubei China

This paper firstly introduces the importance of satellite illumination analysis in aerospace fields and points out that illumination conditions will influence the design of shape of satellite body and the installation of satellite equipments. Then, it discusses two methods for analyzing satellite solar-panel shadow and illumination conditions: ray tracing illumination algorithm and polyhedral mesh contour edge projection algorithm and compares their efficiency and feasibility. We designed solar panel single-axis rotation experiment to validate the proposed algorithm. The experimental results show that contour edge projection algorithm has fine accuracy and costs less time.

关键词： Satellites Lighting Sun Ray tracing Algorithm design and analysis Satellite broadcasting projection algorithms

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Parallel projections for Manifold Learning

Parallel Projections for Manifold Learning

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International Conference on Machine Learning and Applications (ICMLA)

作者： Harry Strange Reyer Zwiggelaar Department of Computer Science Aberystwyth University UK

Manifold learning is a widely used statistical tool which reduces the dimensionality of a data set while aiming to maintain both local and global properties of the data. We present a novel manifold learning technique which aligns local hyper planes to build a global representation of the data. A Minimum Spanning Tree provides the skeleton needed to traverse the manifold so that the local hyper planes can be merged using parallel projections to build a global hyper plane of the data. We show state of the art results when compared against existing manifold learning algorithm on both artificial and real world image data.

关键词： Manifolds Strips Principal component analysis Kernel projection algorithms Video sequences Laplace equations

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Pixel level multifocus image fusion based on variational decomposition

Pixel level multifocus image fusion based on variational dec...

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IEEE Workshop on Signal Processing Systems (SIPS)

作者： Yongping Zhang Zhongkun He Fang Cheng Liang Ding Quanmao Mi School of Electronic and Information Engineering Ningbo University of Technology Ningbo China College of Information Engineering Taiyuan University of Technology Taiyuan Shanxi China

An image fusion Algorithm based on variational decomposition and energy of edge is presented in this paper. Firstly, each source image is decomposed into a geometrical component and a textured component by applying Rudin-Osher-Fatemi (ROF) model in combination with Chambolle's projection algorithm. Secondly, the corresponding components are fused separately. For fusing the geometrical components, a weighted average method is adopted. To construct the textured component, an indirect smoothing method is firstly applied to the composed textured component of each source image, and then a selection mode is used according to the local Energy of Laplacian measure. Finally, the sum of the fused components is calculated to obtain the fused resultant. Experiments show that the proposed algorithm works well in multi focus image fusion.

关键词： Image fusion Smoothing methods Filtering algorithms Noise Pixel projection algorithms Signal processing algorithms

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Iterative reweighted least squares for matrix rank minimization

Iterative reweighted least squares for matrix rank minimizat...

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Annual Allerton Conference on Communication, Control, and Computing

作者： Karthik Mohan Maryam Fazel Electrical Engineering Department University of Washington Seattle USA

The classical compressed sensing problem is to find the sparsest solution to an underdetermined system of linear equations. A good convex approximation to this problem is to minimize the ℓ 1 norm subject to affine constraints. The Iterative Reweighted Least Squares (IRLSp) algorithm (0 p (p ≤ 1) minimization problem with affine constraints. Recently Chartrand et al observed that IRLS-p with p 1 minimization, and Daubechies et al gave `local' linear and super-linear convergence results for IRLS-p with p = 1 and p

关键词： Minimization Convergence projection algorithms Null space Compressed sensing Clustering algorithms Approximation algorithms

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Fault detection using Linear Discriminant Analysis with selection of process variables and time lags

Fault detection using Linear Discriminant Analysis with sele...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Anderson da Silva Soares Roberto Kawakami Harrop Galvão Divisão de Ciência da Computação Instituto Tecnológico de Aeronáutica São José dos Campos SP Brazil Divisão de Engenharia Eletrônica Instituto Tecnológico de Aeronáutica São José dos Campos SP Brazil

ISBN: (纸本)9781424456956

This paper is concerned with the selection of process variables and time lags for fault detection. For this purpose, a feature selection technique known as Successive projections Algorithm (SPA) is employed with Linear Discriminant Analysis (LDA) classifiers to discriminate between normal operating conditions and faults. SPA was originally designed to minimize multicollinearity among the selected features, which is a known cause of generalization problems for LDA. In the present work, a modification to the basic SPA formulation is proposed to place larger emphasis on the selection of relevant features for the classification task. The proposed SPA-LDA methodology is illustrated in a case study involving the Tennessee Eastman benchmark process. For comparison, a genetic algorithm (GA) for feature selection is also employed. In this study, the pre-selection of process variables was found to improve the accuracy of the resulting classifiers. In practice, such a pre-selection would have the extra advantage of reducing the number of sensors required to detect a given fault. Moreover, the proposed modification in SPA-LDA resulted in an improvement of average classification accuracy from 88% to 96%. This result was similar to that obtained by GA-LDA (97%). However, the SPA-LDA classifiers were found to be less sensitive to measurement noise.

关键词： Fault detection Linear discriminant analysis projection algorithms Genetic algorithms Decision trees Noise measurement Industrial training Industrial plants Principal component analysis Wavelet analysis

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A Novel Image Smoothing Algorithm Based on Variational Decomposition

A Novel Image Smoothing Algorithm Based on Variational Decom...

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International Conference on Genetic and Evolutionary Computing (ICGEC)

作者： Zhongkun He Yongping Zhang Rina Su Shaojing Fan School of Electronic and Information Engineering Ningbo University of Technology Ningbo China

A new image smoothing algorithm based on variational decomposition and vector median filtering is presented in this paper. First, the noisy image is decomposed into a bounded variation component and a oscillating component by applying Meyer-Vese-Osher model in combination with Chambolle's fixed point algorithm. Next, the oscillating component is smoothed by using vector median filter. Then, the sum of the bounded variation component and the smoothed oscillating component is calculated to obtain the filtered resultant. Experiments show that the proposed algorithm works well in noisy image with Gaussian white noise.

关键词： Smoothing methods Noise Filtering algorithms Image edge detection projection algorithms Surface fitting Noise measurement

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