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检索条件"机构=Department of Computing and Automation Engineering"

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Dynamic Modeling with Nonlinear Inputs and Backstepping Control for a Hexarotor Micro-Aerial Vehicle

Dynamic Modeling with Nonlinear Inputs and Backstepping Cont...

引用

IEEE Latin American Robotics Symposium, LARS

作者： Armando S. Sanca P.J. Alsina Jes de Jesus F. Cerqueira Computing Engineering and Automation Department Federal University of Rio Grande do Norte Natal RN Brazil

In this paper the hexarotor dynamic model with nonlinear inputs and full state back stepping technique is presented. Dead zone and saturation nonlinear inputs are considered reflected to the rotors. The goal is to obtain a faithful mathematical representation of the mechanical system for analysis and control design, not only in hover, but also in motion when take-off, land and flight for aerial navigation tasks. The model was implemented in Matlab/Simulink to optimize the design of control system. Simulations of the hexarotor model shows the performance of the control law and stabilizes with good tracking.

关键词： Rotors Vehicles Torque Backstepping Blades Mathematical model Aerodynamics

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Power quality disturbances classification using specialized neural networks

Power quality disturbances classification using specialized ...

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International Conference on Harmonics and Quality of Power

作者： Cerqueira, Augusto S. Ferreira, Danton D. Pinto, Marcia B. S. Salmento, Marlon L. G. De Seixas, José M. Department of Electrical Circuits Federal University of Juiz de Fora Juiz de Fora MG 36036000 Brazil Automation and Control Engineering Federal University of Lavras Lavras MG 432434 Brazil Department of Electrical and Computing Engineering Federal University of Rio de Janeiro Rio de Janeiro RJ 300300 Brazil

ISBN: (纸本)9781424472444

This work presents an automatic classification system for power quality disturbances based on specialized neural networks. The proposed system is capable to classify multiple disturbances, which is very important in some power systems scenarios. A system was designed and tested with synthetic signals generated by software and the global efficiency achieved was 98.9%. The system was also evaluated with real data achieving good results. ©2010 IEEE.

关键词： Software testing

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Two-dimensional neighborhood discriminant projection

Two-dimensional neighborhood discriminant projection

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2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010

作者： Zhang, Shan-Wen Lei, Ying-Ke Huang, De-Shuang Intelligent Computing Lab. Hefei Institute of Intelligent Machines Chinese Academy of Sciences P.O.Box 1130 Hefei Anhui 230031 China Department of Automation University of Science and Technology of China Hefei 230027 China Department of Information Electronic Engineering Institute Hefei 230037 China

ISBN: (纸本)9781424469178

Classical linear dimensional reduction algorithms, such as Linear Discriminant Analysis (LDA) and Locality Preserving Projections (LPP) have been widely used in computer vision and pattern recognition. However, when dealing with the multidimensional dataset, they usually first transform the original data to vectors, and then analyze the data in such a high dimensional space. This process inevitably results in some obvious disadvantages. This paper proposes a novel two-dimensional dimensionality reduction algorithm called 2D Neighborhood Discriminant Projection (2D-NDP), which is based directly on 2D image matrices rather than 1D vectors. 2D-NDP detects the intrinsic class-relationships between the images by incorporating both class label information and neighborhood information. It can optimally preserve not only the local class information but discriminant information as well. Under the orthogonal constrain, 2D-NDP is developed as orthogonal 2D-NDP for classification. Experiments on the face database and the plant leaf database demonstrate that orthogonal 2D-NDP is effective and feasible for classification. © 2010 IEEE.

关键词： Discriminant analysis

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Orthogonal linear local spline discriminant embedding for face recognition

Orthogonal linear local spline discriminant embedding for fa...

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2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010

作者： Lei, Ying-Ke Hu, Rong-Xiang Tang, Lei Zhang, Shan-Wen Huang, De-Shuang Intelligent Computing Lab. Hefei Institute of Intelligent Machines Chinese Academy of Sciences P.O.Box 1130 Hefei Anhui 230031 China Department of Automation University of Science and Technology of China Hefei 230027 China Department of Information Electronic Engineering Institute Hefei 230037 China

ISBN: (纸本)9781424469178

In this paper, an efficient feature extraction algorithm called orthogonal linear local spline discriminant embedding (O-LLSDE) is proposed for face recognition. Derived from local spline embedding (LSE), O-LLSDE not only inherits the advantages of LSE which uses local tangent space as a representation of the local geometry so as to preserve the local structure, but also makes full use of class information and orthogonal subspace to improve discriminant power. Extensive experiments on standard face databases demonstrate the effectiveness of the proposed method. © 2010 IEEE.

关键词： Face recognition

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Dimensionality reduction based on minimax risk criterion for face recognition

Dimensionality reduction based on minimax risk criterion for...

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2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010

作者： Tang, Lei Lei, Ying-Ke Zhu, Lin Huang, De-Shuang Intelligent Computing Lab. Hefei Institute of Intelligent Machines Chinese Academy of Sciences P.O.Box 1130 Hefei Anhui 230031 China Department of Automation University of Science and Technology of China Hefei 230027 China Department of Information Electronic Engineering Institute Hefei 230037 China

ISBN: (纸本)9781424469178

In the field of pattern recognition and machine learning, many problems are involved in the tasks of dimensionality reduction and then classification. In this paper, we develop an efficient dimensionality reduction method named MiniRisk Supervised Discrimiant Projection (MRSDP), which extracts effective low-dimensional features for classification purpose. The proposed method utilizes discriminant information to guide the procedure of extracting intrinsic low-dimensional features and provides a linear projection matrix. Since MRSDP is based on minimax risk criterion, it can minimize the maximal probability of misclassification in the common borders of different classes of data by contracting within-class scatter and maximizing between-class scatter. The advantage of our method is borne out by comparison with other widely used methods. In the experiments on Yale face database and ORL face database, our method achieves constantly superior performance than those competing methods. © 2010 IEEE.

关键词： Face recognition

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Medical image colorization using learning

Medical image colorization using learning

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作者： Bochko, Vladimir Välisuc, Petri Alho, Timo M.R. Sutlnen, Severi Parkkinen, Jussl Alander, Jarmo T. Department of Electrical Engineering and Automation University of Vaasa P.O. BOX 700 FIN-65101 Vaasa Finland School of Computing University of Eastern Finland P.O. Box 111 FIN-80101 Joensuu Finland

ISBN: (纸本)9781617388897

We propose a method for colorization of medical grayscale images using color learning. The colors are learned from a color image and predicted for a grayscale image. Earlier we introduced an efficient algorithm for image colorization which uses a dichromatic reflection model. The colorization algorithm is further developed in this study. First, we improve the algorithm performance by extending its capability to work with the grayscale images the contrast of which is lower than the contrast of the color images. Then, we propose a reliable technique to prevent negative contrast during colorization. In addition, we develop a simple approach for grayscale image colorization by a given RGB value. We give two medical applications of our algorithm: realistic color labeling of skin wounds and colorization of a dental cast models. In the former case we use grayscale images and labeling obtained after support vector classification as input data and for the latter application we use photometric stereo images.

关键词： Medical applications

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A Concentration-based Artificial Immune Network for continuous optimization

A Concentration-based Artificial Immune Network for continuo...

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Congress on Evolutionary Computation

作者： Guilherme Palermo Coelho Fernando J. Von Zuben Laboratory of Bioinformatics and Bioinspired Computing Department of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering State University of Campinas-UNICAMP Sao Paulo Brazil

Metaheuristics based on the Artificial Immune System (AIS) framework, especially those inspired by the Immune Network theory, are known to be capable of stimulating the generation of diverse sets of solutions for a given problem, even though they generally implement very simple mechanisms to control the dynamics of the network. In the AIS literature, several studies propose different models that try to explain the behavior of immune networks, which are generally based on the concentration of antibodies and tend to better mimic some aspects of such complex systems. Therefore, in this work we propose a novel immune-inspired algorithm for optimization, named cob-aiNet (Concentration-based Artificial Immune Network), that intends to explore such network models and introduce new mechanisms to better control the dynamics of the network, so that a broader coverage of promising regions of the search space can be achieved. This property of cob-aiNet was verified in experimental analyses, in which the algorithm was compared to two other AIS proposals and also to all the competitors from the 2005 CEC Special Session on RealParameter Optimization.

关键词： Cloning Immune system Heuristic algorithms Optimization Search problems Measurement Benchmark testing

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Global robust output regulation for nonlinear systems with iISS inverse dynamics

Global robust output regulation for nonlinear systems with i...

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IEEE International Conference on automation and Logistics

作者： Dabo Xu Jie Huang Applied Control and Computing Laboratory Department of Mechanical and Automation Engineering Chinese University of Hong Kong New Territories Hong Kong China

This paper develops an approach to dealing with the global robust output regulation problem for a class of nonlinear systems with integral input-to-state stable (iISS) inverse dynamics by using output feedback control. As iISS condition is strictly weaker than ISS condition, the result of this paper applies to a larger class of nonlinear systems.

关键词： Robustness Trajectory Mathematical model Output feedback Equations Radio frequency Nonlinear systems

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Finding a high coverage set of 5-biclusters with swarm intelligence

Finding a high coverage set of 5-biclusters with swarm intel...

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Congress on Evolutionary Computation

作者： Fabrício O. de França Fernando J. Von Zuben Laboratory of Bioinformatics and Bioinspired Computing Department of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering State University of Campinas-UNICAMP Sao Paulo Brazil

Biclustering is usually referred to as the process of finding subsets of rows and columns from a given dataset. Each subset is a bicluster and corresponds to a sub-matrix whose elements tend to present a high degree of coherence with each other. In order to find such structures, the δ-biclustering problem was formulated, being denoted as the problem of finding a set of biclusters limited by a maximum degree of coherence, measured by a mean-squared residue, while maximizing the bicluster total size. Additionally, it is expected a reduced overlap among the biclusters in the set, in other words, a minimization of the number of common elements shared by them. This also leads to a high coverage of the original dataset given the number of biclusters found. Most algorithms intended to find such biclusters focus only on the mean-squared residue and/or the bicluster size. This usually leads to a set of biclusters that do not fully cover the whole data and, as a consequence, shares a high overlap among them. This may generate redundant information on some portions of the dataset and lack of information on other portions. Also, some methods introduce noise into the dataset in order to promote a better coverage, but sometimes misleading the search. In this paper, a swarm-based approach, named SwarmBcluster, is created to effectively find biclusters without introducing noise and with the main objective of achieving maximum coverage. Experiments were performed considering two well-known datasets and a comparative analysis considering other approaches indicates that SwarmBcluster is capable of finding a set of biclusters with high coverage, while maintaining a high average volume and also obeying the coherence constraint imposed.

关键词： Search problems Particle swarm optimization Coherence Indexes Noise Data mining Ant colony optimization

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A k-hyperplane-based neural network for non-linear regression

A k-hyperplane-based neural network for non-linear regressio...

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IEEE International Conference on Cognitive Informatics

作者： Hongmei He Zengchang Qin Department of Engineering Mathematics University of Bristol Bristol UK Intelligent Computing and Machine Learning Lab School of Automation and Electrical Engineering Beihang University Beijing China

For the time series prediction problem, the relationship between the abstracted independent variables and the response variable is usually strong non-linear. We propose a neural network fusion model based on k-hyperplanes for non-linear regression. A k-hyperplane clustering algorithm is developed to split the data to several clusters. The experiments are done on an artificial time series, and the convergence of k-hyperplane clustering algorithm and neural network gradient training algorithm is examined. The dimension of inputs affect the clustering performance very much. Neural network fusion can get some compensation in performance. It is shown that the prediction performance of the model for the time series is very good. The model can be further exploited for many real applications.

关键词： Artificial neural networks Clustering algorithms Convergence Linear regression Mathematical model Training Prediction algorithms

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