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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...

引用

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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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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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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Delay-dependent adaptive reconfiguration control in the presence of input saturation and actuator faults

Delay-dependent adaptive reconfiguration control in the pres...

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作者： Guo, Yuying Jiang, Bin Shi, Peng College of Automation Engineering University of Aeronautics and Astronautics 29 YuDao Street Nanjing 210012 China School of Information Engineering Southwest University of Science and Technology Mianyang 621010 China Department of Computing and Mathematical Sciences University of Glamorgan Pontypridd CF37 1DL United Kingdom School of Engineering and Science Victoria University Melbourne VIC 8001 Australia

In this paper, an active fault tolerant control strategy is developed for a class of linear state-delayed systems with unknown actuator faults and input constraints. The desigh is a combibation between a direct adaptive control algorithm and multiple model switching, and the μ-modification is introduced in the model reference control architecture. The main features of the proposed control strategy are the reliability and simplicity in tracking against actuator faults. By Lyapunov-Krasovskii theory, the stability of overall system is guaranteed and the boundness of all signals is ensured. Numerical simulation results demonstrate the effectiveness of the proposed fault-tolerant control scheme.

关键词： Time delay

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Fault diagnosis for switched systems with time delay

Fault diagnosis for switched systems with time delay

引用

2010 8th IEEE International Conference on Control and automation, ICCA 2010

作者： Du, Dongsheng Jiang, Bin Shi, Peng College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210016 Jiangsu China Aeronautic Science Key Laboratory For Aeronautic Testing and Evaluation China School of Engineering and Science Victoria University Melbourne 8001 VIC Australia Department of Computing and Mathematical Sciences University of Glamorgan Pontypridd CF37 1DL United Kingdom

ISBN: (纸本)9781424451951

This paper investigates the fault diagnosis problem for switched system with time delay. Based on an adaptive fault diagnosis observer, an adaptive estimate algorithm is developed to estimate the fault. The method proposed in this article can not only estimate the fault information quickly and exactly, but also is suitable for two kinds of fault: constant fault and time-varying fault. Simulation results are given to illustrate the effectiveness of the presented method. © 2010 IEEE.

关键词： Failure analysis

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Dissipative analysis and control for discrete-time state-space symmetric systems

Dissipative analysis and control for discrete-time state-spa...

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Chinese Control Conference (CCC)

作者： Shuping Wang Guoshan Zhang Wanquan Liu School of Electrical Engineering and Automation Tianjin University Tianjin P.R.China The Department of Computing Curtin University of Technology WA Australia

In this paper, the problem of dissipativity analysis and output feedback control synthesis for discrete linear time-invariant systems with state-space symmetry is investigated. Firstly, an explicit expression of H ∞ norm for discrete-time symmetric system is given under the mixed H ∞ and positive real performance criterion, and this is a particular case of dissipative systems. Subsequently, we consider the control synthesis problems for such systems and obtain an explicit parameterized expression of the static output feedback controllers. Finally, two numerical examples are employed to show the effectiveness and reliableness of the proposed approach.

关键词： Symmetric matrices Output feedback Linear matrix inequalities Matrix decomposition Performance analysis Artificial intelligence

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A Framework for Nonlinear Output Regulation for Time-varying Uncertain Systems

A Framework for Nonlinear Output Regulation for Time-varying...

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2010 49th IEEE Conference on Decision and Control

作者： Xi Yang Jie Huang Applied Control and Computing Laboratory Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin N.T. Hong Kong

ISBN: (纸本)9781424477456

Recently, the global robust output regulation problem for nonlinear time-varying systems is considered in [14] under the assumption that the exosystem is exactly known. This paper further considers the case where the time-varying exosystem is unknown. Conditions are given under which the problem can be converted into an adaptive stabilization problem of an augmented time-varying nonlinear system. The problem is solved for a class of time-varying output feedback systems with a time-varying uncertain exosystem.

关键词： Nonlinear output regulation Time-varying systems Adaptive control

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A study of all common subsequences in kernel machine

A study of all common subsequences in kernel machine

引用

International Conference on Machine Learning and Cybernetics

作者： Guo, Zhi-Qun Wang, Hui Lin, Zhi-Wei Guo, Xiao-Lian College of Automation Harbin Engineering University Harbin 150001 Heilongjiang Province China School of Computing and Mathematics University of Ulster Northern Ireland BT37 OQB United Kingdom School of Mathematics and Computer Science Fujian Normal University China Department of Computer and Information Science Fujian University of Technology China

ISBN: (纸本)9781424465262

Counting all common subsequences (ACS) was proposed as a similarity measurement, which is conceptually different from the sequence kernel (SK) in that ACS only considers the occurrence of subsequences while SK uses the frequency of occurrences of su bseq uences. This difference evidently results in significant performance variety. ACS has been very competitive in the kNN classifier, however, its performance with kernel machine has been rarely investigated. This is due to the fact that whether ACS is suitable for a kernel classifier is not clear. To this end, this paper firstly proves that ACS is a valid kernel, with a delicate analysis. Then, ACS is further proved to be a good kernel with a comparison with SK in the support vector machine. © 2010 IEEE.

关键词： Nearest neighbor search

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