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Application of conformal predictors to tea classification based on electronic nose

Application of conformal predictors to tea classification ba...

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作者： Nouretdinov, Ilia Li, Guang Gammerman, Alexander Luo, Zhiyuan Computer Learning Research Centre Royal Holloway University of London Egham Hill Egham Surrey TW20 0EX United Kingdom Department of Control Science and Engineering Zhejiang University 38 ZheDa Road HangZhou 310027 Zhejiang China

ISBN: (纸本)364216238X

In this paper, we present an investigation into the performance of conformal predictors for discriminating the aroma of different types of tea using an electronic nose system based on gas sensors. We propose a new non-conformity measure for the implementation of conformal predictors based on Support Vector Machine for multi-class classification problems. The experimental results have shown the good performance of the implemented conformal predictors. © 2010 IFIP.

关键词： Electronic nose

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Spectral representation of DNA sequences and its application

Spectral representation of DNA sequences and its application

引用

International Conference on Bio-Inspired Computing: Theories and Applications, BICTA

作者： Zhujin Zhang Lin Liu Jiuyong Li Zheng Zhang School of Computer and Information Science University of South Australia Adelaide SA Australia Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan China

Randić et al. proposed a famous spectral graphical representation of DNA sequences, and claimed that it avoids loss of information. In this paper we build two mathematical models for this graphical representation and prove that the claim is correct, and that it also avoids degeneracy. Moreover, we propose a new method to do similarity analysis of DNA sequences based on the spectral representation. The method adopts M value to characterize a graphical representation and uses 24-component vector as descriptor. The approach is illustrated on the complete coding sequence of beta-globin genes from 7 different species.

关键词： Humans Rabbits Mice Variable speed drives Silicon Sun Bovine

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Use of adaptive filtering for noise reduction in communications systems

Use of adaptive filtering for noise reduction in communicati...

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International Conference on Applied Electronics, AE

作者： Radek Martinek Jan Zídek Department of Measurement and Control Faculty of Electrical Engineering and Computer Science VšB Technical University of Ostrava Ostrava Czech Republic

This article deals with noise reduction in modern communication systems. Primarily it focuses on cases where conventional filtering techniques based on linear filtering fail. It is modern based approach to adaptive filtering. The article presents a comprehensive hardware and software solutions to the adaptive system using the two main leaders of adaptive LMS (least mean square) and RLS (recursive least squares) algorithms. Adaptive system for noise reduction in radio (mobile) communication is designed in Matlab. This system has its practical use in communication especially in noisy environments (transport, factories, sporting events, etc.).

关键词： Adaptive filters Finite impulse response filter IIR filters Noise Filtration Least squares approximation

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Certifying software robustness using program slicing

Certifying software robustness using program slicing

引用

International Conference on Software Maintenance (ICSM)

作者： Mohammad Abdallah Malcolm Munro Keith Gallagher ãDepartment of Telecommunications and Control Polytechnic School of Engineering Durham University Durham UK Department of Computer Science Florida Institute of Technology FL USA

Robustness of a program is the degree of system correctness of all parts. Measuring robustness is a goal for many researchers. In this paper, program slicing is used to build a robustness hierarchy, where this hierarc... 详细信息

关键词： Software Robustness Testing Fault tolerance Fault tolerant systems Conferences computers

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Fuzzy C-means cluster segmentation algorithm based on hybridized particle swarm optimization

Fuzzy C-means cluster segmentation algorithm based on hybrid...

引用

International Conference on Bio-Inspired Computing: Theories and Applications, BICTA

作者： Yan-ling Li Yi Shen College of Computer and Information Technology Xinyang Normal University Xinyang China Institute of System Engineering Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan China

Fuzzy C-Means(FCM) algorithm is one of the most popular methods for image segmentation, but it is in essence a technology of searching local optimal solution. The algorithm's initial clustering centers are the stochastic selection which causes it to depend on the selection of the initial cluster centers excessively. It always converges at the local optimum and is sensitive to noise. In order to overcome those defects, the fuzzy C-means cluster segmentation algorithm based on hybridized particle swarm optimization is proposed in this paper. Firstly, the hybridized particle swarm algorithm is used to get the initial cluster centers. Then, the images are segmented using standard FCM algorithm. Experimental results show that the proposed algorithm used for image segmentation can segment images more effectively and can provide more robust segmentation results.

关键词： Magnetic resonance imaging Integrated circuits Legged locomotion Image segmentation Educational institutions

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The Minimum Principle for Hybrid Systems with Partitioned State Space and Unspecified Discrete State Sequence

The Minimum Principle for Hybrid Systems with Partitioned St...

引用

2010 49th IEEE Conference on Decision and control

作者： Benjamin Passenberg Peter E. Caines Marion Sobotka Olaf Stursberg Martin Buss Institute of Automatic Control Department of Electrical Engineering and Information Technology Technische Universitat Munchen Munchen Germany Department of Electrical and Computer Engineering and the Center for Intelligent Machines McGill University Montreal Canada Institute of Control and System Theory Department of Electrical Engineering and Computer Science University of Kassel Kassel Germany

ISBN: (纸本)9781424477456

The hybrid minimum principle (HMP) gives necessary conditions to be satisfied for optimal solutions of a hybrid dynamical system. In particular, the HMP accounts for autonomous switching between discrete states that occurs whenever the trajectory hits switching manifolds. In this paper, the existing HMP is extended for hybrid systems with partitioned state space to provide necessary conditions for optimal trajectories that pass through an intersection of switching manifolds. This extension is especially useful for the numerical solution of hybrid optimal control problems as it allows for algorithms with significant reduction of computational complexity. Algorithms based on previous versions of the HMP solve separate optimal control problems for each possible sequence of discrete states. The extension enables us to consider the optimal sequence as subject of optimal control that is varied and finally determined during a single optimization run. A first numerical result illustrates the effectiveness of an algorithm based on the extended HMP.

关键词： state space minimum principle hybrid systems Manifolds Optimal control DYNAMICAL SYSTEMS discrete element

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A modified fuzzy c-means algorithm for breast tissue density segmentation in mammograms

A modified fuzzy c-means algorithm for breast tissue density...

引用

IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Zhili Chen Reyer Zwiggelaar Department of Computer Science Aberystwyth University Aberystwyth UK Faculty of Information and Control Engineering Shenyang Jianzhu University Shenyang China

The fuzzy c-means (FCM) algorithm has been applied in a variety of medical image segmentation applications. The conventional FCM algorithm uses the greylevel information at a single pixel as the feature space and this contains no spatial contextual information, which makes it very sensitive to noise and intensity inhomogeneities. Recently, some modified FCM algorithms with spatial constraints have been published. However, these have individual disadvantages and are not robust enough with different types of noise. In this paper, we propose a modified FCM algorithm incorporating local spatial and intensity information based on an adaptive local window filter whose weighting coefficients differentiate the neighbouring pixels within the local window. Fast clustering is afterwards performed on the intensity histogram of the filtered image. To demonstrate the robustness and insensitivity to noise of the proposed algorithm, it is extensively tested using synthetic images corrupted by a variety of noise. The experimental results are quantitatively evaluated and compared. This algorithm is then applied to mammographic images for breast tissue density segmentation. The segmentation results indicate its effectiveness to the presence of intensity inhomogeneities in mammograms from different density categories.

关键词： Image segmentation Variable speed drives Europe Nonhomogeneous media Robustness computers Pixel

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An Algorithm for Discrete State Sequence and Trajectory Optimization for Hybrid Systems with Partitioned State Space

An Algorithm for Discrete State Sequence and Trajectory Opti...

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2010 49th IEEE Conference on Decision and control

作者： Benjamin Passenberg Marion Sobotka Olaf Stursberg Martin Buss Peter E. Caines Institute of Automatic Control Department of Electrical Engineering and Information Technology Technische Universitat Munchen Munchen Germany Institute of Control and System Theory Department of Electrical Engineering and Computer Science University of Kassel Kassel Germany Department of Electrical and Computer Engineering and the Center for Intelligent Machines McGill University Montreal Canada

ISBN: (纸本)9781424477456

An algorithm for hybrid optimal control is proposed that varies the discrete state sequence based on gradient information during the search for an optimal trajectory. The algorithm is developed for hybrid systems with partitioned state space. It uses a version of the hybrid minimum principle that allows optimal trajectories to pass through intersections of switching manifolds, which enables the algorithm to vary the sequence. Consequently, the combinatorial complexity of former algorithms can be avoided, since not each possible sequence has to be investigated separately anymore. The convergence of the algorithm is proven and a numerical example demonstrates the efficiency of the algorithm.

关键词： Switches Manifolds Trajectory Complexity theory Partitioning algorithms Convergence Aerospace electronics

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A Fourier series-based adaptive sliding-mode controller for nonlinear pneumatic servo systems

A Fourier series-based adaptive sliding-mode controller for ...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Lian-Wang Lee I-Hsum Li Institute of Automation and Control National Taiwan University of Science and Technology Taiwan Department of Computer Science and Information Engineering Lee Ming Institute of Technology Taiwan

The behavior of nonlinearity and time-varying cause the pneumatic actuator systems are difficult to be controlled. This paper proposes a Fourier series-based adaptive sliding-mode controller for nonlinear pneumatic servo systems. The Fourier series-based functional approximation technique can approximate an unknown function, thus bypassing the model-based prerequisite. The learning laws for the coefficients of the Fourier-series functions are derived from a Lyapunov function to guarantee the system stability. Consequently, practical experiments on a rodless pneumatic servo system are successfully implemented with different path tracking profiles, which validates the proposed method.

关键词： Artificial neural networks Conferences Robots Fluid dynamics Trajectory

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Power quality disturbances classification by ensemble and hybrid neural networks

Power quality disturbances classification by ensemble and hy...

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2010 International Conference on Power System Technology: Technological Innovations Making Power Grid Smarter, POWERCON2010

作者： Huang, N.T. Zhang, Y.J. Xu, D.G. Liu, X.S. Qi, J.J. Department of Electrical Engineering Harbin Institute of Technology Harbin Heilongjiang 150001 China College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin 132022 China Department of Computer Science and Technology Harbin Institute of Technology Harbin Heilongjiang 150001 China Hangzhou Electric Power Bureau State Grid Corporation of China Hangzhou Zhejiang 310009 China

ISBN: (纸本)9781424459407

A novel ensemble neural network structure is presented for automatic classification of power quality disturbances. Power quality (PQ) disturbances analysis is the focus of power quality control. The characteristics of PQ disturbances include short duration, variety of types and so on. Power quality disturbances classification is the foundation of power quality control automation. Different types of Neural network, such as BP neural networks, RBF neural networks and probabilistic neural network etc, is already applied in the area of PQ disturbances classification and recognition. The researches about the neural network for PQ disturbances recognition are mainly focused on the optimizing for the signal type of neural network. But the accuracy rate of the classification is still needed to be improved. Ensemble and hybrid algorithms research is currently flourishing in pattern classification machine learning and decision sciences. Compare to traditional NN, the ensemble and hybrid NN classifier achieves higher classification rate. In this paper, a novel PQ classification system using S-transform and ensemble and hybrid NN is designed. There are 2 stages in the novel system. Firstly, the PQ disturbances signals are transformed by S-transform and the subset of features extracted from the result of S-transform is used as the input vector of the ensemble and hybrid NN. Secondly in the pattern classification process, BP network and RBF neural network are utilized as two classification agents. Through choosing different parameters and different samples, every agent includes a group of neural networks. The classification results, generated by different agents, are fuzzified into fuzzy numbers. The centroid of all fuzzy numbers is compared with the threshold. Finally we obtain the classification results. In the simulation, 6 types of disturbances signals which are simulated by Matlab 7.0 use for test the new classification system. Simulation result shows that when the new syste

关键词： Power quality

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