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检索条件"任意字段=International Conference on Soft Computing and Pattern Recognition"

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Method of Laplacian Eigenmap-Based pattern recognition and Diagnosis for Incipient Fault of Pipelines

Method of Laplacian Eigenmap-Based Pattern Recognition and D...

引用

international conference on Internet computing and Information Services (ICICIS)

作者： Zhigang Lou Hongzhao Liu The Faculty of Mechanical and Precision Instrument Engineering Xi'an University of Technology Xi'an Shaanxi China

There is a considerable noise in the measured signal of pressure and flow of a running pipeline due to friction drag and medium diffusion, which poses an obstacle to the quick detection and precise classification of pipeline leakage, especially to the acquiring of weak incipient fault. This paper offers an incipient fault detection method based on nonlinear manifold learning algorithm, which treats the negative pressure wave signal as transient signal and reduces noise of original signal by using multi-scale wavelet transform. The method also learns original fault signal and extracts the intrinsic manifold features of data by using a nonlinear dimensionality reduction algorithm based on Laplacian Eigenmaps. With this method, the identification efficiency of optimal fault characteristics is noticeably improved, and the advantage of this method has been proved by simulation experiments.

关键词： Wavelet transforms Pipelines Laplace equations Feature extraction Manifolds Noise

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Solving language recognition problems

Solving language recognition problems

引用

2011 IEEE international conference on Intelligent computing and Intelligent Systems(ICIS 2011)

作者： Luong Viet Nguyen Trinh Nhat Tien Ho Van Canh University of Engineering and Technology VNU 144 Xuan ThuyCau GiayHanoiVietnam Dept.of Professional Technique I Ministry of Public Security 58A Tran Nhan TongHanoiVietnam

＜正＞We explain how to apply statistical techniques to solve several language recognition problems arising in many other *** model a language as a finite Markov process. This model has been specially adapted to suit ... 详细信息

关键词： Language recognition pattern recognition

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Hand-Dorsa Vein recognition Based on Coded and Weighted Partition Local Binary patterns

Hand-Dorsa Vein Recognition Based on Coded and Weighted Part...

引用

international conference on Hand-Based Biometrics (ICHB)

作者： Yiding Wang Kefeng Li Lik-Kwan Shark Martin R. Varley College of Information Engineering North China University of Technology Beijing China School of Computing Engineering and Physical Sciences University of Central Lancashire Preston UK

In this paper, a new feature descriptor is presented and proposed for personal verification based on near infrared images of hand-dorsa veins. This new feature descriptor is a modification of the previously proposed partition local binary patterns (PLBP) by adding feature weighting and error correction coding (ECC). While addition of feature weighting aims to reduce the influence of insignificant local binary patterns, addition of ECC aims to increase the distances between feature classes by utilizing the systematic redundancy that has been widely used to achieve reliable data transmission in noisy channels. Using a large database with more than two thousand hand-dorsa vein images, the resulting new feature descriptor, named Coded and Weighted PLBP (WCPLBP), is shown to be more effective than the original PLBP without feature weighting and ECC, and offers a better performance in recognition of hand-dorsa vein images with a correct recognition rate reaching approximately 99% using a simple nearest neighbor classifier.

关键词： Veins Convolutional codes Image recognition Error correction codes pattern recognition Histograms Biometrics

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New possibilistic noise rejection clustering algorithm with simulated annealing

New possibilistic noise rejection clustering algorithm with ...

引用

conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： M. H. Fazel Zarandi M. Avazbeigi M. H. Anssari Industrial Engineering Department Amirkabir University of Technology슠 Tehran Iran Soft Computing and Intelligent Data Analysis European Centre for Soft Computing Micros Mieres Spain Industrial Management Department University of Tehran Tehran Iran

Fuzzy C-Means has been used as a popular fuzzy clustering method due to its simplicity and high speed in clustering large data sets. However, C-Means has two shortcomings: dependency on the initial state and convergence to local optima. In this paper a new algorithm based on simulated annealing and possibilistic noise rejection clustering is proposed to reduce the problem of converging to local minima and dependency on initial states. The comparison of the proposed algorithms and some other algorithms in the literature shows that the algorithms outperforms other algorithms in terms of optimization objective function and is capable of doing clustering in noisy environments more efficiently.

关键词： Clustering algorithms Simulated annealing Prototypes Algorithm design and analysis Noise pattern recognition Iterative methods

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Class-Incremental Kernel Discriminative Common Vectors

Class-Incremental Kernel Discriminative Common Vectors

引用

international conference on Computational Intelligence and Security

作者： Yunhui He Department of Communications Engineering Nanjing University of Information Science and Technology Nanjing China

In this paper, we propose an efficient algorithm for implementing the class-incremental kernel discriminative common vectors method via kernel method. One nonlinear discriminative common vector is computed for each class by projecting a sample in each class onto the orthonormal nonlinear discriminative vector. The orthogonalization procedure is performed twice in feature space which is only involved computing a kernel matrix and performing Cholesky decomposition on the kernel matrix. Thus, the real-time performance of classification is guaranteed. The theoretical justification is presented in this paper.

关键词： Kernel Matrix decomposition Vectors pattern recognition Algorithm design and analysis Symmetric matrices Computational complexity

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Sampling step importance in hierarchical semantic segmentation of microscopic images

Sampling step importance in hierarchical semantic segmentati...

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international conference on System Theory, Control, and computing (ICSTCC)

作者： Cristian Smochina Vasile Manta Walter Kropatsch Department of Computer Science and Engineering Gheorghe Asachi Technical University of Iasi Romania Pattern Recognition and Image Processing Group Institute of Computer Graphics and Algorithms Vienna University of Technology Austria

The objective of semantic segmentation in microscopic images is to extract the cellular, nuclear or tissue components. This problem is challenging due to the large variations of these components features (size, shape, orientation or texture). In this paper we improve the technique presented in [17] used to identify the epithelial nuclei (crypt) against interstitial nuclei in microscopic images taken from colon tissues. In the proposed enhanced approach, the crypt inner boundary is detected using the closing morphological pyramid instead of morphological hierarchy. The outer crypt border is determined by the epithelial nuclei, overlapped by the maximal isoline of the inner boundary. The use of sampling in building the pyramid offers computational efficiency, reduces the amount of used memory, increase the robustness and preserve the quality results. An analysis of the two approaches is performed considering the number of pixels processed to create each level. Also the relation between the levels of the hierarchical structures is established.

关键词： Cryptography Image segmentation Image reconstruction Image resolution Microscopy Buildings Shape

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Automated Inspection Using X-Ray Imaging

Automated Inspection Using X-Ray Imaging

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Wenfei Chen Zuohua Miao Delie Ming Computer School Wuhan University Wuhan China School of Resource and Environmental Engine Wuhan University of Science and Technology Wuhan China Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China

With increasing concern on environmental contamination due to pipeline leak, the electronics industry is coming under increasing pressure to develop and apply automated inspection techniques for the inspection of welding line of steel tubes and structural casting. Automatic X-ray inspection systems are taking the high cost out of production inspection for casting manufacturers who previously relied on manual inspection methods while simultaneously wiping out the drudgery and potential for human error common to manual inspection methods in processing and manufacturing applications. Based on the analysis of basics of X-ray Imaging Principle, the interactive process of automatic X-ray inspection was discussed and a new defect inspection method using top-hat operator was put forward. Lastly, this method is applied for many samples of X-ray images, and proved to be effective.

关键词： X-ray imaging Inspection Casting Welding Materials Attenuation

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A novel facial feature extraction method based on ICM network for affective recognition

A novel facial feature extraction method based on ICM networ...

引用

international Joint conference on Neural Networks (IJCNN)

作者： F. Mokhayeri M.-R. Akbarzadeh-T. Mashhad Branch Islamic Azad University Mashhad Iran Islamic Azad University Mashhad Branch Mashhad Iran Center for Applied Research on Soft Computing and Intelligent Systems Departments of Electrical Engineering and Computer Engineering Ferdowsi University of Mashhad Iran

This paper presents a facial expression recognition approach to recognize the affective states. Feature extraction is a vital step in the recognition of facial expressions. In this work, a novel facial feature extraction method based on Intersecting Cortical Model (ICM) is proposed. The ICM network which is a simplified model of Pulse-Coupled Neural Network (PCNN) model has great potential to perform pixel grouping. In the proposed method the normalized face image is segmented into two regions including mouth, eyes using fuzzy c-means clustering (FCM). Segmented face images are imported into an ICM network with 300 iteration number and pulse image produced by the ICM network is chosen as the face code, then the support vector machine (SVM) is trained for discrimination of different expressions to distinguish the different affective states. In order to evaluate the performance of the proposed algorithm, the face image dataset is constructed and the proposed algorithm is used to classify seven basic expressions including happiness, sadness, fear, anger, surprise and hate The experimental results confirm that ICM network has great potential for facial feature extraction and the proposed method for human affective recognition is promising. Fast feature extraction is the most advantage of this method which can be useful for real world application.

关键词： Feature extraction Face Support vector machines Face recognition Image segmentation Facial features Databases

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Artificial Intelligence in River Quality Assessment

Artificial Intelligence in River Quality Assessment

引用

international conference on Information Management, Innovation Management and Industrial Engineering, ICIII

作者： Raza Hasan Akshyadeep Raghav Salman Mahmood M. Asim Hasan Computer Engineering Department S.S.U.E.T Karachi Pakistan C.I.E.S. School of Computing Staffordshire University Stafford UK

Importance of biological monitoring in river quality assessment was well understood and various systems were developed in 20th century. But most of them were based on scoring system and later use of Artificial Intelligence (AI) in River Quality Assessment was started. AI has wide scope in river quality assessment problem and few systems were developed to model human ways of reasoning and finding the river water quality from the ecological data. The paper discusses the approaches of AI which can model human way of solving the problem, namely Neural Networks and Expert System. In this paper system based on pattern recognition (SOM, MIR-Max, RPDS) and Bayesian belief network (RPBBN) were described. RPDS and RPBBN were developed at CEIS, Stafford shire University and have shown very good results as compared to previous systems. All the system developed so far are not able to explore the relationship between chemical, environmental and biological data set of ecological data of rivers. The scope for this in AI is also proposed for development of 3D data analysis system.

关键词： Rivers Biology Chemicals Artificial intelligence Educational institutions pattern recognition Monitoring

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Initial pattern Library Algorithm Based upon Variance and Mean Sorting for Machine Learning

Initial Pattern Library Algorithm Based upon Variance and Me...

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2011 IEEE international conference on Intelligent computing and Intelligent Systems(ICIS 2011)

作者： Hongsong Li School of Information and Communications Guilin University of Electronic Technology Guilin,China Jie Yuan Department of Computer Science Oklahoma State University Stillwater,USA

A new initial pattern library algorithm based upon variance and mean sorting for machine learning is *** first stage sorts the training vector set based on variance of pattern *** divide the training vector set equally into a number of segments, ***, sorts each subset by vector ***, selects a certain number of pattern vectors with an interval from the sorted subsets to form the initial pattern *** new initial pattern library algorithm has been tested by self-organizing maps (SOM ) *** results in image coding showed that this new initial pattern library is better than the commonlyused random sampling initial pattern library and random data setting initial pattern library.

关键词： self-organizing maps formatting machine learning image coding pattern recognition

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