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共 3165 条记录，以下是2801-2810 订阅

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Condition Monitoring Using pattern recognition Techniques on data from Acoustic Emissions

Condition Monitoring Using Pattern Recognition Techniques on...

引用

international conference on machine learning and Applications (ICMLA)

作者： Siril Yella Naren Gupta Mark Dougherty Department of Computer Engineering Dalarna University Borlange Sweden School of Engineering Napier University Edinburgh UK

Condition monitoring applications deploying the usage of impact acoustic techniques are mostly done intuitively by skilled personnel. In this article, a pattern recognition approach is taken to automate such intuitive human skills for the development of more robust and reliable testing methods. the focus of this work is to use the approach as a part of a major research project in the rail inspection area, within the domain of intelligent transport systems. data from impact acoustic tests made on wooden beams have been used. the relation between condition of the wooden beams and respective sounds they make when struck, has been analyzed experimentally. Features were extracted from the acoustic emissions of wooden beams and were used for pattern classification. Features such as magnitude of the signal, natural logarithm of the magnitude and Mel-frequency cepstral coefficients, yielded good results. the extracted feature vectors were used as input to various pattern classifiers for further pattern recognition task. the effect of using classifiers like support vector machines and multi-layer perceptron has been tested and compared. Results obtained experimentally, demonstrate that support vector machines provide good detection rates for the classification of impact acoustic signals in the NDT domain

关键词： Condition monitoring pattern recognition Acoustic emission Acoustic testing Acoustic beams Cepstral analysis Feature extraction Support vector machines Support vector machine classification Acoustic applications

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Formal Concept Analysis for Digital Ecosystem

Formal Concept Analysis for Digital Ecosystem

引用

international conference on machine learning and Applications (ICMLA)

作者： Huaiguo Fu Telecommunications Software & Systems Group Waterford Institute of Technology Waterford Ireland

Formal concept analysis (FCA) is an effective tool for data analysis and knowledge discovery. Concept lattice, which is derived from mathematical order theory and lattice theory, is the core of FCA. Many research works of various areas show that concept lattices structures is an effective platform for data mining, machine learning, information retrieval, software engineer, etc. this paper offers a brief overview of FCA and proposes to apply FCA as a tool for analysis and visualization of data in digital ecosystem, and also discusses the applications of data mining for digital ecosystem

关键词： Ecosystems Lattices data mining data analysis Association rules machine learning Application software Information retrieval Information management Knowledge representation

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ROAD CENTRAL CONTOUR EXTRACTION FROM HIGH RESOLUTION SATELLITE IMAGE USING TENSOR VOTING FRAMEWORK

ROAD CENTRAL CONTOUR EXTRACTION FROM HIGH RESOLUTION SATELLI...

引用

5th international conference on machine learning and Cybernetics

作者： Zheng, Sheng Liu, Jian Shi, Wen-Zhong Zhu, Guang-Xi Huazhong Univ Sci & Technol Elect & Informat Engn Dept Wuhan 430074 Peoples R China China Three Gorges Univ Yichang 443002 Peoples R China Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence State Educ Commiss Key LabImage Proc & Intelligent Control Wuhan 430074 Peoples R China Hong Kong Polytech Univ Adv Res Ctr Spatial Informat Technol Dept Land Surveying & Geo Informat Hong Kong Peoples R China Huazhong Univ Sci & Technol Chinese Key lab opt & Elect Wuhan 430074 Peoples R China

ISBN: (纸本)1424400600

In this paper, a unique road contour extraction approach from high resolution satellite image is proposed, in which the road contour was extracted in two steps. Firstly, support vector machines (SVM) was employed merely to classify the image into two groups of categories: a road group and a non-road group. the identified road group images are the discrete and irregularly distributed sampled points, and they are an uncompleted data set for the road. Secondly, the road contour was extracted from the road group images using the tensor voting framework, since the tensor voting technique is superior to the traditional methods in extracting the geometrical structure from the uncompleted data set. the experimental results on the high resolution satellite image demonstrate that the proposed approach worked well with images comprised by both rural and urban area features.

关键词： tensor voting framework road central line extraction high-resolution satellite image

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Improving Dynamic learning Vector Quantization

Improving Dynamic Learning Vector Quantization

引用

international conference on pattern recognition

作者： C. De Stefano C. D'Elia A. Marcelli A. Scotto di Freca DAEIIMI Universita' di Cassino Italy DIIIE Universita' di Salerno Italy DIIIE-Universita di Salerno Italy

We introduce some improvements to the dynamic learning vector quantization algorithm proposed by us for tackling the two major problems of those networks, namely neuron over-splitting and their distribution in the feature space. We suggest to explicitly estimate the potential improvement on the recognition rate achievable by splitting neurons in those regions of the feature space in which two or more classes overlap. We also suggest to compute the neuron splitting frequency, and to combine these information for selecting the most promising neuron to split. Experimental results on both synthetic and real data extracted from UCI machine learning Repository show substantial improvements of the proposed algorithm with respect to the state of the art

关键词： Vector quantization Neurons Clustering algorithms Power capacitors Frequency pattern recognition data mining machine learning machine learning algorithms Supervised learning

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Robust Multiclass Ensemble Classifiers via Symmetric Functions

Robust Multiclass Ensemble Classifiers via Symmetric Functio...

引用

international conference on pattern recognition

作者： P. Lefaucheur R. Nock DSI Université Antilles-Guyane Martinique France

We introduce a generalization to the multiclass framework of a previous approach to boosting by constructing symmetric functions. this approach contrasts with the usual AdaBoost-type boosting algorithms using linear separators. Indeed, multiclass induction does not necessitate combination tricks such as those for linear separators, and it achieves some novel agnostic learning properties, as well as significant malicious noise tolerance. Experiments on a large testbed against AdaBoost and C4.5 display the efficiency of the approach proned

关键词： Robustness Boosting Particle separators machine learning algorithms Voting Testing Displays machine learning data mining Delta modulation

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Nonlinear Manifold Clustering By Dimensionality

Nonlinear Manifold Clustering By Dimensionality

引用

international conference on pattern recognition

作者： Wenbo Cao R. Haralick Computer Science Pattern Recognition Laboratory The Graduate Center City University of New York New York NY USA

ISBN: (纸本)0769525210

Because of variable dependence, high dimensional data typically have much lower intrinsic dimensionality than the number of its variables. Hence high dimensional data can be expected to lie in (nonlinear) lower dimensional manifold. In this paper, we describe a nonlinear manifold clustering algorithm. By connecting data vectors with their neighbors in feature space, we construct a neighborhood graph from given set data vectors. Furthermore, geometrical invariance, namely dimensionality, are extracted from the neighborhood of vectors, and used to facilitate the clustering procedure. In addition, we discuss a latent model for data cluster descriptions and an EM algorithm to find such descriptions. Preliminary experiments illustrate that this new algorithm can be used to explore the nonlinear structure of data

关键词： Clustering algorithms machine learning algorithms pattern recognition Partitioning algorithms Vectors Computer science Joining processes data mining machine learning Computer vision

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5th international conference on Field and Service Robotics, FSR 2005

5th International Conference on Field and Service Robotics, ...

引用

5th international conference on Field and Service Robotics, FSR 2005

ISBN: (纸本)3540334521

the proceedings contain 50 papers. the special focus in this conference is on Field and Service Robotics. the topics include: A navigation system for automated loaders in underground mines;Outdoor simultaneous localisation and mapping using RatSLAM;Implementation issues and experimental evaluation of D-SLAM;Scan-SLAM: Combining EKF-SLAM and scan correlation;a non-rigid approach to scan alignment and change detection using range sensor data;an efficient extension of elevation maps for outdoor terrain mapping;online reconstruction of vehicles in a car park;wavelet occupancy grids: A method for compact map building;the berkeley lower extremity exoskeleton;further results with localization and mapping using range from radio;experiments with robots and sensor networks for mapping and navigation;applying a new model for machine perception and reasoning in unstructured environments;constrained motion planning in discrete state spaces;vision-based grasping points determination by multifingered hands;embodied social interaction for service robots in hallway environments;intentional motion online learning and prediction;design and locomotion of a semi-passive mobile platform;wheel control based on body configuration for step-climbing vehicle;Ball-shaped robots: An historical overview and recent developments at TKK;autonomous helicopter tracking and localization using a self-surveying camera array;development of a water-hydraulic self-propelled robotic drill for underground mining;A wearable GUI for field robots;design and implementation of machine control systems with modern software development tools;long-term activities for autonomous mobile robot - Autonomous insertion of a plug into real electric outlet by a mobile manipulator;synthesized scene recollection for robot teleoperation;bimodal active stereo vision.

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Scalable Representative Instance Selection and Ranking

Scalable Representative Instance Selection and Ranking

引用

international conference on pattern recognition

作者： Xingquan Zhu Xindong Wu Department of Computer Science University of Vermont VT USA

ISBN: (纸本)0769525210

Finding a small set of representative instances for large datasets can bring various benefits to data mining practitioners so they can (1) build a learner superior to the one constructed from the whole massive data; and (2) avoid working on the whole original dataset all the time. We propose in this paper a scalable representative instance selection and ranking (SRISTAR pronounced 3STAR) mechanism, which carries two unique features: (1) it provides a representative instance ranking list, so that users can always select instances from the top to the bottom, based on the number of examples they prefer; and (2) it investigates the behaviors of the underlying examples for instance selection, and the selection procedure tries to optimize the expected future error. Given a dataset, we first cluster instances into small data cells, each of which consists of instances with similar behaviors. then we progressively evaluate data cells and their combinations, and order them into a list such that the learners built from the top cells are more accurate

关键词： data mining Sampling methods data privacy Scalability Computer science machine learning algorithms Computational complexity machine learning Bagging Boosting

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Boosting in Random Subspaces for Face recognition

Boosting in Random Subspaces for Face Recognition

引用

international conference on pattern recognition

作者： Yong Gao Yangsheng Wang Institute of Automation Chinese Academy and Sciences Beijing China

ISBN: (纸本)0769525210

Boosting is an excellent machine learning algorithm. In this paper, we propose a novel boosting method - boosting in random subspaces. Instead of boosting in original feature space, whose dimensionality is usually very high, multiple feature subspaces with lower dimensionality are randomly generated, and boosting is carried out in each random subspace. then the trained classifiers are further combined with simple fusion method. Compared with boosting in original feature space, there are two advantages. the first is that the computation complexity of training is reduced, which is obvious. the second is that fusion further improves accuracy, which is verified by our extensive experiments on FERET database

关键词： Boosting Face recognition Spatial databases Automation machine learning algorithms Fusion power generation Bagging Kernel data mining pattern recognition

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15th international conference on Computing-Title

15th International Conference on Computing-Title

引用

international conference on Computing (CIC)

the following topics are dealt with: knowledge management, reasoning, neural networks, and evolutionary programming; image processing and pattern recognition; machine learning and data mining; natural language processing and speech recognition; information retrieval; multi-agent systems and ontologies; bioinformatics and medical applications; intelligent tutoring systems; formal languages and automations; software engineering and data warehousing; cryptography and security; computer networks and distributed systems; mobile computing; and control

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