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international conference on Recent Advances in Space Technologies (RAST)

作者： T. Colak R. Qahwaji Department of Electronic Imaging and Media Communications Chesham B1.14 Richmond Road Bradford BD7 1DP England U.K. t.colak@bradford.ac.uk Department of Electronic Imaging and Media Communications Bradford England U.K

Solar imaging is currently an active area of research. A fast hybrid system for the automated detection and classification of sunspot groups on MDI Continuum images using active regions data extracted from MDI Magnetogram images is presented in this paper. The system has three major stages: sunspots detection from MDI Continuum images, sunspots grouping and Mcintosh classification of sunspot groups. Image processing and machine learning are integrated in all these stages.

关键词： Earth Weather forecasting Sun data mining Image processing Space exploration Storms Space missions Testing machine learning

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Soft Computing Signal Processing for Health Monitoring of Tie-Bar of Rotor Head Structure

Soft Computing Signal Processing for Health Monitoring of Ti...

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Intelligent Sensors, Sensor Networks and Information Processing conference (ISSNIP)

作者： P. J. Escamilla-Ambrosio N. Lieven Department of AeroSpace Engineering University of Bristol Bristol UK

The need for robust health monitoring and prognostics of structural components in remote or difficult-to-access locations, e.g. helicopter rotor-head structure, is driving the advancement of wireless intelligent sensor devices (WISD). Damage detection techniques, combined with advanced signal processing, are the core components of a structural health monitoring (SHM) system. In this context, feature extraction is an essential component of a SHM system that converts raw sensor data into useful information about the structure health condition. The level of signal processing that can be performed in a WISD depends on the capability of the processing element in terms of speed, memory and energy consumption. But the real bottleneck for energy efficiency is the fact that communications dominate the WISD energy consumption. Therefore, running intelligent local data interrogation algorithms on-board the WISD is a mechanism through which considerable battery power can be preserved. In that sense, in this paper a soft histogram feature extraction algorithm is developed to extract damage-sensitive information from measured response data of tie-bar component of the main rotor hub of a Lynx helicopter. In addition, a method for pattern recognition and critical degradation detection of tie-bar is proposed based on the extracted features and a combination of statistical process control and fuzzy sets theory. Results show the applicability of the proposed approaches.

关键词： Signal processing Remote monitoring Feature extraction Signal processing algorithms Helicopters Intelligent sensors Energy consumption data mining Robustness Wireless sensor networks

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Call for Papers The Third international conference on Rough Sets and Knowledge Technology （RSKT2008）

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计算机研究与发展 2007年第10期44卷 1740-1740页

Since the introduction of rough sets in 1982 by Professor Zdzisaw Pawlak, we have witnessed great advances in both theory and applications. Rough set theory is closely related to knowledge technology in a variety of forms such as knowledge discovery, approximate reasoning, intelligent and multiagent system design, knowledge intensive computations. The cutting-edge knowledge technologies have great impact on learning, pattern recognition, machine intelligence and automation of acquisition, transformation, communication, exploration and exploitation of knowledge. A principal thrust of such technologies is the utilization of methodologies that facilitate knowledge processing. To present the state-of-the-art scientific results, encourage academic and industrial interaction, and promote collaborative research in rough sets and knowledge technology worldwide, the 3rd international conference on Rough Sets and Knowledge Technology will be held in Chengdu, China, May 17～19, 2008. It will provide a forum for researchers to discuss new results and exchange ideas, following the successful RSKT'06 (Chongqing, China) and JRS'07 (RSKT'07 together with RSFDGrC'07) (Toronto, Canada).

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Call for Papers The Third international conference on Rough Sets and Knowledge Technology (RSKT2008)

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计算机研究与发展 2007年第11期44卷 1872-1872页

关键词： Call for Papers The Third international conference on Rough Sets and Knowledge Technology RSKT2008

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Using association rule mining for the QSAR problem

Using association rule mining for the QSAR problem

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3rd IEEE international conference on Intelligent Systems

作者： Dumitriu, L. Craciun, M-V. Segal, C. Cocu, A. Georgescu, L. P. Dunarea Jos Univ Dept Comp Sci Galati 800201 Romania Dunarea Jos Univ Chem Dept Galati 800201 Romania

ISBN: (纸本)9781424401956

There are several approaches in trying to solve the Quantitative Structure-Activity (QSAR) problem. These approaches are based either on statistical methods or on predictive data mining. Among the statistical methods, one should consider regression analysis, pattern recognition (such as cluster analysis, factor analysis and principal components analysis) or partial least squares. Predictive data mining techniques use either neural networks, or genetic programming, or neuro-fuzzy knowledge. These approaches have a low explanatory capability or non at all. This paper attempts to establish a new approach in solving QSAR problems using descriptive data mining. This way, the relationship between the chemical properties and the activity of a substance would be comprehensibly modeled.

关键词： Quantitative Structure-Activity relationship data mining association rules classification

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Naive Bayes classifier: True and estimated errors for 2-class, 2-features case

Naive Bayes classifier: True and estimated errors for 2-clas...

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3rd IEEE international conference on Intelligent Systems

作者： Hoare, Zoe Univ Wales Sch Informat Bangor LL57 1UT Gwynedd Wales

ISBN: (纸本)9781424401956

The low error rate of Naive Bayes (NB) classifier has been described as surprising. It is known that class conditional independence of the features is sufficient but not a necessary condition for optimality of NB. This study is about the difference between the estimated error and the true error of NB taking into account feature dependencies. Analytical results are derived for two binary features. Illustration examples are also provided.

关键词： machine learning pattern recognition Naive Bayes error rates

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Recent trends in data mining (DM): Document clustering of DM publications

Recent trends in data mining (DM): Document clustering of DM...

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3rd international conference on Service Systems and Service Management

作者： Peng, Yi Kou, Gang Chen, Zhengxin Shi, Yong Univ Nebraska Peter Kiewit Inst Informat Sci Technol & Engn Omaha NE 68182 USA Grad Univ Chinese Acad Sci Chinese Acad Sci Res Ctr Data Technol & Knowledge Beijing 100080 Peoples R China

ISBN: (纸本)1424404509

data mining (DM) brings knowledge and theories from several fields including databases, machine learning, optimization, statistics, and data visualization and has been applied to various real-life applications. A large amount of data mining articles have been published. The goal of this study is to establish an overview of the past and current data mining research activities from the title and abstract more than 1400 textual documents collected from premier data mining journals and conference proceedings. Specifically, this study applied document clustering approaches to determine which subjects had been studied over the last several years, which subjects are currently popular, and describe the longitudinal changes of data mining publications.

关键词： document clustering data mining field content analysis categorization

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Integrative data mining for assessing international conflict events

Integrative data mining for assessing international conflict...

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3rd IEEE international conference on Intelligent Systems

作者： Azuaje, Francisco Wang, Haiying Zheng, Huiru Liu, Chang Wang, Hui Rios-Morales, Ruth Univ Ulster Comp Sci Res Inst Jordanstown BT37 0QB North Ireland Univ Ulster Sch Comp & Math Jordanstown BT37 0QB North Ireland Trinity Coll Dublin Sch Business Studies Dublin Ireland

ISBN: (纸本)9781424401956

State failure has been traditionally defined as the collapse of national authority, which may be reflected in disasters such as wars and disruptive regime transitions. The availability of comprehensive datasets and the limitations exhibited by previous forecasting analyses led us to integrate different predictive resources and models through statistical analysis and machine learning. Here we demonstrate the predictive ability of unsupervised and supervised learning approaches to detecting meaningful relationships between country cases, encoded by several socio-economic indicators, and the emergence of violent conflicts. Two clustering-based analyses (Kohonen maps and a network-based approach) provided the basis for exploratory analyses that confirmed hypotheses about the relevance of the data and the differences between state failure types. We also illustrate the potential of a novel network-based clustering approach for sub-class discovery in the area of political instability analysis. Furthermore, we show significant relationships between the emergence of violent conflicts and a dataset of quantitative indicators of good governance, which allows the design of effective supervised and unsupervised classifiers. This study contributes to the development of intelligent data analysis techniques for supporting hypothesis generation and testing in international conflict analyses.

关键词： data mining machine learning clustering-based analysis state failure data analysis good governance

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The angle difference based state transition modeling for the signal pattern classification of the sensor array

The angle difference based state transition modeling for the...

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3rd international conference on Cybernetics and Information Technologies, Systems and Applications/4th international conference on Computing, Communications and Control Technologies

作者： Lee, SeungJae Kim, Changhwa Kim, Sangkyung Kim, Aram Kangnung Natl Univ Dept Comp Engn Gangnung Si 210702 Gangwon Do South Korea

ISBN: (纸本)9789806560840

Sensors have been used with various purposes in the human life. A sensor which can be functioned as a part of a signal process unit or a mechanical machine is defined as "a part of a measuring instrument which detects and responds immediately changes of a environment". As a sensor just reports the voltage level respect to detected physical or chemical quantity, it is needed to convert properly into meaning data. In most of cases, a sensor array, which consists of various kinds of sensors are used to detect a environment. There are two classes of methods to analyze signal patterns from a sensor array;the statistical method and the neural network method. One method has weak points comparing with another. One of each method's weak points is that most of statistical methods cannot consider shape characteristics of the signal pattern and neural network methods take too long time in the learning process. In spite of this weakness, the neural network process has been used in most of gas pattern recognition in recent studies. In this paper, we introduce a statistical method using state transition model for gas recognition. This paper focuses on making the accurate state transition model. We call this state transition model as ADSTM(Angle Difference based State Transition Model). Through various experiments, we analyze the proposed ADSTM modeling method. The results of experiments show that ADSTM is a fast and reliable statistical method for recognizing a signal pattern of the sensor array.

关键词： gas recognition gas sensor sensor array signal pattern state transition model

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A learning based model for Chinese Co-reference resolution by mining contextual evidence

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3rd international conference on Fuzzy Systems and Knowledge Discovery, FSKD 2006

作者： Feifan, Liu Jun, Zhao National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China

ISBN: (纸本)3540459162

This paper presents a learning based model for Chinese co-reference resolution, in which diverse contextual features are explored inspired by related linguistic theory. Our main motivation is to try to boost the co-reference resolution performance only by leveraging multiple shallow syntactic and semantic features, which can escape from tough problems such as deep syntactic and semantic structural analysis. Also, reconstruction of surface features based on contextual semantic similarity is conducted to approximate the syntactic and semantic parallel preferences in resolution linguistic theories. Furthermore, we consider two classifiers in the machine learning framework for the co-reference resolution, and performance comparison and combination between them are conducted and investigated. We experimentally evaluate our approaches on standard ACE (Automatic Content Extraction) corpus with promising results. © Springer-Verlag Berlin Heidelberg 2006.

关键词： learning systems

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