37264651
Rough Set Approach to Sunspot Classification Problem
This paper presents an application of hierarchical learning method based rough set theory to the problem of sunspot classification from satellite images. The Modified Zurich classification scheme [3] is defined by a set of rules containing many complicated and unprecise concepts, which cannot be determined directly from solar images. The idea is to represent the domain knowledge by an ontology of concepts – a treelike structure that describes the relationship between the target concepts, intermediate concepts and attributes. We show that such ontology can be constructed by a decision tree algorithm and demonstrate the proposed method on the data set containing sunspot extracted from satellite images of solar disk.
11160591
Learning Sunspot Classification
This paper describes automatic sunspot recognition and classification from satellite images. Some experimental results on learning sunspot classification using data mining techniques are presented. The classification scheme used was the seven-class Modified Zurich scheme. Daily images of the solar disk were taken from the NASA SOHO satellite’s MDI instrument and sunspots extracted using image processing techniques. Two data mining tools, WEKA and RSES, were used for learning sunspot classification. In the training dataset sunspots were manually classified by comparing extracted sunspots with corresponding active region maps.
15188179
Advanced Program Analyses and Verifications
Software validation and verification are critical for almost systems whose failure is unacceptable. This is a very expensive process, typically costing 50% of the total softwar e development costs. We show in this paper how program analyses can be used automatically to perform efficient and effective ver ifications, e.garray bound checking and uninitialized variable checking. These verifications are based onarray regions, the analyses that collect information about the way array elements used and de fined by programs. With useful knowledge of the program properties provided by these static analyses, the amount of additional code necess ary to instrument the application is reduced to a minimum. Run-time checks are generated only when we do not know how to prove that they are useless. Dynamic errors that may cause potentially failure are caught and their consequences can be reduced by exceptio n handling. Information provided by our analyses is helpful for the debugging process of large scale programs. All verification s are implemented in PIPS, the research compiler developed at́ Ecole des Mines de Paris. Qualitative and quantitative experimen ts have done with industrial applications up to hundreds of thousand lines of code.
11789833
Collaborative Multidisciplinary Design in Virtual Environments
The application designers can usually define their own "virtual environments" by selecting the appropriate computing resources required or reuse and compose existing environments. The approach is generic by allowing various application domains to benefit from potential hardware and software resources located on remote computing facilities in a simple and intuitive way. The computing resources are defined by services made available as sets of standardized interfaces performing specific tasks: application workflow, input data streams, output visualization tools, monitoring facilities, etc. Services can be composed and hierarchically defined. Transparent access to heterogeneous hardware and software operating systems is guaranteed. An aero-elasticity example in airliner design is given
2066487
Maximum likelihood parameter estimation for image ringing artifact removal
At low bit rates, image compression codecs based on overlapping transforms introduce spurious oscillation known as ringing artifacts in the vicinity of major edges. The image quality can be enhanced considerably by removing the artifacts. We present a maximum likelihood approach to the ringing artifact removal problem. Our approach employs a parameter estimation method based on the k-means algorithm with the number of clusters determined by a cluster separation measure. The proposed algorithm and its simplified approximation are applied to JPEG2000 compressed images to demonstrate their effectiveness.
6207354
Development of Some Methods and Tools for Discovering Conceptual Knowledge
This abstract summarizes key points of our ongoing project that aims at developing several new methods in knowledge discovery in databases (KDD) and implementing them together with related techniques in an interactive-graphic environment. This research lies in several areas if KDD such as supervised classi fication, conceptual clustering, discovery of association rules, parallel and distributed data mining, genetic programming. Some of them are the continuation of our work done so far [1], [2], [3], and some others have been started recently
35277097
Maximum-likelihood parameter estimation for image ringing-artifact removal
At low bit rates, image compression codecs based on overlapping transforms introduce spurious oscillations known as ringing artifacts in the vicinity of major edges. Unlike previous works, we present a maximum-likelihood approach to the ringing-artifact removal problem. Our approach employs a parameter estimation method based on the k-means algorithm with the number of clusters determined by a cluster-separation measure. The proposed algorithm and its simplified approximation are applied to JPEG2000 compressed images. Our results show effective and efficient removal of ringing artifacts.