Knowledge Discovery On Properties Of Magnetic And Flow Fields Of Solar Quiet Regions

With the development of optical technology, the technology of data storage andspace exploration, more and more advanced systems are used in solar observation, sothere are more and more high-resolution solar images. These images record a variety ofthe process of solar activity and the physical rules. The fast and effective methods are urgentlyneeded to discover the knowledge hidden in the images. Currently, solar physicistsanalyze some image based on manual mode or semi-automatical mode of image processing.The efficiency of this mode is low and the accuracy of analysis can be influencedby subjective factors of researchers. Therefore, this paper puts forward a method of automaticalknowledge discovery based on concept objects. The framework of the basicalgorithms of this method is given and be used into the knowledge discovery of images ofsolar magnetic and flow fields in the quiet regions.Firstly, the expression of solar physical knowledge and images by physicists is analyzed.The existing modes of knowledge extraction: the mode based on mutual, semiautomaticalimage processing and theoretical speculation are also analyzed. The knowledgeexpression is based on concept objects. The main objective of knowledge extractionis to find new concept or study the properties of the existing concept. The studied objectsof solar images are needed to concept objects. The concept objects have the physical significance.The following researches of physical knowledge discovery can be continued.The studies based on pixels or grouped pixels can't reflect the physical concept. Therefore,this paper puts forward the knowledge discovery of solar images based on conceptobjects and gives its framework. This framework includes extraction of concept objectsof solar images, expression and subdivision of concept objects, and knowledge expressionbased on concept objects. This structure not only integrates the idea of cognitivepsychology in artificial intelligence filed and information granularity idea - one featureof artificial intelligence, but also provide the interface of prior knowledge, which can improvethe application of information. What's more, some rules of concepts on differentand high-dimension spaces can be mining.Secondly, the extraction of concept from images is discussed. Concept object is thebasic unit with physical meaning to express the information of solar images. And it is the premise of automatical knowledge discovery of the Sun. Through domain knowledge,we can know that there are two concepts—granule and granular lanes in the continuumimages and one concept—magnetic element in the magnetogram. Therefore the conceptcan be obtained by image segmentation after denosing. As for continuum image,2-dimensional Wiener filter is used to denoise and the central area of granule is extractedas a maker based on morphological methods. The magnetogram is denoised in the polarizationsignal. The pixels with total polarization degree below the noise level are set to 0in the polarization continuum. Then the local extremum in all directions are set as markers.The marker-controlled watershed method is applied to segment continuum imagesand magnetograms and the segmentation results are evaluated and compared. The analysisshowed the algorithm of marker-controlled watershed can effectively suppress thenoise in the image, prevent over-segmentation and under segmentation and get the conceptobjects of images with greater accuracy. This laid the foundation for the followingphysical knowledge discovery.Then, the description and division of concepts such as magnetic elements and granulesare analyzed. In order to study the structure of concept objects in feature space, 6features of magnetic elements such as perimeter, area, magnetic properties, and 10 featuresof granules such as perimeter, area, continuum intensity, velocity, are extraction torepresent the concept objects. Now the magnetic elements or granules can be representedas a point in the space. The structure of these concept objects is explored with priorknowledge and the algorithm of automatical clustering. With prior knowledge, granulescan be divided into granules with upward velocity and granules with downward velocityor large granules and small granules. Based on the X-means clustering, granules with fivefeatures can be divided into 2 clusters, magnetic elements with six features can be dividedinto 2 clusters, and magnetic elements with four features can be divided into 4 clusters.Finally, the data mining of solar images in the quiet region is discussed based onconcept objects. The mean, standard variance and the property of probability distributionof concept objects expressed by one feature are analyzed. The rule of granules with2 classes expressed by 5 features is extracted by the algorithm of 1-rule and decisiontree. The rule show that granules are classified into 2 classes by their size. The rules ofmagnetic elements with 2 classes expressed by 6 features and with 4 classes expressed by4 features are extracted by the algorithm of decision tree. The magnetic elements can be classified into 2 classes with their continuum intensity and can be classified into 4 classeswith flux and area. The two classes are bright magnetic elements and dark magneticelements. And the four classes are magnetic elements with large area and large flux, largearea but small flux, median area, and small area. The related degree of velocity and other6 properties such as diameter are analyzed by the linear correlation coefficient, entropy,and support vector machines. The results show that the related features of the directionof velocity are continuum intensity, diameter and the velocity of lanes, but the magneticproperties of granules are almostly no influence over the granular velocity. The aboveresearches as a example show the effective of the methods proposed by this paper.