Research On Theoreies, Methodologic And Application For Character Detection Of Time Series And Image

Character_detection for identifying object is foundation on which intellective Systems such as robot and medical diagnose actualize intellective information disposal. It's increasingly universality in application,complexity in role, uncertainty and particularity in environmental, restricted self resource and real time demand of character detection , which have determined its research tasks are challenge and difficult . However , the demand ,for reliability and security of Character detection to be increasingly enhanced ,continuing enhance the pressure to resolve it.Thus,exploring true and resultful theory,method and technique about Character_detection has become one of important research contents.In this paper, the relative theory and technique how to effectively implement real time Character_detection are discussed by restricted system resources and based on conjunction machine learning with others approach.For issue of Character detection in time series and image , some relevant algorithm have been put forward and its validity have been validated. Main contributions of the dissertation are shown as following,By means of research and analysis the characteristic about the model of OCSVM and PSO and based on integration of father fruitm,an model of anomaly detection based on OCSVM_CPSO is put forward,which actualize that system adaptive adjustment and solve the problem about detecting system online run, and clean off obstacle for its real application. It is used as solving the real problem of fault detection of robot sensor,and a good result is abtainedThe DLS model is put forword in order to overcome the bug that SAX easily lose information on boundary .DLS partition fluctuate boundary according to extremum of the time series ,and select optimization character set based on the most entropy ,more, neatly set optimization partition interval,thus can effectively reduce losing information on boundary .Another Model, for solving the bug of the EXT_SAX,the VSB model is put forword, which increase the component to reduce calculating cost. Its availability is proved by the experiments.A vector symbolic model for Time Series Data Based on Statistic Feature,SFVS,and relavant mothod for estimating the most compress ratio about time series data, are put forward in order to surmount the bugs with which SAX Algorithm can not describe time series information fully,which is helpful to implement more accurate analysis in application of feature detection of time series.Its validity have been proved by experimentsFor the bug of Self-tuning spectral clustering, A new algorithm ASC(called as the adaptive spectral clustering), is put foward,which takes average distance of N-near-neighbour as scaling parameterσ, automatically estimates optimal clustering grouping k by means of information about enginvector difference .It can reduce calculating cost for constructing appetency matrix as well as get highter efficiency and implement easyer .Farther, the correlative semi supervised algorithm for improving its'performance are put forward on the base.The results of experiments in application about color image detection and segmentation shown that the algorithm is valid .