Search

Hyperspectral images contain abundant spectral and spatial information about the earth's surface, labeling data processing and analysis more difficult, as well as the problem of sample labeling. In this paper, local binary pattern (LBP), sparse representation and mixed logistic regression model are introduced, and a sample labeling method based on neighborhood information and priority classifier discrimination is presented. Then, a hyperspectral remote sensing image classification method based on texture features and semi-supervised learning is implemented. The LBP is employed to extract features of spatial texture information from remote sensing images and enrich the feature information of samples. Then the multivariate logistic regression model is used to select the unlabeled samples with the largest amount of information, and the unlabeled samples with neighborhood information and priority classifier tags are selected to obtain the pseudo-labeled samples after learning. By making full use of the advantages of sparse representation and mixed logistic regression model, a new hyperspectral remote sensing image classification model based on semi-supervised learning is constructed to effectively achieve accurate classification of hyperspectral images. The data of Indian Pines, Salinas scene and Pavia University are selected to verify the validity of the proposed method. The experiment results show that the proposed classification method can obtain higher classification accuracy and show stronger timeliness and generalization ability.

Recently, the advancement of cognitive computing and three-way decisions has enabled in-depth sequential pattern understanding through temporal association analysis. The main challenge is to obtain concise patterns that express richer semantics for multivariate time series (MTS) analysis. In this paper, we propose a tri-partition state alphabet-based sequential pattern (Tri-SASP) for MTSs. First, a tri-wildcard gap inserted between each pair of adjacent states enhances the flexibility of the method. Second, a given set of states is partitioned into positive (POS), negative (NEG) and boundary (BND) regions. The states in POS can only be used to construct a Tri-SASP, the states in NEG can only be matched by a tri-wildcard gap, and the states in BND can be used in both ways. Finally, horizontal and vertical algorithms are proposed to obtain frequent Tri-SASPs in a breadth-first manner. The experimental results on four real-world datasets show that (1) the discovered Tri-SASPs and temporal rules can enrich human cognition; (2) the two tri-partition strategies can bring us very meaningful and varied Tri-SASPs; and (3) the two algorithms are effective and scalable.

Recently, predicting multivariate time-series (MTS) has attracted much attention to obtain richer semantics with similar or better performances. In this paper, we propose a tri-partition alphabet-based state (tri-state) prediction method for symbolic MTSs. First, for each variable, the set of all symbols, i.e., alphabets, is divided into strong, medium, and weak using two user-specified thresholds. With the tri-partitioned alphabet, the tri-state takes the form of a matrix. One order contains the whole variables. The other is a feature vector that includes the most likely occurring strong, medium, and weak symbols. Second, a tri-partition strategy based on the deviation degree is proposed. We introduce the piecewise and symbolic aggregate approximation techniques to polymerize and discretize the original MTS. This way, the symbol is stronger and has a bigger deviation. Moreover, most popular numerical or symbolic similarity or distance metrics can be combined. Third, we propose an along–across similarity model to obtain the k-nearest matrix neighbors. This model considers the associations among the time stamps and variables simultaneously. Fourth, we design two post-filling strategies to obtain a completed tri-state. The experimental results from the four-domain datasets show that (1) the tri-state has greater recall but lower precision; (2) the two post-filling strategies can slightly improve the recall; and (3) the along–across similarity model composed by the Triangle and Jaccard metrics are first recommended for new datasets.

Service composition is one of the main behavior in the SOC(Service-Oriented Computing) process, which direct and indirect influences effectiveness and precision of service computing; But at present, relation researches mainly focus on semantics recognition and QoS(Quality of Service). In the paper, according to semantics characteristic classification, we proposed a semantics web service characteristic composition approach based on particle swarm optimization, and set up a characteristic selsection mechanism of semantics web service, and adopt charecteristic distance relation to implement service characteristic classification, and use the distance relation to build characteristic tendency degree, sufficiency and characteristic extractor computing formula of semantics web service, at the same time, according to the formula, to implement service characteristic composition algorithm. Then, we set up a optimal mathematical model via characteristic extractor formula. And employ particle swarm to optimize the model and Amazon service set to make experiment, which showed that it is feasible and effective.

All kinds of information systems in service-oriented have been applied many relation fields, Therefore, according to service-oriented component, we proposed a component composition model and algorithm, and established a component composition model oriented service, which employ a immune genetic ant colony algorithm to solve. Finally, make experiments showed that have improved efficiency of component composition.

In view of the semantic questions of the WSDL in describing service componentpsilas discovering, matching, choosing and so on. an automata based on Ontology is proposed to implement the semantic recognition service, which can finally meet the request of service component. Firstly Analysis of OWL-S and WSMO based on Ontology is made, then the semantic Web service component antomata is established. Finally, under the support of WSMO this theory is applied in the information system of Aba Prefecture. The practice indicates that the automata effectively resolved the service component flow modelling and description in the semantics recognition.