Feng Xiaoning

Hierarchical Object-Oriented Petri Net Modeling Method Based on Ontology

This paper presents a Hierarchical Object-Oriented Petri Net (HOOPN) modeling method based on Ontology that should not only enable sharing Petri nets models on the Semantic Web but also present a high level Petri net. Previous work on formal methods for representing Petri nets mainly focuses on modeling and analyzing aspects or formats for Petri net model interchange. However, such efforts do not provide a suitable model description for using Petri nets on the Semantic Web. This paper uses the HOOPN with the Ontology concepts as a starting point for implementing the Petri net ontology. Moreover this paper uses HOOPN as the Petri net model method. HOOPN supports a wide range of Object-Oriented features including abstract, encapsulated and modularized objects, object interaction by message passing, inheritance, and polymorphism.

A path planning strategy for data acquisition task using multiple autonomous underwater vehicles

This paper proposes to investigate the performance of a path planning method for a data acquisition task using multiple cooperative underwater vehicles (MAUV). We present a multiple objective path planning (MOPP) method to find the optimal solutions for the data acquisition task with certain constraints. This method is based on an Interval Programming (IvP) algorithm introduced in [1] for representing and optimizing over multiple competing objective functions. We made improvements to deal with multiple objective optimizations in the underwater environment. The MOPP model is built to respect with different behaviors. Preliminary simulation trials based on two simplified scenarios have been carried out. The results show that the cooperative data acquisition task could be finished satisfactorily and safety.

Symmetry theory based classification algorithm in CT image database

CT imaging shows that it is approximately symmetrical about the perpendicular bisector. Based on this medical knowledge guidance, symmetry theory based classification algorithm in CT image database is presented in this paper. First of all, the definitions of the weak symmetry and strong symmetry were given. Then, the weak symmetry was applied to the first stage classification of the CT images. Secondly, we proposed the combination of weak symmetry and strong symmetry for the second stage classification. Finally, according to the tumor edge profile, tumors are divided into benign and malignant lesions by extracting some features of the tumor in the third stage classification. In this paper, sample size requirements of SVM (Support Vector Machine) classifier were selected to classify the CT images. Experimental results show that symmetry theory based classification algorithm in CT image database can increase the accuracy of the classification and reduce the time of the doctors diagnosis.