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Articles by Boqin Feng
Total Records ( 9 ) for Boqin Feng
  Haichang Gao , Boqin Feng , Yun Hou , Bin Guo and Li Zhu
  A hybrid adaptive SAGA based on mutative scale chaos optimization strategy (CASAGA) is proposed to solve the slow convergence, incident getting into local optimum characteristics of the Standard Genetic Algorithm (SGA). The algorithm combined the parallel searching structure of Genetic Algorithm (GA) with the probabilistic jumping property of Simulated Annealing (SA), also used adaptive crossover and mutation operators. The mutative scale Chaos optimization strategy was used to accelerate the optimum seeking. Compared with SGA and MSCGA on some complex function optimization and several TSP combination optimization problems, the CASAGA improved the global convergence ability and enhanced the capability of breaking away from local optimal solution.
  Hongfang Zhou , Boqin Feng , Lintao Lv and Yue Hui
  Subspace clustering has been studied extensively and widely since traditional algorithms are ineffective in high-dimensional data spaces. Firstly, they were sensitive to noises, which are inevitable in high-dimensional data spaces; secondly, they were too severely dependent on some distance metrics, which cannot act as virtual indicators as in high-dimensional data spaces; thirdly, they often use a global threshold, but different groups of features behave differently in various dimensional subspaces. Accordingly, traditional clustering algorithms are not suitable in high-dimensional spaces. On the analysis of the advantages and disadvantages inherent to the traditional clustering algorithm, we propose a robust algorithm JPA (Joining-Pruning Algorithm). Our algorithm is based on an efficient two-phase architecture. The experiments show that our algorithm achieves a significant gain of runtime and quality in comparison to nowadays subspace clustering algorithms.
  Mao Ye , Boqin Feng and Li Zhu
  Graphical User Interfaces (GUI) software has characteristics different from traditional software. The oracle for GUI software testing must validate the correctness of the GUI. An automated oracle based on multi-weighted Neural Networks (NN) is proposed in this paper to validate the GUI from users` viewpoint. In this approach the multi-weighted NN is used to learn the topological information in the feature space for the expected images of the graphical interface. The topological information is then used to verify the correctness of the GUI. By this method, trivial difference in the graphical interfaces can be ignored and GUI be automatically tested in the manner of human being. Experimental results show the method is of potential application in automated GUI testing.
  Hongjie Liu , Boqin Feng and Jianjie Wei
  In the oil reservoir prediction, it is not that the more index variables of seismic data, the better effect of classification is. On the contrary, the classification accuracy will reduce because of the redundant index variable influenced by the calculation error. Therefor, a method of the least reduction of oil reservoir data is presented in this study. In this method, we directly adopt the dependency of rough set and PSO algorithm by binary encoding, which make the two algorithm organic affiliated. The actual application on the data of oil reservoir not only shows that using this method presented in this paper achieves very notable effect, but also shows that this method has great actual meaning to further construct higher efficient attribute reduction method.
  Huimin Lu and Boqin Feng
  In this study, we propose a novel concept of intelligent topic map, which embodies the multi-level, multi-granularity and inherent relevant characteristics of knowledge and realizes knowledge reasoning. With the intelligent topic map as infrastructure, we design a specific ontology fusion process for multi-resource knowledge fusion. Also, we define the taxonomy of merging conflicts which occur during the process of intelligent topic maps merging. We define and classify merging conflicts into data-level conflicts, structure-level conflicts, rule-level conflicts and temporary-level conflicts. We propose the detection and resolution schemes for each merging conflict. Additionally, we implement the multi-resource knowledge fusion conflicts detection and resolution system based on rules. The experimental results show that our method can correctly detect and resolve the conflicts in topic maps merging and it is helpful to improve the quality of multi-resource knowledge fusion.
  Huimin Lu and Boqin Feng
  We propose a novel concept of Intelligent Topic Map, which extends the conventional topic map in structure and enhances the reasoning functions. With the Intelligent Topic Map as infrastructure, a mechanism of distributed knowledge integration is designed. The structure is divided into three layers: local Intelligent Topic Map layer, similarity measure layer and global Intelligent Topic Map layer. It provides a uniform query interface to a multitude of knowledge sources and lays the foundation for high-quality knowledge services. Moreover, we propose a new similarity measure algorithm based on comprehensive information theory and merging rules for knowledge integration. The experimental results show that our method is feasible and it has the significance of reference and value of further study for the distributed knowledge integration.
  Hongfang Zhou , Boqin Feng , Lintao Lv and Hui Yue
  Traditional clustering algorithms are designed for isolated datasets. But in most cases, relationships among different datasets are always existed. So we must consider the actual circumstances from the cooperative aspects. A new collaborative model is proposed and based on this model a new cooperative clustering algorithm is presented. In theorem, the algorithm is proved to converge to the local minimum. Finally, experimental results demonstrate that the clustering structures obtained by new algorithm are different from those of conventional algorithms for the consideration of collaboration and the performances of these collaborative clustering algorithms can be much better than those traditional separated algorithms under the cooperating circumstances.
  Liang Xue and Boqin Feng
  Business constraints restrict the enterprise structure, govern the business behavior and contribute to the business goals. Due to stuff turnover and lack of documentation the knowledge of what constraints exist and how they interact are often poorly defined. This can seriously hinder an organization to understand how the business behaves and operates and to seize new opportunities or improve the business performance. A model-driven discovery approach is proposed to help the user to uncover the business constraints from the enterprise documents. This approach proposes a meta-model to describe the ontological constructs of the business constraints enforcing environment, defines a set of formal templates to document the business constraints to facilitate communication, sharing and reuse, gives a verification algorithm to ensure the completeness and validness of the discovered business constraints, defines the interactions among business constraints and finally proposes an elicitation procedure to efficiently guide the business users to discover the business constraints. Moreover, an example is presented to demonstrate the discovery approach of the business constraints and their usage for decision-making.
  Liang Xue and Boqin Feng
  Enterprise model is a representation of the knowledge an organization has about itself and of what it would like this knowledge to be. The changing business environment makes a big challenge for the continuous improvement from as-is to to-be of the enterprise. Business constraints are defined as relationships maintained or enforced in the business structure and process to form the most volatile part of the business requirements. The proposed meta-model for enterprise modeling introduces the business constraints as the key element to make the model responsive to the business changes. It consists of two layers including conceptual layer and specification layer where the former provides a foundation for better communication and the latter provides business constraint language and constraint flow language to accommodate the changes and decrease the model maintaining effort. The meta-model forms a precise business as-is description based on which an Analytic Hierarchy Process based what-if analysis mechanism is proposed to analyze the business alternatives to give recommendations for the to-be design. Examples are presented to demonstrate the validness of the meta-model and its what-if analysis mechanism.
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