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Comparative Study of Two Approaches to Automatic Summarization of Arabic Documents
Weihua Gui,Khaled Alwesabi,Abdullah Ayedh 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.4
We present an automatic summarization of Arabic texts. In this context, two methods for automatic summarization of Arabic documents are proposed and comparatively analyzed. The first method is based on rhetorical structure theory RST that uses linguistic knowledge. The second method is based on digital learning theory that relies on mathematical rules. Both methods are implemented using the Arabic summarization RST ASRST and Arabic summarization digital learning ASDL systems. Thereafter, results obtained by the two systems are presented and a comparative study between the obtained results is conducted to highlight the advantages and limitations of each method. Evaluation results showed that the numerical approach has better performance than the symbolic approach, particularly when the texts are short. By contrast, the symbolic approach provides better result than the numerical approach when the texts are long.
Feature Selection based on Rough Sets and Minimal Attribute Reduction Algorithm
Khaled Alwesabi,Weihua Gui,Chunhua Yang,Hamdi Rajeh 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.8
Numerous studies have focused on feature selection using many algorithms, but most of these algorithms encounter problems when the amount of data is large. In this paper, we propose an algorithm that handles a large amount of data by partitioning the data to process a reduction, and then selecting the intersection of all reducts as a stable reduct. This algorithm is successful but may suffer from loss of information if the samples are unsuitable. The proposed algorithm is based on discernibility matrix and function. Furthermore, the method can address the case in which the data consist of a significant amount of information. Our results show that the proposed algorithm is powerful and flexible enough to successfully target a range of different domains and can effectively reduce computational complexity as well as increase reduction efficiency. The efficiency of Proposed Algorithm is illustrated by experiments with UCI datasets further.
Ning Chen,Jiayang Dai,Xiaojun Zhou,Qingqing Yang,Weihua Gui 제어·로봇·시스템학회 2019 International Journal of Control, Automation, and Vol.17 No.5
Iron precipitation is a key process in zinc hydrometallurgy. The process consists of a series of continuousreactors arranged in descending order, overflowing zinc leach solution from one reactor to the next. In this paper,according to the law of mass conservation and the reaction kinetics, a continuously stirred tank reactor model ofa single reactor is first established. Then, a distributed model of cascade reactors is built with coupled controlbased on the single reactor model, considering the unreacted oxygen in leaching solution. Secondly, four reactorsin the iron precipitation process are considered as four subsystems, the optimization control problem of the processis solved by a distributed model predictive control strategy. Moreover, the control information feedback betweensuccessive subsystems is used to solve the optimization problem of each subsystem, because of the existing controlcoupling in their optimization objective function of pre and post subsystems. Next, considering the intractability ofthe optimization problem for subsystems with various constraints, a distributed dual iterative algorithm is proposedto simplify the calculation. With the consideration of its cascade structure and control couplings, the proposedalgorithm iteratively solves the primal problem and the dual problem of each subsystem. The application case showsthat distributed model predictive control based on dual iteration algorithm can handle coupled control effectivelyand reduce the oxygen consumption.