Seven distributors with different configurations are designed and optimized by constructal approach. Their flow distribution performance and energy dissipation are investigated and compared by computational fluid dyna...Seven distributors with different configurations are designed and optimized by constructal approach. Their flow distribution performance and energy dissipation are investigated and compared by computational fluid dynamics （CFD） simulation. The reliability of CFD simulation is verified by experiments on the distributor that has all distributing rectangle channels on a plate. The results show that the symmetry of the distributing channels has decisive influence on the performance of flow distribution. Increasing the generations of channel branching will improve the flow distribution uniformity, but on the other hand increase the energy dissipation. Among all the seven constructal distributors, the distributor that has dichotomy configuration, Y-type junctions and straight interconnecting channels, is recommended for its better flow distribution performance and less energy dissipation.展开更多
An effective domain ontology automatically constructed is proposed in this paper. The main concept is using the Formal Concept Analysis to automatically establish domain ontology. Finally, the ontology is acted as the...An effective domain ontology automatically constructed is proposed in this paper. The main concept is using the Formal Concept Analysis to automatically establish domain ontology. Finally, the ontology is acted as the base for the Naive Bayes classifier to approve the effectiveness of the domain ontology for document classification. The 1752 documents divided into 10 categories are used to assess the effectiveness of the ontology, where 1252 and 500 documents are the training and testing documents, respectively. The Fl-measure is as the assessment criteria and the following three results are obtained. The average recall of Naive Bayes classifier is 0.94. Therefore, in recall, the performance of Naive Bayes classifier is excellent based on the automatically constructed ontology. The average precision of Naive Bayes classifier is 0.81. Therefore, in precision, the performance of Naive Bayes classifier is gored based on the automatically constructed ontology. The average Fl-measure for 10 categories by Naive Bayes classifier is 0.86. Therefore, the performance of Naive Bayes classifier is effective based on the automatically constructed ontology in the point of F 1-measure. Thus, the domain ontology automatically constructed could indeed be acted as the document categories to reach the effectiveness for document classification.展开更多
基金Supported by the National Natural Science Foundation of China （20476026）, the Program for New Century Excellent Talents in University （05-0416）, the Creative Team Development Project of Ministry of Education （IRT0721）, and the 111 Project of Ministry of Education and State Administration of Foreign Experts Affairs （B08021 ）.
文摘Seven distributors with different configurations are designed and optimized by constructal approach. Their flow distribution performance and energy dissipation are investigated and compared by computational fluid dynamics （CFD） simulation. The reliability of CFD simulation is verified by experiments on the distributor that has all distributing rectangle channels on a plate. The results show that the symmetry of the distributing channels has decisive influence on the performance of flow distribution. Increasing the generations of channel branching will improve the flow distribution uniformity, but on the other hand increase the energy dissipation. Among all the seven constructal distributors, the distributor that has dichotomy configuration, Y-type junctions and straight interconnecting channels, is recommended for its better flow distribution performance and less energy dissipation.
文摘An effective domain ontology automatically constructed is proposed in this paper. The main concept is using the Formal Concept Analysis to automatically establish domain ontology. Finally, the ontology is acted as the base for the Naive Bayes classifier to approve the effectiveness of the domain ontology for document classification. The 1752 documents divided into 10 categories are used to assess the effectiveness of the ontology, where 1252 and 500 documents are the training and testing documents, respectively. The Fl-measure is as the assessment criteria and the following three results are obtained. The average recall of Naive Bayes classifier is 0.94. Therefore, in recall, the performance of Naive Bayes classifier is excellent based on the automatically constructed ontology. The average precision of Naive Bayes classifier is 0.81. Therefore, in precision, the performance of Naive Bayes classifier is gored based on the automatically constructed ontology. The average Fl-measure for 10 categories by Naive Bayes classifier is 0.86. Therefore, the performance of Naive Bayes classifier is effective based on the automatically constructed ontology in the point of F 1-measure. Thus, the domain ontology automatically constructed could indeed be acted as the document categories to reach the effectiveness for document classification.
