In the present research, artificial artificial networks hare be applied to establish the constitutive rela- tionship model of Ti - 5Al - 2Sn - 2Zr - 4Mo - 4Cr (wt - % ) alloy. In the first stage of the re- search...In the present research, artificial artificial networks hare be applied to establish the constitutive rela- tionship model of Ti - 5Al - 2Sn - 2Zr - 4Mo - 4Cr (wt - % ) alloy. In the first stage of the re- search, an isothermal compressive experiment using Thermecmastor - Z hot simulator is studied to ac- quire the flow stress at different deformation temperature,equivalent strain and equivalent strain rate. Then,a feed - forward neural network is trained by using the experimental data.After the training process is finished, the neural networks become a knowledge-based constitutive relationship system. Comparison of the predicted and experimental results results shows that the neural network model has good le- arning precision and good generalization.The neural neural network methods are found to show much better agreement than existing methods with the experiment data, and have the advantage of being able to deal with noisy for or data with strong non - linear reationships. At last, this model can be aused to simulate the flow behavior of Ti - 5Al - 2Sn - 2Zr - 4Mo - 4Ca alloy.展开更多
Manual construction of ontologies by domain experts and knowledge engineers is an expensive and time consuming task so, automatic and/or semiautomatic approaches are needed. Ontology learning looks for identifying ont...Manual construction of ontologies by domain experts and knowledge engineers is an expensive and time consuming task so, automatic and/or semiautomatic approaches are needed. Ontology learning looks for identifying ontology elements like non-taxonomic relationships from information sources. These relationships correspond to slots in a frame-based ontology. This article proposes an initial process for semiautomatic extraction of non-taxonomic relationships of ontologies from textual sources. It uses Natural Language Processing (NLP) techniques to identify good candidates of non-taxonomic relationships and a data mining technique to suggest their possible best level in the ontology hierarchy. Once the extraction of these relationships is essentially a retrieval task, the metrics of this field like recall, precision and f-measure are used to perform evaluation.展开更多
文摘In the present research, artificial artificial networks hare be applied to establish the constitutive rela- tionship model of Ti - 5Al - 2Sn - 2Zr - 4Mo - 4Cr (wt - % ) alloy. In the first stage of the re- search, an isothermal compressive experiment using Thermecmastor - Z hot simulator is studied to ac- quire the flow stress at different deformation temperature,equivalent strain and equivalent strain rate. Then,a feed - forward neural network is trained by using the experimental data.After the training process is finished, the neural networks become a knowledge-based constitutive relationship system. Comparison of the predicted and experimental results results shows that the neural network model has good le- arning precision and good generalization.The neural neural network methods are found to show much better agreement than existing methods with the experiment data, and have the advantage of being able to deal with noisy for or data with strong non - linear reationships. At last, this model can be aused to simulate the flow behavior of Ti - 5Al - 2Sn - 2Zr - 4Mo - 4Ca alloy.
文摘Manual construction of ontologies by domain experts and knowledge engineers is an expensive and time consuming task so, automatic and/or semiautomatic approaches are needed. Ontology learning looks for identifying ontology elements like non-taxonomic relationships from information sources. These relationships correspond to slots in a frame-based ontology. This article proposes an initial process for semiautomatic extraction of non-taxonomic relationships of ontologies from textual sources. It uses Natural Language Processing (NLP) techniques to identify good candidates of non-taxonomic relationships and a data mining technique to suggest their possible best level in the ontology hierarchy. Once the extraction of these relationships is essentially a retrieval task, the metrics of this field like recall, precision and f-measure are used to perform evaluation.
