Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
The work reported here primarily aims to realize the automatic generation of couplets using the linguistic topology of deep neural network (DNN). First, the symmetry, topology, and cognitive linguistics of language are explored to lay a theoretical foundation for subsequent model establishment and analysis. Then, the recurrent neural network (RNN) is employed to build the Seq2Seq model, and Liweng’s Guide to Rhyme (an ancient Chinese enlightenment reading material to poetry creation) is imported into the Seq2Seq model as a basic corpus. Eventually, the entire system is implemented automatically on TensorFlow. The system undergoes tests of the five-character quatrain, the seven-character quatrain, the couplet, and the part-of-speech detection. Results demonstrate that both the first and the second lines of the couplet present an excellent correspondence regarding sentences and words. After some famous verses are entered, the second line of the couplet obtained is quite vivid and appropriate. Meanwhile, the results can be generated quickly and meet the requirements on rhyme and couplet matching. This model can input verses according to users’ own needs and generate the second line of the couplet quickly, showing good correspondence in words, part-of-speech, and sentence pattern. Because the couplet belongs to Chinese traditional culture, it has a strong local Chinese cultural flavor. The system designed based on computer technology can help people learn and experience the charm of couplets.
The work reported here primarily aims to realize the automatic generation of couplets using the linguistic topology of deep neural network (DNN). First, the symmetry, topology, and cognitive linguistics of language are explored to lay a theoretical foundation for subsequent model establishment and analysis. Then, the recurrent neural network (RNN) is employed to build the Seq2Seq model, and Liweng’s Guide to Rhyme (an ancient Chinese enlightenment reading material to poetry creation) is imported into the Seq2Seq model as a basic corpus. Eventually, the entire system is implemented automatically on TensorFlow. The system undergoes tests of the five-character quatrain, the seven-character quatrain, the couplet, and the part-of-speech detection. Results demonstrate that both the first and the second lines of the couplet present an excellent correspondence regarding sentences and words. After some famous verses are entered, the second line of the couplet obtained is quite vivid and appropriate. Meanwhile, the results can be generated quickly and meet the requirements on rhyme and couplet matching. This model can input verses according to users’ own needs and generate the second line of the couplet quickly, showing good correspondence in words, part-of-speech, and sentence pattern. Because the couplet belongs to Chinese traditional culture, it has a strong local Chinese cultural flavor. The system designed based on computer technology can help people learn and experience the charm of couplets.
Buried hill oil reservoirs have become a key area for offshore oil and gas exploration. In this paper, a typical oil field in the western South China Sea is used as the research object, and a study on the characterization, cause of formation and prediction of the fracture–cavity reservoir distribution is carried out. The reservoir in the study area is a complex fracture–cavity reservoir that developed due to weathering and leaching, tectonic movement and dissolution reconstruction on the limestone skeleton. The reservoir spaces are composed of karst caves, fractures and pores. The main controlling factors include lithological changes, karst landforms, tectonic deformation and faulting. To address the controlling mechanisms of the lithological changes on the formation of fracture–cavity reservoirs, a new parameter, the lithology standard deviation, to evaluate lithological changes is proposed based on the characteristics of the lithological changes, and the distribution of these lithological changes is portrayed in combination with the seismic attributes. The tectonic deformation principal curvature inversion algorithm is used to simulate the distribution of the tectonic principal curvature at the top of the Carboniferous. The larger the tectonic principal curvature is, the stronger the deformation of the rock formation and the more favorable the conditions are for fracture formation. The karst geomorphology controls the overall reservoir distribution, and the karst highlands and karst slope areas are the zones with the most–developed secondary pore space (or fractures and karst caves). The faulting control area is the fracture and dissolution pore development area, major faults control the distribution of the karst cave reservoirs, and secondary faults influence the formation of fractures in the faulted area. The study predicts and evaluates the distribution of fracture–cavity reservoirs from the perspective of fracture–cavity genesis quantification, and by gridding and normalizing the four major genesis quantification evaluation parameters and fusing the geological factors that control the formation of fractures and karst caves by using Back–Propagation neural network deep learning algorithms, a method for predicting the distribution of fracture–cavity reservoirs constrained by geological genesis analysis is developed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.