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Intelligent Voice Instructor-Assistant System for Collaborative and Interactive Classes 被引量:1
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作者 matthew baker Xiaohui Hu +1 位作者 Gennaro De Luca and Yinong Chen 《Journal of Artificial Intelligence and Technology》 2021年第2期121-130,共10页
College classes are becoming increasingly large.A critical component in scaling class size is the collaboration and interactions among instructors,teaching assistants,and students.We develop a prototype of an intellig... College classes are becoming increasingly large.A critical component in scaling class size is the collaboration and interactions among instructors,teaching assistants,and students.We develop a prototype of an intelligent voice instructorassistant system for supporting large classes,in which Amazon Web Services,Alexa Voice Services,and self-developed services are used.It uses a scraping service for reading the questions and answers from the past and current course discussion boards,organizes the questions in JavaScript object notation format,and stores them in the database,which can be accessed by Amazon web services Alexa skills.When a voice question from a student comes,Alexa is used for translating the voice sentence into texts.Then,Siamese deep long short-term memory model is introduced to calculate the similarity between the question asked and the questions in the database to find the best-matched answer.Questions with no match will be sent to the instructor,and instructor’s answer will be added into the database.Experiments show that the implemented model achieves promising results that can lead to a practical system.Intelligent voice instructor-assistant system starts with a small set of questions.It can grow through learning and improving when more and more questions are asked and answered. 展开更多
关键词 natural language processing voice processing machine learning Long short-term memory(LSTM)network questions and answers
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Cross-Polarized GPR Imaging of Fracture Flow Channeling 被引量:3
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作者 Georgios P.Tsoflias Christopher Perll +1 位作者 matthew baker matthew W.Becker 《Journal of Earth Science》 SCIE CAS CSCD 2015年第6期776-784,共9页
Ground penetrating radar(GPR) can be used to image fractures and monitor fluid flow in the subsurface. Conventional GPR imaging uses single-polarization, co-polarized acquisition. We examine the use of cross-polariz... Ground penetrating radar(GPR) can be used to image fractures and monitor fluid flow in the subsurface. Conventional GPR imaging uses single-polarization, co-polarized acquisition. We examine the use of cross-polarized GPR signals for imaging flow channeling in a discrete horizontal fracture. Numerical modeling(FDTD) demonstrates that when the fracture channel is oriented at an oblique angle to the survey line, depolarization of the GPR signal results in scattered energy along the cross-polarized components. When the channel is oriented parallel or orthogonal to the survey line, all scattered energy is captured by the co-polarized components and no signal is present in the cross-polarized orientation. Multipolarization, time-lapse 3D GPR field data were acquired at the Altona Flat Rock test site in New York State. The GPR surveys were conducted during background fresh fracture water conditions and during a natural gradient saline tracer test which was used to highlight flow channels along a sub-horizontal fracture. Amplitude analysis of the cross-polarized data reveals flow channeling that is in agreement with the co-polarized GPR images and with independent hydraulic tests. This investigation demonstrates that cross-polarized components of GPR signals can be used to enhance imaging of flow channels in fractured media. 展开更多
关键词 GPR single-polarization cross-polarized data rapid flow saline tracer fractured rock.
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