No prior recognition method of modulation mode by partition-fractal and SVM learning method

Li, Shanshan; Zhang, Qi; Xin, Xiangjun; Zhou, Sitong; Tao, Ying; Shen, Yufei; Chang, Huan; Tian, Qinghua; Tian, Feng; Wang, Yongjun; Yang, Leijing

doi:10.3788/col202018.111404

Cited by 2 publications

(1 citation statement)

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“…Among them, in addition to the question bank itself and the reference answers, the meanings of the relevant index parameters are G1, G2, G3: the corresponding position of the content and knowledge points reflected in the question bank in the textbook (or syllabus); F: the score of the question bank; T: reference time for answers; D: difficulty coefficient of question bank; L: the type of question bank; Z: key item bank mark and random selection of the preferred object; N: mark the interval times of the selected item bank, so as to ensure that the item bank is not selected repeatedly in consecutive examination papers. e practice shows that the system of the index value and the index value need to be quantified with examination theory, statistics theory, and expert experience to ensure the quality of paper in the future [18].…”

Section: Item Bank Index Systemmentioning

confidence: 99%

Design and Application of English Grammar Intelligent Question Bank System

Liu

2021

Scientific Programming

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The English grammar question bank can reserve a large number of special knowledge points and special categories of questions, which is helpful for English learners. Therefore, we design and verify the application effect of an intelligent question bank system. We construct an English grammar knowledge map, which is used to design the system architecture, including basic layer, data layer, service layer, business layer, and user layer. On the basis of the system structure, the index system of the item bank is established, and the functional modules of the system are designed comprehensively. Finally, we design a system database based on MySQL and give the table of knowledge points of English grammar. In order to verify the performance of the designed English grammar intelligent test bank system, a verification experiment is carried out. The experiment selects the accuracy rate of English grammatical feature extraction, recall rate, convergence, and system configuration accuracy. The experimental results show that the proposed system can extract English grammatical features with higher accuracy, and the recall rate is more satisfactory. Compared with the reference systems, the designed system configuration accuracy is higher.

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Section: Item Bank Index Systemmentioning

confidence: 99%