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浙江大学学报(理学版)  2020, Vol. 47 Issue (4): 397-407    DOI: 10.3785/j.issn.1008-9497.2020.04.002
数字书法     
云端结合的书法大数据平台
杨颐1, 李国清1, 王健1,2, 王海军1, 翟翊辰1, 黄卫星1,2
1.中国科学院 自动化研究所,北京 100190
2.中科君胜(深圳)智能数据科技发展有限公司,广东深圳 518000
O2O service-based Chinese calligraphy big data platform
YANG Yi1, LI Guoqing1, WANG Jian1,2, WANG Haijun1, ZHAI Yichen1, HUANG Weixing1,2
1.Institute of Automation Chinese Academy of Sciences, Beijing 100190, China
2.CASIA-Junsheng (Shenzhen) Intelligent & Big Data Sci-Tech Development Ltd, Shenzhen 518000, Guangdong Province, China
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摘要: 基于文化强国的国家重大战略,为满足传承和创新我国传统文化这一重要需求,实现文化休闲与共享、书法普及与教育的具体目标,研发了基于大数据和人工智能等核心技术建构的云端结合的书法大数据平台。平台连通了线下和线上的信息通道,实现古代经典与当下书写的智能对接、线上与线下作品的共创共享以及个体创作与群体创作的有机结合。在此基础上,实现了数字书法创作与交流、作品集的生成与推广、线上线下协同创作、书法作品的智能竞技、书法创作数据分析等形式的智能书法服务。
关键词: 系统架构可视分析数字书法人工智能云平台大数据    
Abstract: Inheritance and innovation play an import role for the soft-power strategy of China. We build an AI-based calligraphy big data platform in order to realize cultural leisure and sharing, calligraphy popularization and education. The platform integrates the online site and offline site in order to connect the classical and fashionable calligraphy art, share between online work and offline work, and individual creation and crowd creation. Furthermore, it facilitates smart calligraphy services, such as the communication and creation of digital calligraphy, the share of artworks, and collaboration between online site and offline site, intelligent games, data analytics of calligraphy.
Key words: digital Chinese calligraphy    big data    AI    architecture    visual analytics    cloud platform
收稿日期: 2019-12-17 出版日期: 2020-07-25
CLC:  TP 31  
通讯作者: ORCID:http://orcid.org/0000-0002-0544-3543,E-mail:weixing.huang@ia.ac.cn.     E-mail: weixing.huang@ia.ac.cn
作者简介: 杨颐(1978—),ORCID:http://orcid.org/0000-0002-1437-8288,男,博士,助理研究员,主要从事大数据与机器学习研究.。
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引用本文:

杨颐, 李国清, 王健, 王海军, 翟翊辰, 黄卫星. 云端结合的书法大数据平台[J]. 浙江大学学报(理学版), 2020, 47(4): 397-407.

YANG Yi, LI Guoqing, WANG Jian, WANG Haijun, ZHAI Yichen, HUANG Weixing. O2O service-based Chinese calligraphy big data platform. Journal of Zhejiang University (Science Edition), 2020, 47(4): 397-407.

链接本文:

https://www.zjujournals.com/sci/CN/10.3785/j.issn.1008-9497.2020.04.002        https://www.zjujournals.com/sci/CN/Y2020/V47/I4/397

1 SHVACHKO K, RONG K H,RADIA S, et al . The Hadoop distributed file system: Mass storage systems and technologies (MSST)[C]// 2010 IEEE 26th Symposium. Incline Village, NV: IEEE, 2010:1-10. DOI: 10.1109/MSST.2010.5496972 .
2 ZAHARIA M, CHOWDHURY M, FRANKLIN M J, et al . Spark: Cluster computing with working sets[C]//Proceeding Hotcloud’10 Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing. Berkeley: USENIX Association,2010:10.
3 ZHANG G G, WANG J, HUANG W X . A study of Chinese character culture big data platform[C]// 2015 International Conference on Cloud Computing and Big Data (CCBD). Shanghai: IEEE Computer Society, 2015:161-168. DOI: 10.1109/CCBD.2015.18 .
4 杨颐,张桂刚,王健,等 .基于云计算的汉字文化数字化平台的架构研究[J]. 计算机科学,2016(7): 28-34. DOI: 10.11896/j.issn.1002-137X.2016.7.004 . YANG . Y, ZHANG G G, WANG J, et al . Cloud computing architecture of Chinese character culture digitization system [J]. Computer Science,2016(7): 28-34. DOI: 10.11896/j.issn.1002-137X.2016.7.004 .
5 YANG Y, ZHANG G G, WANG J, et al . Integrated recommendation for public cultural service[C]// 2017 IEEE Third International Conference on Multimedia Big Data (BigMM) . Laguna Hills, CA:IEEE, 2017: 46-49. DOI: 10.1109/BigMM.2017.21 .
6 YE S F, YANG Y, HUANG W X, et al . Public cultural services recommendation system architecture[C]// 2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C).Prague: IEEE, 2017: 404-409. DOI: 10.1109/QRS-C.2017.73 .
7 ZHAI X S, JIN F, WANG J . A kind of precision recommendation method for massive public digital cultural resources: A preliminary report[C]// 2016 IEEE Second International Conference on Multimedia Big Data (BigMM). Taipei: IEEE, 2016:56-59. DOI: 10.1109/BigMM.2016.46 .
8 YE S F, YANG Y, HUANG W X, et al . Personalized recommended system for digital cultural resources[C]//2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). Lisbon: IEEE, 2018: 559-565. DOI: 10.1109/qrs-c.2018.00099 .
9 YANG Y, ZHANG G G, WANG J, et al . Public cultural knowledge graph platform[C]// 2017 IEEE 11th International Conference on Semantic Computing (ICSC). San Diego: IEEE, 2017: 322-327. DOI: 10.1109/ICSC.2017.37 .
10 中科君胜(深圳)智能数据发展有限公司 . 中科智能书法台[EB/OL]. [2019-06-15]. http://shufatai.com/. CASIA-Junshen (Shenzhen) Intelligent & Big Data Sci-Tech Development Ltd . Zhongke Intelligent Calligraphy Desk [EB/OL].[2018-06-15].http://shufatai.com/.
11 GONG Y Z, NI Z Q, HUANG W X, et al . A real-time Chinese calligraphy creation system[C] // 2017 IEEE International Symposium on Multimedia (ISM). Taichung: IEEE, 2017: 536-542. DOI: 10.1109/ISM.2017.105 .
12 KUMAR L, RAJAWAT S, JOSHI K . Comparative analysis of NoSQL (MongoDB) with MySQL database[J]. International Journal of Modern Trends in Engineering and Research, 2015, 2(5): 120-127.
13 GYŐRÖDI C, GYŐRÖDI R, PECHERLE G, et al . A comparative study: MongoDB vs. MySQL[C]//3th International Conference on Engineering of Modern Electric Systems (EMES). Oradea:IEEE, 2015: 1-6. DOI: 10.1109/EMES.2015.7158433 .
14 ABRAMOVA V, BERNARDINO J . NoSQL databases: MongoDB vs cassandra[C]//Proceedings of the International Conference on Computer Science and Software Engineering. Montreal:ACM, 2013: 14-22. DOI:10.1145/2494444.249447
15 DEDE E, GOVINDARAJU M, GUNTER D, et al . Performance evaluation of a MongoDB and Hadoop platform for scientific data analysis[C]//Proceedings of the 4th ACM Workshop on Scientific Cloud Computing. Tucson:ACM, 2013: 13-20. DOI: 10.1145/2465848.2465849 .
16 SHANKAR V,LIN R . Performance study of Ceph storage with intel cache acceleration software: Decoupling Hadoop mapreduce and hdfs over Ceph storage[C]// 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud). New York: IEEE, 2017: 10-13. DOI: 10.1109/CSCloud.2017.40 .
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