{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T18:42:11Z","timestamp":1777056131487,"version":"3.51.4"},"reference-count":24,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2019,6,13]],"date-time":"2019-06-13T00:00:00Z","timestamp":1560384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["J. Comput. Cult. Herit."],"published-print":{"date-parts":[[2019,10,4]]},"abstract":"<jats:p>Fourth Industrial Revolution technologies, such as artificial intelligence, big data, the Internet of Things (IoT), and virtual reality, have disrupted legacy methods of operations and have led to progress in many industries worldwide. These technologies also affect the cultural and national heritage. IoT generates large volumes of streaming data; therefore, advanced data analytics using big data analytics and artificial neural networks is an important research topic. In this study, IoT sensor data was collected at the restored Woljeong Bridge, which was originally built in the eighth century, or AD 760, during the Silla Dynasty (57 BC--AD 935) in South Korea. We empirically evaluate a recurrent neural network with recurrent units, including a long short-term memory (LSTM) unit and a gated recurrent unit (GRU). Additionally, we evaluate hybrid deep-learning models (convolution neural networks [CNN]-LSTM and CNN-GRU) to build a prediction model, facilitating the preventive conservation of an invaluable cultural and national heritage site. The experimental results show that the LSTM unit is an effective and robust model. When comparing the hybrid models (i.e., the joint CNN-LSTM and CNN-GRU architectures), we found that the vanilla LSTM and GRU models had superior time-series prediction capabilities.<\/jats:p>","DOI":"10.1145\/3316414","type":"journal-article","created":{"date-parts":[[2019,6,14]],"date-time":"2019-06-14T12:36:28Z","timestamp":1560515788000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":26,"title":["Cultural Heritage and the Intelligent Internet of Things"],"prefix":"10.1145","volume":"12","author":[{"given":"Woosik","family":"Lee","sequence":"first","affiliation":[{"name":"Illinois Institute of Technology, Chicago, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong-hoon","family":"Lee","sequence":"additional","affiliation":[{"name":"a Seoul School of Integrated Sciences 8 Technologies, Seodaemun-gu, Seoul, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,6,13]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2016.09.068"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCE.2017.2717198"},{"key":"e_1_2_1_4_1","volume-title":"Neural Networks: Tricks of the Trade"},{"key":"e_1_2_1_5_1","first-page":"1259","article-title":"On the properties of neural machine translation: Encoder--Decoder approaches","volume":"1409","author":"Cho K.","year":"2014","journal-title":"Arxiv"},{"key":"e_1_2_1_6_1","first-page":"3555","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","volume":"1412","author":"Chung J.","year":"2014","journal-title":"Arxiv"},{"key":"e_1_2_1_7_1","unstructured":"J. Duchi E. Hazan and Y. Singer. 2011. Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. (2011) 2121--59.  J. Duchi E. Hazan and Y. Singer. 2011. Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. (2011) 2121--59."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.3390\/s130809729"},{"key":"e_1_2_1_9_1","volume-title":"LSTM: A search space odyssey. Corr abs\/1503.04069.","author":"Greff K.","year":"2015"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_2_1_11_1","article-title":"{Project report} restoration work of woljeong-gyo","volume":"53","author":"Jang H.","year":"2009","journal-title":"Rev. Archit. Build. Sci."},{"key":"e_1_2_1_12_1","doi-asserted-by":"crossref","unstructured":"K. Knight. 1999. Mathematical Statistics. Chapman 8 Hall\/CRC 198.  K. Knight. 1999. Mathematical Statistics. Chapman 8 Hall\/CRC 198.","DOI":"10.1201\/9781584888567"},{"key":"e_1_2_1_13_1","volume-title":"Proceedings of the 25th International Conference on Neural Information Processing Systems. 1097--105","author":"Krizhevsky A."},{"key":"e_1_2_1_14_1","article-title":"A study on provenance of the stone relics of WoljungGyo bridge built in Silla Kingdom based on geological properties","volume":"15","author":"Lee K.","year":"2016","journal-title":"J. Korean Geosynthetics Soc."},{"key":"e_1_2_1_15_1","article-title":"A deep learning analysis of the KOSPI\u2019s directions","volume":"28","author":"Lee W.","year":"2017","journal-title":"J. Korean Data Inf. Sci. Soc."},{"key":"e_1_2_1_16_1","article-title":"A deep learning analysis of the Chinese Yuan\u2019s volatility in the onshore and offshore markets","volume":"27","author":"Lee W.","year":"2016","journal-title":"J. Korean Data Inf. Sci. Soc."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2015.03.014"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1186\/1752-153X-6-145"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2014.03.057"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2017.06.030"},{"key":"e_1_2_1_21_1","unstructured":"UNESCO World Heritage Centre. 2008. World heritage information kit.  UNESCO World Heritage Centre. 2008. World heritage information kit."},{"key":"e_1_2_1_22_1","unstructured":"X. Xie D. Wu S. Liu and R. Li. 2017. IoT data analytics using deep learning. Corr abs\/1708.03854.  X. Xie D. Wu S. Liu and R. Li. 2017. IoT data analytics using deep learning. 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