An Automatic Event Detection Method for Massive Wireless Access Prediction
The scale of mobile users for parallel access is constrained by the capacity of the base stations. When extremely dense terminal access exceeds the capacity of the base stations, access failure and a performance degradation will occur. The early detection and prediction of important events and the t...
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description | The scale of mobile users for parallel access is constrained by the capacity of the base stations. When extremely dense terminal access exceeds the capacity of the base stations, access failure and a performance degradation will occur. The early detection and prediction of important events and the timely detection of possible large-scale terminal access are significant aspects in ensuring the quality of the communication achieved. For the automatic detection of events, methods based on a neural network can learn features automatically without feature engineering and have been proven to be efficient for event detection. As is well known, constructing an adequate input vector that can represent sufficient information is a challenge to a neural network-based approach, particularly for problems caused by Chinese word segmentation and too many unknown communication words. To cope with this problem, a novel representation method that combines the different features with word vectors is proposed to deal with the problem of Chinese event trigger identification. We then use a gated recurrent unit network to train and predict the event trigger and carry out comparative experiments on different methods and feature combinations. The experiment results of the proposed model show that the F1 value can reach 84% for the experimental dataset. Furthermore, the combination of lexical and syntactic features with a neural network was proven to be helpful for this task, although the contributions vary in magnitude for different features. Our study provides directions for further research on the use of lexical and syntactic features with a neural network for an event detection task. |
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When extremely dense terminal access exceeds the capacity of the base stations, access failure and a performance degradation will occur. The early detection and prediction of important events and the timely detection of possible large-scale terminal access are significant aspects in ensuring the quality of the communication achieved. For the automatic detection of events, methods based on a neural network can learn features automatically without feature engineering and have been proven to be efficient for event detection. As is well known, constructing an adequate input vector that can represent sufficient information is a challenge to a neural network-based approach, particularly for problems caused by Chinese word segmentation and too many unknown communication words. To cope with this problem, a novel representation method that combines the different features with word vectors is proposed to deal with the problem of Chinese event trigger identification. We then use a gated recurrent unit network to train and predict the event trigger and carry out comparative experiments on different methods and feature combinations. The experiment results of the proposed model show that the F1 value can reach 84% for the experimental dataset. Furthermore, the combination of lexical and syntactic features with a neural network was proven to be helpful for this task, although the contributions vary in magnitude for different features. 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(IEEE) 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-68bf4e372d818a3771ace29db5eb6f6c8e4ccab26b33f7c04d376e351f9300073</citedby><cites>FETCH-LOGICAL-c408t-68bf4e372d818a3771ace29db5eb6f6c8e4ccab26b33f7c04d376e351f9300073</cites><orcidid>0000-0003-4119-8052 ; 0000-0002-3970-9290</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8794596$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>315,781,785,865,2103,4025,27637,27927,27928,27929,54937</link.rule.ids></links><search><creatorcontrib>Yin, Mingyong</creatorcontrib><creatorcontrib>Chen, Xingshu</creatorcontrib><creatorcontrib>Wang, Haizhou</creatorcontrib><creatorcontrib>Wang, Qixu</creatorcontrib><creatorcontrib>Ma, Chenxi</creatorcontrib><creatorcontrib>Qin, Xue</creatorcontrib><title>An Automatic Event Detection Method for Massive Wireless Access Prediction</title><title>IEEE access</title><addtitle>Access</addtitle><description>The scale of mobile users for parallel access is constrained by the capacity of the base stations. When extremely dense terminal access exceeds the capacity of the base stations, access failure and a performance degradation will occur. The early detection and prediction of important events and the timely detection of possible large-scale terminal access are significant aspects in ensuring the quality of the communication achieved. For the automatic detection of events, methods based on a neural network can learn features automatically without feature engineering and have been proven to be efficient for event detection. As is well known, constructing an adequate input vector that can represent sufficient information is a challenge to a neural network-based approach, particularly for problems caused by Chinese word segmentation and too many unknown communication words. To cope with this problem, a novel representation method that combines the different features with word vectors is proposed to deal with the problem of Chinese event trigger identification. We then use a gated recurrent unit network to train and predict the event trigger and carry out comparative experiments on different methods and feature combinations. The experiment results of the proposed model show that the F1 value can reach 84% for the experimental dataset. Furthermore, the combination of lexical and syntactic features with a neural network was proven to be helpful for this task, although the contributions vary in magnitude for different features. Our study provides directions for further research on the use of lexical and syntactic features with a neural network for an event detection task.</description><subject>Base stations</subject><subject>Chinese event extraction</subject><subject>Event detection</subject><subject>Feature extraction</subject><subject>GRU</subject><subject>Neural networks</subject><subject>Performance degradation</subject><subject>Segmentation</subject><subject>Semantics</subject><subject>Stations</subject><subject>Task analysis</subject><subject>Trigger identification</subject><subject>Wireless communication</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUV1LwzAULaLgmPsFeyn43Jk0X81jmVMnE4UpPoY0vdGOrZlJN_Dfm61jeF_u5XLOuR8nScYYTTBG8q6cTmfL5SRHWE5ySSgT6CIZ5JjLjDDCL__V18kohBWKUcQWE4PkuWzTcte5je4ak8720HbpPXRgusa16Qt0365OrfPpiw6h2UP62XhYQwhpacwhvXmomyP6Jrmyeh1gdMrD5ONh9j59yhavj_NpucgMRUWX8aKyFIjI6wIXmgiBtYFc1hWDiltuCqDG6CrnFSFWGERrIjgQhq0kcXFBhsm8162dXqmtbzba_yqnG3VsOP-ltI_XrEHxOEkTsLbC8S-IS2Gt1aiWtkLMGB61bnutrXc_OwidWrmdb-P6KqeMcUKpkBFFepTxLgQP9jwVI3XwQPUeqIMH6uRBZI17VgMAZ0YhJGWSkz_nI4JX</recordid><startdate>2019</startdate><enddate>2019</enddate><creator>Yin, Mingyong</creator><creator>Chen, Xingshu</creator><creator>Wang, Haizhou</creator><creator>Wang, Qixu</creator><creator>Ma, Chenxi</creator><creator>Qin, Xue</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-4119-8052</orcidid><orcidid>https://orcid.org/0000-0002-3970-9290</orcidid></search><sort><creationdate>2019</creationdate><title>An Automatic Event Detection Method for Massive Wireless Access Prediction</title><author>Yin, Mingyong ; Chen, Xingshu ; Wang, Haizhou ; Wang, Qixu ; Ma, Chenxi ; Qin, Xue</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c408t-68bf4e372d818a3771ace29db5eb6f6c8e4ccab26b33f7c04d376e351f9300073</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Base stations</topic><topic>Chinese event extraction</topic><topic>Event detection</topic><topic>Feature extraction</topic><topic>GRU</topic><topic>Neural networks</topic><topic>Performance degradation</topic><topic>Segmentation</topic><topic>Semantics</topic><topic>Stations</topic><topic>Task analysis</topic><topic>Trigger identification</topic><topic>Wireless communication</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yin, Mingyong</creatorcontrib><creatorcontrib>Chen, Xingshu</creatorcontrib><creatorcontrib>Wang, Haizhou</creatorcontrib><creatorcontrib>Wang, Qixu</creatorcontrib><creatorcontrib>Ma, Chenxi</creatorcontrib><creatorcontrib>Qin, Xue</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yin, Mingyong</au><au>Chen, Xingshu</au><au>Wang, Haizhou</au><au>Wang, Qixu</au><au>Ma, Chenxi</au><au>Qin, Xue</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Automatic Event Detection Method for Massive Wireless Access Prediction</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><date>2019</date><risdate>2019</risdate><volume>7</volume><spage>113404</spage><epage>113416</epage><pages>113404-113416</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract>The scale of mobile users for parallel access is constrained by the capacity of the base stations. When extremely dense terminal access exceeds the capacity of the base stations, access failure and a performance degradation will occur. The early detection and prediction of important events and the timely detection of possible large-scale terminal access are significant aspects in ensuring the quality of the communication achieved. For the automatic detection of events, methods based on a neural network can learn features automatically without feature engineering and have been proven to be efficient for event detection. As is well known, constructing an adequate input vector that can represent sufficient information is a challenge to a neural network-based approach, particularly for problems caused by Chinese word segmentation and too many unknown communication words. To cope with this problem, a novel representation method that combines the different features with word vectors is proposed to deal with the problem of Chinese event trigger identification. We then use a gated recurrent unit network to train and predict the event trigger and carry out comparative experiments on different methods and feature combinations. The experiment results of the proposed model show that the F1 value can reach 84% for the experimental dataset. Furthermore, the combination of lexical and syntactic features with a neural network was proven to be helpful for this task, although the contributions vary in magnitude for different features. Our study provides directions for further research on the use of lexical and syntactic features with a neural network for an event detection task.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2019.2934570</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0003-4119-8052</orcidid><orcidid>https://orcid.org/0000-0002-3970-9290</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Base stations Chinese event extraction Event detection Feature extraction GRU Neural networks Performance degradation Segmentation Semantics Stations Task analysis Trigger identification Wireless communication |
title | An Automatic Event Detection Method for Massive Wireless Access Prediction |
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