典型文献
Underground Pipeline Surveillance with an Algorithm Based on Statistical Time-Frequency Acoustic Features
文献摘要：
Underground pipeline networks suffer from severe damage by earth-moving devices due to rapid urbanization.Thus,designing a round-the-clock intelligent surveillance system has become crucial and urgent.In this study,we develop an acoustic signal-based excavation device recognition system for underground pipeline protection.The front-end hardware system is equipped with an acoustic sensor array,an Analog-to-Digital Converter(ADC)module(ADS1274),and an industrial processor Advanced RISC Machine(ARM)cortex-A8 for signal collection and algorithm implementation.Then,a novel Statistical Time-Frequency acoustic Feature(STFF)is proposed,and a fast Extreme Learning Machine(ELM)is adopted as the classifier.Experiments on real recorded data show that the proposed STFF achieves better discriminative capability than the conventional acoustic cepstrum features.In addition,the surveillance platform is applicable for encountering big data owing to the fast learning speed of ELM.
文献关键词：
中图分类号：
作者姓名：
Tianlei Wang;Jiuwen Cao;Ru Xu;Jianzhong Wang
作者机构：
Artificial Intelligence Institute,and also with the Key Lab for IoT and Information Fusion Technology of Zhejiang,Hangzhou Dianzi University,Hangzhou 310018,China;Zhejiang Sanhua Automotive Components Co.Ltd.,Hangzhou 310008,China
文献出处：
引用格式：
[1]Tianlei Wang;Jiuwen Cao;Ru Xu;Jianzhong Wang-.Underground Pipeline Surveillance with an Algorithm Based on Statistical Time-Frequency Acoustic Features)[J].清华大学学报自然科学版（英文版）,2022(02):358-371
A类：
ADS1274,STFF
B类：
Underground,Pipeline,Surveillance,Algorithm,Based,Statistical,Time,Frequency,Acoustic,Features,pipeline,networks,suffer,from,severe,damage,by,earth,moving,devices,due,rapid,urbanization,Thus,designing,clock,intelligent,surveillance,system,has,become,crucial,urgent,In,this,study,we,develop,acoustic,signal,excavation,recognition,underground,protection,front,end,hardware,equipped,sensor,array,Analog,Digital,Converter,ADC,module,industrial,processor,Advanced,RISC,Machine,ARM,cortex,A8,collection,algorithm,implementation,Then,novel,proposed,fast,Extreme,Learning,ELM,adopted,classifier,Experiments,real,recorded,data,show,that,achieves,better,discriminative,capability,than,conventional,cepstrum,features,addition,platform,applicable,encountering,big,owing,learning,speed
AB值：
0.684568
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机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
