Research on stress curve clustering algorithm of Fiber Bragg grating sensor
The global stress distribution and state parameter analysis of the building's main structure is an urgent problem to be solved in the online state assessment technology of building structure health. In this paper, a stress curve clustering algorithm of fiber Bragg grating stress sensor based on...
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| Veröffentlicht in: | Scientific reports Jg. 13; H. 1; S. 11815 - 12 |
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| Abstract | The global stress distribution and state parameter analysis of the building's main structure is an urgent problem to be solved in the online state assessment technology of building structure health. In this paper, a stress curve clustering algorithm of fiber Bragg grating stress sensor based on density clustering algorithm is proposed. To solve the problem of large dimension and sparse sample space of sensor stress curve, the distance between samples is measured based on improved cosine similarity. Aiming at the problem of low efficiency and poor effect of traditional clustering algorithm, density clustering algorithm based on mutual nearest neighbor is used to cluster. Finally, the classification of the daily stress load characteristics of the sensor is realized, which provides a basis for constructing the mathematical analysis model of building health. The experimental results show that the stress curve clustering method proposed in this paper is better than the latest clustering algorithms such as HDBSCAN, CBKM, K-mean++,FINCH and NPIR, and is suitable for the feature classification of stress curves of fiber Bragg grating sensors. |
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| AbstractList | The global stress distribution and state parameter analysis of the building's main structure is an urgent problem to be solved in the online state assessment technology of building structure health. In this paper, a stress curve clustering algorithm of fiber Bragg grating stress sensor based on density clustering algorithm is proposed. To solve the problem of large dimension and sparse sample space of sensor stress curve, the distance between samples is measured based on improved cosine similarity. Aiming at the problem of low efficiency and poor effect of traditional clustering algorithm, density clustering algorithm based on mutual nearest neighbor is used to cluster. Finally, the classification of the daily stress load characteristics of the sensor is realized, which provides a basis for constructing the mathematical analysis model of building health. The experimental results show that the stress curve clustering method proposed in this paper is better than the latest clustering algorithms such as HDBSCAN, CBKM, K-mean++,FINCH and NPIR, and is suitable for the feature classification of stress curves of fiber Bragg grating sensors.The global stress distribution and state parameter analysis of the building's main structure is an urgent problem to be solved in the online state assessment technology of building structure health. In this paper, a stress curve clustering algorithm of fiber Bragg grating stress sensor based on density clustering algorithm is proposed. To solve the problem of large dimension and sparse sample space of sensor stress curve, the distance between samples is measured based on improved cosine similarity. Aiming at the problem of low efficiency and poor effect of traditional clustering algorithm, density clustering algorithm based on mutual nearest neighbor is used to cluster. Finally, the classification of the daily stress load characteristics of the sensor is realized, which provides a basis for constructing the mathematical analysis model of building health. The experimental results show that the stress curve clustering method proposed in this paper is better than the latest clustering algorithms such as HDBSCAN, CBKM, K-mean++,FINCH and NPIR, and is suitable for the feature classification of stress curves of fiber Bragg grating sensors. Abstract The global stress distribution and state parameter analysis of the building's main structure is an urgent problem to be solved in the online state assessment technology of building structure health. In this paper, a stress curve clustering algorithm of fiber Bragg grating stress sensor based on density clustering algorithm is proposed. To solve the problem of large dimension and sparse sample space of sensor stress curve, the distance between samples is measured based on improved cosine similarity. Aiming at the problem of low efficiency and poor effect of traditional clustering algorithm, density clustering algorithm based on mutual nearest neighbor is used to cluster. Finally, the classification of the daily stress load characteristics of the sensor is realized, which provides a basis for constructing the mathematical analysis model of building health. The experimental results show that the stress curve clustering method proposed in this paper is better than the latest clustering algorithms such as HDBSCAN, CBKM, K-mean++,FINCH and NPIR, and is suitable for the feature classification of stress curves of fiber Bragg grating sensors. The global stress distribution and state parameter analysis of the building's main structure is an urgent problem to be solved in the online state assessment technology of building structure health. In this paper, a stress curve clustering algorithm of fiber Bragg grating stress sensor based on density clustering algorithm is proposed. To solve the problem of large dimension and sparse sample space of sensor stress curve, the distance between samples is measured based on improved cosine similarity. Aiming at the problem of low efficiency and poor effect of traditional clustering algorithm, density clustering algorithm based on mutual nearest neighbor is used to cluster. Finally, the classification of the daily stress load characteristics of the sensor is realized, which provides a basis for constructing the mathematical analysis model of building health. The experimental results show that the stress curve clustering method proposed in this paper is better than the latest clustering algorithms such as HDBSCAN, CBKM, K-mean++,FINCH and NPIR, and is suitable for the feature classification of stress curves of fiber Bragg grating sensors. |
| ArticleNumber | 11815 |
| Author | Qu, Huichen Lin, Yisen Wang, Ye Xiong, Yiwen |
| Author_xml | – sequence: 1 givenname: Yisen surname: Lin fullname: Lin, Yisen organization: School of Computer Science and Engineering, Guilin University of Aerospace Technology – sequence: 2 givenname: Ye surname: Wang fullname: Wang, Ye email: wangye@guat.edu.cn organization: School of Computer Science and Engineering, Guilin University of Aerospace Technology – sequence: 3 givenname: Huichen surname: Qu fullname: Qu, Huichen organization: School of Computer Science and Engineering, Guilin University of Aerospace Technology – sequence: 4 givenname: Yiwen surname: Xiong fullname: Xiong, Yiwen organization: School of Computer Science and Engineering, Guilin University of Aerospace Technology |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37479882$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.patcog.2019.04.014 10.1088/1361-665X/aa9797 10.1007/978-3-030-81716-9_16 10.1016/j.neucom.2015.10.020 10.1016/j.patcog.2020.107589 10.1109/TNN.2005.845141 10.5121/ijdms.2013.5108 10.1016/j.measurement.2013.07.029 10.3788/YJYXS20203502.0173 10.3390/s20010110 10.1016/j.engstruct.2015.04.024 10.1049/el.2016.2810 10.1109/ACCESS.2022.3229582 10.1016/j.patrec.2019.10.019 10.1145/2733381 10.1016/j.neucom.2015.05.109 10.1016/j.patcog.2016.03.008 10.1016/j.optcom.2014.12.079 10.1126/science.1242072 10.1016/j.optcom.2021.127286 10.21105/joss.00205 10.1007/s13042-019-01027-z 10.1016/j.is.2012.09.001 10.1137/1.9781611973440.47 10.1109/CVPR.2019.00914 10.1145/2723372.2737792 |
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| References | Fränti, Sieranoja (CR17) 2019; 93 Lv, Ma, Tang, Cao, Tian, Al-Dhelaan, Al-Rodhaan (CR11) 2016; 171 CR19 McInnes, Healy, Astels (CR23) 2017; 2 CR14 Alex, Alessandro (CR26) 2014; 344 Hayle, Manie, Dehnaw (CR6) 2021; 499 Gao, Hu (CR15) 2020; 35 Qaddoura, Faris, Aljarah (CR27) 2020; 11 Ester, Kriegel, Sander (CR18) 1996 Tan, Hu, Lin (CR2) 2007; 29 Zhang, Wang, Liang (CR5) 2015; 343 Campello, Moulavi, Zimek, Sander (CR12) 2015; 10 Cassisi, Ferro, Giugno, Pigola, Pulvirenti (CR21) 2013; 38 Song, Liu, Cheng, Wei, Yu, Huang, Liang (CR4) 2018; 38 Liu, Sun, Chen, Liu, Zhong (CR13) 2016; 175 Sierra-Pérez, Torres-Arredondo, Alvarez-Montoya (CR9) 2017 Otair (CR29) 2013; 5 Luckey, Fritz, Legatiuk (CR10) 2021 Kumar, Reddy (CR20) 2016; 58 Wang, Chen, Yu (CR30) 2017; 53 CR24 Kahandawa, Epaarachchi, Wang (CR3) 2013; 46 CR22 Lin, Zhang, Liu, Qu (CR31) 2022; 10 Zhang, Guo, Wu (CR8) 2015; 99 Cheng, Wu, Liu (CR1) 2018; 40 Rui, Wunsch (CR16) 2005; 16 Abbas, El-Zoghabi, Shoukry (CR28) 2021; 109 Jiang, Qiao, Li, Luo, Shen, Wu, Zhang (CR7) 2019; 20 Sieranoja, Fränti (CR25) 2019; 128 R Qaddoura (39058_CR27) 2020; 11 39058_CR19 X Gao (39058_CR15) 2020; 35 R Alex (39058_CR26) 2014; 344 M Abbas (39058_CR28) 2021; 109 X Wang (39058_CR30) 2017; 53 C Cassisi (39058_CR21) 2013; 38 KM Kumar (39058_CR20) 2016; 58 M Otair (39058_CR29) 2013; 5 X Rui (39058_CR16) 2005; 16 39058_CR14 D Luckey (39058_CR10) 2021 Y Lin (39058_CR31) 2022; 10 XL Zhang (39058_CR5) 2015; 343 ST Hayle (39058_CR6) 2021; 499 YH Lv (39058_CR11) 2016; 171 S Sieranoja (39058_CR25) 2019; 128 QG Tan (39058_CR2) 2007; 29 SF Jiang (39058_CR7) 2019; 20 L Liu (39058_CR13) 2016; 175 RJGB Campello (39058_CR12) 2015; 10 XG Song (39058_CR4) 2018; 38 J Sierra-Pérez (39058_CR9) 2017 Y Cheng (39058_CR1) 2018; 40 P Fränti (39058_CR17) 2019; 93 39058_CR24 GC Kahandawa (39058_CR3) 2013; 46 39058_CR22 L McInnes (39058_CR23) 2017; 2 M Ester (39058_CR18) 1996 J Zhang (39058_CR8) 2015; 99 |
| References_xml | – volume: 93 start-page: 95 year: 2019 end-page: 112 ident: CR17 article-title: How much can k-means be improved by using better initialization and repeats? publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2019.04.014 – ident: CR22 – year: 2017 ident: CR9 article-title: Damage detection methodology under variable load conditions based on strain field pattern recognition using FBGs, nonlinear principal component analysis, and clustering techniques publication-title: Smart Mater. Struct. doi: 10.1088/1361-665X/aa9797 – year: 2021 ident: CR10 article-title: Explainable artificial intelligence to advance structural health monitoring publication-title: Struct. Health Monitor. Based Data Sci. Techn. doi: 10.1007/978-3-030-81716-9_16 – volume: 175 start-page: 65 year: 2016 end-page: 80 ident: CR13 article-title: K-PRSCAN: A clustering method based on PageRank publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.10.020 – volume: 109 year: 2021 ident: CR28 article-title: DenMune: Density peak based clustering using mutual nearest neighbors publication-title: Pattern Recogn. doi: 10.1016/j.patcog.2020.107589 – volume: 16 start-page: 645 year: 2005 end-page: 678 ident: CR16 article-title: Survey of clustering algorithms publication-title: IEEE Trans. Neural Netw. doi: 10.1109/TNN.2005.845141 – ident: CR14 – volume: 5 start-page: 97 issue: 1 year: 2013 end-page: 108 ident: CR29 article-title: Approximate K-nearest neighbour based spatial clustering using K- D tree publication-title: Int. J. Database Manag. Syst. doi: 10.5121/ijdms.2013.5108 – volume: 38 start-page: 165 year: 2018 end-page: 172 ident: CR4 article-title: An algorithm of dynamic load identification based on FBG sensor and Kalman filter publication-title: Acta Optic. Sin. – volume: 46 start-page: 4045 issue: 10 year: 2013 end-page: 4051 ident: CR3 article-title: Extraction and processing of real time strain of embedded FBG sensors using a fixed filter FBG circuit and an artificial neural network publication-title: Meas. J. Int. Meas. Confed. doi: 10.1016/j.measurement.2013.07.029 – volume: 35 start-page: 173 year: 2020 end-page: 179 ident: CR15 article-title: Digital image clustering based on improved k-means algorithm publication-title: Chin. J. Liq. Cryst. Disp. doi: 10.3788/YJYXS20203502.0173 – year: 1996 ident: CR18 publication-title: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise – volume: 20 start-page: 110 issue: 1 year: 2019 ident: CR7 article-title: Structural health monitoring system based on FBG sensing technique for Chinese ancient timber buildings publication-title: Sensors doi: 10.3390/s20010110 – volume: 99 start-page: 173 year: 2015 end-page: 183 ident: CR8 article-title: Structural identification and damage detection through long-gauge strain measurements publication-title: Eng. Struct. doi: 10.1016/j.engstruct.2015.04.024 – volume: 53 start-page: 156 issue: 3 year: 2017 end-page: 158 ident: CR30 article-title: Optimised quantisation method for approximate nearest neighbour search publication-title: Electron. Lett. doi: 10.1049/el.2016.2810 – volume: 10 start-page: 132031 year: 2022 end-page: 132039 ident: CR31 article-title: DEDIC: Density estimation clustering method using directly interconnected cores publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3229582 – volume: 128 start-page: 551 year: 2019 end-page: 558 ident: CR25 article-title: Fast and general density peaks clustering publication-title: Pattern Recogn. Lett. doi: 10.1016/j.patrec.2019.10.019 – volume: 29 start-page: 696 year: 2007 end-page: 698 ident: CR2 article-title: A novel kind of multi-access interference cancellation scheme based on fiber Bragg gratings publication-title: J. Electron. Inf. Technol. – volume: 10 start-page: 1 year: 2015 end-page: 51 ident: CR12 article-title: Hierarchical density estimates for data clustering, visualization, and outlier detection publication-title: ACM Trans. Knowl. Discov. Data doi: 10.1145/2733381 – ident: CR19 – volume: 171 start-page: 9 year: 2016 end-page: 22 ident: CR11 article-title: An efficient and scalable density-based clustering algorithm for datasets with complex structures publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.05.109 – volume: 40 start-page: 386 year: 2018 end-page: 393 ident: CR1 article-title: A repaired algorithm based on improved compressed sensing to repair damaged fiber bragg grating sensing signal publication-title: J. Electron. Inf. Technol. – volume: 58 start-page: 39 year: 2016 end-page: 48 ident: CR20 article-title: A fast DBSCAN clustering algorithm by accelerating neighbor searching using groups method publication-title: Pattern Recogn. J. Pattern Recogn. Soc. doi: 10.1016/j.patcog.2016.03.008 – volume: 343 start-page: 38 year: 2015 end-page: 46 ident: CR5 article-title: A soft self-repairing for FBG sensor network in SHM system based on PSO-SVR model reconstruction publication-title: Optic. Commun. doi: 10.1016/j.optcom.2014.12.079 – volume: 344 start-page: 1492 year: 2014 end-page: 1496 ident: CR26 article-title: Clustering by fast search and find of density peaks publication-title: Science doi: 10.1126/science.1242072 – volume: 499 issue: 1 year: 2021 ident: CR6 article-title: Reliable self-healing FBG sensor network for improvement of multipoint strain sensing publication-title: Optic. Commun. doi: 10.1016/j.optcom.2021.127286 – volume: 2 start-page: 205 issue: 11 year: 2017 ident: CR23 article-title: Hdbscan: Hierarchical density based clustering publication-title: J. Open Sour. Softw. doi: 10.21105/joss.00205 – volume: 11 start-page: 675 issue: 3 year: 2020 end-page: 714 ident: CR27 article-title: An efficient clustering algorithm based on the k-nearest neighbors with an indexing ratio publication-title: Int. J. Mach. Learn. Cybern. doi: 10.1007/s13042-019-01027-z – volume: 38 start-page: 317 year: 2013 end-page: 330 ident: CR21 article-title: Enhancing density-based clustering: Parameter reduction and outlier detection publication-title: Inf. Syst. doi: 10.1016/j.is.2012.09.001 – ident: CR24 – volume: 35 start-page: 173 year: 2020 ident: 39058_CR15 publication-title: Chin. J. Liq. Cryst. Disp. doi: 10.3788/YJYXS20203502.0173 – volume: 58 start-page: 39 year: 2016 ident: 39058_CR20 publication-title: Pattern Recogn. J. Pattern Recogn. Soc. doi: 10.1016/j.patcog.2016.03.008 – volume: 40 start-page: 386 year: 2018 ident: 39058_CR1 publication-title: J. Electron. Inf. Technol. – year: 2021 ident: 39058_CR10 publication-title: Struct. Health Monitor. Based Data Sci. Techn. doi: 10.1007/978-3-030-81716-9_16 – volume: 46 start-page: 4045 issue: 10 year: 2013 ident: 39058_CR3 publication-title: Meas. J. Int. Meas. Confed. doi: 10.1016/j.measurement.2013.07.029 – volume: 344 start-page: 1492 year: 2014 ident: 39058_CR26 publication-title: Science doi: 10.1126/science.1242072 – volume: 109 year: 2021 ident: 39058_CR28 publication-title: Pattern Recogn. doi: 10.1016/j.patcog.2020.107589 – volume: 343 start-page: 38 year: 2015 ident: 39058_CR5 publication-title: Optic. Commun. doi: 10.1016/j.optcom.2014.12.079 – volume: 10 start-page: 132031 year: 2022 ident: 39058_CR31 publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3229582 – volume: 38 start-page: 317 year: 2013 ident: 39058_CR21 publication-title: Inf. Syst. doi: 10.1016/j.is.2012.09.001 – ident: 39058_CR22 doi: 10.1137/1.9781611973440.47 – year: 2017 ident: 39058_CR9 publication-title: Smart Mater. Struct. doi: 10.1088/1361-665X/aa9797 – ident: 39058_CR14 – volume: 38 start-page: 165 year: 2018 ident: 39058_CR4 publication-title: Acta Optic. Sin. – volume: 99 start-page: 173 year: 2015 ident: 39058_CR8 publication-title: Eng. Struct. doi: 10.1016/j.engstruct.2015.04.024 – volume: 175 start-page: 65 year: 2016 ident: 39058_CR13 publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.10.020 – ident: 39058_CR24 doi: 10.1109/CVPR.2019.00914 – volume: 11 start-page: 675 issue: 3 year: 2020 ident: 39058_CR27 publication-title: Int. J. Mach. Learn. Cybern. doi: 10.1007/s13042-019-01027-z – volume: 5 start-page: 97 issue: 1 year: 2013 ident: 39058_CR29 publication-title: Int. J. Database Manag. Syst. doi: 10.5121/ijdms.2013.5108 – volume: 499 issue: 1 year: 2021 ident: 39058_CR6 publication-title: Optic. Commun. doi: 10.1016/j.optcom.2021.127286 – volume: 29 start-page: 696 year: 2007 ident: 39058_CR2 publication-title: J. Electron. Inf. Technol. – volume: 10 start-page: 1 year: 2015 ident: 39058_CR12 publication-title: ACM Trans. Knowl. Discov. Data doi: 10.1145/2733381 – volume: 93 start-page: 95 year: 2019 ident: 39058_CR17 publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2019.04.014 – volume-title: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise year: 1996 ident: 39058_CR18 – volume: 16 start-page: 645 year: 2005 ident: 39058_CR16 publication-title: IEEE Trans. Neural Netw. doi: 10.1109/TNN.2005.845141 – volume: 171 start-page: 9 year: 2016 ident: 39058_CR11 publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.05.109 – volume: 128 start-page: 551 year: 2019 ident: 39058_CR25 publication-title: Pattern Recogn. Lett. doi: 10.1016/j.patrec.2019.10.019 – ident: 39058_CR19 doi: 10.1145/2723372.2737792 – volume: 2 start-page: 205 issue: 11 year: 2017 ident: 39058_CR23 publication-title: J. Open Sour. Softw. doi: 10.21105/joss.00205 – volume: 20 start-page: 110 issue: 1 year: 2019 ident: 39058_CR7 publication-title: Sensors doi: 10.3390/s20010110 – volume: 53 start-page: 156 issue: 3 year: 2017 ident: 39058_CR30 publication-title: Electron. Lett. doi: 10.1049/el.2016.2810 |
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| Title | Research on stress curve clustering algorithm of Fiber Bragg grating sensor |
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