| 4/2024 - 1 | View TOC | « Previous Article | Next Article » |
Non-parametric Vibration-based Structural Damage Detection for Coastal Structures: Multi-Dimension to Single Input Convolutional Neural Network ApproachDANG, X.-K.   , CORCHADO, J. M. , LE, V.-V. , DO, V.-D. |
Extra paper information in    |
| Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science |
Download PDF  (3,283 KB) | Citation | Downloads: 46 | Views: 49 |
Author keywords
detection algorithms, marine safety, neural networks, risk analysis, surface structures
References keywords
learning(22), ocean(21), damage(21), offshore(20), structures(19), joceaneng(19), detection(19), structural(16), network(15), deep(14)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2024-11-30
Volume 24, Issue 4, Year 2024, On page(s): 3 - 18
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2024.04001
Abstract
Based on multi-station spatial diversity capability, GEO-UAV distributed radar could achieve high-precision aerial target localization with the single-transmitting and multiple-receiving configuration. However, the actual observation area can hardly be covered by several receiving stations simultaneously. Thus, it is necessary to explore a novel target localization method under a single receiving station condition. In this manuscript, an aerial target localization method with GEO-UAV bistatic configuration is presented, where O and AOA measurements are employed. Firstly, measurement models, including bistatic range-delay, pitching AOA, and azimuth AOA, are established using the spatial geometric relationship between the bistatic radar and the target. Then, the receiving range can be estimated using digital beamforming technology based on the receiving array antenna, where the antenna beam coverage information and the prior target altitude information are combined. Finally, the three-dimensional target localization is skillfully derived according to the bistatic configuration, and thus to avoid the parameter unrecognizable problem caused by insufficient degrees of freedom. The proposed algorithm fully exploits the intrinsic correlation characteristics between the measurement information and the bistatic configuration, which provides an effective way for aerial target localization. Simulation results verify the effectiveness of the proposed algorithm. |
| References | | | Cited By «-- Click to see who has cited this paper |
| [1] H. Pezeshki, H. Adeli, D. Pavlou, S. C. Siriwardane, "State of the art in structural health monitoring of offshore and marine structures," Maritime Engineering, vol. 176, no. 2, pp. 89-108, 2023. [CrossRef] [Web of Science Times Cited 64] [SCOPUS Times Cited 69] [2] M. Li, A. Kefal, E. Oterkus, S. Oterkus, "Structural health monitoring of an offshore wind turbine tower using iFEM methodology," Ocean Engineering, vol. 204, pp. 107291, 2020. [CrossRef] [Web of Science Times Cited 69] [SCOPUS Times Cited 75] [3] Y. Zheng, R. Zhang, "Experimental study on the damage characteristic and assessment of transverse bent frame of high-piled wharf under impact load," Developments in the Built Environment, vol. 14, pp. 100124, 2023. [CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 5] [4] K. Li, T. Yu, T. Q. Bui, "Adaptive extended isogeometric upper-bound limit analysis of cracked structures," Engineering Fracture Mechanics, vol. 235, pp. 107131, 2020. [CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 17] [5] V. D. Do, X. K. Dang, T. D. Tran, T. D. A. Pham, "Jacking and energy consumption control over network for jack-up rig: Simulation and experiment," Polish Maritime Research, vol. 29, no. 3, pp. 89-98, 2022. [CrossRef] [Web of Science Record] [SCOPUS Times Cited 3] [6] R. Liu, B. Yang, E. Zio, X. Chen, "Artificial intelligence for fault diagnosis of rotating machinery: a review," Mechanical Systems and Signal Processing, vol. 108, pp. 33-47, 2018. [CrossRef] [Web of Science Times Cited 1335] [SCOPUS Times Cited 1640] [7] H. Salehi, R. Burgueno, "Emerging artificial intelligence methods in structural engineering," Engineering Structures, vol. 171, pp. 170-189, 2018. [CrossRef] [Web of Science Times Cited 552] [SCOPUS Times Cited 695] [8] O. Avci, O. Abdeljaber, S. Kiranyaz, M. Hussein, M. Gabbouj, D. J. Inman, "A review of vibration-based damage detection in civil structures: From traditional methods to machine learning and deep learning applications," Mechanical Systems and Signal Processing, vol. 147, pp. 1-45, 2021. [CrossRef] [Web of Science Times Cited 681] [SCOPUS Times Cited 833] [9] A. Tessler, J. L. Spangler, "A least-squares variational method for full-field recon- struction of elastic deformations in shear-deformable plates and shells," Computer Methods in Applied Mechanics and Engineering, vol. 194, pp. 327-339, 2005. [CrossRef] [Web of Science Times Cited 224] [SCOPUS Times Cited 274] [10] V. D. Do, X. K. Dang, A. T. Le, "Fuzzy adaptive interactive algorithm for rig balancing optimization," 2017 International Conference on Recent Advances in Signal Processing, Telecommunications and Computing, pp. 143-148, Jan. 2017. [CrossRef] [SCOPUS Times Cited 13] [11] V. J. Sharmila, D. A. Jemi Florinabel, "Two-step unsupervised learning approach to diagnose machine fault using big data," Information Technology and Control, vol. 51, no. 1, pp. 78-85, 2022. [CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 3] [12] Z. Mousavi, S. Varahram, M. M. Ettefagh, M. H. Sadeghi, S. N. Razavi, "Deep neural networks-based damage detection using vibration signals of finite element model and real intact State: An evaluation via a lab-scale offshore jacket structure," Structural Health Monitoring, vol. 20, no. 1, pp. 379-405, 2021. [CrossRef] [Web of Science Times Cited 53] [SCOPUS Times Cited 63] [13] S. Xiao, Z. Wang, X. Li, K. A. Harries, Q. Xu, R. Gao, "Study of effects of sleeve grouting defects on the seismic performance of precast concrete shear walls," Engineering Structures, vol. 236, pp. 111833, 2021. [CrossRef] [Web of Science Times Cited 72] [SCOPUS Times Cited 83] [14] Y. J. Kim, K. A. Harries, "Fatigue behavior of damaged steel beams repaired with CFRP strips," Engineering Structures, vol. 33, no. 5, pp. 1491-1502, 2011. [CrossRef] [Web of Science Times Cited 108] [SCOPUS Times Cited 141] [15] X. Zhen, Y. Ning, W. Du, Y. Huang, J. E. Vinnem, "An interpretable and augmented machine-learning approach for causation analysis of major accident risk indicators in the offshore petroleum industry," Process Safety and Environmental Protection, vol. 173, pp. 922-933, 2023. [CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 13] [16] T. Nagao, P. Lu, "A simplified reliability estimation method for pile-supported wharf on the residual displacement by earthquakem," Soil Dynamics and Earthquake Engineering, vol. 129, pp. 105904-105914, 2020. [CrossRef] [Web of Science Times Cited 16] [SCOPUS Times Cited 19] [17] J. Zhang, J. Zhang, S. Teng, G. Chen, Z. Teng, "Structural damage detection based on vibration signal fusion and deep learning," Journal of Vibration Engineering and Technologies, vol. 10, no. 4, pp. 1205-1220, 2022. [CrossRef] [Web of Science Times Cited 12] [SCOPUS Times Cited 15] [18] L. A. H. Ho, V. D. Do, X. K. Dang, T. D. A. Pham, "Early state prediction model for offshore jacket platform structural using EfficientNet-B0 neural network," EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, vol. 11, no. 2, pp. 1-10, 2024. [CrossRef] [SCOPUS Record] [19] Y. Zhang, P. Liu, X. Zhao, "Structural displacement monitoring based on mask regions with convolutional neural network," Construction and Building Materials, vol. 267, pp. 120923, 2021. [CrossRef] [Web of Science Times Cited 22] [SCOPUS Times Cited 24] [20] J. Leng, A. Incecik, M. Wang, S. Feng, Y. Li, C. Yang, Z. Li, "Damage detection of offshore jacket structures using structural vibration measurements: Application of a new hybrid machine learning method," Ocean Engineering, vol. 288, pp. 116078-116096, 2023. [CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 4] [21] M. J. Martinez, "Harbor and coastal structures: A review of mechanical fatigue under random wave loading," Heliyon, vol. 7, no. 10, pp. 08241, 2021. [CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 15] [22] C. Li, Q. Wang, R. Zhu, Y. Zhu, Y. Hu, "Damage identification for pile foundation in high-piled wharf using composite energy factors driven by dynamic response under wave impact excitation," Ocean Engineering, vol. 291, pp. 116286-116299, 2024. [CrossRef] [Web of Science Record] [SCOPUS Times Cited 1] [23] Y. Hu, Q. M. Wang, R. H. Zhu, C. M. Li, N. Wang, "Online robustness damage identification of dynamic response of high pile wharf under wave excitation," Ocean Engineering, vol. 273, pp. 113950-113961, 2023. [CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 5] [24] J. Chen, I. Milne, P. H. Taylor, D. Gunawan, W. Zhao, "Forward prediction of surface wave elevations and motions of offshore floating structures using a data- driven model," Ocean Engineering, vol. 281, pp. 114680-114688, 2023. [CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 9] [25] Z. Lei, G. Liu, Y. Cong, W. Tang, "Research on fatigue damage mitigation of offshore wind turbines by a bi-directional PSTMD under stochastic wind-wave actions," Engineering Structures, vol. 301, pp. 117275, 2024. [CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 7] [26] L. Li, G. Zou, "A novel computational approach for assessing system reliability and damage detection delay: Application to fatigue deterioration in offshore structures," Ocean Engineering, vol. 297, pp. 117275-117289, 2024. [CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 3] [27] F. Goerlandt, S. Islam, "A bayesian network risk model for estimating coastal maritime transportation delays following an earthquake in british columbia," Reliability Engineering and System Safety, vol. 214, pp. 107708-107722, 2021. [CrossRef] [Web of Science Times Cited 32] [SCOPUS Times Cited 34] [28] L. Su, H. P. Wan, Y. Dong, D. Frangopol, X. Z. Ling, "Efficient uncertainty quantification of wharf structures under seismic scenarios using gaussian process surrogate model," Computer Methods in Applied Mechanics and Engineering, vol. 25, no. 1, pp. 117-138, 2021. [CrossRef] [Web of Science Times Cited 23] [SCOPUS Times Cited 22] [29] L. Tang, Z. Zhang, X. Ling, S. Cong, P. Si, "Inertial and kinematic demands of isolated pile-supported wharves in liquefiable soils: Centrifuge tests," Soil Dynamics and Earthquake Engineering, vol. 178, pp. 108441-108458, 2024. [CrossRef] [Web of Science Record] [SCOPUS Times Cited 1] [30] W. Zhang, X. Ren, X. Geng, Y. Zhang, W. Yang, "Study on influence coefficients of hydrodynamic force on dynamic response of deep-water pier under earthquake," Ocean Engineering, vol. 299, pp. 117261-117275, 2024. [CrossRef] [Web of Science Record] [SCOPUS Record] [31] V. Aghaeidoost, S. Afshar, N. Ziaie Tajaddod, B. Asgarian, H. Rahman Shokr-gozar, "Damage detection in jacket-type offshore platforms via generalized flexibility matrix and optimal genetic algorithm (GFM-OGA)," Ocean Engineering, vol. 281, pp. 114841-114854, 2023. [CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 5] [32] Z. Zheng, Z. Chang, L. Zhao, "Mitigating deepwater jacket offshore platform vibration under wave and earthquake loadings utilizing nonlinear energy sinks," Ocean Engineering, vol. 283, pp. 115096-115106, 2023. [CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 8] [33] R. Wang, F. Ji, Y. Jiang, S. H. Wu, S. Kwong, J. Zhang, Z. H. Zhan, "An adaptive ant colony system based on variable range receding horizon control for berth allocation problem," IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 11, pp. 21675-21686, 2022. [CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 14] [34] S. Goksu, O. Arslan, "A quantitative dynamic risk assessment for ship operation using the fuzzy FMEA: The case of ship berthing/unberthing operation," Ocean Engineering, vol. 287, pp. 115548, 2023. [CrossRef] [Web of Science Times Cited 12] [SCOPUS Times Cited 14] [35] R. Skulstad, G. Li, T. I. Fossen, B. Vik, H. Zhang, "A hybrid approach to motion prediction for ship docking - integration of a neural network model into the ship dynamic model," IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-11, 2021. [CrossRef] [Web of Science Times Cited 65] [SCOPUS Times Cited 72] [36] Z. Peng, C. Wang, Y. Yin, J. Wang, "Safety-certified constrained control of maritime autonomous surface ships for automatic berthing," IEEE Transactions on Vehicular Technology, vol. 72, no. 7, pp. 8541-8552, 2023. [CrossRef] [Web of Science Times Cited 19] [SCOPUS Times Cited 26] [37] H. Lin, L. Yang, H. Luan, C. Han, P. Han, H. Xu, G. Chen, "A data-driven assessment model for collision responses of offshore platform structure with ship using hybrid intelligent approaches," Process Safety and Environmental Protection, vol. 167, pp. 225-246, 2022. [CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 6] [38] K. Y. Ma, J. H. Kim, J. S. Park, J. M. Lee, J. K. Seo, "A study on collision strength assessment of a jack-up rig with attendant vessel," International Journal of Naval Architecture and Ocean Engineering, vol. 12, pp. 241-257, 2020. [CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 8] [39] M. P. Mujeeb-Ahmed, J. K. Paik, "A probabilistic approach to determine design loads for collision between an offshore supply vessel and offshore installations," Ocean Engineering, vol. 173, pp. 358-374, 2019. [CrossRef] [Web of Science Times Cited 12] [SCOPUS Times Cited 14] [40] L. Tang, Y. Zhang, X. Ling, S. Tian, "Fuzzy optimization for ground motion intensity measures to characterize the response of the pile-supported wharf in liquehable soils," Ocean Engineering, vol. 265, pp. 112645-112658, 2022. [CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 8] [41] L. Zhou, M. S. Alam, Y. Dong, R. Feng, "Seismic resilience assessment of extended pile shaft supported coastal bridges considering scour and uniform corrosion effects," Engineering Structures, vol. 304, pp. 117643-117657, 2024. [CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 12] [42] S. Babaei, R. Amirabadi, T. Taghikhany, M. Sharifi, "Optimal ground motion intensity measure selection for probabilistic seismic demand modeling of fixed pile-founded offshore platforms," Ocean Engineering, vol. 242, pp. 110116-110142, 2021. [CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 9] [43] M. Hallo, A. Imtiaz, M. Koroni, V. Perron, D. Fa, "Characterization and modeling of ground motion at depth in soft sedimentary rocks: Application to the swiss molasse basin," Soil Dynamics and Earthquake Engineering, vol. 173, pp. 108089-108107, 2023. [CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 1] [44] Y. Shi, L. Lu, S. Fan, C. Liu, C. Zuo, X. Sun, "Theoretical modeling of seafloor spatial earthquake ground motion with random fluctuation sea surface," Ocean Engineering, vol. 280, pp. 114953-114964, 2023. [CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 3] [45] H. F. Burcharth, S. A. Hughes, "Chapter 2 types and functions of coastal structures," Coastal Engineering Manual, vol. 56, 2006 [46] H. Fatemi, S. A. Hadigheh, Y. Tao, G. Adam, "Development of a novel and specialised cementitious matrix overlay for anode embedment in impressed current cathodic protection (ICCP) systems for reinforced concrete bridges," Case Studies in Construction Materials, vol. 20, pp. 02908, 2024. [CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 5] [47] G. Misuriya, T. I. Eldho, B. S. Mazumder, "Turbulent flow field and scour characteristics around bridge pier in combined wave and current conditions," Journal of Waterway, Port, Coastal, and Ocean Engineering, vol. 150, no. 5, pp. 04024010, 2024. [CrossRef] [Web of Science Record] [SCOPUS Record] [48] C. Guo, J. Zou, K. Sun, "Analysis on structural health monitoring system of high-pile wharf based on optical fiber sensor," Journal of Physics: Conference Series, vol. 1181, no. 4, pp. 042018, 2021. [CrossRef] [SCOPUS Times Cited 5] [49] Y. Zhou, Y. Zheng, Y. Liu, T. Pan, Y. Zhou, "A hybrid methodology for structural damage detection uniting FEM and 1D-CNNS: Demonstration on the typical high-pile wharf," Mechanical Systems and Signal Processing, vol. 168, pp. 108738, 2022. [CrossRef] [Web of Science Times Cited 16] [SCOPUS Times Cited 18] [50] L. Su, J. Lu, A. Elgamal, A. K. Arulmoli, "Seismic performance of a pile-supported wharf: Three-dimensional finite element simulation," Soil Dynamics and Earthquake Engineering, vol. 95, pp. 167-179, 2017. [CrossRef] [Web of Science Times Cited 85] [SCOPUS Times Cited 97] [51] C. Zhang, Z. Zhou, G. Hu, L. Yang, S. Tang, "Health assessment of the wharf based on evidential reasoning rule considering optimal sensor placement," Measurement: Journal of the International Measurement Confederation, vol. 186, pp. 110184, 2021. [CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 15] [52] H. B. Liu, Q. Zhang, B. H. Zhang, "Structural health monitoring of a newly built high-piled wharf in a harbor with fiber Bragg grating sensor technology: Design and deployment," Smart Structures and Systems, vol. 20, no. 2, pp. 163-173, 2017. [CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 9] [53] S. Han, B. Li, W. Li, Y. Zhang, P. Liu, "Intelligent analysis of corrosion characteristics of steel pipe piles of offshore construction wharfs based on computer vision," Heliyon, vol. 10, no. 2, pp. 24142, 2024. [CrossRef] [Web of Science Record] [SCOPUS Times Cited 2] [54] B. Liu, X. Wang, X. Liang, "Neural network-based prediction system for port throughput: A case study of ningbo-zhoushan port," Research in Transportation Business and Management, vol. 51, pp. 101067-101079, 2023. [CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 4] [55] M. Shen, F. Shao, Q. Xu, L. Bai, Q. Ma, X. Yan, "Relative motion prediction of pontoon bridge module offshore connection based on deep learning," Ocean Engineering, vol. 286, pp. 115541-115551, 2023. [CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 1] [56] A. Abbasi, F. Nazari, C. Nataraj, "Application of long short-term memory neural network to crack propagation prognostics," 2020 IEEE International Conference on Prognostics and Health Management, pp. 1-6, Jun. 2020. [CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 7] [57] A. A. S. R. Sousa, J. Silva Coelho, M. R. Machado, M. Dutkiewicz, "Multiclass supervised machine learning algorithms applied to damage and assessment using beam dynamic response," Journal of Vibration Engineering and Technologies, vol. 11, no. 6, pp. 2709-2731, 2023. [CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 7] [58] S. Hanumanthappa, "Damage detection in steel beams using generalized flexibility quotient difference based damage index and artificial neural network," Journal of Vibration Engineering and Technologies, vol. 12, no. 2, pp. 2715-2728, 2024. [CrossRef] [Web of Science Record] [SCOPUS Record] [59] N. Wang, M. Wu, K. F. Yuen, "Assessment of port resilience using bayesian network: A study of strategies to enhance readiness and response capacities," Reliability Engineering and System Safety, vol. 237, pp. 109394-109413, 2023. [CrossRef] [Web of Science Times Cited 28] [SCOPUS Times Cited 15] [60] Z. Q. Xiang, J. T. Wang, W. Wang, J. W. Pan, J. F. Liu, Z. J. Le, X. Y. Cai, "Vibration-based health monitoring of the offshore wind turbine tower using machine learning with Bayesian optimisation," Ocean Engineering, vol. 292, pp. 116513-116536, 2024. [CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 7] [61] X. P. Nguyen, X. K. Dang, L. A. H. Ho, H. M. Luu, N. T. Nguyen, "Design of a scalogram-based data acquisition and processing system for a multi-sensor network application for marine structures," Lecture Notes in Civil Engineering, vol. 532, pp. 67-76, 2024. [CrossRef] [SCOPUS Times Cited 1] [62] H. Hejazitalab, T. Taghikhany, "Damage localization and quantification in offshore jacket structures using signal processing and intelligent system," Ocean Engineering, vol. 285, pp. 115325-115347, 2023. [CrossRef] [Web of Science Record] [SCOPUS Times Cited 1] [63] W. Zhang, Z. Lin, X. Liu, "Short-term offshore wind power forecasting - A hybrid model based on discrete wavelet transform (DWT), seasonal autoregressive integrated moving average (SARIMA), and deep-learning-based long short-term memory (LSTM)," Renewable Energy, vol. 185, pp. 611-628, 2022. [CrossRef] [Web of Science Times Cited 116] [SCOPUS Times Cited 136] [64] D. Protic, M. Stankovic, "XOR-based detector of different decisions on anomalies in the computer network traffic," Science and Technology, vol. 26, no. 3-4, pp. 323-338, 2023. [CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 11] [65] I. D. Borlea, R. E. Precup, A. B. Borlea, "Improvement of K-means cluster quality by post processing resulted clusters," Procedia Computer Science, vol. 199, pp. 63-70, 2022. [CrossRef] [Web of Science Times Cited 80] [SCOPUS Times Cited 95] [66] A. Asghari, G. Ghodrati Amiri, E. Darvishan, A. Asghari, "A novel approach for structural damage detection using multi-headed stacked deep ensemble learning," Journal of Vibration Engineering and Technologies, vol. 12, no. 3, pp. 4209-4224, 2024. [CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 2] [67] I. D. Borlea, R. E. Precup, F. Dragan, A. B. Borlea, "Centroid update approach to K-means clustering," Advances in Electrical & Computer Engineering, vol. 17, no. 4, pp. 3-10, 2017. [CrossRef] [Full Text] [Web of Science Times Cited 20] [SCOPUS Times Cited 30] [68] I. D. Borlea, R. E. Precup, A. B. Borlea, D. Iercan, "A unified form of fuzzy C-means and K-means algorithms and its partitional implementation," Knowledge-Based Systems, vol. 214, p. 106731, 2021. [CrossRef] [Web of Science Times Cited 125] [SCOPUS Times Cited 145] [69] C. Zhang, Y. Lu, "Study on artificial intelligence: The state of the art and future prospects," Journal of Industrial Information Integration, vol. 23, pp. 100224, 2021. [CrossRef] [Web of Science Times Cited 352] [SCOPUS Times Cited 525] [70] Y. Zhang, K. V. Yuen, "Crack detection using fusion features-based broad learning system and image processing," Computer-Aided Civil and Infrastructure Engineering, vol. 36, no. 12, pp. 1568-1584, 2021. [CrossRef] [Web of Science Times Cited 90] [SCOPUS Times Cited 95] [71] R. He, H. Li, J. Tang, Z. Hu, Z. Zhang, "Experimental method for modal parameter identification of civil engineering structures based on improved wavelet transform," Results in Engineering, vol. 20, pp. 101402, 2023. [CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 1] [72] X. Li, M. Chen, Y. Liu, Z. Zhang, D. Liu, S. Mao, "Graph neural networks for joint communication and sensing optimization in vehicular networks," IEEE Journal on Selected Areas in Communications, vol. 41, no. 12, pp. 3893-3907, 2023. [CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 6] [73] K. Yang, C. Shi, C. Shen, J. Yang, S. P. Yeh, J. J. Sydir, "Offline reinforcement learning for wireless network optimization with mixture datasets," IEEE Transactions on Wireless Communications, vol. 23, no. 10, pp. 12703-12716, 2024. [CrossRef] [Web of Science Record] [SCOPUS Times Cited 3] [74] X. K. Dang, H. N. Truong, V. C. Nguyen, T. D. A. Pham, "Applying convolutional neural networks for limited-memory application," Telkomnika, vol. 19, no. 1, pp. 244-251, 2021. [CrossRef] [SCOPUS Times Cited 8] [75] V. P. Ta, X. K. Dang, V. H. Dong, V. D. Do, "Designing dynamic positioning system based on robust recurrent cerebellar model articulation controller," The 4th International Conference on Green Technology and Sustainable Development, pp. 652-657, Nov. 2018. [CrossRef] [SCOPUS Times Cited 7] [76] K. Chen, J. Yang, Q. Li, X. Ge, "Sub-array hybrid precoding for massive mimo systems: A CNN-based approach," IEEE Communications Letters, vol. 25, no. 1, pp. 191-195, 2020. [CrossRef] [Web of Science Times Cited 12] [77] J. Zhang, K. Zhang, "Improved multi-objective sensor optimization method for structural damage identification based on genetic algorithm," 2019 2nd International Symposium on Traffic Transportation and Civil Architecture, pp. 32022-32026, Dec. 2019. [CrossRef] [SCOPUS Times Cited 4] [78] M. Lei, Q. Li, X. Ge, A. Pandharipande, "Partially collaborative edge caching based on federated deep reinforcement learning," IEEE Transactions on Vehicular Technology," vol. 72, no. 1, pp. 1389-1394, 2023. [CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 5] [79] T. Q. Nguyen, "A data-driven approach to structural health monitoring of bridge structures based on the discrete model and FFT-deep learning," Journal of Vibration Engineering and Technologies, vol. 9, no. 8, pp. 1959-1981, 2021. [CrossRef] [Web of Science Times Cited 17] [SCOPUS Times Cited 18] [80] S. Vasuhi, V. Vaidehi, "Target tracking using Interactive Multiple Model for Wireless Sensor Network," Information Fusion, vol. 27, pp. 41-53, 2016. [CrossRef] [Web of Science Times Cited 82] [SCOPUS Times Cited 96] [81] Z. Wang, J. Wu, J. Yan, "Distributed estimation of a spatially correlated random field in decentralized sensor networks," 2017 IEEE International Conference on Communications, pp. 1-6, May 2017. [CrossRef] [SCOPUS Times Cited 1] [82] Z. Jiahao, G. Shesheng, L. Guo, X. Juan, Q. Xiaomin, G. Bingbing, "Distributed recursive filtering for multi-sensor networked systems with multi-step sensor delays, missing measurements, and correlated noise," Signal Processing, vol. 181, pp. 107868-107892, 2021. [CrossRef] [Web of Science Times Cited 30] [SCOPUS Times Cited 36] [83] H. Wang, W. Mao, L. Eriksson, "A three-dimensional Dijkstraâs algorithm for multi-objective ship voyage optimization," Ocean Engineering, vol. 186, pp. 106131-106146, 2019. [CrossRef] [Web of Science Times Cited 88] [SCOPUS Times Cited 118] [84] S. Garrido, M. Malfaz, D. Blanco, "Application of the fast marching method for outdoor motion planning in robotics," Robotics and Autonomous Systems, vol. 61, no. 2, pp. 106-114, 2013. [CrossRef] [Web of Science Times Cited 34] [SCOPUS Times Cited 39] [85] E. Treister, E. Haber, "A fast marching algorithm for the factored eikonal equation," Journal of Computational Physics, vol. 324, pp. 210-225, 2016. [CrossRef] [Web of Science Times Cited 42] [SCOPUS Times Cited 51] [86] X. K. Dang, V. C. Nguyen, T. P. Nguyen, T. D. A. Pham, C. P. Vo, "A vision based system design for over-sized vessel detecting and warning using convolutional neural network," Lecture Notes of the Institute for Computer Sciences, Social- Informatics and Telecommunications Engineering, vol. 379, pp. 416-430, 2021. [CrossRef] [SCOPUS Times Cited 3] [87] M. A. Khan, S. Kadry, P. Parwekar, R. DamaÅ¡eviÄius, A. Mehmood, J. A. Khan, S. R. Naqvi, "Human gait analysis for osteoarthritis prediction: A framework of deep learning and kernel extreme learning machine," Complex and Intelligent Systems, vol. 9, no. 3, pp. 2665-2683, 2023. [CrossRef] [Web of Science Times Cited 33] [SCOPUS Times Cited 28] [88] T. Uktveris, V. Jusas, "Application of convolutional neural networks to four-class motor imagery classification problem," Information Technology and Control, vol. 46, no. 2, pp. 94-107, 2017. [CrossRef] [Web of Science Times Cited 52] [SCOPUS Times Cited 62] [89] X. K. Dang, L. A. H. Ho, X. P. Nguyen, B. L. Mai, "Applying artihcial intelligence for the application of bridges deterioration detection system," Telkomnika, vol. 20, no. 1, pp. 149-157, 2022. [CrossRef] [SCOPUS Times Cited 6] [90] S. Jadon, A. A. Srinivasan, "Improving siamese networks for one-shot learning using kernel-based activation functions," Data Management, Analytics and Innovation, Advances in Intelligent Systems and Computing, vol. 1175, pp. 353- 367, 2021. [CrossRef] [SCOPUS Times Cited 12] [91] M. Nobahari, S. M. Seyedpoor, "Structural damage detection using an efficient correlation-based index and a modified genetic algorithm," Mathematical and Computer Modelling, vol. 53, no. 9-10, pp. 1798-1809, 2021. [CrossRef] [Web of Science Times Cited 80] [SCOPUS Times Cited 95] [92] A. Messina, E. J. Williams, T. Contursi, "Structural damage detection by a sensitivity and statistical-based method," Journal of Sound and Vibration, vol. 216, pp. 791-808, 1998. [CrossRef] [Web of Science Times Cited 314] [SCOPUS Times Cited 391] [93] A. Shahnejat Bushehri, A. Amirnia, A. Belkhiri, S. Keivanpour, F. G. De Magalhaes, G. Nicolescu, "Deep learning-driven anomaly detection for green IoT edge networks," IEEE Transactions on Green Communications and Networking, vol. 8, no. 1, pp. 498-513, 2024. [CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 2] [94] H. Li, Y. Hu, L. Li, D. Xu, "Influence of backing layer on the non-metallic encapsulated acoustic emission sensor for concrete monitoring," Case Studies in Construction Materials, vol. 19, pp. 02416, 2023. [CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 3] [95] X. Zhang, J. Zou, K. He, J. Sun, "Accelerating very deep convolutional networks for classihcation and detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 10, pp. 1943-1955, 2016. [CrossRef] [Web of Science Times Cited 510] [SCOPUS Times Cited 649] [96] K. Qiu, Y. Ai, B. Tian, B. Wang, D. Cao, "Siamese-resnet: Implementing loop closure detection based on siamese network," 2018 IEEE Intelligent Vehicles Symposium (IV), pp. 716-721, Jun. 2018. [CrossRef] [SCOPUS Times Cited 13] [97] S. Sykiotis, S. Athanasoulias, M. Kaselimi, A. Doulamis, N. Doulamis, L. Stankovic, V. Stankovic, "Performance-aware nilm model optimization for edge deployment," IEEE Transactions on Green Communications and Networking, vol. 7, no. 3, pp. 1434-1446, 2023. [CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 16] [98] M. Weil, N. Sadeghi, N. Noppe, W. Weijtjens, C. Devriendt, "Machine learning based predictive modelling of a steel railway bridge for damage modelling of train passages and different usage scenario," Lecture Notes in Civil Engineering, vol. 270, pp. 320-329, 2023. [CrossRef] [Web of Science Record] [SCOPUS Record] [99] Z. Huang, X. Yin, Y. Liu, S. Tang, "Bridge scour detection method based on siamese neural networks under bridge-vehicle-wave interaction," Ocean Engineering, vol. 290, pp. 116327-116344, 2016. [CrossRef] [Web of Science Record] [SCOPUS Times Cited 1] [100] J. Kun, Z. Zhenhai, Y. Jiale, D. Jianwu, "A deep learning-based method for pixel-level crack detection on concrete bridges," IET Image Processing, vol. 16, no. 10, pp. 2609-2622, 2022, [CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 12] Web of Science® Citations for all references: 5,813 TCR SCOPUS® Citations for all references: 7,205 TCR Web of Science® Average Citations per reference: 58 ACR SCOPUS® Average Citations per reference: 72 ACR TCR = Total Citations for References / ACR = Average Citations per Reference We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more Citations for references updated on 2024-12-01 19:18 in 662 seconds. Note1: Web of Science® is a registered trademark of Clarivate Analytics. Note2: SCOPUS® is a registered trademark of Elsevier B.V. Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site. |
Copyright ©2001-2024
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
