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Efficient modal parameter identification using DMD-DBSCAN and rank stabilization diagrams

AEROSPACE SCIENCE AND TECHNOLOGY 2025年 161卷

作者： Wu, Chengyuan Yang, Zhichun He, Shun Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China Natl Key Lab Strength & Struct Integr Xian 710072 Peoples R China

Modal parameter identification of aerospace structures, particularly improving the efficient using time-domain vibration responses, is currently a topic of great interest in structural dynamics. Data-driven Dynamic Mode Decomposition (DMD) technique in fluid dynamics has shown good potential for identifying the flow mode. In this respect, there have been a few studies on the application of DMD technique in structural dynamics. This paper proposes an efficient structural modal parameter identification method that combines the DMD technique and density-based spatial clustering of applications with Noise (DBSCAN) algorithm, called the DMD-DBSCAN method. Combined with the proposed rank stabilization diagram, the spurious modes produced by the DMD technique can be effectively cleaned by using DBSCAN algorithm to obtain the physical modal parameter. A numerical case of a composite wing model is presented to validate the proposed method, particularly when identifying the closely spaced modes. It has been shown that the rank stabilization diagram is more appropriate for DMD technique when compared with the solely existing sampling frequency stabilization diagram. An experiment on an aircraft model with response from three-dimensional optical technique is used to demonstrate the method's ability to deal with real complex structures. Compared with traditional methods, results show that the proposed DMD-DBSCAN method can accurately identify modal parameters of complex structures and has excellent operational efficiency because it is free from high order Hankel matrix construction. The proposed datadriven modal parameter identification method is efficient and promising for analysis of complex structures with large datasets.

关键词： Dynamic mode decomposition Data-driven density-based spatial clustering of applications with noise modal parameter identification stabilization diagram

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Adaptive sensor attack detection and defense framework for autonomous vehicles based on density

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COMPUTERS & SECURITY 2025年 148卷

作者： Miao, Zujia Shao, Cuiping Li, Huiyun Cui, Yunduan Tang, Zhimin Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Guangdong Peoples R China Univ Chinese Acad Sci Beijing 10049 Peoples R China Shenzhen Univ Adv Technol Guangdong Prov Key Lab Computil Microelect Shenzhen 518055 Guangdong Peoples R China

The security of autonomous vehicles heavily depends on localization systems that integrate multiple sensors, which are vulnerable to sensor attacks and increase the risk of accidents. Given the diversity of sensor attacks and the dynamic changing of driving scenarios of autonomous vehicles, an adaptive and effective attack detection and defense framework faces a considerable challenge. This paper proposes a novel real-time adaptive attack detection and defense framework based on density, which can detect and identify attacked sensors and effectively recover data. We first develop a reinforcement learning multi-armed Bandit-based density-based spatial clustering of applications with Noise (BDBSCAN) algorithm that selects hyperparameters adaptively. The Adaptive Extended Kalman Filter (AEKF) combines with the vehicle dynamic model on the localization system and extracts data features used for the BDBSCAN algorithm to monitor potential sensor attacks. If attack detection indicates possible system compromise, AEKF is further employed on localization sensors with anomalies identified through the BDBSCAN algorithm of the attacked sensors. To ensure precision and reliability, the data recovery incorporates a redundancy mechanism to apply a decision tree to select the optimal state estimation between AEKF and Extended Kalman Filter (EKF) to replace corrupted sensor data. To evaluate the effectiveness and adaptability of the proposed framework, we conducted 15,000 experiments using the real-world KITTI and V2V4Real datasets across various driving and sensor attack scenarios. The results demonstrate that our proposed framework achieves 100% accuracy and 0% false alarm rate in various driving scenarios for attack detection within 0.15 s, with a recovery time of 0.08 s.

关键词： Autonomous vehicles Sensor attack Anomaly detection density-based spatial clustering of applications with noise algorithm

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Bridge substructure damage morphology identification based on the underwater sonar point cloud data

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ADVANCED ENGINEERING INFORMATICS 2024年 62卷

作者： Zhang, Shuaihui Zhu, Yanjie Xiong, Wen Cai, C. S. Zhang, Jinquan Southeast Univ Sch Transportat Dept Bridge Engn Nanjing 211189 Peoples R China Minist Transport Res Inst Highway Beijing 100088 Peoples R China

Bridge underwater foundation inspection is always a prominent and challenging issue due to an unknown and unsafe underwater environment. Effective identification of bridge foundations is significant for the safety assessment of water-related bridges. However, due to the interference of numerous objective factors in the water environment (e.g., water quality, flow velocity, water depth, etc.), reliable and valid data are often difficult to obtain, and the inspection of bridge substructures remains a major challenge, especially for deep water bridge foundations. To solve this problem, a damage morphology identification method based on underwater sonar point cloud data (USPCD) is proposed in this paper for underwater bridge structures. The method is divided into two stages, including potential damage region attention and fine damage morphology identification. The former considers the regional connectivity properties of the damage, focusing on potential damage regions employing a curve fitting method based on iterative median absolute deviation. The latter gives a significant density difference between intact and damaged regions based on the density-based spatial clustering of applications with the noise clustering method to separate damaged data points from normal data points while preserving fine damage morphology features. based on the swept sonar point cloud of underwater piles from a cross-Yangtze River bridge, we simulated spalling and cavity damage at different scales to comprehensively evaluate our proposed method. The results show that the method can detect damage at different scales and can identify most of the damaged regions. For larger-scale damage, four evaluation indicators are kept at a high level, in which the maximum GTOR and IOU can reach 95.8 % and 85.9 %, respectively. For small-scale damage, based on the synthesized high-resolution point cloud, the method can accurately identify even the damage as small as 12 cm with GTOR above 94 % and IOU over 85

关键词： Bridge substructure Damage morphology identification Iterative median absolute deviation (IMAD) density-based spatial clustering of applications (DBSCAN) Underwater sonar point cloud data (USPCD)

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Wind power forecasting method of large-scale wind turbine clusters based on DBSCAN clustering and an enhanced hunter-prey optimization algorithm

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ENERGY CONVERSION AND MANAGEMENT 2024年 307卷

作者： Hou, Guolian Wang, Junjie Fan, Yuzhen North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China

As the large-scale grid connection of wind turbines poses challenges to the safe and stable operation of the power grid, it is necessary to forecast the power of wind turbine clusters. However, with the rapid increase in the installed capacity of wind turbines, simultaneously improving the accuracy and efficiency of large-scale windturbine cluster power prediction models has become a challenge. In this research, a novel hybrid powerforecasting model for wind-turbine clusters has been designed using density-based spatial clustering of applications with noise (DBSCAN) and an enhanced hunter-prey optimization algorithm (ENHPO). First, the Pearson correlation coefficient was used to select multiple variables that significantly affected the wind power. Second, multiple wind turbines are clustered into different groups using the DBSCAN clustering algorithm, and ENHPO is employed to optimize the DBSCAN parameters to strengthen the clustering performance. Finally, one wind turbine with a high correlation was selected as the representative wind turbine in each group, and power prediction was achieved using the multivariate long short-term memory. Simulation results of four datasets in different seasons show that, compared with other clustering methods, such as fuzzy C-means, balanced iterative reduction, and clustering using hierarchies, K-means, and density peak clustering, the prediction accuracy and prediction efficiency of the proposed hybrid prediction model are improved by 21.85% and 18.07%, respectively, on average. The experimental results effectively demonstrate that the designed model can enhance the accuracy and efficiency of power forecasting simultaneously.

关键词： Wind-turbine cluster prediction Pearson correlation coefficient density-based spatial clustering of applications with noise Hunter-prey optimization algorithm Variable selection

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Equivalent Modeling of LVRT Characteristics for Centralized DFIG Wind Farms based on PSO and DBSCAN

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ENERGIES 2023年第6期16卷 2551页

作者： Zhou, Ning Ma, Huan Chen, Junchao Fang, Qiao Jiang, Zhe Li, Changgang Power Grid Technol Ctr State Grid Shandong Elect Power Res Inst Jinan 250003 Peoples R China Shandong Univ Sch Elect Engn Jinan 250061 Peoples R China

As large-scale wind turbines are connected to the grid, modeling studies of wind farms are essential to the power system dynamic research. Due to the large number of wind turbines in the wind farm, detailed modeling of each wind turbine leads to high model complexity and low simulation efficiency. An equivalent modeling method for the wind farm is needed to reduce the complexity. For wind farms with widely used doubly-fed induction generators (DFIGs), the existing equivalent studies mainly focus on such continuous control parts as electrical control. These methods are unsuitable for the low voltage ride through (LVRT) part which is discontinuous due to switching control. based on particle swarm optimization (PSO) and density-based spatial clustering of applications (DBSCAN), this paper proposes an equivalent method for LVRT characteristics of wind farms. Firstly, the multi-turbine equivalent model of the wind farm is established. Each wind turbine in the model represents a cluster of wind turbines with similar voltage variation characteristics. A single equivalent transmission line connects all wind turbines to the power grid. By changing the terminal voltage threshold to enter LVRT, each equivalent turbine can be in different LVRT states. Secondly, an LVRT parameter optimization method based on PSO is used to obtain the dynamic parameters of the equivalent wind turbines. This method of parameter optimization is applicable to the equivalent of LVRT parameters. Thirdly, a clustering method based on DBSCAN is used to obtain suitable clusters of wind turbines. This clustering method can classify wind turbines with similar electrical distances into the same cluster. Finally, two examples are set up to verify the proposed method.

关键词： doubly-fed induction generator low voltage ride through dynamic equivalence particle swarm optimization density-based spatial clustering of applications

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Enhancing atrial fibrillation classification from single-lead electrocardiogram signals using attention-based networks and generative adversarial networks with density-based clustering

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ENGINEERING applications OF ARTIFICIAL INTELLIGENCE 2024年 133卷

作者： Msigwa, Godwin Ntambala, Ester Yun, Jaeseok Soonchunhyang Univ Dept Future Convergence Technol 22 Soonchunhyang Ro Asan 31538 Choongchungnam South Korea Soonchunhyang Univ Dept Internet Things 22 Soonchunhyang Ro Asan 31538 South Korea

Wearable electrocardiogram devices have grown in popularity and are reliable means to record single-lead electrocardiograms continuously and in real-time. However, the challenges still exist with the noise and abnormal rhythms that make it difficult to extract powerful features from these signals. To address this, we developed a deep learning model, that merges multi-streams convolution recurrent neural networks with attention mechanisms. clustering technique and generative adversarial networks-based data augmentation technique were used to improve this model. The method involves three main steps: data preprocessing for noise removal, quality assessment for outliers removal, and the creation of 9-second electrocardiogram segments. Finally, our newly proposed model processes these refined 9-second segments for accurate atrial fibrillation classification. Our methodology, tested on the 2017 PhysioNet Challenge dataset, initially achieved an F1score of 0.876, which increased to an impressive 0.962 after performing data augmentation using 10-fold cross-validation. This demonstrates the effectiveness of our approach in identifying atrial fibrillation from single-lead electrocardiogram data, surpassing existing state-of-the-art methods.

关键词： Atrial fibrillation Electrocardiogram Electrocardiogram signal quality evaluation Multi-streams convolution recurrent neural network with densely attention-based block density-based spatial clustering of applications with noise Generative adversarial network

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A global optimization feedback model with PSO for large scale group decision making in hesitant fuzzy linguistic environments

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EXPERT SYSTEMS WITH applications 2023年第1期228卷

作者： Zhao, Meng-Ke Guo, Ji Wu, Jian Xu, Ze-Shui Shanghai Maritime Univ Sch Econ & Management Shanghai 201306 Peoples R China Sichuan Univ Business Sch Chengdu 610064 Sichuan Peoples R China

In an increasingly complex and uncertain decision-making environment, large-scale group decision-making (LSGDM) can offer a more efficient method, allowing a large number of decision-makers (DMs) to truly participate in the decision-making process. The consensus-reaching process (CRP) is an effective method for resolving conflicting opinions among large-scale DMs. However, in the existing CRP of LSGDM, the new consensus state and the adjustment cost borne by inconsistent DMs after implementing feedback suggestions are not taken into consideration. To address this issue, this paper proposes a global optimization feedback model with particle swarm optimization (PSO) for LSGDM in hesitant fuzzy linguistic environments. An improved density-based spatial clustering of applications with noise (DBSCAN) on hesitant fuzzy linguistic term sets (HFLTSs) is introduced to classify large-scale DMs into several clusters, and a weight determination method that combines cluster size and intra-cluster tightness is also presented. The consensus degree of clusters is calculated at two levels: intra-consensus and inter-consensus. To improve the global consensus level with minimum cost, a global optimization feedback model is established to generate recommendation advice for inconsistent DMs, and the model is solved by PSO. A numerical example related to "COVID-19" and some comparisons are provided to verify the feasibility and advantages of the proposed method.

关键词： Large scale group decision making Hesitant fuzzy linguistic term sets density-based spatial clustering of applications with noise Global optimized feedback model Particle swarm optimization Minimum cost

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