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data-driven method for Real-Time Reconstruction of the Structural Displacement Field

JOURNAL OF AEROSPACE ENGINEERING 2024年第3期37卷

作者： Yan, Jun Du, Hongze Bu, Yufeng Jiang, Lizhe Xu, Qi Zhao, Chunyu Dalian Univ Technol Dept Engn Mech State Key Lab Struct Anal Ind Equipment Dalian 116024 Peoples R China Dalian Univ Technol Ningbo Res Inst Marine Equipment Strength Anal & Innovat Design Te Ningbo 315016 Peoples R China

Accurate real-time displacement field reconstruction based on limited measurement points is crucial for spacecraft on-orbit monitoring. This study proposes a data-driven displacement field reconstruction method called stacked convolutional autoencoder with denoising autoencoder and filter. Precise reconstruction of the structural displacement from a small number of local strains was made possible by the two primary components of the method: low-resolution displacement field reconstruction and result optimization. Given the significant imbalance between the limited strain information input and the structural displacement field output, a deep learning model with multiple deconvolution layers was built in the low-resolution displacement field reconstruction part using the layer-wise training property of a stacked autoencoder and the sparse mapping property of a convolutional neural network. The result optimization part utilized a denoising autoencoder and a linear density filter to effectively alleviate the checkerboard phenomenon and displacement field discontinuity caused by the deconvolution operation. The results of the case study indicate that the proposed method can accurately reconstruct the structural displacement field of both simple regular geometric structures and irregular geometric structures with complex boundaries without prior information. Additionally, the method exhibits excellent robustness to unavoidable measurement noise, providing a new implementation approach for real-time monitoring of spacecraft.

关键词： Real-time displacement field reconstruction data-driven method Deep learning

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Transforming Conventional Waveform Measurements Into Synchro-Waveforms: A data-driven method for Event Signature Alignment and Synchronization Operator Estimation

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IEEE TRANSACTIONS ON SMART GRID 2025年第2期16卷 1495-1509页

作者： Ye, Zong-Jhen Mohsenian-Rad, Hamed Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92521 USA

A series of methodologies are proposed to transform conventional waveform measurements from legacy power quality meters into synchro-waveforms. This study is motivated by the presence of thousands of legacy power quality meters in operation worldwide that provide event-triggered waveform measurements but lack time-synchronization among their data. Consequently, the waveform measurements from these legacy meters cannot be directly used as synchro-waveforms, limiting their applicability in the promising synchro-waveform applications that have been introduced in the literature in recent years. We address this issue without requiring legacy power quality meters to be equipped with GPS receivers or other time-synchronization hardware. Our data-driven methods operate in two steps: first, they perform optimization-based event signature alignment, and then they use the results to estimate a synchronization operator between any two legacy meters. The proposed methods are accurate, robust, and computationally efficient. All case studies presented in this paper are based on real-world waveform measurements.

关键词： Sensors Power quality Synchronization Power measurement Voltage measurement Time measurement Current measurement Meters Clocks Sensor systems Synchro-waveforms synchronization operator conventional waveform sensors data-driven method real-world measurements event signature alignment optimization

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A data-driven method for Determining DRASTIC Weights to Assess Groundwater Vulnerability to Nitrate: Application in the Lake Baiyangdian Watershed, North China Plain

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APPLIED SCIENCES-BASEL 2025年第5期15卷 2866-2866页

作者： Hou, Xianglong Peng, Liqin Zhang, Yuan Zhang, Yan Wang, Yunxia Feng, Wenzhao Yang, Hui Hebei Acad Sci Inst Geog Sci Shijiazhuang 050011 Peoples R China Hebei Technol Innovat Ctr Geog Informat Applicat Shijiazhuang 050011 Peoples R China Hebei GEO Univ Hebei Prov Collaborat Innovat Ctr Sustainable Util Shijiazhuang 050031 Peoples R China Chinese Acad Sci Inst Genet & Dev Biol Ctr Agr Resources Res Key Lab Agr Water ResourcesHebei Key Lab Agr Wate Shijiazhuang 050001 Peoples R China

Nitrate pollution due to agricultural activities challenges the management of groundwater resources. The most popular technique used for groundwater vulnerability assessments is the DRASTIC. The subjectivity introduced by the DRASTIC has always been questioned. Therefore, the determination of rating scores and weights of parameters has become the main difficulty in DRASTIC applications. In this paper, a new data-driven weighting method based on Monte Carlo or genetic algorithm was developed. The new method considers both single factors and the relationship among factors, overcomes the subjectivity of weight determination, and is theoretically applicable to various hydrogeological environments and as a general weight determination method. In addition, a new method for the verification of the evaluation results on a temporal scale was established, which is based on changes in the nitrate concentration over the past 20 years. To verify and test these methods, they were used for the evaluation of groundwater vulnerability to nitrate in the plain area of the Baiyangdian watershed in the North China Plain and compared with other commonly used methods. The Pearson correlation coefficient increased by 15%. From a time perspective, the changes in nitrate concentration confirmed that the correctness of the assessment is 88%. In this study, the effect of the revision of the rating ranges on the improvement of the evaluation results is very obvious. Therefore, the focus of future work should be on determining the rating ranges and their rating scores, and whether the corresponding weights based on the data-driven method will yield more reliable results.

关键词： groundwater vulnerability nitrate DRASTIC data-driven method North China plain

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Interpretable data-driven method for sub/super-synchronous oscillation stability evaluation

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ELECTRIC POWER SYSTEMS RESEARCH 2025年 241卷

作者： Zhan, Ying Luo, Yonghong Wang, Linguang Xie, Xiaorong China Univ Min & Technol Beijing Sch Mech & Elect Engn Beijing 100083 Peoples R China Huawei Digital Power Technol Co Ltd Digital Power Shenzhen 518031 Peoples R China Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Operat & Control Beijing 100084 Peoples R China

The stability of sub/super-synchronous oscillation (SSO) in wind power integrated power systems is closely related to system operating conditions. To avoid SSO issues, it is necessary to conduct the online stability evaluation. The data-driven methods have the potential to achieve fast and accurate stability evaluations for large-scale power systems. However, the lack of interpretability poses a significant obstacle to its practical application. To address the issue, an interpretable data-driven method is proposed in this paper. Firstly, the SSO stability evaluation model is built based on neural network (NN) and the SSO stability boundary is derived. Then, the regularization and pruning techniques are adopted to streamline the model and activation functions are piecewise linearized to obtain SSO stability classification hyperplanes, so that the SSO stability rules can be extracted. Furthermore, the input sensitivity and SSO stability margin index are proposed to obtain more information on the current operating points. Finally, the proposed method is applied on a single-wind-farm system and a practical multiple-wind-farms system. The NN evaluation model is built and the SSO stability rules are extracted to clarify the evaluation logic. Compared with existing interpretable methods, the accuracy is greatly improved by the proposed method.

关键词： Interpretability data-driven method Stability evaluation Sub/super-synchronous oscillation

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Impact of Feature-Selection in a data-driven method for Flow Curve Identification of Sheet Metal

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METALS 2025年第4期15卷 392-392页

作者： Hoang, Quang Ninh Park, Hyungbum Lai, Dang Giang Nguyen, Sy-Ngoc Pham, Quoc Tuan Dinh, Van Duy Incheon Natl Univ Dept Mech Engn Incheon 22012 South Korea Le Quy Don Tech Univ Hanoi 100000 Vietnam Dongguk Univ Dept Mech Robot & Energy Engn Seoul 04620 South Korea Hanoi Univ Sci & Technol Sch Mech Engn Hanoi 100000 Vietnam

This study presents an innovative data-driven methodology to model the hardening behavior of sheet metals across a broad strain range, crucial for understanding sheet metal mechanics. Conventionally, true stress-strain data from such tests are used to analyze plastic flow within the pre-necking regime, often requiring additional experiments to inverse finite element methods, which demand extensive field data for improved accuracy. Although digital image correlation offers precise data, its implementation is costly. To address this, we integrate experimental data from standard tensile tests with a machine-learning approach to estimate the flow curve. Subsequently, we conduct finite element simulations on uniaxial tensile tests, using materials characterized by the Swift constitutive equation to build a comprehensive database. Loading force-gripper displacement curves from these simulations are then transformed into input features for model training. We propose and compare three models-Models A, B, and C-each employing different input feature selections to estimate the flow curve. Experimental validation including uniaxial tensile, plane strain, and simple shear tests on the DP590 and DP780 sheets are then carefully considered. Results demonstrate the effectiveness of our proposed method, with Model C showing the highest efficacy.

关键词： machine learning finite element analysis data-driven method plastic flow curve sheet metals

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Fault Detection and Fault-Tolerant Control for Discrete-Time Multiagent Systems With Sensor Faults: A data-driven method

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IEEE SENSORS JOURNAL 2024年第14期24卷 22601-22609页

作者： Zhang, Ji Ma, Linlin Zhao, Jingbo Zhu, Yanzheng Qingdao Univ Technol Sch Informat & Control Engn Qingdao 266525 Peoples R China Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China

This article investigates the fault detection and fault-tolerant control (FTC) problems for discrete-time multiagent systems (MASs) with sensor faults. First, the dynamic linearization method is introduced to describe the unknown MASs with sensor faults. Afterward, a decentralized fault detection method based on data-driven observers is proposed. And a fault estimator based on RBF neural networks for estimating multiple sensor faults is designed for estimating multiple sensor faults. Then, on the basis of the designed estimator, a distributed model-free sliding mode FTC strategy is provided to ensure the stability of the considered MASs when suffering from certain sensor faults. Finally, a simulated example is used to illustrate the efficiency of the proposed method.

关键词： data-driven method fault detection (FD) fault-tolerant control (FTC) multiagent systems (MASs) sensor faults data-driven method fault detection (FD) fault-tolerant control (FTC) multiagent systems (MASs) sensor faults

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Fatigue life prediction of cold expansion hole using physics-enhanced data-driven method

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INTERNATIONAL JOURNAL OF FATIGUE 2025年 190卷

作者： Mao, Jian-Xing Xian, Zhi-Fan Wang, Xin Hu, Dian-Yin Pan, Jin-Chao Wang, Rong-Qiao Zou, Shi-Kun Gao, Yang Beihang Univ Res Inst Aeroengine Beijing 100191 Peoples R China United Res Ctr Midsmall Aeroengine Beijing 100191 Peoples R China Beijing Key Lab Aeroengine Struct & Strength Beijing 100191 Peoples R China AECC Beijing Inst Aeronaut Mat Beijing 100095 Peoples R China Beihang Univ Sch Energy & Power Engn Beijing 100191 Peoples R China AVIC Mfg Technol Inst Beijing 100024 Peoples R China AECC Guiyang Engine Res Inst Guiyang 550081 Peoples R China

Cold expansion (CE) serves as a practical surface enhancement to improve the fatigue life of hole structures by improving surface integrity in both macro-scale and micro-scale. Due to the inaccessibility and high cost of experimental measurements, the physical relation between surface integrity and fatigue life are always implicit, serving as the major challenge for accurate life prediction. To address this issue, a novel method is proposed by introducing physical information to traditional data-driven method, where surface integrity enriched by multiscale simulation is mapped to fatigue life via machine learning (ML) mechanism. As integrated to four typical ML algorithms, the proposed physics-enhanced data-driven method exhibit outstanding capability for accuracy improvement, decreasing the scatter band by amplitude between 27.3 % and 71.4 %. The proposed method offers a promising option for fatigue life prediction on surface treated structures with limited physical information.

关键词： Cold expansion Surface integrity data-driven method Machine learning

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Incorporating medical domain knowledge into data-driven method: A vessel attention guided multi-granularity network for automatic cataract classification

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 241卷

作者： Xu, Xi Zhao, Linna Li, Jianqiang Li, Li Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Capital Med Univ Beijing Childrens Hosp Beijing 100045 Peoples R China Beijing Univ Technol Fac Informat Technol Beijing Engn Res Ctr IoT Software & Syst Beijing 100124 Peoples R China

Early cataract screening is essential to help patients prevent irreversible vision loss. Existing data-driven methods can automatically extract deep fundus features for automatic cataract classification. However, due to the lack of medical domain knowledge, these deep features may contain large pathological irrelevant information, thus reducing the classification accuracy of data-driven methods. To fully take the advantages of domain knowledge to enhance the discriminative representation and avoid the irrelevant impact, we comprehensively investigate how to incorporate it into data-driven models. Considering the vessel information and multi-granularity representation are two key points of domain knowledge for cataract diagnosis, we propose a vessel attention guided multi-granularity network (VAM-Net), which contains three main components. Specifically, the vessel-level segmentation subnet is firstly designed by leveraging U-shape segmentation network. The refined vessel information obtained in this step can be regarded as visual attention to enable deep network extract discriminative features. With the guidance of vessel attention, the global-local classification subnet is further proposed to eliminate the interference of irrelevant information and enhance the multi-granularity feature learning (including global structural features and local subtle features). Finally, the multiple levels predictions are integrated into the final cataract diagnosis decision via constructing multi-granularity ensemble network. In this manner, both domain knowledge and deep networks are organically combined for pursuing better diagnosis performance. By setting extensive comparative and ablation experiments, we evaluate the effectiveness of the proposed VAM-Net on the real-world cataract dataset (92.78% detection accuracy and 87.68% grading accuracy).

关键词： Automatic cataract classification data-driven method Vessel attention Multi-granularity Fundus image

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Gas Production Prediction Model of Volcanic Reservoir Based on data-driven method

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ENERGIES 2024年第21期17卷 5461页

作者： Zhang, Haijie Pu, Junwei Zhang, Li Deng, Hengjian Yu, Jihao Xie, Yingming Tong, Xiaochang Man, Xiangjie Liu, Zhonghua Chong Qing Shale Gas Explorat & Dev Co Ltd Chongqing 401121 Peoples R China Chongqing Univ Sci & Technol Sch Petr & Nat Gas Engn Chongqing 401331 Peoples R China CNOOC EnerTech Drilling & Prod Co Beijing 100028 Peoples R China Pancasia Holding Co Ltd Chongqing 400000 Peoples R China

Based on on-site construction experience, considering the time-varying characteristics of gas well quantity, production time, effective reservoir thickness, controlled reserves, reserve abundance, formation pressure, and the energy storage coefficient, a data-driven method was used to establish a natural gas production prediction model based on differential simulation theory. The calculation results showed that the average error between the actual production and predicted production was 12.49%, and the model determination coefficient was 0.99, indicating that the model can effectively predict natural gas production. Additionally, we observed that the influence of factors such as reserve abundance, the number of wells in operation, controlled reserves, the previous year's gas production, formation pressure, the energy storage coefficient, effective matrix thickness, and annual production time on the annual gas production increases progressively as the F-values decrease. These insights are pivotal to a more profound understanding of gas production dynamics in volcanic reservoirs and are instrumental in optimizing stimulation treatments and enhancing resource recovery in such reservoirs and other unconventional hydrocarbon formations.

关键词： production prediction model volcanic reservoir data-driven method data nondimensionalization dimension recovery

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A data-driven method for feature assessment of historical settlements: A case study of Northeast Hubei, China

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Frontiers of Architectural Research 2024年第2期13卷 387-405页

作者： Gangyi Tan Zhanxiang Chen Jiangkun Zhu Kai Wang School of Architecture&Urban Planning Huazhong University of Science and TechnologyWuhan 430074China Hubei Engineering and Technology Research Center of Urbanization Wuhan 430074China Hubei Rural Construction Center Wuhan 430074China HUST Built Heritage Research Center Wuhan 430074China

Formulating criteria for the assessment system of historic settlements is challenging due to complex geographical conditions and evaluator knowledge limitations, leading to subjective bias in the assessment process. To address this issue, this study proposes a data-driven method for assessing the features of historical settlements to carry out scientific and refined assessment and result analysis. Focusing on Northeast Hubei as the study area, this paper selects 3 historical settlements for validation and analysis. The results of the study show that (1) the data-driven method expands the methodological chain of assessing historical settlement features, and improves the assessment efficiency and scientificity of the assessment results by applying it to the new assessment process;(2) Through comparing the assessment results of the validation cases and data samples, the study establishes a comprehensive quantitative ranking of the assessment of historical settlement features and identifies the main influencing factors, thus enhancing the precision of result analysis;(3) By comparing the resulting assessment framework with the current assessment system, this study confirms the advantages of the proposed framework in identifying nuanced features and aligning with geographical conditions, thereby verifying the effectiveness of the data-driven method.

关键词： Historical settlement Feature assessment data-driven method Northeast Hubei

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