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Data-Driven Model Identification and Control of the Inertial Systems 认领 引用 被引量：1
1
作者 Irina Cojuhari 《Intelligent Control and Automation》 2023年第1期1-18,共18页
In the synthesis of the control algorithm for complex systems, we are often faced with imprecise or unknown mathematical models of the dynamical systems, or even with problems in finding a mathematical model of the sy... In the synthesis of the control algorithm for complex systems, we are often faced with imprecise or unknown mathematical models of the dynamical systems, or even with problems in finding a mathematical model of the system in the open loop. To tackle these difficulties, an approach of data-driven model identification and control algorithm design based on the maximum stability degree criterion is proposed in this paper. The data-driven model identification procedure supposes the finding of the mathematical model of the system based on the undamped transient response of the closed-loop system. The system is approximated with the inertial model, where the coefficients are calculated based on the values of the critical transfer coefficient, oscillation amplitude and period of the underdamped response of the closed-loop system. The data driven control design supposes that the tuning parameters of the controller are calculated based on the parameters obtained from the previous step of system identification and there are presented the expressions for the calculation of the tuning parameters. The obtained results of data-driven model identification and algorithm for synthesis the controller were verified by computer simulation. 展开更多
关键词 Data-Driven Model Identification Controller Tuning Undamped Transient Response Closed-Loop System Identification PID Controller
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Noisy data-driven identification for errors-in-variables MISO Hammerstein nonlinear models 认领 引用
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作者 Jie Hou Haoran Wang +1 位作者 Penghua Li Hao Su 《Control Theory and Technology》 EI CSCD 2026年第1期111-126,共16页
In this paper,we consider a multiple-input single-output(MISO)Hammerstein system whose inputs and output are disturbed by unknown Gaussian white measurement noises.The parameter estimation of such a system is a typica... In this paper,we consider a multiple-input single-output(MISO)Hammerstein system whose inputs and output are disturbed by unknown Gaussian white measurement noises.The parameter estimation of such a system is a typical errors-in-variables(EIV)nonlinear system identification problem.This paper proposes a bias-correction least squares(BCLS)identification methods to compute a consistent estimate of EIV MISO Hammerstein systems from noisy data.To obtain the unbiased parameter estimates of EIV MISO Hammerstein system,the analytical expression of estimated bias for the standard least squares(LS)algorithm is derived first,which is a function about the variances of noises.And then a recursive algorithm is proposed to estimate the unknown term of noises variances from noisy data.Finally,based on bias estimation scheme,the bias caused by the correlation between the input–output signals exciting the true system and the corresponding measurement noise,resulting in unbiased parameter estimates of the EIV MISO Hammerstein system.The performance of the proposed method is demonstrated through a simulation example and a chemical continuously stirred tank reactor(CSTR)system. 展开更多
关键词 Biased-corrected least squares Errors-in-variables MISO Hammerstein models Parameter estimation System identification
Data-driven non-Gaussian stochastic dynamical system identification meets knowledge-based model predictive control 认领 引用
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作者 Yicheng Mao Xianbin Liu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第3期71-85,共15页
Randomness and nonlinearity are essential properties of the real world,and their interaction gives rise to highly complex phenomena.With the advancement of technology,merely observing data of the current system state ... Randomness and nonlinearity are essential properties of the real world,and their interaction gives rise to highly complex phenomena.With the advancement of technology,merely observing data of the current system state is no longer sufficient for prediction and application in various fields.Consequently,extracting the nonlinear evolution nature of the system from noisy data has become a prominent and challenging issue.To address this,we propose an integrated approach that combines data-driven stochastic model identification with a knowledge-based model predictive control strategy.By leveraging high-precision model identification,our data-driven control design is particularly effective for continuous target tracking problems that are difficult to address using traditional precise-model-based control theory.Furthermore,the central challenge in data science lies in maximizing the informational value of datasets while minimizing the effects of observation noise.In this study,we propose and rigorously demonstrate the stochastic Occam’s razor principle,a stochastic error estimation theory that evaluates and enhances the design of data-driven schemes to mitigate the effect of observation noise.Notably,our approach offers valuable insights for contemporary data-driven,end-to-end control challenges,particularly those involving uncertain governing equations and substantial non-Gaussian observation noise. 展开更多
关键词 Nonlinear stochastic dynamical system Data-driven system identification Non-Gaussian Lévy noise Data-driven model predictive control
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Parameter identification method of multi-particle model for lithium-ion batteries 认领 引用
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作者 Junfu Li Xiaolong Li +2 位作者 Xueli Hu Quanqing Yu Zhaowei Zhang 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第1期440-452,共13页
Electrochemical models,characterized by high fidelity and physical interpretability,have been applied in var-ious fields such as fast charging,battery state estimation,and battery material design.Currently,widely util... Electrochemical models,characterized by high fidelity and physical interpretability,have been applied in var-ious fields such as fast charging,battery state estimation,and battery material design.Currently,widely utilized single particle-based model exhibits high computational efficiency but suffers from low simulation accuracy under high-rate charge/discharge conditions.In this work,an electrochemical model for lithium-ion batteries based on multi-particle hypothesis is developed.Two particles are employed to represent the electrode char-acteristics of the positive and negative electrodes,respectively.Through theoretical derivation,mathematical equations are established to describe various processes within the battery,including solid-phase diffusion,li-quidphase diffusion,reaction polarization,and ohmic polarization.In addition,a method for obtaining model parameters is proposed.Finally,the model is experimentally validated by using lithium iron phosphate and nickel-cobalt-manganese lithium-ion batteries under constant current conditions.The identified battery elec-trochemical model parameters are within reasonable accuracy as evidenced by the experimental validation results. 展开更多
关键词 Lithium-ion battery Electrochemical model Multi-particle assumption Parameter identification
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Geometry-based image modeling method for intelligent state identification and fault prediction of wind turbines 认领 引用
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作者 Jinman Luo Xiaoxia Li +2 位作者 Yuqing Li Haiji Wang Pu Zhang 《Control Theory and Technology》 EI CSCD 2026年第3期514-525,共12页
This paper introduces a geometry-based method to model wind turbine states and predict faults using a deep convolutional neural network(DCNN).Initially,3D-point cloud models are constructed in spaces defined by wind s... This paper introduces a geometry-based method to model wind turbine states and predict faults using a deep convolutional neural network(DCNN).Initially,3D-point cloud models are constructed in spaces defined by wind speed,power,and various auxiliary variables.Subsequently,the geometry feature of the point cloud in the 3D space is extracted,forming a 3D surface represented in the R,G,or B channels.For identification purposes,this 3D surface is transformed into a 2D image,with grayscale used to depict height.Additionally,the grayscale representing external environmental information sets the background of the 2D image.Consequently,the resulting stacked RGB image model encapsulates the necessary dynamic behavior and operating states of the wind turbine system.Finally,the DCNN,trained using the stacked image model through Xception-based transfer learning,identifies operating states and predicts faults.This paper’s method,which relies on the geometric distribution characteristics of sampling points rather than horizon characteristics,offers a novel perspective on wind turbine state identification and fault prediction.Experimental results demonstrate that the proposed method achieves exceptionally high accuracy in identifying the wind turbine’s operating state and predicts faults with up to 93.875%accuracy. 展开更多
关键词 Wind turbine State identification Fault prediction Geometry feature Image model
Intelligent Identification of Natural Fractures in Tight Sandstone:Optimal Model Coupling in Ensemble Frameworks 认领 引用 被引量：1
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作者 Ma Sheng-lun Zhang Zhao-hui +3 位作者 Zhang Jiao-sheng Liao jian-bo Zhang Wen-ting Zou Jian-dong 《Applied Geophysics》 SCIE CSCD 2026年第1期262-284,432,共23页
The formation and development of natural fractures in tight sandstone reservoirs are governed by a combination of stratigraphic structure,lithological properties,and stress conditions.These fractures often exhibit irr... The formation and development of natural fractures in tight sandstone reservoirs are governed by a combination of stratigraphic structure,lithological properties,and stress conditions.These fractures often exhibit irregular geometries,signicant variations in height,and complex lling materials,leading to intricate conventional logging responses with pronounced multi-solution ambiguities that complicate accurate identication.To address this challenge,this study proposes a multi-model selective coupling identication method.This approach incorporated data cleaning,augmentation,and resampling techniques during the preprocessing phase.Subsequently,multi-dimensional feature extraction and cascade-based feature selection were performed,followed by optimizing model parameters using random search,Bayesian optimization,and grid search algorithms.High-performing models were selected via an evaluation framework.These models were then coupled through voting mechanisms to construct a robust identication model capable of deeply exploring the nonlinear relationship between fractures and logging data.The proposed method achieved an 85.19%fracture identication accuracy in blind tests involving 27 fracture segments across three wells,demonstrating strong identication capability.This methodology provides a valuable reference for fracture identication in hydrocarbon reservoirs within the Hongde area. 展开更多
关键词 Algorithm models Data processing Selective coupling Fracture identification
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Towards Threat Identification for the BACnet Protocol Using Large Language Models 认领 引用
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作者 Hsuan-Chih Ku Jyun-Kai Yang +1 位作者 Pang-Wei Tsai Shih-Hsiung Lee 《Computers, Materials & Continua》 SCIE EI 2026年第8期474-497,共24页
With the rapid proliferation of the Industrial Internet of Things(IIoT),Building Automation Systems(BAS)and Industrial Control Systems(ICS)are increasingly exposed to sophisticated cyber threats.Conventional Intrusion... With the rapid proliferation of the Industrial Internet of Things(IIoT),Building Automation Systems(BAS)and Industrial Control Systems(ICS)are increasingly exposed to sophisticated cyber threats.Conventional Intrusion Detection Systems(IDS)often encounter significant limitations when addressing emerging or hybrid attack patterns,primarily due to delayed signature updates and high false-positive rates.Meanwhile,existing anomaly detection approaches frequently lack sufficient awareness of the physical domain,making them ineffective in identifying falsification attacks that comply with communication protocol specifications while violating underlying physical laws.To address these challenges,this study proposes a hybrid threat detection architecture that integrates Retrieval-Augmented Generation(RAG)with a Physics Rule Engine.The proposed approach leverages the semantic reasoning capabilities of Large Language Models(LLMs)to enhance protocol-level threat interpretation,while incorporating physical constraints to validate system behavior at the cyber–physical level.The core contribution of this work lies in employing an LLM to transform unstructured BACnet packet data into structured reasoning representations using the DSPy framework.These representations are subsequently examined through physics-based validation rules derived from thermodynamics and fluid mechanics,enabling the detection of attacks that are logically valid at the protocol layer but implausible in the physical domain.This layered verification process effectively reduces spurious alerts and improves detection reliability.Experimental evaluations conducted under various BACnet attack scenarios demonstrate that the proposed system,implemented with the Mistral-7B model,achieves an accuracy of 95.12%and an F1-score of 96.0%.Compared with a baseline LLM-only approach without physical validation,the proposed method significantly lowers the false-positive rate.Moreover,the system is capable of automatically generating evidence chains that support explainable security forensics,thereby enhancing situational awareness for security operators.The results of this study suggest that the integration of domain knowledge with generative AI constitutes a promising and effective strategy for strengthening the resilience of critical cyber–physical infrastructures. 展开更多
关键词 Threat identification BACnet protocol large language models
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Exploration and Reform of a Multidimensional Teaching Model in the Identification of Chinese Materia Medica 认领 引用
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作者 Demei HU Jiao MU +2 位作者 Yujun XIAO Tianwei ZENG Yan SHEN 《Medicinal Plant》 2026年第3期108-111,共4页
Identification of Chinese Materia Medica is a core professional course in the discipline of Chinese Materia Medica,providing essential skills for identifying medicinal materials and ensuring quality control.To better ... Identification of Chinese Materia Medica is a core professional course in the discipline of Chinese Materia Medica,providing essential skills for identifying medicinal materials and ensuring quality control.To better align this course with talent training objectives,this paper explores and reforms a multidimensional teaching model based on an analysis of students learning contexts.Teaching strategies,including visual teaching methods,matrix QR code-based learning,flipped classroom,integration of licensed pharmacist examinations,and curriculum-based ideological and political education,are introduced in the reform,in order to address key challenges in student learning,foster active learning and problem-solving abilities,cultivate innovative thinking,and ultimately achieve the desired talent development goals. 展开更多
关键词 Identification of Chinese Materia Medica Teaching model Exploration Reform
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A data-driven identification method for reaction rate constant and diffusion coefficient in the P2D model 认领 引用
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作者 Gaoyang Li Xiaoyu Guo +8 位作者 Yongshuai Li Jialong Huang Zhirui Wang Yizheng Ma Litao Zhu Hui Pan Feng Shao Hao Ling Yulin Min 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2025年第12期188-197,共10页
To ensure the safe operation of batteries,accurately obtaining key internal state parameters is essential.However,traditional parameter measurement methods either require opening the battery or long-term measurements,... To ensure the safe operation of batteries,accurately obtaining key internal state parameters is essential.However,traditional parameter measurement methods either require opening the battery or long-term measurements,which are impractical.Therefore,the fixed values are commonly used for these parameters in electrochemical models and have significant limitations.To overcome these limitations,this paper proposes a deep neural network(DNN)based data-driven evaluation method to determine model parameters.By coupling an improved one-dimensional isothermal pseudo-twodimensional(P2D)model with DNN,this study identified concentration-dependent parameters through detailed discharge curve analysis.The results show that the data-driven method can effectively obtain the change trend of concentration-dependent parameters through the charge and discharge curve,and the method can be extended to different battery systems in different discharge rates and aging applications.This work is expected to provide new parameter selection insights for data-driven battery prediction and monitoring models. 展开更多
关键词 Internal state parameters of batteries P2D model Parameter identification Deep neural network(DNN) Data-driven evaluation method
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Unified physics-informed subspace identification and transformer learning for lithium-ion battery state-of-health estimation 认领 引用
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作者 Yong Li Hao Wang +3 位作者 Chenyang Wang Liye Wang Chenglin Liao Lifang Wang 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2026年第1期350-369,I0009,共20页
The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches ... The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches often suffer from reduced accuracy under dynamically uncertain state-of-charge(SOC)operating ranges and heterogeneous aging stresses.This study presents a unified SOH estimation framework that integrates physics-informed modeling,subspace identification,and Transformer-based learning.A reduced-order model is derived from simplified electrochemical dynamics,providing an interpretable and computationally efficient representation of battery behavior.Subspace identification across a wide SOC and SOH range yields degradation-sensitive features,which the Transformer uses to capture long-range aging dynamics via multi-head self-attention.Experiments on LiFePO4 cells under joint-cell training show consistently accurate SOH estimation,with a maximum error of 1.39%,demonstrating the framework’s effectiveness in decoupling SOC and SOH effects.In cross-cell validation,where training and validation are performed on different cells,the model maintains a maximum error of 2.06%,confirming strong generalization to unseen aging trajectories.Comparative experiments on LiFePO4and public LiCoO2datasets confirm the framework’s cross-chemistry applicability.By extracting low-dimensional,physically interpretable features via subspace identification,the framework significantly reduces training cost while maintaining high SOH estimation accuracy,outperforming conventional data-driven models lacking physical guidance. 展开更多
关键词 Lithium-ion battery Transformer learning Physics-informed modeling Subspace identification State-of-health estimation
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Automatic identification of discontinuities and refined modeling of rock blocks from 3D point cloud data of rock surfaces 认领 引用 被引量：3
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作者 Yaopeng Ji Shengyuan Song +5 位作者 Jianping Chen Jingyu Xue Jianhua Yan Yansong Zhang Di Sun Qing Wang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第5期3093-3106,共14页
The spatial distribution of discontinuities and the size of rock blocks are the key indicators for rock mass quality evaluation and rockfall risk assessment.Traditional manual measurement is often dangerous or unreach... The spatial distribution of discontinuities and the size of rock blocks are the key indicators for rock mass quality evaluation and rockfall risk assessment.Traditional manual measurement is often dangerous or unreachable at some high-steep rock slopes.In contrast,unmanned aerial vehicle(UAV)photogrammetry is not limited by terrain conditions,and can efficiently collect high-precision three-dimensional(3D)point clouds of rock masses through all-round and multiangle photography for rock mass characterization.In this paper,a new method based on a 3D point cloud is proposed for discontinuity identification and refined rock block modeling.The method is based on four steps:(1)Establish a point cloud spatial topology,and calculate the point cloud normal vector and average point spacing based on several machine learning algorithms;(2)Extract discontinuities using the density-based spatial clustering of applications with noise(DBSCAN)algorithm and fit the discontinuity plane by combining principal component analysis(PCA)with the natural breaks(NB)method;(3)Propose a method of inserting points in the line segment to generate an embedded discontinuity point cloud;and(4)Adopt a Poisson reconstruction method for refined rock block modeling.The proposed method was applied to an outcrop of an ultrahigh steep rock slope and compared with the results of previous studies and manual surveys.The results show that the method can eliminate the influence of discontinuity undulations on the orientation measurement and describe the local concave-convex characteristics on the modeling of rock blocks.The calculation results are accurate and reliable,which can meet the practical requirements of engineering. 展开更多
关键词 Three-dimensional(3D)point cloud Rock mass Automatic identification Refined modeling Unmanned aerial vehicle(UAV)
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Review of System Identification for Manoeuvring Modelling of Marine Surface Ships 认领 引用 被引量：1
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作者 Haitong Xu C.Guedes Soares 《哈尔滨工程大学学报（英文版）》 CSCD 2025年第3期459-478,共20页
A state-of-the-art review is presented of mathematical manoeuvring models for surface ships and parameter estimation methods that have been used to build mathematical manoeuvring models for surface ships. In the first... A state-of-the-art review is presented of mathematical manoeuvring models for surface ships and parameter estimation methods that have been used to build mathematical manoeuvring models for surface ships. In the first part, the classical manoeuvring models, such as the Abkowitz model, MMG, Nomoto and their revised versions, are revisited and the model structure with the hydrodynamic coefficients is also presented.Then, manoeuvring tests, including both the scaled model tests and sea trials, are introduced with the fact that the test data is critically important to obtain reliable results using parameter estimation methods. In the last part, selected papers published in journals and international conferences are reviewed and the statistical analysis of the manoeuvring models, test data, system identification methods and environmental disturbances used in the paper is presented. 展开更多
关键词 Manoeuvring simulation System identification Manoeuvring model Manoeuvring test
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Koopman-Based Robust Model Predictive Control With Online Identification for Nonlinear Dynamical Systems 认领 引用
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作者 Ruiqi Ke Jingchuan Tang +1 位作者 Zongyu Zuo Yan Shi 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第9期1947-1949,共3页
Dear Editor,This letter presents a novel approach to the data-driven control of unknown nonlinear systems.By leveraging online sparse identification based on the Koopman operator,a high-dimensional linear system model... Dear Editor,This letter presents a novel approach to the data-driven control of unknown nonlinear systems.By leveraging online sparse identification based on the Koopman operator,a high-dimensional linear system model approximating the actual system is obtained online.The upper bound of the discrepancy between the identified model and the actual system is estimated using real-time prediction error,which is then utilized in the design of a tube-based robust model predictive controller.The effectiveness of the proposed approach is validated by numerical simulation. 展开更多
关键词 koopman operatora online identification tube based control real time prediction error online sparse identification identified model Koopman based control robust model predictive control
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A new rope-sheave traction contact force model incorporating complex geometric features developed through parameter identification methods 认领 引用
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作者 Yunting HAN Hui HU +1 位作者 Haoran SUN Xi SHI 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI CSCD 2025年第10期1983-2006,共24页
The complex geometrical features of mechanical components significantly influence contact interactions and system dynamics.However,directly modeling contact forces on surfaces with intricate geometries presents consid... The complex geometrical features of mechanical components significantly influence contact interactions and system dynamics.However,directly modeling contact forces on surfaces with intricate geometries presents considerable challenges.This study focuses on the helically twisted wire rope-sheave contact and proposes a contact force model that incorporates complex geometric features through a parameter identification approach.The model's impact on contact forces and system dynamics is thoroughly investigated.Leveraging a point contact model and an elliptic integral approximation,a loss function is formulated using the finite element(FE)contact model results as the reference data.Geometric parameters are subsequently determined by optimizing this loss function via a genetic algorithm(GA).The findings reveal that the contact stiffness increases with the wire rope pitch length,the radius of principal curvature,and the elliptic eccentricity of the contact zone.The proposed contact force model is integrated into a rigid-flexible coupled dynamics model,developed by the absolute node coordinate formulation,to examine the effects of contact geometry on system dynamics.The results demonstrate that the variations in wire rope geometry alter the contact stiffness,which in turn affects dynamic rope tension through frictional energy dissipation.The enhanced model's predictions exhibit superior alignment with the experimental data,thereby validating the methodology.This approach provides new insights for deducing the contact geometry from kinetic parameters and monitoring the performance degradation of mechanical components. 展开更多
关键词 complex contact geometry contact force modeling parameter identification helical wire rope rigid-flexible couple dynamics modeling
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Adaptive Multi-Learning Cooperation Search Algorithm for Photovoltaic Model Parameter Identification 认领 引用
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作者 Xu Chen Shuai Wang Kaixun He 《Computers, Materials & Continua》 SCIE EI 2025年第10期1779-1806,共28页
Accurate and reliable photovoltaic(PV)modeling is crucial for the performance evaluation,control,and optimization of PV systems.However,existing methods for PV parameter identification often suffer from limitations in... Accurate and reliable photovoltaic(PV)modeling is crucial for the performance evaluation,control,and optimization of PV systems.However,existing methods for PV parameter identification often suffer from limitations in accuracy and efficiency.To address these challenges,we propose an adaptive multi-learning cooperation search algorithm(AMLCSA)for efficient identification of unknown parameters in PV models.AMLCSA is a novel algorithm inspired by teamwork behaviors in modern enterprises.It enhances the original cooperation search algorithm in two key aspects:(i)an adaptive multi-learning strategy that dynamically adjusts search ranges using adaptive weights,allowing better individuals to focus on local exploitation while guiding poorer individuals toward global exploration;and(ii)a chaotic grouping reflection strategy that introduces chaotic sequences to enhance population diversity and improve search performance.The effectiveness of AMLCSA is demonstrated on single-diode,double-diode,and three PV-module models.Simulation results show that AMLCSA offers significant advantages in convergence,accuracy,and stability compared to existing state-of-the-art algorithms. 展开更多
关键词 Photovoltaic model parameter identification cooperation search algorithm adaptive multiple learning chaotic grouping reflection
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Nonlinear Frictions Identification in Time-Variant Automotive Systems 认领 引用
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作者 Davide Tebaldi Roberto Zanasi 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第3期611-619,共9页
In this paper,the problem of nonlinear frictions identification in a class of nonlinear systems embedding different automotive case studies is addressed.The power-oriented modeling of the system dynamics is first addr... In this paper,the problem of nonlinear frictions identification in a class of nonlinear systems embedding different automotive case studies is addressed.The power-oriented modeling of the system dynamics is first addressed.Next,the identification of the nonlinear friction coefficients representing the system losses,which can have different symmetric or asymmetric characteristics,is addressed using a parabolic interpolation.To show the versatility of the procedure,two automotive physical systems composing the vehicle powertrain are considered as case studies for the identification,namely a Full Toroidal Variator and a Gearbox.The novelty of this work consists of the proposal of a general approach to model nonlinear frictions in a wide class of automotive systems,and in their identification using the proposed least-square-based algorithm.With reference to the latter,we also provide a necessary condition to avoid the rank deficiency problem and considerations about how to increase the identification accuracy. 展开更多
关键词 Automotive identification modeling powertrain components time-varying systems
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Intelligent approach for mucky soil identification during shield tunnelling by enhanced YOLO model 认领 引用 被引量：2
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作者 Wei-Wei Zhao Shui-Long Shen +1 位作者 Tao Yan Annan Zhou 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第6期3327-3338,共12页
An efficient determination of the geological characteristics and soil-rock type ahead of a tunnel face is critical for adjusting construction parameters during shield tunnelling.In general,operational engineers rely o... An efficient determination of the geological characteristics and soil-rock type ahead of a tunnel face is critical for adjusting construction parameters during shield tunnelling.In general,operational engineers rely on visual observations of mucky soil types from belt conveyors.This results in shield halting and involves both time and cost implications.This paper proposes a deep learning approach designed to identify mucky soil by monitoring a video installed on the strut of a belt conveyer.The proposed approach comprises four steps:(1)image acquisition,(2)enhanced you-only-look-once(YOLO)modelling,(3)model performance evaluation,and(4)soil identification based on an optimal analysis.The enhanced YOLO model is a deep image detection algorithm.It was introduced by integrating two innovative strategies:data augmentation and imbalance learning.This enhancement accelerates the speed of image identification and improves the overall classification performance.A case study of shield tunnelling in the soil-rock mixed strata of the GuangzhoueFoshan intercity railway line was conducted to validate the proposed approach.The results indicate that the enhanced YOLO model achieves a classification performance comparable to that of the highly optimised AlexNet and GoogleNet.Additionally,the proposed approach more effectively detects the muck soil content than manual observation.This demonstrates its potential for real-time applications in shield tunnelling operations. 展开更多
关键词 Mucky soil identification Data augmentation Imbalanced learning Shield tunnelling Enhanced you-only-look-once(YOLO) model
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Research and Practice of Blended Teaching of Identification of Chinese Materia Medica under TfU Model : A Case Study of the Lesson "Authentic Medicinal Materials and Quality of Traditional Chinese Medicine" 认领 引用
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作者 Ya GAO Xiaohua WANG +2 位作者 Lunli LAN Deqing HUANG Wenjuan XIE 《Agricultural Biotechnology》 2025年第3期67-70,共4页
In order to improve the traditional teaching model of traditional Chinese medicine identification course for graduate students of pharmacy,this paper described the research on constructing and practicing the blended t... In order to improve the traditional teaching model of traditional Chinese medicine identification course for graduate students of pharmacy,this paper described the research on constructing and practicing the blended teaching model of"online+offline"based on the teaching concept of TfU by taking the course of"Authentic Medicinal Materials and Quality of Traditional Chinese Medicine"as an example.In the preparatory phase,through resource integration and course content decomposition,it identifies"generative topics"to engage students in pre-class online discussions.During the instructional phase,"comprehension-oriented objectives"are established based on learning analytics,followed by the implementation of"understanding-focused activities"for guided inquiry in offline classrooms.The post-class phase employs online extended materials to conduct"sustained assessment"through evaluations and summaries,thereby continuously optimizing subsequent teaching practices.This pedagogical framework not only effectively cultivates investigative research thinking among graduate students but also enhances standardized management and scientific development of the teaching team.The practical research outcomes and experiences derived from this model can provide valuable references for analogous course reforms. 展开更多
关键词 TfU teaching model Identification of Chinese materia medica Blended teaching
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Overview of Data-Driven Models for Wind Turbine Wake Flows 认领 引用 被引量：2
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作者 Maokun Ye Min Li +2 位作者 Mingqiu Liu Chengjiang Xiao Decheng Wan 《哈尔滨工程大学学报（英文版）》 CSCD 2025年第1期1-20,共20页
With the rapid advancement of machine learning technology and its growing adoption in research and engineering applications,an increasing number of studies have embraced data-driven approaches for modeling wind turbin... With the rapid advancement of machine learning technology and its growing adoption in research and engineering applications,an increasing number of studies have embraced data-driven approaches for modeling wind turbine wakes.These models leverage the ability to capture complex,high-dimensional characteristics of wind turbine wakes while offering significantly greater efficiency in the prediction process than physics-driven models.As a result,data-driven wind turbine wake models are regarded as powerful and effective tools for predicting wake behavior and turbine power output.This paper aims to provide a concise yet comprehensive review of existing studies on wind turbine wake modeling that employ data-driven approaches.It begins by defining and classifying machine learning methods to facilitate a clearer understanding of the reviewed literature.Subsequently,the related studies are categorized into four key areas:wind turbine power prediction,data-driven analytic wake models,wake field reconstruction,and the incorporation of explicit physical constraints.The accuracy of data-driven models is influenced by two primary factors:the quality of the training data and the performance of the model itself.Accordingly,both data accuracy and model structure are discussed in detail within the review. 展开更多
关键词 Data-driven Machine learning Artificial neural networks Wind turbine wake Wake models
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Machine learning-based grayscale analyses for lithofacies identification of the Shahejie formation,Bohai Bay Basin,China 认领 引用 被引量：2
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作者 Yu-Fan Wang Shang Xu +4 位作者 Fang Hao Hui-Min Liu Qin-Hong Hu Ke-Lai Xi Dong Yang 《Petroleum Science》 SCIE EI CAS CSCD 2025年第1期42-54,共13页
It is of great significance to accurately and rapidly identify shale lithofacies in relation to the evaluation and prediction of sweet spots for shale oil and gas reservoirs.To address the problem of low resolution in... It is of great significance to accurately and rapidly identify shale lithofacies in relation to the evaluation and prediction of sweet spots for shale oil and gas reservoirs.To address the problem of low resolution in logging curves,this study establishes a grayscale-phase model based on high-resolution grayscale curves using clustering analysis algorithms for shale lithofacies identification,working with the Shahejie For-mation,Bohai Bay Basin,China.The grayscale phase is defined as the sum of absolute grayscale and relative amplitude as well as their features.The absolute grayscale is the absolute magnitude of the gray values and is utilized for evaluating the material composition(mineral composition+total organic carbon)of shale,while the relative amplitude is the difference between adjacent gray values and is used to identify the shale structure type.The research results show that the grayscale phase model can identify shale lithofacies well,and the accuracy and applicability of this model were verified by the fitting relationship between absolute grayscale and shale mineral composition,as well as corresponding re-lationships between relative amplitudes and laminae development in shales.Four lithofacies are iden-tified in the target layer of the study area:massive mixed shale,laminated mixed shale,massive calcareous shale and laminated calcareous shale.This method can not only effectively characterize the material composition of shale,but also numerically characterize the development degree of shale laminae,and solve the problem that difficult to identify millimeter-scale laminae based on logging curves,which can provide technical support for shale lithofacies identification,sweet spot evaluation and prediction of complex continental lacustrine basins. 展开更多
关键词 Shale Machine learning Absolute grayscale Relative amplitude Grayscale phase model Lithofacies identification
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