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nonparametric modeling of a high-speed USV at three speed regions based on Gaussian process regression with a hybrid kernel function

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OCEAN ENGINEERING 2024年 300卷

作者： Ding, Shuo Jin, Jiucai Liu, Deqing Li, Hongyu Ma, Yi Zhang, Jie Minist Nat Resources Inst Oceanog 1 Lab Marine Phys & Remote Sensing Qingdao 266061 Shandong Peoples R China Shandong Univ Sci & Technol Coll Ocean Sci & Engn Qingdao 266590 Shandong Peoples R China China Univ Petr East China Coll Oceanog & Space Informat Qingdao 266580 Shandong Peoples R China

Due to the complexity and variability of the ocean environment, it is difficult for high-speed unmanned surface vehicles (USVs) to construct a unified mathematical model for nonlinear motion under different Froude numbers. In this paper, a nonparametric modeling method for high-speed USVs at three speed regions is presented based on Gaussian process regression with a hybrid kernel function (HKF-GPR). First, the hybrid kernel functions are designed based on the forecasting performance of Gaussian process regression with different single kernel functions (SKF-GPR) using sea trial speed data from the "Jiuhang 750" USV. Second, to better capture the dynamic performance of the USVs, the sea trial speed data are clustered into three speed regions by an improved kmeans++ algorithm;these regions are the displacement, semiplanning, and planning regions. Finally, USV modeling and forecasting are performed in the three speed regions using HKF-GPR. The trained model shows good modeling accuracy and forecasting, verifying the effectiveness of the proposed method.

关键词： High-speed USV Froude numbers Speed regions HKF-GPR nonparametric modeling

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Multiobjective optimization of carbon fiber-reinforced epoxy composite properties using nonparametric modeling

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MATERIALS & DESIGN 2024年 242卷

作者： Zhang, Si He, Chaoshuai Wang, Zifan An, Chuanbo Chen, Yun Jiangsu Univ Sci & Technol Zhenjiang 212000 Peoples R China Jiangsu Automat Res Inst Lianyungang 222006 Peoples R China

Carbon fiber-reinforced epoxy composites are widely used in the marine and aviation sectors due to their superior mechanical properties. As research into these materials progresses, the demand for materials with even more complex and superior properties has increased. Traditional trial -and -error methods are limited to optimizing single factors and are plagued by heavy workloads and prohibitive costs when applied to multifactor composite designs. Addressing these problems, this study introduces a nonparametric multiobjective optimization process for composite materials. This approach employs a Gaussian process (GP) to establish a multiobjective regression model and uses nondominated sorting genetic algorithm II (NSGA II) to determine the Pareto front. Pareto solutions were ranked in accordance with their similarity to ideal solutions and on the basis of predetermined preference weights. The results demonstrate variations in optimal outcomes contingent on the weight values assigned. The differences between the optimized results and experimental validations reached 12.9%, with the minimum deviation being 0.6%. The effectiveness of the proposed method was demonstrated. The strategy combines GP and NSGA II to extend design methods for small samples and materials with multiple attributes. This method enables the simultaneous optimization of multiple conflicting objectives, offering greater efficiency than addressing them individually.

关键词： Materials design nonparametric modeling NSGA-II Multiobjective optimization Carbon fiber-reinforced epoxy

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Adaptive Ensemble of Multi-Kernel Gaussian Process Regressions Based on Heuristic Model Screening for nonparametric modeling of Ship Maneuvering Motion

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JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING-TRANSACTIONS OF THE ASME 2025年第1期147卷 011204页

作者： Jiang, Lichao Shang, Xiaobing Qi, Xinyu Ouyang, Zilu Zhang, Zhi Harbin Engn Univ Coll Intelligent Syst Sci & Engn 145 Nantong St Harbin 150001 Heilongjiang Peoples R China Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn 800 Dongchuan Rd Shanghai 200240 Peoples R China

Gaussian process regression (GPR) is a commonly used approach for establishing the non-parametric models of ship maneuvering motion, and its performance depends on the selec-tion of the kernel function. However, no single kernel function can be universally applied toall nonparametric models of ship maneuvering motion, which may compromise the robust-ness of GPR. To address this issue, an adaptive ensemble of multi-kernel GPRs based onheuristic model screening (AEGPR-HMS) is proposed in this paper. In the proposedmethod, four kernel functions are involved in constructing the ensemble model. The HMSmethod is introduced to determine the weights of individual-based GPR models, whichcan be adaptively assigned according to the baseline GPR model. To determine thehyper-parameters of these kernel functions, the genetic algorithm is also employed tocompute the optimal values. The KVLCC2 tanker provided by the SIMMAN 2008 workshopis used to validate the performance of the proposed method. The results demonstrate that theAEGPR-HMS is an efficient and robust method for nonparametric modeling of ship maneu-vering motion.

关键词： ship maneuvering nonparametric modeling Gaussian process regression genetic algorithm ensemble model ship motions

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nonparametric modeling of ship maneuvering motions in calm water and regular waves based on R-LSTM hybrid method

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OCEAN ENGINEERING 2023年第Part1期285卷

作者： Zhou, Xiao Zou, Lu Ouyang, Zi-Lu Liu, Si-Yu Zou, Zao-Jian Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ State Key Lab Ocean Engn Shanghai 200240 Peoples R China

It is essential to establish reliable method to predict the ship maneuvering motions in complex navigation environments for navigation efficiency and safety. In this paper, a novel hybrid nonparametric modeling method (R-LSTM) is developed by coupling Bidirectional Long Short-Term Memory (Bi-LSTM) with residual network and attention mechanism. Firstly, the prediction capability of the proposed hybrid method is evaluated in the predictions of zigzag maneuvers in calm water. The model test data of a KVLCC2 tanker model are utilized for modeling and validation, and the optimal parameters are determined by Tree-structured Parzen Estimator algorithm. The results demonstrate that the proposed method has higher prediction accuracy and better generalization ability than the classical LSTM method. On this basis, the online modeling method for predicting the turning motion in regular waves is developed along with sliding time window and time series split crossvalidation. The model test data of a KCS container ship and a ONRT tumblehome ship under different wave conditions are used for online modeling and real-time prediction. The results indicate that the ship trajectories with trajectory drift and fluctuation features during the turning motion under the wave impacts are captured well by the developed method with satisfactory confidence intervals.

关键词： Real-time maneuvering motion prediction nonparametric modeling Bidirectional long short-term memory Residual network Attention mechanism Time series split cross-validation

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nonparametric modeling of ship maneuvering motion in waves based on Gaussian process regression

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OCEAN ENGINEERING 2022年 264卷

作者： Ouyang, Zi-Lu Liu, Si -Yu Zou, Zao-Jian Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ State Key Lab Ocean Engn Shanghai 200240 Peoples R China

A robust and easy-to-operate method based on Gaussian process regression (GPR) is proposed for nonparametric modeling of ship maneuvering in waves. The wave parameters that are difficult to measure are not needed when identifying the dynamic model. In order to extract the wave effect from the ship motion data, the black-box model is modified relative to that used for calm water case. Taking the KCS container ship and the S-175 container ship as study objects, the nonparametric models based on GPR are identified by utilizing the experi-mental data of turning circle maneuver of KCS model and zigzag maneuvers of S-175 model in regular waves. Using the identified nonparametric models, turning circle maneuver of KCS model and zigzag maneuvers of S-175 model in regular waves are predicted, and the prediction results are compared with the model test data to verify the proposed method of nonparametric modeling and to evaluate the performances of the identified nonparametric models. It shows that the trajectory of turning circle maneuver and the heading angle of zigzag maneuvers are well predicted, indicating that the nonparametric model based on GPR with the modified black -box model can predict the maneuvering motion in regular waves accurately.

关键词： Ship maneuvering in waves System identification nonparametric modeling Gaussian process regression

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nonparametric modeling of ship maneuvering motion based on self-designed fully connected neural network

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OCEAN ENGINEERING 2022年 251卷 111113-111113页

作者： He, Hong-Wei Wang, Zi-Hao Zou, Zao-Jian Liu, Yi Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn Shanghai 200240 Peoples R China Shanghai Univ Sch Artificial Intelligence Shanghai 200444 Peoples R China Minist Educ China Engn Res Ctr Unmanned Intelligent Marine Equipment Shanghai 200444 Peoples R China Shanghai Jiao Tong Univ State Key Lab Ocean Engn Shanghai 200240 Peoples R China Marine Design & Res Inst China Shanghai 200011 Peoples R China

A self-designed fully connected neural network is proposed for nonparametric modeling of ship maneuvering motion by extracting the dynamic characteristics of ship motion from observed input-output data. The automatic design is conducted by Bayesian optimization, which aims at minimizing the validation error with respect to different hyperparameter settings of the neural network, including learning rate, activation function, as well as network depth and widths. Gaussian process is adopted as the surrogate model to estimate the unobserved validation errors, and lower confidence bound is adopted for trading off exploration and exploitation in the process of optimization. Taking the KVLCC2 ship as the study object, the experimental data provided by Hamburg Ship Model Basin is utilized for modeling and validation of prediction, and the detailed procedures to prepare and preprocess the input-output data are discussed. After well designed and trained, the selected neural network model is evaluated by the prediction results of ship maneuvers in comparison with the experimental data. It shows the high prediction accuracy and strong generalization ability of the established neural network model, indicating the effectiveness of the proposed modeling method.

关键词： nonparametric modeling Ship maneuvering Fully connected neural network Automatic design

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Propagative broad learning for nonparametric modeling of ambient effects on structural health indicators

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2021年第4期20卷 1409-1427页

作者： Kuok, Sin-Chi Yuen, Ka-Veng Roberts, Stephen Girolami, Mark A. Univ Macau State Key Lab Internet Things Smart City Macau 999078 Peoples R China Univ Macau Dept Civil & Environm Engn Macau 999078 Peoples R China Univ Cambridge Dept Engn Cambridge England Univ Oxford Dept Engn Sci Oxford England British Lib Alan Turing Inst London England

In this article, a novel propagative broad learning approach is proposed for nonparametric modeling of the ambient effects on structural health indicators. Structural health indicators interpret the structural health condition of the underlying dynamical system. Long-term structural health monitoring on in-service civil engineering infrastructures has demonstrated that commonly used structural health indicators, such as modal frequencies, depend on the ambient conditions. Therefore, it is crucial to detrend the ambient effects on the structural health indicators for reliable judgment on the variation of structural integrity. However, two major challenging problems are encountered. First, it is not trivial to formulate an appropriate parametric expression for the complicated relationship between the operating conditions and the structural health indicators. Second, since continuous data stream is generated during long-term structural health monitoring, it is required to handle the growing data efficiently. The proposed propagative broad learning provides an effective tool to address these problems. In particular, it is a model-free data-driven machine learning approach for nonparametric modeling of the ambient-influenced structural health indicators. Moreover, the learning network can be updated and reconfigured incrementally to adapt newly available data as well as network architecture modifications. The proposed approach is applied to develop the ambient-influenced structural health indicator model based on the measurements of 3-year full-scale continuous monitoring on a reinforced concrete building.

关键词： Structural health monitoring propagative broad learning nonparametric modeling modal frequencies ambient conditions

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Improved PER-DDPG based nonparametric modeling of ship dynamics with uncertainty

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OCEAN ENGINEERING 2023年第Part1期286卷

作者： Zhu, Man Tian, Kang Wen, Yuan-Qiao Cao, Ji-Ning Huang, Liang Wuhan Univ Technol State Key Lab Maritime Technol & Safety Wuhan 430063 Peoples R China Wuhan Univ Technol Natl Engn Res Ctr Water Transport Safety Wuhan 430063 Peoples R China Delft Univ Technol Fac Technol Policy & Management NL-2628 BX Delft Netherlands Wuhan Univ Technol Intelligent Transportat Syst Res Ctr Wuhan 430063 Peoples R China

This study contributes to addressing the challenge of quickly obtaining an effective and accurate nonparametric model for describing ship maneuvering motion in three degrees of freedom (3-DOF). To achieve this, an intelligent ship dynamics nonparametric modeling method named improved PER-DDPG is proposed. This method leverages the deep deterministic policy gradient algorithm (DDPG) and prioritized experience replay mechanism (PER) and analyzes the characteristics between the goal of deep reinforcement learning (DRL) and the modeling process of the nonparametric model. The PER mechanism is utilized to enhance the agent's understanding of the overall mechanism of ship motion by improving the utilization of samples. The meaning of target value is redefined due to transforming DRL aiming at maximizing cumulative rewards into maximizing the set of immediate rewards at each time step. To validate the performance of the proposed modeling method, we conduct simulation studies using a benchmark ship model i.e., a Mariner cargo ship dynamic model, and experimental studies using a real unmanned surface vehicle (USV). In the simulation test, we demonstrate the effectiveness and generalization of the proposed method through zigzag and turning circle tests. Furthermore, we verify the robustness and applicability of the proposed method by using datasets with uncertain environmental disturbances and datasets with different sampling frequencies. Additionally, the experimental tests conducted on the USV indicate the consistency of the proposed approach.

关键词： Ship dynamic model nonparametric modeling Deep deterministic policy gradient (DDPG) Prioritized experience replay (PER) Uncertainty

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Adaptive hybrid-kernel function based Gaussian process regression for nonparametric modeling of ship maneuvering motion

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OCEAN ENGINEERING 2023年第1期268卷

作者： Ouyang, Zi-Lu Zou, Zao-Jian Zou, Lu Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ State Key Lab Ocean Engn Shanghai 200240 Peoples R China

A novel adaptive hybrid-kernel function based Gaussian process regression (AHKGPR) is proposed for nonparametric modeling of ship maneuvering motion. With the aid of Gaussian process regression (GPR) and nonlinear kernel function, the nonparametric ship dynamic model is identified based on the training data collected during free-running tests. The hybrid-kernel function which combines the good properties of radial basis function (RBF) and polynomial function with adaptive gain coefficients is designed to improve the generalization ability and the prediction accuracy of GPR model. The hyperparameters in the kernel function and the gain coefficients are tuned and optimized by genetic algorithm (GA). To verify the effectiveness of AHKGPR, a comparative study between AHKGPR and the widely used RBF kernel function based GPR (RBFGPR) is carried out. Two cases are considered, one for the Mariner class vessel with the dataset generated from simulation and the other for the container ship KCS with the real measured dataset of free-running model tests provided by SIMMAN 2020 Workshop. The results show that AHKGPR has higher prediction accuracy and better general-ization ability.

关键词： Ship maneuvering nonparametric modeling Gaussian process regression Hybrid -kernel function Genetic algorithm

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nonparametric dynamics modeling for underwater vehicles using local adaptive moment estimation Gaussian processes learning

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NONLINEAR DYNAMICS 2024年第7期112卷 5229-5245页

作者： Zhang, Zhao Ren, Junsheng Dalian Maritime Univ Naut Dynam Simulat & Control Lab 1 Linghai Rd Dalian 116026 Liaoning Peoples R China

This paper investigates a nonparametric modeling scheme for underwater vehicles to achieve continuous-time dynamics modeling, which is essential for various marine missions, control, and navigation of these vehicles. The proposed scheme addresses the challenges posed by the nonlinearity, strong coupling, and complex structure of underwater vehicles through local adaptive moment estimation Gaussian processes learning. This approach constructs mappings between hydrodynamics and motion states while providing uncertainty estimates of the dynamics model. A local weighted strategy is used to construct local models to localize Gaussian processes learning, and an adaptive moment estimation method is designed using gradients of innovation to tune hyperparameters of Gaussian processes automatically. Moreover, a subspace index is created and updated based on feature distance measures to improve the computational efficiency of Gaussian processes learning in each local model. The developed scheme can perform real-time simulation considering environmental disturbances and is applied to a 6 degree-of-freedom autonomous underwater vehicle. The results demonstrate that this scheme is an effective mathematical modeling tool for underwater vehicles dynamics.

关键词： nonparametric modeling AUV dynamics model Local Gaussian processes learning Adaptive moment estimation

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