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System reliability analysis based on non-parametric modeling and melding of multi-source data

RELIABILITY ENGINEERING & SYSTEM SAFETY 2025年 262卷

作者： Wang, Cong Chen, Yunxia Zheng, Jiawei Zhou, Yuan Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China Beihang Univ Sci & Technol Reliabil & Environm Engn Lab Beijing 100191 Peoples R China City Univ Hong Kong Dept Syst Engn Hong Kong Peoples R China

In system reliability analysis, multi-source lifetime, degradation, and pass/fail data of multiple levels can compensate for the scarce data from full-system tests. However, existing research mainly uses parametric methods in modeling these data, which rely on strong prior assumptions on data uncertainty and correlation structures, thus having limited adaptability. To analyze system reliability more adaptively, a non-parametric methodology is developed. It begins with non-parametric modeling of single-source data, where adaptive kernel density estimation methods are proposed with adaptive bandwidth and local correlation coefficient to flexibly depict data uncertainty and correlation, respectively. They are verified to surpass commonly used parametric methods in modeling lifetime data with single and double peaks, and degradation data with linear, nonlinear, and double-cluster features. Next, rules for cross-level data transmission, multi-prior combination, and Bayesian melding based on equivalent parameterization are proposed, which enable the melding of nonparametric modeling results of multi-source data for the first time. Analytical melding results are derived to facilitate engineering applications. Finally, system reliability indices can be calculated, supporting system health management. The proposed methodology is applied to two real-world systems with different features of multisource data, and detailed quantitative and comparative studies fully verify its effectiveness and superiority.

关键词： System reliability analysis Multi-source data non-parametric modeling Adaptive kernel density estimation Bayesian melding

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Assessment of Blast Furnace Slags as a Potential Catalyst in Ozonation to Degrade Bezafibrate: Degradation Study and Kinetic Study via non-parametric modeling

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PROCESSES 2024年第9期12卷 1998页

作者： Galina-Licea, Alexandra Alfaro-Ponce, Mariel Chairez, Isaac Reyes, Elizabeth Perez-Martinez, Arizbeth Univ La Salle Fac Ciencias Quim Mexico City 06140 Mexico Inst Adv Mat Sustainable Mfg Tecnol Monterrey Mexico City 14380 Mexico Tecnol Monterrey Inst Adv Mat Sustainable Mfg Zapopan 45210 Mexico Univ La Salle Mexico Grp Invest Desarrollo Innovac Ciencia & Tecnol Amb Vicerrectoria Invest Mexico City 06140 Mexico

This study investigates the effectiveness of blast furnace slags (BFSs) as catalysts in the ozonation process to degrade complex contaminants such as bezafibrate (BFZ) at different pH levels. The findings reveal that the presence of BFS enhances degradation efficiency, achieving a 10% improvement at pH 10 and a 30% improvement at pH 5.5 compared to simple ozonation. The highest degradation efficiency was observed in the Ozonation-BFS system at pH 10, with 90% decomposition of BFZ. These results were corroborated through ozone consumption analysis, BOD5 measurements, and the identification of oxalic acid as the final decomposition product. Due to the complexity of the reaction system, kinetic characterization was performed using non-parametric modeling based on differential neural networks. The model indicated that the observed reaction rate for BFZ degradation in the presence of ozone and BFS was 4.12 times higher at pH 5.0 and 1.08 times higher at pH 10.0 compared to simple ozonation. These results underscore the potential of using BFS in catalytic ozonation processes for the effective treatment of recalcitrant contaminants in wastewater.

关键词： bezafibrate metallic slags catalytic ozonation non-parametric modeling recalcitrant contaminants

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Oxygen uptake response to switching stairs exercise by non-parametric modeling

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Control Theory and Technology 2024年第2期22卷 315-325页

作者： Hairong Yu Chenyu Zhang Kai Cao Hamzah M.Alqudah Steven Weidong Su Key Laboratory of Child Development and Learning Science of Ministry of Education and the School of Biological Science and Medical Engineering Southeast UniversityNanjing210096JiangsuChina College of Artificial Intelligence and Big Data for Medical Science Shandong First Medical University and Shandong Academy of Medical SciencesJinan250117ShandongChina Faculty of Engineering and Information Technology University of Technology SydneyUltimoNSW2007Australia

Oxygen uptake plays a crucial role in the evaluation of endurance performance during exercise and is extensively utilized for metabolic assessment. This study records the oxygen uptake during the exercise phase (i.e., ascending or descending) of the stair exercise, utilizing an experimental dataset that includes ten participants and covers various exercise periods. Based on the designed experiment protocol, a non-parametric modeling method with kernel-based regularization is generally applied to estimate the oxygen uptake changes during the switching stairs exercise, which closely resembles daily life activities. The modeling results indicate the effectiveness of the non-parametric modeling approach when compared to fixed-order models in terms of accuracy, stability, and compatibility. The influence of exercise duration on estimated fitness reveals that the model of the phase-oxygen uptake system is not time-invariant related to respiratory metabolism regulation and muscle fatigue. Consequently, it allows us to study the humans’ conversion mechanism at different metabolic rates and facilitates the standardization and development of exercise prescriptions.

关键词： non-parametric modeling Interval stair training exercise Kernel method.Cardiorespiratory response Oxygen uptake

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SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies

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GENOME BIOLOGY 2021年第1期22卷 1-25页

作者： Zhu, Jiaqiang Sun, Shiquan Zhou, Xiang Univ Michigan Dept Biostat Ann Arbor MI 48109 USA Univ Michigan Ctr Stat Genet Ann Arbor MI 48109 USA

Spatial transcriptomic studies are becoming increasingly common and large, posing important statistical and computational challenges for many analytic tasks. Here, we present SPARK-X, a non-parametric method for rapid and effective detection of spatially expressed genes in large spatial transcriptomic studies. SPARK-X not only produces effective type I error control and high power but also brings orders of magnitude computational savings. We apply SPARK-X to analyze three large datasets, one of which is only analyzable by SPARK-X. In these data, SPARK-X identifies many spatially expressed genes including those that are spatially expressed within the same cell type, revealing new biological insights.

关键词： Spatial transcriptomics SE analysis Covariance test non-parametric modeling Slide-seq HDST SPARK SPARK-X Spatial expression pattern

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MIMO non-parametric modeling of ship maneuvering motion for marine simulator using adaptive moment estimation locally weighted learning

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OCEAN ENGINEERING 2022年 261卷

作者： Zhang, Zhao Ren, Junsheng Bai, Weiwei Dalian Maritime Univ Naut Dynam Simulat & Control Lab Dalian 116026 Liaoning Peoples R China Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Sichuan Peoples R China

This work explores an adaptive moment estimation locally weighted learning (AME-LWL) method to develop a novel high-precision non-parametric modeling technology for ship maneuvering motion and conducts full-scale tests. First, a non-parametric learning framework is used to avoid unmodeled dynamics and parameter drift;second, Tikhonov method and multi-innovation adaptive moment estimation (AME) algorithm are proposed for local model adaptive learning;third, considering rudder and propeller joint effect for hydrodynamic forces, a multiple-input multiple-output (MIMO) continuous-time model is established to realize ship motion dynamic simulation under interferences. This scheme leads to a high-precision continuous-time non-parametric model that can be easily implemented, robust, less time-consuming and insensitive to initial parameters. In order to verify the properties of AME-LWL non-parametric model, 'Mariner' vessel and 'Delta Linda' tug two ship types were taken as objects for simulation experiments of comprehensive maneuvering tests, rudder-propeller coupling tests and wind interferences test. Finally, simulation results verify the effectiveness of the proposed scheme.

关键词： Ship maneuvering motion Marine simulator non-parametric modeling Locally weighted learning Moment estimation

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Performance analysis of a semi-active suspension system using coupled CFD-FEA based non-parametric modeling of low capacity shear mode monotube MR damper

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2019年第5期233卷 1214-1231页

作者： Mata, Gurubasavaraju Tharehalli Kumar, Hemantha Mahalingam, Arun Natl Inst Technol Karnataka Dept Mech Engn Mangalore 575025 Karnataka India

In this work, an approach for formulation of a non-parametric-based polynomial representative model of magnetorheological damper through coupled computational fluid dynamics and finite element analysis is presented. Using this, the performance of a quarter car suspension subjected to random road excitation is estimated. Initially, prepared MR fluid is characterized to obtain a relationship between the field-dependent shear stress and magnetic flux density. The amount of magnetic flux induced in the shear gap of magnetorheological damper is computed using finite element analysis. The computed magnetic field is used in the computational fluid dynamic analysis to calculate the maximum force induced under specified frequency, displacement and applied current using ANSYS CFX software. Experiments have been conducted to verify the credibility of the results obtained from computational analysis, and a comparative study has been made. From the comparison, it was found that a good agreement exists between experimental and computed results. Furthermore, the influence of fluid flow gap length and frequency on the induced force of the damper is investigated using the computational methods (finite element analysis and computational fluid dynamic) for various values. This proposed approach would serve in the preliminary design for estimation of magnetorheological damper dynamic performance in semi-active suspensions computationally prior to experimental analysis.

关键词： Magneto-rheological damper magnetostatic analysis non-parametric modeling finite element analysis computational fluid dynamics

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FracGen: Natural fracture networks reconstruction and upscaling using generative adversarial networks

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INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES 2025年 191卷

作者： Zhou, Changtai Lyu, Borui Wang, Yu City Univ Hong Kong Dept Architecture & Civil Engn Kowloon Tat Chee Ave Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Clear Water Bay Hong Kong Peoples R China

Accurate modeling of natural fracture networks is crucial for understanding subsurface structures and their properties, yet traditional methods often struggle with complex geometries and scale transitions. In this study, we present a novel non-parametric machine learning model, FracGen, utilizing the Single Image Generative Adversarial Network (SinGAN) to reconstruct and upscale natural fracture networks. After training, using one of three outcrops from different geological sites, the FracGen model can replicate key statistical properties of natural fractures without explicit parameterization, which is validated further through comparisons with real fracture networks. Furthermore, we address the challenge of fracture network upscaling, ensuring that largescale simulations retain the critical characteristics observed at small scales. Quantitative analysis, including cosine similarity measurements and probability distribution fitting, validates the model's accuracy (e.g., high cosine similarity values indicate strong correspondence between generated and real fracture networks). The proposed methodology advances our ability to model complex fracture networks and paves the way for more effective resource exploitation, better risk assessment, and improved design of engineering projects.

关键词： Fracture networks Generative Adversarial Networks (GANs) Machine learning Reconstruction and upscaling non-parametric modeling

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Neuro-Identifier for the Musculoskeletal System of the Upper-Limb to Map Electromyographic Signals to Inverse Kinematics 11

Neuro-Identifier for the Musculoskeletal System of the Upper...

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11th International Conference on Control, Mechatronics and Automation (ICCMA)

作者： Villela, Ulises Gomez-Correa, Manuela Ballesteros, Mariana Cruz-Ortiz, David Salgado, Ivan Gasparini, Francesca Inst Politecn Nacl CIDETEC Mexico City Mexico Inst Politecn Nacl Med Robot & Biosignals Lab Mexico City Mexico Univ Milano Bicocca DISCo Milan Italy

ISBN: (纸本)9798350315684

This paper presents a non-parametric mathematical model based on differential neural networks (DNNs) for the upper limb's neuromusculoskeletal system. The U-LIMB dataset is the source of input-output pairs that relate electromyographic (EMG) signals from different arm muscles with daily patient movements. This information trains the free weights of the DNN through Lyapunov stable learning laws. With the model provided by the DNN, it is possible to get online the inverse kinematic information that defines the desired trajectories for a virtual designed arm with five degrees of freedom. Simulation results demonstrate the application of these trajectories by implementing a set of proportional derivative controllers that ensured the correct movement of the virtual arm in response to a given set of EMG signals.

关键词： Neuromusculoskeletal system electromyographic signals non-parametric modeling differential neural networks

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A Bayesian Monotonic non-parametric Dose-Response Model

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Human and Ecological Risk Assessment 2021年第8期27卷 2104-2123页

作者： Alamri, Faten S. Boone, Edward L. Edwards, David J. Department of Mathematical Sciences Faculty of Science Princess Nourah Bint AbdulRahman University Riyadh Saudi Arabia Virginia Commonwealth University Richmond VA United States

Toxicologists are often concerned with determining the dosage to which an individual can be exposed with an acceptable risk of adverse effect. These types of studies have been conducted widely in the past, and many novel approaches have been developed. parametric techniques utilizing ANOVA and non-linear regression models are well represented in the literature. The biggest drawback of parametric approaches is the need to specify the adequate model. Recently, there has been an interest in nonparametric approaches to tolerable dosage estimation. In this work, we focus on the monotonically decreasing dose-response model where the response is a percent to control. This imposes two constraints on the nonparametric approach: the dose-response function must be monotonic and always positive. Here, we propose a Bayesian solution to this problem using a novel class of non-parametric models. A set of basis functions developed in this research is Alamri Monotonic spline (AM-spline). Our approach is illustrated using two simulated datasets and two experimental datasets from pesticide related research at the US Environmental Protection Agency. © 2021 Taylor & Francis Group, LLC.

关键词： Alamri Monotonic spline Bayesian statistics Benchmark tolerable region non-parametric modeling toxicology data

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Atlantic forest woody carbon stock estimation for different successional stages using Sentinel-2 data

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ECOLOGICAL INDICATORS 2023年 146卷

作者： Verly, Otavio Miranda Leite, Rodrigo Vieira Tavares-Junior, Ivaldo da Silva da Rocha, Samuel Jose Silva Soares Leite, Helio Garcia Gleriani, Jose Marinaldo Rufino, Maria Paula Miranda Xavier Silva, Valeria de Fatima Torres, Carlos Moreira Miquelino Eleto Plata-Rueda, Angelica Castro, Barbara Monteiro Zanuncio, Jose Cola Javocine, Laercio Antonio Goncalves Univ Fed Vicosa Dept Forest Engn Peter Henry Rolfs Ave BR-36570900 Vicosa MG Brazil Univ Fed Lavras Dept Forest Sci Doutor Sylvio Menicucci Ave BR-37200900 Lavras MG Brazil Univ Fed Vicosa Dept Entomol BIOAGRO Peter Henry Rolfs Ave BR-36570900 Vicosa MG Brazil

The Atlantic Forest is one of the most threatened biodiversity hotspots and environmental impacts has made its landscape fragmented and heterogeneous. The heterogeneity of the fragments is a challenge for the character-ization and quantification of forest resources, such as the stock of biomass and carbon. Methodologies based on remote sensing have been used, to improve these estimates without compromising execution costs. The objective was to estimate, with high spatial resolution passive remote sensing, the aboveground carbon stock in fragments of different successional stages of the Atlantic Forest. Forests were classified into initial, intermediate, and advanced successional stages. In each stratum, 10 plots (20x50 m) were established, and the carbon stock was calculated by adjusted Schumacher and Hall model. The reflectances of the blue, green, red, and near-infrared bands and vegetation indices (VIs) were obtained in the dry and rainy seasons, from MSI/Sentinel-2 images, with a resolution of 10 m. Artificial Neural Networks (ANN), with different combinations of variables, were trained and validated with simulated reflectance values. Carbon was estimated by ANN with the best perfor-mance in training and validation. The average carbon stock in the initial, intermediate, and advanced strata was 24.99, 35.79 and 82.28 Mg ha -1, respectively, with a general average of 47.68 Mg ha -1. The carbon estimates were better with the ANN trained with the reflectances of the rainy season. The addition of VIs did not improve ANN performance. The simulated spectral data were consistent and adequate to validate the selected ANN. The total carbon stock, modeled was 41,962.15 Mg, ranging from 6.68 to 108.29 Mg ha -1, with an average of 48.70 Mg ha -1. The carbon stock in the advanced stratum is more than three times that observed in the initial stratum, and they were efficiently estimated using high-resolution multispectral data, obtained in the rainy season, as inputs.

关键词： Semideciduous Seasonal Forest non-parametric modeling Artificial Neural Networks Passive remote sensing Forest succession

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