检索结果-内蒙古大学图书馆

multi-output gaussian process regression Model with Combined Kernel Function for Polyester Esterification processes

Journal of Donghua University(English Edition) 2023年第1期40卷 27-33页

作者：王恒骞耿君先陈磊 Engineering Research Center of Digitized Textile&Apparel Technology Ministry of EducationDonghua UniversityShanghai 201620China College of Information Science and Technology Donghua UniversityShanghai 201620China

In polyester fiber industrial processes,the prediction of key performance indicators is vital for product *** esterification process is an indispensable step in the polyester polymerization *** has the characteristics of strong coupling,nonlinearity and complex *** solve these problems,we put forward a multi-output gaussian process regression(MGPR)model based on the combined kernel function for the polyester esterification *** the seasonal and trend decomposition using loess(STL)can extract the periodic and trend characteristics of time series,a combined kernel function based on the STL and the kernel function analysis is constructed for the *** effectiveness of the proposed model is verified by the actual polyester esterification process data collected from fiber production.

关键词： seasonal and trend decomposition using loess(STL) multi-output gaussian process regression combined kernel function polyester esterification process

来源：评论

学校读者我要写书评

暂无评论

State of Health Trajectory Prediction Based on multi-output gaussian process regression for Lithium-Ion Battery

引用

BATTERIES-BASEL 2022年第10期8卷 134页

作者： Wang, Jiwei Deng, Zhongwei Li, Jinwen Peng, Kaile Xu, Lijun Guan, Guoqing Abudula, Abuliti Hirosaki Univ Grad Sch Sci & Technol Hirosaki Aomori 0368560 Japan Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 611731 Peoples R China Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Xinjiang Inst Engn Xinjiang Coal Mine Electromech Engn Technol Res C Urumqi 830023 Peoples R China

Lithium-ion battery state of health (SOH) accurate prediction is of great significance to ensure the safe reliable operation of electric vehicles and energy storage systems. However, safety issues arising from the inaccurate estimation and prediction of battery SOH have caused widespread concern in academic and industrial communities. In this paper, a method is proposed to build an accurate SOH prediction model for battery packs based on multi-output gaussian process regression (MOGPR) by employing the initial cycle data of the battery pack and the entire life cycling data of battery cells. Firstly, a battery aging experimental platform is constructed to collect battery aging data, and health indicators (HIs) that characterize battery aging are extracted. Then, the correlation between the HIs and the battery capacity is evaluated by the Pearson correlation analysis method, and the HIs that own a strong correlation to the battery capacity are screened. Finally, two MOGPR models are constructed to predict the HIs and SOH of the battery pack. Based on the first MOGPR model and the early HIs of the battery pack, the future cycle HIs can be predicted. In addition, the predicted HIs and the second MOGPR model are used to predict the SOH of the battery pack. The experimental results verify that the approach has a competitive performance;the mean and maximum values of the mean absolute error (MAE) and root mean square error (RMSE) are 1.07% and 1.42%, and 1.77% and 2.45%, respectively.

关键词： lithium-ion battery health indicators state of health multi-output gaussian process regression health prediction

来源：评论

学校读者我要写书评

暂无评论

multi-decadal temporal reconstruction of Sentinel-3 OLCI-based vegetation products with multi-output gaussian process regression

引用

ECOLOGICAL INFORMATICS 2024年 83卷

作者： Kovacs, David D. Reyes-Munoz, Pablo Berger, Katja Meszaros, Viktor Ixion Caballero, Gabriel Verrelst, Jochem Univ Valencia IPL Catedrat Agustin Scardino Benlloch 9 Paterna 46980 Spain GFZ German Res Ctr Geosci D-14473 Potsdam Germany

Operational Earth observation missions, like the Sentinel-3 (S3) satellites, aim to provide imagery for long-term environmental assessment to monitor and analyze vegetation changes and dynamics. However, the S3 archive is limited in temporal availability to the year 2016. Although S3 provides continuity of previous missions, key vegetation products (VPs) including leaf area index (LAI), fraction of photosynthetically active radiation (FAPAR), fractional vegetation cover (FVC), and leaf chlorophyll content (LCC), can be reliably produced from Ocean and Land Colour Instrument (OLCI) data only since the sensors' launch. To overcome this limitation, our study proposes a reconstruction workflow that extends the data record beyond its data acquisition. By using multi-output gaussian process regression (MOGPR) fusion, we explored guiding predictor VPs from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor for the reconstruction of multi-decadal (spanning two decades, 2002-2022) temporal profiles of four OLCI-derived VPs (S3-MOGPR), moving past S3's launch. We first evaluated three MODIS-derived inputs as predictor variables: LAI, FAPAR, and the Normalised Difference Vegetation Index (NDVI) over nine sites with distinct land covers from the Ground-Based Observations for Validation (GBOV) service. Each predictor produced a distinct time series for the four reconstructed S3 VPs. To determine which predictor variable most accurately reconstructs data streams of the targeted variable, all S3-MOGPR VPs were compared to satellite-based products from the Copernicus Global Land Service (CGLS). MOGPR models were trained for 2019 and compared to reference data. Since MODIS LAI demonstrated the best reconstruction performance of all predictors, S3-MOGPR VPs were fully reconstructed from 2022 back to 2002 using guiding MODIS LAI and evaluated with in-situ data. The most consistent reconstructed product was FVC (R = 0.96, NRMSE = 0.17) over mixed forests compared to CGLS

关键词： multi-output gaussian process regression Long-term temporal reconstruction Sentinel-3 MODIS5: time series

来源：评论

学校读者我要写书评

暂无评论

FEW-SHOT PERSONALIZED SALIENCY PREDICTION USING PERSON SIMILARITY BASED ON COLLABORATIVE multi-output gaussian process regression

FEW-SHOT PERSONALIZED SALIENCY PREDICTION USING PERSON SIMIL...

引用

IEEE International Conference on Image processing (ICIP)

作者： Moroto, Yuya Maeda, Keisuke Ogawa, Takahiro Haseyama, Miki Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido Japan Hokkaido Univ Off Inst Res Sapporo Hokkaido Japan Hokkaido Univ Fac Informat Sci & Technol Sapporo Hokkaido Japan

ISBN: (纸本)9781665441155

A few-shot personalized saliency prediction method using person similarity based on collaborative multi-output gaussian process regression is presented in this paper. Contrary to prediction of general saliency maps, that of personalized saliency maps (PSMs), which is a focus of attention owing to its heterogeneity among individuals, is a challenging problem since the amount of training gaze data is limited due to the burden on new persons. Thus, the proposed method focuses on the similarity of gaze tendency between persons. In the proposed method, collaborative gaussian process regression (CoMOGP) is adopted for PSM prediction. CoMOGP enables to represent similarity of gaze tendency between the target person and other persons as weights, and then consider the similarity for each image by using visual features obtained from images as inputs. The contributions of the few-shot PSM prediction based on CoMOGP are twofolds. 1) CoMOGP, which is one of probabilistic methods, can avoid the overfitting to small amount of training data. 2) Similarity for each image can be considered by using visual features as inputs. In the experiment using the open dataset, the proposed method outperforms comparative methods including the state-of-the-art method.

关键词： Saliency map personalized prediction multi-output gaussian process regression multi-task learning image selection

来源：评论

学校读者我要写书评

暂无评论

Long-term prediction enhancement based on multi-output gaussian process regression integrated with production plans for oxygen supply network

引用

COMPUTERS & CHEMICAL ENGINEERING 2022年 163卷

作者： Zhou, Pengwei Xu, Zuhua Peng, Xudong Zhao, Jun Shao, Zhijiang Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China Hangzhou Oxygen Plant Grp Co Ltd Hangzhou Peoples R China

The variation tendency of oxygen demand plays a crucial role in planning and scheduling in the steel industry. Traditional prediction methods merely utilize historical data without considering any future production information. In steel enterprises, the production plan is a kind of easy-to-obtain future production information, which specifies the load adjustment, start-up, and shutdown of equipment in the future. In this paper, a prediction enhancement method based on multi-output gaussian process regression (MOGPR) for the oxygen demand prediction is first developed. The proposed method models the correlations over three data sources, i.e., the plan-based oxygen prediction, the history-based oxygen prediction, and the oxygen measurement, and transfers the meaningful knowledge between them. Two longterm oxygen demand prediction methods integrated with prediction enhancement are further proposed. Finally, two industrial case studies are used to compare and demonstrate the effectiveness and performance of the proposed methods.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Prediction enhancement Production plan Oxygen supply network multi-output gaussian process regression

来源：评论

学校读者我要写书评

暂无评论

Probabilistic impact localization in composites using wavelet scattering transform and multi-output gaussian process regression

引用

MEASUREMENT 2024年 236卷

作者： Ojha, Shivam Jangid, Naveen Shelke, Amit Habib, Anowarul Indian Inst Technol Guwahati Dept Civil Engn Gauhati 781039 Assam India UiT Arctic Univ Norway Dept Phys & Technol N-9037 Tromso Norway

Data -driven machine -learning models offer considerable promise for acoustic source localization. However, many existing models rely on training data that correlates time -of -flight (TOF) measurements with source locations, yet they struggle to handle the complexities arising from nonlinear wave propagation in materials with varying properties. Furthermore, these models overlook the noise and uncertainties inherent in realworld experiments when predicting outputs. This paper aims to bridge a gap in impact localization for such structures, particularly focusing on scenarios involving noisy field measurements. This study proposes a framework based on probabilistic machine learning to identify impact locations, utilizing wavelet scattering transform (WST) and multi -output gaussian process regression (moGPR). WST extracts informative features from Lamb waves, capturing relevant signatures for training the probabilistic machine learning model, while moGPR estimates correlated impact location coordinates (x, y) while accounting for inherent uncertainties in the data. To assess the proposed method's performance in handling measurement uncertainties, an experiment was conducted using a CFRP composite panel instrumented with a sparse array of piezoelectric transducers. The results demonstrate that the probabilistic framework effectively addresses measurement uncertainties, enabling reliable source location estimation with confidence intervals and providing valuable insights for decision -making.

关键词： Acoustic emission AE source localization multi-output gaussian process regression Probabilistic machine learning Structural health monitoring Wavelet scattering transformation

来源：评论

学校读者我要写书评

暂无评论

Batch active learning for microstructure-property relations in energetic materials

引用

MECHANICS OF MATERIALS 2025年 205卷

作者： Ozbayram, Ozge Olsen, Daniel Annamaraju, Maruthi Robertson, Andreas E. Venkatraman, Aditya Kalidindi, Surya R. Zhou, Min Graham-Brady, Lori Johns Hopkins Univ Dept Civil & Syst Engn 3400 N Charles St Baltimore MD 21218 USA Georgia Inst Technol George W Woodruff Sch Mech Engn 801 Ferst Dr Atlanta GA 30332 USA

Polymer-bonded explosives (PBX) exhibit complex microstructure-property relationships, particularly in their shock-to-detonation transition (SDT) behavior. Traditionally physics-based simulations to explore these relationships are computationally expensive and time-consuming for a number of reasons. We present a material informatics framework that leverages batch active learning to efficiently investigate the intricate microstructure-macroscopic property relationships for PBX, significantly reducing simulation time. Our framework integrates multi-output gaussian process regression (MOGPR) to capture complex relationships between microstructural features (including void volume fraction, shape, and distribution) and reaction response (characterized by shock pressure and run-to-detonation distance). The batch active learning component efficiently traverses the microstructure space by strategically selecting the most informative microstructures for additional simulations, maximizing information gain while minimizing computational costs. By iteratively refining the MOGPR model with the most informative samples, we accelerate the learning process and improve the predictive accuracy of the microstructure-property relationships. Our results demonstrate rapid model convergence and high predictive accuracy, with r2 scores of 0.97 for both pressure and run distance predictions in leave-one-out cross-validation after only eight iterations. This approach efficiently navigates the diverse microstructure space, uncovering key factors governing the SDT behavior in PBX. It also has the potential to significantly improve the design and optimization of PBX materials, enabling the development of tailored explosives with enhanced performance and safety characteristics.

关键词： Batch active learning multi-output gaussian process regression Polymer-bonded explosives Materials modeling

来源：评论

学校读者我要写书评

暂无评论

An integrated machine learning framework for effluent quality prediction in Sewage Treatment Units

引用

URBAN WATER JOURNAL 2023年第4期20卷 487-497页

作者： Srungavarapu, Chandra Sainadh Sheik, Abdul Gaffar Tejaswini, E. S. S. Yousuf, Sheik Mohammed Ambati, Seshagiri Rao Natl Inst Technol Dept Chem Engn Warangal Telangana India Natl Inst Technol Dept Comp Sci & Engn Warangal Telangana India Indian Inst Petr & Energy Dept Chem Engn Visakhapatnam Andhra Prades India

The influent and effluent data from wastewater treatment plants being highly correlated with multi-variable coupling and time-varying features may degrade the performance of conventional soft sensors over time. Adaptive strategies based on just-in-time learning (JIT), moving windows (MW), and time difference (TD) are used in this work to develop an adaptive soft sensor. multi-output gaussian-process regression (MGPR) is selected and hybrid methods such as TD JIT, MW TD, and JIT MW TD along with TD and MGPR methods are implemented. Data from the benchmark simulation model No.1, closed-loop architecture after applying PI controller, and real-time data from the Rithala Plant of Delhi are obtained. The improved error percentage is 15.03% for total phosphorus (open-loop) using the JIT TD method when compared with the MW TD method. Fair results are observed with JIT TD on real time data with a strong correlation between predicted and observed values, above 0.8 for any variable being estimated.

关键词： Activated sludge models benchmark simulation models machine learning multi-output gaussian process regression sewage treatment

来源：评论

学校读者我要写书评

暂无评论

Evaluation of machine tool dynamics in dry gear hobbing considering time-varying thermal effects

引用

CASE STUDIES IN THERMAL ENGINEERING 2024年 61卷

作者： Li, Benjie Yu, Changlin Zhang, Chao Yang, Xiao Li, Huangshuai Li, Binglin Southwest Petr Univ Sch Mechatron Engn Chengdu 610500 Peoples R China Chongqing Technol & Business Univ Chongqing Key Lab Mfg Equipment Mech Design & Cont Chongqing 400067 Peoples R China

Effective stability analysis strongly depends on accurately predicting machine tool dynamics. However, the nonlinear thermal effects during the machining operations present a significant challenge to accurately modeling the machine tool dynamics. To this end, the evolution of the machine tool dynamics under the thermal growth during the dry gear hobbing process is investigated, and a rapid evaluation model of the temperature-dependent dynamics is developed. Firstly, a preliminary test was conducted to explore thermal effects on the machine tool dynamics. Then, the free vibrations at the spindle nose during the hobbing operations were filtered, and the corresponding modal parameters were estimated. Subsequently, the influence of temperature rise on the modal parameters is analyzed. The results show that the first- and third-order natural frequencies increase by 142 and 95 Hz, respectively, and the damping ratios for all modes are reduced with the machine tool temperature rise. Finally, the relationship between the feature temperatures and modal parameters is described using the multi-output gaussian process regression (MOGPR) model. The overall prediction accuracy is 95.6 %, and the relative errors in the test data set are within 10 %. The findings can provide an essential basis for the machining stability analysis of dry gear hobbing.

关键词： Machine tool dynamics Modal parameters Thermal effects multi-output gaussian process regression Dry gear hobbing

来源：评论

学校读者我要写书评

暂无评论

Lithium battery state-of-health estimation and remaining useful lifetime prediction based on non-parametric aging model and particle filter algorithm

引用

ETRANSPORTATION 2022年 11卷

作者： Li, Xiaoyu Yuan, Changgui Wang, Zhenpo He, Jiangtao Yu, Shike Hebei Univ Technol Sch Mech Engn Tianjin 300130 Peoples R China China State Shipbldg Corp Ltd Res Inst 705 Kunming Branch Kunming 650106 Yunnan Peoples R China Beijing Inst Technol Natl Engn Lab Elect Vehicles Beijing 100081 Peoples R China FinDreams Battery Corp Ltd Shenzhen 518122 Peoples R China

State of health estimation (SOH) and remaining useful lifetime (RUL) prediction are significant health indicators for improving the safety and reliability of battery systems. Herein, a data-fusion method is developed to establish a non-parametric degradation model and a particle filter algorithm for forecasting battery health conditions. Firstly, a dynamic battery aging state-space system is developed, in which gaussian process regression is applied to establish state equation using historical capacity series and current capacity as input and output variables, respectively. Meanwhile, multi-output gaussian process regression maps the relationship between capacity degradation and battery health indicators to construct an observation equation. Second, two filter methods are unitized to obtain the smooth differential thermal voltammetry curves and the significant health indicators are extracted from partial differential thermal voltammetry curves. Third, the short-term SOH estimation and long-term RUL prediction are carried out using a particle filter algorithm. Moreover, two types of five batteries with various designed cases are conducted to verify and analyze the proposed method. The results show that the estimation errors of short-term SOH are within 4% and prediction errors of long-term RUL are around 7% (relative error/EOL, 12/159), which indicate the proposed method has an excellent capability for accurate and robust forecasting battery health conditions. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Lithium battery State of health Remaining useful life multi-output gaussian process regression Particle filter algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：