检索结果-内蒙古大学图书馆

A data-driven Approach for Predicting Industrial Dyeing Recipes of Polyester Fabrics

FIBERS AND POLYMERS 2024年第8期25卷 2985-2991页

作者： Xie, Yutao Zhang, Hao Zhang, Shujuan Xiao, Shunli Li, Qi Qin, Xianan Zhejiang Sci Tech Univ Qixin Honor Program Hangzhou 310018 Peoples R China Zhejiang Sci Tech Univ Sch Mat Sci & Engn Hangzhou 310018 Peoples R China Zhejiang Sci Tech Univ Natl Engn Lab Text Fiber & Proc Technol Hangzhou 310018 Peoples R China Zhejiang Prov Innovat Ctr Adv Text Technol Shaoxing 312000 Peoples R China Tongkun Inc Co Ltd Tongxiang 314500 Peoples R China

Polyester is extensively used in the textile industry for fabricating fibers and fabrics. Dyeing for polyester fabrics has a huge demand. Given that dyeing is one of the least environmentally friendly industrial processes, decreasing the attempts for dyeing (a.k.a realizing "one-shot" successful dyeing) is of vital importance to the dyeing manufacturing for polyester fabrics. This can be achieved by accurately predicting the dye concentrations for a dyeing recipe with provided target color information on the polyester fabrics. In this paper, we report a data-driven approach for accurately predicting industrial dyeing recipes of polyester fabrics. We intensively discuss the data preprocessing skills for this purpose. We show that log-transform and using full reflectance spectra for the color as input are two effective preprocessing techniques to improve the model performance. An effective model based on gradient-boosting regression tree (GBRT) has been developed to quantitatively model the relationship between the colorimetric information and the dye concentrations of industrial dyeing data of polyester fabrics. The developed approach can predict dye concentrations for dyeing tasks for polyester fabrics with error at 10-20%.

关键词： Polyester Dyeing recipes data-driven modeling Gradient boosting regression tree

来源：评论

学校读者我要写书评

暂无评论

data-driven Closed-Loop Process Fault Diagnosis With Enhanced Fault Dynamics

引用

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年

作者： Liu, Qiang Wen, Li Wu, Gaochang Chai, Tianyou Qin, S. Joe Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Lingnan Univ Sch Data Sci Hong Kong 999077 Peoples R China

In a closed-loop system, feedback control may transfer process faults from source variables to other variables that obscure the diagnosis of the root cause of the fault. Taking the fused magnesium furnace (FMF) as an example, the effects of process faults, e.g., the semimolten condition,may propagate through the controlled system. To address this issue, this article proposes a new data-driven process fault diagnosis method for dynamic processes in the presence of feedback control. A new residual analysis (RA) method is first proposed to extract features of process faults in the feedback-invariant subspace. Moreover, a new fault diagnosis algorithm, which incorporates support vector machines (SVM) with leaky integrate-and-fire neurons (LIF), named LIF-SVM, is proposed. Unlike traditional diagnosis methods which classify each element independently, LIF-SVM efficiently takes into account the fault dynamics. The features of process faults extracted by RA are used by LIF-SVM for diagnosis to constitute the new RALIF-SVM method. Results from experimental studies on a simulated 4x4 dynamic process and a real FMF show that the diagnosis accuracies using the proposed method increase by 10.9% and 9.15%, respectively, compared to the traditional subspace reconstruction method.

关键词： Fault diagnosis Feature extraction Support vector machines Process control Training Testing Magnesium data models Closed loop systems Accuracy Closed-loop systems data-driven modeling dynamic processes fault diagnosis process faults

来源：评论

学校读者我要写书评

暂无评论

Polymer extrusion die design using a data-driven autoencoders technique

引用

INTERNATIONAL JOURNAL OF MATERIAL FORMING 2024年第1期17卷 1-10页

作者： Ghnatios, Chady Gravot, Eloi Champaney, Victor Verdon, Nicolas Hascoet, Nicolas Chinesta, Francisco HESAM Univ Arts & Metiers Inst Technol Lab PIMM CNRSCnamPIMM LabSKF Chaire ENSAM 151 Blvd Hop F-75013 Paris France HESAM Univ Arts & Metiers Inst Technol PIMM Lab CNRSCnam 151 Blvd Hop F-75013 Paris France Goodyear Paris Def 1Tour First1 Sq Saisons F-92400 Courbevoie France ENSAM Inst Technol ESI Grp Chair 151 Blvd Hop F-75013 Paris France ENSAM Inst Technol PIMM Lab 151 Blvd Hop F-75013 Paris France

Designing extrusion dies remains a tricky issue when considering polymers. In fact, polymers exhibit strong non-Newtonian rheology that manifest in noticeable viscoelastic behaviors as well as significant normal stress differences. As a consequence, when they are pushed through a die, an important die-swelling is observed, and consequently the final geometry of the extruded profile differs significantly from the one of the die. This behavior turns the die's design into a difficult task, and its geometry must be defined in such a way that the extruded profile results in the targeted one. Numerical simulation was identified as a natural way for building and solving the inverse problem of defining the die, leading to the targeted extruded geometry. However, state-of-the-art rheological models reveal inaccuracies for the desired level of precision. In this paper, we propose a data-driven approach that, based on the accumulated experience on the extruded profiles for different dies, learns the relation enabling efficient die design.

关键词： Die design Machine learning Artificial intelligence Autoencoder data-driven modeling

来源：评论

学校读者我要写书评

暂无评论

Gradient preserving Operator Inference: data-driven reduced-order models for equations with gradient structure

引用

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2024年 427卷

作者： Geng, Yuwei Singh, Jasdeep Ju, Lili Kramer, Boris Wang, Zhu Univ South Carolina Dept Math Columbia SC 29208 USA Univ Calif San Diego Dept Mech & Aerosp Engn La Jolla CA 92093 USA

Hamiltonian Operator Inference has been introduced in Sharma et al. (2022) to learn structurepreserving reduced -order models (ROMs) for Hamiltonian systems. This approach constructs a low -dimensional model using only data and knowledge of the Hamiltonian function. Such ROMs can keep the intrinsic structure of the system, allowing them to capture the physics described by the governing equations. In this work, we extend this approach to more general systems that are either conservative or dissipative in energy, and which possess a gradient structure. We derive the optimization problems for inferring structure -preserving ROMs that preserve the gradient structure. We further derive an a priori error estimate for the reduced -order approximation. To test the algorithms, we consider semi-discretized partial differential equations with gradient structure, such as the parameterized wave and Korteweg-de-Vries equations, and equations of three-dimensional linear elasticity in the conservative case and the one- and two-dimensional Allen -Cahn equations in the dissipative case. The numerical results illustrate the accuracy, structure -preservation properties, and predictive capabilities of the gradient -preserving Operator Inference ROMs.

关键词： Dissipative and conservative systems Gradient-flow equation Operator inference Reduced-order modeling data-driven modeling

来源：评论

学校读者我要写书评

暂无评论

data-driven Fault Diagnosis of Nonlinear Systems With Parameter Uncertainty Using Deep Koopman Operator and Weighted Window Extended Dynamic Mode Decomposition

引用

INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2024年第11期22卷 3314-3328页

作者： Lee, Jayden Dongwoo Kim, Lamsu Lee, Seongheon Bang, Hyochoong Korea Adv Inst Sci & Technol Dept Aerosp Engn Daejeon 34141 South Korea

In this study, we propose a data-driven fault diagnosis method for nonlinear systems with parameter uncertainty using Koopman operator. The Koopman operator is an infinite-dimensional linear operator that transforms a nonlinear dynamical system to a high-dimensional linear system. Using this property, we obtain an equivalent linear system to detect and identify the fault situation by analyzing the system matrices A and B. In this paper, a deep Koopman operator is proposed to find an observable function automatically by leveraging the capability of deep neural networks. A weighted window extended dynamic mode decomposition (WW-EDMD) is used to obtain the Koopman operator through a recursive procedure reducing computation time and memory usage. A forgetting factor is also implemented to enhance the fault detection ability, giving a higher weight to the latest data. To detect a loss of effectiveness (LoE) fault under a parameter uncertainty, the equivalent linear model is updated at each time, and if the norm of the input matrix B is less than the designed threshold, the LoE fault is detected and identified. The results of the numerical simulation show that the proposed method has a better fault detection capability than the method using window extended dynamic mode decomposition that only updates the matrix B under parameter variation.

关键词： data-driven modeling deep Koopman operator fault diagnosis parameter uncertainty weighted window extended dynamic mode decomposition

来源：评论

学校读者我要写书评

暂无评论

data-driven Fault Detection and Isolation for Multirotor System Using Koopman Operator

引用

JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2024年第3期110卷 128页

作者： Lee, Jayden Dongwoo Im, Sukjae Kim, Lamsu Ahn, Hyungjoo Bang, Hyochoong Korea Adv Inst Sci & Technol Dept Aerosp Engn 291 Daehak Ro Daejeon 34141 South Korea

This paper presents a data-driven fault detection and isolation (FDI) for a multirotor system using Koopman operator and Luenberger observer. Koopman operator is an infinite-dimensional linear operator that can transform nonlinear dynamical systems into linear ones. Using this transformation, our aim is to apply the linear fault detection method to the nonlinear system. Initially, a Koopman operator-based linear model is derived to represent the multirotor system, considering factors like non-diagonal inertial tensor, center of gravity variations, aerodynamic effects, and actuator dynamics. Various candidate lifting functions are evaluated for prediction performance and compared using the root mean square error to identify the most suitable one. Subsequently, a Koopman operator-based Luenberger observer is proposed using the lifted linear model to generate residuals for identifying faulty actuators. Simulation and experimental results demonstrate the effectiveness of the proposed observer in detecting actuator faults such as bias and loss of effectiveness, without the need for an explicitly defined fault dataset.

关键词： Fault detection and isolation (FDI) data-driven modeling Koopman operator Extended dynamic mode decomposition (EDMD) Multirotor UAV

来源：评论

学校读者我要写书评

暂无评论

Development and Evaluation of Artificial Neural Networks for Real-World data-driven Virtual Sensors in Vehicle Suspension

引用

IEEE ACCESS 2024年 12卷 13183-13195页

作者： Sabanovic, Eldar Kojis, Paulius Ivanov, Valentin Dhaens, Miguel Skrickij, Viktor Vilnius Gediminas Tech Univ Transport Engn Fac LT-10223 Vilnius Lithuania Tenneco Automot Europe B-3800 St Truiden Belgium

Vehicle comfort, handling, and stability can be improved by a semi-active suspension with advanced control algorithms using the vertical velocities of the sprung mass (SM) and the unsprung masses (UMs) as inputs. Displacement and acceleration sensors are often used to estimate vertical velocities of UMs. However, these sensors are expensive and are susceptible to degradation. Virtual sensors (VS) have been proposed as a solution, and previous research using simulation data has shown that artificial neural networks (ANNs) can provide usable UM vertical velocity estimates. This study aims at finding ANNs structure and input sample window size to achieve best performance on real-world data. Novel dataset was created and used to test VSs based on eight structures of ANNs combining multilayer perceptron, convolutional neural network, long-term short-term memory (LSTM), and bidirectional LSTM layers. This article presents the results of 104 combinations of ANN structure and sample window size, which required 6240 training sessions. A Bayesian search was used to tune the hyperparameters of ANNs' layers minimizing the root-mean-square error (RMSE) of estimations on the validation data, while a grid search was used to select the sample window size that minimizes RMSE of estimation on test data, ensuring that selected combinations are well generalized. The VS based on ANN with convolutional layers, achieved the lowest RMSE of 0.0210 m/s, and processing time of 0.421 ms for a window size of 23 samples while estimating vertical velocities of vehicle UMs from real-world data.

关键词： Suspensions (mechanical systems) Soft sensors Stability analysis Power system stability Multilayer perceptrons Degradation Convolutional neural networks Bayes methods Artificial neural networks data models Vehicle dynamics data-driven modeling virtual sensor velocity estimation vehicle suspension

来源：评论

学校读者我要写书评

暂无评论

data-driven model predictive control using road-based disturbance estimations in longitudinal driving of e-bike

引用

JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2024年第4期46卷 1-15页

作者： Genc, Mehmet Onur Bursa Tech Univ Dept Mechatron Engn Bursa Turkiye

In this study, the electric bike is investigated under the data-driven model predictive control (MPC) model approach to develop road disturbance estimation. The focus of the research study is to develop a new control algorithm proposal for cargo e-bikes subjecting to uncertain road conditions in urban transportation. The developed model is proposed for model predictive driving option in e-bikes to provide optimal motor torque operation against predicted road disturbances. The driving profile is designed to simulate urban driving in different road types by considering pedal usage frequency and pedal load measurement in combined drive. Firstly, a datalogger is designed, and the microcontroller is programmed to measure pedal force independently in all road profiles. Road profiles are divided into two groups, pedal-assisted routes and the full electric driving mode, and all range capacities are recorded to make allover comparisons. Road types consist of asphalt and graded gravel at different slope levels. Pedal load statistical relationship based on each route is also indicated, and the road-based difference is also presented. The obtained data are used for data-driven model development in the MPC control model as measured disturbances. The control model then is integrated into the one-dimensional model to provide road estimation. The design of the developed model provides sensorless road disturbance estimation in urban driving. The proposed control model presents an innovative approach for system designing of micro-e-mobility vehicles in optimum longitudinal vehicle control considering the data-driven MPC method.

关键词： Model predictive control Electric bike Driving profile data-driven modeling Road-based disturbance estimations

来源：评论

学校读者我要写书评

暂无评论

Visually Guided Acquisition of Contact Dynamics and Case Study in data-driven Haptic Texture modeling

引用

IEEE TRANSACTIONS ON HAPTICS 2020年第3期13卷 611-627页

作者： Abdulali, Arsen Atadjanov, Ibragim R. Jeon, Seokhee Kyung Hee Univ Dept Comp Sci & Engn Yongin 17104 South Korea

data-driven modeling of human hand contact dynamics starts with a tedious process of data collection. The data of contact dynamics consist of an input describing an applied action and response stimuli from the environment. The quality and stability of the model mainly depend on how well data points cover the model space. Thus, in order to build a reliable data-driven model, a user usually collects data dozens of times. In this article, we aim to build an interactive system that assists a user in data collection. We develop an online segmentation framework that partitions a multivariate streaming signal. Real-time segmentation allows for tracking the process of how the model space is being populated. We applied the proposed framework for a haptic texture modeling use-case. In order to guide a user in data collection, we designed a user interface mapping applied input to alternative visual modalities based on the theory of direct perception. A combination of the segmentation framework and user interface implements a human-in-loop system, where the user interface assigns the target combination of input variables and the user tries to acquire them. Experimental results show that the proposed data collection schema considerably increases the approximation quality of the model, whereas the proposed user interface considerably reduces mental workload experienced during data collection.

关键词： data collection data models Haptic interfaces Solid modeling Visualization User interfaces Robots data-driven modeling data segmentation visual guidance data collection interface

来源：评论

学校读者我要写书评

暂无评论

Confidence score: a data-driven measure for inclusive systematic reviews considering unpublished preprints

引用

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2024年第4期31卷 809-819页

作者： Tong, Jiayi Luo, Chongliang Sun, Yifei Duan, Rui Saine, M. Elle Lin, Lifeng Peng, Yifan Lu, Yiwen Batra, Anchita Pan, Anni Wang, Olivia Li, Ruowang Marks-Anglin, Arielle Yang, Yuchen Zuo, Xu Liu, Yulun Bian, Jiang Kimmel, Stephen E. Hamilton, Keith Cuker, Adam Hubbard, Rebecca A. Xu, Hua Chen, Yong Univ Penn Ctr Hlth Analyt & Synth Evidence CHASE Dept Biostat Epidemiol & Informat Philadelphia PA 19104 USA Univ Penn Perelman Sch Med Dept Biostat Epidemiol & Informat Philadelphia PA 19104 USA Washington Univ Div Publ Hlth Sci Sch Med St Louis MO 63110 USA Columbia Univ Dept Biostat New York NY 10032 USA Harvard Univ Harvard TH Chan Sch Publ Hlth Dept Biostat Cambridge MA 02115 USA Univ Arizona Dept Epidemiol & Biostat Tucson AZ 85724 USA Weill Cornell Med Dept Populat Hlth Sci New York NY 11101 USA Univ Penn Sch Arts & Sci Grad Grp Appl Math & Computat Sci Philadelphia PA 19104 USA Cedars Sinai Med Ctr Dept Computat Biomed West Hollywood CA USA Univ Texas Hlth Sci Ctr Houston McWilliams Sch Biomed Informat Houston TX 77030 USA Univ Texas Southwestern Med Ctr Dallas Peter ODonnell Jr Sch Publ Hlth Dallas TX 75390 USA Univ Florida Coll Med Dept Hlth Outcomes & Biomed Informat Gainesville FL 32611 USA Univ Florida Coll Publ Hlth & Hlth Profess Dept Epidemiol Gainesville FL 32610 USA Univ Florida Coll Med Gainesville FL 32610 USA Hosp Univ Penn Dept Med Philadelphia PA 19104 USA Univ Penn Perelman Sch Med Dept Med Philadelphia PA 19104 USA Univ Penn Perelman Sch Med Dept Pathol & Lab Med Philadelphia PA 19104 USA Yale Sch Med Sect Biomed Informat & Data Sci New Haven CT 06510 USA Penn Med Leonard Davis Inst Hlth Econ Philadelphia PA 19104 USA Ctr Evidence Based Practice CEP Philadelphia PA 19104 USA Penn Inst Biomed Informat IBI Philadelphia PA 19104 USA

Objectives COVID-19, since its emergence in December 2019, has globally impacted research. Over 360 000 COVID-19-related manuscripts have been published on PubMed and preprint servers like medRxiv and bioRxiv, with preprints comprising about 15% of all manuscripts. Yet, the role and impact of preprints on COVID-19 research and evidence synthesis remain uncertain. Materials and Methods We propose a novel data-driven method for assigning weights to individual preprints in systematic reviews and meta-analyses. This weight termed the "confidence score" is obtained using the survival cure model, also known as the survival mixture model, which takes into account the time elapsed between posting and publication of a preprint, as well as metadata such as the number of first 2-week citations, sample size, and study type. Results Using 146 preprints on COVID-19 therapeutics posted from the beginning of the pandemic through April 30, 2021, we validated the confidence scores, showing an area under the curve of 0.95 (95% CI, 0.92-0.98). Through a use case on the effectiveness of hydroxychloroquine, we demonstrated how these scores can be incorporated practically into meta-analyses to properly weigh preprints. Discussion It is important to note that our method does not aim to replace existing measures of study quality but rather serves as a supplementary measure that overcomes some limitations of current approaches. Conclusion Our proposed confidence score has the potential to improve systematic reviews of evidence related to COVID-19 and other clinical conditions by providing a data-driven approach to including unpublished manuscripts.

关键词： evidence synthesis data-driven modeling preprint systematic review

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：