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A Novel Hybrid Machine Learning Algorithm for Limited and big data modeling With Application in Industry 4.0

IEEE ACCESS 2020年 8卷 111381-111393页

作者： Khayyam, Hamid Jamali, Ali Bab-Hadiashar, Alireza Esch, Thomas Ramakrishna, Seeram Jalili, Mahdi Naebe, Minoo RMIT Univ Sch Engn Melbourne Vic 3000 Australia Univ Guilan Fac Mech Engn Rasht *** Iran FH Aachen Univ Appl Sci Mech Engn D-52066 Aachen Germany Natl Univ Singapore Dept Mech Engn Singapore 119077 Singapore Deakin Univ Isnt Frontier Mat Burwood Vic 3217 Australia

To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements.

关键词： data models Support vector machines big data Machine learning Computational modeling Principal component analysis Industries Industry 40 big data modeling limited data modeling multi-objective optimization

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Manufacturing big data modeling Algorithm Based on GM (1,1) - LSTM and Its Application in Sales Forecasting 12

Manufacturing Big Data Modeling Algorithm Based on GM (1,1) ...

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IEEE 12th data Driven Control and Learning Systems Conference (DDCLS)

作者： Long, Yinren Xiao, Yi Ren, Hongru Lu, Renquan Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Guangdong Univ Technol Guangdong Prov Key Lab Intelligent Decis & Cooper Guangzhou 510006 Peoples R China

ISBN: (纸本)9798350321050

It is a new period for the development of automobile industry, the economic situation is complex and changing, and the policies of automobile industry are frequently issued, so accurate prediction of automobile sales is extremely important and practical for both government and enterprises. In this paper, the GM(1,1) model and the long short-term memory (LSTM) neural network model are combined and optimized, and the sales of a brand of cars from January 2019 to September 2022 are used as sample data, and the car sales in the next three months are predicted by two single models and linear combination forecasting models, respectively. The experimental results show that the linear combined forecasting model outperforms the other two single models in terms of forecasting results and has better resistance to the interference of external factors.

关键词： big data modeling Sales Forecasting Manufacturing Enterprise

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Industrial big data-Driven modeling and Prediction for Hot-Rolled Strip Crown with Multigrade and Multispecification data

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STEEL RESEARCH INTERNATIONAL 2024年第7期95卷

作者： Xu, Dewei Ding, Chengyan Liu, Yu Sun, Jie Peng, Wen Zhang, Dianhua Northeastern Univ State Key Lab Rolling & Automat Shenyang 110819 Peoples R China

In the field of hot rolling big data, the presence of different steel types, specifications, and data heterogeneity poses significant challenges to the accuracy and stability of using single machine learning regression technology for prediction. Therefore, this study proposes a hot-rolled strip crown prediction method that combines data clustering and fusion modeling. First, this article introduces a relevant mechanism for designing cluster strategies. The optimal clustering strategy is determined through comparative experiments using rolling process parameters, strip size, and main material components as the clustering features. Subsequently, the K-Means++ algorithm is used to effectively cluster the training and testing datasets based on this strategy, generating multiple clusters for both datasets. Finally, this study establishes seven different training models to match the most suitable regression prediction model for each cluster, and matching between each cluster and the model is determined through rigorous testing. The evaluation of the fusion model shows an R2 value of 0.829 and a root mean square error value of 3.974. The experimental results show that the proposed method outperforms traditional methods in solving the challenges of multiclass classification and data heterogeneity, providing strong data support for the intelligent control of the hot-rolled strip crown in the future. This article proposes a method that combines data clustering and fusion modeling to predict the crown of hot rolled strip steel in hot rolled big data with multiple steel grades, specifications, and data heterogeneity. The clustering strategy is designed by introducing mechanisms that are related to hot rolling. On this basis, the fusion model is constructed using the K-Means++ algorithm and the matched regression prediction *** (c) 2024 WILEY-VCH GmbH

关键词： big data modeling clustering method fusion modeling hot-rolled strip crowns

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Grey linguistic term sets for decision-making

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ANNALS OF OPERATIONS RESEARCH 2025年第1期348卷 489-509页

作者： Du, Junliang Xie, Naiming Liu, Sifeng Goh, Mark Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing 211106 Peoples R China Natl Univ Singapore NUS Business Sch Singapore 119613 Singapore Natl Univ Singapore Logist Inst Asia Pacific Singapore 119613 Singapore

In the era of big data, decision-making has become more complex and more uncertain. Faced with this situation, fuzzy linguistic approach may be an information representation model that is closer to natural language and people's cognition habits than exact numerical models. Although big data has a large amount of data, the useful information is incomplete, scattered and poor. Thus, an expert may use a more uncertain linguistic expression, i.e., there exists one term, several non-consecutive terms and linguistic intervals at the same time. In view of grey system theory for presenting objective uncertainty, we propose a new concept of grey linguistic term set (GLTS). GLTS has realized a unified description of uncertain linguistic term, hesitant fuzzy linguistic term set, extended hesitant fuzzy linguistic term set, and can provide an effective tool for data generation, processing and fusion of big data. Firstly, inspired by generalised grey numbers, we propose the basic representation model, kernel, and degree of greyness for GLTS. Secondly, we study some basic operations for GLTSs, e.g., whitenisation, subset, complement, union, intersection, merge, meet, and two aggregation operators. Finally, we develop a novel multicriteria group decision making method for quality function deployment with grey linguistic information. An illustrative example about electric vehicle manufacturing company is used to demonstrate the application and effectiveness of the developed method.

关键词： big data modeling Decision-making technique Linguistic terms Quality function deployment

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BiDaML in Practice: Collaborative modeling of big data Analytics Application Requirements 15th

BiDaML in Practice: Collaborative Modeling of Big Data Analy...

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15th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE)

作者： Khalajzadeh, Hourieh Simmons, Andrew J. Verma, Tarun Abdelrazek, Mohamed Grundy, John Hosking, John He, Qiang Ratnakanthan, Prasanna Zia, Adil Law, Meng Monash Univ Clayton Vic 3800 Australia Deakin Univ Burwood Vic 3125 Australia Univ Auckland Auckland 1010 New Zealand Swinburne Univ Hawthorn Vic 3122 Australia Alfred Hlth Melbourne Vic 3000 Australia

ISBN: (纸本)9783030700058;9783030700065

Using data analytics to improve industrial planning and operations has become increasingly popular and data scientists are more and more in demand. However, complex data analytics-based software development is challenging. It involves many new roles lacking in traditional software engineering teams - e.g. data scientists and data engineers;use of sophisticated machine learning (ML) approaches replacing many programming tasks;uncertainty inherent in the models;as well as interfacing with models to fulfill software functionalities. These challenges make communication and collaboration within the team and with external stakeholders challenging. In this paper, we describe our experiences in applying our BiDaML (big data Analytics modeling Languages) approach to several large-scale industrial projects. We used our BiDaML modeling toolset that brings all stakeholders around one tool to specify, model and document their big data applications. We report our experience in using and evaluating this tool on three real-world, large-scale applications with teams from: *** - a property price prediction website for home buyers;VicRoads - a project seeking to build a digital twin (simulated model) of Victoria's transport network updated in real-time by a stream of sensor data from inductive loop detectors at traffic intersections;and the Alfred Hospital - Intracranial hemorrhage (ICH) prediction through Computed Tomography (CT) Scans. These show that our approach successfully supports complex data analytics software development in industrial settings.

关键词： big data analytics big data modeling big data toolkits BiDaML Domain specific visual languages End-user tools

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Bidimensionally partitioned online sequential broad learning system for large-scale data stream modeling

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SCIENTIFIC REPORTS 2024年第1期14卷 1-19页

作者： Guo, Wei Yu, Jianjiang Zhou, Caigen Yuan, Xiaofeng Wang, Zhanxiu Yancheng Teachers Univ Key Lab Child Cognit Dev & Mental Hlth Jiangsu Prov Univ Yancheng 224002 Peoples R China Yancheng Teachers Univ Coll Informat Engn Yancheng 224002 Peoples R China

Incremental broad learning system (IBLS) is an effective and efficient incremental learning method based on broad learning paradigm. Owing to its streamlined network architecture and flexible dynamic update scheme, IBLS can achieve rapid incremental reconstruction on the basis of the previous model without the entire retraining from scratch, which enables it adept at handling streaming data. However, two prominent deficiencies still persist in IBLS and constrain its further promotion in large-scale data stream scenarios. Firstly, IBLS needs to retain all historical data and perform associated calculations in the incremental learning process, which causes its computational overhead and storage burden to increase over time and as such puts the efficacy of the algorithm at risk for massive or unlimited data streams. Additionally, due to the random generation rule of hidden nodes, IBLS generally necessitates a large network size to guarantee approximation accuracy, and the resulting high-dimensional matrix calculation poses a greater challenge to the updating efficiency of the model. To address these issues, we propose a novel bidimensionally partitioned online sequential broad learning system (BPOSBLS) in this paper. The core idea of BPOSBLS is to partition the high-dimensional broad feature matrix bidimensionally from the aspects of instance dimension and feature dimension, and consequently decompose a large least squares problem into multiple smaller ones, which can then be solved individually. By doing so, the scale and computational complexity of the original high-order model are substantially diminished, thus significantly improving its learning efficiency and usability for large-scale complex learning tasks. Meanwhile, an ingenious recursive computation method called partitioned recursive least squares is devised to solve the BPOSBLS. This method exclusively utilizes the current online samples for iterative updating, while disregarding the previous historical sam

关键词： Broad learning system Online sequential learning big data modeling Matrix partitioning Partitioned recursive least squares

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Random forest framework customized to handle highly correlated variables: an extensive experimental study applied to feature selection in genetic data 5

Random forest framework customized to handle highly correlat...

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5th IEEE International Conference on data Science and Advanced Analytics (IEEE DSAA)

作者： Sinoquet, Christine Mekhnacha, Kamel Univ Nantes LS2N UMR CNRS 6004 Nantes France Probayes Grenoble France

ISBN: (纸本)9781538650905

The random forest model is a popular framework used in classification and regression. In cases where high correlations exist within the data, it may be beneficial to capture these dependencies through latent variables, for an enhanced use of the random forest framework. In this paper, we present Sylva, the second proposal of a random forest with latent variables after T-Trees, derived from the seminal works of Botta and co-workers (Botta et al., 2008). Sylva is an innovative hybrid approach in which the dynamic generation of latent variables used to learn the random forest is driven by an additional forest model, this time a forest of latent tree models. The latter forest model, a class of Bayesian networks devised in (Mourad et al., 2011), allows a flexible modeling of the dependencies existing within the data. In the comprehensive study reported here, three variants of Sylva, instantiated by different clustering methods (CAST, DBSCAN, Louvain method), are compared to T-Trees using high-dimensional real-world datasets (161 datasets each describing around 5,000 observations and between 5,700 and 39,000 variables) in the context of genetic association studies. We show that T-Trees and Sylva have comparable high predictive powers (aeras under the ROC curves), that lie in range [0.887, 0.961] (T-Trees), and in interval [0.885, 0.979] (over the three Sylva instantiations). Interestingly, T-Trees and Sylva are shown to differ significantly in their importance measure distributions: in Sylva, the importance measure distribution corresponding to top-ranked variables is significantly skewed towards higher values than in T-Trees, which meets the feature selection enhancement objective. This property holds true for the three instantiations of Sylva. In addition, the thorough analysis of the number of top-ranked variables jointly identified by T-Trees and Sylva highlights the possibility to cross-validate the findings, in order to constitute a priorized list of features (e.g.,

关键词： feature selection dimensionality reduction big data modeling highly correlated variables high-dimensional data random forest with latent variables importance measure Bayesian network with latent variables case study genetic association study

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BiDaML: A Suite of Visual Languages for Supporting End-user data Analytics 8

BiDaML: A Suite of Visual Languages for Supporting End-user ...

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IEEE International Congress on Internet of Things (IEEE ICIOT) / IEEE International Congress on big data (IEEE bigdata Congress) held as part of IEEE World Congress on Services (IEEE SERVICES)

作者： Khalajzadeh, Hourieh Abdelrazek, Mohamed Grundy, John Hosking, John G. He, Qiang Monash Univ Fac Informat Technol Clayton Vic Australia Deakin Univ Sch Informat Technol Geelong Vic Australia Univ Auckland Fac Sci Auckland New Zealand Swinburne Univ Technol Sch Software & Elect Engn Hawthorn Vic Australia

ISBN: (纸本)9781728127729

We introduce big data Analytics modeling Languages (BiDaML), a novel integrated suite of visual languages aimed at supporting end users during the process of designing big data analytics solutions. BiDaML comprises five diagrammatic notations: a data analytics brainstorming diagram;a process diagram;technique diagrams;data diagrams;and output diagrams. Tool support in the form of an integrated design environment for creating BiDaML diagrams has also been developed. To demonstrate the utility of BiDaML, we illustrate our approach with a real-world example of property price prediction and evaluate BiDaML using the physics of notations and cognitive walkthroughs with target end users - data scientists and software engineers.

关键词： big data analytics big data modeling big data toolkits domain specific visual languages end user tools

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Map-Reduce Decentralized PCA for big data Monitoring and Diagnosis of Faults in High-Speed Train Bearings

Map-Reduce Decentralized PCA for Big Data Monitoring and Dia...

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10th IFAC Symposium on Advanced Control of Chemical Processes (ADCHEM)

作者： Liu, Qiang Kong, Dezhi Qin, S. Joe Xu, Quan Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Liaoning Peoples R China Univ Southern Calif Mork Family Dept Chem Engn & Mat Sci Los Angeles CA 90089 USA

Real-time fault detection and diagnosis of high speed trains is essential for the operation safety. Traditional methods mainly employ rule-based alarms to detect faults when the measured single variable deviates too far from the expected range, with multivariate data correlations ignored. In this paper, a Map-Reduce decentralized PCA algorithm and its dynamic extension are proposed to deal with the large amount of data collected from high speed trains. In addition, the Map-Reduce algorithm is implemented in a Hadoop-based big data platform. The experimental results using real high-speed train operation data demonstrate the advantages and effectiveness of the proposed methods for five faulty cases. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： big data modeling Decentralized Principal Component Analysis Fault Diagnosis High-Speed Train Operation Safety

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Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2018年第1期29卷 10-24页

作者： Chen, C. L. Philip Liu, Zhulin Univ Macau Fac Sci & Technol Dept Comp & Informat Sci Macau 99999 Peoples R China Dalian Maritime Univ Dalian 116026 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100080 Peoples R China

Broad Learning System (BLS) that aims to offer an alternative way of learning in deep structure is proposed in this paper. Deep structure and learning suffer from a time-consuming training process because of a large number of connecting parameters in filters and layers. Moreover, it encounters a complete retraining process if the structure is not sufficient to model the system. The BLS is established in the form of a flat network, where the original inputs are transferred and placed as "mapped features" in feature nodes and the structure is expanded in wide sense in the "enhancement nodes." The incremental learning algorithms are developed for fast remodeling in broad expansion without a retraining process if the network deems to be expanded. Two incremental learning algorithms are given for both the increment of the feature nodes (or filters in deep structure) and the increment of the enhancement nodes. The designed model and algorithms are very versatile for selecting a model rapidly. In addition, another incremental learning is developed for a system that has been modeled encounters a new incoming input. Specifically, the system can be remodeled in an incremental way without the entire retraining from the beginning. Satisfactory result for model reduction using singular value decomposition is conducted to simplify the final structure. Compared with existing deep neural networks, experimental results on the Modified National Institute of Standards and Technology database and NYU NORB object recognition dataset benchmark data demonstrate the effectiveness of the proposed BLS.

关键词： big data big data modeling broad learning system (BLS) deep learning incremental learning random vector functional-link neural networks (RVFLNN) single layer feedforward neural networks (SLFN) singular value decomposition (SVD)

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