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5th International Conference on Through-Life Engineering Services

作者： Okoh, C. Roy, R. Mehnen, J. Cranfield Univ Mfg Dept EPSRC Ctr Innovat Mfg Life Engn Serv Cranfield MK43 0AL Beds England

predictive maintenance needs to forecast the numbers of rejections at any overhaul point before any failure occurs in order to accurately and proactively take adequate maintenance action. In healthcare, prediction has been applied to foretell when and how to administer medication to improve the health condition of the patient. The same is true for maintenance where the application of prognostics can help make better decisions. In this paper, an overview of prognostic maintenance strategies is presented. The proposed data-driven prognostics approach employs a statistical technique of (i) the parameter estimation methods of the time-to failure data to predict the relevant statistical model parameters and (ii) prognostics modelling incorporating the reliability Weibull Cumulative Distribution Function to predict part rejection, replacement, and reuse. The analysis of the modelling uses synthetic data validated by industry domain experts. The outcome of the prediction can further proffer solution to designers, manufacturers and operators of industrial product-service systems. The novelty in this paper is the development of the through life performance approach. The approach ascertains when the system needs to undergo maintenance, repair and overhaul before failure occurs. (C) 2016 Elsevier B.V.

关键词： Through-life engineering services maintenance strategies data-driven prediction predictive modelling parameter estimation

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Evidence, Explanation and predictive Data modelling

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Philosophy and Technology 2017年第4期30卷 461-473页

作者： Mckinlay, Steve T. School of Information Technology Wellington Institute of Technology Private Bag 39803 Petone Wellington New Zealand

predictive risk modelling is a computational method used to generate probabilities correlating events. The output of such systems is typically represented by a statistical score derived from various related and often arbitrary datasets. In many cases, the information generated by such systems is treated as a form of evidence to justify further action. This paper examines the nature of the information generated by such systems and compares it with more orthodox notions of evidence found in epistemology. The paper focuses on a specific example to illustrate the issues: The New Zealand Government has proposed implementing a predictive risk modelling system which purportedly identifies children at risk of a maltreatment event before the age of five. Timothy Williamson’s (2002) conception of epistemology places a requirement on knowledge that it be explanatory. Furthermore, Williamson argues that knowledge is equivalent to evidence. This approach is compared to the claim that the output of such computational systems constitutes evidence. While there may be some utility in using predictive risk modelling systems, I argue, since an explanatory account of the output of such algorithms that meets Williamson’s requirements cannot be given, doubt is cast upon the resulting statistical scores as constituting evidence on generally accepted epistemic grounds. The algorithms employed in such systems are geared towards identifying patterns which turn out to be good correlations. However, rather than providing information about specific individuals and their exposure to risk, a more valid explanation of a high probability score is that the particular variables related to incidents of maltreatment are just higher amongst certain subgroups in a population than they are amongst others. The paper concludes that any justification of the information generated by such systems is generalised and pragmatic at best and the application of this information to individual cases raises various eth

关键词： Big data Epistemology Explanation Information and computing ethics predictive modelling

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predictive modelling for shelf life determination of nutricereal based fermented baby food

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JOURNAL OF FOOD SCIENCE AND TECHNOLOGY-MYSORE 2015年第8期52卷 5003-5011页

作者： Rasane, Prasad Jha, Alok Sharma, Nitya Banaras Hindu Univ Ctr Food Sci & Technol Varanasi 221005 Uttar Pradesh India Banaras Hindu Univ Dept Farm Engn Varanasi 221005 Uttar Pradesh India

A shelf life model based on storage temperatures was developed for a nutricereal based fermented baby food formulation. The formulated baby food samples were packaged and stored at 10, 25, 37 and 45 A degrees C for a test storage period of 180 days. A shelf life study was conducted using consumer and semi-trained panels, along with chemical analysis (moisture and acidity). The chemical parameters (moisture and titratable acidity) were found inadequate in determining the shelf life of the formulated product. Weibull hazard analysis was used to determine the shelf life of the product based on sensory evaluation. Considering 25 and 50 % rejection probability, the shelf life of the baby food formulation was predicted to be 98 and 322 days, 84 and 271 days, 71 and 221 days and 58 and 171 days for the samples stored at 10, 25, 37 and 45 A degrees C, respectively. A shelf life equation was proposed using the rejection times obtained from the consumer study. Finally, the formulated baby food samples were subjected to microbial analysis for the predicted shelf life period and were found microbiologically safe for consumption during the storage period of 360 days.

关键词： Baby food Consumer study predictive modelling Shelf life Weibull hazard analysis

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predictive modelling of running and dwell times in railway traffic

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PUBLIC TRANSPORT 2015年第3期7卷 295-319页

作者： Kecman, Pavle Goverde, Rob M. P. Linkoping Univ Dept Sci & Technol Linkoping Sweden Delft Univ Technol Dept Transport & Planning Delft Netherlands

Accurate estimation of running and dwell times is important for all levels of planning and control of railway traffic. The availability of historical track occupation data with a high degree of granularity inspired a data-driven approach for estimating these process times. In this paper we present and compare the accuracy of several approaches to model running and dwell times in railway traffic. Three global predictive model approaches are presented based on advanced statistical learning techniques: LTS robust linear regression, regression trees and random forests. Also local models are presented for a particular train line, station or block section, based on LTS robust linear regression with some refinements. The models are validated and compared using a test set independent from the training set. The applicability of the proposed data-driven approach for real-time applications is proved by the accuracy of the obtained estimates and the low computation times. Overall, the local models perform best both in accuracy and computation time.

关键词： predictive modelling Robust regression Running and dwell times Tree-based models

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predictive modelling of gene expression from transcriptional regulatory elements

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BRIEFINGS IN BIOINFORMATICS 2015年第4期16卷 616-628页

作者： Budden, David M. Hurley, Daniel G. Crampin, Edmund J. Melbourne Sch Engn Syst Biol Lab Melbourne Vic Australia Univ Melbourne Rowden White Chair Syst & Computat Biol Melbourne Vic 3010 Australia Melbourne Sch Engn Dept Math & Stat Melbourne Vic Australia Melbourne Med Sch Melbourne Vic Australia

predictive modelling of gene expression provides a powerful framework for exploring the regulatory logic underpinning transcriptional regulation. Recent studies have demonstrated the utility of such models in identifying dysregulation of gene and miRNA expression associated with abnormal patterns of transcription factor (TF) binding or nucleosomal histone modifications (HMs). Despite the growing popularity of such approaches, a comparative review of the various modelling algorithms and feature extraction methods is lacking. We define and compare three methods of quantifying pairwise gene-TF/HM interactions and discuss their suitability for integrating the heterogeneous chromatin immunoprecipitation (ChIP)-seq binding patterns exhibited by TFs and HMs. We then construct log-linear and I mu-support vector regression models from various mouse embryonic stem cell (mESC) and human lymphoblastoid (GM12878) data sets, considering both ChIP-seq- and position weight matrix- (PWM)-derived in silico TF-binding. The two algorithms are evaluated both in terms of their modelling prediction accuracy and ability to identify the established regulatory roles of individual TFs and HMs. Our results demonstrate that TF-binding and HMs are highly predictive of gene expression as measured by mRNA transcript abundance, irrespective of algorithm or cell type selection and considering both ChIP-seq and PWM-derived TF-binding. As we encourage other researchers to explore and develop these results, our framework is implemented using open-source software and made available as a preconfigured bootable virtual environment.

关键词： gene expression transcriptional regulation transcription factors histone modifications predictive modelling

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Large-scale ligand-based predictive modelling using support vector machines

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JOURNAL OF CHEMINFORMATICS 2016年第1期8卷 39-39页

作者： Alvarsson, Jonathan Lampa, Samuel Schaal, Wesley Andersson, Claes Wikberg, Jarl E. S. Spjuth, Ola Uppsala Univ Dept Pharmaceut Biosci S-75124 Uppsala Sweden Uppsala Univ Sci Life Lab S-75124 Uppsala Sweden Uppsala Univ Dept Med Sci S-75185 Uppsala Sweden

The increasing size of datasets in drug discovery makes it challenging to build robust and accurate predictive models within a reasonable amount of time. In order to investigate the effect of dataset sizes on predictive performance and modelling time, ligand-based regression models were trained on open datasets of varying sizes of up to 1.2 million chemical structures. For modelling, two implementations of support vector machines (SVM) were used. Chemical structures were described by the signatures molecular descriptor. Results showed that for the larger datasets, the LIBLINEAR SVM implementation performed on par with the well-established libsvm with a radial basis function kernel, but with dramatically less time for model building even on modest computer resources. Using a non-linear kernel proved to be infeasible for large data sizes, even with substantial computational resources on a computer cluster. To deploy the resulting models, we extended the Bioclipse decision support framework to support models from LIBLINEAR and made our models of logD and solubility available from within Bioclipse.

关键词： predictive modelling Support vector machine Bioclipse Molecular signatures QSAR

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predictive modelling and Optimization of Machining Parameters to Minimize Surface Roughness using Artificial Neural Network Coupled with Genetic Algorithm 15

Predictive Modelling and Optimization of Machining Parameter...

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15th CIRP Conference on modelling of Machining Operations (CMMO)

作者： Kant, Girish Sangwan, Kuldip Singh Birla Inst Technol & Sci Dept Mech Engn Pilani 333031 Rajasthan India

genetic algorithm - as an alternative to conventional approaches in predicting the optimal value of machining parameters leading to minimum surface roughness. A real machining experiment has been referred in this study to check the capability of the proposed model for prediction and optimization of surface roughness. The results predicted by the proposed model indicate good agreement between the predicted values and experimental values. The analysis of this study proves that the proposed approach is capable of determining the optimum machining parameters. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： Roughness Artificial neural network Genetic Algorithm Optimization predictive modelling

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predictive Maintenance modelling for Through-Life Engineering Services

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Procedia CIRP 2017年 59卷 196-201页

作者： C. Okoh R. Roy J. Mehnen EPSRC Centre for Innovative Manufacturing in Through-Life Engineering Services Manufacturing Department Cranfield University MK43 0AL Cranfield United Kingdom

predictive maintenance needs to forecast the numbers of rejections at any overhaul point before any failure occurs in order to accurately and proactively take adequate maintenance action. In healthcare, prediction has been applied to foretell when and how to administer medication to improve the health condition of the patient. The same is true for maintenance where the application of prognostics can help make better decisions. In this paper, an overview of prognostic maintenance strategies is presented. The proposed data-driven prognostics approach employs a statistical technique of (i) the parameter estimation methods of the time-to-failure data to predict the relevant statistical model parameters and (ii) prognostics modelling incorporating the reliability Weibull Cumulative Distribution Function to predict part rejection, replacement, and reuse. The analysis of the modelling uses synthetic data validated by industry domain experts. The outcome of the prediction can further proffer solution to designers, manufacturers and operators of industrial product-service systems. The novelty in this paper is the development of the through-life performance approach. The approach ascertains when the system needs to undergo maintenance, repair and overhaul before failure occurs.

关键词： Through-life engineering services maintenance strategies data-driven prediction predictive modelling parameter estimation

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Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles

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JOURNAL OF CHEMINFORMATICS 2016年第1期8卷 67-67页

作者： Lampa, Samuel Alvarsson, Jonathan Spjuth, Ola Uppsala Univ Dept Pharmaceut Biosci Box 591 S-75124 Uppsala Sweden Uppsala Univ Sci Life Lab Box 3037 S-75003 Uppsala Sweden

predictive modelling in drug discovery is challenging to automate as it often contains multiple analysis steps and might involve cross-validation and parameter tuning that create complex dependencies between tasks. With large-scale data or when using computationally demanding modelling methods, e-infrastructures such as high-performance or cloud computing are required, adding to the existing challenges of fault-tolerant automation. Workflow management systems can aid in many of these challenges, but the currently available systems are lacking in the functionality needed to enable agile and flexible predictive modelling. We here present an approach inspired by elements of the flow-based programming paradigm, implemented as an extension of the Luigi system which we name SciLuigi. We also discuss the experiences from using the approach when modelling a large set of biochemical interactions using a shared computer cluster.

关键词： predictive modelling Machine learning Workflows Drug discovery Flow-based programming

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predictive hydraulic conductivity modelling of wide gradation spectrum sandy soils using stepwise multiple linear and LASSO regression

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INTERNATIONAL JOURNAL OF HYDROLOGY SCIENCE AND TECHNOLOGY 2025年第4期19卷 375-403页

作者： Khaja, Mohammad Aasif Shah, Shagoofta Rasool Jha, Ramakar Natl Inst Technol Srinagar Dept Civil Engn Srinagar 190006 J&K India Natl Inst Technol Patna Dept Civil Engn Patna 800005 Bihar India

Accurate estimation of soil hydraulic conductivity (K) is crucial in groundwater hydrology and geo-environmental engineering applications. This study introduces novel K predictive modelling for wide gradation spectrum sandy soils, employing stepwise multiple linear regression (SMLR) and least absolute shrinkage and selection operator (LASSO) techniques. Using an 81-sample dataset with key variables, including particle sizes, gradation parameters, porosity, and dry density, this study addresses limitations in existing K prediction methods. Correlation analysis reveals variable associations and multicollinearity issues, necessitating feature selection and the development of SMLR and LASSO regression models. While both models perform well on the training dataset, LASSO excels in mitigating overfitting, achieving a high coefficient of determination (R2) of 0.82 and 0.87 on the training and testing datasets. Comparative analyses with existing models in the literature underscored LASSO's superiority in approximating laboratory-measured K-values, establishing it as the preferred choice for hydraulic conductivity estimation.

关键词： hydraulic conductivity sandy soils predictive modelling stepwise multiple linear regression SMLR LASSO regression

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