scispace - formally typeset
Journal ArticleDOI: 10.1080/24725854.2020.1741741

A hybrid transfer learning framework for in-plane freeform shape accuracy control in additive manufacturing

04 Mar 2021-Vol. 53, Iss: 3, pp 298-312
Abstract: Shape accuracy control is one of the quality issues of greatest concern in Additive Manufacturing (AM). An efficient approach to improving the shape accuracy of a fabricated product is to compensat...

... read more

Topics: Product (mathematics) (51%)
Citations
  More

7 results found


Journal ArticleDOI: 10.1080/24725854.2021.1875520
Kai Wang1, Jian Li1, Fugee Tsung2Institutions (2)
01 Mar 2021-
Abstract: Data streams are prevalent in current manufacturing and service systems where real-time data arrive progressively. A quick distribution inference from such data streams at their early stages is ext...

... read more

Topics: Data stream mining (54%), Inference (52%)

4 Citations


Journal ArticleDOI: 10.1080/24725854.2020.1851824
Lening Wang1, Xiaoyu Chen1, Daniel Henkel, Ran Jin1Institutions (1)
09 Feb 2021-
Abstract: A Cyber-Additive Manufacturing Network (CAMNet) integrates connected additive manufacturing processes with advanced data analytics as computation services to support personalized product realizatio...

... read more

Topics: Process modeling (55%), Product (mathematics) (52%)

3 Citations


Open accessJournal ArticleDOI: 10.1016/J.IMU.2021.100568
Moses Ekpenyong1, Mercy E. Edoho1, Ifiok J. Udo1, Philip I. Etebong1  +3 moreInstitutions (2)
Abstract: As we advance towards individualized therapy, the ‘one-size-fits-all’ regimen is gradually paving the way for adaptive techniques that address the complexities of failed treatments. Treatment failure is associated with factors such as poor drug adherence, adverse side effect/reaction, co-infection, lack of follow-up, drug-drug interaction and more. This paper implements a transfer learning approach that classifies patients' response to failed treatments due to adverse drug reactions. The research is motivated by the need for early detection of patients' response to treatments and the generation of domain-specific datasets to balance under-represented classification data, typical of low-income countries located in Sub-Saharan Africa. A soft computing model was pre-trained to cluster CD4+ counts and viral loads of treatment change episodes (TCEs) processed from two disparate sources: the Stanford HIV drug resistant database ( https://hivdb.stanford.edu ), or control dataset, and locally sourced patients' records from selected health centers in Akwa Ibom State, Nigeria, or mixed dataset. Both datasets were experimented on a traditional 2-layer neural network (NN) and a 5-layer deep neural network (DNN), with odd dropout neurons distribution resulting in the following configurations: NN (Parienti et al., 2004) [32], NN (Deniz et al., 2018) [53] and DNN [9 7 5 3 1]. To discern knowledge of failed treatment, DNN1 [9 7 5 3 1] and DNN2 [9 7 5 3 1] were introduced to model both datasets and only TCEs of patients at risk of drug resistance, respectively. Classification results revealed fewer misclassifications, with the DNN architecture yielding best performance measures. However, the transfer learning approach with DNN2 [9 7 3 1] configuration produced superior classification results when compared to other variants/configurations, with classification accuracy of 99.40%, and RMSE values of 0.0056, 0.0510, and 0.0362, for test, train, and overall datasets, respectively. The proposed system therefore indicates good generalization and is vital as decision-making support to clinicians/physicians for predicting patients at risk of adverse drug reactions. Although imbalanced features classification is typical of disease problems and diminishes dependence on classification accuracy, the proposed system still compared favorably with the literature and can be hybridized to improve its precision and recall rates.

... read more

2 Citations


Open accessPosted Content
Sahand Hajifar1, Hongyue Sun1Institutions (1)
Abstract: Accurate evaluation of liver viability during its procurement is a challenging issue and has traditionally been addressed by taking invasive biopsy on liver. Recently, people have started to investigate on the non-invasive evaluation of liver viability during its procurement using the liver surface thermal images. However, existing works include the background noise in the thermal images and do not consider the cross-subject heterogeneity of livers, thus the viability evaluation accuracy can be affected. In this paper, we propose to use the irregular thermal data of the pure liver region, and the cross-subject liver evaluation information (i.e., the available viability label information in cross-subject livers), for the real-time evaluation of a new liver's viability. To achieve this objective, we extract features of irregular thermal data based on tools from graph signal processing (GSP), and propose an online domain adaptation (DA) and classification framework using the GSP features of cross-subject livers. A multiconvex block coordinate descent based algorithm is designed to jointly learn the domain-invariant features during online DA and learn the classifier. Our proposed framework is applied to the liver procurement data, and classifies the liver viability accurately.

... read more

1 Citations


Open accessJournal ArticleDOI: 10.1080/24725854.2021.1949762
Sahand Hajifar1, Hongyue Sun1Institutions (1)
08 Jul 2021-
Abstract: Accurate evaluation of liver viability during its procurement is a challenging issue and has traditionally been addressed by taking an invasive biopsy of the liver. Recently, people have started to...

... read more

1 Citations


References
  More

30 results found


Journal ArticleDOI: 10.1109/TKDE.2009.191
Sinno Jialin Pan1, Qiang Yang1Institutions (1)
Abstract: A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research.

... read more

Topics: Semi-supervised learning (69%), Inductive transfer (68%), Multi-task learning (67%) ... read more

13,267 Citations


Reference EntryDOI: 10.1002/0471667196.ESS3138
15 Aug 2006-
Abstract: When either data or the models for them involve functions, and when only weak assumptions about these functions such as smoothness are permitted, familiar statistical methods must be modified and new approaches developed in order to take advantage of this smoothness. The first part of the article considers some general issues such as characteristics of functional data, uses of derivatives in functional modelling, estimation of phase variation by the alignment or registration of curve features, the nature of error, and so forth. The second section describes functional versions of traditional methods such principal components analysis and linear modelling, and also mentions purely functional approaches that involve working with and estimating differential equations in the functional data analysis process.

... read more

2,967 Citations


Journal ArticleDOI: 10.1016/J.CAD.2015.04.001
Wei Gao1, Yunbo Zhang1, Devarajan Ramanujan1, Karthik Ramani1  +6 moreInstitutions (3)
Abstract: Additive manufacturing (AM) is poised to bring about a revolution in the way products are designed, manufactured, and distributed to end users. This technology has gained significant academic as well as industry interest due to its ability to create complex geometries with customizable material properties. AM has also inspired the development of the maker movement by democratizing design and manufacturing. Due to the rapid proliferation of a wide variety of technologies associated with AM, there is a lack of a comprehensive set of design principles, manufacturing guidelines, and standardization of best practices. These challenges are compounded by the fact that advancements in multiple technologies (for example materials processing, topology optimization) generate a "positive feedback loop" effect in advancing AM. In order to advance research interest and investment in AM technologies, some fundamental questions and trends about the dependencies existing in these avenues need highlighting. The goal of our review paper is to organize this body of knowledge surrounding AM, and present current barriers, findings, and future trends significantly to the researchers. We also discuss fundamental attributes of AM processes, evolution of the AM industry, and the affordances enabled by the emergence of AM in a variety of areas such as geometry processing, material design, and education. We conclude our paper by pointing out future directions such as the "print-it-all" paradigm, that have the potential to re-imagine current research and spawn completely new avenues for exploration. The fundamental attributes and challenges/barriers of Additive Manufacturing (AM).The evolution of research on AM with a focus on engineering capabilities.The affordances enabled by AM such as geometry, material and tools design.The developments in industry, intellectual property, and education-related aspects.The important future trends of AM technologies.

... read more

1,338 Citations


Open accessBook
14 Oct 2005-
Abstract: PART I AN OVERVIEW INTRODUCTION Experiments and Their Statistical Designs Some Concepts in Experimental Design Computer Experiments Examples of Computer Experiments Space-Filling Designs Modeling Techniques Sensitivity Analysis Strategies for Computer Experiments and An Illustration Case Study Remarks on Computer Experiments Guidance of Reading This Book PART II DESIGNS FOR COMPUTER EXPERIMENTS Latin Hypercube Sampling and its Modifications Uniform Experimental Design Optimization in Construction of Designs for Computer Experiments PART III MODELING FOR COMPUTER EXPERIMENTS METAMODELING Model Interpretation Functional Response APPENDIX Abbreviation References Index Author Index

... read more

904 Citations


Open accessJournal ArticleDOI: 10.1111/1467-9868.00233
Jianqing Fan1, Jin-ting Zhang1Institutions (1)
Abstract: Functional linear models are useful in longitudinal data analysis. They include many classical and recently proposed statistical models for longitudinal data and other functional data. Recently, smoothing spline and kernel methods have been proposed for estimating their coefficient functions nonparametrically but these methods are either intensive in computation or inefficient in performance. To overcome these drawbacks, in this paper, a simple and powerful two-step alternative is proposed. In particular, the implementation of the proposed approach via local polynomial smoothing is discussed. Methods for estimating standard deviations of estimated coefficient functions are also proposed. Some asymptotic results for the local polynomial estimators are established. Two longitudinal data sets, one of which involves time-dependent covariates, are used to demonstrate the approach proposed. Simulation studies show that our two-step approach improves the kernel method proposed by Hoover and co-workers in several aspects such as accuracy, computational time and visual appeal of the estimators.

... read more

Topics: Smoothing (60%), Smoothing spline (56%), Kernel method (55%) ... read more

397 Citations