Multiple Tensor-on-Tensor Regression: An Approach for Modeling Processes With Heterogeneous Sources of Data

Gahrooei, Mostafa Reisi; Yan, Hao; Paynabar, Kamran; Shi, Jianjun

doi:10.1080/00401706.2019.1708463

Cited by 42 publications

(17 citation statements)

References 27 publications

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“…To extract latent features from the raw data, we used a tensor decomposition technique that divides a tensor into smaller tensors or matrices. Tensor decomposition has been commonly used to extract latent features from data whose form is a tensor and has demonstrated its effectiveness in data analysis [ 28 , 29 ]. Among various methods for tensor decomposition, we used the Tucker decomposition because it is a generalized form of tensor decomposition [ 30 , 31 ].…”

Section: Methodsmentioning

confidence: 99%

Cluster-Based Analysis of Infectious Disease Occurrences Using Tensor Decomposition: A Case Study of South Korea

Jung

Moon

Hwang

2020

IJERPH

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For a long time, various epidemics, such as lower respiratory infections and diarrheal diseases, have caused serious social losses and costs. Various methods for analyzing infectious disease occurrences have been proposed for effective prevention and proactive response to reduce such losses and costs. However, the results of the occurrence analyses were limited because numerous factors affect the outbreak of infectious diseases and there are complex interactions between these factors. To alleviate this limitation, we propose a cluster-based analysis scheme of infectious disease occurrences that can discover commonalities or differences between clusters by grouping elements with similar occurrence patterns. To do this, we collect and preprocess infectious disease occurrence data according to time, region, and disease. Then, we construct a tensor for the data and apply Tucker decomposition to extract latent features in the dimensions of time, region, and disease. Based on these latent features, we conduct k-means clustering and analyze the results for each dimension. To demonstrate the effectiveness of this scheme, we conduct a case study on data from South Korea and report some of the results.

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Section: Methodsmentioning

confidence: 99%

Cluster-Based Analysis of Infectious Disease Occurrences Using Tensor Decomposition: A Case Study of South Korea

Jung

Moon

Hwang

2020

IJERPH

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“…One possible extension of the MOSS is to multiple response regression under the non-parametric estimation framework [ 55 ]. Next, the spatial process variables and quality responses, such as the thermal video and 3d profile of the product, can be incorporated into the MOSS to reasonably quantify the spatio-temporal relationship contained in both process variables and quality variables [ 56 , 57 ]. Finally, the monitoring and control strategy can also be integrated with the MOSS in a real-time manner to effectively detect the anomaly event during the fabrication process, and further improve process reliability and reduce process variation [ 58 ].…”

Section: Discussionmentioning

confidence: 99%

MOSS—Multi-Modal Best Subset Modeling in Smart Manufacturing

Wang

Jin

2021

Sensors

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Smart manufacturing, which integrates a multi-sensing system with physical manufacturing processes, has been widely adopted in the industry to support online and real-time decision making to improve manufacturing quality. A multi-sensing system for each specific manufacturing process can efficiently collect the in situ process variables from different sensor modalities to reflect the process variations in real-time. However, in practice, we usually do not have enough budget to equip too many sensors in each manufacturing process due to the cost consideration. Moreover, it is also important to better interpret the relationship between the sensing modalities and the quality variables based on the model. Therefore, it is necessary to model the quality-process relationship by selecting the most relevant sensor modalities with the specific quality measurement from the multi-modal sensing system in smart manufacturing. In this research, we adopted the concept of best subset variable selection and proposed a new model called Multi-mOdal beSt Subset modeling (MOSS). The proposed MOSS can effectively select the important sensor modalities and improve the modeling accuracy in quality-process modeling via functional norms that characterize the overall effects of individual modalities. The significance of sensor modalities can be used to determine the sensor placement strategy in smart manufacturing. Moreover, the selected modalities can better interpret the quality-process model by identifying the most correlated root cause of quality variations. The merits of the proposed model are illustrated by both simulations and a real case study in an additive manufacturing (i.e., fused deposition modeling) process.

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“…Gaussian and Poisson models are the two popular distribution models for generating random noise; however, spatial correlation is not formally considered through these noise generation processes. Gahrooei et al 25 addressed the problem of generating heterogeneous source of data that often contains scalars waveform signal, images, and point-cloud data. They introduced a tensor-on-tensor regression approach that each set of input and output data can be represented by tensors.…”

Section: Cross-correlated Image Simulationmentioning

confidence: 99%

A multi‐image monitoring framework for statistical process control to improve manufacturing systems

Chen

Wells

2021

Quality & Reliability Eng

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Machine vision system has been widely used for detecting surface defects in a manufacturing process. This paper proposes a new framework for image monitoring with control charts that aims to improve the overall performance in detecting a variety of surface-related process shifts. Specifically, multiple images of the same object are acquired under different capturing parameters. To illustrate this framework, this paper considers two multi-image control chart approaches:(1) fusing multiple images together with multilinear principal component analysis and monitoring with a single-image control chart and (2) using a combined single-image control chart. A two-image simulation study, based upon real cross-correlated images, is accomplished to compare the performances of these two multi-image control chart approaches to a traditional single-image control charts. The results indicate that multi-image control charts outperform singleimage control charts when multiple shifts are considered. Another two-image simulation study investigates the effect of cross-correlations to multi-image control charts' performances. This study indicates that the two multi-image control charts have similar performances at low cross-correlation but the fused-image control chart outperforms the combined single-image control chart at high crosscorrelation. In addition, a case study with real images is conducted to demonstrate the proposed multi-image monitoring framework's ability in detecting shifts.

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Multiple Tensor-on-Tensor Regression: An Approach for Modeling Processes With Heterogeneous Sources of Data

Cited by 42 publications

References 27 publications

Cluster-Based Analysis of Infectious Disease Occurrences Using Tensor Decomposition: A Case Study of South Korea

Cluster-Based Analysis of Infectious Disease Occurrences Using Tensor Decomposition: A Case Study of South Korea

MOSS—Multi-Modal Best Subset Modeling in Smart Manufacturing

A multi‐image monitoring framework for statistical process control to improve manufacturing systems

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