SVAE-WGAN-Based Soft Sensor Data Supplement Method for Process Industry

Gao, Shiwei; Qiu, Sulong; Ma, Zhongyu; Tian, Ruifeng; Liu, Yanxing

doi:10.1109/jsen.2021.3128562

Cited by 28 publications

(17 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In general, a generic generative adversarial network framework aims to learn a generative model where an encoder can be extended to the model to aggregate the corresponding input features to generate a low-dimensional generative factor [20]. And this representation factor can be considered a stream of information capturing the necessary details of the input.…”

Section: Psp-ganmentioning

confidence: 99%

The Application of 3D Virtual Technology in the Teaching of Clinical Medicine

Zhang

Kong

Yan

et al. 2022

Advances in Multimedia

View full text Add to dashboard Cite

The booming development of 3D virtual technology provides the basis for the innovation of teaching mode of clinical medicine. Integrating 3D virtual technology into the clinical education teaching of medical students can help medical students learn clinical expertise more intuitively, enrich practical experience, improve hands-on ability, and make up for the shortcomings of traditional medical teaching. However, the traditional 3D modeling method has the problems of sparse and insufficient accuracy of output results, which greatly limits the specific application of 3D virtual technology in clinical teaching. Therefore, this paper addresses the shortcomings of previous 3D virtual technology in 3D modeling of organs, introduces deep learning, and then proposes a pyramidal shape perception network with the ability to generate samples on point clouds. Experimental results show that the proposed model not only has a large improvement in accuracy but also shows a high degree of confidence in providing real-time feedback of local image attributes.

show abstract

Section: Psp-ganmentioning

confidence: 99%

The Application of 3D Virtual Technology in the Teaching of Clinical Medicine

Zhang

Kong

Yan

et al. 2022

Advances in Multimedia

View full text Add to dashboard Cite

show abstract

“…The CADA strategy proposed in this paper is a horizontal augmentation method, which increases the attribute columns of the data while maintaining the original amount of data. Hence, the vertical expansion methods in refs ( 3 ) and ( 31 ) are not discussed in this paper.…”

Section: Related Workmentioning

confidence: 99%

“…These methods are considered to help improve the generalization of the model. In regression analysis, such as predicting the weather, industrial product quality forecasting, and soft sensor modeling prediction, data augmentation is typically performed using generative adversarial networks (GAN) − and linear interpolation methods. , The horizontal augmentation of data can be seen as expanding the number of attributes for each piece of data while maintaining the current data size. For example, the horizontal dimension of the data is raised to a larger extent using an autoregressive moving average model…”

Section: Related Workmentioning

confidence: 99%

Horizontal Data Augmentation Strategy for Industrial Quality Prediction

et al. 2022

Self Cite

View full text Add to dashboard Cite

In recent years, neural network-based soft sensor technology has been widely used in industrial production processes and has excellent optimization, monitoring, and quality prediction performance. This paper proposes a horizontal data augmentation strategy to provide highly available data for subsequent prediction models, called the combined autoencoder data augmentation (CADA) strategy. This paper has developed a CADA-based convolutional neural network (CADA-CNN) soft sensor model and applied it to the process of industrial debutanizer and industrial steam volume. In terms of method validation, this paper compares the output data of the proposed CADA by the Spearman correlation coefficient to verify the strategy’s feasibility. Then, the output data of the CADA strategy is fed into the artificial neural network (NN), support vector regression (SVR), and convolutional neural network (CNN) for comparison experiments. The final experimental results show that our proposed CADA-CNN model has lower prediction error and better prediction error distribution.

show abstract

“…Nowadays, information technology (IT) by integrating different data-driven systems, plays the pivot role in collecting a large amount of data in different sensitive industries. The raw data obtained by the IT paradigm provide the necessary conditions for conducting data-oriented actions instead of experience-based operations in all tasks and responsibilities of system operators [1]- [5]. Such restructuring in the decision-making process will be possible through data mining (DM) technology which triangulated machine learning (ML), statistical learning (SL), and dataset to discover useful patterns for predicting different phenomena [6], [7].…”

Section: Introductionmentioning

confidence: 99%

“…Testing procedure of model based on param and f i . HB [1][2] First-second hybrid blocks of IWMs. S i i th contingency sample in TMEs.…”

mentioning

confidence: 99%

Applying Cross-Permutation-Based Quad-Hybrid Feature Selection Algorithm on Transient Univariates to Select Optimal Features for Transient Analysis

Mosavi

2022

IEEE Access

View full text Add to dashboard Cite

Neglect feature selection matter for high-dimensional transient data obtained from phasor measurement units (PMUs) negatively affect the inconsistent-linked indices, namely data labeling time (DLT) and data labeling accuracy (DLA) in the transient analysis (TA). A reasonable trade-off between DLT and DLA or a win-win solution (low DLT and high DLA) necessitates feature-based mining on transient multivariate excursions (TMEs) via designing the comprehensive feature selection scheme (FSS). Hence, to achieve high-performance TA, we offer the cross-permutation-based quad-hybrid FSS (CPQHFSS) to select optimal features from TMEs. The CPQHFSS consists of four filter-wrapper blocks (FWBs) in the form of twin two-FWBs mounted on two-mechanism of the incremental wrapper, namely incremental wrapper subset selection (IWSS) and IWSS with replacement (IWSSr). The IWSS 2FWBs and IWSSr 2FWBs contain filter-fixed and wrapper-varied approaches (F f W v ) that first block-specific F f W v of IWSS 2FWBs and IWSSr 2FWBs includes relevancy ratio-support vector machine (RR-SVM) and second block-specific F f W v of IWSS 2FWBs and IWSSr 2FWBs accompanied by relevancy ratio-twin support vector machine (RR-TWSVM). Generally, RR IWSS SVM and RR IWSS TWSVM is in IWSS 2FWBs , and RR IWSSr SVM and RR IWSSr TWSVM is in IWSSr 2FWBs . Besides direct relations in two-F f W v Bs per incremental wrapper mechanism, by plugging different kernels into the hyperplane-based wrapper, all possible cross-permutations of hybrid FSS are applied on transient data to extract the optimal transient features (OTFs). Finally, the evaluation of the effectiveness of the CPQHFSS-based OTFs in TA is conducted based on the cross-validation technique. The obtained results show that the proposed framework has a DLA of 98.87 % and a DLT of 152.525 milliseconds

show abstract

SVAE-WGAN-Based Soft Sensor Data Supplement Method for Process Industry

Cited by 28 publications

References 21 publications

The Application of 3D Virtual Technology in the Teaching of Clinical Medicine

The Application of 3D Virtual Technology in the Teaching of Clinical Medicine

Horizontal Data Augmentation Strategy for Industrial Quality Prediction

Applying Cross-Permutation-Based Quad-Hybrid Feature Selection Algorithm on Transient Univariates to Select Optimal Features for Transient Analysis

Contact Info

Product

Resources

About