Deep Probabilistic Principal Component Analysis for Process Monitoring.

Probabilistic latent variable models (PLVMs), such as probabilistic principal component analysis (PPCA), are widely employed in process monitoring and fault detection of industrial processes. This article proposes a novel deep PPCA (DePPCA) model, which has the advantages of both probabilistic modeling and deep learning. The construction of DePPCA includes a greedy layer-wise pretraining phase and a unified end-to-end fine-tuning phase.

Deep Learning of Partially Labeled Data for Quality Prediction Based on Stacked Target-Related Laplacian Autoencoder.

Partially labeled data, which is common in industrial processes due to the low sampling rate of quality variables, remains an important challenge in soft sensor applications. In order to exploit the information from partially labeled data, a target-related Laplacian autoencoder (TLapAE) is proposed in this work. In TLapAE, a novel target-related Laplacian regularizer is developed, which aims to extract structure-preserving and quality-related features by preserving the feature-target mapping according to the local geometrical structure of the data.

Visual Process Monitoring by Data-Dependent Kernel Discriminant Analysis with t-Distributed Similarities.

Visual process monitoring would provide more directly appreciable and more easily comprehensible information about the process operating status as well as clear depictions of the occurrence path of faults; however, as a more challenging task, it has been sporadically discussed in the research literature on conventional process monitoring. In this paper, the Data-Dependent Kernel Discriminant Analysis (DK-DA) model is proposed. A special data-dependent kernel function is constructed and learned from the measured data, so that the low-dimensional visualizations are guaranteed, combined with intraclass compactness, interclass separability, local geometry preservation, and global geometry preservation.

Real-Time Forecasting of Subsurface Inclusion Defects for Continuous Casting Slabs: A Data-Driven Comparative Study.

Subsurface inclusions are one of the most common defects that affect the inner quality of continuous casting slabs. This increases the defects in the final products and increases the complexity of the hot charge rolling process and may even cause breakout accidents. The defects are, however, hard to detect online by traditional mechanism-model-based and physics-based methods.

Security Versus Accuracy: Trade-Off Data Modeling to Safe Fault Classification Systems.

While the data-driven fault classification systems have achieved great success and been widely deployed, machine-learning-based models have recently been shown to be unsafe and vulnerable to tiny perturbations, i.e., adversarial attack.

Multi-scale Fusion of Stretched Infrared and Visible Images.

Infrared (IR) band sensors can capture digital images under challenging conditions, such as haze, smoke, and fog, while visible (VIS) band sensors seize abundant texture information. It is desired to fuse IR and VIS images to generate a more informative image. In this paper, a novel multi-scale IR and VIS images fusion algorithm is proposed to integrate information from both the images into the fused image and preserve the color of the VIS image.

Deep Neural Network-Embedded Stochastic Nonlinear State-Space Models and Their Applications to Process Monitoring.

Process complexities are characterized by strong nonlinearities, dynamics, and uncertainties. Monitoring such a complex process requires a high-quality model describing the corresponding nonlinear dynamic behavior. The proposed model is constructed using deep neural networks (DNNs) to represent the state transition and observation generation, both of which constitute a stochastic nonlinear state-space model.

Hessian Semisupervised Scatter Regularized Classification Model With Geometric and Discriminative Information for Nonlinear Process.

The integration of semisupervised modeling and discriminative information has been sporadically discussed in the research literature of traditional classification modeling, while the former one would make full use of the collected data and the latter one would further improve the classification performance. In this article, the Hessian semisupervised scatter regularized classification model is proposed as a coherent framework for the nonlinear process classification upon both labeled and unlabeled data. It is innovatively designed with a loss function to evaluate the classification accuracy and three regularization terms, respectively, corresponding to the geometry information, discriminative information, and model complexity.

Semisupervised Bayesian Gaussian Mixture Models for Non-Gaussian Soft Sensor.

Soft sensors have been widely accepted for online estimating key quality-related variables in industrial processes. The Gaussian mixture models (GMM) is one of the most popular soft sensing methods for the non-Gaussian industrial processes. However, in industrial applications, the quantity of samples with known labels is usually quite limited because of the technical limitations or economical reasons.

Ensemble semi-supervised Fisher discriminant analysis model for fault classification in industrial processes.

In this paper, an ensemble form of the semi-supervised Fisher Discriminant Analysis (FDA) model is developed for fault classification in industrial processes. This method uses the K Nearest Neighbor (KNN) algorithm to merge the metric level outputs, which are obtained by the sub-classifiers in the ensemble model, to get the final classification result. An adaptive form is further proposed to improve the classification performance by putting forward to a new method of weight adjustment.

Student's- Mixture Regression-Based Robust Soft Sensor Development for Multimode Industrial Processes.

Because of multiple manufacturing phases or operating conditions, a great many industrial processes work with multiple modes. In addition, it is inevitable that some measurements of industrial variables obtained through hardware sensors are incorrectly observed, recorded or imported into databases, resulting in the dataset available for statistic analysis being contaminated by outliers. Unfortunately, these outliers are difficult to recognize and remove completely.

Active surge control for variable speed axial compressors.

This paper discusses active surge control in variable speed axial compressors. A compression system equipped with a variable area throttle is investigated. Based on a given compressor model, a fuzzy logic controller is designed for surge control and a proportional speed controller is used for speed control.

Coronary artery segmentation using geometric moments based tracking and snake-driven refinement.

Automatic or semi-automatic segmentation and tracking of artery trees from computed tomography angiography (CTA) is an important step to improve the diagnosis and treatment of artery diseases, but it still remains a significant challenging problem. In this paper, we present an artery extraction method to address the challenge. The proposed method consists of two steps: (1) a geometric moments based tracking to secure a rough centerline, and (2) a fully automatic generalized cylinder structure-based snake method to refine the centerlines and estimate the radii of the arteries.

Multi scale and slice-based approach for automatic spine detection.

Dendritic spines play an essential role in the central nervous system. Recent experiments have revealed that neuron functional properties are highly correlated with the statistical and morphological changes of the dendritic spines. In this paper, we propose a new multi scale approach for detecting dendritic spines in a 2D Maximum Intensity Projection (MIP) image of the 3D neuron data stacks collected from a 2-photon laser scanning confocal microscope.

A neurocomputational method for fully automated 3D dendritic spine detection and segmentation of medium-sized spiny neurons.

Acquisition and quantitative analysis of high resolution images of dendritic spines are challenging tasks but are necessary for the study of animal models of neurological and psychiatric diseases. Currently available methods for automated dendritic spine detection are for the most part customized for 2D image slices, not volumetric 3D images. In this work, a fully automated method is proposed to detect and segment dendritic spines from 3D confocal microscopy images of medium-sized spiny neurons (MSNs).

[The online measurement for pulp Kappa number based on near infrared spectroscopy and support vector machine].

A new method for online measurement of pulp Kappa number by means of near infrared diffuse reflectance spectroscopy and support vector machine (SVM) modeling has been developed in this paper. The near infrared diffuse reflectance spectroscopy of 45 Chinese red pine wood pulp samples was acquired. Selecting the absorption rates in 15 vibration absorption peaks of each sample and using dynamic independent component analysis (DICA) to distill the characters of input sample data, the pulp Kappa number predictive model based on SVM was built.