In this paper, some drawbacks of original kernel independent component analysis (KICA) and support vector machine (SVM) algorithms are analyzed for the purpose of multivariate statistical process ...monitoring (MSPM). When the measured variables follow non-Gaussian distribution, KICA provides more meaningful knowledge by extracting higher-order statistics compared with PCA and kernel principal component analysis (KPCA). However, in real industrial processes, process variables are complex and are not absolutely Gaussian or non-Gaussian distributed. Any single technique is not sufficient to extract the hidden information. Hence, both KICA (non-Gaussion part) and KPCA (Gaussion part) are used for fault detection in this paper, which combine the advantages of KPCA and KICA to develop a nonlinear dynamic approach to detect fault online compared to other nonlinear approaches. Because SVM is available for classifying faults, it is used to diagnose fault in this paper.
For above mentioned kernel methods, the calculation of eigenvectors and support vectors will be time consuming when the sample number becomes large. Hence, some dissimilar data are analyzed in the input and feature space.
The proposed approach is applied to the fault detection and diagnosis in the Tennessee Eastman process. Application of the proposed approach indicates that proposed method effectively captures the nonlinear dynamics in the process variables.
In this article, the nonlinear dynamic process monitoring method based on kernel independent component analysis (KICA) is developed. Compared to the Support Vector Machine (SVM) method, KICA is ...unsupervised and available for fault detection. Hence, in this article, KICA is used to detect faults. Because the dimension of the feature space is far less than the rank of kernel matrix, a basis in feature space is selected. Specifically, the basis in feature space is first constructed based on the similarity factor of data in one group in this article. A contribution plot is impossible, because the nonlinear mapping function from input space into feature space is unknown. Therefore, KICA is difficult for nonlinear fault diagnosis. In this article, once a fault is detected, the kernel-transformed scores from improved KICA will be directly introduced as the inputs of SVM to diagnose the fault. The classification rate of SVM plus improved KICA is higher than the classification rate of SVM plus KICA when the same number of independent components (nICs) is selected. The reason is that the negentropy in improved KICA plus SVM could take into account the more-useful information of original inputs than that of original KICA plus SVM. The training time of SVM plus improved KICA is shorter than that of SVM plus KICA, because the former attenuates the expensive computation load. The proposed approach is applied to the fault detection and diagnosis in the Tennessee Eastman process and a wastewater treatment process (WWTP). Applications indicate that the proposed approach effectively captures the nonlinear dynamic in the process variables and shows superior fault detectability, compared to conventional methods.
•A quality-related fault detection approach based on dynamic model is proposed.•Measurements space is separated into quality-related part and quality-unrelated part.•The proposed method can identify ...accurately whether the faults are related to quality indices or not.
In this paper, a new dynamic kernel partial least squares (D-KPLS) modeling approach and corresponding process monitoring method are proposed. The contributions are as follows: (1) Different from standard kernel partial least squares, which performs an oblique decomposition on measurement space. D-KPLS performs an orthogonal decomposition on measurement space, which separates measurement space into quality-related part and quality-unrelated part. (2) Compared with the standard KPLS algorithm, the new KPLS algorithm, D-KPLS, builds a dynamic relationship between measurements and quality indices. (3) By introducing the forgetting factor to the model, i.e., the samples gathered at the different history time are assigned to different weights, so the D-KPLS model builds a more robust relationship between input and output variables than standard KPLS model. On the basis of proposed D-KPLS algorithm, corresponding process monitoring and quality prediction methods are proposed. The D-KPLS monitoring method is used to monitor the numerical example and Tennessee Eastman (TE) process, and faults are detected accurately by the proposed D-KPLS model. The case studies show the effeteness of the proposed approach.
New approaches are proposed for nonlinear process monitoring and fault diagnosis based on kernel principal component analysis (KPCA) and kernel partial least analysis (KPLS) models at different ...scales, which are called multiscale KPCA (MSKPCA) and multiscale KPLS (MSKPLS). KPCA and KPLS are applied to these multiscale data to capture process variable correlations occurring at different scales. Main contribution of the paper is to propose nonlinear fault diagnosis methods based on multiscale contribution plots. In particular, the nonlinear scores of the variables at each scale are derived. These nonlinear scale contributions can be computed, which is very useful in diagnosing faults that occur mainly at a single scale. The proposed methods are applied to process monitoring of a continuous annealing process and fused magnesium furnace. Application results indicate that the proposed approach effectively captures the complex relations in the process and improves the diagnosis ability.
Dynamic DNA Structures Zhang, Yingwei; Pan, Victor; Li, Xue ...
Small (Weinheim an der Bergstrasse, Germany),
06/2019, Letnik:
15, Številka:
26
Journal Article
Recenzirano
Odprti dostop
Dynamic DNA structures, a type of DNA construct built using programmable DNA self‐assembly, have the capability to reconfigure their conformations in response to environmental stimulation. A general ...strategy to design dynamic DNA structures is to integrate reconfigurable elements into conventional static DNA structures that may be assembled from a variety of methods including DNA origami and DNA tiles. Commonly used reconfigurable elements range from strand displacement reactions, special structural motifs, target‐binding DNA aptamers, and base stacking components, to DNA conformational change domains, etc. Morphological changes of dynamic DNA structures may be visualized by imaging techniques or may be translated to other detectable readout signals (e.g., fluorescence). Owing to their programmable capability of recognizing environmental cues with high specificity, dynamic DNA structures embody the epitome of robust and versatile systems that hold great promise in sensing and imaging biological analytes, in delivering molecular cargos, and in building programmable systems that are able to conduct sophisticated tasks.
Dynamic DNA structures are able to sense and actuate in response to environmental stimulations. Design strategies and emerging applications of dynamic DNA structures are summarized and discussed.
In this communication, we demonstrate for the first time the proof of concept that carbon nanoparticles (CNPs) can be used as an effective fluorescent sensing platform for nucleic acid detection with ...selectivity down to single-base mismatch. The dye-labeled single-stranded DNA (ssDNA) probe is adsorbed onto the surface of the CNP via π-π interaction, quenching the dye. In the target assay, a double-stranded DNA (dsDNA) hybrid forms, recovering dye fluorescence.
An aqueous dispersion of graphene nanosheets (GNs) has been successfully prepared via chemical reduction of graphene oxide (GO) by hydrazine hydrate in the presence of ...poly(2-ethyldimethylammonioethyl methacrylate ethyl sulfate)-co-(1-vinylpyrrolidone) (PQ11), a cationic polyelectrolyte, for the first time. The noncovalent functionalization of GN by PQ11 leads to a GN dispersion that can be very stable for several months without the observation of any floating or precipitated particles. Several analytical techniques including UV−vis spectroscopy, Raman spectroscopy, X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS) have been used to characterize the resulting GNs. Taking advantages of the fact that PQ11 is a positively charged polymer exhibiting reducing ability, we further demonstrated the subsequent decoration of GN with Ag nanoparticles (AgNPs) by two routes: (1) direct adsorption of preformed, negatively charged AgNPs; (2) in-situ chemical reduction of silver salts. It was found that such Ag/GN nanocomposites exhibit good catalytic activity toward the reduction of hydrogen peroxide (H2O2), leading to an enzymeless sensor with a fast amperometric response time of less than 2 s. The linear detection range is estimated to be from 100 μM to 40 mM (r = 0.996), and the detection limit is estimated to be 28 μM at a signal-to-noise ratio of 3.
For fault classification in industrial processes, the performance of the classification model highly depends on the size of labeled dataset. Unfortunately, labeling the fault types of data samples ...need expert experiences and prior knowledge of the process, which is costly and time consuming. As a result, semisupervised modeling with both labeled and unlabeled data have recently become an interest in industrial processes. In this paper, a kernel-driven semisupervised fisher discriminant analysis (FDA) model is proposed for nonlinear fault classification. Two discriminant analytical strategies are introduced for online fault assignment, namely k-nearest neighborhood and Bayesian inference. Detailed comparative studies are carried out through two industrial benchmark processes between the linear and kernel-driven semisupervised FDA models, in which the best fault classification performance is obtained by the kernel semisupervised model with Bayesian inference as its discriminant strategy.
Increasing reaction temperature produces photoluminescent polymer nanodots (PPNDs) with decreased particle size and increased quantum yield. Such PPNDs are used as an effective fluorescent sensing ...platform for label‐free sensitive and selective detection of Cu(II) ions with a detection limit as low as 1 nM. This method is successfully applied to determine Cu2+ in real water samples.
The present article reports on a simple, economical, and green preparative strategy toward water-soluble, fluorescent carbon nanoparticles (CPs) with a quantum yield of approximately 6.9% by ...hydrothermal process using low cost wastes of pomelo peel as a carbon source for the first time. We further explore the use of such CPs as probes for a fluorescent Hg2+ detection application, which is based on Hg2+-induced fluorescence quenching of CPs. This sensing system exhibits excellent sensitivity and selectivity toward Hg2+, and a detection limit as low as 0.23 nM is achieved. The practical use of this system for Hg2+ determination in lake water samples is also demonstrated successfully.
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