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1.
ACS Omega ; 8(22): 19273-19286, 2023 Jun 06.
Article in English | MEDLINE | ID: mdl-37305238

ABSTRACT

An acid gas removal unit (AGRU) in a natural gas processing plant is designed specifically to remove acidic components, such as carbon dioxide (CO2) and hydrogen sulfide (H2S), from the natural gas. The occurrence of faults, such as foaming, and to a lesser extent, damaged trays and fouling, in AGRUs is a commonly encountered problem; however, they are the least studied in the open literature. Hence, in this paper, shallow and deep sparse autoencoders with SoftMax layers are investigated to facilitate early detection of these three faults before any significant financial loss. The dynamic behavior of process variables in AGRUs in the presence of fault conditions was simulated using Aspen HYSYS Dynamics. The simulated data were used to compare five closely related fault diagnostic models, i.e., a model based on principal component analysis, a shallow sparse autoencoder without fine-tuning, a shallow sparse autoencoder with fine-tuning, a deep sparse autoencoder without fine-tuning, and a deep sparse autoencoder with fine-tuning. All models could distinguish reasonably well between the different fault conditions. The deep sparse autoencoder with fine-tuning was best able to do so with very high accuracy. Visualization of the autoencoder features yielded further insight into the performance of the models, as well as the dynamic behavior of the AGRU. Foaming was relatively difficult to distinguish from normal operating conditions. The features obtained from the fine-tuned deep autoencoder in particular can be used to construct bivariate scatter plots as a basis for automatic monitoring of the process.

2.
Pediatr Neurol ; 61: 28-37, 2016 08.
Article in English | MEDLINE | ID: mdl-27255413

ABSTRACT

BACKGROUND: Electroencephalography (EEG) has been used for almost a century to identify seizure-related disorders in humans, typically through expert interpretation of multichannel recordings. Attempts have been made to quantify EEG through frequency analyses and graphic representations. These "traditional" quantitative EEG analysis methods were limited in their ability to analyze complex and multivariate data and have not been generally accepted in clinical settings. There has been growing interest in identification of novel EEG biomarkers to detect early risk of autism spectrum disorder, to identify clinically meaningful subgroups, and to monitor targeted intervention strategies. Most studies to date have, however, used quantitative EEG approaches, and little is known about the emerging multivariate analytical methods or the robustness of candidate biomarkers in the context of the variability of autism spectrum disorder. METHODS: Here, we present a targeted review of methodological and clinical challenges in the search for novel resting-state EEG biomarkers for autism spectrum disorder. RESULTS: Three primary novel methodologies are discussed: (1) modified multiscale entropy, (2) coherence analysis, and (3) recurrence quantification analysis. Results suggest that these methods may be able to classify resting-state EEG as "autism spectrum disorder" or "typically developing", but many signal processing questions remain unanswered. CONCLUSIONS: We suggest that the move to novel EEG analysis methods is akin to the progress in neuroimaging from visual inspection, through region-of-interest analysis, to whole-brain computational analysis. Novel resting-state EEG biomarkers will have to evaluate a range of potential demographic, clinical, and technical confounders including age, gender, intellectual ability, comorbidity, and medication, before these approaches can be translated into the clinical setting.


Subject(s)
Autism Spectrum Disorder/diagnosis , Autism Spectrum Disorder/physiopathology , Brain/physiopathology , Electroencephalography/methods , Humans , Rest
4.
J Surg Res ; 145(2): 244-50, 2008 Apr.
Article in English | MEDLINE | ID: mdl-18067925

ABSTRACT

BACKGROUND: Omega-3 fatty acids (n-3 FA) demonstrate significant anti-inflammatory properties thought to occur through three principal mechanisms; (1) displacement of arachidonic acid from the cellular membrane, (2) differential prostaglandin E2 (PGE2) and LTB4 production, and (3) molecular level alterations such as diminished nuclear factor kappa B and AP-1 activation. Recently, n-3 FA have been demonstrated to significantly decrease nitric oxide (NO) production in a lipopolysaccharide (LPS)-stimulated M Phi model. We hypothesized that decreased NO production by n-3 FA occurs through inhibition of cyclooxygenase-2 (COX-2) derived PGE2 and that repletion of the system with PGE2 would obliterate these effects. Selective COX-2 inhibitor (L-748,731) experiments and separate PGE2 repletion studies were used to test this hypothesis. METHODS: NO production was assessed following 24 h with or without LPS/PGE2 in the presence of n-3 FA, L-748,731 (a selective COX-2 inhibitor), or combination (n-3 FA + L-748,731) treatment. Western blots were used to assess inducible NO synthase protein expression. RESULTS: Independently or in the presence of LPS, treatment with a COX-2 inhibitor significantly increased NO production compared with control, n-3 FA, and combination treatment. NO production in combination treatment is slightly increased compared to n-3 FA treatment. In control cells treated with LPS, PGE2 repletion resulted in a significant decrease in NO. All other treatment groups repleted with PGE2 demonstrated no significant alterations in NO production. Inducible NO synthase protein expression levels were similar to NO production across all treatments. CONCLUSION: These experiments disproved our original hypothesis that the decrease in NO production associated with n-3 FA treatment occurs through a COX-2 derived PGE2 dependent mechanism. Eliminating COX-2 derived PGE2 by a selective inhibitor actually increased NO production. Exogenous PGE2 repletion did not restore the system. Therefore, mechanisms other than n-3 FA associated alterations in COX-2 derived PGE2 are likely involved in decreasing NO production in LPS stimulated M Phi.


Subject(s)
Cyclooxygenase 2/metabolism , Dinoprostone/metabolism , Fatty Acids, Omega-3/pharmacology , Lipopolysaccharides/pharmacology , Macrophages/drug effects , Nitric Oxide Synthase Type II/metabolism , Animals , Cell Line , Cyclooxygenase Inhibitors/pharmacology , Drug Interactions , Macrophages/cytology , Macrophages/enzymology , Mice , Nitric Oxide/metabolism
5.
Neural Netw ; 12(1): 175-189, 1999 Jan.
Article in English | MEDLINE | ID: mdl-12662726

ABSTRACT

A number of techniques exist with which neural network architectures such as multilayer perceptrons and radial basis function networks can be trained. These include backpropagation, k-means clustering and evolutionary algorithms. The latter method is particularly useful as it is able to avoid local optima in the search space and can optimise parameters for which no gradient information exists. Unfortunately, only moderately sized networks can be trained by this method, owing to the fact that evolutionary optimisation is very computationally intensive. In this paper a novel algorithm (CERN) is therefore proposed which uses a special form of combinatorial search to optimise groups of neural nodes. Oriented, ellipsoidal basis nodes optimised with CERN achieved significantly better accuracy with fewer nodes than spherical basis nodes optimised by k-means clustering. Multilayer perceptrons optimised by CERN were found to be as accurate as those trained by advanced gradient descent techniques. CERN was also found to be significantly more efficient than a conventional evolutionary algorithm that does not use a combinatorial search.

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