Explainability: Relevance based Dynamic Deep Learning Algorithm for Fault Detection and Diagnosis in Chemical Processes

The focus of this work is on Statistical Process Control (SPC) of a manufacturing process based on available measurements. Two important applications of SPC in industrial settings are fault detection and diagnosis (FDD). In this work a deep learning (DL) based methodology is proposed for FDD. We investigate the application of an explainability concept to enhance the FDD accuracy of a deep neural network model trained with a data set of relatively small number of samples. The explainability is quantified by a novel relevance measure of input variables that is calculated from a Layerwise Relevance Propagation (LRP) algorithm. It is shown that the relevances can be used to discard redundant input feature vectors/ variables iteratively thus resulting in reduced over-fitting of noisy data, increasing distinguishability between output classes and superior FDD test accuracy. The efficacy of the proposed method is demonstrated on the benchmark Tennessee Eastman Process.Comment: Under Review. arXiv admin note: text overlap with arXiv:2012.0386

PLS-Based Robust Inferential Control for a Packed-Bed Reactor

This paper compares the performance of two different inferential schemes when applied to an experimental packed-bed reactor. The first scheme, proposed initially by Brosilow, is designed based on Kalman filter estimation. The second less traditional design uses an estimator computed from the Partial Least Squares regression method (PLS). The second approach was found to give superior performance when the nonlinear system under study is operated is a wide range of operating points. Due to the nonlinearity of the system it is essential to address the issue of robustness of the proposed schemes. This is formally done in this work using Structured Singular Value Theory. For the robustness analysis it is crucial to develop a realistic but not overly conservative uncertainty description. Since the PLS estimator uses a large number of measurements, a robust design based on the uncertainty associated with each one of the measurements would be very conservative. To overcome this problem a lumped uncertainty description is proposed which is identified directly from experiments

Robust Inferential Control for a Packed-Bed Reactor

An inferential robust control technique is applied to an experimental fixed bed reactor. The controlled variables are the exit concentration and the maximum bed temperature. Both controlled variables are inferred from one single temperature measurement. The location of this measurement is selected to optimize the performance of the closed-loop system when model uncertainty is allowed. Closed-loop experiments are conducted to test the robustness characteristics of the controller. From these experiments, the operating regions most sensitive to modelling uncertainty are determined. The nonlinear system characteristics can cause significant offset in the inferred controlled variables

NEKI ASPEKTI NAUTIČKOG TURIZMA U HRVATSKOJ I SVIJETU

Da bi se izučavao nautički turizam potrebno ga je uzročno-posljedična sagledati, pobliže odrediti sadržaj pojma i okvire mogućih učinaka, što je svrha ovog rada, u čiji kontekst se smješta ovovremeno stanje i kretanje te vrste turizma u Republici Hrvatskoj kao europskoj zemlji koja spada među one s najpovoljnijim uvjetima za njegov razvitak

Characterization of a mutation demonstrating a link between the lack of S-adenosylmethionine and cell division in Escherichia coli

The metK gene of Escherichia coli encodes for the enzyme S-adenosylmethionine (SAM) synthetase, which catalyzes the formation of S-adenosylmethionine. Isolated as a $\gamma$-glutamylmethyl ester-resistant mutant by Greene et al. (1973), a metK84 mutant grows normally on rich media (Greene et al. 1973; Lin et al., 1990). This mutation has been transferred via transduction to an lrp\sp+ background, where normal growth rates on minimal medium require the presence of 50 $\mu$g/ml L-leucine. When the leucine concentration is reduced to 25 $\mu$g/ml or less, cells fail to divide, resulting in the formation of long filaments. These filaments can undergo DNA replication, but fail to form crosswalls. SAM synthetase activity assays indicate that metK84 mutants maintain only 1.7% of the wild type activity when grown on minimal medium without added leucine. A mutation in lrp, or the addition of leucine increases metK expression in both wild type and mutant strains. When metK activity is increased to a level of 0.26 mmol/min/mg of protein (11.3% of wild type activity), the mutant strain undergoes normal growth and cell division. The wild type metK gene has been cloned, and expression of the plasmid-borne gene complements for the growth-deficiency phenotypes of metK84. However, when the plasmid is over-expressed, the culture becomes auxotrophic for methionine. This work indicates that a SAM deficiency results in a cell division defect in E. coli

Can abuse deterrent formulations make a difference? Expectation and speculation

It is critical that issues surrounding the abuse and misuse of prescription opioids be balanced with the need for these medications for the treatment of pain. One way to decrease the abuse of prescription opioid medications is to develop abuse deterrent formulations (or ADFs) that in some way prevent drug abusers from extracting out the active ingredient in order to employ alternate routes of administration, such as injection, snorting, and smoking. Several factors including the pharmacokinetic profile of the drug, the features of the drug formulation that make it attractive or unattractive for abuse, the type of drug abuser, the progression of one's addiction pathway, and one's social environment may all play a role in the abuse of prescription opioids and what methods are used to abuse these drugs. This paper will examine these factors in order to understand how they affect the abuse of prescription opioids and routes of administration, and how the development of ADFs may alter these patterns