VODCA: Verification of Diagnosis Using CAM-Based Approach for Explainable Process Monitoring.

Process monitoring at industrial sites contributes to system stability by detecting and diagnosing unexpected changes in a system. Today, as the infrastructure of industrial sites is advancing because of the development of communication technology, vast amounts of data are generated, and the importance of a way to effectively monitor such data in order to diagnose a system is increasing daily. Because a method based on a deep neural network can effectively extract information from a large amount of data, methods have been proposed to monitor processes using such networks to detect system faults and abnormalities.

Large-scale pattern storage and retrieval using generalized brain-state-in-a-box neural networks.

In this paper, a generalized Brain-State-in-a-Box (gBSB)-based hybrid neural network is proposed for storing and retrieving pattern sequences. The hybrid network consists of autoassociative and heteroassociative parts. Then, a large-scale image storage and retrieval neural system is constructed using the gBSB-based hybrid neural network and the pattern decomposition concept.

Development of GCxGC/TOF-MS metabolomics for use in ecotoxicological studies with invertebrates.

The majority of metabolomic studies used in ecotoxicology have implemented (1)H NMR analysis. Despite constant improvement, major limitations of NMR-based techniques include relatively low sensitivity that results in an examination of a limited number of metabolites. An alternative approach is the use of liquid or gas chromatography (GC) for separation of metabolites and mass spectrometry (MS) for their quantification and identification.

Comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry peak sorting algorithm.

We report a novel peak sorting method for the two-dimensional gas chromatography/time-of-flight mass spectrometry (GC x GC/TOF-MS) system. The objective of peak sorting is to recognize peaks from the same metabolite occurring in different samples from thousands of peaks detected in the analytical procedure. The developed algorithm is based on the fact that the chromatographic peaks for a given analyte have similar retention times in all of the chromatograms.

Neural network prediction of peptide separation in strong anion exchange chromatography.

Motivation: The still emerging combination of technologies that enable description and characterization of all expressed proteins in a biological system is known as proteomics. Although many separation and analysis technologies have been employed in proteomics, it remains a challenge to predict peptide behavior during separation processes. New informatics tools are needed to model the experimental analysis method that will allow scientists to predict peptide separation and assist with required data mining steps, such as protein identification.

Autoassociative memory design using interconnected generalized brain-state-in-a-box neural networks.

A class of interconnected neural networks composed of generalized Brain-State-in-a-Box (gBSB) neural subnetworks is considered. Interconnected gBSB neural network architectures are proposed along with their stability conditions. The design of the interconnected neural networks is reduced to the problem of solving linear matrix inequalities (LMIs) to determine the interconnection parameters.

Associative memory design using overlapping decomposition and generalized brain-state-in-a-box neural networks.

This paper is concerned with large scale associative memory design. A serious problem with neural associative memories is the quadratic growth of the number of interconnections with the problem size. An overlapping decomposition algorithm is proposed to attack this problem.