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Cyber-physical systems are ever more common in manufacturing industries. Increasing their autonomy has been declared an explicit goal, for example, as part of the Industry 4.0 vision. To achieve this system intelligence, principled and software-driven methods are required to analyze sensing data, make goal-directed decisions, and eventually execute and monitor chosen tasks. In this chapter, we present a number of knowledge-based approaches to these problems and case studies with in-depth evaluation results of several different implementations for groups of autonomous mobile robots performing in-house logistics in a smart factory. We focus on knowledge-based systems because besides providing expressive languages and capable reasoning techniques, they also allow for explaining how a particular sequence of actions came about, for example, in the case of a failure.
20 Years of RoboCup
(2016)
An immunochromatographic lateral flow dipstick assay for the fast detection of microcystin-LR was developed. Colloid gold particles with diameters of 40 nm were used as red-colored antibody labels for the visual detection of the antigen. The new dipstick sensor is capable of detecting down to 5 µg·l−1 (ppb; total inversion of the color signal) or 1 ppb (observation of color grading) of microcystin-LR. The course of the labeling reaction was observed via spectrometric wave shifts caused by the change of particle size during the binding of antibodies. Different stabilizing reagents showed that especially bovine serum albumin (BSA) and casein increase the assays sensitivity and the conjugate stability. Performance of the dipsticks was quantified by pattern processing of capture zone CCD images. Storage stability of dipsticks and conjugate suspensions over 115 days under different conditions were monitored. The ready-to-use dipsticks were successfully tested with microcystin-LR-spiked samples of outdoor drinking- and salt water and applied to the tissue of microcystin-fed mussels.
Implikationen der Digitalisierung für den Finanzbereich der Unternehmung und das Rollenbild des CFO
(2017)
Three amperometric biosensors have been developed for the detection of L-malic acid, fumaric acid, and L -aspartic acid, all based on the combination of a malate-specific dehydrogenase (MDH, EC 1.1.1.37) and diaphorase (DIA, EC 1.8.1.4). The stepwise expansion of the malate platform with the enzymes fumarate hydratase (FH, EC 4.2.1.2) and aspartate ammonia-lyase (ASPA, EC 4.3.1.1) resulted in multi-enzyme reaction cascades and, thus, augmentation of the substrate spectrum of the sensors. Electrochemical measurements were carried out in presence of the cofactor β-nicotinamide adenine dinucleotide (NAD+) and the redox mediator hexacyanoferrate (III) (HCFIII). The amperometric detection is mediated by oxidation of hexacyanoferrate (II) (HCFII) at an applied potential of + 0.3 V vs. Ag/AgCl. For each biosensor, optimum working conditions were defined by adjustment of cofactor concentrations, buffer pH, and immobilization procedure. Under these improved conditions, amperometric responses were linear up to 3.0 mM for L-malate and fumarate, respectively, with a corresponding sensitivity of 0.7 μA mM−1 (L-malate biosensor) and 0.4 μA mM−1 (fumarate biosensor). The L-aspartate detection system displayed a linear range of 1.0–10.0 mM with a sensitivity of 0.09 μA mM−1. The sensor characteristics suggest that the developed platform provides a promising method for the detection and differentiation of the three substrates.