Dynamic operation, efficient calibration, and advanced data analysis of gas sensors : from modelling to real-world operation

This thesis demonstrates the use of dynamic operation, efficient calibration and advanced data analysis using metal oxide semiconductor (MOS) gas sensors as an example – from modeling to real-world operation. The necessary steps for an applicationspecific, selective indoor volatile organic compound (VOC) measurement system are addressed, analyzed and improved. Factors such as sensors, operation, electronics and calibration are considered. The developed methods and tools are universally transferable to other gas sensors and applications. The basis for selective measurement is temperature cyclic operation (TCO). The model-based understanding of a semiconductor gas sensor in TCO for the optimized development of operating modes and data evaluation is addressed and, for example, the tailored and stable detection of short gas pulses is developed. Two successful interlaboratory tests for the measurement of VOCs in independent laboratories are described. Selective measurements of VOCs in the laboratory and in the field are successfully demonstrated. Calibrations using the proposed techniques of randomized design of experiment (DoE), model-based data evaluation and calibration with machine learning methods are employed. The calibrated models are compared with analytical measurements using release tests. The high agreement of the results is unique in current research.Diese Thesis zeigt den Einsatz von dynamischem Betrieb, effizienter Kalibrierung, und fortschrittlicher Datenanalyse am Beispiel von Metalloxid Halbleiter (MOS) Gassensoren – von der Modellierung bis zum realen Betrieb. Die notwendigen Schritte für ein anwendungsspezifisches, selektives Messystem für flüchtige organische Verbindungen (VOC) im Innenraum werden adressiert, analysiert und verbessert. Faktoren wie z.B. Sensoren, Funktionsweise, Elektronik und Kalibrierung werden berücksichtigt. Die entwickelten Methoden und Tools sind universell auf andere Gassensoren und Anwendungen übertragbar. Grundlage für die selektive Messung ist der temperaturzyklische Betrieb (TCO). Auf das modellbasierte Verständnis eines Halbleitergassensors im TCO für die optimierte Entwicklung von Betriebsmodi und Datenauswertung wird eingegangen und z.B. die maßgeschneiderte und stabile Detektion von kurzen Gaspulsen entwickelt. Zwei erfolgreiche Ringversuche zur Messung von VOCs in unabhängigen Laboren werden beschrieben. Selektive Messungen verschiedener VOCs im Labor und im Feld werden erfolgreich demonstriert. Dabei kommen Kalibrierungen mit den vorgeschlagenen Techniken des randomisierten Design of Experiment (DoE), der modellbasierten Datenauswertung und Kalibrierung mit Methoden des maschinellen Lernens zum Einsatz. Die kalibrierten Modelle werden anhand von Freisetzungstests mit analytischen Messungen verglichen. Die hohe Übereinstimmung der Ergebnisse ist einzigartig in der aktuellen Forschung

Function-Orientated Structural Analysis of the Proximal Human Femur

In his model of the biomechanics of the proximal human femur, Friedrich Pauwels assumes a resultant force acting on the femoral head that is created by the partial body weight and the force of the abductor muscles inserting at the greater trochanter. This model suggests a tensile force in the region of the greater trochanter. An exact examination of the muscle insertions at the greater trochanter resulted in a contrasting hypothesis assuming a local compression stress in the region of the greater trochanter. The aim of this study was to examine which hypothesis is favored by the internal architecture of the proximal femur. Based on the architectural software Allplan (R), we performed an extended analysis of the trabecular structure within the proximal femur using CT scans of 10 human cadaver femora altogether. According to our results, both the medial and the trochanteric trabecular systems are orientated approximately perpendicular to the arcuate trabecular system {[}angles between systems ranging from 84.6 to 93.0 degrees (mean angle 90.7 degrees) and from 80.9 to 86.5 degrees, (mean angle 84.9 degrees), respectively]; furthermore, the medial trabecular system is orientated perpendicular to the epiphysis of the femoral head (mean of angles: 94.7). The biomechanical interpretation of these results strongly supports the idea of compressive stress in the region of the greater trochanter and makes a predominant tensile force of the abductor muscles highly unlikely. Copyright (C) 2009 S. Karger AG, Base

A transparent framework towards the context-sensitive recognition of conversational engagement

Modelling and recognising affective and mental user states is an urging topic in multiple research fields. This work suggests an approach towards adequate recognition of such states by combining state-of-the-art behaviour recognition classifiers in a transparent and explainable modelling framework that also allows to consider contextual aspects in the inference process. More precisely, in this paper we exemplify the idea of our framework with the recognition of conversational engagement in bi-directional conversations. We introduce a multi-modal annotation scheme for conversational engagement. We further introduce our hybrid approach that combines the accuracy of state-of-the art machine learning techniques, such as deep learning, with the capabilities of Bayesian Networks that are inherently interpretable and feature an important aspect that modern approaches are lacking - causal inference. In an evaluation on a large multi-modal corpus of bi-directional conversations, we show that this hybrid approach can even outperform state-of-the-art black-box approaches by considering context information and causal relations

I see what you did there: understanding when to trust a ML model with NOVA

In this demo paper we present NOVA, a machine learning and explanation interface that focuses on the automated analysis of social interactions. NOVA combines Cooperative Machine Learning (CML) and explainable AI (XAI) methods to reduce manual labelling efforts while simultaneously generating an intuitive understanding of the learning process of a classification system. Therefore, NOVA features a semi-automated labelling process in which users are provided with immediate visual feedback on the predictions, which gives insights into the strengths and weaknesses of the underlying classification system. Following an interactive and exploratory workflow, the performance of the model can be improved by manual revision of the predictions

Facile Quantification and Identification Techniques for Reducing Gases over a Wide Concentration Range Using a MOS Sensor in Temperature-Cycled Operation

Dedicated methods for quantification and identification of reducing gases based on model-based temperature-cycled operation (TCO) using a single commercial MOS gas sensor are presented. During high temperature phases the sensor surface is highly oxidized, yielding a significant sensitivity increase after switching to lower temperatures (differential surface reduction, DSR). For low concentrations, the slope of the logarithmic conductance during this low-temperature phase is evaluated and can directly be used for quantification. For higher concentrations, the time constant for reaching a stable conductance during the same low-temperature phase is evaluated. Both signals represent the reaction rate of the reducing gas on the strongly oxidized surface at this low temperature and provide a linear calibration curve, which is exceptional for MOS sensors. By determining these reaction rates on different low-temperature plateaus and applying pattern recognition, the resulting footprint can be used for identification of different gases. All methods are tested over a wide concentration range from 10 ppb to 100 ppm (4 orders of magnitude) for four different reducing gases (CO, H2, ammonia and benzene) using randomized gas exposures

Measuring Hydrogen in Indoor Air with a Selective Metal Oxide Semiconductor Sensor

Hydrogen is a ubiquitous but often neglected gas. In analytical measurements hydrogen—as a harmless gas—often is not considered so no studies on hydrogen in indoor air can be found. For metal oxide semiconductor (MOS) gas sensors that are increasingly pushed into the application as TVOC (total volatile organic compounds) sensors, hydrogen is a severe disturbance. On the other hand, hydrogen can be an intentional choice as indicator for human presence similar to carbon dioxide. We present a field-study on hydrogen in indoor air using selective MOS sensors accompanied by an analytical reference device for hydrogen with an accuracy of 10 ppb. Selectivity is achieved by siloxane treatment combined with temperature cycled operation and training with a complex lab calibration using randomized gas mixtures, yielding an uncertainty of 40–60 ppb. The feasibility is demonstrated by release tests with several gases inside a room and by comparison to the reference device. The results show that selective MOS sensors can function as cheap and available hydrogen detectors. Fluctuations in hydrogen concentration without human presence are measured over several days to gain insight in this highly relevant parameter for indoor air quality. The results indicate that the topic needs further attention and that the usage of hydrogen as indicator for human presence might be precluded by other sources and fluctuations

Field Study of Metal Oxide Semiconductor Gas Sensors in Temperature Cycled Operation for Selective VOC Monitoring in Indoor Air

More and more metal oxide semiconductor (MOS) gas sensors with digital interfaces are entering the market for indoor air quality (IAQ) monitoring. These sensors are intended to measure volatile organic compounds (VOCs) in indoor air, an important air quality factor. However, their standard operating mode often does not make full use of their true capabilities. More sophisticated operation modes, extensive calibration and advanced data evaluation can significantly improve VOC measurements and, furthermore, achieve selective measurements of single gases or at least types of VOCs. This study provides an overview of the potential and limits of MOS gas sensors for IAQ monitoring using temperature cycled operation (TCO), calibration with randomized exposure and data-based models trained with advanced machine learning. After lab calibration, a commercial digital gas sensor with four different gas-sensitive layers was tested in the field over several weeks. In addition to monitoring normal ambient air, release tests were performed with compounds that were included in the lab calibration, but also with additional VOCs. The tests were accompanied by different analytical systems (GC-MS with Tenax sampling, mobile GC-PID and GC-RCP). The results show quantitative agreement between analytical systems and the MOS gas sensor system. The study shows that MOS sensors are highly suitable for determining the overall VOC concentrations with high temporal resolution and, with some restrictions, also for selective measurements of individual components