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Beim Ausbau nachhaltiger, regenerativer Energieversorgung hat die Umwandlung von organischer Biomasse in Biogas ein großes Potential. Der zugrundeliegende, komplexe biologische Prozess wird noch immer unzureichend verstanden und bedarf systematischer Untersuchungen der Prozessparameter, um einen hohen Ertrag bei guter Gasqualität zu ermöglichen. Die Fragestellungen zur Entschlüsselung des Prozesses sind sowohl verfahrenstechnischer als auch mikrobiologischer Natur. Aus mikrobiologischer Sicht ist die Kenntnis der tatsächlich beteiligten prozesstragenden Mikroorganismen von erheblicher Bedeutung, aus verfahrenstechnischer Sicht die Kenntnis der physikalischen und chemischen Faktoren, welche die mikrobiologischen Prozesse und kontrollieren. Im Zusammenspiel aller dieser Parameter wird die Biogasbildung befördert oder behindert, bis zum Abbruch des Prozesses.
Eine mögliche Kontrollmethode ist die Messung der metabolischen Aktivität prozesstragender Organismen.
Diese soll, beruhend auf fundierten Prozessdaten, gewonnen durch eine Parallelanlage, mit einem lichtadressierbaren potentiometrischen Sensor-System (LAPS) realisiert werden. Dieser Sensor ist in der Lage, pH-Wert-änderungen zu detektieren, die durch den Stoffwechsel der auf dem Chip immobilisierten Organismen hervorgerufen werden, um eine Online-Überwachung von Biogasanlagen zu ermöglichen.
Multi-parameter detection for supporting monitoring and control of biogas processes in agriculture
(2014)
Chemische Sensoren mit Bariumstrontiumtitanat als funktionelle Schicht zur Multiparameterdetektion
(2013)
The discovery of human induced pluripotent stem cells reprogrammed from somatic cells [1] and their ability to differentiate into cardiomyocytes (hiPSC-CMs) has provided a robust platform for drug screening [2]. Drug screenings are essential in the development of new components, particularly for evaluating the potential of drugs to induce life-threatening pro-arrhythmias. Between 1988 and 2009, 14 drugs have been removed from the market for this reason [3]. The microelectrode array (MEA) technique is a robust tool for drug screening as it detects the field potentials (FPs) for the entire cell culture. Furthermore, the propagation of the field potential can be examined on an electrode basis. To analyze MEA measurements in detail, we have developed an open-source tool.
The paper presents a method for the quantitative assessment of choroidal blood flow using an OCT-A system. The developed technique for processing of OCT-A scans is divided into two stages. At the first stage, the identification of the boundaries in the selected portion was performed. At the second stage, each pixel mark on the selected layer was represented as a volume unit, a voxel, which characterizes the region of moving blood. Three geometric shapes were considered to represent the voxel. On the example of one OCT-A scan, this work presents a quantitative assessment of the blood flow index. A possible modification of two-stage algorithm based on voxel scan processing is presented.
Biomechanical simulation of different prosthetic meshes for repairing uterine/vaginal vault prolapse
(2017)
The human arm consists of the humerus (upper arm), the medial ulna and the lateral radius (forearm). The joint between the humerus and the ulna is called humeroulnar joint and the joint between the humerus and the radius is called humeroradial joint. Lateral and medial collateral ligaments stabilize the elbow. Statistically, 2.5 out of 10,000 people suffer from radial head fractures [1]. In these fractures the cartilage is often affected. Caused by the injured cartilage, degenerative diseases like posttraumatic arthrosis may occur. The resulting pain and reduced range of motion have an impact on the patient’s quality of life. Until now, there has not been a treatment which allows typical loads in daily life activities and offers good long-term results. A new surgical approach was developed with the motivation to reduce the progress of the posttraumatic arthrosis. Here, the radius is shortened by 3 mm in the proximal part [2]. By this means, the load of the radius is intended to be reduced due to a load shift to the ulna. Since the radius is the most important stabilizer of the elbow it has to be confirmed that the stability is not affected. In the first test (Fig. 1 left), pressure distributions within the humeroulnar and humeroradial joints a native and a shortened radius were measured using resistive pressure sensors (I5076 and I5027, Tekscan, USA). The humerus was loaded axially in a tension testing machine (Z010, Zwick Roell, Germany) in 50 N steps up to 400 N. From the humerus the load is transmitted through both the radius and the ulna into the hand which is fixed on the ground. In the second test (Fig. 1 right), the joint stability was investigated using a digital image correlation system to measure the displacement of the ulna. Here, the humerus is fixed with a desired flexion angle and the unconstrained forearm lies on the ground. A rope connects the load actuator with a hook fixed in the ulna. A guide roller is used so that the rope pulls the ulna horizontally when a tensile load is applied. This creates a moment about the elbow joint with a maximum value of 7.5 Nm. Measurements were performed with varying flexion angles (0°, 30°, 60°, 90°, 120°). For both tests and each measurement, seven specimens were used. Student ́s t-test was employed to determine whether the mean values of the measurements in native specimen and operated specimens differ significantly.
The overall objective of this study is to develop a new external fixator, which closely maps the native kinematics of the elbow to decrease the joint force resulting in reduced rehabilitation time and pain. An experimental setup was designed to determine the native kinematics of the elbow during flexion of cadaveric arms. As a preliminary study, data from literature was used to modify a published biomechanical model for the calculation of the joint and muscle forces. They were compared to the original model and the effect of the kinematic refinement was evaluated. Furthermore, the obtained muscle forces were determined in order to apply them in the experimental setup. The joint forces in the modified model differed slightly from the forces in the original model. The muscle force curves changed particularly for small flexion angles but their magnitude for larger angles was consistent.
Mathematical morphology is a part of image processing that has proven to be fruitful for numerous applications. Two main operations in mathematical morphology are dilation and erosion. These are based on the construction of a supremum or infimum with respect to an order over the tonal range in a certain section of the image. The tonal ordering can easily be realised in grey-scale morphology, and some morphological methods have been proposed for colour morphology. However, all of these have certain limitations.
In this paper we present a novel approach to colour morphology extending upon previous work in the field based on the Loewner order. We propose to consider an approximation of the supremum by means of a log-sum exponentiation introduced by Maslov. We apply this to the embedding of an RGB image in a field of symmetric 2x2 matrices. In this way we obtain nearly isotropic matrices representing colours and the structural advantage of transitivity. In numerical experiments we highlight some remarkable properties of the proposed approach.
Effective training requires high muscle forces potentially leading to training-induced injuries. Thus, continuous monitoring and controlling of the loadings applied to the musculoskeletal system along the motion trajectory is required. In this paper, a norm-optimal iterative learning control algorithm for the robot-assisted training is developed. The algorithm aims at minimizing the external knee joint moment, which is commonly used to quantify the loading of the medial compartment. To estimate the external knee joint moment, a musculoskeletal lower extremity model is implemented in OpenSim and coupled with a model of an industrial robot and a force plate mounted at its end-effector. The algorithm is tested in simulation for patients with varus, normal and valgus alignment of the knee. The results show that the algorithm is able to minimize the external knee joint moment in all three cases and converges after less than seven iterations.
Heavy metal detection with semiconductor devices based on PLD-prepared chalcogenide glass thin films
(2007)
In recent years, the development of large pretrained language models, such as BERT and GPT, significantly improved information extraction systems on various tasks, including relation classification. State-of-the-art systems are highly accurate on scientific benchmarks. A lack of explainability is currently a complicating factor in many real-world applications. Comprehensible systems are necessary to prevent biased, counterintuitive, or harmful decisions.
We introduce semantic extents, a concept to analyze decision patterns for the relation classification task. Semantic extents are the most influential parts of texts concerning classification decisions. Our definition allows similar procedures to determine semantic extents for humans and models. We provide an annotation tool and a software framework to determine semantic extents for humans and models conveniently and reproducibly. Comparing both reveals that models tend to learn shortcut patterns from data. These patterns are hard to detect with current interpretability methods, such as input reductions. Our approach can help detect and eliminate spurious decision patterns during model development. Semantic extents can increase the reliability and security of natural language processing systems. Semantic extents are an essential step in enabling applications in critical areas like healthcare or finance. Moreover, our work opens new research directions for developing methods to explain deep learning models.
Multi-attribute relation extraction (MARE): simplifying the application of relation extraction
(2021)
Natural language understanding’s relation extraction makes innovative and encouraging novel business concepts possible and facilitates new digitilized decision-making processes. Current approaches allow the extraction of relations with a fixed number of entities as attributes. Extracting relations with an arbitrary amount of attributes requires complex systems and costly relation-trigger annotations to assist these systems. We introduce multi-attribute relation extraction (MARE) as an assumption-less problem formulation with two approaches, facilitating an explicit mapping from business use cases to the data annotations. Avoiding elaborated annotation constraints simplifies the application of relation extraction approaches. The evaluation compares our models to current state-of-the-art event extraction and binary relation extraction methods. Our approaches show improvement compared to these on the extraction of general multi-attribute relations.
Supervised machine learning and deep learning require a large amount of labeled data, which data scientists obtain in a manual, and time-consuming annotation process. To mitigate this challenge, Active Learning (AL) proposes promising data points to annotators they annotate next instead of a subsequent or random sample. This method is supposed to save annotation effort while maintaining model performance.
However, practitioners face many AL strategies for different tasks and need an empirical basis to choose between them. Surveys categorize AL strategies into taxonomies without performance indications. Presentations of novel AL strategies compare the performance to a small subset of strategies. Our contribution addresses the empirical basis by introducing a reproducible active learning evaluation (ALE) framework for the comparative evaluation of AL strategies in NLP.
The framework allows the implementation of AL strategies with low effort and a fair data-driven comparison through defining and tracking experiment parameters (e.g., initial dataset size, number of data points per query step, and the budget). ALE helps practitioners to make more informed decisions, and researchers can focus on developing new, effective AL strategies and deriving best practices for specific use cases. With best practices, practitioners can lower their annotation costs. We present a case study to illustrate how to use the framework.
The progress in natural language processing (NLP) research over the last years, offers novel business opportunities for companies, as automated user interaction or improved data analysis. Building sophisticated NLP applications requires dealing with modern machine learning (ML) technologies, which impedes enterprises from establishing successful NLP projects. Our experience in applied NLP research projects shows that the continuous integration of research prototypes in production-like environments with quality assurance builds trust in the software and shows convenience and usefulness regarding the business goal. We introduce STAMP 4 NLP as an iterative and incremental process model for developing NLP applications. With STAMP 4 NLP, we merge software engineering principles with best practices from data science. Instantiating our process model allows efficiently creating prototypes by utilizing templates, conventions, and implementations, enabling developers and data scientists to focus on the business goals. Due to our iterative-incremental approach, businesses can deploy an enhanced version of the prototype to their software environment after every iteration, maximizing potential business value and trust early and avoiding the cost of successful yet never deployed experiments.
In energy economy forecasts of different time series are rudimentary. In this study, a prediction for the German day-ahead spot market is created with Apache Spark and R. It is just an example for many different applications in virtual power plant environments. Other examples of use as intraday price processes, load processes of machines or electric vehicles, real time energy loads of photovoltaic systems and many more time series need to be analysed and predicted.
This work gives a short introduction into the project where this study is settled. It describes the time series methods that are used in energy industry for forecasts shortly. As programming technique Apache Spark, which is a strong cluster computing technology, is utilised. Today, single time series can be predicted. The focus of this work is on developing a method to parallel forecasting, to process multiple time series simultaneously with R and Apache Spark.
Conventional EEG devices cannot be used in everyday life and hence, past decade research has been focused on Ear-EEG for mobile, at-home monitoring for various applications ranging from emotion detection to sleep monitoring. As the area available for electrode contact in the ear is limited, the electrode size and location play a vital role for an Ear-EEG system. In this investigation, we present a quantitative study of ear-electrodes with two electrode sizes at different locations in a wet and dry configuration. Electrode impedance scales inversely with size and ranges from 450 kΩ to 1.29 MΩ for dry and from 22 kΩ to 42 kΩ for wet contact at 10 Hz. For any size, the location in the ear canal with the lowest impedance is ELE (Left Ear Superior), presumably due to increased contact pressure caused by the outer-ear anatomy. The results can be used to optimize signal pickup and SNR for specific applications. We demonstrate this by recording sleep spindles during sleep onset with high quality (5.27 μVrms).
Useful market simulations are key to the evaluation of diferent market designs existing of multiple market mechanisms or rules. Yet a simulation framework which has a comparison of diferent market mechanisms in mind was not found. The need to create an objective view on different sets of market rules while investigating meaningful agent strategies concludes that such a simulation framework is needed to advance the research on this subject. An overview of diferent existing market simulation models is given which also shows the research gap and the missing capabilities of those systems. Finally, a methodology is outlined how a novel market simulation which can answer the research questions can be developed.
Market abstraction of energy markets and policies - application in an agent-based modeling toolbox
(2023)
In light of emerging challenges in energy systems, markets are prone to changing dynamics and market design. Simulation models are commonly used to understand the changing dynamics of future electricity markets. However, existing market models were often created with specific use cases in mind, which limits their flexibility and usability. This can impose challenges for using a single model to compare different market designs. This paper introduces a new method of defining market designs for energy market simulations. The proposed concept makes it easy to incorporate different market designs into electricity market models by using relevant parameters derived from analyzing existing simulation tools, morphological categorization and ontologies. These parameters are then used to derive a market abstraction and integrate it into an agent-based simulation framework, allowing for a unified analysis of diverse market designs. Furthermore, we showcase the usability of integrating new types of long-term contracts and over-the-counter trading. To validate this approach, two case studies are demonstrated: a pay-as-clear market and a pay-as-bid long-term market. These examples demonstrate the capabilities of the proposed framework.
Due to the transition to renewable energies, electricity markets need to be made fit for purpose. To enable the comparison of different energy market designs, modeling tools covering market actors and their heterogeneous behavior are needed. Agent-based models are ideally suited for this task. Such models can be used to simulate and analyze changes to market design or market mechanisms and their impact on market dynamics. In this paper, we conduct an evaluation and comparison of two actively developed open-source energy market simulation models. The two models, namely AMIRIS and ASSUME, are both designed to simulate future energy markets using an agent-based approach. The assessment encompasses modelling features and techniques, model performance, as well as a comparison of model results, which can serve as a blueprint for future comparative studies of simulation models. The main comparison dataset includes data of Germany in 2019 and simulates the Day-Ahead market and participating actors as individual agents. Both models are comparable close to the benchmark dataset with a MAE between 5.6 and 6.4 €/MWh while also modeling the actual dispatch realistically.
In the research domain of energy informatics, the importance of open datais rising rapidly. This can be seen as various new public datasets are created andpublished. Unfortunately, in many cases, the data is not available under a permissivelicense corresponding to the FAIR principles, often lacking accessibility or reusability.Furthermore, the source format often differs from the desired data format or does notmeet the demands to be queried in an efficient way. To solve this on a small scale atoolbox for ETL-processes is provided to create a local energy data server with openaccess data from different valuable sources in a structured format. So while the sourcesitself do not fully comply with the FAIR principles, the provided unique toolbox allows foran efficient processing of the data as if the FAIR principles would be met. The energydata server currently includes information of power systems, weather data, networkfrequency data, European energy and gas data for demand and generation and more.However, a solution to the core problem - missing alignment to the FAIR principles - isstill needed for the National Research Data Infrastructure.