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Cybersecurity of Industrial Control Systems (ICS) is an important issue, as ICS incidents may have a direct impact on safety of people or the environment. At the same time the awareness and knowledge about cybersecurity, particularly in the context of ICS, is alarmingly low. Industrial honeypots offer a cheap and easy to implement way to raise cybersecurity awareness and to educate ICS staff about typical attack patterns. When integrated in a productive network, industrial honeypots may not only reveal attackers early but may also distract them from the actual important systems of the network. Implementing multiple honeypots as a honeynet, the systems can be used to emulate or simulate a whole Industrial Control System. This paper describes a network of honeypots emulating HTTP, SNMP, S7communication and the Modbus protocol using Conpot, IMUNES and SNAP7. The nodes mimic SIMATIC S7 programmable logic controllers (PLCs) which are widely used across the globe. The deployed honeypots' features will be compared with the features of real SIMATIC S7 PLCs. Furthermore, the honeynet has been made publicly available for ten days and occurring cyberattacks have been analyzed
Process mining gets more and more attention even outside large enterprises and can be a major benefit for small and medium sized enterprises (SMEs) to gain competitive advantages. Applying process mining is challenging, particularly for SMEs because they have less resources and process maturity. So far, IS researchers analyzed process mining challenges with a focus on larger companies. This paper investigates the application of process mining by means of a case study and sheds light into the particular challenges of an IT SME. The results reveal 13 SME process mining challenges and seven guidelines to address them. In this way, the paper contributes to the understanding of process mining application in SME and shows similarities and differences to larger companies.
In the Laser Powder Bed Fusion (LPBF) process, parts are built out of metal powder material by exposure of a laser beam. During handling operations of the powder material, several influencing factors can affect the properties of the powder material and therefore directly influence the processability during manufacturing. Contamination by moisture due to handling operations is one of the most critical aspects of powder quality. In order to investigate the influences of powder humidity on LPBF processing, four materials (AlSi10Mg, Ti6Al4V, 316L and IN718) are chosen for this study. The powder material is artificially humidified, subsequently characterized, manufactured into cubic samples in a miniaturized process chamber and analyzed for their relative density. The results indicate that the processability and reproducibility of parts made of AlSi10Mg and Ti6Al4V are susceptible to humidity, while IN718 and 316L are barely influenced.
The future of industrial manufacturing and production will increasingly manifest in the form of cyber-physical production systems. Here, Digital Shadows will act as mediators between the physical and digital world to model and operationalize the interactions and relationships between different entities in production systems. Until now, the associated concepts have been primarily pursued and implemented from a technocentric perspective, in which human actors play a subordinate role, if they are considered at all. This paper outlines an anthropocentric approach that explicitly considers the characteristics, behavior, and traits and states of human actors in socio-technical production systems. For this purpose, we discuss the potentials and the expected challenges and threats of creating and using Human Digital Shadows in production.
In this paper research activities developed within the FutureCom project are presented. The project, funded by the European Metrology Programme for Innovation and Research (EMPIR), aims at evaluating and characterizing: (i) active devices, (ii) signal- and power integrity of field programmable gate array (FPGA) circuits, (iii) operational performance of electronic circuits in real-world and harsh environments (e.g. below and above ambient temperatures and at different levels of humidity), (iv) passive inter-modulation (PIM) in communication systems considering different values of temperature and humidity corresponding to the typical operating conditions that we can experience in real-world scenarios. An overview of the FutureCom project is provided here, then the research activities are described.
Many of today’s factors make software development more and more complex, such as time pressure, new technologies, IT security risks, et cetera. Thus, a good preparation of current as well as future software developers in terms of a good software engineering education becomes progressively important. As current research shows, Competence Developing Games (CDGs) and Serious Games can offer a potential solution.
This paper identifies the necessary requirements for CDGs to be conducive in principle, but especially in software engineering (SE) education. For this purpose, the current state of research was summarized in the context of a literature review. Afterwards, some of the identified requirements as well as some additional requirements were evaluated by a survey in terms of subjective relevance.
Kawasaki Heavy Industries, Ltd. (KHI), Aachen University of Applied Sciences, and B&B-AGEMA GmbH have investigated the potential of low NOx micro-mix (MMX) hydrogen combustion and its application to an industrial gas turbine combustor. Engine demonstration tests of a MMX combustor for the M1A-17 gas turbine with a co-generation system were conducted in the hydrogen-fueled power generation plant in Kobe City, Japan.
This paper presents the results of the commissioning test and the combined heat and power (CHP) supply demonstration. In the commissioning test, grid interconnection, loading tests and load cut-off tests were successfully conducted. All measurement results satisfied the Japanese environmental regulation values. Dust and soot as well as SOx were not detected. The NOx emissions were below 84 ppmv at 15 % O2. The noise level at the site boundary was below 60 dB. The vibration at the site boundary was below 45 dB.
During the combined heat and power supply demonstration, heat and power were supplied to neighboring public facilities with the MMX combustion technology and 100 % hydrogen fuel. The electric power output reached 1800 kW at which the NOx emissions were 72 ppmv at 15 % O2, and 60 %RH. Combustion instabilities were not observed. The gas turbine efficiency was improved by about 1 % compared to a non-premixed type combustor with water injection as NOx reduction method. During a total equivalent operation time of 1040 hours, all combustor parts, the M1A-17 gas turbine as such, and the co-generation system were without any issues.
Direct methods comprising limit and shakedown analysis is a branch of computational mechanics. It plays a significant role in mechanical and civil engineering design. The concept of direct method aims to determinate the ultimate load bearing capacity of structures beyond the elastic range. For practical problems, the direct methods lead to nonlinear convex optimization problems with a large number of variables and onstraints. If strength and loading are random quantities, the problem of shakedown analysis is considered as stochastic programming. This paper presents a method so called chance constrained programming, an effective method of stochastic programming, to solve shakedown analysis problem under random condition of strength. In this our investigation, the loading is deterministic, the strength is distributed as normal or lognormal variables.
Masonry infill walls are the most traditional enclosure system that is still widely used in RC frame buildings all over the world, particularly in seismic active regions. Although infill walls are usually neglected in seismic design, during an earthquake event they are subjected to in-plane and out-of-plane forces that can act separately or simultaneously. Since observations of damage to buildings after recent earthquakes showed detrimental effects of in-plane and out-of-plane load interaction on infill walls, the number of studies that focus on influence of in-plane damage on out-of-plane response has significantly increased. However, most of the xperimental campaigns have considered only solid infills and there is a lack of combined in-plane and out-of-plane experimental tests on masonry infills with openings, although windows and doors strongly affect seismic performance. In this paper, two types of experimental tests on infills with window openings are presented. The first is a pure out-of-plane test and the second one is a sequential in-plane and out-of-plane test aimed at investigating the effects of existing in-plane damage on outof-plane response. Additionally, findings from two tests with similar load procedure that were carried out on fully infilled RC frames in the scope of the same project are used for comparison. Test results clearly show that window opening increased vulnerability of infills to combined seismic actions and that prevention of damage in infills with openings is of the utmost importance for seismic safety.
The seismic performance and safety of major European industrial facilities has a global interest for Europe, its citizens and economy. A potential major disaster at an industrial site could affect several countries, probably far beyond the country where it is located. However, the seismic design and safety assessment of these facilities is practically based on national, often outdated seismic hazard assessment studies, due to many reasons, including the absence of a reliable, commonly developed seismic hazard model for whole Europe. This important gap is no more existing, as the 2020 European Seismic Hazard Model ESHM20 was released in December 2021. In this paper we investigate the expected impact of the adoption of ESHM20 on the seismic demand for industrial facilities, through the comparison of the ESHM20 probabilistic hazard at the sites where industrial facilities are located with the respective national and European regulations. The goal of this preliminary work in the framework of Working Group 13 of the European Association for Earthquake Engineering (EAEE), is to identify potential inadequacies in the design and safety control of existing industrial facilities and to highlight the expected impact of the adoption of the new European Seismic Hazard Model on the design of new industrial facilities and the safety assessment of existing ones.
An interdisciplinary view on humane interfaces for digital shadows in the internet of production
(2022)
Digital shadows play a central role for the next generation industrial internet, also known as Internet of Production (IoP). However, prior research has not considered systematically how human actors interact with digital shadows, shaping their potential for success. To address this research gap, we assembled an interdisciplinary team of authors from diverse areas of human-centered research to propose and discuss design and research recommendations for the implementation of industrial user interfaces for digital shadows, as they are currently conceptualized for the IoP. Based on the four use cases of decision support systems, knowledge sharing in global production networks, human-robot collaboration, and monitoring employee workload, we derive recommendations for interface design and enhancing workers’ capabilities. This analysis is extended by introducing requirements from the higher-level perspectives of governance and organization.
A capacitive electrolyte-insulator-semiconductor (EISCAP) biosensor modified with Tobacco mosaic virus (TMV) particles for the detection of acetoin is presented. The enzyme acetoin reductase (AR) was immobilized on the surface of the EISCAP using TMV particles as nanoscaffolds. The study focused on the optimization of the TMV-assisted AR immobilization on the Ta 2 O 5 -gate EISCAP surface. The TMV-assisted acetoin EISCAPs were electrochemically characterized by means of leakage-current, capacitance-voltage, and constant-capacitance measurements. The TMV-modified transducer surface was studied via scanning electron microscopy.
In the past, CSP and PV have been seen as competing technologies. Despite massive reductions in the electricity generation costs of CSP plants, PV power generation is - at least during sunshine hours - significantly cheaper. If electricity is required not only during the daytime, but around the clock, CSP with its inherent thermal energy storage gets an advantage in terms of LEC. There are a few examples of projects in which CSP plants and PV plants have been co-located, meaning that they feed into the same grid connection point and ideally optimize their operation strategy to yield an overall benefit. In the past eight years, TSK Flagsol has developed a plant concept, which merges both solar technologies into one highly Integrated CSP-PV-Hybrid (ICPH) power plant. Here, unlike in simply co-located concepts, as analyzed e.g. in [1] – [4], excess PV power that would have to be dumped is used in electric molten salt heaters to increase the storage temperature, improving storage and conversion efficiency. The authors demonstrate the electricity cost sensitivity to subsystem sizing for various market scenarios, and compare the resulting optimized ICPH plants with co-located hybrid plants. Independent of the three feed-in tariffs that have been assumed, the ICPH plant shows an electricity cost advantage of almost 20% while maintaining a high degree of flexibility in power dispatch as it is characteristic for CSP power plants. As all components of such an innovative concept are well proven, the system is ready for commercial market implementation. A first project is already contracted and in early engineering execution.
Technical assessment of Brayton cycle heat pumps for the integration in hybrid PV-CSP power plants
(2022)
The hybridization of Concentrated Solar Power (CSP) and Photovoltaics (PV) systems is a promising approach to reduce costs of solar power plants, while increasing dispatchability and flexibility of power generation. High temperature heat pumps (HT HP) can be utilized to boost the salt temperature in the thermal energy storage (TES) of a Parabolic Trough Collector (PTC) system from 385 °C up to 565 °C. A PV field can supply the power for the HT HP, thus effectively storing the PV power as thermal energy. Besides cost-efficiently storing energy from the PV field, the power block efficiency of the overall system is improved due to the higher steam parameters. This paper presents a technical assessment of Brayton cycle heat pumps to be integrated in hybrid PV-CSP power plants. As a first step, a theoretical analysis was carried out to find the most suitable working fluid. The analysis included the fluids Air, Argon (Ar), Nitrogen (N2) and Carbon dioxide (CO2). N2 has been chosen as the optimal working fluid for the system. After the selection of the ideal working medium, different concepts for the arrangement of a HT HP in a PV-CSP hybrid power plant were developed and simulated in EBSILON®Professional. The concepts were evaluated technically by comparing the number of components required, pressure losses and coefficient of performance (COP).
Concentrated Solar Power (CSP) systems are able to store energy cost-effectively in their integrated thermal energy storage (TES). By intelligently combining Photovoltaics (PV) systems with CSP, a further cost reduction of solar power plants is expected, as well as an increase in dispatchability and flexibility of power generation. PV-powered Resistance Heaters (RH) can be deployed to raise the temperature of the molten salt hot storage from 385 °C up to 565 °C in a Parabolic Trough Collector (PTC) plant. To avoid freezing and decomposition of molten salt, the temperature distribution in the electrical resistance heater is investigated in the present study. For this purpose, a RH has been modeled and CFD simulations have been performed. The simulation results show that the hottest regions occur on the electric rod surface behind the last baffle. A technical optimization was performed by adjusting three parameters: Shell-baffle clearance, electric rod-baffle clearance and number of baffles. After the technical optimization was carried out, the temperature difference between the maximum temperature and the average outlet temperature of the salt is within the acceptable limits, thus critical salt decomposition has been avoided. Additionally, the CFD simulations results were analyzed and compared with results obtained with a one-dimensional model in Modelica.
The Solar-Institut Jülich (SIJ) and the companies Hilger GmbH and Heliokon GmbH from Germany have developed a small-scale cost-effective heliostat, called “micro heliostat”. Micro heliostats can be deployed in small-scale concentrated solar power (CSP) plants to concentrate the sun's radiation for electricity generation, space or domestic water heating or industrial process heat. In contrast to conventional heliostats, the special feature of a micro heliostat is that it consists of dozens of parallel-moving, interconnected, rotatable mirror facets. The mirror facets array is fixed inside a box-shaped module and is protected from weathering and wind forces by a transparent glass cover. The choice of the building materials for the box, tracking mechanism and mirrors is largely dependent on the selected production process and the intended application of the micro heliostat. Special attention was paid to the material of the tracking mechanism as this has a direct influence on the accuracy of the micro heliostat. The choice of materials for the mirror support structure and the tracking mechanism is made in favor of plastic molded parts. A qualification assessment method has been developed by the SIJ in which a 3D laser scanner is used in combination with a coordinate measuring machine (CMM). For the validation of this assessment method, a single mirror facet was scanned and the slope deviation was computed.
New materials often lead to innovations and advantages in technical applications. This also applies to the particle receiver proposed in this work that deploys high-temperature and scratch resistant transparent ceramics. With this receiver design, particles are heated through direct-contact concentrated solar irradiance while flowing downwards through tubular transparent ceramics from top to bottom. In this paper, the developed particle receiver as well as advantages and disadvantages are described. Investigations on the particle heat-up characteristics from solar irradiance were carried out with DEM simulations which indicate that particle temperatures can reach up to 1200 K. Additionally, a simulation model was set up for investigating the dynamic behavior. A test receiver at laboratory scale has been designed and is currently being built. In upcoming tests, the receiver test rig will be used to validate the simulation results. The design and the measurement equipment is described in this work.
In this work, three patent pending calibration methods for heliostat fields of central receiver systems (CRS) developed by the Solar-Institut Jülich (SIJ) of the FH Aachen University of Applied Sciences are presented. The calibration methods can either operate in a combined mode or in stand-alone mode. The first calibration method, method A, foresees that a camera matrix is placed into the receiver plane where it is subjected to concentrated solar irradiance during a measurement process. The second calibration method, method B, uses an unmanned aerial vehicle (UAV) such as a quadrocopter to automatically fly into the reflected solar irradiance cross-section of one or more heliostats (two variants of method B were tested). The third calibration method, method C, foresees a stereo central camera or multiple stereo cameras installed e.g. on the solar tower whereby the orientations of the heliostats are calculated from the location detection of spherical red markers attached to the heliostats. The most accurate method is method A which has a mean accuracy of 0.17 mrad. The mean accuracy of method B variant 1 is 1.36 mrad and of variant 2 is 1.73 mrad. Method C has a mean accuracy of 15.07 mrad. For method B there is great potential regarding improving the measurement accuracy. For method C the collected data was not sufficient for determining whether or not there is potential for improving the accuracy.
This work presents a basic forecast tool for predicting direct normal irradiance (DNI) in hourly resolution, which the Solar-Institut Jülich (SIJ) is developing within a research project. The DNI forecast data shall be used for a parabolic trough collector (PTC) system with a concrete thermal energy storage (C-TES) located at the company KEAN Soft Drinks Ltd in Limassol, Cyprus. On a daily basis, 24-hour DNI prediction data in hourly resolution shall be automatically produced using free or very low-cost weather forecast data as input. The purpose of the DNI forecast tool is to automatically transfer the DNI forecast data on a daily basis to a main control unit (MCU). The MCU automatically makes a smart decision on the operation mode of the PTC system such as steam production mode and/or C-TES charging mode. The DNI forecast tool was evaluated using historical data of measured DNI from an on-site weather station, which was compared to the DNI forecast data. The DNI forecast tool was tested using data from 56 days between January and March 2022, which included days with a strong variation in DNI due to cloud passages. For the evaluation of the DNI forecast reliability, three categories were created and the forecast data was sorted accordingly. The result was that the DNI forecast tool has a reliability of 71.4 % based on the tested days. The result fulfils SIJ’s aim to achieve a reliability of around 70 %, but SIJ aims to still improve the DNI forecast quality.
Concerning current efforts to improve operational efficiency and to lower overall costs of concentrating solar power (CSP) plants with prediction-based algorithms, this study investigates the quality and uncertainty of nowcasting data regarding the implications for process predictions. DNI (direct normal irradiation) maps from an all-sky imager-based nowcasting system are applied to a dynamic prediction model coupled with ray tracing. The results underline the need for high-resolution DNI maps in order to predict net yield and receiver outlet temperature realistically. Furthermore, based on a statistical uncertainty analysis, a correlation is developed, which allows for predicting the uncertainty of the net power prediction based on the corresponding DNI forecast uncertainty. However, the study reveals significant prediction errors and the demand for further improvement in the accuracy at which local shadings are forecasted.
A promising approach to reduce the system costs of molten salt solar receivers is to enable the irradiation of the absorber tubes on both sides. The star design is an innovative receiver design, pursuing this approach. The unconventional design leads to new challenges in controlling the system. This paper presents a control concept for a molten salt receiver system in star design. The control parameters are optimized in a defined test cycle by minimizing a cost function. The control concept is tested in realistic cloud passage scenarios based on real weather data. During these tests, the control system showed no sign of unstable behavior, but to perform sufficiently in every scenario further research and development like integrating Model Predictive Controls (MPCs) need to be done. The presented concept is a starting point to do so.
Reliable methods for automatic readability assessment have the potential to impact a variety of fields, ranging from machine translation to self-informed learning. Recently, large language models for the German language (such as GBERT and GPT-2-Wechsel) have become available, allowing to develop Deep Learning based approaches that promise to further improve automatic readability assessment. In this contribution, we studied the ability of ensembles of fine-tuned GBERT and GPT-2-Wechsel models to reliably predict the readability of German sentences. We combined these models with linguistic features and investigated the dependence of prediction performance on ensemble size and composition. Mixed ensembles of GBERT and GPT-2-Wechsel performed better than ensembles of the same size consisting of only GBERT or GPT-2-Wechsel models. Our models were evaluated in the GermEval 2022 Shared Task on Text Complexity Assessment on data of German sentences. On out-of-sample data, our best ensemble achieved a root mean squared error of 0:435.
Digital twins enable the modeling and simulation of real-world entities
(objects, processes or systems), resulting in improvements in the associated value
chains. The emerging field of quantum computing holds tremendous promise for
evolving this virtualization towards Quantum (Digital) Twins (QDT) and
ultimately Quantum Twins (QT). The quantum (digital) twin concept is not a
contradiction in terms - but instead describes a hybrid approach that can be
implemented using the technologies available today by combining classical
computing and digital twin concepts with quantum processing. This paper
presents the status quo of research and practice on quantum (digital) twins. It also
discuses their potential to create competitive advantage through real-time
simulation of highly complex, interconnected entities that helps companies better
address changes in their environment and differentiate their products and
services.
In this article we describe an Internet-of-Things sensing device with a wireless interface which is powered by the oftenoverlooked harvesting method of the Wiegand effect. The sensor can determine position, temperature or other resistively measurable quantities and can transmit the data via an ultra-low power ultra-wideband (UWB) data transmitter. With this approach we can energy-self-sufficiently acquire, process, and wirelessly transmit data in a pulsed operation. A proof-of-concept system was built up to prove the feasibility of the approach. The energy consumption of the system is analyzed and traced back in detail to the individual components, compared to the generated energy and processed to identify further optimization options. Based on the proof-of-concept, an application demonstrator was developed. Finally, we point out possible use cases.
A Gamified Information System (GIS) implements game concepts and elements, such as affordances and game design principles to motivate people. Based on the idea to develop a GIS to increase the motivation of software developers to perform software quality tasks, the research work at hand aims at investigating relevant requirements from that target group. Therefore, 14 interviews with software development experts are conducted and analyzed. According to the results, software developers prefer the affordances points, narrative storytelling in a multiplayer and a round-based setting. Furthermore, six design principles for the development of a GIS are derived.
When confining pressure is low or absent, extensional fractures are typical, with fractures occurring on unloaded planes in rock. These “paradox” fractures can be explained by a phenomenological extension strain failure criterion. In the past, a simple empirical criterion for fracture initiation in brittle rock has been developed. But this criterion makes unrealistic strength predictions in biaxial compression and tension. A new extension strain criterion overcomes this limitation by adding a weighted principal shear component. The weight is chosen, such that the enriched extension strain criterion represents the same failure surface as the Mohr–Coulomb (MC) criterion. Thus, the MC criterion has been derived as an extension strain criterion predicting failure modes, which are unexpected in the understanding of the failure of cohesive-frictional materials. In progressive damage of rock, the most likely fracture direction is orthogonal to the maximum extension strain. The enriched extension strain criterion is proposed as a threshold surface for crack initiation CI and crack damage CD and as a failure surface at peak P. Examples show that the enriched extension strain criterion predicts much lower volumes of damaged rock mass compared to the simple extension strain criterion.
The fourth industrial revolution presents a multitude of challenges for industries, one of which being the increased flexibility required of manufacturing lines as a result of increased consumer demand for individualised products. One solution to tackle this challenge is the digital twin, more specifically the standardised model of a digital twin also known as the asset administration shell. The standardisation of an industry wide communications tool is a critical step in enabling inter-company operations. This paper discusses the current state of asset administration shells, the frameworks used to host them and their problems that need to be addressed. To tackle these issues, we propose an event-based server capable of drastically reducing response times between assets and asset administration shells and a multi-agent system used for the orchestration and deployment of the shells in the field.
Industrial facilities must be thoroughly designed to withstand seismic
actions as they exhibit an increased loss potential due to the possibly wideranging
damage consequences and the valuable process engineering equipment.
Past earthquakes showed the social and political consequences of seismic damage
to industrial facilities and sensitized the population and politicians worldwide
for the possible hazard emanating from industrial facilities. However, a holistic
approach for the seismic design of industrial facilities can presently neither be
found in national nor in international standards. The introduction of EN 1998-4
of the new generation of Eurocode 8 will improve the normative situation with
specific seismic design rules for silos, tanks and pipelines and secondary process
components. The article presents essential aspects of the seismic design of
industrial facilities based on the new generation of Eurocode 8 using the example
of tank structures and secondary process components. The interaction effects of
the process components with the primary structure are illustrated by means of
the experimental results of a shaking table test of a three story moment resisting
steel frame with different process components. Finally, an integrated approach of
digital plant models based on building information modelling (BIM) and structural
health monitoring (SHM) is presented, which provides not only a reliable
decision-making basis for operation, maintenance and repair but also an excellent
tool for rapid assessment of seismic damage.
Inference on the basis of high-dimensional and functional data are two topics which are discussed frequently in the current statistical literature. A possibility to include both topics in a single approach is working on a very general space for the underlying observations, such as a separable Hilbert space. We propose a general method for consistently hypothesis testing on the basis of random variables with values in separable Hilbert spaces. We avoid concerns with the curse of dimensionality due to a projection idea. We apply well-known test statistics from nonparametric inference to the projected data and integrate over all projections from a specific set and with respect to suitable probability measures. In contrast to classical methods, which are applicable for real-valued random variables or random vectors of dimensions lower than the sample size, the tests can be applied to random vectors of dimensions larger than the sample size or even to functional and high-dimensional data. In general, resampling procedures such as bootstrap or permutation are suitable to determine critical values. The idea can be extended to the case of incomplete observations. Moreover, we develop an efficient algorithm for implementing the method. Examples are given for testing goodness-of-fit in a one-sample situation in [1] or for testing marginal homogeneity on the basis of a paired sample in [2]. Here, the test statistics in use can be seen as generalizations of the well-known Cramérvon-Mises test statistics in the one-sample and two-samples case. The treatment of other testing problems is possible as well. By using the theory of U-statistics, for instance, asymptotic null distributions of the test statistics are obtained as the sample size tends to infinity. Standard continuity assumptions ensure the asymptotic exactness of the tests under the null hypothesis and that the tests detect any alternative in the limit. Simulation studies demonstrate size and power of the tests in the finite sample case, confirm the theoretical findings, and are used for the comparison with concurring procedures. A possible application of the general approach is inference for stock market returns, also in high data frequencies. In the field of empirical finance, statistical inference of stock market prices usually takes place on the basis of related log-returns as data. In the classical models for stock prices, i.e., the exponential Lévy model, Black-Scholes model, and Merton model, properties such as independence and stationarity of the increments ensure an independent and identically structure of the data. Specific trends during certain periods of the stock price processes can cause complications in this regard. In fact, our approach can compensate those effects by the treatment of the log-returns as random vectors or even as functional data.
Recent earthquakes showed that low-rise URM buildings following codecompliant seismic design and details behaved in general very well without substantial damages. Although advances in simulation tools make nonlinear calculation methods more readily accessible to designers, linear analyses will still be the standard design method for years to come. The present paper aims to improve the linear seismic design method by providing a proper definition of the q-factor of URM buildings. Values of q-factors are derived for low-rise URM buildings with rigid diaphragms, with reference to modern structural configurations realized in low to moderate seismic areas of Italy and Germany. The behaviour factor components for deformation and energy dissipation capacity and for overstrength due to the redistribution of forces are derived by means of pushover analyses. As a result of the investigations, rationally based values of the behaviour factor q to be used in linear analyses in the range of 2.0 to 3.0 are proposed.
Using optimization to design a renewable energy system has become a computationally demanding task as the high temporal fluctuations of demand and supply arise within the considered time series. The aggregation of typical operation periods has become a popular method to reduce effort. These operation periods are modelled independently and cannot interact in most cases. Consequently, seasonal storage is not reproducible. This inability can lead to a significant error, especially for energy systems with a high share of fluctuating renewable energy. The previous paper, “Time series aggregation for energy system design: Modeling seasonal storage”, has developed a seasonal storage model to address this issue. Simultaneously, the paper “Optimal design of multi-energy systems with seasonal storage” has developed a different approach. This paper aims to review these models and extend the first model. The extension is a mathematical reformulation to decrease the number of variables and constraints. Furthermore, it aims to reduce the calculation time while achieving the same results.
The development of protype applications with sensors and actuators in the automation industry requires tools that are independent of manufacturer, and are flexible enough to be modified or extended for any specific requirements. Currently, developing prototypes with industrial sensors and actuators is not straightforward. First of all, the exchange of information depends on the industrial protocol that these devices have. Second, a specific configuration and installation is done based on the hardware that is used, such as automation controllers or industrial gateways. This means that the development for a specific industrial protocol, highly depends on the hardware and the software that vendors provide. In this work we propose a rapid-prototyping framework based on Arduino to solve this problem. For this project we have focused to work with the IO-Link protocol. The framework consists of an Arduino shield that acts as the physical layer, and a software that implements the IO-Link Master protocol. The main advantage of such framework is that an application with industrial devices can be rapid-prototyped with ease as its vendor independent, open-source and can be ported easily to other Arduino compatible boards. In comparison, a typical approach requires proprietary hardware, is not easy to port to another system and is closed-source.
Digital twins are seen as one of the key technologies of Industry 4.0. Although many research groups focus on digital twins and create meaningful outputs, the technology has not yet reached a broad application in the industry. The main reasons for this imbalance are the complexity of the topic, the lack of specialists, and the unawareness of the twin opportunities. The project "Digital Twin Academy" aims to overcome these barriers by focusing on three actions: Building a digital twin community for discussion and exchange, offering multi-stage training for various knowledge levels, and implementing realworld use cases for deeper insights and guidance. In this work, we focus on creating a flexible learning platform that allows the user to select a training path adjusted to personal knowledge and needs. Therefore, a mix of basic and advanced modules is created and expanded by individual feedback options. The usage of personas supports the selection of the appropriate modules.
Quantitative evaluation of health management designs for fuel cell systems in transport vehicles
(2022)
Focusing on transport vehicles, mainly with regard to aviation applications, this paper presents compilation and subsequent quantitative evaluation of methods aimed at building an optimum integrated health management solution for fuel cell systems. The methods are divided into two different main types and compiled in a related scheme. Furthermore, different methods are analysed and evaluated based on parameters specific to the aviation context of this study. Finally, the most suitable method for use in fuel cell health management systems is identified and its performance and suitability is quantified.
Having well-defined control strategies for fuel cells, that can efficiently detect errors and take corrective action is critically important for safety in all applications, and especially so in aviation. The algorithms not only ensure operator safety by monitoring the fuel cell and connected components, but also contribute to extending the health of the fuel cell, its durability and safe operation over its lifetime. While sensors are used to provide peripheral data surrounding the fuel cell, the internal states of the fuel cell cannot be directly measured. To overcome this restriction, Kalman Filter has been implemented as an internal state observer.
Other safety conditions are evaluated using real-time data from every connected sensor and corrective actions automatically take place to ensure safety. The algorithms discussed in this paper have been validated thorough Model-in-the-Loop (MiL) tests as well as practical validation at a dedicated test bench.
The industrial revolution IR4.0 era have driven many states of the art technologies to be introduced especially in the automotive industry. The rapid development of automotive industries in Europe have created wide industry gap between European Union (EU) and developing countries such as in South-East Asia (SEA). Indulging this situation, FH Joanneum, Austria together with European partners from FH Aachen, Germany and Politecnico Di Torino, Italy is taking initiative to close the gap utilizing the Erasmus+ United grant from EU. A consortium was founded to engage with automotive technology transfer using the European ramework to Malaysian, Indonesian and Thailand Higher Education Institutions (HEI) as well as automotive industries. This could be achieved by establishing Engineering Knowledge Transfer Unit (EKTU) in respective SEA institutions guided by the industry partners in their respective countries. This EKTU could offer updated, innovative, and high-quality training courses to increase graduate’s employability in higher education institutions and strengthen relations between HEI and the wider economic and social environment by addressing Universityindustry cooperation which is the regional priority for Asia. It is expected that, the Capacity Building Initiative would improve the quality of higher education and enhancing its relevance for the labor market and society in the SEA partners. The outcome of this project would greatly benefit the partners in strong and complementary partnership targeting the automotive industry and enhanced larger scale international cooperation between the European and SEA partners. It would also prepare the SEA HEI in sustainable partnership with Automotive industry in the region as a mean of income generation in the future.
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.
This paper presents an approach to predicting the sound exposure on the ground caused by a landing aircraft with recuperating propellers. The noise source along the trajectory of a flight specified for a steeper approach is simulated based on measurements of sound power levels and additional parameters of a single propeller placed in a wind tunnel. To validate the measured data/measurement results, these simulations are also supported by overflight measurements of a test aircraft. It is shown that the simple source models of propellers do not provide fully satisfactory results since the sound levels are estimated too low. Nevertheless, with a further reference comparison, margins for an acceptable increase in the sound power level of the aircraft on its now steeper approach path could be estimated. Thus, in this case, a +7 dB increase in SWL would not increase the SEL compared to the conventional approach within only 2 km ahead of the airfield.
Dynamic loads significantly impact the structural design of propeller blades due to fatigue and static strength. Since propellers are elastic structures, deformations and aerodynamic loads are coupled. In the past, propeller manufacturers established procedures to determine unsteady aerodynamic loads and the structural response with analytical steady-state calculations. According to the approach, aeroelastic coupling primarily consists of torsional deformations. They neglect bending deformations, deformation velocities, and inertia terms. This paper validates the assumptions above for a General Aviation propeller and a lift propeller for urban air mobility or large cargo drones. Fully coupled reduced-order simulations determine the dynamic loads in the time domain. A quasi-steady blade element momentum approach transfers loads to one-dimensional finite beam elements. The simulation results are in relatively good agreement with the analytical method for the General Aviation propeller but show increasing errors for the slender lift propeller. The analytical approach is modified to consider the induced velocities. Still, inertia and velocity proportional terms play a significant role for the lift propeller due to increased elasticity. The assumption that only torsional deformations significantly impact the dynamic loads of propellers is not valid. Adequate determination of dynamic loads of such designs requires coupled aeroelastic simulations or advanced analytical procedures.
In order to reduce energy consumption of homes, it is important to make transparent which devices consume how much energy. However, power consumption is often only monitored aggregated at the house energy meter. Disaggregating this power consumption into the contributions of individual devices can be achieved using Machine Learning. Our work aims at making state of the art disaggregation algorithms accessibe for users of the open source home automation platform Home Assistant.
The feasibility study presents results of a hydrogen combustor integration for a Medium-Range aircraft engine using the Dry-Low-NOₓ Micromix combustion principle. Based on a simplified Airbus A320-type flight mission, a thermodynamic performance model of a kerosene and a hydrogen-powered V2530-A5 engine is used to derive the thermodynamic combustor boundary conditions. A new combustor design using the Dry-Low NOx Micromix principle is investigated by slice model CFD simulations of a single Micromix injector for design and off-design operation of the engine. Combustion characteristics show typical Micromix flame shapes and good combustion efficiencies for all flight mission operating points. Nitric oxide emissions are significant below ICAO CAEP/8 limits. For comparison of the Emission Index (EI) for NOₓ emissions between kerosene and hydrogen operation, an energy (kerosene) equivalent Emission Index is used.
A full 15° sector model CFD simulation of the combustion chamber with multiple Micromix injectors including inflow homogenization and dilution and cooling air flows investigates the combustor integration effects, resulting NOₓ emission and radial temperature distributions at the combustor outlet. The results show that the integration of a Micromix hydrogen combustor in actual aircraft engines is feasible and offers, besides CO₂ free combustion, a significant reduction of NOₓ emissions compared to kerosene operation.
Residential and commercial buildings account for more than one-third of global energy-related greenhouse gas emissions. Integrated multi-energy systems at the district level are a promising way to reduce greenhouse gas emissions by exploiting economies of scale and synergies between energy sources. Planning district energy systems comes with many challenges in an ever-changing environment. Computational modelling established itself as the state-of-the-art method for district energy system planning. Unfortunately, it is still cumbersome to combine standalone models to generate insights that surpass their original purpose. Ideally, planning processes could be solved by using modular tools that easily incorporate the variety of competing and complementing computational models. Our contribution is a vision for a collaborative development and application platform for multi-energy system planning tools at the district level. We present challenges of district energy system planning identified in the literature and evaluate whether this platform can help to overcome these challenges. Further, we propose a toolkit that represents the core technical elements of the platform. Lastly, we discuss community management and its relevance for the success of projects with collaboration and knowledge sharing at their core.
The integration of high temperature thermal energy storages into existing conventional power plants can help to reduce the CO2 emissions of those plants and lead to lower capital expenditures for building energy storage systems, due to the use of synergy effects [1]. One possibility to implement that, is a molten salt storage system with a powerful power-to-heat unit. This paper presents two possible control concepts for the startup of the charging system of such a facility. The procedures are implemented in a detailed dynamic process model. The performance and safety regarding the film temperatures at heat transmitting surfaces are investigated in the process simulations. To improve the accuracy in predicting the film temperatures, CFD simulations of the electrical heater are carried out and the results are merged with the dynamic model. The results show that both investigated control concepts are safe regarding the temperature limits. The gradient controlled startup performed better than the temperature-controlled startup. Nevertheless, there are several uncertainties that need to be investigated further.
Despite the challenges of pioneering molten salt towers (MST), it remains the leading technology in central receiver power plants today, thanks to cost effective storage integration and high cost reduction potential. The limited controllability in volatile solar conditions can cause significant losses, which are difficult to estimate without comprehensive modeling [1]. This paper presents a Methodology to generate predictions of the dynamic behavior of the receiver system as part of an operating assistance system (OAS). Based on this, it delivers proposals if and when to drain and refill the receiver during a cloudy period in order maximize the net yield and quantifies the amount of net electricity gained by this. After prior analysis with a detailed dynamic two-phase model of the entire receiver system, two different reduced modeling approaches where developed and implemented in the OAS. A tailored decision algorithm utilizes both models to deliver the desired predictions efficiently and with appropriate accuracy.
This paper describes the potential for developing a digital twin of society- a dynamic model that can be used to observe, analyze, and predict the evolution of various societal aspects. Such a digital twin can help governmental agencies and policy makers in interpreting trends, understanding challenges, and making decisions regarding investments or policies necessary to support societal development and ensure future prosperity. The paper reviews related work regarding the digital twin paradigm and its applications. The paper presents a motivating case study- an analysis of opportunities and challenges faced by the German federal employment agency, Bundesagentur f¨ur Arbeit (BA), proposes solutions using digital twins, and describes initial proofs of concept for such solutions.
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.
Messenger apps like WhatsApp and Telegram are frequently used for everyday communication, but they can also be utilized as a platform for illegal activity. Telegram allows public groups with up to 200.000 participants. Criminals use these public groups for trading illegal commodities and services, which becomes a concern for law enforcement agencies, who manually monitor suspicious activity in these chat rooms. This research demonstrates how natural language processing (NLP) can assist in analyzing these chat rooms, providing an explorative overview of the domain and facilitating purposeful analyses of user behavior. We provide a publicly available corpus of annotated text messages with entities and relations from four self-proclaimed black market chat rooms. Our pipeline approach aggregates the extracted product attributes from user messages to profiles and uses these with their sold products as features for clustering. The extracted structured information is the foundation for further data exploration, such as identifying the top vendors or fine-granular price analyses. Our evaluation shows that pretrained word vectors perform better for unsupervised clustering than state-of-the-art transformer models, while the latter is still superior for sequence labeling.
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 work in modern open-pit and underground mines requires the transportation of large amounts of resources between fixed points. The navigation to these fixed points is a repetitive task that can be automated. The challenge in automating the navigation of vehicles commonly used in mines is the systemic properties of such vehicles. Many mining vehicles, such as the one we have used in the research for this paper, use steering systems with an articulated joint bending the vehicle’s drive axis to change its course and a hydraulic drive system to actuate axial drive components or the movements of tippers if available. To address the difficulties of controlling such a vehicle, we present a model-predictive approach for controlling the vehicle. While the control optimisation based on a parallel error minimisation of the predicted state has already been established in the past, we provide insight into the design and implementation of an MPC for an articulated mining vehicle and show the results of real-world experiments in an open-pit mine environment.
Extracting workflow nets from textual descriptions can be used to simplify guidelines or formalize textual descriptions of formal processes like business processes and algorithms. The task of manually extracting processes, however, requires domain expertise and effort. While automatic process model extraction is desirable, annotating texts with formalized process models is expensive. Therefore, there are only a few machine-learning-based extraction approaches. Rule-based approaches, in turn, require domain specificity to work well and can rarely distinguish relevant and irrelevant information in textual descriptions. In this paper, we present GUIDO, a hybrid approach to the process model extraction task that first, classifies sentences regarding their relevance to the process model, using a BERT-based sentence classifier, and second, extracts a process model from the sentences classified as relevant, using dependency parsing. The presented approach achieves significantly better resul ts than a pure rule-based approach. GUIDO achieves an average behavioral similarity score of 0.93. Still, in comparison to purely machine-learning-based approaches, the annotation costs stay low.
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.
The complex questions of today for a world of tomorrow are characterized by their global impact. Solutions must therefore not only be sustainable in the sense of the three pillars of sustainability (economic, environmental, and social) but must also function globally. This goes hand in hand with the need for intercultural acceptance of developed services and products. To achieve this, engineers, as the problem solvers of the future, must be able to work in intercultural teams on appropriate solutions, and be sensitive to intercultural perspectives. To equip the engineers of the future with the so-called future skills, teaching concepts are needed in which students can acquire these methods and competencies in application-oriented formats. The presented course "Applying Design Thinking - Sustainability, Innovation and Interculturality" was developed to teach future skills from the competency areas Digital Key Competencies, Classical Competencies and Transformative Competencies. The CDIO Standard 3.0, in particular the standards 5, 6, 7 and 8, was used as a guideline. The course aims to prepare engineering students from different disciplines and cultures for their future work in an international environment by combining a digital teaching format with an interdisciplinary, transdisciplinary and intercultural setting for solving sustainability challenges. The innovative moment lies in the digital application of design thinking and the inclusion of intercultural as well as trans- and interdisciplinary perspectives in innovation development processes. In this paper, the concept of the course will be presented in detail and the particularities of a digital implementation of design thinking will be addressed. Subsequently, the potentials and challenges will be reflected and practical advice for integrating design thinking in engineering education will be given.
The popularity of social media and particularly Instagram grows steadily. People use the different platforms to share pictures as well as videos and to communicate with friends. The potential of social media platforms is also being used for marketing purposes and for selling products. While for Facebook and other online social media platforms the purchase decision factors are investigated several times, Instagram stores remain mainly unattended so far. The present research work closes this gap and sheds light into decisive factors for purchasing products offered in Instagram stores. A theoretical research model, which contains selected constructs that are assumed to have a significant influence on Instagram user´s purchase intention, is developed. The hypotheses are evaluated by applying structural equation modelling on survey data containing 127 relevant participants. The results of the study reveal that ‘trust’, ‘personal recommendation’, and ‘usability’ significantly influences user’s buying intention in Instagram stores.
Autonomous agents require rich environment models for fulfilling their missions. High-definition maps are a well-established map format which allows for representing semantic information besides the usual geometric information of the environment. These are, for instance, road shapes, road markings, traffic signs or barriers. The geometric resolution of HD maps can be as precise as of centimetre level. In this paper, we report on our approach of using HD maps as a map representation for autonomous load-haul-dump vehicles in open-pit mining operations. As the mine undergoes constant change, we also need to constantly update the map. Therefore, we follow a lifelong mapping approach for updating the HD maps based on camera-based object detection and GPS data. We show our mapping algorithm based on the Lanelet 2 map format and show our integration with the navigation stack of the Robot Operating System. We present experimental results on our lifelong mapping approach from a real open-pit mine.
Due to the decarbonization of the energy sector, the electric distribution grids are undergoing a major transformation, which is expected to increase the load on the operating resources due to new electrical loads and distributed energy resources. Therefore, grid operators need to gradually move to active grid management in order to ensure safe and reliable grid operation. However, this requires knowledge of key grid variables, such as node voltages, which is why the mass integration of measurement technology (smart meters) is necessary. Another problem is the fact that a large part of the topology of the distribution grids is not sufficiently digitized and models are partly faulty, which means that active grid operation management today has to be carried out largely blindly. It is therefore part of current research to develop methods for determining unknown grid topologies based on measurement data. In this paper, different clustering algorithms are presented and their performance of topology detection of low voltage grids is compared. Furthermore, the influence of measurement uncertainties is investigated in the form of a sensitivity analysis.
Digital forensics of smartphones is of utmost importance in many criminal cases. As modern smartphones store chats, photos, videos etc. that can be relevant for investigations and as they can have storage capacities of hundreds of gigabytes, they are a primary target for forensic investigators. However, it is exactly this large amount of data that is causing problems: extracting and examining the data from multiple phones seized in the context of a case is taking more and more time. This bears the risk of wasting a lot of time with irrelevant phones while there is not enough time left to analyze a phone which is worth examination. Forensic triage can help in this case: Such a triage is a preselection step based on a subset of data and is performed before fully extracting all the data from the smartphone. Triage can accelerate subsequent investigations and is especially useful in cases where time is essential. The aim of this paper is to determine which and how much data from an Android smartphone can be made directly accessible to the forensic investigator – without tedious investigations. For this purpose, an app has been developed that can be used with extremely limited storage of data in the handset and which outputs the extracted data immediately to the forensic workstation in a human- and machine-readable format.
Due to the increasing complexity of software projects, software development is becoming more and more dependent on teams. The quality of this teamwork can vary depending on the team composition, as teams are always a combination of different skills and personality types. This paper aims to answer the question of how to describe a software development team and what influence the personality of the team members has on the team dynamics. For this purpose, a systematic literature review (n=48) and a literature search with the AI research assistant Elicit (n=20) were conducted. Result: A person’s personality significantly shapes his or her thinking and actions, which in turn influences his or her behavior in software development teams. It has been shown that team performance and satisfaction can be strongly influenced by personality. The quality of communication and the likelihood of conflict can also be attributed to personality.
The RoboCup Logistics League (RCLL) is a robotics competition in a production logistics scenario in the context of a Smart Factory. In the competition, a team of three robots needs to assemble products to fulfill various orders that are requested online during the game. This year, the Carologistics team was able to win the competition with a new approach to multi-agent coordination as well as significant changes to the robot’s perception unit and a pragmatic network setup using the cellular network instead of WiFi. In this paper, we describe the major components of our approach with a focus on the changes compared to the last physical competition in 2019.
Modern implementations of driver assistance systems are evolving from a pure driver assistance to a independently acting automation system. Still these systems are not covering the full vehicle usage range, also called operational design domain, which require the human driver as fall-back mechanism. Transition of control and potential minimum risk manoeuvres are currently research topics and will bridge the gap until full autonomous vehicles are available. The authors showed in a demonstration that the transition of control mechanisms can be further improved by usage of communication technology. Receiving the incident type and position information by usage of standardised vehicle to everything (V2X) messages can improve the driver safety and comfort level. The connected and automated vehicle’s software framework can take this information to plan areas where the driver should take back control by initiating a transition of control which can be followed by a minimum risk manoeuvre in case of an unresponsive driver. This transition of control has been implemented in a test vehicle and was presented to the public during the IEEE IV2022 (IEEE Intelligent Vehicle Symposium) in Aachen, Germany.
KNX is a protocol for smart building automation, e.g., for automated heating, air conditioning, or lighting. This paper analyses and evaluates state-of-the-art KNX devices from manufacturers Merten, Gira and Siemens with respect to security. On the one hand, it is investigated if publicly known vulnerabilities like insecure storage of passwords in software, unencrypted communication, or denialof-service attacks, can be reproduced in new devices. On the other hand, the security is analyzed in general, leading to the discovery of a previously unknown and high risk vulnerability related to so-called BCU (authentication) keys.
Clinical assessment of newly developed sensors is important for ensuring their validity. Comparing recordings of emerging electrocardiography (ECG) systems to a reference ECG system requires accurate synchronization of data from both devices. Current methods can be inefficient and prone to errors. To address this issue, three algorithms are presented to synchronize two ECG time series from different recording systems: Binned R-peak Correlation, R-R Interval Correlation, and Average R-peak Distance. These algorithms reduce ECG data to their cyclic features, mitigating inefficiencies and minimizing discrepancies between different recording systems. We evaluate the performance of these algorithms using high-quality data and then assess their robustness after manipulating the R-peaks. Our results show that R-R Interval Correlation was the most efficient, whereas the Average R-peak Distance and Binned R-peak Correlation were more robust against noisy data.
Achieving the 17 Sustainable Development Goals (SDGs) set by the United Nations (UN) in 2015 requires global collaboration between different stakeholders. Industry, and in particular engineers who shape industrial developments, have a special role to play as they are confronted with the responsibility to holistically reflect sustainability in industrial processes. This means that, in addition to the technical specifications, engineers must also question the effects of their own actions on an ecological, economic and social level in order to ensure sustainable action and contribute to the achievement of the SDGs. However, this requires competencies that enable engineers to apply all three pillars of sustainability to their own field of activity and to understand the global impact of industrial processes. In this context, it is relevant to understand how industry already reflects sustainability and to identify competences needed for sustainable development.
In addition to the technical content, modern courses at university should also teach professional skills to enhance the competencies of students towards their future work. The competency driven approach including technical as well as professional skills makes it necessary to find a suitable way for the integration into the corresponding module in a scalable and flexible manner. Agile development, for example, is essential for the development of modern systems and applications and makes use of dedicated professional skills of the team members, like structured group dynamics and communication, to enable the fast and reliable development. This paper presents an easy to integrate and flexible approach to integrate Scrum, an agile development method, into the lab of an existing module. Due to the different role models of Scrum the students have an individual learning success, gain valuable insight into modern system development and strengthen their communication and organization skills. The approach is implemented and evaluated in the module Vehicle Systems, but it can be transferred easily to other technical courses as well. The evaluation of the implementation considers feedback of all stakeholders, students, supervisor and lecturers, and monitors the observations during project lifetime.
In times of social climate protection movements, such as Fridays for Future, the priorities of society, industry and higher education are currently changing. The consideration of sustainability challenges is increasing. In the context of sustainable development, social skills are crucial to achieving the United Nations Sustainable Development Goals (SDGs). In particular, the impact that educational activities have on people, communities and society is therefore coming to the fore. Research has shown that people with high levels of social competence are better able to manage stressful situations, maintain positive relationships and communicate effectively. They are also associated with better academic performance and career success. However, especially in engineering programs, the social pillar is underrepresented compared to the environmental and economic pillars.
In response to these changes, higher education institutions should be more aware of their social impact - from individual forms of teaching to entire modules and degree programs. To specifically determine the potential for improvement and derive resulting change for further development, we present an initial framework for social impact measurement by transferring already established approaches from the business sector to the education sector. To demonstrate the applicability, we measure the key competencies taught in undergraduate engineering programs in Germany.
The aim is to prepare the students for success in the modern world of work and their future contribution to sustainable development. Additionally, the university can include the results in its sustainability report. Our method can be applied to different teaching methods and enables their comparison.
Existing residential buildings have an average lifetime of 100 years. Many of these buildings will exist for at least another 50 years. To increase the efficiency of these buildings while keeping costs at reasonable rates, they can be retrofitted with sensors that deliver information to central control units for heating, ventilation and electricity. This retrofitting process should happen with minimal intervention into existing infrastructure and requires new approaches for sensor design and data transmission. At FH Aachen University of Applied Sciences, students of different disciplines work together to learn how to design, build, deploy and operate such sensors. The presented teaching project already created a low power design for a combined CO2, temperature and humidity measurement device that can be easily integrated into most home automation systems
This paper presents an approach for reducing the cognitive load for humans working in quality control (QC) for production processes that adhere to the 6σ -methodology. While 100% QC requires every part to be inspected, this task can be reduced when a human-in-the-loop QC process gets supported by an anomaly detection system that only presents those parts for manual inspection that have a significant likelihood of being defective. This approach shows good results when applied to image-based QC for metal textile products.
This work proposes a hybrid algorithm combining an Artificial Neural Network (ANN) with a conventional local path planner to navigate UAVs efficiently in various unknown urban environments. The proposed method of a Hybrid Artificial Neural Network Avoidance System is called HANNAS. The ANN analyses a video stream and classifies the current environment. This information about the current Environment is used to set several control parameters of a conventional local path planner, the 3DVFH*. The local path planner then plans the path toward a specific goal point based on distance data from a depth camera. We trained and tested a state-of-the-art image segmentation algorithm, PP-LiteSeg. The proposed HANNAS method reaches a failure probability of 17%, which is less than half the failure probability of the baseline and around half the failure probability of an improved, bio-inspired version of the 3DVFH*. The proposed HANNAS method does not show any disadvantages regarding flight time or flight distance.
In this paper, the use of reinforcement learning (RL) in control systems is investigated using a rotatory inverted pendulum as an example. The control behavior of an RL controller is compared to that of traditional LQR and MPC controllers. This is done by evaluating their behavior under optimal conditions, their disturbance behavior, their robustness and their development process. All the investigated controllers are developed using MATLAB and the Simulink simulation environment and later deployed to a real pendulum model powered by a Raspberry Pi. The RL algorithm used is Proximal Policy Optimization (PPO). The LQR controller exhibits an easy development process, an average to good control behavior and average to good robustness. A linear MPC controller could show excellent results under optimal operating conditions. However, when subjected to disturbances or deviations from the equilibrium point, it showed poor performance and sometimes instable behavior. Employing a nonlinear MPC Controller in real time was not possible due to the high computational effort involved. The RL controller exhibits by far the most versatile and robust control behavior. When operated in the simulation environment, it achieved a high control accuracy. When employed in the real system, however, it only shows average accuracy and a significantly greater performance loss compared to the simulation than the traditional controllers. With MATLAB, it is not yet possible to directly post-train the RL controller on the Raspberry Pi, which is an obstacle to the practical application of RL in a prototyping or teaching setting. Nevertheless, RL in general proves to be a flexible and powerful control method, which is well suited for complex or nonlinear systems where traditional controllers struggle.
Im Hinblick auf die Klimaziele der Bundesrepublik Deutschland konzentriert sich das Projekt Diggi Twin auf die nachhaltige Gebäudeoptimierung. Grundlage für eine ganzheitliche Gebäudeüberwachung und -optimierung bildet dabei die Digitalisierung und Automation im Sinne eines Smart Buildings. Das interdisziplinäre Projekt der FH Aachen hat das Ziel, ein bestehendes Hochschulgebäude und einen Neubau an klimaneutrale Standards anzupassen. Im Rahmen des Projekts werden bekannte Verfahren, wie das Building Information Modeling (BIM), so erweitert, dass ein digitaler Gebäudezwilling entsteht. Dieser kann zur Optimierung des Gebäudebetriebs herangezogen werden, sowie als Basis für eine Erweiterung des Bewertungssystems Nachhaltiges Bauen (BNB) dienen. Mithilfe von Sensortechnologie und künstlicher Intelligenz kann so ein präzises Monitoring wichtiger Gebäudedaten erfolgen, um ungenutzte Energieeinsparpotenziale zu erkennen und zu nutzen. Das Projekt erforscht und setzt methodische Erkenntnisse zu BIM und digitalen Gebäudezwillingen praxisnah um, indem es spezifische Fragen zur Energie- und Ressourceneffizienz von Gebäuden untersucht und konkrete Lösungen für die Gebäudeoptimierung entwickelt.
Magnetic nanoparticles (MNP) are investigated with great interest for biomedical applications in diagnostics (e.g. imaging: magnetic particle imaging (MPI)), therapeutics (e.g. hyperthermia: magnetic fluid hyperthermia (MFH)) and multi-purpose biosensing (e.g. magnetic immunoassays (MIA)). What all of these applications have in common is that they are based on the unique magnetic relaxation mechanisms of MNP in an alternating magnetic field (AMF). While MFH and MPI are currently the most prominent examples of biomedical applications, here we present results on the relatively new biosensing application of frequency mixing magnetic detection (FMMD) from a simulation perspective. In general, we ask how the key parameters of MNP (core size and magnetic anisotropy) affect the FMMD signal: by varying the core size, we investigate the effect of the magnetic volume per MNP; and by changing the effective magnetic anisotropy, we study the MNPs’ flexibility to leave its preferred magnetization direction. From this, we predict the most effective combination of MNP core size and magnetic anisotropy for maximum signal generation.
Pulmonary arterial cannulation is a common and effective method for percutaneous mechanical circulatory support for concurrent right heart and respiratory failure [1]. However, limited data exists to what effect the positioning of the cannula has on the oxygen perfusion throughout the pulmonary artery (PA). This study aims to evaluate, using computational fluid dynamics (CFD), the effect of different cannula positions in the PA with respect to the oxygenation of the different branching vessels in order for an optimal cannula position to be determined. The four chosen different positions (see Fig. 1) of the cannulas are, in the lower part of the main pulmonary artery (MPA), in the MPA at the junction between the right pulmonary artery (RPA) and the left pulmonary artery (LPA), in the RPA at the first branch of the RPA and in the LPA at the first branch of the LPA.
Humic substances possess distinctive chemical features enabling their use in many advanced applications, including biomedical fields. No chemicals in nature have the same combination of specific chemical and biological properties as humic substances. Traditional medicine and modern research have demonstrated that humic substances from different sources possess immunomodulatory and anti-inflammatory properties, which makes them suitable for the prevention and treatment of chronic dermatoses, allergic rhinitis, atopic dermatitis, and other conditions characterized by inflammatory and allergic responses [1-4]. The use of humic compounds as agentswith antifungal and antiviral properties shows great potential [5-7].
This study presents the concept of AstroBioLab, an autonomous astrobiological field laboratory tailored for the exploration of (sub)glacial habitats. AstroBioLab is an integral component of the TRIPLE (Technologies for Rapid Ice Penetration and subglacial Lake Exploration) DLR-funded project, aimed at advancing astrobiology research through the development and deployment of innovative technologies. AstroBioLab integrates diverse measurement techniques such as fluorescence microscopy, DNA sequencing and fluorescence spectrometry, while leveraging microfluidics for efficient sample delivery and preparation.