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Keywords
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Flexibility estimation is the first step necessary to incorporate building energy systems into demand side management programs. We extend a known method for temporal flexibility estimation from literature to a real-world residential heat pump system, solely based on historical cloud data. The method proposed relies on robust simplifications and estimates employing process knowledge, energy balances and manufacturer's information. Resulting forced and delayed temporal flexibility, covering both domestic hot water and space heating demands as constraints, allows to derive a flexibility range for the heat pump system. The resulting temporal flexibility lay within the range of 24 minutes and 6 hours for forced and delayed flexibility, respectively. This range provides new insights into the system's behaviour and is the basis for estimating power and energy flexibility - the first step necessary to incorporate building energy systems into demand side management programs.
The paper deals with the optimization of 2x2 optical switch for photonic integrated circuits based on two 2x2 MMI splitters and two phase-modulators. The optical switch was modelled in the RSoftCAD with the simulation tool BeamPROP. The optimization was done to minimise the insertion losses and broaden the spectral band at 1550 nm by using linear tapers in a 2x2 MMI splitter topology. The 2x2 optical switch is a common element for creating more complex 1xN or NxN optical switches in all-optical signal processing.
The design and development of smart products and services with data science enabled solutions forms a core topic of the current trend of digitalisation in industry. Enabling skilled staff, employees, and students to use data science in their daily work routine of designing such products and services is a key concern of higher education institutions, including universities, company workshop providers and in further education. The scope and usage scenario of this paper is to assess software modules (‘tools’) for integrated data and analytics as service (DAaaS). The tools are usually driven by machine learning, may be deployed in cloud infrastructures, and are specifically targeted at particular needs of the industrial manufacturing, production, or supply chain sector.
The paper describes existing theories and previous work, namely methods used in didactics, work done for visually designing and using machine learning algorithms (no-code / low- code tools), as well as combinations of these two topics. For tools available on the market, an extended assessment of their suitability for a set of learning scenarios and personas is discussed.
A trend from centralized to decentralized production is emerging in the manufacturing domain leading to new and innovative approaches for long-established production methods. A technology supporting this trend is Cloud Manufacturing, which adapts technologies and concepts known from cloud computing to the manufacturing domain. A core aspect of Cloud Manufacturing is representing knowledge about manufacturing, e.g., machine capabilities, in a suitable form. This knowledge representation should be flexible and adaptable so that it fits across various manufacturing domains, but, at the same time, should also be specific and exhaustive. We identify three core capabilities that such a platform has to support, i.e., the product, the process and the production.We propose representing this knowledge in semantically specified knowledge graphs, essentially creating three through features interconnected ontologies each representing a facet of manufacturing. Finally, we present an exemplary implementation of a Cloud Manufacturing platform using this representation and its advantages.
Bubble column humidifiers (BCHs) are frequently used for the humidification of air in various water treatment applications. A potential but not yet profoundly investigated application of such devices is the treatment of oily wastewater. To evaluate this application, the accumulation of an oil-water emulsion using a BCH is experimentally analyzed. The amount of evaporating water vapor can be evaluated by measuring the humidity ratio of the outlet air. However, humidity measurements are difficult in close to saturated conditions, as the formation of liquid droplets on the sensor impacts the measurement accuracy. We use a heating section after the humidifier, such that no liquid droplets are formed on the sensor. This enables us a more accurate humidity measurement. Two batch measurement runs are conducted with (1) tap water and (2) an oil-water emulsion as the respective liquid phase. The humidity measurement in high humidity conditions is highly accurate with an error margin of below 3 % and can be used to predict the oil concentration of the remaining liquid during operation. The measured humidity ratio corresponds with the removed amount of water vapor for both tap water and the accumulation of an oil-water emulsion. Our measurements show that the residual water content
in the oil-water emulsion is below 4 %.
Die vorliegende Studie untersucht die Auswirkungen der Covid-Pandemie auf die Finanzierungssituation von Unternehmen in Vorarlberg. Besonderes Augenmerk wird dabei auf kleine und mittlere Unternehmen sowie deren Eigenkapitalausstattung gelegt. Die Analyse erfolgt anhand einer quantitativen Befragung von 569 Unternehmen im Zeitraum Ende Oktober/Anfang November 2021. Ein Großteil der befragten Unternehmen schätzt die Finanzierungssituation insgesamt als befriedigend oder besser ein. Eigenkapital wird die höchste Relevanz aller Finanzierungsquellen zugesprochen. Obwohl 39% der befragten Unternehmen eine Eigenkapitalerhöhung für Ihr Unternehmen als nötig erachten, haben aktuell nur 14% Überlegungen in diese Richtung. Die Ergebnisse der Studie weisen darauf hin, dass eine Beseitigung der steuerlichen Bevorzugung von Fremdkapital (Debt Bias) einen relevanten Anreiz für Eigenkapitalerhöhungen liefern könnte.
Recent developments in the area of Natural Language Processing (NLP) increasingly allow for the extension of such techniques to hitherto unidentified areas of application. This paper deals with the application of state-of-the-art NLP techniques to the domain of Product Safety Risk Assessment (PSRA). PSRA is concerned with the quantification of the risks a user is exposed to during product use. The use case arises from an important process of maintaining due diligence towards the customers of the company OMICRON electronics GmbH.
The paper proposes an approach to evaluate the consistency of human-made risk assessments that are proposed by potentially changing expert panels. Along the stages of this NLP-based approach, multiple insights into the PSRA process allow for an improved understanding of the related risk distribution within the product portfolio of the company. The findings aim at making the current process more transparent as well as at automating repetitive tasks. The results of this paper can be regarded as a first step to support domain experts in the risk assessment process.
A new software tool, called AWG-Channel-Spacing, is developed to calculate accurate channel spacing of an arrayed waveguide gratings (AWG) optical multiplexer/demultiplexer. This tool has been developed with the application framework QT in the programming language C++. The tool was evaluated with a design of 20-channel 200 GHz AWG. The achieved simulated transmission characteristics prove the correct functionality of the tool.
Deep etched structures in GaAs with high aspect ratio have promising applications in optoelectronics and MEMS devices. The key factors in their fabrication process are the choosing of proper mask material and etching conditions which results in high selectivity and an anisotropic etch profile with smooth sidewalls. In this work, we studied several types of mask materials (Al, Ni, Cr, SiO2) for deep reactive ion etching of GaAs using inductively coupled plasma system. Thus, several sets of experiments were performed with varying gas mixture, pressure and ICP/RF power. As a result, we find optimized conditions and minimal thickness of mask material for achieving deep etched (>140 m) GaAs structures.
Small and medium-sized enterprises often face resource deficits and there- fore depend on cooperating with other actors to stay innovative in a competitive environment. Establishing and maintaining actual co-creation and service inter- action strategies however is challenging. A reason for this is the complexity of finding methodologies and tools to create valuable outcome and the lack of knowledge of collaboration toolsets, also in virtual environments. This paper introduces an Innovation-Method-Framework consisting of innovation methods for increased service interaction and value co-creation among service stakeholders. Also, toolsets for the framework’s practical application are provided.
A new software tool, called AWG-Wuckler, is developed to calculate geometric parameters of arrayed waveguide grating structures for telecommunication and medical applications. These parameters are crucial for a AWG layout which will be created and simulated using commercial photonic design tools. The design process of AWG is very complex because its geometric dimensions depend on a large number of input design parameters and other input design parameters. Often geometric constraints require an adjustment of the input design parameters and vice versa. Calculation and adjustment of the geometric parameters is a time-consuming process that is currently not fully supported by any commercial photonic tool. AWG-Wuckler tool overcomes this issue and offers a fast and easy to use solution. The tool was already applied in various AWG designs and is technologically well proven.
Various carbon (nano-) forms, so-called allotropes, have become one of the most supporting activities in fundamental and applied research trends. Therefore, a universal deposition process capable of “adjusting” system parameters in one “deposition chamber” is highly demanding. Here, we present a low-pressure large area deposition system combining radiofrequency (RF) and microwave (MW) plasma in one chamber in different configurations, which offers a wide deposition window for the growth of sp2 carbon (carbon nanotubes, amorphous carbon), a mixture of sp2 and sp3 (diamond-like films) and pure sp3 carbon represented by diamond films. We will show that not only the type of plasma source (RF vs. MW) but also the gas mixture and plasma chemistry are crucial parameters for the controllable and reproducible growth of these allotropes at temperatures from 250 to 800 °C.
As the boundary between real and virtual life is becoming increasingly blurred, researchers and practitioners are looking for ways to integrate the two intending to improve human lives in a plethora of domains. A cutting-edge concept is the design of Digital Twins (DT), having a broad range of implications and applications, spanning from education, training, as well as safety and productivity in the workplace. An emergent approach for implementing DTs is the usage of mixed reality (MR) and augmented reality (AR), which are well aligned with merging real and virtual objects to enhance the human’s ability to interact with and manage DTs. Yet, this is still a novel area of research and, as such, a grounded understanding of the current state, challenges, and open questions is still lacking. Towards this, we conducted a PRISMA-based literature review of scientific articles and book chapters dealing with the use of MR and AR for digital twins. After a thorough screening phase and eligibility check, 25 papers were analyzed, sorted and compared by different categories like research topic (e.g., visualization, guidance), domain (e.g., manufacturing, education), paper type (e.g., design study, evaluation), evaluation type (user study, case study or none), used hardware (e.g., Microsoft HoloLens, mobile devices) as well as the different outcomes (result type and topic, problems, outlook). The major finding of this research survey is the predominant focus of the reviewed papers on the technology itself and the neglect of factors regarding the users. We, therefore, encourage researchers in this area to keep the importance of ease and joy of use in mind and include users in multiple stages of their work.
PV hosting capacity provides utilities the knowledge of the maximum amount of solar installations possible to accommodate in low voltage grids such that no operational problems arise. As the quantification of the hosting capacity requires data collection, grid modelling, and often time-consuming simulations, simplified estimations for large-scale applications are of interest. In this paper, Bayesian statistical inference is applied to estimate the hosting capacities of more than 5000 real feeders in Austria. The results show that the hosting capacity of 95% of the total feeders can be estimated with a mean error below 20% by only having knowledge of a random sample of 5%. Moreover, the hosting capacity estimation at a regional level shows a maximum error below 9%, also relying on a random sample of 5% of the total feeders. Furthermore, the approach proposed provides a methodology to assess new parameters aiming to improve the accuracy of the hosting capacity estimation at a feeder level.
Purpose: Although there is an apparent potential in using data for advanced services in manufacturing environments, SMEs are reluctant to share data with their ecosystem partners, which prevents them from leveraging this potential. Therefore, the purpose of this paper is to analyse the reasons behind these resistances. The argumentation paves the way for elaborating countermeasures that are adequate for the specific situation and the typical capabilities of SMEs.
Design/Methodology/Approach: The analysis is based on literature research and in-depth interviews with management representatives of 15 companies in manufacturing service ecosystems. Half of these are manufacturers and the other half technology or service providers for manufacturers. They are SMEs or partly larger companies operating in structures that are typical for SMEs.
Findings: Data sharing hurdles are investigated in the five dimensions, 1. quantifying the value of data, 2. willingness to share data and trust, 3. organizational culture and mindset, 4. legal aspects, and 5. security and privacy. The ability to quantify the value of data is a necessary but not sufficient precondition for data sharing, which must be enabled by adequate measures in the other four dimensions.
Originality/Value: The findings of this empirical study and the solution approach provide an SME-specific framework to analyze hurdles that must be overcome for sharing data in an ecosystem.
Manufacturing SMEs can apply the framework to overcome the hurdles by specific insights and solution approaches. Furthermore, the analysis illustrates the future research direction of the project towards a comprehensive solution approach for data sharing in a manufacturing ecosystem.
Due to the increasing trend of photonic element miniaturisation and the need for optical splitting, we propose and simulate a new type of three-dimensional (3D) optical splitter based on multimode interference (MMI) for the wavelength of 1550 nm. We present various designs and simulations of various parameters for the optimized MMI splitter. We focus on the possibility of its integration on an optical fiber. The design is focused on a possible production process using 3D laser lithography for the prepared experiments. The MMI splitter was prepared by laser lithography using direct writing process and finally investigated by output characterisation by the near-field measurement.
ROS 2 in Embedded Systemen
(2022)
Das Robot Operating System in seiner zweiten Version (ROS 2) findet zunehmend Verwendung und das nicht nur in Robotern. Dieser Beitrag gibt einen Überblick über den Aufbau und die Funktion von ROS 2. Die wesentlichen Elemente werden vorgestellt, das Publish-Subscribe-Konzept, das der Kommunikation zugrunde liegt, wird erläutert. Die Anforderungen von ROS 2 an Hardware und Betriebssystem werden beleuchtet und es werden Betrachtungen zu dessen Echtzeitverhalten gemacht.