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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.
This paper introduces an inexpensive Wiegand-sensor-based rotary encoder that avoids rotating magnets and is suitable for electrical-drive applications. So far, Wiegand-sensor-based encoders usually include a magnetic pole wheel with rotating permanent magnets. These encoders combine the disadvantages of an increased magnet demand and a limited maximal speed due to the centripetal force acting on the rotating magnets. The proposed approach reduces the total demand of permanent magnets drastically. Moreover, the rotating part is manufacturable from a single piece of steel, which makes it very robust and cheap. This work presents the theoretical operating principle of the proposed approach and validates its benefits on a hardware prototype. The presented proof-of-concept prototype achieves a mechanical resolution of 4.5 ° by using only 4 permanent magnets, 2Wiegand sensors and a rotating steel gear wheel with 20 teeth.
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.
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.
Software development projects often fail because of insufficient code quality. It is now well documented that the task of testing software, for example, is perceived as uninteresting and rather boring, leading to poor software quality and major challenges to software development companies. One promising approach to increase the motivation for considering software quality is the use of gamification. Initial research works already investigated the effects of gamification on software developers and come to promising. Nevertheless, a lack of results from field experiments exists, which motivates the chapter at hand. By conducting a gamification experiment with five student software projects and by interviewing the project members, the chapter provides insights into the changing programming behavior of information systems students when confronted with a leaderboard. The results reveal a motivational effect as well as a reduction of code smells.
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.
The problem of fair and privacy-preserving ordered set reconciliation arises in a variety of applications like auctions, e-voting, and appointment reconciliation. While several multi-party protocols have been proposed that solve this problem in the semi-honest model, there are no multi-party protocols that are secure in the malicious model so far. In this paper, we close this gap. Our newly proposed protocols are shown to be secure in the malicious model based on a variety of novel non-interactive zero-knowledge-proofs. We describe the implementation of our protocols and evaluate their performance in comparison to protocols solving the problem in the semi-honest case.
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.
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.
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.
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.
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.
Nowadays, the most employed devices for recoding videos or capturing images are undoubtedly the smartphones. Our work investigates the application of source camera identification on mobile phones. We present a dataset entirely collected by mobile phones. The dataset contains both still images and videos collected by 67 different smartphones. Part of the images consists in photos of uniform backgrounds, especially collected for the computation of the RSPN. Identifying the source camera given a video is particularly challenging due to the strong video compression. The experiments reported in this paper, show the large variation in performance when testing an highly accurate technique on still images and videos.
Automated driving is now possible in diverse road and traffic conditions. However, there are still situations that automated vehicles cannot handle safely and efficiently. In this case, a Transition of Control (ToC) is necessary so that the driver takes control of the driving. Executing a ToC requires the driver to get full situation awareness of the driving environment. If the driver fails to get back the control in a limited time, a Minimum Risk Maneuver (MRM) is executed to bring the vehicle into a safe state (e.g., decelerating to full stop). The execution of ToCs requires some time and can cause traffic disruption and safety risks that increase if several vehicles execute ToCs/MRMs at similar times and in the same area. This study proposes to use novel C-ITS traffic management measures where the infrastructure exploits V2X communications to assist Connected and Automated Vehicles (CAVs) in the execution of ToCs. The infrastructure can suggest a spatial distribution of ToCs, and inform vehicles of the locations where they could execute a safe stop in case of MRM. This paper reports the first field operational tests that validate the feasibility and quantify the benefits of the proposed infrastructure-assisted ToC and MRM management. The paper also presents the CAV and roadside infrastructure prototypes implemented and used in the trials. The conducted field trials demonstrate that infrastructure-assisted traffic management solutions can reduce safety risks and traffic disruptions.
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.
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.
This paper addresses the pixel based recognition of 3D objects with bidirectional associative memories. Computational power and memory requirements for this approach are identified and compared to the performance of current computer architectures by benchmarking different processors. It is shown, that the performance of special purpose hardware, like neurocomputers, is between one and two orders of magnitude higher than the performance of mainstream hardware. On the other hand, the calculation of small neural networks is performed more efficiently on mainstream processors. Based on these results a novel concept is developed, which is tailored for the efficient calculation of bidirectional associative memories. The computational efficiency is further enhanced by the application of algorithms and storage techniques which are matched to characteristics of the application at hand.
This paper addresses the pixel based classification of three dimensional objects from arbitrary views. To perform this task a coding strategy, inspired by the biological model of human vision, for pixel data is described. The coding strategy ensures that the input data is invariant against shift, scale and rotation of the object in the input domain. The image data is used as input to a class of self organizing neural networks, the Kohonen-maps or self-organizing feature maps (SOFM). To verify this approach two test sets have been generated: the first set, consisting of artificially generated images, is used to examine the classification properties of the SOFMs; the second test set examines the clustering capabilities of the SOFM when real world image data is applied to the network after it has been preprocessed to be invariant against shift, scale and rotation. It is shown that the clustering capability of the SOFM is strongly dependant on the invariance coding of the images.
This paper describes the realization of a novel neurocomputer which is based on the concepts of a coprocessor. In contrast to existing neurocomputers the main interest was the realization of a scalable, flexible system, which is capable of computing neural networks of arbitrary topology and scale, with full independence of special hardware from the software's point of view. On the other hand, computational power should be added, whenever needed and flexibly adapted to the requirements of the application. Hardware independence is achieved by a run time system which is capable of using all available computing power, including multiple host CPUs and an arbitrary number of neural coprocessors autonomously. The realization of arbitrary neural topologies is provided through the implementation of the elementary operations which can be found in most neural topologies.
Aim of the AXON2 project (Adaptive Expert System for Object Recogniton using Neuml Networks) is the development of an object recognition system (ORS) capable of recognizing isolated 3d objects from arbitrary views. Commonly, classification is based on a single feature extracted from the original image. Here we present an architecture adapted from the Mixtures of Eaqerts algorithm which uses multiple neuml networks to integmte different features. During tmining each neural network specializes in a subset of objects or object views appropriate to the properties of the corresponding feature space. In recognition mode the system dynamically chooses the most relevant features and combines them with maximum eficiency. The remaining less relevant features arz not computed and do therefore not decelerate the-recognition process. Thus, the algorithm is well suited for ml-time applications.
In this paper we report on CO2 Meter, a do-it-yourself carbon dioxide measuring device for the classroom. Part of the current measures for dealing with the SARS-CoV-2 pandemic is proper ventilation in indoor settings. This is especially important in schools with students coming back to the classroom even with high incidents rates. Static ventilation patterns do not consider the individual situation for a particular class. Influencing factors like the type of activity, the physical structure or the room occupancy are not incorporated. Also, existing devices are rather expensive and often provide only limited information and only locally without any networking. This leaves the potential of analysing the situation across different settings untapped. Carbon dioxide level can be used as an indicator of air quality, in general, and of aerosol load in particular. Since, according to the latest findings, SARS-CoV-2 can be transmitted primarily in the form of aerosols, carbon dioxide may be used as a proxy for the risk of a virus infection. Hence, schools could improve the indoor air quality and potentially reduce the infection risk if they actually had measuring devices available in the classroom. Our device supports schools in ventilation and it allows for collecting data over the Internet to enable a detailed data analysis and model generation. First deployments in schools at different levels were received very positively. A pilot installation with a larger data collection and analysis is underway.
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
With the growing interest in small distributed sensors for the “Internet of Things”, more attention is being paid to energy harvesting techologies. Reducing or eliminating the need for external power sources or batteries make devices more self-sufficient, more reliable, and reduces maintenance requirements. The Wiegand effect is a proven technology for harvesting small amounts of electrical power from mechanical motion.
This article describes an Internet of things (IoT) sensing device with a wireless interface which is powered by the energy-harvesting method of the Wiegand effect. The Wiegand effect, in contrast to continuous sources like photovoltaic or thermal harvesters, provides small amounts of energy discontinuously in pulsed mode. To enable an energy-self-sufficient operation of the sensing device with this pulsed energy source, the output energy of the Wiegand generator is maximized. This energy is used to power up the system and to acquire and process data like position, temperature or other resistively measurable quantities as well as transmit these data via an ultra-low-power ultra-wideband (UWB) data transmitter. A proof-of-concept system was built to prove the feasibility of the approach. The energy consumption of the system during start-up was analysed, 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 prototype was developed.
In this study, the performance of an integrated body-imaging array for 7 T with 32 radiofrequency (RF) channels under consideration of local specific absorption rate (SAR), tissue temperature, and thermal dose limits was evaluated and the imaging performance was compared with a clinical 3 T body coil.
Thirty-two transmit elements were placed in three rings between the bore liner and RF shield of the gradient coil. Slice-selective RF pulse optimizations for B1 shimming and spokes were performed for differently oriented slices in the body under consideration of realistic constraints for power and local SAR. To improve the B1+ homogeneity, safety assessments based on temperature and thermal dose were performed to possibly allow for higher input power for the pulse optimization than permissible with SAR limits.
The results showed that using two spokes, the 7 T array outperformed the 3 T birdcage in all the considered regions of interest. However, a significantly higher SAR or lower duty cycle at 7 T is necessary in some cases to achieve similar B1+ homogeneity as at 3 T. The homogeneity in up to 50 cm-long coronal slices can particularly benefit from the high RF shim performance provided by the 32 RF channels. The thermal dose approach increases the allowable input power and the corresponding local SAR, in one example up to 100 W/kg, without limiting the exposure time necessary for an MR examination.
In conclusion, the integrated antenna array at 7 T enables a clinical workflow for body imaging and comparable imaging performance to a conventional 3 T clinical body coil.
Carbon nanofiber nonwovens represent a powerful class of materials with prospective application in filtration technology or as electrodes with high surface area in batteries, fuel cells, and supercapacitors. While new precursor-to-carbon conversion processes have been explored to overcome productivity restrictions for carbon fiber tows, alternatives for the two-step thermal conversion of polyacrylonitrile precursors into carbon fiber nonwovens are absent. In this work, we develop a continuous roll-to-roll stabilization process using an atmospheric pressure microwave plasma jet. We explore the influence of various plasma-jet parameters on the morphology of the nonwoven and compare the stabilized nonwoven to thermally stabilized samples using scanning electron microscopy, differential scanning calorimetry, and infrared spectroscopy. We show that stabilization with a non-equilibrium plasma-jet can be twice as productive as the conventional thermal stabilization in a convection furnace, while producing electrodes of comparable electrochemical performance.
Benchmarking of various LiDAR sensors for use in self-driving vehicles in real-world environments
(2022)
Abstract
In this paper, we report on our benchmark results of the LiDAR sensors Livox Horizon, Robosense M1, Blickfeld Cube, Blickfeld Cube Range, Velodyne Velarray H800, and Innoviz Pro. The idea was to test the sensors in different typical scenarios that were defined with real-world use cases in mind, in order to find a sensor that meet the requirements of self-driving vehicles. For this, we defined static and dynamic benchmark scenarios. In the static scenarios, both LiDAR and the detection target do not move during the measurement. In dynamic scenarios, the LiDAR sensor was mounted on the vehicle which was driving toward the detection target. We tested all mentioned LiDAR sensors in both scenarios, show the results regarding the detection accuracy of the targets, and discuss their usefulness for deployment in self-driving cars.
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.
Because of customer churn, strong competition, and operational inefficiencies, the telecommunications operator ME Telco (fictitious name due to confidentiality) launched a strategic transformation program that included a Business Process Management (BPM) project. Major problems were silo-oriented process management and missing cross-functional transparency. Process improvements were not consistently planned and aligned with corporate targets. Measurable inefficiencies were observed on an operational level, e.g., high lead times and reassignment rates of the incident management process.
Due to the high number of customer contacts, fault clearances, installations, and product provisioning per year, the automation level of operational processes has a significant impact on financial results, quality, and customer experience. Therefore, the telecommunications operator Deutsche Telekom (DT) has defined a digital strategy with the objectives of zero complexity and zero complaint, one touch, agility in service, and disruptive thinking. In this context, Robotic Process Automation (RPA) was identified as an enabling technology to formulate and realize DT’s digital strategy through automation of rule-based, routine, and predictable tasks in combination with structured and stable data.
Information technologies, such as big data analytics, cloud computing,
cyber physical systems, robotic process automation, and the internet of things, provide a sustainable impetus for the structural development of business sectors as well as the digitalization of markets, enterprises, and processes. Within the consulting industry, the proliferation of these technologies opened up the new segment of digital transformation, which focuses on setting up, controlling, and implementing projects for enterprises from a broad range of sectors. These recent developments raise the question, which requirements evolve for IT consultants as important success factors of those digital transformation projects. Therefore, this empirical contribution provides indications regarding the qualifications and competences necessary for IT consultants in the era of digital transformation from a labor market perspective. On the one hand, this knowledge base is interesting for the academic education of consultants, since it supports a market-oriented design of adequate training measures. On the other hand, insights into the competence requirements for consultants are considered relevant for skill and talent management processes in consulting practice. Assuming that consulting companies pursue a strategic human resource management approach, labor market information may also be useful to discover strategic behavioral patterns.
Recently, novel AI-based services have emerged in the consumer market. AI-based services can affect the way consumers take commercial decisions. Research on the influence of AI on commercial interactions is in its infancy. In this chapter, a framework creating a first overview of the influence of AI on commercial interactions is introduced. This framework summarizes the findings of comparing numerous customer journeys of novel AI-based services with corresponding non-AI equivalents.
Intelligent autonomous software robots replacing human activities and performing administrative processes are reality in today’s corporate world. This includes, for example, decisions about invoice payments, identification of customers for a marketing campaign, and answering customer complaints. What happens if such a software robot causes a damage? Due to the complete absence of human activities, the question is not trivial. It could even happen that no one is liable for a damage towards a third party, which could create an uncalculatable legal risk for business partners. Furthermore, the implementation and operation of those software robots involves various stakeholders, which result in the unsolvable endeavor of identifying the originator of a damage. Overall it is advisable to all involved parties to carefully consider the legal situation. This chapter discusses the liability of software robots from an interdisciplinary perspective. Based on different technical scenarios the legal aspects of liability are discussed.
The benefits of robotic process automation (RPA) are highly related to the usage of commercial off-the-shelf (COTS) software products that can be easily implemented and customized by business units. But, how to find the best fitting RPA product for a specific situation that creates the expected benefits? This question is related to the general area of software evaluation and selection. In the face of more than 75 RPA products currently on the market, guidance considering those specifics is required. Therefore, this chapter proposes a criteria-based selection method specifically for RPA. The method includes a quantitative evaluation of costs and benefits as well as a qualitative utility analysis based on functional criteria. By using the visualization of financial implications (VOFI) method, an application-oriented structure is provided that opposes the total cost of ownership to the time savings times salary (TSTS). For the utility analysis a detailed list of functional criteria for RPA is offered. The whole method is based on a multi-vocal review of scientific and non-scholarly literature including publications by business practitioners, consultants, and vendors. The application of the method is illustrated by a concrete RPA example. The illustrated
structures, templates, and criteria can be directly utilized by practitioners in their real-life RPA implementations. In addition, a normative decision process for selecting RPA alternatives is proposed before the chapter closes with a discussion and outlook.
Robotic process automation (RPA) has attracted increasing attention in research and practice. This chapter positions, structures, and frames the topic as an introduction to this book. RPA is understood as a broad concept that comprises a variety of concrete solutions. From a management perspective RPA offers an innovative approach for realizing automation potentials, whereas from a technical perspective the implementation based on software products and the impact of artificial intelligence (AI) and machine learning (ML) are relevant. RPA is industry-independent and can be used, for example, in finance, telecommunications, and the public sector. With respect to RPA this chapter discusses definitions, related approaches, a structuring framework, a research framework, and an inside as well as outside architectural view. Furthermore, it provides an overview of the book combined with short summaries of each chapter.
Subject of this case is Deutsche Telekom Services Europe (DTSE), a service center for administrative processes. Due to the high volume of repetitive tasks (e.g., 100k manual uploads of offer documents into SAP per year), automation was identified as an important strategic target with a high management attention and commitment. DTSE has to work with various backend application systems without any possibility to change those systems. Furthermore, the complexity of administrative processes differed. When it comes to the transfer of unstructured data (e.g., offer documents) to structured data (e.g., MS Excel files), further cognitive technologies were needed.
This book reflects the tremendous changes in the telecommunications industry in the course of the past few decades – shorter innovation cycles, stiffer competition and new communication products. It analyzes the transformation of processes, applications and network technologies that are now expected to take place under enormous time pressure. The International Telecommunication Union (ITU) and the TM Forum have provided reference solutions that are broadly recognized and used throughout the value chain of the telecommunications industry, and which can be considered the de facto standard. The book describes how these reference solutions can be used in a practical context: it presents the latest insights into their development, highlights lessons learned from numerous international projects and combines them with well-founded research results in enterprise architecture management and reference modeling. The complete architectural transformation is explained, from the planning and set-up stage to the implementation. Featuring a wealth of examples and illustrations, the book offers a valuable resource for telecommunication professionals, enterprise architects and project managers alike.
How does the implementation of a next generation network influence a telecommunication company?
(2009)
As the potential of a Next Generation Network (NGN) is recognized, telecommunication companies consider switching to it. Although the implementation of an NGN seems to be merely a modification of the network infrastructure, it may trigger or require changes in the whole company and even influence the company strategy. To capture the effects of NGN we propose a framework based on concepts of business engineering and technical recommendations for the introduction of NGN technology. The specific design of solutions for the layers "Strategy", "Processes" and "Information Systems" as well as their interdependencies are an essential characteristic of the developed framework. We have per-formed a case study on NGN implementation and observed that all layers captured by our framework are influenced by the introduction of an NGN.
Market changes have forced telecommunication companies to transform their business. Increased competition, short innovation cycles, changed usage patterns, increased customer expectations and cost reduction are the main drivers. Our objective is to analyze to what extend transformation projects have improved the orientation towards the end-customers. Therefore, we selected 38 real-life case studies that are dealing with customer orientation. Our analysis is based on a telecommunication-specific framework that aligns strategy, business processes and information systems. The result of our analysis shows the following: transformation projects that aim to improve the customer orientation are combined with clear goals on costs and revenue of the enterprise. These projects are usually directly linked to the customer touch points, but also to the development and provisioning of products. Furthermore, the analysis shows that customer orientation is not the sole trigger for transformation. There is no one-fits-all solution; rather, improved customer orientation needs aligned changes of business processes as well as information systems related to different parts of the company.
Development of a subject-oriented reference process model for the telecommunications industry
(2016)
Generally the usage of reference models can be structured top-down or bottom-up. The practical need of agile change and flexible organizational implementation requires a consistent mapping to an operational level. In this context, well-established reference process models are typically structured top-down. The subject-oriented Business Process Management (sBPM) offers a modeling concept that is structured bottom-up and concentrates on the process actors on an
operational level. This paper applies sBPM to the enhanced Telecom Operations Map (eTOM), a well-accepted reference process model in the telecommunications industry. The resulting design artifact is a concrete example for a combination of a bottom-up and top-down developed reference model. The results are evaluated and confirmed in practical context through the involvement of the industry body TMForum.
The telecommunications industry is currently going through a major transformation. In this context, the enhanced Telecom Operations Map (eTOM) is a domain-specific process reference model that is offered by the industry organization TM Forum. In practice, eTOM is well accepted and confirmed as de facto standard. It provides process definitions and process flows on different levels of detail. This article discusses the reference modeling of eTOM, i.e., the design, the resulting artifact, and its evaluation based on three project cases. The application of eTOM in three projects illustrates the design approach and concrete models on strategic and operational levels. The article follows the Design Science Research (DSR) paradigm. It contributes with concrete design artifacts to the transformational needs of the telecommunications industry and offers lessons-learned from a general DSR perspective.
The potential of electronic markets in enabling innovative product bundles through flexible and sustainable partnerships is not yet fully exploited in the telecommunication industry. One reason is that bundling requires seamless de-assembling and re-assembling of business processes, whilst processes in telecommunication companies are often product-dependent and hard to virtualize. We propose a framework for the planning of the virtualization of processes, intended to assist the decision maker in prioritizing the processes to be virtualized: (a) we transfer the virtualization pre-requisites stated by the Process Virtualization Theory in the context of customer-oriented processes in the telecommunication industry and assess their importance in this context, (b) we derive IT-oriented requirements for the removal of virtualization barriers and highlight their demand on changes at different levels of the organization. We present a first evaluation of our approach in a case study and report on lessons learned and further steps to be performed.
As the potential of a next generation network (NGN) is recognised, telecommunication companies consider switching to it. Although the implementation of an NGN seems to be merely a modification of the network infrastructure, it may trigger or require changes in the whole company, because it builds upon the separation between service and transport, a flexible bundling of services to products and the streamlining of the IT infrastructure. We propose a holistic framework, structured into the layers ‘strategy’, ‘processes’ and ‘information systems’ and incorporate into each layer all concepts necessary for the implementation of an NGN, as well as the alignment of these concepts. As a first proof-of-concept for our framework we have performed a case study on the introduction of NGN in a large telecommunication company; we show that our framework captures all topics that are affected by an NGN implementation.
The continuing growth of scientific publications raises the question how research processes can be digitalized and thus realized more productively. Especially in information technology fields, research practice is characterized by a rapidly growing volume of publications. For the search process various information systems exist. However, the analysis of the published content is still a highly manual task. Therefore, we propose a text analytics system that allows a fully digitalized analysis of literature sources. We have realized a prototype by using EBSCO Discovery Service in combination with IBM Watson Explorer and demonstrated the results in real-life research projects. Potential addressees are research institutions, consulting firms, and decision-makers in politics and business practice.
The initial idea of Robotic Process Automation (RPA) is the automation of business processes through the presentation layer of existing application systems. For this simple emulation of user input and output by software robots, no changes of the systems and architecture is required. However, considering strategic aspects of aligning business and technology on an enterprise level as well as the growing capabilities of RPA driven by artificial intelligence, interrelations between RPA and Enterprise Architecture (EA) become visible and pose new questions. In this paper we discuss the relationship between RPA and EA in terms of perspectives and implications. As workin- progress we focus on identifying new questions and research opportunities related to RPA and EA.
The initial idea of Robotic Process Automation (RPA) is the automation of business processes through a simple emulation of user input and output by software robots. Hence, it can be assumed that no changes of the used software systems and existing Enterprise Architecture (EA) is
required. In this short, practical paper we discuss this assumption based on a real-life implementation project. We show that a successful RPA implementation might require architectural work during analysis, implementation, and migration. As practical paper we focus on exemplary lessons-learned and new questions related to RPA and EA.
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 forevolving 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 classicalcomputing and digital twin concepts with quantum processing. This paperpresents the status quo of research and practice on quantum (digital) twins. It alsodiscuses their potential to create competitive advantage through real-timesimulation of highly complex, interconnected entities that helps companies better
address changes in their environment and differentiate their products andservices.
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.
This paper covers the use of the magnetic Wiegand effect to design an innovative incremental encoder. First, a theoretical design is given, followed by an estimation of the achievable accuracy and an optimization in open-loop operation.
Finally, a successful experimental verification is presented. For this purpose, a permanent magnet synchronous machine is controlled in a field-oriented manner, using the angle information of the prototype.
Upcoming gasoline engines should run with a larger number of fuels beginning from petrol over methanol up to gas by a wide range of compression ratios and a homogeneous charge. In this article, the microwave (MW) spark plug, based on a high-speed frequency hopping system, is introduced as a solution, which can support a nitrogen compression ratio up to 1:39 in a chamber and more. First, an overview of the high-speed frequency hopping MW ignition and operation system as well as the large number of applications are presented. Both gives an understanding of this new base technology for MW plasma generation. Focus of the theoretical part is the explanation of the internal construction of the spark plug, on the achievable of the high voltage generation as well as the high efficiency to hold the plasma. In detail, the development process starting with circuit simulations and ending with the numerical multiphysics field simulations is described. The concept is evaluated with a reference prototype covering the frequency range between 2.40 and 2.48 GHz and working over a large power range from 20 to 200 W. A larger number of different measurements starting by vector hot-S11 measurements and ending by combined working scenarios out of hot temperature, high pressure and charge motion are winding up the article. The limits for the successful pressure tests were given by the pressure chamber. Pressures ranged from 1 to 39 bar and charge motion up to 25 m/s as well as temperatures from 30◦ to 125◦.
his report summarizes the results of a workshop on Groupware related task design which took place at the International Conference on Supporting Group Work Group'99, Arizona, from 14 th to 17 th November 1999.
The workshop was addressed to people from different
viewpoints, backgrounds, and domains:
- Researchers dealing with questions of task analysis
and task modeling for Groupware application from an
academic point of view. They may contribute modelbased design
approaches or theoretically oriented
work
- Practitioners with experience in the design and
everyday use of groupware systems. They might refer
to the practical side of the topic: "real" tasks, "real"
problems, "real" users, etc.
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
This paper introduces a Competence Developing Game (CDG) for the purpose of a cybersecurity awareness training for businesses. The target audience will be discussed in detail to understand their requirements. It will be explained why and how a mix of business simulation and serious game meets these stakeholder requirements. It will be shown that a tablet and touchscreen based approach is the most suitable solution. In addition, an empirical study will be briefly presented. The study was carried out to examine how an interaction system for a 3D-tablet based CDG has to be designed, to be manageable for non-game experienced employees. Furthermore, it will be explained which serious content is necessary for a Cybersecurity awareness training CDG and how this content is wrapped in the game
Competence Developing Games (CDGs) are a new concept of how to think about games with serious intentions. In order to emphasize on this topic, a new framework has been developed. It basically relies on learning and motivation theories. This ‘motivational Competence Developing Game Framework’ demonstrates how it is possible to use these theories in a CDG development process. The theoretical derivation and use of the framework is explained in this paper.
During the development of a Competence Developing Game’s (CDG) story it is indispensable to understand the target audience. Thereby, CDGs stories represent more than just the plot. The Story is about the
Setting, the Characters and the Plot. As a toolkit to support the
development of such a story, this paper introduces the UserFocused Storybuilding (short UFoS) Framework for CDGs. The Framework and its utilization will be explained, followed by a description of its development and derivation, including an empirical study. In addition, to simplify the Framework use regarding the CDG’s target audience, a new concept of Nine Psychographic Player Types will be explained. This concept of Player Types provides an approach to handle the differences in between players during the UFoS Framework use. Thereby,
this article presents a unique approach to the development of
target group-differentiated CDGs stories.
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.
In this paper we investigate the use of deep neural networks for 3D object detection in uncommon, unstructured environments such as in an open-pit mine. While neural nets are frequently used for object detection in regular autonomous driving applications, more unusual driving scenarios aside street traffic pose additional challenges. For one, the collection of appropriate data sets to train the networks is an issue. For another, testing the performance of trained networks often requires tailored integration with the particular domain as well. While there exist different solutions for these problems in regular autonomous driving, there are only very few approaches that work for special domains just as well. We address both the challenges above in this work. First, we discuss two possible ways of acquiring data for training and evaluation. That is, we evaluate a semi-automated annotation of recorded LIDAR data and we examine synthetic data generation. Using these datasets we train and test different deep neural network for the task of object detection. Second, we propose a possible integration of a ROS2 detector module for an autonomous driving platform. Finally, we present the performance of three state-of-the-art deep neural networks in the domain of 3D object detection on a synthetic dataset and a smaller one containing a characteristic object from an open-pit mine.
Water suppliers are faced with the great challenge of achieving high-quality and, at the same time, low-cost water supply. Since climatic and demographic influences will pose further challenges in the future, the resilience enhancement of water distribution systems (WDS), i.e. the enhancement of their capability to withstand and recover from disturbances, has been in particular focus recently. To assess the resilience of WDS, graph-theoretical metrics have been proposed. In this study, a promising approach is first physically derived analytically and then applied to assess the resilience of the WDS for a district in a major German City. The topology based resilience index computed for every consumer node takes into consideration the resistance of the best supply path as well as alternative supply paths. This resistance of a supply path is derived to be the dimensionless pressure loss in the pipes making up the path. The conducted analysis of a present WDS provides insight into the process of actively influencing the resilience of WDS locally and globally by adding pipes. The study shows that especially pipes added close to the reservoirs and main branching points in the WDS result in a high resilience enhancement of the overall WDS.
Cheap does not imply cost-effective -- this is rule number one of zeitgeisty system design. The initial investment accounts only for a small portion of the lifecycle costs of a technical system. In fluid systems, about ninety percent of the total costs are caused by other factors like power consumption and maintenance. With modern optimization methods, it is already possible to plan an optimal technical system considering multiple objectives. In this paper, we focus on an often neglected contribution to the lifecycle costs: downtime costs due to spontaneous failures. Consequently, availability becomes an issue.
In times of planned obsolescence the demand for sustainability keeps growing. Ideally, a technical system is highly reliable, without failures and down times due to fast wear of single components. At the same time, maintenance should preferably be limited to pre-defined time intervals. Dispersion of load between multiple components can increase a system’s reliability and thus its availability inbetween maintenance points. However, this also results in higher investment costs and additional efforts due to higher complexity. Given a specific load profile and resulting wear of components, it is often unclear which system structure is the optimal one. Technical Operations Research (TOR) finds an optimal structure balancing availability and effort. We present our approach by designing a hydrostatic transmission system.
The conference center darmstadtium in Darmstadt is a prominent example of energy efficient buildings. Its heating system consists of different source and consumer circuits connected by a Zortström reservoir. Our goal was to reduce the energy costs of the system as much as possible. Therefore, we analyzed its supply circuits. The first step towards optimization is a complete examination of the system: 1) Compilation of an object list for the system, 2) collection of the characteristic curves of the components, and 3) measurement of the load profiles of the heat and volume-flow demand. Instead of modifying the system manually and testing the solution by simulation, the second step was the creation of a global optimization program. The objective was to minimize the total energy costs for one year. We compare two different topologies and show opportunities for significant savings.
Gearboxes are mechanical transmission systems that provide speed and torque conversions from a rotating power source. Being a central element of the drive train, they are relevant for the efficiency and durability of motor vehicles. In this work, we present a new approach for gearbox design: Modeling the design problem as a mixed-integer nonlinear program (MINLP) allows us to create gearbox designs from scratch for arbitrary requirements and—given enough time—to compute provably globally optimal designs for a given objective. We show how different degrees of freedom influence the runtime and present an exemplary solution.
Resilience as a concept has found its way into different disciplines to describe the ability of an individual or system to withstand and adapt to changes in its environment. In this paper, we provide an overview of the concept in different communities and extend it to the area of mechanical engineering. Furthermore, we present metrics to measure resilience in technical systems and illustrate them by applying them to load-carrying structures. By giving application examples from the Collaborative Research Centre (CRC) 805, we show how the concept of resilience can be used to control uncertainty during different stages of product life.
The understanding that optimized components do not automatically lead to energy-efficient systems sets the attention from the single component on the entire technical system. At TU Darmstadt, a new field of research named Technical Operations Research (TOR) has its origin. It combines mathematical and technical know-how for the optimal design of technical systems. We illustrate our optimization approach in a case study for the design of a ventilation system with the ambition to minimize the energy consumption for a temporal distribution of diverse load demands. By combining scaling laws with our optimization methods we find the optimal combination of fans and show the advantage of the use of multiple fans.
Energy-efficient components do not automatically lead to energy-efficient systems. Technical Operations Research (TOR) shifts the focus from the single component to the system as a whole and finds its optimal topology and operating strategy simultaneously. In previous works, we provided a preselected construction kit of suitable components for the algorithm. This approach may give rise to a combinatorial explosion if the preselection cannot be cut down to a reasonable number by human intuition. To reduce the number of discrete decisions, we integrate laws derived from similarity theory into the optimization model. Since the physical characteristics of a production series are similar, it can be described by affinity and scaling laws. Making use of these laws, our construction kit can be modeled more efficiently: Instead of a preselection of components, it now encompasses whole model ranges. This allows us to significantly increase the number of possible set-ups in our model. In this paper, we present how to embed this new formulation into a mixed-integer program and assess the run time via benchmarks. We present our approach on the example of a ventilation system design problem.
Planning the layout and operation of a technical system is a common task
for an engineer. Typically, the workflow is divided into consecutive stages: First,
the engineer designs the layout of the system, with the help of his experience or of
heuristic methods. Secondly, he finds a control strategy which is often optimized
by simulation. This usually results in a good operating of an unquestioned sys-
tem topology. In contrast, we apply Operations Research (OR) methods to find a
cost-optimal solution for both stages simultaneously via mixed integer program-
ming (MILP). Technical Operations Research (TOR) allows one to find a provable
global optimal solution within the model formulation. However, the modeling error
due to the abstraction of physical reality remains unknown. We address this ubiq-
uitous problem of OR methods by comparing our computational results with mea-
surements in a test rig. For a practical test case we compute a topology and control
strategy via MILP and verify that the objectives are met up to a deviation of 8.7%.
Pure analytical or experimental methods can only find a control strategy for technical systems with a fixed setup. In former contributions we presented an approach that simultaneously finds the optimal topology and the optimal open-loop control of a system via Mixed Integer Linear Programming (MILP). In order to extend this approach by a closed-loop control we present a Mixed Integer Program for a time discretized tank level control. This model is the basis for an extension by combinatorial decisions and thus for the variation of the network topology. Furthermore, one is able to appraise feasible solutions using the global optimality gap.
Nach Stand von Wissenschaft und Technik werden Komponenten hinsichtlich ihrer Eigenschaften, wie Lebensdauer oder Energieeffizienz, optimiert. Allerdings können selbst hervorragende Komponenten zu ineffizienten oder instabilen Systemen führen, wenn ihr Zusammenspiel nur unzureichend berücksichtigt wird. Eine Systembetrachtung schafft ein größeres Optimierungspotential - dem erhöhten Potential steht jedoch auch ein erhöhter Komplexitätsgrad gegenüber. Die vorliegende Arbeit ist im Rahmen des Sonderforschungsbereichs 805 entstanden, dessen Ziel die Beherrschung von Unsicherheit in Systemen des Maschinenbaus ist. Die Arbeit zeigt anhand eines realen Systems aus dem Bereich der Hydraulik, wie Unsicherheit in der Entwicklungsphase beherrscht werden kann. Hierbei ist neu, dass die durch den späteren Betrieb zu erwartende Systemdegradation eines jeden möglichen Systemvorschlags antizipiert werden kann. Dadurch können Betriebs- und Wartungskosten vorausgesagt und minimiert werden und durch eine optimale Betriebs- und Wartungsstrategie die Verfügbarkeit des Systems garantiert werden. Wesentliche Fragen bei der optimalen Auslegung des betrachteten hydrostatischen Getriebes sind dessen physikalische Modellierung, die Darstellung des Optimierungsproblems als gemischt-ganzzahliges lineares Programm, und dessen algorithmische Behandlung zur Lösungsfindung. Hierzu werden Heuristiken zum schnelleren Auffinden sinnvoller Systemtopologien vorgestellt und mittels mathematischer Dekomposition eine Bewertung des dynamischen Verschleiß- und Wartungsverlaufs möglicher Systemvorschläge vorgenommen. Die Arbeit stellt die Optimierung technischer Systeme an der Schnittstelle von Mathematik, Informatik und Ingenieurwesen sowohl gründlich als auch anschaulich und nachvollziehbar dar.
Finding a good system topology with more than a handful of components is a
highly non-trivial task. The system needs to be able to fulfil all expected load cases, but at the
same time the components should interact in an energy-efficient way. An example for a system
design problem is the layout of the drinking water supply of a residential building. It may be
reasonable to choose a design of spatially distributed pumps which are connected by pipes in at
least two dimensions. This leads to a large variety of possible system topologies. To solve such
problems in a reasonable time frame, the nonlinear technical characteristics must be modelled
as simple as possible, while still achieving a sufficiently good representation of reality. The
aim of this paper is to compare the speed and reliability of a selection of leading mathematical
programming solvers on a set of varying model formulations. This gives us empirical evidence
on what combinations of model formulations and solver packages are the means of choice with the current state of the art.
The UN sets the goal to ensure access to water and sanitation for all people by 2030. To address this goal, we present a multidisciplinary approach for designing water supply networks for slums in large cities by applying mathematical optimization. The problem is modeled as a mixed-integer linear problem (MILP) aiming to find a network describing the optimal supply infrastructure. To illustrate the approach, we apply it on a small slum cluster in Dhaka, Bangladesh.
The energy-efficiency of technical systems can be improved by a systematic design approach. Technical Operations Research (TOR) employs methods known from Operations Research to find a global optimal layout and operation strategy of technical systems. We show the practical usage of this approach by the systematic design of a decentralized water supply system for skyscrapers. All possible network options and operation strategies are modeled by a Mixed-Integer Nonlinear Program. We present the optimal system found by our approach and highlight the energy savings compared to a conventional system design.
The overall energy efficiency of ventilation systems can be improved by considering not only single components, but by considering as well the interplay between every part of the system. With the help of the method "TOR" ("Technical Operations Research"), which was developed at the Chair of Fluid Systems at TU Darmstadt, it is possible to improve the energy efficiency of the whole system by considering all possible design choices programmatically. We show the ability of this systematic design approach with a ventilation system for buildings as a use case example.
Based on a Mixed-Integer Nonlinear Program (MINLP) we model the ventilation system. We use binary variables to model the selection of different pipe diameters. Multiple fans are model with the help of scaling laws. The whole system is represented by a graph, where the edges represent the pipes and fans and the nodes represents the source of air for cooling and the sinks, that have to be cooled. At the beginning, the human designer chooses a construction kit of different suitable fans and pipes of different diameters and different load cases. These boundary conditions define a variety of different possible system topologies. It is not possible to consider all topologies by hand. With the help of state of the art solvers, on the other side, it is possible to solve this MINLP.
Next to this, we also consider the effects of malfunctions in different components. Therefore, we show a first approach to measure the resilience of the shown example use case. Further, we compare the conventional approach with designs that are more resilient. These more resilient designs are derived by extending the before mentioned model with further constraints, that consider explicitly the resilience of the overall system. We show that it is possible to design resilient systems with this method already in the early design stage and compare the energy efficiency and resilience of these different system designs.
Highly competitive markets paired with tremendous production volumes demand particularly cost efficient products. The usage of common parts and modules across product families can potentially reduce production costs. Yet, increasing commonality typically results in overdesign of individual products. Multi domain virtual prototyping enables designers to evaluate costs and technical feasibility of different single product designs at reasonable computational effort in early design phases. However, savings by platform commonality are hard to quantify and require detailed knowledge of e.g. the production process and the supply chain. Therefore, we present and evaluate a multi-objective metamodel-based optimization algorithm which enables designers to explore the trade-off between high commonality and cost optimal design of single products.
To increase pressure to supply all floors of high buildings with water, booster stations, normally consisting of several parallel pumps in the basement, are used. In this work, we demonstrate the potential of a decentralized pump topology regarding energy savings in water supply systems of skyscrapers. We present an approach, based on Mixed-Integer Nonlinear Programming, that allows to choose an optimal network topology and optimal pumps from a predefined construction kit comprising different pump types. Using domain-specific scaling laws and Latin Hypercube Sampling, we generate different input sets of pump types and compare their impact on the efficiency and cost of the total system design. As a realistic application example, we consider a hotel building with 325 rooms, 12 floors and up to four pressure zones.
Given industrial applications, the costs for the operation and maintenance of a pump system typically far exceed its purchase price. For finding an optimal pump configuration which minimizes not only investment, but life-cycle costs, methods like Technical Operations Research which is based on Mixed-Integer Programming can be applied. However, during the planning phase, the designer is often faced with uncertain input data, e.g. future load demands can only be estimated. In this work, we deal with this uncertainty by developing a chance-constrained two-stage (CCTS) stochastic program. The design and operation of a booster station working under uncertain load demand are optimized to minimize total cost including purchase price, operation cost incurred by energy consumption and penalty cost resulting from water shortage. We find optimized system layouts using a sample average approximation (SAA) algorithm, and analyze the results for different risk levels of water shortage. By adjusting the risk level, the costs and performance range of the system can be balanced, and thus the
system’s resilience can be engineered
On obligations in the development process of resilient systems with algorithmic design methods
(2018)
Advanced computational methods are needed both for the design of large systems and to compute high accuracy solutions. Such methods are efficient in computation, but the validation of results is very complex, and highly skilled auditors are needed to verify them. We investigate legal questions concerning obligations in the development phase, especially for technical systems developed using advanced methods. In particular, we consider methods of resilient and robust optimization. With these techniques, high performance solutions can be found, despite a high variety of input parameters. However, given the novelty of these methods, it is uncertain whether legal obligations are being met. The aim of this paper is to discuss if and how the choice of a specific computational method affects the developer’s product liability. The review of legal obligations in this paper is based on German law and focuses on the requirements that must be met during the design and development process.
The paper industry is the industry with the third highest energy consumption in the European Union. Using recycled paper instead of fresh fibers for papermaking is less energy consuming and saves resources. However, adhesive contaminants in recycled paper are particularly problematic since they reduce the quality of the resulting paper-product. To remove as many contaminants and at the same time obtain as many valuable fibres as possible, fine screening systems, consisting of multiple interconnected pressure screens, are used. Choosing the best configuration is a non-trivial task: The screens can be interconnected in several ways, and suitable screen designs as well as operational parameters have to be selected. Additionally, one has to face conflicting objectives. In this paper, we present an approach for the multi-criteria optimization of pressure screen systems based on Mixed-Integer Nonlinear Programming. We specifically focus on a clear representation of the trade-off between different objectives.
Ensuring access to water and sanitation for all is Goal No. 6 of the 17 UN Sustainability Development Goals to transform our world. As one step towards this goal, we present an approach that leverages remote sensing data to plan optimal water supply networks for informal urban settlements. The concept focuses on slums within large urban areas, which are often characterized by a lack of an appropriate water supply. We apply methods of mathematical optimization aiming to find a network describing the optimal supply infrastructure. Hereby, we choose between different decentral and central approaches combining supply by motorized vehicles with supply by pipe systems. For the purposes of illustration, we apply the approach to two small slum clusters in Dhaka and Dar es Salaam. We show our optimization results, which represent the lowest cost water supply systems possible. Additionally, we compare the optimal solutions of the two clusters (also for varying input parameters, such as population densities and slum size development over time) and describe how the result of the optimization depends on the entered remote sensing data.
Water suppliers are faced with the great challenge of achieving high-quality and, at the same time, low-cost water supply. In practice, the focus is set on the most beneficial maintenance measures and/or capacity adaptations of existing water distribution systems (WDS). Since climatic and demographic influences will pose further challenges in the future, the resilience enhancement of WDS, i.e. the enhancement of their capability to withstand and recover from disturbances, has been in particular focus recently. To assess the resilience of WDS, metrics based on graph theory have been proposed. In this study, a promising approach is applied to assess the resilience of the WDS for a district in a major German City. The conducted analysis provides insight into the process of actively influencing the
resilience of WDS
The development of resilient technical systems is a challenging task, as the system should adapt automatically to unknown disturbances and component failures. To evaluate different approaches for deriving resilient technical system designs, we developed a modular test rig that is based on a pumping system. On the basis of this example
system, we present metrics to quantify resilience and an algorithmic approach to improve resilience. This approach enables the pumping system to automatically react on unknown disturbances and to reduce the impact of component failures. In this case, the system is able to automatically adapt its topology by activating additional valves. This enables the system to still reach a minimum performance, even in case of failures. Furthermore, timedependent disturbances are evaluated continuously, deviations from the original state are automatically detected and anticipated in the future. This allows to reduce the impact of future disturbances and leads to a more resilient
system behaviour.
The transition within transportation towards battery electric vehicles can lead to a more sustainable future. To account for the development goal ‘climate action’ stated by the United Nations, it is mandatory, within the conceptual design phase, to derive energy-efficient system designs. One barrier is the uncertainty of the driving behaviour within the usage phase. This uncertainty is often addressed by using a stochastic synthesis process to derive representative driving cycles and by using cycle-based optimization. To deal with this uncertainty, a new approach based on a stochastic optimization program is presented. This leads to an optimization model that is solved with an exact solver. It is compared to a system design approach based on driving cycles and a genetic algorithm solver. Both approaches are applied to find efficient electric powertrains with fixed-speed and multi-speed transmissions. Hence, the similarities, differences and respective advantages of each optimization procedure are discussed.
The course Physics for Electrical Engineering is part of the curriculum of the bachelor program Electrical Engineering at University of Applied Science Aachen.
Before covid-19 the course was conducted in a rather traditional way with all parts (lecture, exercise and lab) face-to-face. This teaching approach changed fundamentally within a week when the covid-19 limitations forced all courses to distance learning. All parts of the course were transformed to pure distance learning including synchronous and asynchronous parts for the lecture, live online-sessions for the exercises and self-paced labs at home. Using these methods, the course was able to impart the required knowledge and competencies. Taking the teacher’s observations of the student’s learning behaviour and engagement, the formal and informal feedback of the students and the results of the exams into account, the new methods are evaluated with respect to effectiveness, sustainability and suitability for competence transfer. Based on this analysis strong and weak points of the concept and countermeasures to solve the weak points were identified. The analysis further leads to a sustainable teaching approach combining synchronous and asynchronous parts with self-paced learning times that can be used in a very flexible manner for different learning scenarios, pure online, hybrid (mixture of online and presence times) and pure presence teaching.
Around 60% of the paper worldwide is made from recovered paper. Especially adhesive contaminants, so called stickies, reduce paper quality. To remove stickies but at the same time keep as many valuable fibers as possible, multi-stage screening systems with several interconnected pressure screens are used. When planning such systems, suitable screens have to be selected and their interconnection as well as operational parameters have to be defined considering multiple conflicting objectives. In this contribution, we present a Mixed-Integer Nonlinear Program to optimize system layout, component selection and operation to find a suitable trade-off between output quality and yield.
In product development, numerous design decisions have to be made. Multi-domain virtual prototyping provides a variety of tools to assess technical feasibility of design options, however often requires substantial computational effort for just a single evaluation. A special challenge is therefore the optimal design of product families, which consist of a group of products derived from a common platform. Finding an optimal platform configuration (stating what is shared and what is individually designed for each product) and an optimal design of all products simultaneously leads to a mixed-integer nonlinear black-box optimization model. We present an optimization approach based on metamodels and a metaheuristic. To increase computational efficiency and solution quality, we compare different types of Gaussian process regression metamodels adapted from the domain of machine learning, and combine them with a genetic algorithm. We illustrate our approach on the example of a product family of electrical drives, and investigate the trade-off between solution quality and computational overhead.
In order to maximize the possible travel distance of battery electric vehicles with one battery charge, it is mandatory to adjust all components of the powertrain carefully to each other. While current vehicle designs mostly simplify the powertrain rigorously and use an electric motor in combination with a gearbox with only one fixed transmission ratio, the use of multi-gear systems has great potential. First, a multi-speed system is able to improve the overall energy efficiency. Secondly, it is able to reduce the maximum momentum and therefore to reduce the maximum current provided by the traction battery, which results in a longer battery lifetime. In this paper, we present a systematic way to generate multi-gear gearbox designs that—combined with a certain electric motor—lead to the most efficient fulfillment of predefined load scenarios and are at the same time robust to uncertainties in the load. Therefore, we model the electric motor and the gearbox within a Mixed-Integer Nonlinear Program, and optimize the efficiency of the mechanical parts of the powertrain. By combining this mathematical optimization program with an unsupervised machine learning algorithm, we are able to derive global-optimal gearbox designs for practically relevant momentum and speed requirements.
The chemical industry is one of the most important industrial sectors in Germany in terms of manufacturing revenue. While thermodynamic boundary conditions often restrict the scope for reducing the energy consumption of core processes, secondary processes such as cooling offer scope for energy optimisation. In this contribution, we therefore model and optimise an existing cooling system. The technical boundary conditions of the model are provided by the operators, the German chemical company BASF SE. In order to systematically evaluate different degrees of freedom in topology and operation, we formulate and solve a Mixed-Integer Nonlinear Program (MINLP), and compare our optimisation results with the existing system.
Component failures within water supply systems can lead to significant performance losses. One way to address these losses is the explicit anticipation of failures within the design process. We consider a water supply system for high-rise buildings, where pump failures are the most likely failure scenarios. We explicitly consider these failures within an early design stage which leads to a more resilient system, i.e., a system which is able to operate under a predefined number of arbitrary pump failures. We use a mathematical optimization approach to compute such a resilient design. This is based on a multi-stage model for topology optimization, which can be described by a system of nonlinear inequalities and integrality constraints. Such a model has to be both computationally tractable and to represent the real-world system accurately. We therefore validate the algorithmic solutions using experiments on a scaled test rig for high-rise buildings. The test rig allows for an arbitrary connection of pumps to reproduce scaled versions of booster station designs for high-rise buildings. We experimentally verify the applicability of the presented optimization model and that the proposed resilience properties are also fulfilled in real systems.
This chapter describes three general strategies to master uncertainty in technical systems: robustness, flexibility and resilience. It builds on the previous chapters about methods to analyse and identify uncertainty and may rely on the availability of technologies for particular systems, such as active components. Robustness aims for the design of technical systems that are insensitive to anticipated uncertainties. Flexibility increases the ability of a system to work under different situations. Resilience extends this characteristic by requiring a given minimal functional performance, even after disturbances or failure of system components, and it may incorporate recovery. The three strategies are described and discussed in turn. Moreover, they are demonstrated on specific technical systems.
Algorithmic design and resilience assessment of energy efficient high-rise water supply systems
(2018)
High-rise water supply systems provide water flow and suitable pressure in all levels of tall buildings. To design such state-of-the-art systems, the consideration of energy efficiency and the anticipation of component failures are mandatory. In this paper, we use Mixed-Integer Nonlinear Programming to compute an optimal placement of pipes and pumps, as well as an optimal control strategy.Moreover, we consider the resilience of the system to pump failures. A resilient system is able to fulfill a predefined minimum functionality even though components fail or are restricted in their normal usage. We present models to measure and optimize the resilience. To demonstrate our approach, we design and analyze an optimal resilient decentralized water supply system inspired by a real-life hotel building.
Successful optimization requires an appropriate model of the system under consideration. When selecting a suitable level of detail, one has to consider solution quality as well as the computational and implementation effort. In this paper, we present a MINLP for a pumping system for the drinking water supply of high-rise buildings. We investigate the influence of the granularity of the underlying physical models on the solution quality. Therefore, we model the system with a varying level of detail regarding the friction losses, and conduct an experimental validation of our model on a modular test rig. Furthermore, we investigate the computational effort and show that it can be reduced by the integration of domain-specific knowledge.