Interview Study on Strategy Options for Platform Operation in B2B Markets - Özcan, Leon; Fichtler, Timm; Kasten, Benjamin; Koldewey, Christian; Dumitrescu, Roman
Keywords: Digital Platform; Platform Strategy; Strategic Management; Platform Life Cycle; Interview Study; Business Model; Business-to-Business; Two-sided Market; Multi-sided Market

Performance Management Approach for Digital Platforms in B2B Markets - Özcan, Leon; Kirchberg, Lisa; Koldewey, Christian; Dumitrescu, Roman (The Role of Innovation: Past, Present, Future)
Digital platforms have already led to disruptions in multiple B2C markets and are becoming increasingly dominant in B2B markets. As a result, more and more companies are trying to participate in the platform economy. However, the successful development and operation of a digital platform is associated with significant challenges, which leads to 85% of all platforms failing. A core challenge is the dynamic nature of the platform economy, with varying strategic objectives at different stages in the platform lifecycle. Platform operators must continuously monitor platform progress and adjust their strategy. Utilizing action research in the real-world platform project AI Marketplace, we developed a lifecycle-oriented performance management approach for digital platforms in B2B markets. It enables platform operators to reflect on their position in the platform lifecycle, derive relevant strategic objectives, and monitor them with suitable key performance indicators. Hence, allowing them to secure the long-term success of their platform business.
Keywords: Digital Platform; Two-Sided Market; Multi-Sided Market; Platform Lifecycle; Platform Monitoring; Performance Management

Why do Digital Platforms succeed or fail? - A Literature Review on Success and Failure Factors - Özcan, Leon; Koldewey, Christian; Duparc, Estelle; van der Valk, Hendrik; Otto, Boris; Dumitrescu, Roman
In the course of digitalization, digital platforms are unleashing their full disruptive potential and are already dominating the first industries (e.g., hotel industry). As a result of this success, more and more companies want to build their own platforms and participate in the success. However, building and operating a digital platform involves multiple challenges and most of such ambitions fail. Since most digital platforms fail, strategic leadership of digital platforms must consider both success factors and reasons for platform failure. For this purpose, we conducted a systematic literature analysis and identified 24 success as well as failure factors in 9 dimensions. From a scientific perspective, the article provides a structured analysis of success and failure factors of digital platforms, which previously did not exist in literature. Practitioners can use the resulting knowledge base to successfully manage platform activities and avoid pitfalls.
Keywords: Digital Platform; Multi-sided Market; Two-sided Market; Success Factor; Failure Factor

A Damage Model for Corrosion Fatigue Due to Hydrogen Embrittlement - Shi, Yuhao; Harzheim, Sven; Hofmann, Martin; Wallmersperger, Thomas (Material Modeling and Structural Mechanics)
Predicting the durability of components subjected to mechanical load under environmental conditions leading to corrosion is one of the most challenging tasks in mechanical engineering. The demand for precise predictions increases with the desire of lightweight design in transportation due to environmental protection. Corrosion with its manifold of mechanisms often occurs together with the production of hydrogen by electrochemical reactions. Hydrogen embrittlement is one of the most feared damage mechanisms for metal constructions often leading to early and unexpected failure. Until now, predictions are mostly based on costly experiments. Hence, a rational predictive model based on the fundamentals of electrochemistry and damage mechanics has to be developed in order to reduce the costs. In this work, a first model approach based on classical continuum damage mechanics is presented to couple both, the damage induced by the mechanical stress and the hydrogen embrittlement. An elaborated two-scale model based on the selfconsistent theory is applied to describe the mechanical damage due to fatigue. The electrochemical kinetics are elucidated through the Langmuir adsorption isotherm and the diffusion equation to consider the impact of hydrogen embrittlement on the fatigue. The modeling of the mechanism of hydrogen embrittlement defines the progress of damage accumulation due to the electrochemistry. The durability results like the S-N diagram show the influence of hydrogen embrittlement by varying, e.g. the fatigue frequency or the stress ratio.
Keywords: Hydrogen embrittlement; Fatigue; Continuum damage mechanics; Numerical simulation; Multi-field problem

Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems - Schön, Oliver; Götte, Ricarda-Samantha; Timmermann, Julia (14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022))
While trade-offs between modeling effort and model accuracy remain a major concern with system identification, resorting to data-driven methods often leads to a complete disregard for physical plausibility. To address this issue, we propose a physics-guided hybrid approach for modeling non-autonomous systems under control. Starting from a traditional physics-based model, this is extended by a recurrent neural network and trained using a sophisticated multi-objective strategy yielding physically plausible models. While purely data-driven methods fail to produce satisfying results, experiments conducted on real data reveal substantial accuracy improvements by our approach compared to a physics-based model.
Keywords: neural networks; physics-guided; data-driven; multi-objective optimization; system identification; machine learning; dynamical systems

BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems - Heins, Jonathan; Rook, Jeroen; Schäpermeier, Lennart; Kerschke, Pascal; Bossek, Jakob; Trautmann, Heike (Parallel Problem Solving from Nature (PPSN XVII))
In multimodal multi-objective optimization (MMMOO), the focus is not solely on convergence in objective space, but rather also on explicitly ensuring diversity in decision space. We illustrate why commonly used diversity measures are not entirely appropriate for this task and propose a sophisticated basin-based evaluation (BBE) method. Also, BBE variants are developed, capturing the anytime behavior of algorithms. The set of BBE measures is tested by means of an algorithm configuration study. We show that these new measures also transfer properties of the well-established hypervolume (HV) indicator to the domain of MMMOO, thus also accounting for objective space convergence. Moreover, we advance MMMOO research by providing insights into the multimodal performance of the considered algorithms. Specifically, algorithms exploiting local structures are shown to outperform classical evolutionary multi-objective optimizers regarding the BBE variants and respective trade-off with HV.
Keywords: Anytime behavior; Benchmarking; Continuous optimization; Multi-objective optimization; Multimodality; Performance metric

On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems - Rook, Jeroen; Trautmann, Heike; Bossek, Jakob; Grimme, Christian (Proceedings of the Genetic and Evolutionary Computation Conference Companion)
Hardness of Multi-Objective (MO) continuous optimization problems results from an interplay of various problem characteristics, e. g. the degree of multi-modality. We present a benchmark study of classical and diversity focused optimizers on multi-modal MO problems based on automated algorithm configuration. We show the large effect of the latter and investigate the trade-off between convergence in objective space and diversity in decision space.
Keywords: configuration; multi-modality; multi-objective optimization

AutoML for Multi-Label Classification: Overview and Empirical Evaluation - Wever, Marcel Dominik; Tornede, Alexander; Mohr, Felix; Hüllermeier, Eyke (IEEE Transactions on Pattern Analysis and Machine Intelligence)
Automated machine learning (AutoML) supports the algorithmic construction and data-specific customization of machine learning pipelines, including the selection, combination, and parametrization of machine learning algorithms as main constituents. Generally speaking, AutoML approaches comprise two major components: a search space model and an optimizer for traversing the space. Recent approaches have shown impressive results in the realm of supervised learning, most notably (single-label) classification (SLC). Moreover, first attempts at extending these approaches towards multi-label classification (MLC) have been made. While the space of candidate pipelines is already huge in SLC, the complexity of the search space is raised to an even higher power in MLC. One may wonder, therefore, whether and to what extent optimizers established for SLC can scale to this increased complexity, and how they compare to each other. This paper makes the following contributions: First, we survey existing approaches to AutoML for MLC. Second, we augment these approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking framework that supports a fair and systematic comparison. Fourth, we conduct an extensive experimental study, evaluating the methods on a suite of MLC problems. We find a grammar-based best-first search to compare favorably to other optimizers.
Keywords: Automated Machine Learning; Multi Label Classification; Hierarchical Planning; Bayesian Optimization

Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning - Schneider, Stefan Balthasar; Khalili, Ramin; Manzoor, Adnan; Qarawlus, Haydar; Schellenberg, Rafael; Karl, Holger; Hecker, Artur (Transactions on Network and Service Management)
Modern services consist of interconnected components,e.g., microservices in a service mesh or machine learning functions in a pipeline. These services can scale and run across multiple network nodes on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities, changing demands, and Quality of Service (QoS) requirements into account. This challenge is usually solved with custom approaches designed by experts. While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e.g., a priori knowledge). We propose DeepCoord, a novel deep reinforcement learning approach that learns how to best coordinate services and is geared towards realistic assumptions. It interacts with the network and relies on available, possibly delayed monitoring information. Rather than defining a complex model or an algorithm on how to achieve an objective, our model-free approach adapts to various objectives and traffic patterns. An agent is trained offline without expert knowledge and then applied online with minimal overhead. Compared to a state-of-the-art heuristic, DeepCoord significantly improves flow throughput (up to 76%) and overall network utility (more than 2x) on realworld network topologies and traffic traces. It also supports optimizing multiple, possibly competing objectives, learns to respect QoS requirements, generalizes to scenarios with unseen, stochastic traffic, and scales to large real-world networks. For reproducibility and reuse, our code is publicly available.
Keywords: network management; service management; coordination; reinforcement learning; self-learning; self-adaptation; multi-objective

DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning - Schneider, Stefan Balthasar; Karl, Holger; Khalili, Ramin; Hecker, Artur
Macrodiversity is a key technique to increase the capacity of mobile networks. It can be realized using coordinated multipoint (CoMP), simultaneously connecting users to multiple overlapping cells. Selecting which users to serve by how many and which cells is NP-hard but needs to happen continuously in real time as users move and channel state changes. Existing approaches often require strict assumptions about or perfect knowledge of the underlying radio system, its resource allocation scheme, or user movements, none of which is readily available in practice. Instead, we propose three novel self-learning and self-adapting approaches using model-free deep reinforcement learning (DRL): DeepCoMP, DD-CoMP, and D3-CoMP. DeepCoMP leverages central observations and control of all users to select cells almost optimally. DD-CoMP and D3-CoMP use multi-agent DRL, which allows distributed, robust, and highly scalable coordination. All three approaches learn from experience and self-adapt to varying scenarios, reaching 2x higher Quality of Experience than other approaches. They have very few built-in assumptions and do not need prior system knowledge, making them more robust to change and better applicable in practice than existing approaches.
Keywords: mobility management; coordinated multipoint; CoMP; cell selection; resource management; reinforcement learning; multi agent; MARL; self-learning; self-adaptation; QoE

A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions - Bender, Amelie (Machines)
While increasing digitalization enables multiple advantages for a reliable operation of technical systems, a remaining challenge in the context of condition monitoring is seen in suitable consideration of uncertainties affecting the monitored system. Therefore, a suitable prognostic approach to predict the remaining useful lifetime of complex technical systems is required. To handle different kinds of uncertainties, a novel Multi-Model-Particle Filtering-based prognostic approach is developed and evaluated by the use case of rubber-metal-elements. These elements are maintained preventively due to the strong influence of uncertainties on their behavior. In this paper, two measurement quantities are compared concerning their ability to establish a prediction of the remaining useful lifetime of the monitored elements and the influence of present uncertainties. Based on three performance indices, the results are evaluated. A comparison with predictions of a classical Particle Filter underlines the superiority of the developed Multi-Model-Particle Filter. Finally, the value of the developed method for enabling condition monitoring of technical systems related to uncertainties is given exemplary by a comparison between the preventive and the predictive maintenance strategy for the use case.
Keywords: prognostics; RUL predictions; particle filter; uncertainty consideration; Multi-Model-Particle Filter; model-based approach; rubber-metal-elements; predictive maintenance

Hybrid Prediction Method for Remaining Useful Lifetime Estimation Considering Uncertainties - Bender, Amelie; Sextro, Walter (Proceedings of the European Conference of the PHM Society 2021)
Predictive Maintenance as a desirable maintenance strategy in industrial applications relies on suitable condition monitoring solutions to reduce costs and risks of the monitored technical systems. In general, those solutions utilize model-based or data-driven methods to diagnose the current state or predict future states of monitored technical systems. However, both methods have their advantages and drawbacks. Combining both methods can improve uncertainty consideration and accuracy. Different combination approaches of those hybrid methods exist to exploit synergy effects. The choice of an appropriate approach depends on different requirements and the goal behind the selection of a hybrid approach. In this work, the hybrid approach for estimating remaining useful lifetime takes potential uncertainties into account. Therefore, a data-driven estimation of new measurements is integrated within a model-based method. To consider uncertainties within the system, a differentiation between different system behavior is realized throughout diverse states of degradation. The developed hybrid prediction approach bases on a particle filtering method combined with a machine learning method, to estimate the remaining useful lifetime of technical systems. Particle filtering as a Monte Carlo simulation technique is suitable to map and propagate uncertainties. Moreover, it is a state-of-the-art model-based method for predicting remaining useful lifetime of technical systems. To integrate uncertainties a multi-model particle filtering approach is employed. In general, resampling as a part of the particle filtering approach has the potential to lead to an accurate prediction. However, in the case where no future measurements are available, it may increase the uncertainty of the prediction. By estimating new measurements, those uncertainties are reduced within the data-driven part of the approach. Hence, both parts of the hybrid approach strive to account for and reduce uncertainties. Rubber-metal-elements are employed as a use-case to evaluate the developed approach. Rubber-metal-elements, which are used to isolate vibrations in various systems, such as railways, trucks and wind turbines, show various uncertainties in their behavior and their degradation. Those uncertainties are caused by diverse inner and outer factors, such as manufacturing influences and operating conditions. By expert knowledge the influences are described, analyzed and if possible reduced. However, the remaining uncertainties are considered within the hybrid prediction method. Relative temperature is the selected measurand to describe the element’s degradation. In lifetime tests, it is measured as the difference between the element’s temperature and the ambient temperature. Thereby, the influence of the ambient temperature on the element’s temperature is taken into account. Those elements show three typical states of degradation that are identified within the temperature measurements. Depending on the particular state of degradation a new measurement is estimated within the hybrid approach to reduce potential uncertainties. Finally, the performance of the developed hybrid method is compared to a model-based method for estimating the remaining useful lifetime of the same elements. Suitable performance indices are implemented to underline the differences between the results.
Keywords: Hybrid prediction method; Multi-model particle filtering; Uncertainty quantification; RUL estimation

Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization - Grimme, Christian; Kerschke, Pascal; Aspar, Pelin; Trautmann, Heike; Preuss, Mike; Deutz, André H.; Wang, Hao; Emmerich, Michael (Computers & Operations Research)
Multi-objective (MO) optimization, i.e., the simultaneous optimization of multiple conflicting objectives, is gaining more and more attention in various research areas, such as evolutionary computation, machine learning (e.g., (hyper-)parameter optimization), or logistics (e.g., vehicle routing). Many works in this domain mention the structural problem property of multimodality as a challenge from two classical perspectives: (1) finding all globally optimal solution sets, and (2) avoiding to get trapped in local optima. Interestingly, these streams seem to transfer many traditional concepts of single-objective (SO) optimization into claims, assumptions, or even terminology regarding the MO domain, but mostly neglect the understanding of the structural properties as well as the algorithmic search behavior on a problem’s landscape. However, some recent works counteract this trend, by investigating the fundamentals and characteristics of MO problems using new visualization techniques and gaining surprising insights. Using these visual insights, this work proposes a step towards a unified terminology to capture multimodality and locality in a broader way than it is usually done. This enables us to investigate current research activities in multimodal continuous MO optimization and to highlight new implications and promising research directions for the design of benchmark suites, the discovery of MO landscape features, the development of new MO (or even SO) optimization algorithms, and performance indicators. For all these topics, we provide a review of ideas and methods but also an outlook on future challenges, research potential and perspectives that result from recent developments.
Keywords: Multimodal optimization; Multi-objective continuous optimization; Landscape analysis; Visualization; Benchmarking; Theory; Algorithms

Transforming into a Platform Provider: Strategic Options for Industrial Smart Service Providers - Beverungen, Daniel; Kundisch, Dennis; Wünderlich, Nancy (Journal of Service Management)
Purpose The purpose of this paper is to identify strategic options and challenges that arise when an industrial firm moves from providing smart service toward providing a platform. Design/methodology/approach This conceptual study takes on a multidisciplinary research perspective that integrates concepts, theories and insights from service management and marketing, information systems and platform economics. Findings The paper outlines three platform types – smart data platform, smart product platform and matching platform – as strategic options for firms that wish to evolve from smart service providers to platform providers. Research limitations/implications Investigating smart service platforms calls for launching interdisciplinary research initiatives. Promising research avenues are outlined to span boundaries that separate different research disciplines today. Practical implications Managing a successful transition from providing smart service toward providing a platform requires making significant investments in IT, platform-related capabilities and skills, as well as implement new approaches toward relationship management and brand-building. Originality/value The findings described in this paper are valuable to researchers in multiple disciplines seeking to develop and to justify theory related to platforms in industrial scenarios.
Keywords: Smart service; Platform; Interdisciplinary research; Manufacturing company; Smart service provider; Platform economics; Information systems; Multi-sided markets; Business-to-business (B2B) markets

The Online Multi-Commodity Facility Location Problem - Castenow, Jannik; Feldkord, Björn; Knollmann, Till; Malatyali, Manuel; Meyer auf der Heide, Friedhelm (Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures)
We consider a natural extension to the metric uncapacitated Facility Location Problem (FLP) in which requests ask for different commodities out of a finite set \( S \) of commodities. Ravi and Sinha (SODA 2004) introduced the model as the Multi-Commodity Facility Location Problem (MFLP) and considered it an offline optimization problem. The model itself is similar to the FLP: i.e., requests are located at points of a finite metric space and the task of an algorithm is to construct facilities and assign requests to facilities while minimizing the construction cost and the sum over all assignment distances. In addition, requests and facilities are heterogeneous; they request or offer multiple commodities out of $S$. A request has to be connected to a set of facilities jointly offering the commodities demanded by it. In comparison to the FLP, an algorithm has to decide not only if and where to place facilities, but also which commodities to offer at each. To the best of our knowledge we are the first to study the problem in its online variant in which requests, their positions and their commodities are not known beforehand but revealed over time. We present results regarding the competitive ratio. On the one hand, we show that heterogeneity influences the competitive ratio by developing a lower bound on the competitive ratio for any randomized online algorithm of \( Ω ( \sqrt{|S|} + \frac{\log n}{\log \log n} ) \) that already holds for simple line metrics. Here, \( n \) is the number of requests. On the other side, we establish a deterministic \( \mathcal{O}(\sqrt{|S|} · \log n) \)-competitive algorithm and a randomized \( \mathcal{O}(\sqrt{|S|} · \frac{\log n}{\log \log n} ) \)-competitive algorithm. Further, we show that when considering a more special class of cost functions for the construction cost of a facility, the competitive ratio decreases given by our deterministic algorithm depending on the function.
Keywords: Online Multi-Commodity Facility Location; Competitive Ratio; Online Optimization; Facility Location Problem

5GOS: Demonstrating multi-domain orchestration of end-to-end virtual RAN services - Camps-Mur, Daniel; Canellas, Ferran; Machwe, Azahar; Paracuellos, Jorge; Choumas, Kostas; Giatsios, Dimitris; Korakis, Thanasis; Razzaghi Kouchaksaraei, Hadi (the 6th IEEE International Conference on Network Softwarization (IEEE NetSoft 2020))
Despite recent progress in orchestration of Virtual Network Functions (VNFs) and in multi-technology SDN connectivity, the automated provisioning of end-to-end network services composed of virtual functions deployed across distributed compute locations remains an open challenge. This problem is especially relevant to support the deployment of future 5G networks, comprising virtual access and core network functions connected through a potentially multi-domain transport network. In this paper, we present and demonstrate the 5GOS, a lightweight end-to-end orchestration framework that enables the automated provisioning of virtual radio access network services. Using an experimental multi-domain testbed we demonstrate that the 5GOS can provision multi-domain virtual Wi-Fi and LTE services in less than three minutes.
Keywords: Orchestration; multi-domain; cellular network virtualization; SDN; 5G

Improvement of a rivet geometry for the self-piercing riveting of high-strength steel and multi-material joints - Uhe, Benedikt; Kuball, Clara-Maria; Merklein, Marion; Meschut, Gerson (Production Engineering)
As a result of lightweight design, increased use is being made of high-strength steel and aluminium in car bodies. Self-piercing riveting is an established technique for joining these materials. The dissimilar properties of the two materials have led to a number of different rivet geometries in the past. Each rivet geometry fulfils the requirements of the materials within a limited range. In the present investigation, an improved rivet geometry is developed, which permits the reliable joining of two material combinations that could only be joined by two different rivet geometries up until now. Material combination 1 consists of high-strength steel on both sides, while material combination 2 comprises aluminium on the punch side and high-strength steel on the die side. The material flow and the stress and strain conditions prevailing during the joining process are analysed by means of numerical simulation. The rivet geometry is then improved step-by-step on the basis of this analysis. Finally, the improved rivet geometry is manufactured and the findings of the investigation are verified in experimental joining tests.
Keywords: Self-piercing riveting; Joining technology; Rivet geometry; Multi-material design; High-strength steel; Aluminium

A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms - Bossek, Jakob; Kerschke, Pascal; Trautmann, Heike (Applied Soft Computing)
We build upon a recently proposed multi-objective view onto performance measurement of single-objective stochastic solvers. The trade-off between the fraction of failed runs and the mean runtime of successful runs – both to be minimized – is directly analyzed based on a study on algorithm selection of inexact state-of-the-art solvers for the famous Traveling Salesperson Problem (TSP). Moreover, we adopt the hypervolume indicator (HV) commonly used in multi-objective optimization for simultaneously assessing both conflicting objectives and investigate relations to commonly used performance indicators, both theoretically and empirically. Next to Penalized Average Runtime (PAR) and Penalized Quantile Runtime (PQR), the HV measure is used as a core concept within the construction of per-instance algorithm selection models offering interesting insights into complementary behavior of inexact TSP solvers.
Keywords: Algorithm selection; Multi-objective optimization; Performance measurement; Combinatorial optimization; Traveling Salesperson Problem

Dynamic Bi-Objective Routing of Multiple Vehicles - Bossek, Jakob; Grimme, Christian; Trautmann, Heike (Proceedings of the Genetic and Evolutionary Computation Conference)
In practice, e.g. in delivery and service scenarios, Vehicle-Routing-Problems (VRPs) often imply repeated decision making on dynamic customer requests. As in classical VRPs, tours have to be planned short while the number of serviced customers has to be maximized at the same time resulting in a multi-objective problem. Beyond that, however, dynamic requests lead to the need for re-planning of not yet realized tour parts, while already realized tour parts are irreversible. In this paper we study this type of bi-objective dynamic VRP including sequential decision making and concurrent realization of decisions. We adopt a recently proposed Dynamic Evolutionary Multi-Objective Algorithm (DEMOA) for a related VRP problem and extend it to the more realistic (here considered) scenario of multiple vehicles. We empirically show that our DEMOA is competitive with a multi-vehicle offline and clairvoyant variant of the proposed DEMOA as well as with the dynamic single-vehicle approach proposed earlier.
Keywords: decision making; dynamic optimization; evolutionary algorithms; multi-objective optimization; vehicle routing

A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms - Bossek, Jakob; Kerschke, Pascal; Trautmann, Heike (Applied Soft Computing)
We build upon a recently proposed multi-objective view onto performance measurement of single-objective stochastic solvers. The trade-off between the fraction of failed runs and the mean runtime of successful runs – both to be minimized – is directly analyzed based on a study on algorithm selection of inexact state-of-the-art solvers for the famous Traveling Salesperson Problem (TSP). Moreover, we adopt the hypervolume indicator (HV) commonly used in multi-objective optimization for simultaneously assessing both conflicting objectives and investigate relations to commonly used performance indicators, both theoretically and empirically. Next to Penalized Average Runtime (PAR) and Penalized Quantile Runtime (PQR), the HV measure is used as a core concept within the construction of per-instance algorithm selection models offering interesting insights into complementary behavior of inexact TSP solvers. \textbullet The multi-objective perspective is naturally generalizable to multiple objectives. \textbullet Proof of relationship between HV and the PAR in the considered bi-objective space. \textbullet New insights into complementary behavior of stochastic optimization algorithms.
Keywords: Algorithm selection; Combinatorial optimization; Multi-objective optimization; Performance measurement; Traveling Salesperson Problem

Approaches for Collaborative Security Defences in Multi Network Environments - Koning, Ralph; Polevoy, Gleb; Meijer, Lydia; de Laat, Cees; Grosso, Paola (2019 6th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/ 2019 5th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom))
Resolving distributed attacks benefits from collaboration between networks. We present three approaches for the same multi-domain defensive action that can be applied in such an alliance: 1) Counteract Everywhere, 2) Minimize Countermeasures, and 3) Minimize Propagation. First, we provide a formula to compute efficiency of a defense; then we use this formula to compute the efficiency of the approaches under various circumstances. Finally, we discuss how task execution order and timing influence defense efficiency. Our results show that the Minimize Propagation approach is the most efficient method when defending against the chosen attack.
Keywords: computer network security; multinetwork environments; multidomain defensive action; task execution order; timing influence defense efficiency; distributed attacks; collaborative security defence approach; minimize propagation approach; minimize countermeasure approach; counteract everywhere approach; Conferences; Cloud computing; Computer crime; Edge computing; Security; Defense Approaches; Multi-Domain Defense; Collaborative Defense; Defense Algorithms; Computer Networks

Using complex multi-dimensional vibration trajectories in ultrasonic bonding and welding - Schemmel, Reinhard; Hemsel, Tobias; Dymel, Collin; Hunstig, Matthias; Brökelmann, Michael; Sextro, Walter (Sensors and Actuators A: Physical)
Ultrasonic joining is a common industrial process. In the electronics industry it is used to form electrical connections, including those of dissimilar materials. Multiple influencing factors in ultrasonic joining are known and extensively investigated; process parameters like ultrasonic power, bond force, and bonding frequency of the ultrasonic vibration are known to have a high impact on a reliable joining process and need to be adapted for each new application with different geometry or materials. This contribution is focused on increasing ultrasonic power transmitted to the interface and keeping mechanical stresses during ultrasonic bonding low by using a multi-dimensional ultrasonic transducer concept. Bonding results for a new designed connector pin in IGBT-modules achieved by multi- and one-dimensional bonding are discussed.
Keywords: Ultrasonic bonding; Ultrasonic welding; Multi-dimensional bonding; Complex vibration; Multi-frequent; Two-dimensional friction model

Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm - Bossek, Jakob; Grimme, Christian; Meisel, Stephan; Rudolph, Günter; Trautmann, Heike (Evolutionary Multi-Criterion Optimization (EMO))
We tackle a bi-objective dynamic orienteering problem where customer requests arise as time passes by. The goal is to minimize the tour length traveled by a single delivery vehicle while simultaneously keeping the number of dismissed dynamic customers to a minimum. We propose a dynamic Evolutionary Multi-Objective Algorithm which is grounded on insights gained from a previous series of work on an a-posteriori version of the problem, where all request times are known in advance. In our experiments, we simulate different decision maker strategies and evaluate the development of the Pareto-front approximations on exemplary problem instances. It turns out, that despite severely reduced computational budget and no oracle-knowledge of request times the dynamic EMOA is capable of producing approximations which partially dominate the results of the a-posteriori EMOA and dynamic integer linear programming strategies.
Keywords: Combinatorial optimization; Dynamic optimization; Metaheuristics; Multi-objective optimization; Vehicle routing

On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem - Bossek, Jakob; Grimme, Christian; Neumann, Frank (Proceedings of the Genetic and Evolutionary Computation Conference)
Research has shown that for many single-objective graph problems where optimum solutions are composed of low weight sub-graphs, such as the minimum spanning tree problem (MST), mutation operators favoring low weight edges show superior performance. Intuitively, similar observations should hold for multi-criteria variants of such problems. In this work, we focus on the multi-criteria MST problem. A thorough experimental study is conducted where we estimate the probability of edges being part of non-dominated spanning trees as a function of the edges’ non-domination level or domination count, respectively. Building on gained insights, we propose several biased one-edge-exchange mutation operators that differ in the used edge-selection probability distribution (biased towards edges of low rank). Our empirical analysis shows that among different graph types (dense and sparse) and edge weight types (both uniformly random and combinations of Euclidean and uniformly random) biased edge-selection strategies perform superior in contrast to the baseline uniform edge-selection. Our findings are in particular strong for dense graphs.
Keywords: biased mutation; combinatorial optimization; minimum spanning tree; multi-objective optimization

Intelligent Production of Wire Bonds using Multi-Objective Optimization – Insights, Opportunities and Challenges - Unger, Andreas; Hunstig, Matthias; Meyer, Tobias; Brökelmann, Michael; Sextro, Walter (In Proceedings of IMAPS 2018 – 51st Symposium on Microelectronics, Pasadena, CA, 2018)
Ultrasonic wire bonding is an indispensable process in the industrial manufacturing of semiconductor devices. Copper wire is increasingly replacing the well-established aluminium wire because of its superior electrical, thermal and mechanical properties. Copper wire processes differ significantly from aluminium processes and are more sensitive to disturbances, which reduces the range of parameter values suitable for a stable process. Disturbances can be compensated by an adaption of process parameters, but finding suitable parameters manually is difficult and time-consuming. This paper presents a physical model of the ultrasonic wire bonding process including the friction contact between tool and wire. This model yields novel insights into the process. A prototype of a multi-objective optimizing bonding machine (MOBM) is presented. It uses multi-objective optimization, based on the complete process model, to automatically select the best operating point as a compromise of concurrent objectives.
Keywords: wire bonding; multi-objective optimization; process model; copper wire; self-optimization

A Peer-to-Peer based Cloud Storage supporting orthogonal Range Queries of arbitrary Dimension - Benter, Markus; Knollmann, Till; Meyer auf der Heide, Friedhelm; Setzer, Alexander; Sundermeier, Jannik (Proceedings of the 4th International Symposium on Algorithmic Aspects of Cloud Computing (ALGOCLOUD))
We present a peer-to-peer network that supports the efficient processing of orthogonal range queries $R=\bigtimes_{i=1}^{d}[a_i,\,b_i]$ in a $d$-dimensional point space.\\ The network is the same for each dimension, namely a distance halving network like the one introduced by Naor and Wieder (ACM TALG'07). We show how to execute such range queries using $\mathcal{O}\left(2^{d'}d\,\log m + d\,|R|\right)$ hops (and the same number of messages) in total. Here $[m]^d$ is the ground set, $|R|$ is the size and $d'$ the dimension of the queried range. Furthermore, if the peers form a distributed network, the query can be answered in $\mathcal{O}\left(d\,\log m + d\,\sum_{i=1}^{d}(b_i-a_i+1)\right)$ communication rounds. Our algorithms are based on a mapping of the Hilbert Curve through $[m]^d$ to the peers.
Keywords: Distributed Storage; Multi-Dimensional Range Queries; Peer-to-Peer; Hilbert Curve

Local Search Effects in Bi-Objective Orienteering - Bossek, Jakob; Grimme, Christian; Meisel, Stephan; Rudolph, Günter; Trautmann, Heike (Proceedings of the Genetic and Evolutionary Computation Conference)
We analyze the effects of including local search techniques into a multi-objective evolutionary algorithm for solving a bi-objective orienteering problem with a single vehicle while the two conflicting objectives are minimization of travel time and maximization of the number of visited customer locations. Experiments are based on a large set of specifically designed problem instances with different characteristics and it is shown that local search techniques focusing on one of the objectives only improve the performance of the evolutionary algorithm in terms of both objectives. The analysis also shows that local search techniques are capable of sending locally optimal solutions to foremost fronts of the multi-objective optimization process, and that these solutions then become the leading factors of the evolutionary process.
Keywords: combinatorial optimization; metaheuristics; multi-objective optimization; orienteering; transportation

Contrasting Risk Perceptions of Technology-Based Service Innovations in Inter-Organizational Settings. - Paluch, Stefanie; Wünderlich, Nancy (Journal of business Research)
Despite the rapid growth and potential of technology-based services, managers' greatest challenges are gaining customer acceptance and increasing usage of these new innovative services. In the B2C field, studies of self-service technology show that perceived risk is an important factor influencing the use of service technology. Though prior research explores different risk types that emerge in consumer settings, risk perception in the B2B setting lacks a detailed examination of different risk types influencing technology-based service adoption. Data from 49 qualitative interviews with providers and customers in two different B2B industries inform this study. The findings emphasize the importance of functional and financial risks in a B2B context and show that business customers' personal and psychological fears hinder their use of technology-based services. Results highlight differences in risk perception and evaluation between customers and providers.
Keywords: Risk perception; Technology-based service innovations; Business-to-business context; Interview study; Risk categories; Smart service

The Value of Self-Service: Long-Term Effects of Technology-Based Self-Service Usage on Customer Retention. - Scherer, Anne; Wünderlich, Nancy; Von Wangenheim, Florian (MIS Quarterly)
Advancements in information technology have changed the way customers experience a service encounter and their relationship with service providers. Especially technology-based self-service channels have found their way into the 21st century service economy. While research embraces these channels for their cost-efficiency, it has not examined whether a shift from personal to self-service affects customer–firm relationships. Drawing from the service-dominant logic and its central concept of value-in-context, we discuss customers’ value creation in self-service and personal service channels and examine the long-term impact of these channels on customer retention. Using longitudinal customer data, we investigate how the ratio of self-service versus personal service use influences customer defection over time. Our findings suggest that the ratio of self-service to personal service used affects customer defection in a U-shaped manner, with intermediate levels of both self-service and personal service use being associated with the lowest likelihood of defection. We also find that this effect mitigates over time. We conclude that firms should not shift customers toward self-service channels completely, especially not at the beginning of a relationship. Our study underlines the importance of understanding when and how self-service technologies create valuable customer experiences and stresses the notion of actively managing customers’ cocreation of value.
Keywords: customer defection; customer retention; e-service; longitudinal; Self-service; value-in-context

A Multi-Theoretical Literature Review on Information Security Investments using the Resource-Based View and the Organizational Learning Theory - Weishäupl, Eva; Yasasin, Emrah; Schryen, Guido (International Conference on Information Systems)
The protection of information technology (IT) has become and is predicted to remain a key economic challenge for organizations. While research on IT security investment is fast growing, it lacks a theoretical basis for structuring research, explaining economic-technological phenomena and guide future research. We address this shortcoming by suggesting a new theoretical model emerging from a multi-theoretical perspective adopt-ing the Resource-Based View and the Organizational Learning Theory. The joint appli-cation of these theories allows to conceptualize in one theoretical model the organiza-tional learning effects that occur when the protection of organizational resources through IT security countermeasures develops over time. We use this model of IT security invest-ments to synthesize findings of a large body of literature and to derive research gaps. We also discuss managerial implications of (closing) these gaps by providing practical ex-amples.
Keywords: Information Security; Investment; Literature review; Resource-based View; Organi-zational Learning Theory; Multi-theoretical Perspective

Hierarchisches Optimierungskonzept für die Laststeuerung von Haushaltsgeräten - Stille, Karl Stephan Christian; Böcker, Joachim; Bettentrup, Ralf; Kaiser, Ingo (ETG-Fachtagung "Von Smart Grids zu Smart Markets")
Haushaltsgeräte aus der Klasse der "Weißen Ware" tragen mit etwa einem Drittel ($34,2%$ \citeBDEW2013) zum privaten Energieverbrauch bei. Diese Veröffentlichung präsentiert eine Struktur und die dafür notwendige optimale Betriebsstrategie für Weiße Ware in einer Umgebung mit Strompreisen, die wegen der Volatilität der Regenerativen Energien stark fluktuieren. Das vorgeschlagene Konzept nutzt dafür ein dezentrales Energiemanagementsystem, das über drei Hierarchieebenen verteilt ist: die Geräteebene, die Haushaltsebene und die Ortsnetzebene. Auf der Geräteebene nutzt dieses Konzept zusätzlich Betriebsflexibilitäten der Haushaltsgeräte aus.
Keywords: Energy management; hybrid energy storage system; self-optimization; multi-objective optimization; adaptive systems; pareto set; SFB614-D1; SFB614-D2; LEA-Publikation; Eigene

Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle - Meisel, Stephan; Grimme, Christian; Bossek, Jakob; Wölck, Martin; Rudolph, Günter; Trautmann, Heike (Proceedings of the Genetic and Evolutionary Computation Conference)
We evaluate the performance of a multi-objective evolutionary algorithm on a class of dynamic routing problems with a single vehicle. In particular we focus on relating algorithmic performance to the most prominent characteristics of problem instances. The routing problem considers two types of customers: mandatory customers must be visited whereas optional customers do not necessarily have to be visited. Moreover, mandatory customers are known prior to the start of the tour whereas optional customers request for service at later points in time with the vehicle already being on its way. The multi-objective optimization problem then results as maximizing the number of visited customers while simultaneously minimizing total travel time. As an a-posteriori evaluation tool, the evolutionary algorithm aims at approximating the related Pareto set for specifically designed benchmarking instances differing in terms of number of customers, geographical layout, fraction of mandatory customers, and request times of optional customers. Conceptional and experimental comparisons to online heuristic procedures are provided.
Keywords: combinatorial optimization; metaheuristics; multi-objective optimization; online algorithms; transportation

Numerical Representations and User Behaviour in Social Networking Sites: Towards a Multi- Theoretical Research Framework - Sjöklint, Mimmi; Constantiou, Ioanna; Trier, Matthias (ECIS 2013 Proceedings)
The new technological enhancements and the accessibility to varieties of online applications, enable users to collect personal data and perform self-evaluation through test, comparison and experimentation. The sparked interest in numbers and numbers as self-representative visualisations is prominent in social networking sites, which are the empirical setting for the present study. This paper sets out to establish a multi-theoretical framework which enables the investigation of emerging phenomena of the role of numbers in social networking sites. The proposed framework rests on three theoretical pillars: self-determination theory, heuristic decision making and behavioural economics. A discussion departs from these convictions to investigate user reactions and behaviour when faced with numerical representations in the SNS.
Keywords: User Behaviour; Social Networking Sites; Numerical Representations; Multi-Theoretical Framework; Quantified Self; Pointification

Efficient Power-Intent Validation Using "Loosely-Timed" Simulation Models: A Non-Invasive Approach - Mischkalla, Fabian; Müller, Wolfgang
Faced with increasing demands on energy efficiency, current electronic systems operate according to complex power management schemes including more and more fine-grained voltage frequency scaling and power shutdown scenarios. Consequently, validation of the power design intent should begin as early as possible at electronic system-level (ESL) together with first executable system specifications for integrity tests. However, today's system-level design methodologies usually focus on the abstraction of digital logic and time, so that typical low-power aspects cannot be considered so far. In this paper, we present a high-level modeling approach on top of the SystemC/TLM standard to simulate power distribution and voltage based implications in a "loosely-timed" functional execution context. The approach reuses legacy TLM models and prevents the need for detailed lock-step process synchronization in contrast to existing methods. A case study derived from an open source low-power design demonstrates the efficiency of our approach in terms of simulation performance and testability.
Keywords: Time-varying systems; Time-domain analysis; Synchronization; Context modeling; Clocks; Semantics; Standards

An Energy-Efficient Heuristic for Hard Real-Time System on Multi-Core Processors - He, Da; Müller, Wolfgang (Proceedings of the International Conference on Applied Computing (AC))
In the electronic system development, energy consumption is clearly becoming one of the most important design concerns. From the system level point of view, Dynamic Power Management (DPM) and Dynamic Voltage and Frequency Scaling (DVFS) are two mostly applied techniques to adjust the tradeoff between the performance and power dissipation at runtime. In this paper, we study the problem of combined application of both techniques with regard to hard real-time systems running on cluster-based multi-core processors. To optimize the processor energy consumption, a heuristic based on simulated annealing with efficient termination criterion is proposed. The experiment results show that the proposed algorithm outperforms the existing approaches in terms of the energy reduction.
Keywords: Dynamic Power Management; Dynamic Voltage and Frequency Scaling; Hard Real-Time; Multi-core Processor

RTOS-Aware Refinement for TLM2.0-based HW/SW Design - Becker, Markus; Di Guglielmo, Giuseppe; Fummi, Franco; Müller, Wolfgang; Pravadelli, Graziano; Xie, Tao (Proceedings of DATE’10)
Refinement of untimed TLM models into a timed HW/SW platform is a step by step design process which is a trade-off between timing accuracy of the used models and correct estimation of the final timing performance. The use of an RTOS on the target platform is mandatory in the case real-time properties must be guaranteed. Thus, the question is when the RTOS must be introduced in this step by step refinement process. This paper proposes a four-level RTOS-aware refinement methodology that, starting from an untimed TLM SystemC description of the whole system, progressively introduce HW/SW partitioning, timing, device driver and RTOS functionalities, till to obtain an accurate model of the final platform, where SW tasks run upon an RTOS hosted by QEMU and HW components are modeled by cycle accurate TLM descriptions. Each refinement level allows the designer to estimate more and more accurate timing properties, thus anticipating design decisions without being constrained to leave timing analysis to the final step of the refinement. The effectiveness of the methodology has been evaluated in the design of two complex platforms.
Keywords: Timing; Hardware; Operating systems; Process design; Accuracy; Standards development; Context modeling; Real time systems; Communication channels; Microprogramming

Work roll cooling system design optimisation in presence of uncertainty and constrains - Azene, Y.T.; Roy, R.; Farrugia, D.; Onisa, C.; Mehnen, J.; Trautmann, Heike (CIRP Journal of Manufacturing Science and Technology)
The paper presents a framework to optimise the design of work roll based on the cooling performance. The framework develops meta-models from a set of finite element analyses (FEA) of the roll cooling. A design of experiment technique is used to identify the FEA runs. The research also identifies sources of uncertainties in the design process. A robust evolutionary multi-objective evaluation technique is applied to the design optimisation in constrained problems with real life uncertainty. The approach handles uncertainties associated both with design variables and fitness functions. Constraints violation within the neighbourhood of a design is considered as part of a measurement for degree of feasibility and robustness of a solution.
Keywords: Roll cooling design; Uncertainty; Design optimisation; Multi-objective optimisation; Constraint in design

Online Diarization of Streaming Audio-Visual Data for Smart Environments - Schmalenstroeer, Joerg; Haeb-Umbach, Reinhold (IEEE Journal of Selected Topics in Signal Processing)
For an environment to be perceived as being smart, contextual information has to be gathered to adapt the system's behavior and its interface towards the user. Being a rich source of context information speech can be acquired unobtrusively by microphone arrays and then processed to extract information about the user and his environment. In this paper, a system for joint temporal segmentation, speaker localization, and identification is presented, which is supported by face identification from video data obtained from a steerable camera. Special attention is paid to latency aspects and online processing capabilities, as they are important for the application under investigation, namely ambient communication. It describes the vision of terminal-less, session-less and multi-modal telecommunication with remote partners, where the user can move freely within his home while the communication follows him. The speaker diarization serves as a context source, which has been integrated in a service-oriented middleware architecture and provided to the application to select the most appropriate I/O device and to steer the camera towards the speaker during ambient communication.
Keywords: audio streaming; audio visual data streaming; context information speech; face identification; face recognition; image segmentation; middleware; multimodal telecommunication; online diarization; service oriented middleware architecture; sessionless telecommunication; software architecture; speaker identification; speaker localization; speaker recognition; steerable camera; telecommunication computing; temporal segmentation; terminal-less telecommunication; video streaming

Joint Parameter Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning - Bevermeier, Maik; Peschke, Sven; Haeb-Umbach, Reinhold (IEEE 69th Vehicular Technology Conference (VTC 2009 Spring))
In this paper we present a novel vehicle tracking method which is based on multi-stage Kalman filtering of GPS and IMU sensor data. After individual Kalman filtering of GPS and IMU measurements the estimates of the orientation of the vehicle are combined in an optimal manner to improve the robustness towards drift errors. The tracking algorithm incorporates the estimation of time-variant covariance parameters by using an iterative block Expectation-Maximization algorithm to account for time-variant driving conditions and measurement quality. The proposed system is compared to an interacting multiple model approach (IMM) and achieves improved localization accuracy at lower computational complexity. Furthermore we show how the joint parameter estimation and localizaiton can be conducted with streaming input data to be able to track vehicles in a real driving environment.
Keywords: computational complexity; expectation-maximisation algorithm; Global Positioning System; inertial measurement unit; inertial navigation; interacting multiple model; iterative block expectation-maximization algorithm; Kalman filters; multi-stage Kalman filter; parameter estimation; road vehicles; vehicle positioning; vehicle tracking

Increased Accuracy through Noise Injection in Abstract RTOS Simulation - Zabel, Henning; Müller, Wolfgang (Proceedings of DATE'09)
Today, mobile and embedded real-time systems have to cope with the migration and allocation of multiple software tasks running on top of a real-time operating system (RTOS) residing on one or multiple system processors. Abstract RTOS simulations and timing analysis applies for fast and early estimation to configure it towards the individual needs of the application and environment. In this context, a high accuracy of the simulation compared to an instruction set simulation (ISS) is of key importance. In this paper, we investigate the accuracy of abstract RTOS simulation and compare it to ISS and the behavior of the physical system. We show that we can reach an increased accuracy of the simulation when we inject noise into the time model. Our results indicate that it is sufficient to inject uniformly distributed random time values to the RTOS real-time clock.
Keywords: Timing; Analytical models; Clocks; Performance analysis; Scheduling; Operating systems; Delay; Real time systems; Application software; Context modeling

Assessment of a Multimodal Interaction and Rendering System against Established Design Principles - Schäfer, Robbie; Müller, Wolfgang (Journal on Multimodal User Interfaces)
To provide user interfaces for a rich set of devices and interaction modalities, we follow a model-based development methodology. We devised an architecture which deploys user interfaces specified as dialogue models with abstract interaction objects and allows context-based adaptations by means of an external transcoding process. For the validation of the applicability of this methodology for developing usable multimodal multi-device systems, we present two case studies based on proof-of-concept implementations and assessed them with a large set of established design principles and different types of modality cooperation.
Keywords: Interaction architecture Abstract interaction objects Dialogue model Transformations Multimodality Multi-device Design principles

How can multimodal cues from child-directed interaction reduce learning complexity in robots? - Rohlfing, Katharina; Fritsch, Jannik; Wrede, Britta; Jungmann, Tanja (Advanced Robotics)
Robots have to deal with an enormous amount of sensory stimuli. One solution in making sense of them is to enable a robot system to actively search for cues that help structuring the information. Studies with infants reveal that parents support the learning-process by modifying their interaction style, dependent on their child's developmental age. In our study, in which parents demonstrated everyday actions to their preverbal children (8-11 months old), our aim was to identify objective parameters for multimodal action modification. Our results reveal two action parameters being modified in adult-child interaction: roundness and pace. Furthermore, we found that language has the power to help children structuring actions sequences by synchrony and emphasis. These insights are discussed with respect to the built-in attention architecture of a socially interactive robot, which enables it to understand demonstrated actions. Our algorithmic approach towards automatically detecting the task structure in child-designed input demonstrates the potential impact of insights from developmental learning on robotics. The presented findings pave the way to automatically detect when to imitate in a demonstration
Keywords: multi-modal motherese; child-directed input; motionese; learning mechanisms

Secure Profile Management in Smart Home Networks - Ziegler, Max; Müller, Wolfgang; Schäfer, Robbie; Loeser, Chris (Proceedings of the 1st International Workshop on Secure and Ubiquitous Networks (SUN-2005))
Currently, middleware for smart home networks with embedded and mobile devices are in the focus of several investigations. In this paper, we propose a middleware for secure management of device and user profiles by integrating a profile database with a generic authentication scheme for an X.509 enabled ticket management in the context of the OSGi framework. After the introduction of the individual system components and their interaction, we also discuss potential system attacks.
Keywords: Intelligent networks; Smart homes; Middleware; Project management; Data security; Ubiquitous computing; Context-aware services; Computer architecture; Home automation; Environmental management

Virtualizing Hardware with Multi-Context Reconfigurable Arrays - Enzler, Rolf; Plessl, Christian; Platzner, Marco (Proc. Int. Conf. on Field Programmable Logic and Applications (FPL))
In contrast to processors, current reconfigurable devices totally lack programming models that would allow for device independent compilation and forward compatibility. The key to overcome this limitation is hardware virtualization. In this paper, we resort to a macro-pipelined execution model to achieve hardware virtualization for data streaming applications. As a hardware implementation we present a hybrid multi-context architecture that attaches a coarse-grained reconfigurable array to a host CPU. A co-simulation framework enables cycle-accurate simulation of the complete architecture. As a case study we map an FIR filter to our virtualized hardware model and evaluate different designs. We discuss the impact of the number of contexts and the feature of context state on the speedup and the CPU load.
Keywords: Zippy; multi-context; FPGA

Visual-Textual Prototyping of 4D Scenes - Dücker, M.; Geiger, Christian; Hunstock, R.; Lehrenfeld, Georg; Müller, Wolfgang (Proceedings of the 1997 IEEE Symposium on Visual Languages)
3D-graphics are becoming popular in a steadily increasing number of areas such as entertainment, scientific visualization, simulation, and virtual reality. Despite this rapid growth the generation of animated 3D scenes is by no means trivial. Since animated 3D objects evolve over time the authors denote these objects as 4D. The article presents a novel approach to the rapid prototyping of 4D models. They introduce the AAL (Animated Agent Layer) system. AAL is an interpreter-based approach covering a textual (AAL-PR) as well as a visual command language (AAL-VL) for the specification of the dynamics in 4D scenes. AAL provides support for different levels of abstraction: primitives, structured objects, animated objects, and animated (autonomous) agents.
Keywords: Prototypes; Layout; Animation; Command languages; Application software; Libraries; Virtual reality; Computer graphics; Hardware; Context modeling

Complete Visual Specification and Animations of Protocols - Müller, Wolfgang; Lehrenfeld, Georg; Tahedl, C. (Proceedings of ASP-DAC'95/CHDL'95/VLSI'95 with EDA Technofair)
This article discusses the application of Pictorial Janus (PJ) for the rapid development and analysis of protocols by animation and complete visualization. In order to make PJ applicable in the context of hardware description we first extend PJ by timing facilities (Timed PJ) and introduce an approach for integrating VHDL models into this visual framework preserving the simulation semantics of VHDL. We finally give the example of the specification and animation of a non interlocked protocol.
Keywords: Animation; Protocols; Timing; Computer languages; Electronic mail; Context modeling; Visualization; Control systems; Flow graphs; Trademarks