@article{27060, author = {{Mesch, Christina M. and Stimming, Madlen and Foterek, Kristina and Hilbig, Annett and Alexy, Ute and Kersting, Mathilde and Libuda, Lars}}, issn = {{0195-6663}}, journal = {{Appetite}}, pages = {{113--119}}, title = {{{Food variety in commercial and homemade complementary meals for infants in Germany. Market survey and dietary practice}}}, doi = {{10.1016/j.appet.2014.01.074}}, year = {{2014}}, } @article{27768, author = {{Riedel, Christina and von Kries, Rüdiger and Buyken, Anette and Diethelm, Katharina and Keil, Thomas and Grabenhenrich, Linus and Müller, Manfred J. and Plachta-Danielzik, Sandra}}, issn = {{1932-6203}}, journal = {{PLoS ONE}}, title = {{{Overweight in Adolescence Can Be Predicted at Age 6 Years: A CART Analysis in German Cohorts}}}, doi = {{10.1371/journal.pone.0093581}}, year = {{2014}}, } @inproceedings{1140, abstract = {{Customized planning, engineering and build-up of factory plants are very complex tasks, where project management contains lots of risks and uncertainties. Existing simulation techniques could help massively to evaluate these uncertainties and achieve improved and at least more robust plans during project management, but are typically not applied in industry, especially at SMEs (small and medium-sized enterprises). This paper presents some results of the joint research project simject of the Universities of Paderborn and Kassel, which aims at the development of a demonstrator for a simulation-based and logistic-integrated project planning and scheduling. Based on the researched state-of-the-art, requirements and a planning process are derived and described, as well as a draft of the current technical infrastructure of the intended modular prototype. First plug-ins for project simulation and multi-project optimization are implemented and already show possible benefits for the project management process.}}, author = {{Gutfeld, Thomas and Jessen, Ulrich and Wenzel, Sigrid and Weber, Jens}}, booktitle = {{Proceedings of the 2014 Winter Simulation Conference}}, editor = {{Tolk, Andreas and Diallo, Saikou Y. and Ryzhov, Ilya O. and Yilmaz, Levent and Buckley, Stephen J. and Miller, John A.}}, isbn = {{9781479974863}}, location = {{Savannah, GA, USA}}, pages = {{3423--3434}}, publisher = {{IEEE Press}}, title = {{{A Technical Concept for Plant Engineering by Simulation-Based and Logistic-Integrated Project Management}}}, doi = {{10.1109/WSC.2014.7020175}}, year = {{2014}}, } @inproceedings{1781, abstract = {{In light of an increasing awareness of environmental challenges, extensive research is underway to develop new light-weight materials. A problem arising with these materials is their increased response to vibration. This can be solved using a new composite material that contains embedded hollow spheres that are partially filled with particles. Progress on the adaptation of molecular dynamics towards a particle-based numerical simulation of this material is reported. This includes the treatment of specific boundary conditions and the adaption of the force computation. First results are presented that showcase the damping properties of such particle-filled spheres in a bouncing experiment.}}, author = {{Steinle, Tobias and Vrabec, Jadran and Walther, Andrea}}, booktitle = {{Proc. Modeling, Simulation and Optimization of Complex Processes (HPSC)}}, editor = {{Bock, Hans Georg and Hoang, Xuan Phu and Rannacher, Rolf and Schlöder, Johannes P.}}, isbn = {{978-3-319-09063-4}}, pages = {{233--243}}, publisher = {{Springer International Publishing}}, title = {{{Numerical Simulation of the Damping Behavior of Particle-Filled Hollow Spheres}}}, doi = {{10.1007/978-3-319-09063-4_19}}, year = {{2014}}, } @inproceedings{22389, author = {{Strop, Malte and Hölscher, Christian and Zimmer, Detmar}}, booktitle = {{OPT-i 2014. 1st International Conference on Engineering and Applied Sciences Optimization}}, isbn = {{978-960-99994-6-5}}, pages = {{626--663}}, publisher = {{Institute of Structural Analyses and Antiseismic Research}}, title = {{{Intelligent Operating Strategies for Multi-Motor Drive Systems}}}, volume = {{1}}, year = {{2014}}, } @article{23091, author = {{Shareef, Zeeshan and Trächtler, Ansgar}}, journal = {{Robotica}}, title = {{{Simultaneous Path Planning and Trajectory Optimization for Robotic Manipulators using Discrete Mechanics and Optimal Control}}}, year = {{2014}}, } @article{21743, author = {{Repenning, A. and C. Webb, D. and Brand, C. and Gluck, F. and Grover, R. and Miller, S. and Nickerson, H. and Song, M.}}, journal = {{IEEE Computer Graphics and Applications}}, number = {{3}}, pages = {{68--71}}, publisher = {{IEEE}}, title = {{{Beyond Minecraft: Facilitating Computational Thinking through Modeling and Programming in 3D}}}, doi = {{10.1109/MCG.2014.46}}, volume = {{34}}, year = {{2014}}, } @inproceedings{5749, author = {{Yigitbas, Enes and Fischer, Holger Gerhard and Sauer, Stefan}}, booktitle = {{Design, User Experience, and Usability. Theories, Methods, and Tools for Designing the User Experience - Third International Conference, {DUXU} 2014, Held as Part of {HCI} International 2014, Heraklion, Crete, Greece, June 22-27, 2014, Proceedings, Part {I}}}, pages = {{206--213}}, title = {{{Model-Based User Interface Development for Adaptive Self-Service Systems}}}, doi = {{10.1007/978-3-319-07668-3\_21}}, year = {{2014}}, } @inproceedings{455, abstract = {{We study the existence of approximate pure Nash equilibria in weighted congestion games and develop techniques to obtain approximate potential functions that prove the existence of alpha-approximate pure Nash equilibria and the convergence of alpha-improvement steps. Specifically, we show how to obtain upper bounds for approximation factor alpha for a given class of cost functions. For example for concave cost functions the factor is at most 3/2, for quadratic cost functions it is at most 4/3, and for polynomial cost functions of maximal degree d it is at at most d + 1. For games with two players we obtain tight bounds which are as small as for example 1.054 in the case of quadratic cost functions.}}, author = {{Hansknecht, Christoph and Klimm, Max and Skopalik, Alexander}}, booktitle = {{Proceedings of the 17th. International Workshop on Approximation Algorithms for Combinatorial Optimization Problems (APPROX)}}, pages = {{242 -- 257}}, title = {{{Approximate pure Nash equilibria in weighted congestion games}}}, doi = {{10.4230/LIPIcs.APPROX-RANDOM.2014.242}}, year = {{2014}}, } @inproceedings{457, abstract = {{Automatically composing service-based software solutionsis still a challenging task. Functional as well as nonfunctionalproperties have to be considered in order to satisfyindividual user requests. Regarding non-functional properties,the composition process can be modeled as optimization problemand solved accordingly. Functional properties, in turn, can bedescribed by means of a formal specification language. Statespacebased planning approaches can then be applied to solvethe underlying composition problem. However, depending on theexpressiveness of the applied formalism and the completenessof the functional descriptions, formally equivalent services maystill differ with respect to their implemented functionality. As aconsequence, the most appropriate solution for a desired functionalitycan hardly be determined without considering additionalinformation. In this paper, we demonstrate how to overcome thislack of information by means of Reinforcement Learning. Inorder to resolve ambiguity, we expand state-space based servicecomposition by a recommendation mechanism that supportsdecision-making beyond formal specifications. The recommendationmechanism adjusts its recommendation strategy basedon feedback from previous composition runs. Image processingserves as case study. Experimental results show the benefit of ourproposed solution.}}, author = {{Jungmann, Alexander and Mohr, Felix and Kleinjohann, Bernd }}, booktitle = {{Proceedings of the 7th International Conference on Service Oriented Computing and Applications (SOCA)}}, pages = {{105--112}}, title = {{{Applying Reinforcement Learning for Resolving Ambiguity in Service Composition}}}, doi = {{10.1109/SOCA.2014.48}}, year = {{2014}}, } @inproceedings{759, author = {{Dräxler, Martin and Dreimann, Philipp and Karl, Holger}}, booktitle = {{IEEE Online Conference on Green Communications, OnlineGreenComm 2014, November 12-14, 2014}}, pages = {{1----7}}, title = {{{Anticipatory power cycling of mobile network equipment for high demand multimedia traffic}}}, doi = {{10.1109/OnlineGreenCom.2014.7114415}}, year = {{2014}}, } @inproceedings{760, author = {{Auroux, Sebastien and Karl, Holger}}, booktitle = {{25th IEEE Annual International Symposium on Personal, Indoor, and Mobile Radio Communication, {PIMRC} 2014, Washington DC, USA, September 2-5, 2014}}, pages = {{1294----1299}}, title = {{{Flow processing-aware controller placement in wireless DenseNets}}}, doi = {{10.1109/PIMRC.2014.7136368}}, year = {{2014}}, } @inproceedings{762, author = {{Schwabe, Arne and Karl, Holger}}, booktitle = {{Proceedings of the third workshop on Hot topics in software defined networking, HotSDN '14, Chicago, Illinois, USA, August 22, 2014}}, pages = {{115----120}}, title = {{{Using MAC addresses as efficient routing labels in data centers}}}, doi = {{10.1145/2620728.2620730}}, year = {{2014}}, } @book{9873, abstract = {{Intelligent technical systems, which combine mechanical, electrical and software engineering with methods from control engineering and advanced mathematics, go far beyond the state of the art in mechatronics and open up fascinating perspectives. Among these systems are so-called self-optimizing systems, which are able to adapt their behavior autonomously and flexibly to changing operating conditions. The Collaborative Research Center 614 "Self-optimizing concepts and structures in mechanical engineering" pursued the long-term aim to enable others to develop dependable self-optimizing systems. Assuring their dependability poses new challenges. However, self-optimization also offers the possibility to adapt the system's behavior to improve dependability during operation. The aim of this book is to provide methods and techniques to master the challenges and to exploit the possibilities given by self-optimization. The reader will be able to develop self-optimizing systems that fulfill and surpass today’s dependability requirements easily. This book is directed to researchers and practitioners alike. It gives a brief introduction to the holistic development approach for self-optimizing mechatronic systems and the steps required to assure a dependable product design starting with the very early conceptual design phase. A guideline to select suitable methods for each step and the methods themselves are included. Each method is individually introduced, many examples and full references are given.}}, author = {{Gausemeier, Jürgen and Josef Rammig, Franz and Schäfer, Wilhelm and Sextro, Walter}}, publisher = {{Springer Berlin Heidelberg}}, title = {{{Dependability of Self-Optimizing Mechatronic Systems}}}, volume = {{Lecture Notes in Mechanical Engineering}}, year = {{2014}}, } @article{9881, abstract = {{The increasing demand for high reliability, safety and availability of technical systems calls for innovative maintenance strategies. The use of prognostic health management (PHM) approach where maintenance action is taken based on current and future health state of a component or system is rapidly gaining popularity in the maintenance industry. Multiclass support vector machines (MC-SVM) has been identified as a promising algorithm in PHM applications due to its high classification accuracy. However, it requires parameter tuning for each application, with the objective of minimizing the classification error. This is a single objective optimization problem which requires the use of optimization algorithms that are capable of exhaustively searching for the global optimum parameters. This work proposes the use of hybrid differential evolution (DE) and particle swarm optimization (PSO) in optimally tuning the MC-SVM parameters. DE identifies the search limit of the parameters while PSO finds the global optimum within the search limit. The feasibility of the approach is verified using bearing run-to-failure data and the results show that the proposed method significantly increases health state classification accuracy.}}, author = {{Kimotho, James Kuria and Sextro, Walter}}, issn = {{1617-7061}}, journal = {{PAMM}}, number = {{1}}, pages = {{815--816}}, publisher = {{WILEY-VCH Verlag}}, title = {{{Optimal Parameter Tuning for Multiclass Support Vector Machines in Machinery Health State Estimation}}}, doi = {{10.1002/pamm.201410388}}, volume = {{14}}, year = {{2014}}, } @inproceedings{9884, abstract = {{So-called reliability adaptive systems are able to adapt their system behavior based on the current reliability of the system. This allows them to react to changed operating conditions or faults within the system that change the degradation behavior. To implement such reliability adaptation, self-optimization can be used. A self-optimizing system pursues objectives, of which the priorities can be changed at runtime, in turn changing the system behavior. When including system reliability as an objective of the system, it becomes possible to change the system based on the current reliability as well. This capability can be used to control the reliability of the system throughout its operation period in order to achieve a pre-defined or user-selectable system lifetime. This way, optimal planning of maintenance intervals is possible while also using the system capabilities to their full extent. Our proposed control system makes it possible to react to changed degradation behavior by selecting objectives of the self-optimizing system and in turn changing the operating parameters in a closed loop. A two-stage controller is designed which is used to select the currently required priorities of the objectives in order to fulfill the desired usable lifetime. Investigations using a model of an automotive clutch system serve to demonstrate the feasibility of our controller. It is shown that the desired lifetime can be achieved reliably.}}, author = {{Meyer , Tobias and Sextro, Walter}}, booktitle = {{Proceedings of the Second European Conference of the Prognostics and Health Management Society 2014}}, keywords = {{self-optimization reliability adaptive}}, title = {{{Closed-loop Control System for the Reliability of Intelligent Mechatronic Systems}}}, volume = {{5}}, year = {{2014}}, } @article{9885, abstract = {{Intelligent mechatronic systems, such as self-optimizing systems, allow an adaptation of the system behavior at runtime based on the current situation. To do so, they generally select among several pre-defined working points. A common method to determine working points for a mechatronic system is to use model-based multiobjective optimization. It allows finding compromises among conflicting objectives, called objective functions, by adapting parameters. To evaluate the system behavior for different parameter sets, a model of the system behavior is included in the objective functions and is evaluated during each function call. Intelligent mechatronic systems also have the ability to adapt their behavior based on their current reliability, thus increasing their availability, or on changed safety requirements; all of which are summed up by the common term dependability. To allow this adaptation, dependability can be considered in multiobjective optimization by including dependability-related objective functions. However, whereas performance-related objective functions are easily found, formulation of dependability-related objective functions is highly system-specific and not intuitive, making it complex and error-prone. Since each mechatronic system is different, individual failure modes have to be taken into account, which need to be found using common methods such as Failure-Modes and Effects Analysis or Fault Tree Analysis. Using component degradation models, which again are specific to the system at hand, the main loading factors can be determined. By including these in the model of the system behavior, the relation between working point and dependability can be formulated as an objective function. In our work, this approach is presented in more detail. It is exemplified using an actively actuated single plate dry clutch system. Results show that this approach is suitable for formulating dependability-related objective functions and that these can be used to extend system lifetime by adapting system behavior.}}, author = {{Meyer , Tobias and Sondermann-Wölke, Christoph and Sextro, Walter}}, journal = {{Conference Proceedings of the 2nd International Conference on System-Integrated Intelligence}}, keywords = {{Self-optimization, multiobjective optimization, objective function, dependability, intelligent system, behavior adaptation}}, pages = {{46--53}}, title = {{{Method to Identify Dependability Objectives in Multiobjective Optimization Problem}}}, doi = {{10.1016/j.protcy.2014.09.033}}, volume = {{15}}, year = {{2014}}, } @inbook{9893, author = {{Trächtler, Ansgar and Hölscher, Christian and Rasche, Christoph and Priesterjahn, Claudia and Zimmer, Detmar and Henning Keßler, Jan and Stahl, Katharin and Flaßkamp, Kathrin and Vaßholz, Mareen and Krüger, Martin and Dellnitz, Michael and Iwanek, Peter and Reinold, Peter and Hartmann, Philip and Meyer, Tobias and Sextro, Walter}}, booktitle = {{Dependability of Self-Optimizing Mechatronic Systems}}, editor = {{Gausemeier, Jürgen and Josef Rammig, Franz and Schäfer, Wilhelm and Sextro, Walter}}, isbn = {{978-3-642-53741-7}}, pages = {{1--24}}, publisher = {{Springer Berlin Heidelberg}}, title = {{{Introduction to Self-optimization and Dependability}}}, doi = {{10.1007/978-3-642-53742-4_1}}, year = {{2014}}, } @inproceedings{9896, abstract = {{In power electronics, ultrasonic wire bonding is used to connect the electrical terminals of power modules. To implement a self-optimization technique for ultrasonic wire bonding machines, a model of the process is essential. This model needs to include the so called ultrasonic softening effect. It is a key effect within the wire bonding process primarily enabling the robust interconnection between the wire and a substrate. However, the physical modeling of the ultrasonic softening effect is notoriously difficult because of its highly non-linear character and the absence of a proper measurement method. In a first step, this paper validates the importance of modeling the ultrasonic softening by showing its impact on the wire deformation characteristic experimentally. In a second step, the paper presents a data-driven model of the ultrasonic softening effect which is constructed from data using machine learning techniques. A typical caveat of data-driven modeling is the need for training data that cover the considered domain of process parameters in order to achieve accurate generalization of the trained model to new process configurations. In practice, however, the space of process parameters can only be sampled sparsely. In this paper, a novel technique is applied which enables the integration of prior knowledge about the process into the datadriven modeling process. It turns out that this approach results in accurate generalization of the data-driven model to unseen process parameters from sparse data.}}, author = {{Unger, Andreas and Sextro, Walter and Althoff, Simon and Meyer, Tobias and Brökelmann, Michael and Neumann, Klaus and Reimann, René Felix and Guth, Karsten and Bolowski, Daniel}}, booktitle = {{Proceedings of 8th International Conference on Integrated Power Electronic Systems}}, pages = {{175--180}}, title = {{{Data-driven Modeling of the Ultrasonic Softening Effect for Robust Copper Wire Bonding}}}, volume = {{141}}, year = {{2014}}, } @inproceedings{6940, author = {{Krauter, Stefan}}, booktitle = {{Proceedings of the 29th European Photovoltaic Solar Energy Conference and Exhibition, Amsterdam (The Netherlands), September 22-16, 2014 }}, title = {{{Plant layout modifications and load adaptation to integrate Photovoltaics in the energy system.}}}, year = {{2014}}, }