TY - JOUR A1 - Allefeld, Carsten A1 - Bialonski, Stephan T1 - Detecting synchronization clusters in multivariate time series via coarse-graining of Markov chains JF - Physical Review E Y1 - 2007 U6 - http://dx.doi.org/10.1103/PhysRevE.76.066207 SN - 2470-0053 VL - 76 IS - 6 SP - 066207 ER - TY - JOUR A1 - Lehnertz, Klaus A1 - Mormann, Florian A1 - Osterhage, Hannes A1 - Andy, Müller A1 - Prusseit, Jens A1 - Chernihovskyi, Anton A1 - Staniek, Matthäus A1 - Krug, Dieter A1 - Bialonski, Stephan A1 - Elger, Christian E. T1 - State-of-the-art of seizure prediction JF - Journal of Clinical Neurophysiology Y1 - 2007 U6 - http://dx.doi.org/10.1097/WNP.0b013e3180336f16 SN - 1537-1603 VL - 24 IS - 2 SP - 147 EP - 153 ER - TY - JOUR A1 - Kroll-Ludwigs, Kathrin T1 - The Reform of German Maintenance Law JF - The International Survey of Family Law Y1 - 2018 SP - 85 EP - 100 ER - TY - CHAP A1 - Drumm, Christian A1 - Schmitt, Matthias A1 - Do, Hong-Hai A1 - Rahm, Erhard T1 - Quickmig: automatic schema matching for data migration projects T2 - Proceedings of the 2007 ACM Conference on Information and Knowledge Management / CIKM'07, Lisboa, Portugal, Nov. 6 - 10, 2007 Y1 - 2007 SN - 978-1-59593-803-9 U6 - http://dx.doi.org/10.1145/1321440.1321458 SP - 107 EP - 116 ER - TY - CHAP A1 - Weber, Ingo A1 - Markovic, Ivan A1 - Drumm, Christian T1 - A conceptual framework for composition in business process management T2 - Business Information Systems : 10th International Conference, BIS 2007, Poznan, Poland, April 25-27, 2007. Proceedings Y1 - 2007 SN - 978-3-540-72035-5 U6 - http://dx.doi.org/10.1007/978-3-540-72035-5_5 SP - 54 EP - 66 PB - Springer CY - Berlin, Heidelberg ER - TY - CHAP A1 - Drumm, Christian A1 - Lemcke, Jens A1 - Oberle, Daniel T1 - Business Process Management And Semantic Technologies T2 - The Semantic Web Y1 - 2007 SN - 978-0-387-48531-7 U6 - http://dx.doi.org/10.1007/978-0-387-48531-7_10 SP - 207 EP - 239 PB - Springer CY - Boston, MA ER - TY - CHAP A1 - Matcha, Heike T1 - Parametric possibilities: designing with parametric modelling T2 - Predicting the Future [25th eCAADe Conference Proceedings] Y1 - 2007 SN - 978-0-9541183-6-5 SP - 849 EP - 856 ER - TY - THES A1 - Trzewik, Jürgen T1 - Experimental analysis of biaxial mechanical tension in cell monolayers and cultured three-dimensional tissues: the celldrum technology Y1 - 2007 N1 - Zugl.: Ilmenau, Techn. Univ., Diss., 2007 PB - Univeristätsverlg Ilmenau CY - Ilmenau ER - TY - JOUR A1 - Waller, Mark P. A1 - Braun, Heiko A1 - Hojdis, Nils A1 - Bühl, Michael T1 - Geometries of Second-Row Transition-Metal Complexes from Density-Functional Theory JF - Journal of Chemical Theory and Computation Y1 - 2007 U6 - http://dx.doi.org/10.1021/ct700178y SN - 1549-9626 VL - 3 IS - 6 SP - 2234 EP - 2242 ER - TY - CHAP A1 - Dachwald, Bernd T1 - Low-Thrust Mission Analysis and Global Trajectory Optimization Using Evolutionary Neurocontrol: New Results T2 - European Workshop on Space Mission Analysis ESA/ESOC, Darmstadt, Germany 10 { 12 Dec 2007 N2 - Interplanetary trajectories for low-thrust spacecraft are often characterized by multiple revolutions around the sun. Unfortunately, the convergence of traditional trajectory optimizers that are based on numerical optimal control methods depends strongly on an adequate initial guess for the control function (if a direct method is used) or for the starting values of the adjoint vector (if an indirect method is used). Especially when many revolutions around the sun are re- quired, trajectory optimization becomes a very difficult and time-consuming task that involves a lot of experience and expert knowledge in astrodynamics and optimal control theory, because an adequate initial guess is extremely hard to find. Evolutionary neurocontrol (ENC) was proposed as a smart method for low-thrust trajectory optimization that fuses artificial neural networks and evolutionary algorithms to so-called evolutionary neurocontrollers (ENCs) [1]. Inspired by natural archetypes, ENC attacks the trajectoryoptimization problem from the perspective of artificial intelligence and machine learning, a perspective that is quite different from that of optimal control theory. Within the context of ENC, a trajectory is regarded as the result of a spacecraft steering strategy that maps permanently the actual spacecraft state and the actual target state onto the actual spacecraft control vector. This way, the problem of searching the optimal spacecraft trajectory is equivalent to the problem of searching (or "learning") the optimal spacecraft steering strategy. An artificial neural network is used to implement such a spacecraft steering strategy. It can be regarded as a parameterized function (the network function) that is defined by the internal network parameters. Therefore, each distinct set of network parameters defines a different network function and thus a different steering strategy. The problem of searching the optimal steering strategy is now equivalent to the problem of searching the optimal set of network parameters. Evolutionary algorithms that work on a population of (artificial) chromosomes are used to find the optimal network parameters, because the parameters can be easily mapped onto a chromosome. The trajectory optimization problem is solved when the optimal chromosome is found. A comparison of solar sail trajectories that have been published by others [2, 3, 4, 5] with ENC-trajectories has shown that ENCs can be successfully applied for near-globally optimal spacecraft control [1, 6] and that they are able to find trajectories that are closer to the (unknown) global optimum, because they explore the trajectory search space more exhaustively than a human expert can do. The obtained trajectories are fairly accurate with respect to the terminal constraint. If a more accurate trajectory is required, the ENC-solution can be used as an initial guess for a local trajectory optimization method. Using ENC, low-thrust trajectories can be optimized without an initial guess and without expert attendance. Here, new results for nuclear electric spacecraft and for solar sail spacecraft are presented and it will be shown that ENCs find very good trajectories even for very difficult problems. Trajectory optimization results are presented for 1. NASA's Solar Polar Imager Mission, a mission to attain a highly inclined close solar orbit with a solar sail [7] 2. a mission to de ect asteroid Apophis with a solar sail from a retrograde orbit with a very-high velocity impact [8, 9] 3. JPL's \2nd Global Trajectory Optimization Competition", a grand tour to visit four asteroids from different classes with a NEP spacecraft Y1 - 2007 ER -