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KASAI, H.; BERTRAM, V. (1996), Artificial intelligence for ship operation control Part I: Automatic navigation including collision avoidance, Hansa 133/5, pp.18-21 KOUSHAN, K.; STEEN, S.; ZHAO, R. (2002), Wake wash minimization by hull form optimization using artificial intelligence, 3 rd Int. Conf. High-Performance Marine Vehicles, HIPER, Bergen, pp.262-273 LINDENAU, O.; BERTRAM, V. (2003), The making of a VRML model for an SES with streamlines and pressure distribution, 2 nd Int. Conf. Computer and IT Applic. Mar. Industries, COMPIT, Hamburg, pp.5-14 LUCO, R.; SALAS, M.; TARFAOUI, M.; BERTRAM, V. (2002), Finite element structural modelling of a composite-material multihull, 3 rd Int. Conf. High-Performance Marine Vehicles, HIPER, Bergen, pp.297-305 MARZI, J.; BERTRAM, V.; STÖHRMANN, H. (1997), User-friendly hydro-numeric hull design by incorporation of expert knowledge, ICCAS'97, Yokohama, pp.351-361 MESBAHI, E. (2003), Artificial neural networks – Fundamentals, 39 th WEGEMT School OPTIMIS- TIC – Optimization in Marine Design, Mensch&Buch Verlag, pp.191-216 MESBAHI, E.; BERTRAM, V. (2000), Empirical design formulae using artificial neural networks, 1 st Int. Conf. Computer and IT Applic. Mar. Industries, COMPIT, Potsdam, pp.292-301 MISTREE, F.; SMITH, W.F.; BRAS, B.; ALLEN, J.K.; MUSTER, D. (1990), Decision-based design: A contemporary paradigm for ship design, Trans. SNAME 98, pp.565-595 REICH, Y.; BERTRAM, V.; FRIESCH, J. (1997), The development of a decision support system for propeller design, ICCAS'97, Yokohama, pp.363-378 RIGO, P. (2003), An integrated software for scantling optimization and least production cost, Ship Technology Research 50, pp.125-140 SCHMODE, D.; BERTRAM, V. (2002), Aerodynamic flow computations for a Superfast ferry, 3 rd Int. Conf. High-Performance Marine Vehicles, HIPER, Bergen, pp.345-354 SALAS, M.; MESBAHI, E.; BRINK, K.E.; BERTRAM, V. (2002), Empirical roll damping formula derived by adaptive neural network application, Schiff+Hafen 54/2, pp.56-57 SASAKI, Y. (2003), Application of factory simulation to the shipyard, 2 nd Int. Conf. Computer and IT Applic. Mar. Industries, COMPIT, Hamburg, pp.362-376 SIMPSON, D.; FURSTENBERG, L.; KALATHAS, E.; BERTRAM, V. (2003), Virtual prototyping for developing South African ports, 2 nd Int. Conf. Computer and IT Applic. Mar. Industries, COMPIT, Hamburg, pp.235-246 STEINHAUER, D. (2003), The virtual shipyard – Simulation in production and logistics at Flensburger, 2 nd Int. Conf. Computer and IT Applic. Mar. Industries, COMPIT, Hamburg, pp.203-209 VAN ES, K.; VAN HEES, M.T. (2003), Application of knowledge management in conceptual naval ship design, 2 nd Int. Conf. Computer and IT Applic. Mar. Industries, COMPIT, Hamburg, pp.350-362 WAUCHOPE, K.; EVERETT, S.; TATE, D.; MANEY, T. (2003), Speech-interactive virtual environments for ship familiarization, 2 nd Int. Conf. Computer and IT Applic. Mar. Industries, COMPIT, Hamburg, pp.70-83 16
Use of Genetic Algorithms in Propeller Foil Design Marco Ferrando, DINAV, Genova/Italy, ferrando@dinav.unige.it Andrea Lommi, CETENA, Genova/Italy, andrea.lommi@cetena.it Antonio Traverso, CETENA, Genova/Italy, antonio.traverso@cetena.it Abstract One of the most popular tools for design of 2-D foil sections is that developed by Prof. Eppler. Unfortunately the required input data are not correlated to the actual shape of the profile in a straightforward way. A mathematical solution to the problem of 2-D foil sections design with arbitrary constraints does not exist, and available design tools (like the Eppler code) do not allow a real optimization of produced solutions, but rather a simple evaluation, with the result that if contrasting characteristics have to be jointly optimized the optimal trade-off is difficult to devise.Depending on the chosen design objective, like for instance the optimization of a cavitation bucket, deterministic optimizers like the gradient method could get stuck in local minima or could even be not exploitable, due to the non-linearity of the search space and therefore a use of a stochastic method is necessary. During the past years, Genetic Algorithms have been applied on a variety of engineering problems, showing a high robustness and reliability on handling non-linear and sub-optimal solutions-filled search spaces.This paper describes the development of a tool for the design of foil shapes that integrates a genetic algorithm, a revised Eppler code and a procedure to compare foil sections having different zero-lift angles. Results are evaluated by means of an objective function that can be adapted to the goal for any project and, in this case, an example is given by optimising a cavitation bucket. 1. Introduction In the last decade traditional approach to propeller design has evolved side by side with new techniques of optimization that moved from the classical numerical approach based on numerical methods toward the field of Genetic Algorithms (GA) which seem to be suitable for wider ranges of application than those based on analytical theory of optimization. In propeller design the starting point is the airfoil section and, since their introduction, NACA Mod 66 and NACA 16 profiles have been the most used blade sections and considered as a good stating point for marine propeller design. Anyway as cavitation phenomena are involved, modification of geometrical parameter such as rake and skew are not sufficient to ensure a complete control of propeller performances and a new starting point might be needed, and this can be a complete different choice of blade sections. Unfortunately propeller operates in a fluid with a non uniform field of velocity so that each blade section operates in different condition with different angle of attack, which in turns changes as the propeller rotates. This makes impossible to design an optimized propeller for the whole range of operation, but it is still possible to choose one or two blade section, which are considered the most important, and try to optimize them. Airfoil design can be achieved with several codes, such as the one developed by Eppler and Somers (1980). The best choice of input parameter is defined and found with a Genetic Algorithm that takes into account the previously mentioned points by acting on the characteristics of the airfoil for which a suitable coding of the parameters has been defined. The obtained airfoil configurations represent optimality-close tradeoffs between the required characteristics. 2. The Eppler Code The code developed by Richard Eppler, Eppler and Somers (1980), uses the potential flow model to generate the geometry of an airfoil which satisfies a certain set of condition imposed by the designer. The input for this program is extremely complex as the flow field around the airfoil is not described either by a velocity or a pressure field. 17
Page 1 and 2: 3 rd International Conference on Co
Page 3 and 4: Patrick Hupe, Uwe Langbecker, Mubas
Page 5 and 6: Towards Intelligent Simulation-Base
Page 7 and 8: ectly damage in terms of greenhouse
Page 9 and 10: Fig.7: FEA of a catamaran made of c
Page 11 and 12: Set the small parts fixing the part
Page 13 and 14: main benefit of 3-d design lies in
Page 15: Applic. Mar. Industries, COMPIT, Ha
Page 19 and 20: 3. Fitness Function Formulation The
Page 21 and 22: where µ α and µ θ are the chose
Page 23 and 24: From another point of view and unde
Page 25 and 26: Y X Benchmark First Optimum Fig.8:
Page 27 and 28: A Systematic Study on Posing and So
Page 29 and 30: expressed as functions of the wave
Page 31 and 32: 4.4. Optimization with modeFRONTIER
Page 33 and 34: Fig.5: SIMPLEX optimization and Par
Page 35 and 36: References HOFFSCHILDT, M.; BIDLOT,
Page 37 and 38: 2.2. Tribon Product Information Mod
Page 39 and 40: 3. New Process The new process is b
Page 41 and 42: 4. Conclusions and next step The ne
Page 43 and 44: 2. E-Commerce of goods versus servi
Page 45 and 46: Main steps on ePING’s road-map ar
Page 47 and 48: anon. client new client registered
Page 49 and 50: Preparing an offer can be hierarchi
Page 51 and 52: Acknowledgment This work was partia
Page 53 and 54: The focus within this paper is the
Page 55 and 56: conditions have to be prevented. Fo
Page 57 and 58: Ship 2 LPP 182.390 m B 26.00 m T D
Page 59 and 60: Fig.9: Ship 1; Simulated Crash stop
Page 61 and 62: Using Simulation in Evaluating Bert
Page 63 and 64: ship. To shorten the time spent by
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around time is the highest against
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to alternative berths instead of pl
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Appendix 1: Data Collected from Con
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Abstract Introduction to BALTPORTS-
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- at the level of operational plann
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- External interoperability - In ad
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5. Setting up of the Baltic Sub-Reg
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Robot Applications in the Field of
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Fig.3: The main components of the D
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The precision of the process depend
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The frame is further divided into 2
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1. Periodicity on the time line, pe
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Quality measures. Let J be the inst
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6.2 Mutation A random number out of
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7.1 Adaptation of Theorem 2 in our
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8. Conclusions and scope for future
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meet the needs of the user. Typical
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2-digit level (e.g., 220 - Engineer
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eviewed in dialogue boxes and on Ex
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Fig.3: Example tanker (HMI BRENTON
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Fig.7: Assigned material tabular en
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Fig.11: Design ship 2-digit SWBS Re
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Communication in Ship Design Networ
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number and volume of communicated i
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The investigated collaborations are
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The four principle communication pa
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6. Information content In design cl
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References ERIKSTAD, S.O., HAGEN, A
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intensive. The terminal's own drive
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A distribution of decision competen
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the order for planning the task to
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3.400.000 Total costs 3.200.000 3.0
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Visualisation of Ship Motions in Wa
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3.3. Panelisation and information e
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5.2. 3D visualisation Fig.4: Refere
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6.2. Examples of the visualisations
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7. Conclusions The presented toolki
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Fig.2: Possibilities for robotic in
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Experiments with the underwater cam
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Fig.13. and 14: Cleaning of the cha
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Acknowledgements The authors want t
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Fig.2: Result of Industrial Enginee
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2. Industrial engineering system fo
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4. Next steps In the next phase, th
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organizations and organizational un
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artifact’s context and extend the
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We take the ship survey process, wh
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device multimedia capabilities: Sur
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4. Future Work The mobile services
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FEM Supported Alignment of Power Tr
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2.2. Causes First researches result
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Fig.5: Sufficient tooth grip patter
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4. Alignment of a RoRo vessel’s p
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4.3. Calibration/Validation of the
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Optimisation of the Survivability o
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Table I: Current UK & US Damage Sta
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Fig.2: Damage Density Distribution,
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An Air-to-Surface Missile (ASM) is
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length according to the existing re
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the use of a stochastic optimizatio
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Each MOGA loop requires approximate
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JIASIONOWSKI, A. (2002), An integra
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Design problems are typically chara
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o planned: systematic; o ad-hoc: op
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o Provide justification for decisio
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o o o o o o Guide the user through
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- for some systems, the initial com
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feasible. The practical implementat
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PARK, J.H.; STORCH, R.L. (2002), "O
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where: a, b - coefficients (weights
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C Fig.2: The fuzzy domain of the bu
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5.2. Ship fuzzy domain in an open a
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PIETRZYKOWSKI, Z. (2003a), Modellin
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means that the number of intersecti
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4. Manipulation of Sets of Data Poi
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Fig.2: The selection function defin
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Let us define the root node of a se
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Fig.4 took up to several minutes on
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Applying Meta-Models to the Probabi
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The assessment of the damage stabil
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Fig.1 shows clearly an important ch
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one is willing to do, the smaller t
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Depending on the number of simulati
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Since none of the points of the lat
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CHEN, S.; WANG, L.; WU, X.; WANG, X
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alance between ship resistance and
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frequent movements of the pitch tha
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"commanding" by itself since a vari
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The numbers shown in the 3 rd row o
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angle of attack of the water flow o
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stationary, i.e. all variables rema
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the engine map runs through the ove
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References GRIMMELIUS, H.T.; STAPER
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their output to be random too. Runn
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simulation runs. It typically takes
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When the system initiates, the star
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The user is guided through various
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• Port Planning & Development •
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Knowledge-based Concurrent Engineer
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or puts components aside to make mo
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Technical specs Make preliminary ca
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ios, and knowledge about the design
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are applied afterwards, they can be
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calculations etc.) The product rela
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For finding the dependency of reque
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GIASSI, A. (2003), Multidisciplinar
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already mentioned. It is based on t
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2.2.2 Geometry Geometry covers the
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2.3 Simulation The simulation engin
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As far as the simulation engine inc
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time results. Table I: Mustering ti
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simulation means. The required time
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design consultants. Starting from a
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different severity, e.g. warning or
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• Execute calculation (including
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and ghostscript, Gnuplot, Ghostscri
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References CASTOR; An open source d
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an accurate description of a densit
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(a) UDS and CDS Schemes (b) Gamma S
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Figure 3: Typical unstructured grid
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(a) 0 degree (b) 5 degrees - Port s
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(a) Computations (b) Experiments Fi
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(a) X/L = +0.10 (b) X/L = +0.20 (c)
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4 Conclusion This paper has describ
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State of the art in climbing and wa
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2.1 REST 1 and REST 2 climbing robo
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Figure 5: ROWER 1 system. The full
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Acknowledgements The REST climbing
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X X X X X X X X Z Z Z Z Z Z Z Z Y Y
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30 25 20 Conjugate gradient 30 25 2
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CARENA is a naval architectural sof
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Fig.6: Cargo vessel carrying “spe
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used especially for shape optimizat
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easily given as simple auto-update
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and mutation concepts for evolving
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govern agents’ interaction with t
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Create generation behaviour Sequent
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Side Casing Hoister Deck Car Deck C
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The selection of the best individua
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Appendix A Table A-I: Optimisation
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2. Project Management tools in ship
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Fig.1: Example of document properti
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Fig.4: Example of query view on doc
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The Product Data Management is comp
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Abstract Applications of Self-Organ
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EMERGENT GLOBAL STRUCTURE LOCAL INT
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dynamically structured, the system
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After a leakage has been detected,
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EXIT door door corridor exit device
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adaptive. Furthermore, from a imple
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The main objective for our approach
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LUCAS, C. (2003), Self-organizing s
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• As a result of the above-mentio
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Table I: Exemplary matching of Refe
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Yard Program time Mile-stone Plan d
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In the following we start with a di
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Material Information Machines Proje
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module (SpaceMan) supports the allo
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10 10 Exp ANN Exp ANN 7.5 7.5 Cr [x
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7 Cr [x 1000] 6 5 4 3 2 0.2 0.4 0.6
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Automation Tools in the Design Proc
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Fig.1: Visual LISP integrated devel
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• Bentley’s adoption of VBA •
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To these outstanding features, it h
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4.2.1. Automation The objective of
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• Direction of the start of the c
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• The FDE program processes the p
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Database access The FORAN System us
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A Heuristic for the Container Pre-M
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When they arrive in the terminal ya
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(3) The number of all badly placed
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still greater than zero, the minimu
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• For each dirty stack s exactly
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in the remaining 7 instances no mor
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DOF motion. Then, alter the input f
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learn of any delay that can occur b
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40 20 30 10 x-x0 (m) 20 roll (deg)
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Since, among the velocity component
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Y (m) -1700 -1500 -1300 -1100 -900
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system framework, the details of it
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Real-time Nonlinear Simulation (RNS
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design modifications are made, and
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Evacuation Notation - a New Concept
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• r(t): response time, which acco
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• The time tk: the time correspon
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5.1. Quantitative assessment Quanti
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Table 2: Summary of total and model
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notation: besides an objective asse
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Neural Networks Model for Ship Mane
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&r V G • • r = a = ( uî & & +
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V a Estimating Forces F Calculating
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Fig. 5: Error variation for the net
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Fig 8: Ship's path in Zigzag test 3
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[4] ALMAN, P.R., BERTSCHE, W.R., BO
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2 Numerical Methods RANSE methods l
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As a conclusion of these parameter
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For correlation of propeller data t
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quadratic functions result in littl
Page 479 and 480:
Index by authors Abels 470 Akinfiev
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