279 research outputs found
A Theory of Discrete Hierarchies as Optimal Cost-Adjusted Productivity Organisations
Hierarchical structures are ubiquitous in human and animal societies, but a
fundamental understanding of their raison d'\^etre has been lacking. Here, we
present a general theory in which hierarchies are obtained as the optimal
design that strikes a balance between the benefits of group productivity and
the costs of communication for coordination. By maximising a generic
representation of the output of a hierarchical organization with respect to its
design, the optimal configuration of group sizes at different levels can be
determined. With very few ingredients, a wide variety of hierarchically ordered
complex organisational structures can be derived. Furthermore, our results
rationalise the ubiquitous occurrence of triadic hierarchies, i.e., of the
universal preferred scaling ratio between and found in many human and
animal hierarchies, which should occur according to our theory when production
is rather evenly contributed by all levels. We also provide a systematic
approach for optimising team organisation, helping to address the question of
the optimal `span of control'. The significantly larger number of
subordinates a supervisor typically manages is rationalised to occur in
organisations where the production is essentially done at the bottom level and
in which the higher levels are only present to optimise coordination and
control
High Resolution Maps of the Vasculature of An Entire Organ
The structure of vascular networks represents a great, unsolved problem in anatomy. Network geometry and topology differ dramatically from left to right and person to person as evidenced by the superficial venation of the hands and the vasculature of the retinae. Mathematically, we may state that there is no conserved topology in vascular networks. Efficiency demands that these networks be regular on a statistical level and perhaps optimal. We have taken the first steps towards elucidating the principles underlying vascular organization, creating the rst map of the hierarchical vasculature (above the capillaries) of an entire organ. Using serial blockface microscopy and fluorescence imaging, we are able to identify vasculature at 5 ÎŒm resolution. We have designed image analysis software to segment, align, and skeletonize the resulting data, yielding a map of the individual vessels. We transformed these data into a mathematical graph, allowing computationally efficient storage and the calculation of geometric and topological statistics for the network. Our data revealed a complexity of structure unexpected by theory. We observe loops at all scales that complicate the assignment of hierarchy within the network and the existence of set length scales, implying a distinctly non-fractal structure of components within
Using MapReduce Streaming for Distributed Life Simulation on the Cloud
Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conwayâs life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MRâs applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithmsâ performance on Amazonâs Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp
Applying the Free-Energy Principle to Complex Adaptive Systems
The free energy principle is a mathematical theory of the behaviour of self-organising systems that originally gained prominence as a unified model of the brain. Since then, the theory has been applied to a plethora of biological phenomena, extending from single-celled and multicellular organisms through to niche construction and human culture, and even the emergence of life itself. The free energy principle tells us that perception and action operate synergistically to minimize an organismâs exposure to surprising biological states, which are more likely to lead to decay. A key corollary of this hypothesis is active inferenceâthe idea that all behavior involves the selective sampling of sensory data so that we experience what we expect to (in order to avoid surprises). Simply put, we act upon the world to fulfill our expectations. It is now widely recognized that the implications of the free energy principle for our understanding of the human mind and behavior are far-reaching and profound. To date, however, its capacity to extend beyond our brainâto more generally explain living and other complex adaptive systemsâhas only just begun to be explored. The aim of this collection is to showcase the breadth of the free energy principle as a unified theory of complex adaptive systemsâconscious, social, living, or not
Machine learning algorithms for efficient process optimisation of variable geometries at the example of fabric forming
FĂŒr einen optimalen Betrieb erfordern moderne Produktionssysteme eine sorgfĂ€ltige Einstellung der eingesetzten Fertigungsprozesse. Physikbasierte Simulationen können die Prozessoptimierung wirksam unterstĂŒtzen, jedoch sind deren Rechenzeiten oft eine erhebliche HĂŒrde. Eine Möglichkeit, Rechenzeit einzusparen sind surrogate-gestĂŒtzte Optimierungsverfahren (SBO1). Surrogates sind recheneffiziente, datengetriebene Ersatzmodelle, die den Optimierer im Suchraum leiten. Sie verbessern in der Regel die Konvergenz, erweisen sich aber bei verĂ€nderlichen Optimierungsaufgaben, etwa hĂ€ufigen Bauteilanpassungen nach Kundenwunsch, als unhandlich.
Um auch solche variablen Optimierungsaufgaben effizient zu lösen, untersucht die vorliegende Arbeit, wie jĂŒngste Fortschritte im Maschinenlernen (ML) â im Speziellen bei neuronalen Netzen â bestehende SBO-Techniken ergĂ€nzen können. Dabei werden drei Hauptaspekte betrachtet: erstens, ihr Potential als klassisches Surrogate fĂŒr SBO, zweitens, ihre Eignung zur effiziente Bewertung der Herstellbarkeit neuer BauteilentwĂŒrfe und drittens, ihre Möglichkeiten zur effizienten Prozessoptimierung fĂŒr variable Bauteilgeometrien. Diese Fragestellungen sind grundsĂ€tzlich technologieĂŒbergreifend anwendbar und werden in dieser Arbeit am Beispiel der Textilumformung untersucht.
Der erste Teil dieser Arbeit (Kapitel 3) diskutiert die Eignung tiefer neuronaler Netze als Surrogates fĂŒr SBO. Hierzu werden verschiedene Netzarchitekturen untersucht und mehrere Möglichkeiten verglichen, sie in ein SBO-Framework einzubinden. Die Ergebnisse weisen ihre Eignung fĂŒr SBO nach: FĂŒr eine feste Beispielgeometrie minimieren alle Varianten erfolgreich und schneller als ein Referenzalgorithmus (genetischer Algorithmus) die Zielfunktion.
Um die Herstellbarkeit variabler Bauteilgeometrien zu bewerten, untersucht Kapitel 4 anschlieĂend, wie Geometrieinformationen in ein Prozess-Surrogate eingebracht werden können. Hierzu werden zwei ML-AnsĂ€tze verglichen, ein merkmals- und ein rasterbasierter Ansatz. Der merkmalsbasierte Ansatz scannt ein Bauteil nach einzelnen, prozessrelevanten Geometriemerkmalen, der rasterbasierte Ansatz hingegen interpretiert die Geometrie als Ganzes. Beide AnsĂ€tze können das Prozessverhalten grundsĂ€tzlich erlernen, allerdings erweist sich der rasterbasierte Ansatz als einfacher ĂŒbertragbar auf neue Geometrievarianten. Die Ergebnisse zeigen zudem, dass hauptsĂ€chlich die Vielfalt und weniger die Menge der Trainingsdaten diese Ăbertragbarkeit bestimmt.
AbschlieĂend verbindet Kapitel 5 die Surrogate-Techniken fĂŒr flexible Geometrien mit variablen Prozessparametern, um eine effiziente Prozessoptimierung fĂŒr variable Bauteile zu erreichen. Hierzu interagiert ein ML-Algorithmus in einer Simulationsumgebung mit generischen Geometriebeispielen und lernt, welche Geometrie, welche Umformparameter erfordert. Nach dem Training ist der Algorithmus in der Lage, auch fĂŒr nicht-generische Bauteilgeometrien brauchbare Empfehlungen auszugeben. Weiter zeigt sich, dass die Empfehlungen mit Ă€hnlicher Geschwindigkeit wie die klassische SBO zum tatsĂ€chlichen Prozessoptimum konvergieren, jedoch kein bauteilspezifisches A-priori-Sampling nötig ist. Einmal trainiert, ist der entwickelte Ansatz damit effizienter.
Insgesamt zeigt diese Arbeit, wie ML-Techniken gegenwĂ€rtige SBOMethoden erweitern und so die Prozess- und Produktoptimierung zu frĂŒhen Entwicklungszeitpunkten effizient unterstĂŒtzen können. Die Ergebnisse der Untersuchungen mĂŒnden in Folgefragen zur Weiterentwicklung der Methoden, etwa die Integration physikalischer Bilanzgleichungen, um die Modellprognosen physikalisch konsistenter zu machen
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On-site installation flexibility for disruption management in modular off-site construction systems
Modular off-site construction is one of the methods adopted by the construction industry in a recent drive to modernise its operations and increase its productivity. Operations that were traditionally performed on-site are instead completed at an off-site factory, with finished modules then being transported on-site for installation. Operating across two locations in this way can provide numerous gains in speed, quality, and costs. However, it does mean that construction companies must now understand and manage a new and wider range of potential disruptions to their operations. This thesis is concerned with addressing disruptions that delay the delivery of modules to site.
To identify operational disruptions and their corresponding disruption management strategies, an exploratory study was performed consisting of five case studies and an industrial workshop. An over-reliance on storing modules as a means of coping with disruptions was uncovered. Construction sites typically follow a fixed module installation sequence because of on-site installation constraints. As such, when delivery of a module is delayed, subsequent modules in the sequence must be stored until the delayed module arrives for installation. As the industry expands towards manufacturing larger projects at higher production rates, storage may become a less viable disruption management strategy given the lack of storage space, particularly in urban areas. To overcome these challenges, a novel disruption management strategy is proposed and evaluated: on-site installation flexibility. There are four types: vertical assignment flexibility, lateral assignment flexibility, vertical sequence flexibility, and lateral sequence flexibility. Each type relaxes one of the on-site installation constraints, thereby allowing completed modules to continue to be installed in the event of a module being disrupted.
Several conclusions were drawn from studying on-site installation flexibility as a disruption management strategy. Implementation roadmaps developed during a workshop using an Impact Matrix Cross-Reference Multiplication Applied to a Classification analysis and Interpretive Structural Modelling revealed that implementing on-site installation flexibility requires coordination and many changes across a range of organisational functions. A Discrete Event Simulation model developed and applied to a case study showed that on-site installation flexibility can reduce installation delay and storage requirements. Furthermore, combining more than one type of on-site installation flexibility can significantly improve system performance. However, greater co-ordination effort would be required to control module installation operations. Finally, a Simulation-Based Optimisation model was formulated and applied to a second case study and showed that investing in a combination of on-site installation flexibilities in conjunction with other disruption management options can achieve cost savings. Hence, on-site installation flexibility was demonstrated to be a promising disruption management strategy for modular off-site construction systems
Recent Advances in Multi Robot Systems
To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems
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