1,188 research outputs found

    Multi-objective reliability based design of complex engineering structures using response surface methods

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    Extensive research contributions have been carried out in the field of Reliability-Based Design Optimisation (RBDO). Traditional RBDO methods deal with a single objective optimisation problem subject to probabilistic constraints. However, realistic problems in engineering practice require a multi-criteria perspective where two or more conflicting objectives need to be optimised. These type of problems are solved with multi-objective optimization methods, known as Multi-Objective Reliability Based Design Optimization (MORBDO) methods. Usually, significant computational efforts are required to solve these types of problems due to the huge number of complex finite element model evaluations. This paper proposes a practical and efficient approach based for talking this challenge. A multiobjective evolutionary algorithms (MOEAs) is combined with response surface method to obtain efficiently, accurate and uniformly distributed Pareto front. The proposed approach has been implemented into the OpenCossan software. Two examples are presented to show the applicability of the approach: an analytical problem where one of the objectives is the system reliability and the classic 25 bars transmission tower

    A new tow maneuver of a damaged boat through a swarm of autonomous sea drones

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    Given the huge rising interest in autonomous drone swarms to be employed in actual marine applications, the present paper explores the possibility to recover a distressed vessel by means of the other agents belonging to the swarm itself. Suitable approaches and control strategies are developed and tested to find the highest performance algorithms. Different rules are exploited to obtain a correct behaviour in terms of swarm interaction, namely collective and coordinated, and individual. An innovative feedback control strategy is adopted and demonstrated its effectiveness. Extensive simulation runs have been conducted, whose results validate the approach

    From Social Simulation to Integrative System Design

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    As the recent financial crisis showed, today there is a strong need to gain "ecological perspective" of all relevant interactions in socio-economic-techno-environmental systems. For this, we suggested to set-up a network of Centers for integrative systems design, which shall be able to run all potentially relevant scenarios, identify causality chains, explore feedback and cascading effects for a number of model variants, and determine the reliability of their implications (given the validity of the underlying models). They will be able to detect possible negative side effect of policy decisions, before they occur. The Centers belonging to this network of Integrative Systems Design Centers would be focused on a particular field, but they would be part of an attempt to eventually cover all relevant areas of society and economy and integrate them within a "Living Earth Simulator". The results of all research activities of such Centers would be turned into informative input for political Decision Arenas. For example, Crisis Observatories (for financial instabilities, shortages of resources, environmental change, conflict, spreading of diseases, etc.) would be connected with such Decision Arenas for the purpose of visualization, in order to make complex interdependencies understandable to scientists, decision-makers, and the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c

    Conceptual multi-agent system design for distributed scheduling systems

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    With the progressive increase in the complexity of dynamic environments, systems require an evolutionary configuration and optimization to meet the increased demand. In this sense, any change in the conditions of systems or products may require distributed scheduling and resource allocation of more elementary services. Centralized approaches might fall into bottleneck issues, becoming complex to adapt, especially in case of unexpected events. Thus, Multi-agent systems (MAS) can extract their automatic and autonomous behaviour to enhance the task effort distribution and support the scheduling decision-making. On the other hand, MAS is able to obtain quick solutions, through cooperation and smart control by agents, empowered by their coordination and interoperability. By leveraging an architecture that benefits of a collaboration with distributed artificial intelligence, it is proposed an approach based on a conceptual MAS design that allows distributed and intelligent management to promote technological innovation in basic concepts of society for more sustainable in everyday applications for domains with emerging needs, such as, manufacturing and healthcare scheduling systems.This work has been supported by FCT - Fundação para a Ciência e a Tecnologia within the R&D Units Projects Scope: UIDB/00319/2020 and UIDB/05757/2020. Filipe Alves is supported by FCT Doctorate Grant Reference SFRH/BD/143745/2019.info:eu-repo/semantics/publishedVersio

    Perspectives on resilience in cloud computing: Review and trends

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    The development of resilient distributed systems is seen as essential to maintaining stable business and state-run processes due to information systems now underpinning most aspects of society. Cloud computing is now one of the most pervasive usage paradigms and due its novelty, research surrounding its resilience is largely lacking and often varied in terms of developed solutions. Therefore this paper provides an up-to-date review of resilience work in cloud computing. This includes methods of measuring and evaluating resilience, solutions for enabling resilience and alternative architectures developed with a focus upon ensuring resilience from the ground up. Firstly, resilience is defined within the context of cloud computing in order to categorise the work appropriately. © 2017 IEEE

    Artificial Collective Intelligence Engineering: a Survey of Concepts and Perspectives

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    Collectiveness is an important property of many systems--both natural and artificial. By exploiting a large number of individuals, it is often possible to produce effects that go far beyond the capabilities of the smartest individuals, or even to produce intelligent collective behaviour out of not-so-intelligent individuals. Indeed, collective intelligence, namely the capability of a group to act collectively in a seemingly intelligent way, is increasingly often a design goal of engineered computational systems--motivated by recent techno-scientific trends like the Internet of Things, swarm robotics, and crowd computing, just to name a few. For several years, the collective intelligence observed in natural and artificial systems has served as a source of inspiration for engineering ideas, models, and mechanisms. Today, artificial and computational collective intelligence are recognised research topics, spanning various techniques, kinds of target systems, and application domains. However, there is still a lot of fragmentation in the research panorama of the topic within computer science, and the verticality of most communities and contributions makes it difficult to extract the core underlying ideas and frames of reference. The challenge is to identify, place in a common structure, and ultimately connect the different areas and methods addressing intelligent collectives. To address this gap, this paper considers a set of broad scoping questions providing a map of collective intelligence research, mostly by the point of view of computer scientists and engineers. Accordingly, it covers preliminary notions, fundamental concepts, and the main research perspectives, identifying opportunities and challenges for researchers on artificial and computational collective intelligence engineering.Comment: This is the author's final version of the article, accepted for publication in the Artificial Life journal. Data: 34 pages, 2 figure

    Clustering Optimized Portrait Matting Algorithm Based on Improved Sparrow Algorithm

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    As a result of the influence of individual appearance and lighting conditions, aberrant noise spots cause significant mis-segmentation for frontal portraits. This paper presents an accurate portrait segmentation approach based on a combination of wavelet proportional shrinkage and an upgraded sparrow search (SSA) clustering algorithm to solve the accuracy challenge of segmentation for frontal portraits. The brightness component of the human portrait in HSV space is first subjected to wavelet scaling denoising. The elite inverse learning approach and adaptive weighting factor are then implemented to optimize the initial center location of the K-Means algorithm to improve the initial distribution and accelerate the convergence speed of SSA population members. The pixel segmentation accuracy of the proposed method is approximately 70% and 15% higher than two comparable traditional methods, while the similarity of color image features is approximately 10% higher. Experiments show that the proposed method has achieved a high level of accuracy in capricious lighting conditions
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