KEER2022

Avanttítol: KEER2022. DiversitiesDescripció del recurs: 25 juliol 202

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. 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

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 Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s 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 algorithms’ performance on Amazon’s 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

Políticas de Copyright de Publicações Científicas em Repositórios Institucionais: O Caso do INESC TEC

A progressiva transformação das práticas científicas, impulsionada pelo desenvolvimento das novas Tecnologias de Informação e Comunicação (TIC), têm possibilitado aumentar o acesso à informação, caminhando gradualmente para uma abertura do ciclo de pesquisa. Isto permitirá resolver a longo prazo uma adversidade que se tem colocado aos investigadores, que passa pela existência de barreiras que limitam as condições de acesso, sejam estas geográficas ou financeiras. Apesar da produção científica ser dominada, maioritariamente, por grandes editoras comerciais, estando sujeita às regras por estas impostas, o Movimento do Acesso Aberto cuja primeira declaração pública, a Declaração de Budapeste (BOAI), é de 2002, vem propor alterações significativas que beneficiam os autores e os leitores. Este Movimento vem a ganhar importância em Portugal desde 2003, com a constituição do primeiro repositório institucional a nível nacional. Os repositórios institucionais surgiram como uma ferramenta de divulgação da produção científica de uma instituição, com o intuito de permitir abrir aos resultados da investigação, quer antes da publicação e do próprio processo de arbitragem (preprint), quer depois (postprint), e, consequentemente, aumentar a visibilidade do trabalho desenvolvido por um investigador e a respetiva instituição. O estudo apresentado, que passou por uma análise das políticas de copyright das publicações científicas mais relevantes do INESC TEC, permitiu não só perceber que as editoras adotam cada vez mais políticas que possibilitam o auto-arquivo das publicações em repositórios institucionais, como também que existe todo um trabalho de sensibilização a percorrer, não só para os investigadores, como para a instituição e toda a sociedade. A produção de um conjunto de recomendações, que passam pela implementação de uma política institucional que incentive o auto-arquivo das publicações desenvolvidas no âmbito institucional no repositório, serve como mote para uma maior valorização da produção científica do INESC TEC.The progressive transformation of scientific practices, driven by the development of new Information and Communication Technologies (ICT), which made it possible to increase access to information, gradually moving towards an opening of the research cycle. This opening makes it possible to resolve, in the long term, the adversity that has been placed on researchers, which involves the existence of barriers that limit access conditions, whether geographical or financial. Although large commercial publishers predominantly dominate scientific production and subject it to the rules imposed by them, the Open Access movement whose first public declaration, the Budapest Declaration (BOAI), was in 2002, proposes significant changes that benefit the authors and the readers. This Movement has gained importance in Portugal since 2003, with the constitution of the first institutional repository at the national level. Institutional repositories have emerged as a tool for disseminating the scientific production of an institution to open the results of the research, both before publication and the preprint process and postprint, increase the visibility of work done by an investigator and his or her institution. The present study, which underwent an analysis of the copyright policies of INESC TEC most relevant scientific publications, allowed not only to realize that publishers are increasingly adopting policies that make it possible to self-archive publications in institutional repositories, all the work of raising awareness, not only for researchers but also for the institution and the whole society. The production of a set of recommendations, which go through the implementation of an institutional policy that encourages the self-archiving of the publications developed in the institutional scope in the repository, serves as a motto for a greater appreciation of the scientific production of INESC TEC

Data-Driven Environmentally Sustainable Product Design: A Shift Toward Increased use of Sustainable Design Activities in the Early Design Phase

Modern product design drives the boundaries of innovation by encompassing complex Design-for-X objectives, and the inclusion of multidimensional stakeholders. This growing complexity in product design has left design teams unequipped, as classical design theory is bounded by designer experience, expertise, and cognition. One novel approach to tackling product complexity is to leverage data into improved design processes through data-driven design methods. In this dissertation, data-driven design approaches are presented as a means to explore applied knowledge discovery in product data, with a particular focus on function-based design, life cycle inventory, and life cycle assessment data. Using artificial intelligence approaches ranging from decision trees to graph neural networks, and statistical methods such as kernel density estimation, function-based sustainable design is more readily achievable. This dissertation presents methods for introducing sustainable design knowledge in the early stages of product design through the use of data-driven approaches on historic LCA product data. Here, functional modeling is identified as the entry point to the early design phase that can be altered to meet sustainable design objectives. By using data-driven design methods, components are related to functional performance and estimated environmental impact. The culmination of this work is the creation of a probabilistic data-driven methodology that assesses the environmental impact of functional chains and provides component suggestions that reduce potential environmental impact. The research presented in this dissertation begins with an introduction to data-driven design literature and research opportunities (manuscript one). From these opportunities, manuscripts two and three introduce two function-based design methods for aiding in component function assignment and automated functional modeling. Manuscripts four and five introduce novel sustainable methods that realize the goals of this dissertation to create data-driven sustainable design methods applicable to the early design phase. Expanding on this work, the disparity in product data encourages the exploration of richer data sets. By leveraging multiple data sources, including manufacturing, digital, and other life cycle data, we can look forward to novel approaches in data-driven design for knowledge enrichment in function-based sustainable design

Products and Services

Today’s global economy offers more opportunities, but is also more complex and competitive than ever before. This fact leads to a wide range of research activity in different fields of interest, especially in the so-called high-tech sectors. This book is a result of widespread research and development activity from many researchers worldwide, covering the aspects of development activities in general, as well as various aspects of the practical application of knowledge

Neuroengineering of Clustering Algorithms

Cluster analysis can be broadly divided into multivariate data visualization, clustering algorithms, and cluster validation. This dissertation contributes neural network-based techniques to perform all three unsupervised learning tasks. Particularly, the first paper provides a comprehensive review on adaptive resonance theory (ART) models for engineering applications and provides context for the four subsequent papers. These papers are devoted to enhancements of ART-based clustering algorithms from (a) a practical perspective by exploiting the visual assessment of cluster tendency (VAT) sorting algorithm as a preprocessor for ART offline training, thus mitigating ordering effects; and (b) an engineering perspective by designing a family of multi-criteria ART models: dual vigilance fuzzy ART and distributed dual vigilance fuzzy ART (both of which are capable of detecting complex cluster structures), merge ART (aggregates partitions and lessens ordering effects in online learning), and cluster validity index vigilance in fuzzy ART (features a robust vigilance parameter selection and alleviates ordering effects in offline learning). The sixth paper consists of enhancements to data visualization using self-organizing maps (SOMs) by depicting in the reduced dimension and topology-preserving SOM grid information-theoretic similarity measures between neighboring neurons. This visualization\u27s parameters are estimated using samples selected via a single-linkage procedure, thereby generating heatmaps that portray more homogeneous within-cluster similarities and crisper between-cluster boundaries. The seventh paper presents incremental cluster validity indices (iCVIs) realized by (a) incorporating existing formulations of online computations for clusters\u27 descriptors, or (b) modifying an existing ART-based model and incrementally updating local density counts between prototypes. Moreover, this last paper provides the first comprehensive comparison of iCVIs in the computational intelligence literature --Abstract, page iv

The Anesthesia Continuing Education Market and the Value Creation From a Sustainable Unified Platform

Practicing anesthesia professionals in the United States are all governed by various profession-specific regulatory bodies that mandate continuing education (CE) requirements. To date, no unified resource exists for anesthesia professionals (i.e., Anesthesiologists, Certified Registered Nurse Anesthetists, and Anesthesiologist Assistants) to explore the CE offerings available within the marketplace. This study endeavored to convey the potential value of a unified anesthesia CE resource. It investigated how to cultivate a sustainable platform to potentially improve how anesthesia professionals search available CE offerings and to potentially enhance how anesthesia CE providers reach anesthesia professionals. This qualitative study was conducted utilizing an integrative review of the literature. The key concepts identified and investigated were network effect, segmentation, first to market, best of breed, search costs, transaction costs, minimally viable product, evolutionary phases of platforms, platform theory, platform business model, platform economy, and types of platforms. Inductive content analysis was chosen as the organizational method for the resultant qualitative data. The goal of the analysis was to create a conceptual, practical, and strategically applicable platform paradigm for the anesthesia CE marketplace driven by the insights and amalgamations from the literature. The analyzed concepts, dimensions, and indicators of platform successes and their applications potentially facilitate anesthesia professionals’ CE explorations and CE providers’ marketing efforts, as well as contextualize the overarching impacts and implications onto the anesthesia CE industry and beyond. The conclusion portrays these impacts and implications

Robotics in Germany and Japan

This book comprehends an intercultural and interdisciplinary framework including current research fields like Roboethics, Hermeneutics of Technologies, Technology Assessment, Robotics in Japanese Popular Culture and Music Robots. Contributions on cultural interrelations, technical visions and essays are rounding out the content of this book

Feasibility investigation of crowdsourcing-based product design and development for manufacturing

In the era of Industry 4.0, to help manufacturers make quick response to rapidly changing market and customer needs, this research explores the feasibility of realizing benefits of crowdsourcing in product design and development from a lifecycle point of view through investigations on product design quality control and crowdsourcing technology theories, product design lifecycle information modelling, and simulation platform prototyping. It intends to help manufacturers create a product-service ecosystem to deliver values to all involved stakeholders of a PDD process. This study started with building up the theoretical foundation of product design quality control in crowdsourcing design environment. Then, key crowdsourcing technologies for realizing a lifecycle PDD process on a crowdsourcing platform while enabling the design quality were explored. Thirdly, a multi-layer product design lifecycle information model was developed to accommodate all design related information in a PDD process and the identified information at each design phase and the relationships and interactions among information entities were evaluated by case studies and ORM modelling method, respectively. Finally, two crowdsourcing platform prototypes based on the PDLIM were developed to test their effectiveness in communicating design information among stakeholders and delivering value to them. The proposed research made contributions to knowledge through the following improvements/advancements: (1) understanding of key factors affecting product design quality in crowdsourcing design environments, (2) a technical foundation of crowdsourcing technologies for PDD process, (3) a novel product design lifecycle information model accommodating design information in crowdsourcing environments, and (4) guidelines on developing intermediary and integrated crowdsourcing platforms for PDD