Uso de árvores aleatórias para classificação sensorial de arroz cozido

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Estatística, 2017.A classificação por floresta aleatória (random forest) dispensa suposições paramétricas e possui aplicabilidade em diversos problemas de predição. Foram consideradas as técnicas de floresta aleatória e floresta aleatória para dados desbalanceados. Os resultados foram comparados ao tradicional classificador de regressão logística. As regiões classificadoras foram apresentadas em gráficos de calor da região formada entre as duas primeiras componentes principais, com a intensidade dada pelo valor da probabilidade. Por fim, foi investigado o uso da medida de discrepância fornecida pelo modelo de floresta aleatória, para a determinação de regiões de incerteza de classificação. A análise foi aplicada em dados de pegajosidade sensorial do arroz cozido. Resultados competitivos foram observados em termos de acurácia do modelo de floresta aleatória quando comparada com a regressão logística. Para o caso específico do arroz de terras altas, do ano de 2014, a floresta aleatória balanceada representou um ganho considerável no desempenho do modelo. Os gráficos de calor apresentados auxiliam na percepção de particularidades do modelo e ajudam no entendimento da construção da floresta aleatória. Finalmente, a barreira construída via valores discrepantes, se mostrou consistente na seleção de observações erroneamente classificadas e quando aplicada à floresta aleatória balanceada dos dados de terras altas, no ano de 2014, resultou em um modelo com apenas um erro de classificação, isso sem custos elevados de não classificação

Quantitative Set-Based Design for Complex System Development

This dissertation comprises a body of research facilitating decision-making and complex system development with quantitative set-based design (SBD). SBD is concurrent product development methodology, which develops and analyzes many design alternatives for longer time periods enabling design maturation and uncertainty reduction. SBD improves design space exploration, facilitating the identification of resilient and affordable systems. The literature contains numerous qualitative descriptions and quantitative methodologies describing limited aspects of the SBD process. However, there exist no methodologies enabling the quantitative management of SBD programs throughout the entire product development cycle. This research addresses this knowledge gap by developing the process framework and supporting methodologies guiding product development from initial system concepts to a final design solution. This research provides several new research contributions. First, we provide a comprehensive SBD state-of-practice assessment identifying key knowledge and methodology gaps. Second, we demonstrate the physical implementation of the integrated analytics framework in a model-based engineering environment. Third, we develop a quantitative methodology enabling program management decision making in SBD. Fourth, we describe a supporting uncertainty reduction methodology using multiobjective value of information analysis to assess design set maturity and higher-resolution model usefulness. Finally, we describe a quantitative SBD process framework enabling sequential design maturation and uncertainty reduction decisions. Using an unmanned aerial vehicle case study, we demonstrate our methodology’s ability to resolve uncertainty and converge a complex design space onto a set of resilient and affordable design solutions

Smart Monitoring and Control in the Future Internet of Things

The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things

PLATFORM-DRIVEN CROWDSOURCED MANUFACTURING FOR MANUFACTURING AS A SERVICE

Platform-driven crowdsourced manufacturing is an emerging manufacturing paradigm to instantiate the adoption of the open business model in the context of achieving Manufacturing-as-a-Service (MaaS). It has attracted attention from both industries and academia as a powerful way of searching for manufacturing solutions extensively in a smart manufacturing era. In this regard, this work examines the origination and evolution of the open business model and highlights the trends towards platform-driven crowdsourced manufacturing as a solution for MaaS. Platform-driven crowdsourced manufacturing has a full function of value capturing, creation, and delivery approach, which is fulfilled by the cooperation among manufacturers, open innovators, and platforms. The platform-driven crowdsourced manufacturing workflow is proposed to organize these three decision agents by specifying the domains and interactions, following a functional, behavioral, and structural mapping model. A MaaS reference model is proposed to outline the critical functions and inter-relationships. A series of quantitative, qualitative, and computational solutions are developed for fulfilling the outlined functions. The case studies demonstrate the proposed methodologies and can pace the way towards a service-oriented product fulfillment process. This dissertation initially proposes a manufacturing theory and decision models by integrating manufacturer crowds through a cyber platform. This dissertation reveals the elementary conceptual framework based on stakeholder analysis, including dichotomy analysis of industrial applicability, decision agent identification, workflow, and holistic framework of platform-driven crowdsourced manufacturing. Three stakeholders require three essential service fields, and their cooperation requires an information service system as a kernel. These essential functions include contracting evaluation services for open innovators, manufacturers' task execution services, and platforms' management services. This research tackles these research challenges to provide a technology implementation roadmap and transition guidebook for industries towards crowdsourcing.Ph.D