17,996 research outputs found
Meso-scale FDM material layout design strategies under manufacturability constraints and fracture conditions
In the manufacturability-driven design (MDD) perspective, manufacturability of the product or system is the most important of the design requirements. In addition to being able to ensure that complex designs (e.g., topology optimization) are manufacturable with a given process or process family, MDD also helps mechanical designers to take advantage of unique process-material effects generated during manufacturing. One of the most recognizable examples of this comes from the scanning-type family of additive manufacturing (AM) processes; the most notable and familiar member of this family is the fused deposition modeling (FDM) or fused filament fabrication (FFF) process. This process works by selectively depositing uniform, approximately isotropic beads or elements of molten thermoplastic material (typically structural engineering plastics) in a series of pre-specified traces to build each layer of the part. There are many interesting 2-D and 3-D mechanical design problems that can be explored by designing the layout of these elements. The resulting structured, hierarchical material (which is both manufacturable and customized layer-by-layer within the limits of the process and material) can be defined as a manufacturing process-driven structured material (MPDSM). This dissertation explores several practical methods for designing these element layouts for 2-D and 3-D meso-scale mechanical problems, focusing ultimately on design-for-fracture. Three different fracture conditions are explored: (1) cases where a crack must be prevented or stopped, (2) cases where the crack must be encouraged or accelerated, and (3) cases where cracks must grow in a simple pre-determined pattern. Several new design tools, including a mapping method for the FDM manufacturability constraints, three major literature reviews, the collection, organization, and analysis of several large (qualitative and quantitative) multi-scale datasets on the fracture behavior of FDM-processed materials, some new experimental equipment, and the refinement of a fast and simple g-code generator based on commercially-available software, were developed and refined to support the design of MPDSMs under fracture conditions. The refined design method and rules were experimentally validated using a series of case studies (involving both design and physical testing of the designs) at the end of the dissertation. Finally, a simple design guide for practicing engineers who are not experts in advanced solid mechanics nor process-tailored materials was developed from the results of this project.U of I OnlyAuthor's request
Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control
This paper provides an overview of the current state-of-the-art in selective
harvesting robots (SHRs) and their potential for addressing the challenges of
global food production. SHRs have the potential to increase productivity,
reduce labour costs, and minimise food waste by selectively harvesting only
ripe fruits and vegetables. The paper discusses the main components of SHRs,
including perception, grasping, cutting, motion planning, and control. It also
highlights the challenges in developing SHR technologies, particularly in the
areas of robot design, motion planning and control. The paper also discusses
the potential benefits of integrating AI and soft robots and data-driven
methods to enhance the performance and robustness of SHR systems. Finally, the
paper identifies several open research questions in the field and highlights
the need for further research and development efforts to advance SHR
technologies to meet the challenges of global food production. Overall, this
paper provides a starting point for researchers and practitioners interested in
developing SHRs and highlights the need for more research in this field.Comment: Preprint: to be appeared in Journal of Field Robotic
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution
Recommended from our members
Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Economia colaborativa
A importância de se proceder à análise dos principais desafios jurÃdicos que a economia colaborativa coloca – pelas implicações que as mudanças de paradigma dos modelos de negócios e dos sujeitos envolvidos suscitam − é indiscutÃvel, correspondendo à necessidade de se fomentar a segurança jurÃdica destas práticas, potenciadoras de crescimento económico e bem-estar social.
O Centro de Investigação em Justiça e Governação (JusGov) constituiu uma equipa multidisciplinar que, além de juristas, integra investigadores de outras áreas, como a economia e a gestão, dos vários grupos do JusGov – embora com especial participação dos investigadores que integram o grupo E-TEC (Estado, Empresa e Tecnologia) – e de outras prestigiadas instituições nacionais e internacionais, para desenvolver um projeto neste domÃnio, com o objetivo de identificar os problemas jurÃdicos que a economia colaborativa suscita e avaliar se já existem soluções para aqueles, refletindo igualmente sobre a conveniência de serem introduzidas alterações ou se será mesmo necessário criar nova regulamentação.
O resultado desta investigação é apresentado nesta obra, com o que se pretende fomentar a continuação do debate sobre este tema.Esta obra é financiada por fundos nacionais através da FCT — Fundação para a Ciência e a Tecnologia, I.P., no âmbito do Financiamento UID/05749/202
Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspective
This paper introduces a comprehensive, multi-stage machine learning
methodology that effectively integrates information systems and artificial
intelligence to enhance decision-making processes within the domain of
operations research. The proposed framework adeptly addresses common
limitations of existing solutions, such as the neglect of data-driven
estimation for vital production parameters, exclusive generation of point
forecasts without considering model uncertainty, and lacking explanations
regarding the sources of such uncertainty. Our approach employs Quantile
Regression Forests for generating interval predictions, alongside both local
and global variants of SHapley Additive Explanations for the examined
predictive process monitoring problem. The practical applicability of the
proposed methodology is substantiated through a real-world production planning
case study, emphasizing the potential of prescriptive analytics in refining
decision-making procedures. This paper accentuates the imperative of addressing
these challenges to fully harness the extensive and rich data resources
accessible for well-informed decision-making
Continual Learning of Hand Gestures for Human-Robot Interaction
In this paper, we present an efficient method to incrementally learn to
classify static hand gestures. This method allows users to teach a robot to
recognize new symbols in an incremental manner. Contrary to other works which
use special sensors or external devices such as color or data gloves, our
proposed approach makes use of a single RGB camera to perform static hand
gesture recognition from 2D images. Furthermore, our system is able to
incrementally learn up to 38 new symbols using only 5 samples for each old
class, achieving a final average accuracy of over 90\%. In addition to that,
the incremental training time can be reduced to a 10\% of the time required
when using all data available
Adjacent LSTM-Based Page Scheduling for Hybrid DRAM/NVM Memory Systems
Recent advances in memory technologies have led to the rapid growth of hybrid systems that combine traditional DRAM and Non Volatile Memory (NVM) technologies, as the latter provide lower cost per byte, low leakage power and larger capacities than DRAM, while they can guarantee comparable access latency. Such kind of heterogeneous memory systems impose new challenges in terms of page placement and migration among the alternative technologies of the heterogeneous memory system. In this paper, we present a novel approach for efficient page placement on heterogeneous DRAM/NVM systems. We design an adjacent LSTM-based approach for page placement, which strongly relies on page accesses prediction, while sharing knowledge among pages with behavioral similarity. The proposed approach leads up to 65.5% optimized performance compared to existing approaches, while achieving near-optimal results and saving 20.2% energy consumption on average. Moreover, we propose a new page replacement policy, namely clustered-LRU, achieving up to 8.1% optimized performance, compared to the default Least Recently Used (LRU) policy
A Design Science Research Approach to Smart and Collaborative Urban Supply Networks
Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness.
A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense.
Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice
Countermeasures for the majority attack in blockchain distributed systems
La tecnologÃa Blockchain es considerada como uno de los paradigmas informáticos más importantes posterior al Internet; en función a sus caracterÃsticas únicas que la hacen ideal para registrar, verificar y administrar información de diferentes transacciones. A pesar de esto, Blockchain se enfrenta a diferentes problemas de seguridad, siendo el ataque del 51% o ataque mayoritario uno de los más importantes. Este consiste en que uno o más mineros tomen el control de al menos el 51% del Hash extraÃdo o del cómputo en una red; de modo que un minero puede manipular y modificar arbitrariamente la información registrada en esta tecnologÃa. Este trabajo se enfocó en diseñar e implementar estrategias de detección y mitigación de ataques mayoritarios (51% de ataque) en un sistema distribuido Blockchain, a partir de la caracterización del comportamiento de los mineros. Para lograr esto, se analizó y evaluó el Hash Rate / Share de los mineros de Bitcoin y Crypto Ethereum, seguido del diseño e implementación de un protocolo de consenso para controlar el poder de cómputo de los mineros. Posteriormente, se realizó la exploración y evaluación de modelos de Machine Learning para detectar software malicioso de tipo Cryptojacking.DoctoradoDoctor en IngenierÃa de Sistemas y Computació
- …