17,235 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
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
TransFusionOdom: Interpretable Transformer-based LiDAR-Inertial Fusion Odometry Estimation
Multi-modal fusion of sensors is a commonly used approach to enhance the
performance of odometry estimation, which is also a fundamental module for
mobile robots. However, the question of \textit{how to perform fusion among
different modalities in a supervised sensor fusion odometry estimation task?}
is still one of challenging issues remains. Some simple operations, such as
element-wise summation and concatenation, are not capable of assigning adaptive
attentional weights to incorporate different modalities efficiently, which make
it difficult to achieve competitive odometry results. Recently, the Transformer
architecture has shown potential for multi-modal fusion tasks, particularly in
the domains of vision with language. In this work, we propose an end-to-end
supervised Transformer-based LiDAR-Inertial fusion framework (namely
TransFusionOdom) for odometry estimation. The multi-attention fusion module
demonstrates different fusion approaches for homogeneous and heterogeneous
modalities to address the overfitting problem that can arise from blindly
increasing the complexity of the model. Additionally, to interpret the learning
process of the Transformer-based multi-modal interactions, a general
visualization approach is introduced to illustrate the interactions between
modalities. Moreover, exhaustive ablation studies evaluate different
multi-modal fusion strategies to verify the performance of the proposed fusion
strategy. A synthetic multi-modal dataset is made public to validate the
generalization ability of the proposed fusion strategy, which also works for
other combinations of different modalities. The quantitative and qualitative
odometry evaluations on the KITTI dataset verify the proposed TransFusionOdom
could achieve superior performance compared with other related works.Comment: Submitted to IEEE Sensors Journal with some modifications. This work
has been submitted to the IEEE for possible publication. Copyright may be
transferred without notice, after which this version may no longer be
accessibl
Composing games into complex institutions
Game theory is used by all behavioral sciences, but its development has long
centered around tools for relatively simple games and toy systems, such as the
economic interpretation of equilibrium outcomes. Our contribution,
compositional game theory, permits another approach of equally general appeal:
the high-level design of large games for expressing complex architectures and
representing real-world institutions faithfully. Compositional game theory,
grounded in the mathematics underlying programming languages, and introduced
here as a general computational framework, increases the parsimony of game
representations with abstraction and modularity, accelerates search and design,
and helps theorists across disciplines express real-world institutional
complexity in well-defined ways. Relative to existing approaches in game
theory, compositional game theory is especially promising for solving game
systems with long-range dependencies, for comparing large numbers of
structurally related games, and for nesting games into the larger logical or
strategic flows typical of real world policy or institutional systems.Comment: ~4000 words, 6 figure
First emergence of cold accretion and supermassive star formation in the early universe
We investigate the first emergence of the so-called cold accretion, the
accretion flows deeply penetrating a halo, in the early universe with
cosmological N-body/SPH simulations. We study the structure of the accretion
flow and its evolution within small halos with
with sufficiently high spatial resolutions down to scale.
While previous studies only follow the evolution for a short period after the
primordial cloud collapse, we follow the long-term evolution until the cold
accretion first appears, employing the sink particle method. We show that the
cold accretion emerges when the halo mass exceeds , the halo
masses above which the accretion flow penetrates halos. We further continue
simulations to study whether the cold accretion provides the dense shock waves,
which have been proposed to give birth to supermassive stars (SMSs). We find
that the accretion flow eventually hits a compact disc near the halo centre,
creating dense shocks over a wide area of the disc surface. The resulting
post-shock gas becomes dense and hot enough with its mass comparable to the
Jeans mass , a sufficient amount to
induce the gravitational collapse, leading to the SMS formation.Comment: 17 pages, 17 figures, MNRAS submitte
Re-prioritizing climate services for agriculture: Insights from Bangladesh
Considerable progress has been made in establishing climate service capabilities over the last few decades, but the gap between the resulting services and national needs remains large. Using climate services for agriculture in Bangladesh as a case study example, we highlight mismatches between local needs on the one hand, and international initiatives that have focused largely on prediction on the other, and we make suggestions for addressing such mismatches in similar settings. To achieve greater benefit at the national level, there should be a stronger focus on addressing important preliminaries for building services. These preliminaries include the identification of priorities, the definition of responsibilities and expectations, the development of climate services skills, and the construction of a high-quality and easily usable national climate record. Once appropriate institutional, human resources and data infrastructure are in place, the implementation of a climate monitoring and watch system would form a more logical basis for initial climate service implementation than attempting to promote sub-seasonal to seasonal climate forecasting, especially when and where the inherent predictability is limited at best. When and where forecasting at these scales is viable, efforts should focus on defining and predicting high-impact events important for decision making, rather than on simple seasonal aggregates that often correlate poorly with outcomes. Some such forecasts may be more skillful than the 3- to 4-month seasonal aggregates that have become the internationally adopted standard. By establishing a firm foundation for climate services within National Meteorological Services, there is a greater chance that individual climate service development initiatives will be sustainable after their respective project lifetimes
Countermeasures for the majority attack in blockchain distributed systems
La tecnología Blockchain es considerada como uno de los paradigmas informáticos más importantes posterior al Internet; en función a sus características 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 extraído o del cómputo en una red; de modo que un minero puede manipular y modificar arbitrariamente la información registrada en esta tecnología. 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 Ingeniería de Sistemas y Computació
A citizen science approach to the characterisation and modelling of urban pluvial flooding
Urban pluvial flooding (UPF), a growing challenge across cities worldwide that is expected to worsen
due to climate change and urbanisation, requires comprehensive response strategies. However, the
characterisation and simulation of UPF is more complex than traditional catchment hydrological modelling because
UPF is driven by a complex set of interconnected factors and modelling constraints. Different integrated approaches
have attempted to address UPF by coupling humans and environmental systems and reflecting on the possible
outcomes from the interactions among varied disciplines. Nonetheless, it is argued that current integrated
approaches are insufficient. To further improve the characterisation and modelling of UPF, this study advances a
citizen science approach that integrates local knowledge with the understanding and interpretation of UPF. The
proposed framework provides an avenue to couple quantitative and qualitative community-based observations
with traditional sources of hydro-information. This approach allows researchers and practitioners to fill spatial and
temporal data gaps in urban catchments and hydrologic/hydrodynamic models, thus yielding a more accurate
characterisation of local catchment response and improving rainfall-runoff modelling of UPF. The results of applying
this framework indicate how community-based practices provide a bi-directional learning context between experts
and residents, which can contribute to resilience building by providing UPF knowledge necessary for risk reduction
and response to extreme flooding events
Accurate and Interpretable Solution of the Inverse Rig for Realistic Blendshape Models with Quadratic Corrective Terms
We propose a new model-based algorithm solving the inverse rig problem in
facial animation retargeting, exhibiting higher accuracy of the fit and
sparser, more interpretable weight vector compared to SOTA. The proposed method
targets a specific subdomain of human face animation - highly-realistic
blendshape models used in the production of movies and video games. In this
paper, we formulate an optimization problem that takes into account all the
requirements of targeted models. Our objective goes beyond a linear blendshape
model and employs the quadratic corrective terms necessary for correctly
fitting fine details of the mesh. We show that the solution to the proposed
problem yields highly accurate mesh reconstruction even when general-purpose
solvers, like SQP, are used. The results obtained using SQP are highly accurate
in the mesh space but do not exhibit favorable qualities in terms of weight
sparsity and smoothness, and for this reason, we further propose a novel
algorithm relying on a MM technique. The algorithm is specifically suited for
solving the proposed objective, yielding a high-accuracy mesh fit while
respecting the constraints and producing a sparse and smooth set of weights
easy to manipulate and interpret by artists. Our algorithm is benchmarked with
SOTA approaches, and shows an overall superiority of the results, yielding a
smooth animation reconstruction with a relative improvement up to 45 percent in
root mean squared mesh error while keeping the cardinality comparable with
benchmark methods. This paper gives a comprehensive set of evaluation metrics
that cover different aspects of the solution, including mesh accuracy, sparsity
of the weights, and smoothness of the animation curves, as well as the
appearance of the produced animation, which human experts evaluated
A Visual Modeling Method for Spatiotemporal and Multidimensional Features in Epidemiological Analysis: Applied COVID-19 Aggregated Datasets
The visual modeling method enables flexible interactions with rich graphical
depictions of data and supports the exploration of the complexities of
epidemiological analysis. However, most epidemiology visualizations do not
support the combined analysis of objective factors that might influence the
transmission situation, resulting in a lack of quantitative and qualitative
evidence. To address this issue, we have developed a portrait-based visual
modeling method called +msRNAer. This method considers the spatiotemporal
features of virus transmission patterns and the multidimensional features of
objective risk factors in communities, enabling portrait-based exploration and
comparison in epidemiological analysis. We applied +msRNAer to aggregate
COVID-19-related datasets in New South Wales, Australia, which combined
COVID-19 case number trends, geo-information, intervention events, and
expert-supervised risk factors extracted from LGA-based censuses. We perfected
the +msRNAer workflow with collaborative views and evaluated its feasibility,
effectiveness, and usefulness through one user study and three subject-driven
case studies. Positive feedback from experts indicates that +msRNAer provides a
general understanding of analyzing comprehension that not only compares
relationships between cases in time-varying and risk factors through portraits
but also supports navigation in fundamental geographical, timeline, and other
factor comparisons. By adopting interactions, experts discovered functional and
practical implications for potential patterns of long-standing community
factors against the vulnerability faced by the pandemic. Experts confirmed that
+msRNAer is expected to deliver visual modeling benefits with spatiotemporal
and multidimensional features in other epidemiological analysis scenarios
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