21,028 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
Rank-based linkage I: triplet comparisons and oriented simplicial complexes
Rank-based linkage is a new tool for summarizing a collection of objects
according to their relationships. These objects are not mapped to vectors, and
``similarity'' between objects need be neither numerical nor symmetrical. All
an object needs to do is rank nearby objects by similarity to itself, using a
Comparator which is transitive, but need not be consistent with any metric on
the whole set. Call this a ranking system on . Rank-based linkage is applied
to the -nearest neighbor digraph derived from a ranking system. Computations
occur on a 2-dimensional abstract oriented simplicial complex whose faces are
among the points, edges, and triangles of the line graph of the undirected
-nearest neighbor graph on . In steps it builds an
edge-weighted linkage graph where
is called the in-sway between objects and . Take to be
the links whose in-sway is at least , and partition into components of
the graph , for varying . Rank-based linkage is a
functor from a category of out-ordered digraphs to a category of partitioned
sets, with the practical consequence that augmenting the set of objects in a
rank-respectful way gives a fresh clustering which does not ``rip apart`` the
previous one. The same holds for single linkage clustering in the metric space
context, but not for typical optimization-based methods. Open combinatorial
problems are presented in the last section.Comment: 37 pages, 12 figure
RAFEN -- Regularized Alignment Framework for Embeddings of Nodes
Learning representations of nodes has been a crucial area of the graph
machine learning research area. A well-defined node embedding model should
reflect both node features and the graph structure in the final embedding. In
the case of dynamic graphs, this problem becomes even more complex as both
features and structure may change over time. The embeddings of particular nodes
should remain comparable during the evolution of the graph, what can be
achieved by applying an alignment procedure. This step was often applied in
existing works after the node embedding was already computed. In this paper, we
introduce a framework -- RAFEN -- that allows to enrich any existing node
embedding method using the aforementioned alignment term and learning aligned
node embedding during training time. We propose several variants of our
framework and demonstrate its performance on six real-world datasets. RAFEN
achieves on-par or better performance than existing approaches without
requiring additional processing steps.Comment: ICCS 202
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
In-situ crack and keyhole pore detection in laser directed energy deposition through acoustic signal and deep learning
Cracks and keyhole pores are detrimental defects in alloys produced by laser
directed energy deposition (LDED). Laser-material interaction sound may hold
information about underlying complex physical events such as crack propagation
and pores formation. However, due to the noisy environment and intricate signal
content, acoustic-based monitoring in LDED has received little attention. This
paper proposes a novel acoustic-based in-situ defect detection strategy in
LDED. The key contribution of this study is to develop an in-situ acoustic
signal denoising, feature extraction, and sound classification pipeline that
incorporates convolutional neural networks (CNN) for online defect prediction.
Microscope images are used to identify locations of the cracks and keyhole
pores within a part. The defect locations are spatiotemporally registered with
acoustic signal. Various acoustic features corresponding to defect-free
regions, cracks, and keyhole pores are extracted and analysed in time-domain,
frequency-domain, and time-frequency representations. The CNN model is trained
to predict defect occurrences using the Mel-Frequency Cepstral Coefficients
(MFCCs) of the lasermaterial interaction sound. The CNN model is compared to
various classic machine learning models trained on the denoised acoustic
dataset and raw acoustic dataset. The validation results shows that the CNN
model trained on the denoised dataset outperforms others with the highest
overall accuracy (89%), keyhole pore prediction accuracy (93%), and AUC-ROC
score (98%). Furthermore, the trained CNN model can be deployed into an
in-house developed software platform for online quality monitoring. The
proposed strategy is the first study to use acoustic signals with deep learning
for insitu defect detection in LDED process.Comment: 36 Pages, 16 Figures, accepted at journal Additive Manufacturin
BotMoE: Twitter Bot Detection with Community-Aware Mixtures of Modal-Specific Experts
Twitter bot detection has become a crucial task in efforts to combat online
misinformation, mitigate election interference, and curb malicious propaganda.
However, advanced Twitter bots often attempt to mimic the characteristics of
genuine users through feature manipulation and disguise themselves to fit in
diverse user communities, posing challenges for existing Twitter bot detection
models. To this end, we propose BotMoE, a Twitter bot detection framework that
jointly utilizes multiple user information modalities (metadata, textual
content, network structure) to improve the detection of deceptive bots.
Furthermore, BotMoE incorporates a community-aware Mixture-of-Experts (MoE)
layer to improve domain generalization and adapt to different Twitter
communities. Specifically, BotMoE constructs modal-specific encoders for
metadata features, textual content, and graphical structure, which jointly
model Twitter users from three modal-specific perspectives. We then employ a
community-aware MoE layer to automatically assign users to different
communities and leverage the corresponding expert networks. Finally, user
representations from metadata, text, and graph perspectives are fused with an
expert fusion layer, combining all three modalities while measuring the
consistency of user information. Extensive experiments demonstrate that BotMoE
significantly advances the state-of-the-art on three Twitter bot detection
benchmarks. Studies also confirm that BotMoE captures advanced and evasive
bots, alleviates the reliance on training data, and better generalizes to new
and previously unseen user communities.Comment: Accepted at SIGIR 202
Construction of radon chamber to expose active and passive detectors
In this research and development, we present the design and manufacture of a radon chamber
(PUCP radon chamber), a necessary tool for the calibration of passive detectors, verification
of the operation of active radon monitors as well as diffusion chamber calibration used in
radon measurements in air, and soils. The first chapter is an introduction to describe radon
gas and national levels of radon concentration given by many organizations. Parameters that
influence the calibration factor of the LR 115 type 2 film detector are studied, such as the
energy window, critical angle, and effective volumes. Those are strongly related to the etching
processes and counting of tracks all seen from a semi-empirical approach studied in the second
chapter. The third chapter presents a review of some radon chambers that have been reported
in the literature, based on their size and mode of operation as well as the radon source they use.
The design and construction of the radon chamber are presented, use of uranium ore (autunite)
as a chamber source is also discussed. In chapter fourth, radon chamber characterization
is presented through leakage lambda, homogeneity of radon concentration, regimes-operation
modes, and the saturation concentrations that can be reached. Procedures and methodology
used in this work are contained in the fifth chapter and also some uses and applications of the
PUCP radon chamber are presented; the calibration of cylindrical metallic diffusion chamber
based on CR-39 chips detectors taking into account overlapping effect; transmission factors of
gaps and pinhole for the same diffusion chambers are determined; permeability of glass fiber
filter for 222Rn is obtained after reach equilibrium through Ramachandran model and taking
into account a partition function as the rate of track density. The results of this research have
been published in indexed journals. Finally, the conclusion and recommendations that reflect
the fulfillment aims of this thesis are presented
Leveraging Hidden Positives for Unsupervised Semantic Segmentation
Dramatic demand for manpower to label pixel-level annotations triggered the
advent of unsupervised semantic segmentation. Although the recent work
employing the vision transformer (ViT) backbone shows exceptional performance,
there is still a lack of consideration for task-specific training guidance and
local semantic consistency. To tackle these issues, we leverage contrastive
learning by excavating hidden positives to learn rich semantic relationships
and ensure semantic consistency in local regions. Specifically, we first
discover two types of global hidden positives, task-agnostic and task-specific
ones for each anchor based on the feature similarities defined by a fixed
pre-trained backbone and a segmentation head-in-training, respectively. A
gradual increase in the contribution of the latter induces the model to capture
task-specific semantic features. In addition, we introduce a gradient
propagation strategy to learn semantic consistency between adjacent patches,
under the inherent premise that nearby patches are highly likely to possess the
same semantics. Specifically, we add the loss propagating to local hidden
positives, semantically similar nearby patches, in proportion to the predefined
similarity scores. With these training schemes, our proposed method achieves
new state-of-the-art (SOTA) results in COCO-stuff, Cityscapes, and Potsdam-3
datasets. Our code is available at: https://github.com/hynnsk/HP.Comment: Accepted to CVPR 202
CrossLoc3D: Aerial-Ground Cross-Source 3D Place Recognition
We present CrossLoc3D, a novel 3D place recognition method that solves a
large-scale point matching problem in a cross-source setting. Cross-source
point cloud data corresponds to point sets captured by depth sensors with
different accuracies or from different distances and perspectives. We address
the challenges in terms of developing 3D place recognition methods that account
for the representation gap between points captured by different sources. Our
method handles cross-source data by utilizing multi-grained features and
selecting convolution kernel sizes that correspond to most prominent features.
Inspired by the diffusion models, our method uses a novel iterative refinement
process that gradually shifts the embedding spaces from different sources to a
single canonical space for better metric learning. In addition, we present
CS-Campus3D, the first 3D aerial-ground cross-source dataset consisting of
point cloud data from both aerial and ground LiDAR scans. The point clouds in
CS-Campus3D have representation gaps and other features like different views,
point densities, and noise patterns. We show that our CrossLoc3D algorithm can
achieve an improvement of 4.74% - 15.37% in terms of the top 1 average recall
on our CS-Campus3D benchmark and achieves performance comparable to
state-of-the-art 3D place recognition method on the Oxford RobotCar. We will
release the code and CS-Campus3D benchmark
Boosting the Cycle Counting Power of Graph Neural Networks with I-GNNs
Message Passing Neural Networks (MPNNs) are a widely used class of Graph
Neural Networks (GNNs). The limited representational power of MPNNs inspires
the study of provably powerful GNN architectures. However, knowing one model is
more powerful than another gives little insight about what functions they can
or cannot express. It is still unclear whether these models are able to
approximate specific functions such as counting certain graph substructures,
which is essential for applications in biology, chemistry and social network
analysis. Motivated by this, we propose to study the counting power of Subgraph
MPNNs, a recent and popular class of powerful GNN models that extract rooted
subgraphs for each node, assign the root node a unique identifier and encode
the root node's representation within its rooted subgraph. Specifically, we
prove that Subgraph MPNNs fail to count more-than-4-cycles at node level,
implying that node representations cannot correctly encode the surrounding
substructures like ring systems with more than four atoms. To overcome this
limitation, we propose I-GNNs to extend Subgraph MPNNs by assigning
different identifiers for the root node and its neighbors in each subgraph.
I-GNNs' discriminative power is shown to be strictly stronger than Subgraph
MPNNs and partially stronger than the 3-WL test. More importantly, I-GNNs
are proven capable of counting all 3, 4, 5 and 6-cycles, covering common
substructures like benzene rings in organic chemistry, while still keeping
linear complexity. To the best of our knowledge, it is the first linear-time
GNN model that can count 6-cycles with theoretical guarantees. We validate its
counting power in cycle counting tasks and demonstrate its competitive
performance in molecular prediction benchmarks
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