208 research outputs found
Modal test and finite element analysis of a turbine disk
Experimental modal analysis of a turbine disk was conducted with the hammering method. The first five modals were obtained, matches well with calculation results of ANSYS, and proves the effectiveness of the experiment, provides a reference for further improvement of a certain engine
H2TF for Hyperspectral Image Denoising: Where Hierarchical Nonlinear Transform Meets Hierarchical Matrix Factorization
Recently, tensor singular value decomposition (t-SVD) has emerged as a
promising tool for hyperspectral image (HSI) processing. In the t-SVD, there
are two key building blocks: (i) the low-rank enhanced transform and (ii) the
accompanying low-rank characterization of transformed frontal slices. Previous
t-SVD methods mainly focus on the developments of (i), while neglecting the
other important aspect, i.e., the exact characterization of transformed frontal
slices. In this letter, we exploit the potentiality in both building blocks by
leveraging the \underline{\bf H}ierarchical nonlinear transform and the
\underline{\bf H}ierarchical matrix factorization to establish a new
\underline{\bf T}ensor \underline{\bf F}actorization (termed as H2TF). Compared
to shallow counter partners, e.g., low-rank matrix factorization or its convex
surrogates, H2TF can better capture complex structures of transformed frontal
slices due to its hierarchical modeling abilities. We then suggest the
H2TF-based HSI denoising model and develop an alternating direction method of
multipliers-based algorithm to address the resultant model. Extensive
experiments validate the superiority of our method over state-of-the-art HSI
denoising methods
Prior Bilinear Based Models for Knowledge Graph Completion
Bilinear based models are powerful and widely used approaches for Knowledge
Graphs Completion (KGC). Although bilinear based models have achieved
significant advances, these studies mainly concentrate on posterior properties
(based on evidence, e.g. symmetry pattern) while neglecting the prior
properties. In this paper, we find a prior property named "the law of identity"
that cannot be captured by bilinear based models, which hinders them from
comprehensively modeling the characteristics of KGs. To address this issue, we
introduce a solution called Unit Ball Bilinear Model (UniBi). This model not
only achieves theoretical superiority but also offers enhanced interpretability
and performance by minimizing ineffective learning through minimal constraints.
Experiments demonstrate that UniBi models the prior property and verify its
interpretability and performance
PolarRec: Radio Interferometric Data Reconstruction with Polar Coordinate Representation
In radio astronomy, visibility data, which are measurements of wave signals
from radio telescopes, are transformed into images for observation of distant
celestial objects. However, these resultant images usually contain both real
sources and artifacts, due to signal sparsity and other factors. One way to
obtain cleaner images is to reconstruct samples into dense forms before
imaging. Unfortunately, existing reconstruction methods often miss some
components of visibility in frequency domain, so blurred object edges and
persistent artifacts remain in the images. Furthermore, the computation
overhead is high on irregular visibility samples due to the data skew. To
address these problems, we propose PolarRec, a transformer-encoder-conditioned
reconstruction pipeline with visibility samples converted into the polar
coordinate representation. This representation matches the way in which radio
telescopes observe a celestial area as the Earth rotates. As a result,
visibility samples distribute in the polar system more uniformly than in the
Cartesian space. Therefore, we propose to use radial distance in the loss
function, to help reconstruct complete visibility effectively. Also, we group
visibility samples by their polar angles and propose a group-based encoding
scheme to improve the efficiency. Our experiments demonstrate that PolarRec
markedly improves imaging results by faithfully reconstructing all frequency
components in the visibility domain while significantly reducing the
computation cost in visibility data encoding. We believe this high-quality and
high-efficiency imaging of PolarRec will better facilitate astronomers to
conduct their research
Size and Shape Determination of Riprap and Large-sized Aggregates Using Field Imaging
Riprap rock and large-sized aggregates are extensively used in transportation, geotechnical, and hydraulic engineering applications. Traditional methods for assessing riprap categories based on particle weight may involve subjective visual inspection and time-consuming manual measurements. Aggregate imaging and segmentation techniques can efficiently characterize riprap particles for their size and morphological/shape properties to estimate particle weights. Particle size and morphological/shape characterization ensure the reliable and sustainable use of all aggregate skeleton materials at quarry production lines and construction sites. Aggregate imaging systems developed to date for size and shape characterization, however, have primarily focused on measurement of separated or non-overlapping aggregate particles. This research study presents an innovative approach for automated segmentation and morphological analyses of stockpile aggregate images based on deep-learning techniques. As a project outcome, a portable, deployable, and affordable field-imaging system is envisioned to estimate volumes of individual riprap rocks for field evaluation. A state-of-the-art object detection and segmentation framework is used to train an image-segmentation kernel from manually labeled 2D riprap images in order to facilitate automatic and user-independent segmentation of stockpile aggregate images. The segmentation results show good agreement with ground-truth validation, which entailed comparing the manual labeling to the automatically segmented images. A significant improvement to the efficiency of size and morphological analyses conducted on densely stacked and overlapping particle images is achieved. The algorithms are integrated into a software application with a user-friendly Graphical User Interface (GUI) for ease of operation. Based on the findings of this study, this stockpile aggregate image analysis program promises to become an efficient and innovative application for field-scale and in-place evaluations of aggregate materials. The innovative imaging-based system is envisioned to provide convenient, reliable, and sustainable solutions for the on-site quality assurance/quality control (QA/QC) tasks related to riprap rock and large-sized aggregate material characterization and classification.IDOT-R27-182Ope
Sodium 5-amino-1,3,4-thiadiazole-2-thiolate dihydrate
There are two 5-amino-1,3,4-thiadiazole-2(3H)-thiolate anions in the asymmetric unit of the title compound, Na+·C2H2N3S2
−·2H2O, which are almost perpendicular to each other [dihedral angle = 84.64 (6)°]. The two Na+ cations are in distorted fourfold coordinations by O atoms of the water molecules. The crystal structure is stabilized by N—H⋯S, O—H⋯N and O—H⋯S hydrogen bonds
3D-STMN: Dependency-Driven Superpoint-Text Matching Network for End-to-End 3D Referring Expression Segmentation
In 3D Referring Expression Segmentation (3D-RES), the earlier approach adopts
a two-stage paradigm, extracting segmentation proposals and then matching them
with referring expressions. However, this conventional paradigm encounters
significant challenges, most notably in terms of the generation of lackluster
initial proposals and a pronounced deceleration in inference speed. Recognizing
these limitations, we introduce an innovative end-to-end Superpoint-Text
Matching Network (3D-STMN) that is enriched by dependency-driven insights. One
of the keystones of our model is the Superpoint-Text Matching (STM) mechanism.
Unlike traditional methods that navigate through instance proposals, STM
directly correlates linguistic indications with their respective superpoints,
clusters of semantically related points. This architectural decision empowers
our model to efficiently harness cross-modal semantic relationships, primarily
leveraging densely annotated superpoint-text pairs, as opposed to the more
sparse instance-text pairs. In pursuit of enhancing the role of text in guiding
the segmentation process, we further incorporate the Dependency-Driven
Interaction (DDI) module to deepen the network's semantic comprehension of
referring expressions. Using the dependency trees as a beacon, this module
discerns the intricate relationships between primary terms and their associated
descriptors in expressions, thereby elevating both the localization and
segmentation capacities of our model. Comprehensive experiments on the
ScanRefer benchmark reveal that our model not only set new performance
standards, registering an mIoU gain of 11.7 points but also achieve a
staggering enhancement in inference speed, surpassing traditional methods by
95.7 times. The code and models are available at
https://github.com/sosppxo/3D-STMN
AutoPrep: An Automatic Preprocessing Framework for In-the-Wild Speech Data
Recently, the utilization of extensive open-sourced text data has
significantly advanced the performance of text-based large language models
(LLMs). However, the use of in-the-wild large-scale speech data in the speech
technology community remains constrained. One reason for this limitation is
that a considerable amount of the publicly available speech data is compromised
by background noise, speech overlapping, lack of speech segmentation
information, missing speaker labels, and incomplete transcriptions, which can
largely hinder their usefulness. On the other hand, human annotation of speech
data is both time-consuming and costly. To address this issue, we introduce an
automatic in-the-wild speech data preprocessing framework (AutoPrep) in this
paper, which is designed to enhance speech quality, generate speaker labels,
and produce transcriptions automatically. The proposed AutoPrep framework
comprises six components: speech enhancement, speech segmentation, speaker
clustering, target speech extraction, quality filtering and automatic speech
recognition. Experiments conducted on the open-sourced WenetSpeech and our
self-collected AutoPrepWild corpora demonstrate that the proposed AutoPrep
framework can generate preprocessed data with similar DNSMOS and PDNSMOS scores
compared to several open-sourced TTS datasets. The corresponding TTS system can
achieve up to 0.68 in-domain speaker similarity
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