316 research outputs found
Study of Deformation-Compensated Modeling for Flexible Material Path Processing Based on Fuzzy Neural Network and Fuzzy Clustering
In this paper, the Flexible Material Path Processing (FMPP) deformation compensation modeling method based on T-S fuzzy neural network is proposed. This method combined with T S fuzzy reasoning and fuzzy neural network.Firstly, fuzzy clustering is introduced to extract fuzzy membership functions and the fitness of fuzzy rules of T S fuzzy neural network antecedent from historical processing data; secondly, through back-propagation iteration to calculate connection weights of the network. Processing experiments shows that T S fuzzy neural network modeling in this paper is superior to typical T S model,the angle error and straightness error processing by NTS FNN is decreased than these of STS FNN
Seasonal variability in spatial patterns of sea surface cold- and warm fronts over the continental shelf of the northern South China Sea
Ubiquitous fronts are a key part of energy transfer from large scales to small scales and exert a great impact on material exchange and biogeochemical processes. The spatial pattern and seasonal variability of cold- and warm fronts over the wide shelf of the northern South China Sea (SCS) are investigated using a 20-year time series (2002−2021) of 1-km spatial resolution Group for High Resolution Sea Surface Temperature (GHRSST) images. Our analysis shows distinct spatial and temporal variability in the occurrence of the cold- and warm fronts. Over the inner shelf (depth <50 m), the band-shaped cold fronts are predominately observed during spring through autumn from east of Hainan Island to Taiwan Shoal, with the presence of the maximum intensity and probability in winter. The frontal formations are possibly associated with the joint effect of the Guangdong Coastal Current (GCC) and the South China Sea Warm Current. During summer, the inshore fronts have relatively low probability and gradient magnitude. The warm fronts mainly occur off the western Guangdong coast possibly due to the southwestward-flowing GCC, whereas the cold fronts dominate off the eastern Guangdong coast and the eastern Hainan Island largely because of the coastal upwelling. Over the outer shelf (depth >50 m), the finer-scale cold- and warm fronts are discretely observed, with relatively weaker intensity and lower probability. The frontal activities are very vigorous in winter but slightly quiescent in summer, apparently resulting from the influence of the rich submesoscale processes in the SCS. This study could help improve our understanding of the SCS oceanic multiscale dynamics
Deformation-compensated modeling of flexible material processing based on T-S fuzzy neural network and fuzzy clustering
According to the factors that influence flexible material processing (FMP), the deformation compensation modeling method based on T-S fuzzy neural network is proposed. This method combines T-S fuzzy reasoning with a fuzzy neural network. Firstly, fuzzy clustering is introduced to extract fuzzy membership functions and the fitness of fuzzy rules of T-S fuzzy neural network antecedent from the past processing data. Secondly, with the steepest descent method, back-propagation iteration is used to calculate the connection weights of the network. The processing of experiments shows that T-S fuzzy neural network modeling is superior to typical T-S model. The angle error and the straightness error processed by NTS-FNN is 40.4Â %, 28.8Â % lower than those of STS-FNN. The minimum processing time processed by NTS-FNN is lower by 46.1Â % than that of STS-FNN
VQ-NeRF: Vector Quantization Enhances Implicit Neural Representations
Recent advancements in implicit neural representations have contributed to
high-fidelity surface reconstruction and photorealistic novel view synthesis.
However, the computational complexity inherent in these methodologies presents
a substantial impediment, constraining the attainable frame rates and
resolutions in practical applications. In response to this predicament, we
propose VQ-NeRF, an effective and efficient pipeline for enhancing implicit
neural representations via vector quantization. The essence of our method
involves reducing the sampling space of NeRF to a lower resolution and
subsequently reinstating it to the original size utilizing a pre-trained VAE
decoder, thereby effectively mitigating the sampling time bottleneck
encountered during rendering. Although the codebook furnishes representative
features, reconstructing fine texture details of the scene remains challenging
due to high compression rates. To overcome this constraint, we design an
innovative multi-scale NeRF sampling scheme that concurrently optimizes the
NeRF model at both compressed and original scales to enhance the network's
ability to preserve fine details. Furthermore, we incorporate a semantic loss
function to improve the geometric fidelity and semantic coherence of our 3D
reconstructions. Extensive experiments demonstrate the effectiveness of our
model in achieving the optimal trade-off between rendering quality and
efficiency. Evaluation on the DTU, BlendMVS, and H3DS datasets confirms the
superior performance of our approach.Comment: Submitted to the 38th Annual AAAI Conference on Artificial
Intelligenc
Networking Architecture and Key Technologies for Human Digital Twin in Personalized Healthcare: A Comprehensive Survey
Digital twin (DT), refers to a promising technique to digitally and
accurately represent actual physical entities. One typical advantage of DT is
that it can be used to not only virtually replicate a system's detailed
operations but also analyze the current condition, predict future behaviour,
and refine the control optimization. Although DT has been widely implemented in
various fields, such as smart manufacturing and transportation, its
conventional paradigm is limited to embody non-living entities, e.g., robots
and vehicles. When adopted in human-centric systems, a novel concept, called
human digital twin (HDT) has thus been proposed. Particularly, HDT allows in
silico representation of individual human body with the ability to dynamically
reflect molecular status, physiological status, emotional and psychological
status, as well as lifestyle evolutions. These prompt the expected application
of HDT in personalized healthcare (PH), which can facilitate remote monitoring,
diagnosis, prescription, surgery and rehabilitation. However, despite the large
potential, HDT faces substantial research challenges in different aspects, and
becomes an increasingly popular topic recently. In this survey, with a specific
focus on the networking architecture and key technologies for HDT in PH
applications, we first discuss the differences between HDT and conventional
DTs, followed by the universal framework and essential functions of HDT. We
then analyze its design requirements and challenges in PH applications. After
that, we provide an overview of the networking architecture of HDT, including
data acquisition layer, data communication layer, computation layer, data
management layer and data analysis and decision making layer. Besides reviewing
the key technologies for implementing such networking architecture in detail,
we conclude this survey by presenting future research directions of HDT
Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction
Understanding Origin-Destination (O-D) travel demand is crucial for
transportation management. However, traditional spatial-temporal deep learning
models grapple with addressing the sparse and long-tail characteristics in
high-resolution O-D matrices and quantifying prediction uncertainty. This
dilemma arises from the numerous zeros and over-dispersed demand patterns
within these matrices, which challenge the Gaussian assumption inherent to
deterministic deep learning models. To address these challenges, we propose a
novel approach: the Spatial-Temporal Tweedie Graph Neural Network (STTD). The
STTD introduces the Tweedie distribution as a compelling alternative to the
traditional 'zero-inflated' model and leverages spatial and temporal embeddings
to parameterize travel demand distributions. Our evaluations using real-world
datasets highlight STTD's superiority in providing accurate predictions and
precise confidence intervals, particularly in high-resolution scenarios.Comment: In proceeding of CIKM 2023. Doi:
https://dl.acm.org/doi/10.1145/3583780.361521
Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction
Understanding Origin-Destination (O-D) travel demand is crucial for transportation management. However, traditional spatialtemporal deep learning models grapple with addressing the sparse
and long-tail characteristics in high-resolution O-D matrices and
quantifying prediction uncertainty. This dilemma arises from the
numerous zeros and over-dispersed demand patterns within these
matrices, which challenge the Gaussian assumption inherent to
deterministic deep learning models. To address these challenges,
we propose a novel approach: the Spatial-Temporal Tweedie Graph
Neural Network (STTD). The STTD introduces the Tweedie distribution as a compelling alternative to the traditional ’zero-inflated’
model and leverages spatial and temporal embeddings to parameterize travel demand distributions. Our evaluations using realworld datasets highlight STTD’s superiority in providing accurate
predictions and precise confidence intervals, particularly in highresolution scenarios. GitHub code is available online
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