672 research outputs found
A multicomponent assembly approach for the design of deep desulfurization heterogeneous catalysts
Deep desulfurization is a challenging task and global efforts are focused on the development of new approaches for the reduction of sulfur-containing compounds in fuel oils. In this work, we have proposed a new design strategy for the development of deep desulfurization heterogeneous catalysts. Based on the adopted design strategy, a novel composite material of polyoxometalate (POM)-based ionic liquid-grafted layered double hydroxides (LDHs) was synthesized by an exfoliation/grafting/assembly process. The structural properties of the as-prepared catalyst were characterized using FT-IR, XRD, TG, NMR, XPS, BET, SEM and HRTEM. The heterogeneous catalyst exhibited high activity in deep desulfurization of DBT (dibenzothiophene), 4,6-DMDBT (4,6-dimethyldibenzothiophene) and BT (benzothiophene) at 70 °C in 25, 30 and 40 minutes, respectively. The catalyst can be easily recovered and reused at least ten times without obvious decrease of its catalytic activity. Such excellent sulfur removal ability as well as the cost efficiency of the novel heterogeneous catalyst can be attributed to the rational design, where the spatial proximity of the substrate and the active sites, the immobilization of ionic liquid onto the LDHs via covalent bonding and the recyclability of the catalyst are carefully considered
Phase Fluctuation Analysis in Functional Brain Networks of Scaling EEG for Driver Fatigue Detection
The characterization of complex patterns arising from electroencephalogram (EEG) is an important problem with significant applications in identifying different mental states. Based on the operational EEG of drivers, a method is proposed to characterize and distinguish different EEG patterns. The EEG measurements from seven professional taxi drivers were collected under different states. The phase characterization method was used to calculate the instantaneous phase from the EEG measurements. Then, the optimization of drivers’ EEG was realized through performing common spatial pattern analysis. The structures and scaling components of the brain networks from optimized EEG measurements are sensitive to the EEG patterns. The effectiveness of the method is demonstrated, and its applicability is articulated.</p
SSC-RS: Elevate LiDAR Semantic Scene Completion with Representation Separation and BEV Fusion
Semantic scene completion (SSC) jointly predicts the semantics and geometry
of the entire 3D scene, which plays an essential role in 3D scene understanding
for autonomous driving systems. SSC has achieved rapid progress with the help
of semantic context in segmentation. However, how to effectively exploit the
relationships between the semantic context in semantic segmentation and
geometric structure in scene completion remains under exploration. In this
paper, we propose to solve outdoor SSC from the perspective of representation
separation and BEV fusion. Specifically, we present the network, named SSC-RS,
which uses separate branches with deep supervision to explicitly disentangle
the learning procedure of the semantic and geometric representations. And a BEV
fusion network equipped with the proposed Adaptive Representation Fusion (ARF)
module is presented to aggregate the multi-scale features effectively and
efficiently. Due to the low computational burden and powerful representation
ability, our model has good generality while running in real-time. Extensive
experiments on SemanticKITTI demonstrate our SSC-RS achieves state-of-the-art
performance.Comment: 8 pages, 5 figures, IROS202
PANet: LiDAR Panoptic Segmentation with Sparse Instance Proposal and Aggregation
Reliable LiDAR panoptic segmentation (LPS), including both semantic and
instance segmentation, is vital for many robotic applications, such as
autonomous driving. This work proposes a new LPS framework named PANet to
eliminate the dependency on the offset branch and improve the performance on
large objects, which are always over-segmented by clustering algorithms.
Firstly, we propose a non-learning Sparse Instance Proposal (SIP) module with
the ``sampling-shifting-grouping" scheme to directly group thing points into
instances from the raw point cloud efficiently. More specifically, balanced
point sampling is introduced to generate sparse seed points with more uniform
point distribution over the distance range. And a shift module, termed bubble
shifting, is proposed to shrink the seed points to the clustered centers. Then
we utilize the connected component label algorithm to generate instance
proposals. Furthermore, an instance aggregation module is devised to integrate
potentially fragmented instances, improving the performance of the SIP module
on large objects. Extensive experiments show that PANet achieves
state-of-the-art performance among published works on the SemanticKITII
validation and nuScenes validation for the panoptic segmentation task.Comment: 8 pages, 3 figures, IROS202
ThumbNet: One Thumbnail Image Contains All You Need for Recognition
Although deep convolutional neural networks (CNNs) have achieved great
success in computer vision tasks, its real-world application is still impeded
by its voracious demand of computational resources. Current works mostly seek
to compress the network by reducing its parameters or parameter-incurred
computation, neglecting the influence of the input image on the system
complexity. Based on the fact that input images of a CNN contain substantial
redundancy, in this paper, we propose a unified framework, dubbed as ThumbNet,
to simultaneously accelerate and compress CNN models by enabling them to infer
on one thumbnail image. We provide three effective strategies to train
ThumbNet. In doing so, ThumbNet learns an inference network that performs
equally well on small images as the original-input network on large images.
With ThumbNet, not only do we obtain the thumbnail-input inference network that
can drastically reduce computation and memory requirements, but also we obtain
an image downscaler that can generate thumbnail images for generic
classification tasks. Extensive experiments show the effectiveness of ThumbNet,
and demonstrate that the thumbnail-input inference network learned by ThumbNet
can adequately retain the accuracy of the original-input network even when the
input images are downscaled 16 times
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