498 research outputs found
Construction of multi-mineral digital rocks for upscaling the numerical simulation of tight rock physical properties
Tight sandstone reservoirs are characterized by multi-scale pore space and high clay content, resulting in intricate rock physical responses. In this work, multi-scale imaging techniques, including computed tomography and stitched scanning electron microscopy, are applied to identify the large intergranular pores and micropores within major minerals. The pore structure of tight sandstones is quantitatively investigated using multi-scale images. Besides, multi-mineral digital rocks are constructed by performing registration and segmentation processing on the images obtained from microcomputed tomography and energy-dispersive scanning electron microscopy. These digital rocks are treated as composite materials consisting of different mineral types and micro-porosities, which enables the upscaling of the numerical simulation of rock physics properties. The results reveal that residual intergranular pores are interconnected through micropores within clay minerals, which significantly influences the electrical conductivities and permeabilities of tight sandstones. The proposed upscaling method can effectively couple the contribution of formation brine in multi-scale pores and clay minerals to bulk rock physics properties. This approach is suitable for the numerical simulation of diverse rock physical properties and can be applied to various tight reservoirs.Document Type: PerspectiveCited as: Hu, J., Xiao, Z., Ni, H., Liu, X. Construction of multi-mineral digital rocks for upscaling the numerical simulation of tight rock physical properties. Advances in Geo-Energy Research, 2023, 9(1): 68-70. https://doi.org/10.46690/ager.2023.07.0
Multi-Objective Evolutionary for Object Detection Mobile Architectures Search
Recently, Neural architecture search has achieved great success on
classification tasks for mobile devices. The backbone network for object
detection is usually obtained on the image classification task. However, the
architecture which is searched through the classification task is sub-optimal
because of the gap between the task of image and object detection. As while
work focuses on backbone network architecture search for mobile device object
detection is limited, mainly because the backbone always requires expensive
ImageNet pre-training. Accordingly, it is necessary to study the approach of
network architecture search for mobile device object detection without
expensive pre-training. In this work, we propose a mobile object detection
backbone network architecture search algorithm which is a kind of evolutionary
optimized method based on non-dominated sorting for NAS scenarios. It can
quickly search to obtain the backbone network architecture within certain
constraints. It better solves the problem of suboptimal linear combination
accuracy and computational cost. The proposed approach can search the backbone
networks with different depths, widths, or expansion sizes via a technique of
weight mapping, making it possible to use NAS for mobile devices detection
tasks a lot more efficiently. In our experiments, we verify the effectiveness
of the proposed approach on YoloX-Lite, a lightweight version of the target
detection framework. Under similar computational complexity, the accuracy of
the backbone network architecture we search for is 2.0% mAP higher than
MobileDet. Our improved backbone network can reduce the computational effort
while improving the accuracy of the object detection network. To prove its
effectiveness, a series of ablation studies have been carried out and the
working mechanism has been analyzed in detail
Mastering Strategy Card Game (Hearthstone) with Improved Techniques
Strategy card game is a well-known genre that is demanding on the intelligent
game-play and can be an ideal test-bench for AI. Previous work combines an
end-to-end policy function and an optimistic smooth fictitious play, which
shows promising performances on the strategy card game Legend of Code and
Magic. In this work, we apply such algorithms to Hearthstone, a famous
commercial game that is more complicated in game rules and mechanisms. We
further propose several improved techniques and consequently achieve
significant progress. For a machine-vs-human test we invite a Hearthstone
streamer whose best rank was top 10 of the official league in China region that
is estimated to be of millions of players. Our models defeat the human player
in all Best-of-5 tournaments of full games (including both deck building and
battle), showing a strong capability of decision making.Comment: cog2023 ful
Edge control in a computer controlled optical surfacing process using a heterocercal tool influence function
Edge effect is regarded as one of the most difficult technical issues in a computer controlled optical surfacing (CCOS) process. Traditional opticians have to even up the consequences of the two following cases. Operating CCOS in a large overhang condition affects the accuracy of material removal, while in a small overhang condition, it achieves a more accurate performance, but leaves a narrow rolled-up edge, which takes time and effort to remove. In order to control the edge residuals in the latter case, we present a new concept of the ‘heterocercal’ tool influence function (TIF). Generated from compound motion equipment, this type of TIF can ‘transfer’ the material removal from the inner place to the edge, meanwhile maintaining the high accuracy and efficiency of CCOS. We call it the ‘heterocercal’ TIF, because of the inspiration from the heterocercal tails of sharks, whose upper lobe provides most of the explosive power. The heterocercal TIF was theoretically analyzed, and physically realized in CCOS facilities. Experimental and simulation results showed good agreement. It enables significant control of the edge effect and convergence of entire surface errors in large tool-to-mirror size-ratio conditions. This improvement will largely help manufacturing efficiency in some extremely large optical system projects, like the tertiary mirror of the Thirty Meter Telescope
Prospects of Searching for Type Ia Supernovae with 2.5-m Wide Field Survey Telescope
Type Ia Supernovae (SNe Ia) are the thermonuclear explosion of a
carbon-oxygen white dwarf (WD) and are well-known as a distance indicator.
However, it is still unclear how WDs increase their mass near the Chandrasekhar
limit and how the thermonuclear runaway happens. The observational clues
associated with these open questions, such as the photometric data within hours
to days since the explosion, are scarce. Thus, an essential way is to discover
SNe Ia at specific epochs with optimal surveys. The 2.5-m Wide Field Survey
Telescope (WFST) is an upcoming survey facility deployed in western China. In
this paper, we assess the detecability of SNe Ia with mock observations of
WFST. Followed by the volumetric rate, we generate a spectral series of SNe Ia
based on a data-based model and introduce the line-of-sight extinction to
calculate the brightness from the observer. By comparing with the detection
limit of WFST, which is affected by the observing conditions, we can count the
number of SNe Ia discovered by mock WFST observations. We expect that WFST can
find more than pre-maximum SNe Ia within one-year running. In
particular, WFST could discover about 45 bright SNe Ia, 99 early-phase SNe Ia,
or well-observed SNe Ia with the hypothesized Wide, Deep, or
Medium mode, respectively, suggesting WFST will be an influential facility in
time-domain astronomy.Comment: Accepted by Univers
DLIP: Distilling Language-Image Pre-training
Vision-Language Pre-training (VLP) shows remarkable progress with the
assistance of extremely heavy parameters, which challenges deployment in real
applications. Knowledge distillation is well recognized as the essential
procedure in model compression. However, existing knowledge distillation
techniques lack an in-depth investigation and analysis of VLP, and practical
guidelines for VLP-oriented distillation are still not yet explored. In this
paper, we present DLIP, a simple yet efficient Distilling Language-Image
Pre-training framework, through which we investigate how to distill a light VLP
model. Specifically, we dissect the model distillation from multiple
dimensions, such as the architecture characteristics of different modules and
the information transfer of different modalities. We conduct comprehensive
experiments and provide insights on distilling a light but performant VLP
model. Experimental results reveal that DLIP can achieve a state-of-the-art
accuracy/efficiency trade-off across diverse cross-modal tasks, e.g.,
image-text retrieval, image captioning and visual question answering. For
example, DLIP compresses BLIP by 1.9x, from 213M to 108M parameters, while
achieving comparable or better performance. Furthermore, DLIP succeeds in
retaining more than 95% of the performance with 22.4% parameters and 24.8%
FLOPs compared to the teacher model and accelerates inference speed by 2.7x
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