85 research outputs found
Dirty Industry Migration and the Environment - China as a Major Case for Study
Ph.DDOCTOR OF PHILOSOPH
CM-GAN: Image Inpainting with Cascaded Modulation GAN and Object-Aware Training
Recent image inpainting methods have made great progress but often struggle
to generate plausible image structures when dealing with large holes in complex
images. This is partially due to the lack of effective network structures that
can capture both the long-range dependency and high-level semantics of an
image. To address these problems, we propose cascaded modulation GAN (CM-GAN),
a new network design consisting of an encoder with Fourier convolution blocks
that extract multi-scale feature representations from the input image with
holes and a StyleGAN-like decoder with a novel cascaded global-spatial
modulation block at each scale level. In each decoder block, global modulation
is first applied to perform coarse semantic-aware structure synthesis, then
spatial modulation is applied on the output of global modulation to further
adjust the feature map in a spatially adaptive fashion. In addition, we design
an object-aware training scheme to prevent the network from hallucinating new
objects inside holes, fulfilling the needs of object removal tasks in
real-world scenarios. Extensive experiments are conducted to show that our
method significantly outperforms existing methods in both quantitative and
qualitative evaluation.Comment: 32 pages, 18 figure
Structure-Guided Image Completion with Image-level and Object-level Semantic Discriminators
Structure-guided image completion aims to inpaint a local region of an image
according to an input guidance map from users. While such a task enables many
practical applications for interactive editing, existing methods often struggle
to hallucinate realistic object instances in complex natural scenes. Such a
limitation is partially due to the lack of semantic-level constraints inside
the hole region as well as the lack of a mechanism to enforce realistic object
generation. In this work, we propose a learning paradigm that consists of
semantic discriminators and object-level discriminators for improving the
generation of complex semantics and objects. Specifically, the semantic
discriminators leverage pretrained visual features to improve the realism of
the generated visual concepts. Moreover, the object-level discriminators take
aligned instances as inputs to enforce the realism of individual objects. Our
proposed scheme significantly improves the generation quality and achieves
state-of-the-art results on various tasks, including segmentation-guided
completion, edge-guided manipulation and panoptically-guided manipulation on
Places2 datasets. Furthermore, our trained model is flexible and can support
multiple editing use cases, such as object insertion, replacement, removal and
standard inpainting. In particular, our trained model combined with a novel
automatic image completion pipeline achieves state-of-the-art results on the
standard inpainting task.Comment: 18 pages, 16 figure
Supercurrent, Multiple Andreev Reflections and Shapiro Steps in InAs Nanosheet Josephson Junctions
High-quality free-standing InAs nanosheets are emerging layered semiconductor
materials with potentials in designing planar Josephson junction devices for
novel physics studies due to their unique properties including strong
spin-orbit couplings, large Land\'e g-factors and the two dimensional nature.
Here, we report an experimental study of proximity induced superconductivity in
planar Josephson junction devices made from free-standing InAs nanosheets. The
nanosheets are grown by molecular beam epitaxy and the Josephson junction
devices are fabricated by directly contacting the nanosheets with
superconductor Al electrodes. The fabricated devices are explored by
low-temperature carrier transport measurements. The measurements show that the
devices exhibit a gate-tunable supercurrent, multiple Andreev reflections, and
a good quality superconductor-semiconductor interface. The superconducting
characteristics of the Josephson junctions are investigated at different
magnetic fields and temperatures, and are analyzed based on the
Bardeen-Cooper-Schrieffer (BCS) theory. The measurements of ac Josephson effect
are also conducted under microwave radiations with different radiation powers
and frequencies, and integer Shapiro steps are observed. Our work demonstrates
that InAs nanosheet based hybrid devices are desired systems for investigating
forefront physics, such as the two-dimensional topological superconductivity
Forest Fire Prevention Early Warning Method Based on Fuzzy Bayesian Network
In the environment of large forest, the factors causing fire are nonlinear and uncertain. If the data collected by the sensor is simply analyzed and compared, the false alarm rate will be higher. How to combine the data of several sensors for effective fire warning is a difficult point. In order to improve the accuracy of prediction, aiming at the shortcomings of traditional forest fire prevention early warning system, we propose a forest fire prevention early warning method based on fuzzy Bayesian network. Firstly, we combine the fuzzy control system and the Bayesian network in series, and pre-process the collected sensor data. The pre-processed data is sent to the previously trained Bayesian network for processing. Then the calculated open fire probability, smoldering fire probability, and no fire probability are used as input data of fuzzy control system, and fuzzy inference is performed. Finally, we de-fuzzify the results of fuzzy reasoning and get the probability of fire. Simulation results show that our method can effectively combine the data collected by multiple sensors, quickly and accurately determine fire occurrence probability, improve the accuracy of forest fire prevention warning, and reduce the false positive rate
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