2,714 research outputs found
Availability simulation model of complex electromechanical systems with the consideration of testability parameters
This paper proposes a stochastic MFBD (maintenance function block diagram) to describe fault diagnosis dynamic behavior of availability fluctuation evaluation for complex electromechanical system, which considers comprehensive diagnostic parameters, maintenance process and resource. The availability evaluation of complex electromechanical systems is achieved by simulation method. Firstly, the faults are divided into several types according to the quantity relationship represented by testability parameters and the logic sequence of fault-related activities is modeled. Math models describing the uncertainty between activities are established, which are embedded within MFBD. The stochastic MFBD is transformed into a simulation model designed via PI (process interaction) algorithm. Finally, a discrete-event simulation example for availability analysis of complex electromechanical system is provided and the accuracy and applicability of the proposed method are verified
Competitive and Weighted Evolving Simplicial Complexes
A simplex-based network is referred to as a higher-order network, in which
describe that the interactions can include more than two nodes. This paper
first proposes a competitive evolving model of higher-order networks. We notice
the batch effect of low-dim simplices during the growth of such a network. We
obtain an analytical expression for the distribution of higher-order degrees by
employing the theory of Poisson processes and the mean field method and use
computers to simulate higher-order networks of competitions. The established
results indicate that the scale-free behavior for the (d-1)-dim simplex with
respect to the d-order degree is controlled by the competitiveness factor. As
the competitiveness increases, the d-order degree of the (d-1)-dim simplex is
bent under the logarithmic coordinates. Second, by considering the weight
changes of the neighboring simplices, as triggered by the selected simplex, a
new weighted evolving model in higher-order networks is proposed. The results
of the competitive evolving model of higher-order networks are used to analyze
the weighted evolving model so that obtained are the analytical expressions of
the higher-order degree distribution and higher-order strength density function
of weighted higher-order networks. The outcomes of the simulation experiments
are consistent with the theoretical analysis. Therefore, the weighted network
belongs to the collection of competition networks
Towards Blind Watermarking: Combining Invertible and Non-invertible Mechanisms
Blind watermarking provides powerful evidence for copyright protection, image
authentication, and tampering identification. However, it remains a challenge
to design a watermarking model with high imperceptibility and robustness
against strong noise attacks. To resolve this issue, we present a framework
Combining the Invertible and Non-invertible (CIN) mechanisms. The CIN is
composed of the invertible part to achieve high imperceptibility and the
non-invertible part to strengthen the robustness against strong noise attacks.
For the invertible part, we develop a diffusion and extraction module (DEM) and
a fusion and split module (FSM) to embed and extract watermarks symmetrically
in an invertible way. For the non-invertible part, we introduce a
non-invertible attention-based module (NIAM) and the noise-specific selection
module (NSM) to solve the asymmetric extraction under a strong noise attack.
Extensive experiments demonstrate that our framework outperforms the current
state-of-the-art methods of imperceptibility and robustness significantly. Our
framework can achieve an average of 99.99% accuracy and 67.66 dB PSNR under
noise-free conditions, while 96.64% and 39.28 dB combined strong noise attacks.
The code will be available in https://github.com/rmpku/CIN.Comment: 9 pages, 9 figures, 5 table
RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors
Existing dehazing approaches struggle to process real-world hazy images owing
to the lack of paired real data and robust priors. In this work, we present a
new paradigm for real image dehazing from the perspectives of synthesizing more
realistic hazy data and introducing more robust priors into the network.
Specifically, (1) instead of adopting the de facto physical scattering model,
we rethink the degradation of real hazy images and propose a phenomenological
pipeline considering diverse degradation types. (2) We propose a Real Image
Dehazing network via high-quality Codebook Priors (RIDCP). Firstly, a VQGAN is
pre-trained on a large-scale high-quality dataset to obtain the discrete
codebook, encapsulating high-quality priors (HQPs). After replacing the
negative effects brought by haze with HQPs, the decoder equipped with a novel
normalized feature alignment module can effectively utilize high-quality
features and produce clean results. However, although our degradation pipeline
drastically mitigates the domain gap between synthetic and real data, it is
still intractable to avoid it, which challenges HQPs matching in the wild.
Thus, we re-calculate the distance when matching the features to the HQPs by a
controllable matching operation, which facilitates finding better counterparts.
We provide a recommendation to control the matching based on an explainable
solution. Users can also flexibly adjust the enhancement degree as per their
preference. Extensive experiments verify the effectiveness of our data
synthesis pipeline and the superior performance of RIDCP in real image
dehazing.Comment: Acceptted by CVPR 202
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