35 research outputs found
There aren't Non-Standard Solutions for the Braid Group Representations of the QYBE Associated with 10-D Representations of SU(4)
It is well known that the quantum Yang-Baxter equations (QYBE) play an important role in various theoretical and mathematical physics, such as completely integrable system in (1 + 1)-dimensions, exactly solvable models in statistical mechanics, the quantum inverse scattering method and the conformal field theories in 2-dimensions. Recently, much remarkable progress has been made in constructing the solutions of the QYBE associated with the representations of lie algebras. It is shown that for some cases except the standard solutions, there also exist new solutions, but the others have not non-standard solutions. In this paper by employing the weight conservation and the diagrammatic techniques we show that the solution associated with the 10-D representations of SU (4) are standard alone
Solutions of the Quantum Yang-Baxter Equations Associated with (1-3/2)-D Representations of SU(sub q) (2)
The solutions of the spectral independent QYBE associated with (1-3/2)-D representations of SU(sub q) (2) are derived, based on the weight conservation and extended Kauffman diagrammatic technique. It is found that there are nonstandard solutions
A General Unfolding Speech Enhancement Method Motivated by Taylor's Theorem
While deep neural networks have facilitated significant advancements in the
field of speech enhancement, most existing methods are developed following
either empirical or relatively blind criteria, lacking adequate guidelines in
pipeline design. Inspired by Taylor's theorem, we propose a general unfolding
framework for both single- and multi-channel speech enhancement tasks.
Concretely, we formulate the complex spectrum recovery into the spectral
magnitude mapping in the neighborhood space of the noisy mixture, in which an
unknown sparse term is introduced and applied for phase modification in
advance. Based on that, the mapping function is decomposed into the
superimposition of the 0th-order and high-order polynomials in Taylor's series,
where the former coarsely removes the interference in the magnitude domain and
the latter progressively complements the remaining spectral detail in the
complex spectrum domain. In addition, we study the relation between adjacent
order terms and reveal that each high-order term can be recursively estimated
with its lower-order term, and each high-order term is then proposed to
evaluate using a surrogate function with trainable weights so that the whole
system can be trained in an end-to-end manner. Given that the proposed
framework is devised based on Taylor's theorem, it possesses improved internal
flexibility. Extensive experiments are conducted on WSJ0-SI84, DNS-Challenge,
Voicebank+Demand, spatialized Librispeech, and L3DAS22 multi-channel speech
enhancement challenge datasets. Quantitative results show that the proposed
approach yields competitive performance over existing top-performing approaches
in terms of multiple objective metrics.Comment: Submitted to TASLP, revised version, 17 page
DMF-Net: A decoupling-style multi-band fusion model for full-band speech enhancement
For the difficulty and large computational complexity of modeling more
frequency bands, full-band speech enhancement based on deep neural networks is
still challenging. Previous studies usually adopt compressed full-band speech
features in Bark and ERB scale with relatively low frequency resolution,
leading to degraded performance, especially in the high-frequency region. In
this paper, we propose a decoupling-style multi-band fusion model to perform
full-band speech denoising and dereverberation. Instead of optimizing the
full-band speech by a single network structure, we decompose the full-band
target into multi sub-band speech features and then employ a multi-stage chain
optimization strategy to estimate clean spectrum stage by stage. Specifically,
the low- (0-8 kHz), middle- (8-16 kHz), and high-frequency (16-24 kHz) regions
are mapped by three separate sub-networks and are then fused to obtain the
full-band clean target STFT spectrum. Comprehensive experiments on two public
datasets demonstrate that the proposed method outperforms previous advanced
systems and yields promising performance in terms of speech quality and
intelligibility in real complex scenarios
Application of evolution-based uncertainty design on gear
The evolution of mechanical parameters, a factor affecting the mechanical reliability, has gathered more attention nowadays. However, studies on time varying uncertainty can hardly be found. A new method based on evolution-based uncertainty design (EBUD) is applied to the design of gear in this paper. Considering the wear evolution over the lifetime, a tooth wear’s time-varying uncertainty model based on the continuous-time model and Ito lemma is established. Drift and volatility functions dependent on the drift rate and volatility rate of rotational speed and torque are used to express the time-varying uncertainty of tooth thickness. The method can predict the reliability and provide an instruction in reliability improving, maintenance and repair of the gear system
SAR2EO: A High-resolution Image Translation Framework with Denoising Enhancement
Synthetic Aperture Radar (SAR) to electro-optical (EO) image translation is a
fundamental task in remote sensing that can enrich the dataset by fusing
information from different sources. Recently, many methods have been proposed
to tackle this task, but they are still difficult to complete the conversion
from low-resolution images to high-resolution images. Thus, we propose a
framework, SAR2EO, aiming at addressing this challenge. Firstly, to generate
high-quality EO images, we adopt the coarse-to-fine generator, multi-scale
discriminators, and improved adversarial loss in the pix2pixHD model to
increase the synthesis quality. Secondly, we introduce a denoising module to
remove the noise in SAR images, which helps to suppress the noise while
preserving the structural information of the images. To validate the
effectiveness of the proposed framework, we conduct experiments on the dataset
of the Multi-modal Aerial View Imagery Challenge (MAVIC), which consists of
large-scale SAR and EO image pairs. The experimental results demonstrate the
superiority of our proposed framework, and we win the first place in the MAVIC
held in CVPR PBVS 2023