322 research outputs found
Planetary gearbox remaining useful life estimation based on state space model
As planetary gearboxes are widely used in various kinds of engineering, the fault diagnosis and prognosis of planetary gearbox is very important. This paper proposes a remaining useful life estimation method based on state space model. The degradation process is assumed to be Gamma distribution. And experience maximization method and particle filter is used to estimate the parameters of state space model. A planetary gearbox life-cycle experiment is done to obtain the degradation data and verify the effectiveness of the proposed method
Phonemic Adversarial Attack against Audio Recognition in Real World
Recently, adversarial attacks for audio recognition have attracted much
attention. However, most of the existing studies mainly rely on the
coarse-grain audio features at the instance level to generate adversarial
noises, which leads to expensive generation time costs and weak universal
attacking ability. Motivated by the observations that all audio speech consists
of fundamental phonemes, this paper proposes a phonemic adversarial tack (PAT)
paradigm, which attacks the fine-grain audio features at the phoneme level
commonly shared across audio instances, to generate phonemic adversarial
noises, enjoying the more general attacking ability with fast generation speed.
Specifically, for accelerating the generation, a phoneme density balanced
sampling strategy is introduced to sample quantity less but phonemic features
abundant audio instances as the training data via estimating the phoneme
density, which substantially alleviates the heavy dependency on the large
training dataset. Moreover, for promoting universal attacking ability, the
phonemic noise is optimized in an asynchronous way with a sliding window, which
enhances the phoneme diversity and thus well captures the critical fundamental
phonemic patterns. By conducting extensive experiments, we comprehensively
investigate the proposed PAT framework and demonstrate that it outperforms the
SOTA baselines by large margins (i.e., at least 11X speed up and 78% attacking
ability improvement)
TFMQ-DM: Temporal Feature Maintenance Quantization for Diffusion Models
The Diffusion model, a prevalent framework for image generation, encounters
significant challenges in terms of broad applicability due to its extended
inference times and substantial memory requirements. Efficient Post-training
Quantization (PTQ) is pivotal for addressing these issues in traditional
models. Different from traditional models, diffusion models heavily depend on
the time-step to achieve satisfactory multi-round denoising. Usually,
from the finite set is encoded to a temporal feature by a
few modules totally irrespective of the sampling data. However, existing PTQ
methods do not optimize these modules separately. They adopt inappropriate
reconstruction targets and complex calibration methods, resulting in a severe
disturbance of the temporal feature and denoising trajectory, as well as a low
compression efficiency. To solve these, we propose a Temporal Feature
Maintenance Quantization (TFMQ) framework building upon a Temporal Information
Block which is just related to the time-step and unrelated to the sampling
data. Powered by the pioneering block design, we devise temporal information
aware reconstruction (TIAR) and finite set calibration (FSC) to align the
full-precision temporal features in a limited time. Equipped with the
framework, we can maintain the most temporal information and ensure the
end-to-end generation quality. Extensive experiments on various datasets and
diffusion models prove our state-of-the-art results. Remarkably, our
quantization approach, for the first time, achieves model performance nearly on
par with the full-precision model under 4-bit weight quantization.
Additionally, our method incurs almost no extra computational cost and
accelerates quantization time by on LSUN-Bedrooms
compared to previous works. Our code is publicly available at
https://github.com/ModelTC/TFMQ-DM
Excitement Surfeited Turns to Errors: Deep Learning Testing Framework Based on Excitable Neurons
Despite impressive capabilities and outstanding performance, deep neural
networks (DNNs) have captured increasing public concern about their security
problems, due to their frequently occurred erroneous behaviors. Therefore, it
is necessary to conduct a systematical testing for DNNs before they are
deployed to real-world applications. Existing testing methods have provided
fine-grained metrics based on neuron coverage and proposed various approaches
to improve such metrics. However, it has been gradually realized that a higher
neuron coverage does \textit{not} necessarily represent better capabilities in
identifying defects that lead to errors. Besides, coverage-guided methods
cannot hunt errors due to faulty training procedure. So the robustness
improvement of DNNs via retraining by these testing examples are
unsatisfactory. To address this challenge, we introduce the concept of
excitable neurons based on Shapley value and design a novel white-box testing
framework for DNNs, namely DeepSensor. It is motivated by our observation that
neurons with larger responsibility towards model loss changes due to small
perturbations are more likely related to incorrect corner cases due to
potential defects. By maximizing the number of excitable neurons concerning
various wrong behaviors of models, DeepSensor can generate testing examples
that effectively trigger more errors due to adversarial inputs, polluted data
and incomplete training. Extensive experiments implemented on both image
classification models and speaker recognition models have demonstrated the
superiority of DeepSensor.Comment: 32 page
Passively Q-switched erbium-doped fiber laser using evanescent field interaction with gold-nanosphere based saturable absorber
We demonstrate an all-fiber passively Q-switched erbiumdoped fiber laser (EDFL) using a gold-nanosphere (GNS) based saturable absorber (SA) with evanescent field interaction. Using the interaction of evanescent field for fabricating SAs, long nonlinear interaction length of evanescent wave and GNSs can be achieved. The GNSs are synthesized from mixing solution of chloroauricacid (HAuCl4) and sodium citrate by the heating effects of the microfiber's evanescent field radiation. The proposed passively Q-switched EDFL could give output pulses at 1562 nm with pulse width of 1.78 ÎĽs, a repetition rate of 58.1 kHz, a pulse energy of 133 nJ and a output power of 7.7 mWwhen pumped by a 980 nm laser diode of 237 mW
AutoOptLib: Tailoring Metaheuristic Optimizers via Automated Algorithm Design
Metaheuristics are prominent gradient-free optimizers for solving hard
problems that do not meet the rigorous mathematical assumptions of analytical
solvers. The canonical manual optimizer design could be laborious, untraceable
and error-prone, let alone human experts are not always available. This arises
increasing interest and demand in automating the optimizer design process. In
response, this paper proposes AutoOptLib, the first platform for accessible
automated design of metaheuristic optimizers. AutoOptLib leverages computing
resources to conceive, build up, and verify the design choices of the
optimizers. It requires much less labor resources and expertise than manual
design, democratizing satisfactory metaheuristic optimizers to a much broader
range of researchers and practitioners. Furthermore, by fully exploring the
design choices with computing resources, AutoOptLib has the potential to
surpass human experience, subsequently gaining enhanced performance compared
with human problem-solving. To realize the automated design, AutoOptLib
provides 1) a rich library of metaheuristic components for continuous,
discrete, and permutation problems; 2) a flexible algorithm representation for
evolving diverse algorithm structures; 3) different design objectives and
techniques for different optimization scenarios; and 4) a graphic user
interface for accessibility and practicability. AutoOptLib is fully written in
Matlab/Octave; its source code and documentation are available at
https://github.com/qz89/AutoOpt and https://AutoOpt.readthedocs.io/,
respectively
Parametrically tunable soliton-induced resonant radiation by three-wave mixing
We show that a temporal soliton can induce resonant radiation by three-wave mixing nonlinearities. This constitutes a new class of resonant radiation whose spectral positions are parametrically tunable. The experimental verification is done in a periodically poled lithium niobate crystal, where a femtosecond near-IR soliton is excited and resonant radiation waves are observed exactly at the calculated soliton phase-matching wavelengths via the sum-and difference-frequency generation nonlinearities. This extends the supercontinuum bandwidth well into the mid IR to span 550-5000 nm, and the mid-IR edge is parametrically tunable over 1000 nm by changing the three-wave mixing phase-matching condition. The results are important for the bright and broadband supercontinuum generation and for the frequency comb generation in quadratic nonlinear microresonators
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