159 research outputs found
Analysis of Electrohysterographic Signal Propagation Direction during Uterine Contraction:the Application of Directed Information
Uterus Modeling from Cell to Organ Level: towards Better Understanding of Physiological Basis of Uterine Activity
Adversarial Light Projection Attacks on Face Recognition Systems: A Feasibility Study
Deep learning-based systems have been shown to be vulnerable to adversarial
attacks in both digital and physical domains. While feasible, digital attacks
have limited applicability in attacking deployed systems, including face
recognition systems, where an adversary typically has access to the input and
not the transmission channel. In such setting, physical attacks that directly
provide a malicious input through the input channel pose a bigger threat. We
investigate the feasibility of conducting real-time physical attacks on face
recognition systems using adversarial light projections. A setup comprising a
commercially available web camera and a projector is used to conduct the
attack. The adversary uses a transformation-invariant adversarial pattern
generation method to generate a digital adversarial pattern using one or more
images of the target available to the adversary. The digital adversarial
pattern is then projected onto the adversary's face in the physical domain to
either impersonate a target (impersonation) or evade recognition (obfuscation).
We conduct preliminary experiments using two open-source and one commercial
face recognition system on a pool of 50 subjects. Our experimental results
demonstrate the vulnerability of face recognition systems to light projection
attacks in both white-box and black-box attack settings.Comment: To appear in the proceedings of the IEEE Computer Vision and Pattern
Recognition (CVPR) Biometrics Workshop 2020 - 9 pages, 8 figure
DPF: Learning Dense Prediction Fields with Weak Supervision
Nowadays, many visual scene understanding problems are addressed by dense
prediction networks. But pixel-wise dense annotations are very expensive (e.g.,
for scene parsing) or impossible (e.g., for intrinsic image decomposition),
motivating us to leverage cheap point-level weak supervision. However, existing
pointly-supervised methods still use the same architecture designed for full
supervision. In stark contrast to them, we propose a new paradigm that makes
predictions for point coordinate queries, as inspired by the recent success of
implicit representations, like distance or radiance fields. As such, the method
is named as dense prediction fields (DPFs). DPFs generate expressive
intermediate features for continuous sub-pixel locations, thus allowing outputs
of an arbitrary resolution. DPFs are naturally compatible with point-level
supervision. We showcase the effectiveness of DPFs using two substantially
different tasks: high-level semantic parsing and low-level intrinsic image
decomposition. In these two cases, supervision comes in the form of
single-point semantic category and two-point relative reflectance,
respectively. As benchmarked by three large-scale public datasets
PASCALContext, ADE20K and IIW, DPFs set new state-of-the-art performance on all
of them with significant margins.
Code can be accessed at https://github.com/cxx226/DPF
Improved fuzzy neural network control for the clamping force of Camellia fruit picking manipulator
During the operation of the vibrating mechanism, the push-shaking camellia fruit picking manipulator needs to ensure a constant force output of the clamping hydraulic motor in order to make sure that the camellia fruit tree trunk wouldn't loosen or damage, which may affect its later growth, during the picking process. In this regard, this paper derived the state space model of the valve-controlled clamping hydraulic motor system of the push-shaking camellia fruit picking manipulator, and the fuzzy wavelet neural network (FWNN) was designed on the basis of the traditional incremental PID control principle and the parameters of the neural network were optimized by the improved grey wolf optimizer (GWO). And then, the control system was simulated with the MATLAB/Simulink software without and with external interference, and compared and analyzed it with traditional PID controller and fuzzy PID (FPID) controller. The results show that the traditional PID controller and the FPID control have slow response and poor robustness, while the improved fuzzy wavelet neural network PID (IFWNN PID) controller possesses the characteristics of fast response and strong robustness, which can well meet the requirement of the constant clamping force of hydraulic motors. Finally, the field clamping test was carried out on the picking manipulator. The results show that the manipulator controlled by the IFWNN PID controller shortens the clamping time by 20.0% and reduces the clamping damage by 13.6% compared with the PID controller, which is verified that the designed controller can meet the clamping operation requirements of the camellia fruit picking machine
Validation of the neural network for 3D photon radiation field reconstruction under various source distributions
Introduction: This paper proposes a five-layer fully connected neural network for predicting radiation parameters in a radiation space based on detector readings.Methods: The network is trained and tested using gamma flux values from individual detector positions as input, and is used to predict the gamma radiation field in 3D space under different source term distributions. The method is evaluated using the mean percentage change error (PCT) for the test set under different source term distributions.Results: The results show that the neural network method can accurately predict radiation parameters with an average PCT error range of 0.53% to 3.11%, within the given measurement input error range of ± 10%. The method also demonstrates its ability to directly reconstruct the 3D radiation field with some simple source terms.Discussion: The proposed method has practical value in real operations within radiation spaces, and can be used to improve the accuracy and efficiency of predicting radiation parameters. Further research could explore the use of more complex source term distributions and the integration of other types of sensors for improved accuracy
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