20,126 research outputs found
Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel Removal
Deep Convolutional Neural Networks (CNNs) are widely employed in modern
computer vision algorithms, where the input image is convolved iteratively by
many kernels to extract the knowledge behind it. However, with the depth of
convolutional layers getting deeper and deeper in recent years, the enormous
computational complexity makes it difficult to be deployed on embedded systems
with limited hardware resources. In this paper, we propose two
computation-performance optimization methods to reduce the redundant
convolution kernels of a CNN with performance and architecture constraints, and
apply it to a network for super resolution (SR). Using PSNR drop compared to
the original network as the performance criterion, our method can get the
optimal PSNR under a certain computation budget constraint. On the other hand,
our method is also capable of minimizing the computation required under a given
PSNR drop.Comment: This paper was accepted by 2018 The International Symposium on
Circuits and Systems (ISCAS
An interactively recurrent functional neural fuzzy network with fuzzy differential evolution and its applications
In this paper, an interactively recurrent functional neural fuzzy network (IRFNFN) with fuzzy differential evolution (FDE) learning method was proposed for solving the control and the prediction problems. The traditional differential evolution (DE) method easily gets trapped in a local optimum during the learning process, but the proposed fuzzy differential evolution algorithm can overcome this shortcoming. Through the information sharing of nodes in the interactive layer, the proposed IRFNFN can effectively reduce the number of required rule nodes and improve the overall performance of the network. Finally, the IRFNFN model and associated FDE learning algorithm were applied to the control system of the water bath temperature and the forecast of the sunspot number. The experimental results demonstrate the effectiveness of the proposed method
Conditioning of BPM pickup signals for operations of the Duke storage ring with a wide range of single-bunch current
The Duke storage ring is a dedicated driver for the storage ring based
oscillator free-electron lasers (FELs), and the High Intensity Gamma-ray Source
(HIGS). It is operated with a beam current ranging from about 1 mA to 100 mA
per bunch for various operations and accelerator physics studies. High
performance operations of the FEL and gamma-ray source require a stable
electron beam orbit, which has been realized by the global orbit feedback
system. As a critical part of the orbit feedback system, the electron beam
position monitors (BPMs) are required to be able to precisely measure the
electron beam orbit in a wide range of the single-bunch current. However, the
high peak voltage of the BPM pickups associated with high single-bunch current
degrades the performance of the BPM electronics, and can potentially damage the
BPM electronics. A signal conditioning method using low pass filters is
developed to reduce the peak voltage to protect the BPM electronics, and to
make the BPMs capable of working with a wide range of single-bunch current.
Simulations and electron beam based tests are performed. The results show that
the Duke storage ring BPM system is capable of providing precise orbit
measurements to ensure highly stable FEL and HIGS operations
Improving Person Re-identification by Attribute and Identity Learning
Person re-identification (re-ID) and attribute recognition share a common
target at learning pedestrian descriptions. Their difference consists in the
granularity. Most existing re-ID methods only take identity labels of
pedestrians into consideration. However, we find the attributes, containing
detailed local descriptions, are beneficial in allowing the re-ID model to
learn more discriminative feature representations. In this paper, based on the
complementarity of attribute labels and ID labels, we propose an
attribute-person recognition (APR) network, a multi-task network which learns a
re-ID embedding and at the same time predicts pedestrian attributes. We
manually annotate attribute labels for two large-scale re-ID datasets, and
systematically investigate how person re-ID and attribute recognition benefit
from each other. In addition, we re-weight the attribute predictions
considering the dependencies and correlations among the attributes. The
experimental results on two large-scale re-ID benchmarks demonstrate that by
learning a more discriminative representation, APR achieves competitive re-ID
performance compared with the state-of-the-art methods. We use APR to speed up
the retrieval process by ten times with a minor accuracy drop of 2.92% on
Market-1501. Besides, we also apply APR on the attribute recognition task and
demonstrate improvement over the baselines.Comment: Accepted to Pattern Recognition (PR
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