1,756 research outputs found
A note on eigenvalues of random block Toeplitz matrices with slowly growing bandwidth
This paper can be thought of as a remark of \cite{llw}, where the authors
studied the eigenvalue distribution of random block Toeplitz band
matrices with given block order . In this note we will give explicit density
functions of when the bandwidth grows
slowly. In fact, these densities are exactly the normalized one-point
correlation functions of Gaussian unitary ensemble (GUE for short).
The series can be seen
as a transition from the standard normal distribution to semicircle
distribution. We also show a similar relationship between GOE and block
Toeplitz band matrices with symmetric blocks.Comment: 6 page
A novel classification method combining adaptive local iterative filtering with singular value decomposition for fault diagnosis
As a novel time-frequency analysis method, adaptive local iterative filtering (ALIF) can decompose the time series into several stable components which contain the main fault information. In addition, the amplitude of singular value obtained by singular value decomposition (SVD) can reflect the energy distribution. Naturally, there are certain differences in the energy produced by different faults such as the broken tooth, wearing and normal. Thus, a novel method of mechanical fault classification method based on adaptive local iterative filtering and singular value decomposition is proposed in this paper. Firstly, ALIF method decomposed the original vibration signal into a number of stable components to establish an initial feature vector matrix. Then, the singular values energy corresponding to the feature matrix is employed as a criterion to identify various faults. Compared with the conventional EMD method by simulation experiments, ALIF method has obvious superiority in solving modal aliasing, which is more conducive to the advanced analysis. In this paper, the proposed method is employed to extract the fault information of rolling bearing fault signals from Case Western Reserve University Bearing Data Center. To further verify the effectiveness of the method, the case study is conducted at Drivetrain Diagnostics Simulator. To further illustrate the effectiveness of the method, the results obtained by this method are compared with EMD and EEMD. The results indicated the proposed method performs better in the classification of different mechanical faulty modes
Developing a Volume Model Using South NTS-372R Total Station without Tree Felling in a Populus canadensis Moench Plantation in Beijing, China
Volume table preparation using the traditional method and a collection model requires the harvest of approximately 200–300 trees of individual species. Although high precision could be achieved using that method, it causes huge damage to the forest. To minimize these losses, in this study, a South NTS-372R total station with a precise angle and distance measurement mode was used to measure 507 trees of Populus canadensis Moench without single tree felling. Moreover, the C# programming language was used in this study and the collected volume data were inserted in the total station. Using this method, a real-time precise measurement of volume could be achieved. After data collection, the optimal binary volume model of Populus canadensis Moench could be obtained through a comparative analysis. It turns out that the Yamamoto model is the optimal binary volume model (also known as two predictor variable model), with 0.9641 as the coefficient of determination (R2) and 0.19 m3 as the standard deviation of estimated value (SEE), which presents a good imitative effect. Moreover, it showed relative stability with the general relative error (TRE) of –0.12% and the mean system error (MSE) of –1.24%. The mean predicted error (MPE) of 1.18% and the mean predicted standard error (MPSE) of 9.25% showed high estimated precision of the average and individual tree volumes. The model has only three parameters, so it is suitable for volume table preparation. Finally, this study will present some new technical methods and means for volume modeling for further application in forestry
A New Biometric Template Protection using Random Orthonormal Projection and Fuzzy Commitment
Biometric template protection is one of most essential parts in putting a
biometric-based authentication system into practice. There have been many
researches proposing different solutions to secure biometric templates of
users. They can be categorized into two approaches: feature transformation and
biometric cryptosystem. However, no one single template protection approach can
satisfy all the requirements of a secure biometric-based authentication system.
In this work, we will propose a novel hybrid biometric template protection
which takes benefits of both approaches while preventing their limitations. The
experiments demonstrate that the performance of the system can be maintained
with the support of a new random orthonormal project technique, which reduces
the computational complexity while preserving the accuracy. Meanwhile, the
security of biometric templates is guaranteed by employing fuzzy commitment
protocol.Comment: 11 pages, 6 figures, accepted for IMCOM 201
PP-YOLOE-R: An Efficient Anchor-Free Rotated Object Detector
Arbitrary-oriented object detection is a fundamental task in visual scenes
involving aerial images and scene text. In this report, we present PP-YOLOE-R,
an efficient anchor-free rotated object detector based on PP-YOLOE. We
introduce a bag of useful tricks in PP-YOLOE-R to improve detection precision
with marginal extra parameters and computational cost. As a result,
PP-YOLOE-R-l and PP-YOLOE-R-x achieve 78.14 and 78.28 mAP respectively on DOTA
1.0 dataset with single-scale training and testing, which outperform almost all
other rotated object detectors. With multi-scale training and testing,
PP-YOLOE-R-l and PP-YOLOE-R-x further improve the detection precision to 80.02
and 80.73 mAP. In this case, PP-YOLOE-R-x surpasses all anchor-free methods and
demonstrates competitive performance to state-of-the-art anchor-based two-stage
models. Further, PP-YOLOE-R is deployment friendly and PP-YOLOE-R-s/m/l/x can
reach 69.8/55.1/48.3/37.1 FPS respectively on RTX 2080 Ti with TensorRT and
FP16-precision. Source code and pre-trained models are available at
https://github.com/PaddlePaddle/PaddleDetection, which is powered by
https://github.com/PaddlePaddle/Paddle.Comment: 6 pages, 2 figures, 3 table
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