123 research outputs found
Adaptive Fuzzy Modelling and Control for Non-Linear Systems Using Interval Reasoning and Differential Evolution
Spatial shift unwrapping for digital fringe profilometry based on spatial shift estimation
An approach is presented to solve the problem of spatial shift wrapping associated with spatial shift estimation-based fringe pattern profilometry (FPP). This problem arises as the result of fringe reuses (that is, use of fringes with periodic light intensity variance), and the spatial shift can only be identified without ambiguity within the range of a fringe width. It is demonstrated that the problem is similar to the phase unwrapping problem associated with the phase-detection-based FPP, and the proposed method is inspired by the existing ideas of using multiple images with different wavelengths proposed for phase unwrapping. The effectiveness of the proposed method is verified by comparing experimental results against several objects, with the last object consisting of more complex surface features. We conclude by showing that our method is successful in reconstructing the fine details of the more complex object
OpenNet: Incremental Learning for Autonomous Driving Object Detection with Balanced Loss
Automated driving object detection has always been a challenging task in
computer vision due to environmental uncertainties. These uncertainties include
significant differences in object sizes and encountering the class unseen. It
may result in poor performance when traditional object detection models are
directly applied to automated driving detection. Because they usually presume
fixed categories of common traffic participants, such as pedestrians and cars.
Worsely, the huge class imbalance between common and novel classes further
exacerbates performance degradation. To address the issues stated, we propose
OpenNet to moderate the class imbalance with the Balanced Loss, which is based
on Cross Entropy Loss. Besides, we adopt an inductive layer based on gradient
reshaping to fast learn new classes with limited samples during incremental
learning. To against catastrophic forgetting, we employ normalized feature
distillation. By the way, we improve multi-scale detection robustness and
unknown class recognition through FPN and energy-based detection, respectively.
The Experimental results upon the CODA dataset show that the proposed method
can obtain better performance than that of the existing methods
A Wireless Covert Channel Based on Constellation Shaping Modulation
Wireless covert channel is an emerging covert communication technique which conceals the very existence of secret information in wireless signal including GSM, CDMA, and LTE. The secret message bits are always modulated into artificial noise superposed with cover signal, which is then demodulated with the shared codebook at the receiver. In this paper, we first extend the traditional KS test and regularity test in covert timing channel detection into wireless covert channel, which can be used to reveal the very existence of secret data in wireless covert channel from the aspect of multiorder statistics. In order to improve the undetectability, a wireless covert channel for OFDM-based communication system based on constellation shaping modulation is proposed, which generates additional constellation points around the standard points in normal constellations. The carrier signal is then modulated with the dirty constellation and the secret message bits are represented by the selection mode of the additional constellation points; shaping modulation is employed to keep the distribution of constellation errors unchanged. Experimental results show that the proposed wireless covert channel scheme can resist various statistical detections. The communication reliability under typical interference is also proved
Fault severity assessment of rolling bearings method based on improved VMD and LSTM
In order to solve the problem of selection of appropriate wavelet basis function and clearly show the physical meaning of Empirical Mode Decomposition (EMD), an improved Variational Mode Decomposition (VMD) method with Long Short-Term Memory (LSTM) neural network is proposed. With the Cuckoo Search (CS) algorithm, the central frequency updating rules of VMD are optimized. And the low efficiency and local optimum problem is avoided. Meanwhile the decomposition layer number is found by the instantaneous frequency theory. For improving the prediction accuracy in traditional regression prediction methods, a LSTM neural network is designed for regression prediction of time sequence characteristics. The proposed method is implemented on actual bearings data which is derived from the bearing laboratory of Case West Reserve University in the United States and the University of Cincinnati Bearing Data Center. The experimental results showed that the improved VMD method was more robust and more accurate than the other traditional methods. And it has some practical value for real application and guiding significance for theory
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