scholarly journals Short-Term Traffic Speed Prediction Method for Urban Road Sections Based on Wavelet Transform and Gated Recurrent Unit

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xin Fu ◽  
Wei Luo ◽  
Chengyao Xu ◽  
Xiaoxuan Zhao
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Prediction Model ◽  
High Frequency ◽  
Low Frequency ◽  
Traffic Prediction ◽  
Spatiotemporal Correlation ◽  
Road Section ◽  
Speed Prediction ◽  
Traffic Speed

As a core component of the urban intelligent transportation system, traffic prediction is significant for urban traffic control and guidance. However, it is challenging to achieve accurate traffic prediction due to the complex spatiotemporal correlation of traffic data. A road section speed prediction model based on wavelet transform and neural network is, therefore, proposed in this article to improve traffic prediction methods. The wavelet transform is used to decompose the original traffic speed data, and then the coefficients obtained after the decomposition are used to reconstruct the high-frequency random sequences and the low-frequency trend sequence. Secondly, a GRU neural network is constructed to learn the trend of low-frequency sequence. The spatiotemporal correlation between input data is extracted by adjusting the input of the model. Meanwhile, an ARMA model is used to fit unstable random fluctuations of high-frequency sequences. Last of all, the prediction results of the two models are added together to obtain the final prediction result. The proposed prediction model is validated by using road section speed data based on the floating car data collected in Ningbo. The results show that the proposed model has high accuracy and robustness.

BLIND IMAGE RESTORATION BY COMBINING WAVELET TRANSFORM AND RBF NEURAL NETWORK

2007 ◽  
Vol 05 (01) ◽  
pp. 15-26 ◽  
Author(s):  
PING GUO ◽  
HONGZHAI LI ◽  
MICHAEL R. LYU
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
High Frequency ◽  
Additive Noise ◽  
Rbf Neural Network ◽  
Low Frequency ◽  
Degraded Image ◽  
Speed Up ◽  
Blind Image ◽  
The Given

In this paper, we present a novel technique for restoring a blurred noisy image without any prior knowledge of the blurring function and the statistics of noise. The technique combines wavelet transform with radial basis function (RBF) neural network to restore the given image which is degraded by Gaussian blur and additive noise. In the proposed technique, the wavelet transform is adopted to decompose the degraded image into high frequency parts and low frequency part. Then the RBF neural network based technique is used to restore the underlying image from the given image. The inverse principal element method (IPEM) is applied to speed up the computation. Experimental results show that the proposed technique inherited the advantages of wavelet transform and IPEM, and the algorithm is efficient in computation and robust to the noise.

scholarly journals A Novel Approach for Transmission Line Protection Using Wavelet Transform and Neural Network

2011 ◽  
pp. 121-126
Author(s):  
Mrunalini M. Rao ◽  
P.M. Deoghare
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Transmission Line ◽  
High Frequency ◽  
Low Frequency ◽  
Line Length ◽  
Frequency Information ◽  
Protection Scheme ◽  
Frequency Components ◽  
Transmission Line Protection

The two most important expected objectives of the transmission line protection are – 1) Differentiating the internal faults from external faults and 2) identifying exactly the fault type using one end data only. In conventional distance protection scheme only 80 percent of line length gets primary protection while for remaining 20 percent of line length a time delay is provided to avoid maloperation due to overreach in case of D.C. offset. In this new scheme a fault generated transients based protection method is introduced by which the whole line length gets primary protection by using the concept of bus capacitance. This scheme implements improved solution based on wavelet transform and self-organized neural network. The measured current and voltage signals are preprocessed first and then decomposed using wavelet multiresolution analysis to obtain the high frequency and low frequency information. The training patterns are formed based on high frequency signal components and the low frequency components of all three phase voltages and current. Zero sequence voltage and current are also used to identify faults involving grounds. The input sets formed based on the high frequency components are arranged as inputs of neural network-1, whose task is to indicate whether the fault is internal or external. The input sets formed based on the low frequency components are arranged as inputs of neural network- 2, whose task is indicate the type of fault. The new method uses both low and high frequency information of the fault signal to achieve an advanced transmission line protection scheme.

scholarly journals Multi-focus Image Fusion Using an Effective Discrete Wavelet Transform Based Algorithm

2014 ◽  
Vol 14 (2) ◽  
pp. 102-108 ◽  
Author(s):  
Yong Yang ◽  
Shuying Huang ◽  
Junfeng Gao ◽  
Zhongsheng Qian
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Low Frequency ◽  
Objective Evaluation ◽  
Discrete Wavelet ◽  
Evaluation Indexes ◽  
Fused Image ◽  
Focus Image

Abstract In this paper, by considering the main objective of multi-focus image fusion and the physical meaning of wavelet coefficients, a discrete wavelet transform (DWT) based fusion technique with a novel coefficients selection algorithm is presented. After the source images are decomposed by DWT, two different window-based fusion rules are separately employed to combine the low frequency and high frequency coefficients. In the method, the coefficients in the low frequency domain with maximum sharpness focus measure are selected as coefficients of the fused image, and a maximum neighboring energy based fusion scheme is proposed to select high frequency sub-bands coefficients. In order to guarantee the homogeneity of the resultant fused image, a consistency verification procedure is applied to the combined coefficients. The performance assessment of the proposed method was conducted in both synthetic and real multi-focus images. Experimental results demonstrate that the proposed method can achieve better visual quality and objective evaluation indexes than several existing fusion methods, thus being an effective multi-focus image fusion method.

scholarly journals An Image Denoising Method with Enhancement of the Directional Features Based on Wavelet and SVD Transforms

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Min Wang ◽  
Zhen Li ◽  
Xiangjun Duan ◽  
Wei Li
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Image Denoising ◽  
High Frequency ◽  
Low Frequency ◽  
Singular Value ◽  
Denoising Method ◽  
Diagonal Part ◽  
Value Decomposition ◽  
Image Part

This paper proposes an image denoising method, using the wavelet transform and the singular value decomposition (SVD), with the enhancement of the directional features. First, use the single-level discrete 2D wavelet transform to decompose the noised image into the low-frequency image part and the high-frequency parts (the horizontal, vertical, and diagonal parts), with the edge extracted and retained to avoid edge loss. Then, use the SVD to filter the noise of the high-frequency parts with image rotations and the enhancement of the directional features: to filter the diagonal part, one needs first to rotate it 45 degrees and rotate it back after filtering. Finally, reconstruct the image from the low-frequency part and the filtered high-frequency parts by the inverse wavelet transform to get the final denoising image. Experiments show the effectiveness of this method, compared with relevant methods.

DEPSO With DTCWT Algorithm for Multimodal Medical Image Fusion

2021 ◽  
Vol 12 (4) ◽  
pp. 78-97
Author(s):  
Hassiba Talbi ◽  
Mohamed-Khireddine Kholladi
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
High Frequency ◽  
Medical Image ◽  
Low Frequency ◽  
Weighted Average ◽  
Optimization Method ◽  
Absolute Maximum ◽  
Complex Wavelet Transform ◽  
Medical Image Fusion

In this paper, the authors propose an algorithm of hybrid particle swarm with differential evolution (DE) operator, termed DEPSO, with the help of a multi-resolution transform named dual tree complex wavelet transform (DTCWT) to solve the problem of multimodal medical image fusion. This hybridizing approach aims to combine algorithms in a judicious manner, where the resulting algorithm will contain the positive features of these different algorithms. This new algorithm decomposes the source images into high-frequency and low-frequency coefficients by the DTCWT, then adopts the absolute maximum method to fuse high-frequency coefficients; the low-frequency coefficients are fused by a weighted average method while the weights are estimated and enhanced by an optimization method to gain optimal results. The authors demonstrate by the experiments that this algorithm, besides its simplicity, provides a robust and efficient way to fuse multimodal medical images compared to existing wavelet transform-based image fusion algorithms.

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