scholarly journals Intelligent Retrieval Method of Approximate Painting in Digital Art Field

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Jixin Wan ◽  
Yu Xiaobo
Keyword(s):  
Neural Network ◽  
Original Artwork ◽  
Wavelet Transform ◽  
Rapid Development ◽  
Random Noise ◽  
Low Frequency ◽  
Digital Art ◽  
Internet Technology ◽  
Retrieval Method ◽  
Trademark Registration

With the rapid development of Internet technology and the wide application of image acquisition equipment, the number of digital artwork images is exploding. The retrieval of near-similar artwork images has a wide application prospect for copyright infringement, trademark registration, and other scenes. However, compared with traditional images, these artwork images have the characteristics of high similarity and complexity, which lead to the retrieval accuracy not meeting the demand. To solve the above problems, an intelligent retrieval method of artwork image based on wavelet transform and dual propagation neural network (WTCPN) is proposed. Firstly, the original artwork image is replaced by the low-frequency subimage after wavelet transform, which not only removes redundant information and reduces the dimension of data but also suppresses random noise. Secondly, in order to make the network assign different competition winning units to different types of modes, the dual propagation neural network is improved by setting the maximum number of times of winning neurons. Experimental results show that the proposed method can improve the accuracy of image retrieval, and the recognition accuracy of verification set can reach over 91%.

scholarly journals Design, Implementation, and Evaluation of Online English Learning Platforms

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jie Li
Keyword(s):  
Neural Network ◽  
Online Learning ◽  
Performance Test ◽  
Rapid Development ◽  
Internet Technology ◽  
English Learning ◽  
Learning Platform ◽  
Learning Platforms ◽  
Questionnaire Surveys ◽  
And Performance

With the rapid development of internet technology, various online learning platforms have emerged. The combination of the internet and education is an inevitable trend, and smart online learning platforms based on neural network become popular. This paper introduces how to design online English learning platforms through a neural network. It proposes the construction of a universally designed online English learning platform and the design of an online English learning platform server development architecture. Then, the implementation of online English learning platforms is discussed. Evaluation of the platforms is also very important, which is conducted through two questionnaire surveys. The first survey is general and the second one is more specific. Results of both surveys show that the learners’ demand for online English learning platforms is still growing, especially among the young learners. In addition, this paper reports the results of the feasibility analysis and performance test of online English learning platforms: (1) The well-designed online English learning platform has relatively complete functions and meets the needs of both students and teachers. It includes a series of functional modules such as students’ registration, analysis of students’ profile, courseware and learning resources management, test management, test score analysis, interactive discussion, online monitor and feedback. (2) There are no major defects in the implementation of the online English learning platform in this experiment. (3) The reliability and security of the online English learning platform are relatively high.

scholarly journals Design of English Automatic Translation System Based on Machine Intelligent Translation and Secure Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Haidong Ban ◽  
Jing Ning
Keyword(s):  
Neural Network ◽  
Machine Translation ◽  
Rapid Development ◽  
Model Performance ◽  
Internet Technology ◽  
Target Language ◽  
Translation System ◽  
Improvement Rate ◽  
Translation Model ◽  
Automatic Translation

With the rapid development of Internet technology and the development of economic globalization, international exchanges in various fields have become increasingly active, and the need for communication between languages has become increasingly clear. As an effective tool, automatic translation can perform equivalent translation between different languages while preserving the original semantics. This is very important in practice. This paper focuses on the Chinese-English machine translation model based on deep neural networks. In this paper, we use the end-to-end encoder and decoder framework to create a neural machine translation model, the machine automatically learns its function, and the data is converted into word vectors in a distributed method and can be directly through the neural network perform the mapping between the source language and the target language. Research experiments show that, by adding part of the voice information to verify the effectiveness of the model performance improvement, the performance of the translation model can be improved. With the superimposition of the number of network layers from two to four, the improvement ratios of each model are 5.90%, 6.1%, 6.0%, and 7.0%, respectively. Among them, the model with an independent recurrent neural network as the network structure has the largest improvement rate and a higher improvement rate, so the system has high availability.

A Rapid Method for Image Compression Based on Wavelet Transform and SOFM Neural Network

2011 ◽  
Vol 135-136 ◽  
pp. 126-131 ◽  
Author(s):  
Hong Ke Xu ◽  
Wei Song Yang ◽  
Jian Wu Fang ◽  
Chang Bao Wen ◽  
Wei Sun
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Image Compression ◽  
Euclidean Distance ◽  
Low Frequency ◽  
Training Time ◽  
Codebook Design ◽  
Network Training ◽  
Sofm Network ◽  
Neural Network Algorithm

The current self-organizing feature map (SOFM) neural network algorithm used for image compression, of which a large amount of network training time and the blocking effect in the reconstructed image existed in codebook design vector calculation. Based on the above issue, this paper proposed an improved SOFM. The new SOFM introduced normalized distance between the sum of input vectors and the sum of the codeword vectors as a constraint in the process of searching for the winning neuron, which can remove redundant Euclidean distance calculation in the competitive process. Furthermore, this paper has done image compression by combining wavelet transform with the improved SOFM (WT & improved SOFM). The method firstly conducted wavelet decomposition for the image, retained low-frequency sub-band, then put the high-frequency sub-band into improved SOFM network, and achieved the purpose of compression. Experimental results showed that this algorithm can greatly reduce the network training time and enhance the learning efficiency of neural network, while effectively improve the PSNR (increased 0.6dB) of reconstructed.

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.

scholarly journals Research on bearing diagnosis technology based on wavelet transform and one-dimensional convolutional neural network

2021 ◽  
Vol 336 ◽  
pp. 01010
Author(s):  
Dazhi Guo ◽  
Qiang Wang ◽  
Fengyan Wu ◽  
Jun Li ◽  
Mo Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Low Frequency ◽  
One Dimensional ◽  
Deep Groove ◽  
Deep Groove Ball Bearing ◽  
Rolling Element ◽  
Object Use ◽  
Neural Network Input

Aiming at the fault diagnosis of rolling element bearings, propose a method for fine diagnosis of bearings based on wavelet transform and one-dimensional convolutional neural network. First use wavelet transform to decompose the experimental data; Use the resulting low-frequency signal as a one-dimensional convolutional neural network input, bearing fault identification. The experiment uses the deep groove ball bearing of Case Western Reserve University as the research object, Use this method to identify the normal and outer ring faults of the bearing. the result shows: This method can be effectively applied to the precise identification of bearings.

scholarly journals Can Wavelet Transform Detect LDDoS Abnormal Traffic in Multipath TCP Transmission System?

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Gang Lei ◽  
Lejun Ji ◽  
Ruiwen Ji ◽  
Yuanlong Cao ◽  
Wei Yang ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Detection Method ◽  
Rapid Development ◽  
Denial Of Service ◽  
Transmission System ◽  
Mobile Internet ◽  
Internet Technology ◽  
Transmission Protocol ◽  
Transmission Performance ◽  
Multipath Tcp

With the rapid development of mobile Internet technology and multihost terminal devices, multipath transmission protocol has been widely concerned. Among them, multipath TCP (MPTCP) has become a hot research protocol in recent years because of its good transmission performance and Internet compatibility. Due to the increasing power of Low-Rate Distributed Denial of Service (LDDoS) attack, the network security situation is becoming increasingly serious. The robustness of MPTCP network has become an urgent performance index to improve. Therefore, it is very necessary to detect LDDoS abnormal traffic timely and effectively in the transmission system based on MPTCP. This paper tries to use wavelet transform technology to decompose and reconstruct network traffic and find a detection method of LDDoS abnormal traffic in the MPTCP transmission system. The experimental results show that in the MPTCP transmission system, the signal processing technology based on wavelet transform can realize the identification of LDDoS abnormal traffic. It indicates a direction worth further exploration for the detection and defense of the LDDoS attack.

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.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

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