A Unified Convolutional Neural Network Classifier Aided Intelligent Channel Decoder for Coexistent Heterogeneous Networks

Yang, Xiaoling; Zhang, Lin; Wu, Zhiqiang

doi:10.1109/jsyst.2020.3040287

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…It is the core technology and effective measure to promote the comprehensive development of the port in the future. erefore, strengthening the in-depth study on the technical characteristics and control measures of port mechanical and electrical automation can provide positive help for the development of port construction towards intelligence, large-scale, and automation and make outstanding contributions to the harmonious and stable development of society [8].…”

Section: Literature Reviewmentioning

confidence: 99%

Remote Control and Fault Diagnosis of Port Mechanical Equipment Based on Wireless Communication Technology

2022

Journal of Control Science and Engineering

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In order to solve the problem that there is a huge amount of port machinery operating state data, the traditional fault diagnosis method is difficult to effectively obtain the mechanical fault information from the massive data and also cannot accurately diagnose the fault. This paper presents a fault diagnosis method for port machinery based on the FastAP algorithm. By analyzing the characteristics of the FastAP algorithm, the algorithm is introduced into port machinery fault diagnosis, and a port machinery fault diagnosis model based on FastAP is proposed. Through simulation experiments, the algorithm and its application in port machinery fault diagnosis are verified. The experimental results show that compared with the standard AP algorithm, the FastAP algorithm performs well in the sum of similarity, clustering accuracy, and clustering calculation time and has better performance, which is more suitable for fault diagnosis. The fault diagnosis method based on the FastAP algorithm has a high diagnosis accuracy of port machinery fault, reaching more than 92%, and can accurately diagnose the normal state, inner and outer ring fault state, and rolling element fault state of the actual port mechanical belt conveyor. The research on remote control and fault diagnosis of port machinery equipment based on wireless communication technology can effectively solve the problem that there is a huge amount of port machinery operating state data, and the traditional fault diagnosis methods are difficult to effectively obtain mechanical fault information from the massive data and thus cannot accurately diagnose the fault.

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Section: Literature Reviewmentioning

confidence: 99%

Remote Control and Fault Diagnosis of Port Mechanical Equipment Based on Wireless Communication Technology

2022

Journal of Control Science and Engineering

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“…Pooling Layer: A filter with a particular stride size makes up the pooling stage, a layer of the matrix measuring system [41]. The amount of strides determines each shift, as shown in Fig.…”

Section: )mentioning

confidence: 99%

Classification of Lombok Songket and Sasambo Batik Motifs Using the Convolution Neural Network (CNN) Algorithm

Ariessaputra,

Vidiasari,

Sasongko

et al. 2024

JOIV : Int. J. Inform. Visualization

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Sasambo batik is a traditional batik from the West Nusa Tenggara province. Sasambo itself is an abbreviation of three tribes, namely the Sasak (sa) in the Lombok Islands, the Samawa (sam), and the Mbojo (bo) tribes in Sumbawa Island. Classification of batik motifs can use image processing technology, one of which is the Convolution Neural Network (CNN) algorithm. Before entering the classification process, the batik image first undergoes image resizing. After that, proceed with the operation of the convolution, pooling, and fully connected layers. The sample image of Lombok songket motifs and Sasambo batik consists of 20 songket fabric data with the same motif and color and 14 songket data with the same motif but different colors. In addition, there are 10 data points on songket fabrics with other motifs and colors. In addition, there are 5 data points on Sasambo batik fabrics with the same motif and color and 5 data points on Sasambo batik fabrics with the same motif but different colors. The training data rotates the image by 150as many as 20 photos. Testing with motifs with the same color shows that the system's success rate is 83.85%. The highest average recognition for Sasambo batik cloth is in testing motifs with the same color for data in the database at 93.66%. The CNN modeling classification results indicate that the Sasambo batik cloth can be a reference for developing songket categorization using a website platform or the Android system.

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“…Semantic features, i.e., bilingual semantic similarity, monolingual semantic similarity, bilingual semantic sensitivity, monolingual semantic sensitivity. Sparse features, i.e., high-frequency feature number [21,22].…”

Section: Decoding Integration Analysismentioning

confidence: 99%

Design of English Translation Mobile Information System Based on Recurrent Neural Network

Gao¹

2022

Mobile Information Systems

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To solve the problem of translating lines of difference in length into English, this article presents a model of neural network recovery (RNN) English translator-based models of end-to-end encoder-decoder. This method promotes machine autonomous learning of features and transforms corpus data into word vectors by constructing end-to-end. By mapping the source language and target language directly through the recurrent neural network and selecting semantic error to construct objective function during training, the influence of each part in semantic can be well balanced, and the alignment information is fully considered, which provides powerful guidance for deep recurrent neural network training. The results of the neural network test define the standard BLEU score by 1.51–11.86. Our test scores and BLEU scores at all levels show that data in equivalence play an important role in modeling. Summary. the English translation model based on the neural repetitive fusion is efficient and stable.

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A Unified Convolutional Neural Network Classifier Aided Intelligent Channel Decoder for Coexistent Heterogeneous Networks

Cited by 7 publications

References 10 publications

Remote Control and Fault Diagnosis of Port Mechanical Equipment Based on Wireless Communication Technology

Remote Control and Fault Diagnosis of Port Mechanical Equipment Based on Wireless Communication Technology

Classification of Lombok Songket and Sasambo Batik Motifs Using the Convolution Neural Network (CNN) Algorithm

Design of English Translation Mobile Information System Based on Recurrent Neural Network

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