Visual detection of liquid leakage based on convolutional neural network

Li, Si-han; Qiu, H.; Jia, Wei; Shen, Yan; 上海西派埃智能化系统有限公司, 上海 Shanghai Sipai Intelligent System Co.

doi:10.37188/cjlcd.2020-0314

Cited by 3 publications

(1 citation statement)

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“…The comparison results of the detection confusion matrix of the two methods on the experimental network are shown in Figure 2 below. The value of the matrix in the figure is 1 for abnormal flow and 0 for normal flow [16].…”

Section: Experimental Data and Analysismentioning

confidence: 99%

Convolutional neural network anomaly traffic detection based on DAPA

Li¹,

Ma²,

Zhang³

et al. 2022

6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022)

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At present, the existing anomaly traffic detection methods rely on the statistical characteristics of traffic for detection, which can not adapt to the unknown nature of network traffic, resulting in low detection accuracy, high false detection rate, and poor generalization ability of the methods. This paper studies the anomaly traffic detection method of a convolutional neural network based on the Dynamic Adaptive Pooling Algorithm (DAPA). The DAPA algorithm is used to improve the pooling layer of the CNN network to reduce the overfitting interference of unknown features; after the t-SNE algorithm reduces the dimensionality of the data, using clustering to transform the data feature map to get anomaly identification output. The experimental results show that the false detection rate is reduced by about 37.46%. The actual detection results are close to the prediction results, and the method has better generalization ability.

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Section: Experimental Data and Analysismentioning

confidence: 99%

Convolutional neural network anomaly traffic detection based on DAPA

Li¹,

Ma²,

Zhang³

et al. 2022

6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022)

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Accelerated identification method of industrial instrument based on yolov5

Zheng

Song

et al. 2022

2022 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML)

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Intrusion detection algorithm based on image enhanced convolutional neural network

Wang

Zhao

Ren

2021

IFS

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Intrusion Detection System (IDS) can reduce the losses caused by intrusion behaviors and protect users’ information security. The effectiveness of IDS depends on the performance of the algorithm used in identifying intrusions. And traditional machine learning algorithms are limited to deal with the intrusion data with the characteristics of high-dimensionality, nonlinearity and imbalance. Therefore, this paper proposes an Intrusion Detection algorithm based on Image Enhanced Convolutional Neural Network (ID-IE-CNN). Firstly, based on the image processing technology of deep learning, oversampling method is used to increase the amount of original data to achieve data balance. Secondly, the one-dimensional data is converted into two-dimensional image data, the convolutional layer and the pooling layer are used to extract the main features of the image to reduce the data dimensionality. Third, the Tanh function is introduced as an activation function to fit nonlinear data, a fully connected layer is used to integrate local information, and the generalization ability of the prediction model is improved by the Dropout method. Finally, the Softmax classifier is used to predict the behavior of intrusion detection. This paper uses the KDDCup99 data set and compares with other competitive algorithms. Both in the performance of binary classification and multi-classification, ID-IE-CNN is better than the compared algorithms, which verifies its superiority.

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Visual detection of liquid leakage based on convolutional neural network

Cited by 3 publications

References 0 publications

Convolutional neural network anomaly traffic detection based on DAPA

Convolutional neural network anomaly traffic detection based on DAPA

Accelerated identification method of industrial instrument based on yolov5

Intrusion detection algorithm based on image enhanced convolutional neural network

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