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Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Chemical process industry fault diagnosis(CPIFD) is an important branch of intelligent manufacturing. In recent years, deep learning has demonstrated unique advantages and potential in feature recognition and classification. Therefore, the research of CPIFD based on deep learning has received extensive attention from scholars. However, there are limited reviews about CPIFD based on deep learning in the published literature. Hence, the purpose of the paper is to offer the state-of-the-art reference for CPIFD and stimulate scholars to further explore the application of deep learning in CPIFD. Firstly, the development of CPIFD technology is described. Secondly, the fundamental theories of representative models in deep learning are expounded and their applications in CPIFD are reviewed. These models include convolutional neural network, deep belief network, stacked autoencoder, long short- term memory network and other emerging neural network models. Finally, the problems faced by deep learning in CPIFD are discussed and the outlooks on future directions worthy of research are also presented. [ABSTRACT FROM AUTHOR]

Due to diversity in local information between categories is relatively subtle in fine-grained image classification tasks, it often causes problems such as insufficient ability of the model to capture discriminative features, and poor interdependence between channels when extracting features. As a result, the network cannot learn the salient and diverse image category features, which ultimately affects the classification performance. Therefore, this paper proposes a bilinear aggregate residual attention network (BARAN). In order to improve the feature capture ability of the network, firstly, based on the original bilinear convolutional neural networks model (B-CNN), the original feature extraction sub-network is transformed into a more learning aggregate residual network. And then, a distraction module is embedded in each aggregate residual block, so that the network focuses on integrating cross-dimensional features, and strengthens the degree of close association between channels in the feature acquisition process. Finally, the fused bilinear feature map is input into the cross-channel attention module, and the discriminative and distinctive sub- components included in the cross- channel attention module are used to further learn more subtle, diverse and mutually exclusive local inter-classes confusing information. Experimental results show that the classification accuracy on the fine-grained image datasets of CUB-200-2011, FGVC-Aircraft and Stanford Cars is 87.9%, 92.9% and 94.7%, which is superior to primary mainstream methods in classification performance. Moreover, the improvement is 0.038, 0.088 and 0.034 compared with the original B-CNN model. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the large data environment, there are many problems in the parallel deep convolution neural network（DCNN） algorithm, such as excessive data redundancy, slow convolution layer operation and poor convergence of loss function. This paper proposed a parallel deep convolution neural network optimization algorithm based on the Im2 col method. First, the algorithm proposed a parallel feature extraction strategy based on Marr-Hildreth operator to extract target features from data as input of convolution neural network, which can effectively avoid the problem of excessive data redundancy. Secondly, the algorithm designed a parallel model training strategy based on the Im2 col method. The redundant convolution kernel is removed by designing the Mahalanobis distance center value, and the convolution layer operation speed is improved by combining the MapReduce and Im2 col methods. Finally, the algorithm proposed an improved small-batch gradient descent strategy, which eliminates the effect of abnormal data on the batch gradient and solves the problem of poor convergence of the loss function. The experimental results show that IA-PDCNNOA algorithm performs well in deep convolution neural network calculation under large data environment and is suitable for parallel DCNN model training of large datasets. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Agricultural Science & Technology (1008-0864) is the property of Journal of Agricultural Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the rapid development of Internet technology, sensitive content images have changed from basic concealed content exchange to mass data sharing. The traditional method of sensitive content detection based on image feature extraction is no longer applicable. To overcome these difficulties, this paper proposed a sensitive content detection method based on sparse semantics and double-layer deep convolution neural network. In this method, the upper network preprocessed the training samples and constructed sparse semantic representation of the image as the input of the neural network, while the lower network further considered the third-party control mechanism (such as government agents) and proposed a sensitive content image detection method for specific groups. Compared with the existing image detection methods for sensitive content, this method can effectively reduce the number of training samples, and the detection accuracy is more than 7% higher than that of traditional image detection methods ( such as visual word bag method). [ABSTRACT FROM AUTHOR]

Copyright of Journal of Image & Graphics is the property of Editorial Office of Journal of Image & Graphics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Wool Textile Journal is the property of National Wool Textile Science & Technology Information Center and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)