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Copyright of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher 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.)

A gear box fault diagnosis method based on improved time series gray image and deep learning is put forward in order to solve the problems such as complex actual working environment of gear box, inadequate performance of extracting features by traditional methods and gray image extraction features. After the vibration signal is decomposed into several intrinsic mode components (IMFs) by EEMD, the IMFs are divided into high-frequency and low-frequency components by cumulative mean criterion, in which the high-frequency components are denoised by wavelet threshold. The high frequency IMFs and low frequency IMFs after noise reduction are reconstructed, and the reconstructed signal is coded by gray image method. Two-dimensional improved time series gray image is sent to convolution neural network for training, so as to exert the advantages of convolution network in feature extraction of pictures and display the results by confusion matrix. Finally, the model results and different gray scale images are compared with traditional diagnosis methods. The results show that compared with common gray image and global denoising gray image, the method proposed in this paper improves the accuracy of gear box fault diagnosis by 4 and 1.8 percentage points respectively, and the convergence speed is significantly faster. Compared with BP neural network and ELM diagnosis method, the method proposed in this paper significantly improves the accuracy of gear box fault diagnosis. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

Objectives: Convolutional neural network (CNN) was widely used in landslide susceptibility assessment because of its powerful feature extraction capability. However, with the demand for scene diversification and high accuracy, the algorithm of CNN was constantly improved. The practice of improving accuracy by deepening the network hierarchy or combining other models tended to increase the number of model parameters and the amount of calculation greatly, which led to the difficulty of model training or the overfitting of results, thus limiting its practical application. We want to solve the above problems from the model structure. of feature maps, one-dimensional CNN（1D-CNN） and two-dimensional CNN（2D-CNN） were connected to maintain network depth, limit model parameters, and reduce computation. The parameters sharing of multidimensional convolution kernels was used to capture the deep coupling features of different dimensions and different landslide factors, so as to make full use of features and avoid overfitting. Taking Sedongpu gully in Xizang,China as the experimental area, this paper selected 11 kinds of landslide influencing factors to analyze the landslide susceptibility. Results: The results show that the multi-dimensional CNN coupling structure had the same training efficiency as the shallow 2D-CNN with fewer parameters due to the reduced computational effort. While compared with the deep 2D-CNN with the approximate number of parameters, the training time of the proposed method was significantly reduced and the training cost was lower. In addition, the coupled model had a stronger feature learning ability than the independent 1D-CNN and 2D-CNN, and therefore obtained higher model accuracy. Under each confusion matrix metric of the testing data, the coupled model received higher scores, and thus obtained more reliable landslide susceptibility assessment results. Conclusions: The multidimensional CNN coupling model proposed in this paper is a reliable method applicable to landslide susceptibility assessment. This study provides new theoretical guidance and technical support for further landslide hazard monitoring and prevention [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology is the property of Automobile Technology Editorial Office 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 order to improve the spatial resolution of brain magnetic resonance imaging (MRI) images, the super resolution (SR) reconstruction method based on deep convolutional neural network (CNN) has achieved remarkable results. Increasing network depth or width usually can expand the receptive field of the network to improve the quality of reconstruction, but it is difficult to train the network due to the large parameter quantity and huge computing requirements. Meanwhile, the network does not pay enough attention to the medium and high frequency features of the image, which affects the quality of reconstruction. To solve the above problems, this paper proposes a cascaded channels-space attention and encode-decode network for 3D-MRI image SR reconstruction. Firstly, feature extraction is carried out in the low resolution space, and the image features are extracted by the symmetric connected encoder-decoder with dilated convolution to alleviate the checkerboard artifacts. Secondly, a serial connected channel-space attention module is constructed to capture the interdependence between the feature channels by channel attention, and the spatial attention is increased to assign weight to information of different positions, effectively enhancing the network' s learning of medium and high frequency information. Finally, subpixel convolution is used to sample the feature map to the target image size and meanwhile reduce memory consumption. Experimental results on public Kirby21 and BraTS datasets show that the proposed method is superior in both quantitative peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and subjective visual quality when compared with traditional SR algorithms and mainstream CNN-based SR algorithms. [ABSTRACT FROM AUTHOR]

Bearing faults can seriously reduce industrial production efficiency and even endanger the safety of people's lives and property. Monitoring the operating conditions of bearings conducting fault diagnosis are of great significance for ensuring the safe operation of the production process. This paper proposes a Multi-Scale and Attentive Convolutional Neural Network (MACNN) based on attention mechanism for bearing faults classification. Firstly, one-dimensional bearing signals are input into a convolution layer, followed by a maximum pooling layer to suppress noise and reduce information redundancy. Then, four MACNN modules are taken as input, each of which adopts a parallel network structure of ordinary convolution and void convolution. Under the premise without increasing model parameters, the model's receptive field is expanded to extract more fault features for improving the accuracy. In addition, the attention mechanism module is connected at the end of each MACNN model and the feature information is further extracted by using the ability of automatic extraction of important features. The average pooling layer is used in the network structure to prevent the overfitting and the full connection layer from the output of experimental classification results. Furthermore, the Boundary Equilibrium Generative Adversarial Networks (BEGAN) model is adopted to enhance fault data, change the proportion of unbalanced data sets, increase the number of dataset samples, reduce overfitting of MACNN model, and improve diagnostic accuracy. Finally, the experimental results on the Paderborn University Dataset show that MACNN can achieve better performance in feature extraction and fault classification, outperforming the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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 Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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 South China University of Technology (Natural Science Edition) is the property of South China 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 Journal of Test & Measurement Technology is the property of Publishing Center of North University of China 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 Automotive Engineer (1674-6546) is the property of Auto Engineering Editorial Office 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 Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control 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.)

Aiming at the problems of low accuracy and poor generalization under the condition of small samples due to the excessive dependence of traditional deep learning models on a large amount of training data, a small sample bearing fault diagnosis method based on improved siamese neural network is proposed. This method is dedicated to using the metric learning to judge the similarity between samples. Compared with complex and multi-layer deep models, its structure is simpler and it is easier to train with small samples. Specifically, this paper designs three modules to realize fault diagnosis. In the feature extraction stage, the long short- term memory network and the convolutional neural network are used to extract the temporal and spatial features of the fault signal, which can make full use of the limited sample information; the relationship measurement stage replaces the common Euclidean distance measurement with an adaptive network measurement method and uses global mean pooling to reduce network parameters; in addition, compared with the standard siamese network, the constructed fault classification network can directly identify the faults of the samples, without tedious sample comparison in the testing stage. The experimental results show that the ISNN method has better fault diagnosis accuracy and generalization performance than various comparative methods under the limited number of training samples. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays 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.)

Aiming at the problems of poor image recognition robustness and poor model generalization in camouflaged object segmentation, inspired by the receptive field structure of the human visual system in neuroscience, this paper proposed a polarization imaging camouflage target detection method based on the receptive field network of focused attention. According to the polarization imaging target detection needs, it constructed the polarization imaging dataset that could effectively contain the background noise as well as obtain the detailed features of the target. The proposed method based on the recognition and localization network framework, which could effectively improve the discriminability and robustness of camouflaged target features by improving the feature extraction module and the decoder module, which exploited the relationship between the eccentricity and the size of the receptive field to cover multi-scale target information. The experimental validation carries out using a self- constructed dataset on multiple typical targets and comparing the subjective visual and objective evaluation metrics of the segmentation results with classical algorithms, and the results of the ablation experiments validate the effectiveness of the present segmentation method. [ABSTRACT FROM AUTHOR]

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 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 Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition 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 Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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 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.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

Aiming at the current phenomenon of single feature extraction of motor imagery EEG signals and low classification recognition rate, this paper combines the classifier of convolutional neural networks and proposes a feature fusion algorithm of multi-feature weighted fusion to improve motor imagery EEG recognition rate. It performs wavelet packet transform on the preprocessed EEG signals, extracts its common space features, energy features, marginal spectral entropy features and nonlinear dynamics features, then performs weighted fusion. Finally, the paper uses convolutional neural network classification to classify. To verify the rationality of the algorithm, it uses dataset which is the BCI-IV Dataset 2a to verify and analyze the proposed algorithm. The results show that the proposed weighted feature fusion algorithm with CNN classifier can effectively improve the recognition rate of motion imagination. In the experiments, the average classi-fication accuracy of 9 volunteers is 75.88%, and the average Kappa coefficient is 0.70. [ABSTRACT FROM AUTHOR]

Most of the Encryption-then-Compression (ETC) methods can only obtain several limited fixed compression ratios. However, arbitrary encryption compression ratios instead of limited fixed compression ratios are more suitable for practical requirements. To this end, this paper proposed a lossy compression algorithm for encrypted color images with arbitrary compression ratios. It combines the uniform and random downsampling to compress the encrypted images, obtaining arbitrary compression ratios of an encrypted image. The receiver receives the compressed sequence of the encrypted image and obtains the decrypted image by decompression and decryption. The proposed scheme then characterizes the lossy reconstruction of the original image from the decrypted image as an optimization problem with the downsampling compression-based constraint. This scheme designs a convolutional neural network-based image reconstruction model for lossy ETC to resolve this problem, which is denoted the ETC-oriented Reconstruction Network (ETRN). ETRN consists of shallow feature extraction (SFE), residual in residual (RIR), residual content supplementation (RCS), and down-sampling constraint (DC). The experimental simulation results show that the encrypted color image lossy compression algorithm proposed in this paper can obtain excellent compression and reconstruction performance, which fully demonstrates the feasibility and effectiveness of this method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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 Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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 Guangxi Normal University - Natural Science Edition is the property of Gai Kan Bian Wei Hui 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 Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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 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 Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications 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 Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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 Sichuan University (Natural Sciences Division) / Sichuan Daxue Xuebao-Ziran Kexueban is the property of Editorial Department of Journal of Sichuan University Natural Science Edition 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 Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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 Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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 China Forest Products Industry is the property of China Forest Products Industry Editorial Office 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.)

The paper proposes a novel fault diagnosis method for hydroelectric units to address the challenge of achieving high-precision fault diagnosis due to the non-stationarity and non-linearity of vibration signals, which makes it difficult to extract single features. The method utilizes convolutional neural networks to extract vibration signal features, and employs empirical mode decomposition and approximate entropy to construct signal feature vectors, which are used to create a fusion feature vector. Furthermore, a BP neural network is trained with the fusion feature vector as input and the fault category as output to obtain a hydroelectric unit fault recognizer that can identify the unit's operating state as normal or with a specific fault type. The validity of the proposed method in signal feature mining and its high fault diagnosis accuracy are verified through rotor experimental data. [ABSTRACT FROM AUTHOR]

Most of the existing fingerprint indexing methods are based on real-valued features, which is compute and storage intensive for large- scale databases. In order to speed up the retrieval process and obtain real- time response results from large-scale databases, a novel fingerprint indexing method based on supervised deep hash is proposed in this paper. This method combines domain knowledge with self-attention deep hash model. The domain knowledge is used for fingerprint image preprocessing to obtain the binary skeleton images, and the self-attention deep hash module maps the binary skeleton images to Hamming space to get binary embeddings for indexing. In the feature extraction module, Transformer structure is used to replace convolutional neural network to extract fingerprint minutiae features. In addition, an automatic alignment module is added to the model and a STN-AE structure is designed to assist in training the module. Experiments conducted on public datasets such as NIST4, NIST14, FVC2000, FVC2002 and FVC2004, have confirmed that this method is effective in improving the retrieval speed and reducing storage consumption. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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 Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

In image retrieval based on deep hashing, to solve the problems of low feature extraction efficiency in convolutional neural networks (CNN ) and underutilization of feature correlation, this paper proposed a novel method combining sparse difference network and multi-supervised hashing( SDNMSH ), and used it for efficient image retrieval. SDNMSH took pairs of images as training inputs, and guided hash codes learning through an elaborately designed sparse difference convolutional neural network and a supervised hash function. The sparse difference convolutional layer and the vanilla convolutional layer composed the sparse difference convolutional neural network. The sparse difference convolutional layer could quickly extract rich feature information, to achieve efficient feature extraction of the entire network. At the same time, in order to make full use of the pairwise correlation of semantic information and features, so as to promote the feature information extracted by the network to be more effectively transformed into discriminative hash codes, and then to achieve efficient image retrieval by using SDNMSH, this paper adopted a multi-supervised hash( MSH) function and designed an objective function for this purpose. Extensive experimental results on three widely used datasets MNIST,CIFAR-10 and NUS-WIDE show that SDNMSH achieves better retrieval performance, compared with the state-of-the-arts. [ABSTRACT FROM AUTHOR]

In order to accurately predict the short-term passenger flow of urban rail transit for multiple stations, this paper proposed a deep learning model, SAE-ConvLSTM, combining convolutional long short-term memory ( ConvLSTM) and stack autoencoder (SAE) . This paper considered thirteen external factors related to passenger flow, whose features would be extracted by SAE with successive layers and thus obtain more representative features. It proposed ConvLSTM to extract spatiotemporal features of passenger flow, which was combined with the resulting external factors to predict short-term passenger flow of multiple stations simultaneously. And it developed latent action Monte Carlo tree search ( LA-MCTS) to optimize the parameters of SAE. Compared with genetic algorithm( GA), particle swarm optimization ( PSO), simulated annealing algorithm (SA) and tabu search (TS), LA-MCTS performed best in terms of effect and efficiency. This paper conducted extensive experiments. The results show that SAE-ConvLSTM works better than shallow machine learning model-back propagation neural network ( BPNN), support vector regression mode(SVR),autoregressive integrated moving average model ( ARIMA ),and deep learning modellong and short time memory network ( LSTM), convolutional neural network (CNN) and Conv LSTM without external features, ConvLSTM external features without SAE, CNN + LSTM and CNN + LSTM with external features, in terms of root mean square errors ( RMSE), mean absolute errors ( MAE) and mean absolute percentage errors ( MAPE), and the goodness of fit ( R² ) . [ABSTRACT FROM AUTHOR]

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]

Copyright of Journal of East China University of Science & Technology is the property of Journal of East China University of Science & Technology Editorial Office 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.)

To solve the problem of inaccurate description and unreliable relation inference in group activity recognition, this paper focused on constructing a scene relationship graph for three aspects: individual, group, and scene, and proposed a scene relationship graph network（SRGN） for group activity recognition. This method included a feature extraction module, a scene relation graph inference module, and a classification module. The feature extraction module extracted individual features, group features, and scene features by convolutional neural network. To fully explore the impact of scene on individual and group descriptions, the scene relation graph inference module learnt individual features and group features by building individual-scene and group-scene relationship graphs in a two-branch framework. Scene graph inference took into account the influence of individual on group and introduced a cross-branch module. It used the classification module to classify individual features and group features for prediction. The experimental results show that the group recognition accuracy of the proposed method on volleyball and collective activity data sets is improved by 1. 1% and 0. 5%,respectively. It verifies the validity of the scene graph in describing individual feature and group feature. [ABSTRACT FROM AUTHOR]

In view of the slow detection speed caused by the huge amount of computation and parameters in deep convolutional neural networks and the low accuracy by complex scenes in nowadays face detection, this paper presents an anchorfree face detection algorithm combing light-weight backbone and multi-feature fusion. The method firstly constructs a light-weight convolutional neural network as the backbone for feature extraction. Then three different modules are used to process the feature fusion:receptive field enhancement module to strengthen image information extraction, weighted feature fusion module to improve detection accuracy, channel shuffle fusion module to simplify the detection head. Finally, the center point anchor-free detection method is used to calculate the fused features and predict the position of faces. The experimental results show that the final model parameter is only 5.1 MB. The detection accuracy of easy, medium and hard in WIDERFACE verification set are improved by 1.4, 2.2 and 4.8 percentage points respectively compared with the benchmark method, which shows that the method has high accuracy in complex scenes face detection with a light-weight model and verifies the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Aiming at the characteristics of complex ocean scene (multi-scale target, diverse object, great style difference, strong spatiotemporal correlation and uncertain target), this paper studies the effective feature extraction method of complex image based on attention mechanism, and proposes a Chinese description generation model of complex ocean scene image based on convolutional neural network (CNN) and long short-term memory (LSTM) network. Combined with Jieba word segmentation tool, the complex ocean scene image is realized automatic translation of ocean scene monitoring images. Using 91 satellite map assistant and UAV high-definition image data, the model is established and the algorithm is verified. The results show that the Inception-v4 model has stronger complex feature extraction ability than VGG16 model, and the image classification ability of Inception-v4 model is about 5.3 percentage points higher than that of VGG16 model. Based on convolutional neural network and long short- term memory model, the image classification ability is basically feasible and can solve the problem of automatic annotation of batch images, but the stability and accuracy of the algorithm need to be further improved. [ABSTRACT FROM AUTHOR]

To accurately and quickly analyze the stability of open-pit mine slopes, this paper proposes AdaBoost-Convolutional Neural Network(AdaBoost-CNN) to establish the nonlinear relationship between open-pit mine slope stability and influencing factors. AdaBoost-CNN is a new machine learning method that combines AdaBoost and CNN. The core idea is to combine the feature extraction capabilities of CNN with the ability of AdaBoost to process unbalanced data, which has the advantages of high reliability, high accuracy, and less training time. AdaBoost-CNN uses migration learning, which not only eliminates the limitation of CNN that require a large number of training samples but also solves the problem of reducing actual performance in the serialization process of AdaBoost. This paper uses BP neural network, Support Vctor Mchine, CNN, and AdaBoost-CNN to model and analyze the measured engineering data. By comparing RMSK and RPK, it is found that AdaBoost-CNN has the highest prediction accuracy and the strongest model generalization ability. The results show that AdaBoost-CNN can predict slope stability more accurately, and is a reliable tool for slope stability prediction. [ABSTRACT FROM AUTHOR]

Mild cognitive decline is the early stage of Alzheimer's disease, and the feature extraction and classification of mild cognitive decline using EEG signals is an important method for diagnosing mild cognitive decline. In the automatic detection technology of mild cognitive decline based on electroencephalogram artificial intelligence, the existing research only extracted a certain feature in the electroencephalogram signal or simply concatenates multiple features, which caused these methods to fail to well consider the correlation between different features and it would cause the problem of dimensional disaster. This paper proposed a convolutional neural network based automatic detection algorithm for resting state electroencephalogram data of mild cognitive decline. By extracting the power spectrum and brain network features of the electroencephalogram, it fused the two features by matrix operation, and designed a convolutional neural network to classify the fused features. This method achieves a high accuracy rate on the data set collected by a hospital in Shanghai. In addition, by inputting different subsets of the feature set, this method found the few groups of features that contribute the most to mild cognitive decline, thereby it also had a certain interpretability. Experiments on the dataset of this paper, it p roves the advantages of the power brain network for the automatic diagnosis of mild cognitive decline. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)