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In order to solve the problem of strong popularity bias in recommendation lists generated by Bayesian personalized ranking algorithm under the condition of unbalanced data, this paper designed a hybrid recommendation algorithm based on feature embedding to remove popularity bias. Firstly, this paper used convolutional neural network to extract user and item features, determine user preferences, and fill original unbalanced data according to user preferences. Secondly, this paper embedded the user preference features extracted from convolutional neural network into the Bayesian personalized ranking algorithm for hybrid recommendation. Finally, it trained the mixed recommendation model with score filled data, and obtained the personalized ranking list without popularity bias. In order to verify the performance of the algorithm, it conducted the analysis and comparison experiments on Movielens-100 K and Movielens-1 M. And the experimental results show that the popularity bias is reduced by about 50%～60% and the accuracy is improved by about twice. [ABSTRACT FROM AUTHOR]

To solve the problem of combustion state recognition in solid oxide fuel cell (SOFC) combustor, this paper proposed a combustion state recognition method based on attention mechanism and image feature pyramid. The method adopted the adaptive gamma correction with weighting distribution (AGCWD) to standardize the input images. It combined two single full connections with 1×1 convolution to replace the squeeze and excitation structure, and proposed a hybrid attention structure combined with spatial attention structure to enhance the ability of feature extraction. To provide multi-scale information communication capability, it constructed the multi-scale bidirectional fusion pyramid by means of bidirectional computation and multi-scale fusion. The experimental results show that the proposed method reaches 99.22% accuracy under the premise of 3.98 M parameters and 397 M floating point operations (FLOPs), and effectively identifies the combustion state in SOFC combustor. [ABSTRACT FROM AUTHOR]

This paper proposed a natural language sentence matching method that combined high-level and low-level semantic information to solve the problems about current natural language sentence matching method fail to integrate common semantic information and it is difficult to capture deep-semantic information. First of all, the method used pre-trained word vector GloVe and character-level word vector to obtained the word embedding representation of sentence P and sentence Q. Secondly, this paper encode red P and Q with bidirectional LSTM, then it contained low-level semantic information through preliminary fusion of P and Q. Thirdly, this paper calculated bidirectional attention between P and Q, then spliced them together to get semantic representation, afterwards it calculated its self-attention to obtained high-level semantic information. Finally, this paper used a heuristic fusion function to fuse the low-level semantic information with the high-level semantic information to obtain the final semantic representation, and it used a convolutional neural network to prediction answers. This paper evaluated the proposed model on two tasks, such as recognition textual entailment, paraphrase recognition. This paper conducted experiments on the SNLI dataset and the Quora dataset. The results show that the accuracy of the proposed algorithm on the SNLI test set is 87. 1%, and the accuracy of the Quora test set is 86. 8%, which verifies the effectiveness of the algorithm in the task of natural language sentence matching. [ABSTRACT FROM AUTHOR]

To solve the problem of insufficient real-time performance of existing optical flow estimation methods, this paper proposed a lightweight improved PSC-NET model with deep separable convolution ( depth pyramid, warping and cost volume, DS-PWC). One of the improvements was decoupling the conventional two-dimensional convolutional network layer into a deep separable convolutional layer. The other one was adding a weight coefficient based on the number of layers in the pyramid layer by DS-PWC, which greatly reduced the number of model parameters in the network structure without loss of precision. In addition, in the training process, the paper applied data enhancement technologies such as I+ ORE to further improve the generalization ability of estimation and prediction results. The experimental results show that the DS-PWC model was tested in the dataset and the operating efficiency reaches about 58 fps while maintaining the quality. To verify the effectiveness of the algorithm, this paper carried out the ablation experiments of model structure and data enhancement. The results verify the effectiveness of the DS-PWC model. [ABSTRACT FROM AUTHOR]

This paper proposed a self-encoding salient region extraction algorithm based on multi-layer convolutional to solve the problem that the existing convolutional neural network wasn’t sufficient in extracting image features, which led to the poor effect of salient extraction. When convolutional networks were used to extract image features, the shallow convolution features generally extracted the detailed features of the image, such as color, texture and location features, and the deep convolution features were generally referred to the semantic features of the image. On the coding layer, this algorithm downsampled the shallow convolution features and fused them into the deep convolution features, and upsampled the deep convolution features to the shallow convolution feature. Experiments show that this method can greatly improve the coding quality. In the decoding, it also fused the convolutional features by encoding to obtain the lost information in the decoding and also obtained a better decoded image. In addition, this paper designed a layer-by-layer supervision method to guide the training of the decoding layer, which used the standard region extraction map by down-sampling as the standard image for supervision training. The experimental results show that this algorithm can increase the F-measure by 0.071 on the basis of PAGRN, and reduce the MEA by 0.031 on average. [ABSTRACT FROM AUTHOR]

In order to recognize complex interactive actions, this paper proposed a feature learning method based on depth information, and used a two-layer classification strategy to solve similar action recognition problems. The method analyzed the depth image action sequence from the frequency domain, extracted the frequency domain features, used VAE for spatial feature compression representation, established HMM to simulate time series changes and performed the first layer action recognition. In order to solve the problem of similar actions, this paper introduced three-dimensional joint point features for the second layer action recognition. Experimental results show that these two types of features can effectively represent the meaning of gestures on the SBU-Kinect action data set, the strategy is simple and effective and can get high recognition accuracy. [ABSTRACT FROM AUTHOR]

In order to solve the problems of high computational complexity, large computational cost and large number of parameters, this paper proposed the parallel group convolution algorithm based on the domestic SW26010P multi-core processor. The core idea was to use the unique data layout, through the multi-core mapping processing, parallel computing. Experimental results show that compared with single-core serial algorithm, the proposed parallel group convolution algorithm can achieve the highest speed-up ratio of 79.5 and the maximum effective computing power of 186.7MFLOPS. After data parallel optimization of the parallel group convolution algorithm by SIMD instruction, the algorithm obtains the highest speed-up ratio of 10.2 compared with the parallel group convolution algorithm before optimization. [ABSTRACT FROM AUTHOR]

The severe occlusion and scale variation in the cotton field reduce the accuracy of the object counting algorithm greatly. To solve above problems, this paper presented a cotton boll counting algorithm based on context multi-scale fusion. This algorithm consisted of the context module of the pyramid structure and the fusion convolutional neural network. Firstly, it adopted global context and local context modules to encode the context information in the cotton images, and then used multicolumn feature conversion module to map the input image into high-dimensional feature . Finally, it combined the context information with the high-dimensional feature through the fusion convolutional neural network, for achieving high-precision cotton boll counting and generating high-quality cotton boll density map. In addition, this paper conducted experiments on the cotton boll dataset from two angles of close-range and ground-air observation. The comparative experimental results show that adopting context information can effectively improve the accuracy of cotton boll counting, and the counting errors MAE and MSE have decreased by 27 . 3 and 29 . 4, respectively. [ABSTRACT FROM AUTHOR]

In order to solve the problems of uneven population distribution and different target scales affecting the crowd counting in public places, this paper proposed a novel crowd counting model based on image field division. Firstly, this model divided the image scene into two parts : the near and far field of vision area. For the near field of vision area, it used the YOLO based network for p edestrian d etection and added scene constraints to avoid repeated counting in the near and far field of vision. For the far field of vision area, it used the improved MobileNets to extract the population density distribution characteristics, and introduced the super-resolution reconstruction module to improve the quality of the population density map . Finally, it obtained the number of the c rowd in the whole image by calculating the sum of the two. This paper tested the proposed model on Shanghai Tech and Mall d atasets. The results show that the model has a significant improvement in accuracy and robustness and prove the model is feasible. [ABSTRACT FROM AUTHOR]

Aiming at the traditional model parallel methods for accelerating convolution neural network (CNN) tend to have low utilization, this paper proposed PipeCNN, which accelerated CNN with software pipeline. Firstly, this paper studied the forward propagation and back propagation, and then explored data correlation during training. Secondly, it parallelized CNN with the support of software pipeline, and then analyzed two feasible gradient updating methods in PipeCNN. Finally, it used circular queue to realize communication between two layers and then proposed a task allocation algorithm to divide CNN into working parts. Experiments show that the method can obtain good speedup and utilization while ensuring the accuracy of the model. It shows that software pipeline can effectively solve the problem of low utilization in model parallel, and accelerate the training of CNN. [ABSTRACT FROM AUTHOR]

Common models of text classification are mostly constructed with recurrent neural network and convolutio-nal neural network in a stacked way. Although this stacked structure can extract more high-dimensional and deeper semantic information, a part of the effective feature information is also dropped when different structures are connected . In order to solve the above problem, this paper proposed a classification model based on dual-channel word vectors, and the model used a shallower structure with attention-mechanism-based Bi-LS TM and CNN to extract features of text representation effectively. In addition, this paper presented a new method to characterize text into two forms, forward and backward, and used CNN to extract feature information of the text . By conducting classification experiments on two different five -classification datasets and comparing with a variety of benchmark models, it verifies that the model is effective and the results show that this model is superior to the other models with stacked structure. [ABSTRACT FROM AUTHOR]

Most of the existing steganalysis methods have weak generalization ability and cannot effectively detect unknown steganalysis algorithms, which makes the accuracy of their classification greatly reduced in the practical application process. To solve this problem, this paper proposed an image steganalysis method based on group convolution and snapshot ensembling（Snapshot Ensembling steganalysis network, SENet）. Firstly, residual convolution block and group convolution block are used to extract the features of the image. Secondly, the model with the best performance is obtained as the snapshot model in each training period. Finally, the selected snapshot models are integrated to classify the images. This method uses the techniques of group convolution and snapshot ensembling to avoid the high training cost of traditional integration methods and the limited generalization ability of a single classifier. Experimental results show that this method can improve the accuracy of steganographic analysis model, and can effectively classify when the training set and test set are mismatched, and has high model generalization ability. [ABSTRACT FROM AUTHOR]

To solve the problems that the objects with different semantic classes are identified as co-salient objects in current co-saliency detection methods, this paper proposed a CNN and semantic correction-based co-saliency detection method (CSCCD). The proposed method first adopted the guided super pixel filter to process the super pixels obtained by SLIC and the saliency results generated by DSS, which showed clear object boundaries. Then it utilized Mask R-CNN to extract semantic features. It proposed the definitions of image semantic feature and semantic consistency. It also defined the image group semantic correction to solve the problem that detected the objects with different pose belonging to a semantic class as different semantic classes. With the concept, this paper defined image group semantic correlation class, solving semantic correlation problem of multiple images. It generated the final co-saliency detection results by fusing the saliency detection regions with the image group semantic consistent regions. The experimental results on public benchmark datasets show that this algorithm can effectively highlight the whole and outline of the object, and its comprehensive performance in objective quantification is obviously improved. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the Transformer model cannot apply to mobile devices with limited computational resources due to its huge number of parameters and computational complexity, this paper proposed a mobile-friendly lightweight image recognition network called FuseNet. FuseNet utilized convolutional neural network to extract local feature information and self-attention to excel in modeling global information, and it integrated the features of both local and global representations into a single feature extraction module, which efficiently combined the advantages of the two different structures to achieve a high accuracy with a small model size. Experiments demonstrate that FuseNet with different model sizes can achieve good performance without using pre-training and it can well apply to mobile devices. FuseNet-B achieves 80. 5% accuracy with 14. 8 M parameters on ImageNet-1 K dataset, the performance exceeds the same volume of Transformer models and convolutional neural networks. [ABSTRACT FROM AUTHOR]

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.)

Aiming at the fact that partial occlusion affected facial expression recognition in practical applications, this paper proposed an expression recognition method based on generative adversarial networks (GAN) _ Firstly, this method filled and repaired the occlusion face images, and then performed the expression recognition. The generator of GAN was composed of a convolutional auto-encode r, the face images generated by adversarial learning between generator and discriminator were more vivid. The discriminator was composed of the convolutional neural network, which had good feature extraction ability. It added a multi-classification layer to construct the expression classifier, which avoided feature re-calculation. In order to solve the problem of insufficient training samples, this paper used the CelebA face dataset to train face filling and repairing, and pretrained the feature extraction part of the expression classifier. Experiments on the CK + dataset show that the face images after filling are real and coherent, and achieves a higher expression recognition rate. Especially it improves the recognition rate of large-area occlusion of the face. [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 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 Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department 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.)

Due to the low randomness of character distribution and high randomness of word combination in word-based DGA domain names, the traditional detection methods are difficult to identify them. The method based on deep learning such as LSTM can capture domain's sequence features, but it lacks the word combination features and has low detection accuracy. In order to solve above problems, this paper proposed a new DGA domain name detection method based on fusion embedding layer. In domain embedding stage, it carried out a fusion vector representation based on characters and word segmentation technology . Then, it constructed a hybrid deep learning model based on CNN and BiGRU. The experimental results show that the proposed model has increased by 3 . 1 % and 4. 3% respectively in accuracy compared with the models based on CNN or LSTM alone. When dealing with the matsnu, suppobox and ngioweb DGAs, the proposed model is more effective. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department 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.)

Because convolutional neural networks( CNN) mostly focus on global feature learning and ignore local feature information containing more details, it is difficult to improve the accuracy of indoor scene recognition. To solve this problem, this paper proposed an indoor scene recognition method based on improved global-local attention network ( GLANet). Firstly, it used GIAN et to capture the global and local features of the scene image to increase the detail information of the image features. Then, it introduced a non-local attention module into the local network to further preserve the detail features of the image through the convolution of attention diagram and feature graph. Finally, it used various features of different stages of the network for classification. Through training and verification on the MIT lndoor67 and the SUN397 dataset, the recognition accuracy of the proposed method was increased by l. 98% and 3. 07% respectively compared with the LGN method. Experimental results show that the algorithm can effectively capture global semantic information and fine spatial details, and significantly improves the recognition accuracy. [ABSTRACT FROM AUTHOR]

Most of the current fake face detection technologies use deep learning to identify the feature differences between real videos and fake videos, and have achieved good results on uncompressed videos. But the detection performance on compressed video will be severely reduced. Therefore, this paper proposed a fake face video detection method based on improved triplet loss to tackle the above problem. Firstly, it used an artifact map generated by the artifact map generator to deepen the feature difference between the fake face and the real face. Secondly, it used improved triplet loss to solve the problem of hard samples that were difficult to detect correctly. Finally, it chose a deep learning network that was more suitable for fake face detection to extract convolutional features. Experiments on the FaceForensics++ dataset show that proposed method is better than other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The size of the receptive field of the convolutional neural network is related to the size of the convolution kernel. And the traditional convolution uses a fixed-size convolution kernel, which limits the feature perception ability of the network model. In addition, due to the parameter sharing mechanism of the convolutional neural network, it used the same feature extraction method for all pixels in the spatial region. However, there are differences in the distribution of noise signals and clean speech signals in the noisy spectrogram, especially in the complex noise environment, the general convolution method is difficult to achieve high-quality speech signal feature extraction and choosing. In order to solve the above problems, this paper proposed a multi-scale region adaptive convolution module, which used multi-scale information to improve the feature perception ability of the model and automatically allocated the area adaptive convolution achieve and improved the denoising ability of the model. The experiments on the TIMIT public datasets show that the proposed algorithm has achieved satisfactory results in the metrics of speech quality and intelligibility. [ABSTRACT FROM AUTHOR]

In order to solve the problems of insufficient feature extraction of sEMG end-to-end gesture recognition and low accuracy of multi-gesture recognition, this paper proposed a multi-stream convolutional EMG gesture recognition network model fusing attention mechanism. The proposed model generated sub-myoelectrograms from multi-channel time-domain sEMG through sliding windows and used a multi-stream convolutional neural network to fully extract the semantic features of each sampled channel sEMG, and then aggregated them to obtain rich multi-channel gesture semantic features. At the same time, the proposed model calculated the attention distribution map of semantic features from temporal and feature channel dimensions to strengthen the useful features and weaken the useless features, further improved the accuracy of multi-gesture recognition. The experiment used the Ninapro dataset for training and testing and compared with the mainstream electromyogram recognition model. The experimental results show that the proposed model has better performance in recognition accuracy, which proves the effectiveness of the proposed model. [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.)

Due to the small amount of microscopic features image data of traditional Chinese medicinal materials powder, unbalanced distribution of sample classes and small difference between classes, it is difficult to achieve a satisfying classification effect through traditional image classification methods. To solve the above problems,this paper proposed an improved method of deep convolution neural network based on dynamic ReLU and attention mechanism model. Firstly, it used Xception as the basic network, which had an obvious effect on small sample data classification. Secondly, it replaced the static ReLU activation function in the network with the improved dynamic ReLU function, so that each sample had its own unique ReLU parameters. Finally, it embedded the improved SE module in the network to enable the network to learn the importance of each feature channel automatically. The proposed method can make the network pay more attention to the detailed information in the image, and can solve the problem of unbalanced distribution of sample classes and small differences between classes. The experimental results show that the image classification accuracy of 56 kinds of traditional Chinese medicinal materials powder vessel is increased by about 1.5% to 93.8%, which demonstrates that the proposed method is advantageous over other image classification methods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department 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.)

In order to solve the difficult problems of low classification accuracy and long training time in conifer species recognition, this paper presents a coniferous species classification network model based on convolutional neural network in airborne hyperspectral images. VGG16 was selected as the basic network to improve the experiment, which simplified the structure of the network layer, reorganized the arrangement of convolution kernel, and better adapted to the task of hyperspectral classification. The airborne hyperspectral image data band of the teapot experimental forest selected for the experiment was enhanced, the Adam optimizer was used for training optimization, and the learning rate inverse time attenuator and the Early-stooping optimizer were used to accelerate the speed of network fitting and increase the generalization ability of the network. The results showed that, in the case of small inter-class gap and large intra-class gap, compared with the unimproved VGG16 network with the best comparative experiment effect, the accuracy of multi-label classification of hyperspectral images of coniferous species was improved by 8.17%, and the classification accuracy was 94.47%, and the training time was reduced by more than 6 times. It can be concluded that the arrangement of the number of convolution kernels from large to small was helpful to the extraction of hyperspectral information and shorten the training time. The simplification of network layers can ensure that the model can be fitted without excessive training and reduce the training time. Data enhancement was of great help to the improvement of coniferous species identification accuracy. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the existing sequence recommendation algorithms only use short-term sequential behaviors to make recommendations, but fail to fully consider the long-term preference of users and the deeper relationship be tween items, this paper proposed a sequential recommendation algorithm CSALSR that it integrated self-attention mechanism and long-term and short-term preference. This model embedded the user and the item into the vector respectively, and regar ded the pairwise encoding of the item in short-term interaction sequence as a three-way tensor. Then, it added self-attention mechanism on the three-way tensor and used convolutional neural network to extract the deeper connection between items from the order pattern of users. At the same time, considering the long-term preference of users, it took the embedding of similar users as a supplement to enhance the user representation. Experimental results show that the proposed method on MovieLens IM and Gowalla data sets is superior to other methods in precision@N.recull@ N and MAP. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering 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: In order to solve the problem of low efficiency of manual visual discrimination in the audit of “problematic map” in China, an end‐to ‐end adaptive detection method for small sample scene based on the multi ‐ scale feature fusion of convolutional neural network (CNN) is proposed in this paper. Methods: The real ‐ time enhancement of the dataset can overcome the shortcoming of CNN, which requires a large number of training samples. By fusing multiple maps at different scales, the intelligent detection of significant error areas of the “problematic map” in multiple scales is realized. The region proposal network is optimized by taking the attributes of wrong regions into account to further improve the detection accuracy. Results: Compared with the existing detection method, the accuracy of the proposed method is increased by 8 times, which verifies its effectiveness. Conclusions: The proposed method provides a new solution for large‐scale “problematic map” detection, and can be quickly applied in production. [ABSTRACT FROM AUTHOR]

Due to the influence of culture and society on the expression of human emotion, the features of speech emotion in different languages vary greatly, which leads to the insufficient generalization ability of speech emotion recognition model in a single language . To solve this problem, this paper proposed a multi-lingual speech emotion recognition method based on multitask attention. By introducing the auxiliary task of language identification, the model could not only learn the emotional features shared by different languages, but also learn the unique emotional characteristics of each language, so as to improve the generalization ability of the multi-language emotion recognition model. Experiments on the dimensional affective corpora of two languages show that the proposed method improves the mean values of relative UAR of the Valence and Arousal tasks by 3 . 66% ~ 5. 58% and 1. 27% ~ 6. 51 %, respectively, compared with the benchmark methods . Experiments on discrete affective corpora of four languages show that the mean values of absolute UAR improves by 13. 82% ~ 15. 75% compared with the benchmark methods. Therefore, the proposed method can effectively extract the language-related emotion feat ures and improves the performance of multi-lingual emotion recognition. [ABSTRACT FROM AUTHOR]

Existing detection algorithms that rely on CNN often adopt the feature fusion to enrich feature expression.Although the methods can effectively improve multi-scale detection,it has no significant improvement in complex scenes due to the three problems.The loss of feature correlation caused by long-path feature fusion;It complements the semantic information only in single direction.The importance of ERF in multi-scale detection is ignored.This paper proposed DFS,MBFM and RFEM to solve these three problems respectively.The proposed method used DFS to shorten the path within feature maps during the second stage fusion,designed MBFM to made up for the loss of semantic information,and designed RFEM to increase the area of ERF.It makes the mAP value reaching 85.4% in the PASCAL VOC 2007 test dataset.Compared with the detection algorithms depending on the traditional modeling method,the algorithm improves the mAP value of 2.6%. [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.)

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.)

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.)

Author identification always plays an important role in the public security and literary inspection work. Texts feature extraction is cumbersome and not universal. To solve this problem, this paper proposed the CABLSTM Chinese microblog author identification model without expert feature modeling, and tested the accuracy of the model in the open microblog corpus. This model maximized the extraction of short text features, fused the attention mechanism in the CNN and removed the pooling layer, and obtained context-related information through the bidirectional LSTM. The identity recognition result was output through the softmax layer. Experimental results show that the model has a certain improvement in accuracy, recall rate, and F-measure in comparison with traditional machine learning algorithms and TextCNN and LSTM algorithms in the identification task of Chinese microblog authors. [ABSTRACT FROM AUTHOR]

In order to solve the problem of high time complexity in the signal recognition process by using convolution neural network, this paper proposed a new algorithm based on denoising auto-encoder and convolution neural network. Firstly, it performed the STFT on the radar emitter signal to obtain the time frequency image. And it used gray and threshold. It put processed image vectorization into the denoising auto-encoder. Then the denoising auto-encoder extracted the feature data. It put the reconstituted image matrix into the convolution neural network and used softmax classifier as classification and recognition. The simulation shows that the model using DAE is better than the original model and the time complexity decreases greatly. The recognition rate can reach 80% under SNR = - 6 dB. Besides, compared with the performance of traditional way, the recognition effect is obviously improved. [ABSTRACT FROM AUTHOR]

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 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.)