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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 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.)

TVM (tensor virtual machine) is a deep learning compiler, that translates the deep learning operators described by tensor expression to TVM IR (TVM intermediate representation) programs. After a series of operatorlevel optimizations on TVM IR, TVM generates the target code across diverse hardware back-ends. Tensor expression, a domain- specific language for tensor computation, performs loop transformation to operators. The result of loop transformation is a number of complicated expressions emerging in nested loop statements, which contain loop invariant code. However, in the context of deep learning applications, the traditional loop invariant code motion algorithm has severe limitations. Firstly, it's difficult to determine the extra-benefit of moving certain invariant code out of loops. Secondly, it's difficult to detect loop invariant code which has different orders of operands. Thirdly, it cannot process nested condition expressions well. Furthermore, there are conflicts with target hardware compiler optimizations. The application of loop invariant code motion technique is constrained by the aforementioned problems. In this paper, a new loop invariant code motion algorithm is proposed, which takes deep learning application characteristics into consideration in a heuristics way. The algorithm normalizes the program by manipulating the expression operands and simplifying the nested condition expression. This paper introduces a new cost model, which evaluates the cost of moving certain loop invariant code while the characteristics of TVM IR and target hardware back-ends are fully considered. The algorithm is implemented as a registered TVM pass on opensource compiler TVM version 0.7. To testify the effectiveness and correctness of this algorithm, experiments are conducted on TVM TOPI benchmark with 27 operators and 511 test cases under different input. Experimental results show that this algorithm improves 47.6% of operators'performance, and achieves speedups up to 40.0%. [ABSTRACT FROM AUTHOR]

In recent years, various human action recognition algorithms have achieved excellent recognition performance based on a large number of labeled samples. However, in practical applications, acquiring training samples and their corresponding labels is time-consuming and laborious, which limits the actual implementation of the algorithm. This paper summarized the deep learning algorithms for action recognition under weak supervision and few-shot learning. Firstly, in the case of weak supervision, it classified and summarized the semi-supervised action recognition methods and unsupervised domain adaptation video action recognition methods separately. Then, it reviewed video action recognition algorithms based on few-shot learning in detail. Further, it summarized the relevant human behavior datasets and analyzed and compared the performance of various relevant video action recognition algorithms on these datasets. Finally, the paper summarized the full text and discusses the future development trend of human action recognition. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology 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.)

In dynamic gesture recognition methods based on deep learning, aiming at the problems of large network scale, simple dimension of extracted spatiotemporal features, and insufficient extraction ability of effective features, this paper proposed a deep network framework. Firstly, this paper applied a novel spatiotemporal convolution module based on multiscale information fusion in the time domain to improve the 3D residual network structure, greatly reduced the size of the network and obtained rich spatiotemporal receptive field characteristics. Then it introduced a spatiotemporal feature channel attention mechanism with global information synchronization, and used a few parameters to construct the global dependency between feature maps, and the module could obtain the key features of dynamic gestures more efficiently. The experimental results on the self-built gesture dataset SHC and the public gesture dataset SKIG show that the proposed gesture recognition method has fewer parameters and more powerful multiscale spatiotemporal feature extraction ability, and gains a higher recognition rate than the current mainstream algorithms. [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 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.)

To accomplish the task of aerial manipulator’s autonomous grasping, it is very important to recognize and locate the object. At present, most of the recognition algorithms of aerial manipulators use traditional feature extraction methods. In order to improve the accuracy and efficiency of object recognition and positioning, this paper designs a visual recognition and positioning algorithm based on YOLOv5 deep learning object detection algorithm and RGB‑D sensor. The algorithm can detect the object in real‑time and estimate its pose, which serves for the grasping task of aerial manipulator. At the same time, aiming at the problems that the deep learning algorithm has a huge amount of calculation and cannot achieve high‑performance real‑time detection in the embedded system, model quantization is introduced to optimize the algorithm, which can greatly improve the processing speed of the algorithm. This paper introduces the overall framework and implementation process of the algorithm and verifies the effectiveness of some algorithms of object detection and pose estimation by using the COCO dataset and motion capture system. [ABSTRACT FROM AUTHOR]

Objectives: In recent years, the brain‑like navigation algorithm is a new research hotspot, which is expected to achieve autonomous navigation by imitating the ability of biological navigation. The core is‑ sue is how to improve generalization ability. Methods: This paper introduces the research background and theoretical basis of the brain‑like navigation algorithm. After investigation, we propose a computational framework of brain ‑ like navigation algorithm. The outstanding works in this field are discussed and analyzed under this framework, and we carried out experimental verification of some basic methods. Results： The main contributions of this paper are: (1) Comprehensively introduces and summarizes the theoretical basis and outstanding works in this field. (2) Proposes the computational framework of the brain‑like navigation algorithm, which scientifically defines the functions of different modules of the algorithm. (3) Through theoretical analysis and experimental verification, we summarized valuable conclusions and expectations. Conclusions: In terms of model design, mature methods of deep learning can also be applied to this problem, but need more modifications to further improve navigation capabilities; in terms of model training, combining the advantages of multiple learning algorithms is hopeful to further improve the generalization ability. [ABSTRACT FROM AUTHOR]

Aiming at the problem of fine-grained visual categorization, this paper proposed an effective algorithm to achieve end-to-end fine-grained visual categorization. The ECA module in ECA-Net was a channel attention mechanism with significant performance advantages. It model-fused with the classic network ResNet-50 to form the ResEca. Then, it used the object-level image positioning module and the part-level image generation module to generate object-level and part-level images. Those images combined with original images could be as the input of the new constructed network Tb-ResEca-Net. This paper trained the 95. 1% and 95. 3% respectively on the test set of the corresponding dataset. The experimental results show that this method has higher classification accuracy and stronger robustness compares with other traditional fine-grained classification methods, which is an effective fine-grained image classification method. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural 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.)

People have widely used air quality prediction models composed of traditional machine learning methods. However, such models still have shortage on data validity, especially the validity of spatial-temporal related data selection. In response to the input data validity of deep learning networks, this paper proposed a prediction model based on spatial-temporal similarity LSTM to select more effective data at the time and space level. STS-LSTM contained three modules, pre-order, middle-order and post-order module. The pre-order module was the time-space similar selection input module, which proposed the GCWDTW algorithm. This paper used it to select data with higher temporal and spatial similarity. The in-order module used LSTM as a deep. learning network for training. The post-order module selected different output groups for integration according to the characteristies of the target station. The STS-LSTM overall model improves the air quality prediction error by about 8% compared with the existing algorithm, and the data selected by the validity improves the accuracy of the model by up to 21%. The experimental results show that this model has achieved significant results in the effective data selection, and the data input and output method as a part of the applied deep learning network can effectively improve the final effect of the deep learning network. [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.)

【Objective】 Aiming at the problems of easy falling off and easy to cause pig infection in traditional pig breeding ear tag recognition, the paper used the improved YOLOv5s model to carry out non-invasive recognition of pig face.【Method】 Firstly, the distance of K-Means was changed to 1-IOU, which improved the adaptability of the target anchor frame of the model; Secondly, the CA coordinate attention mechanism was introduced to improve the model feature extraction; Finally, BiFPN feature fusion was introduced to effectively use the features to improve the detection ability of the model. The pig face dataset used in the experiment was divided into 5 categories, with 12 756 samples after data enhancement, and the training set and test set were divided into a ratio of 9∶1.【Result】 The improved algorithm reached 0.926, 0.897 and 0.955 in accuracy, recall, and average accuracy (IOU=0.5), which were 13.2%, 3.0% and 2.2%, while the improved algorithm has better generalization ability and high recognition accuracy in single, multiple, small target, dense, and occluded scenes. 【Conclusion】 Using deep learning algorithm can get the facial information of pigs and identify them accurately, reduce the situation of missed inspection and wrong inspection, and provide better technical support for intelligent management of pigs. [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.)

To solve the problem that the existing raw cotton impurity analysis requires too much manual participation and the accuracy is not easy to quantify, a Mobile-Net-V2 model automatic recognition method for cotton surface impurities based on machine learning algorithms was proposed. A deep learning algorithm based on Mobile-Net-V2's model was used in this system. First, the image was pre-processed by image binarization and guided filtering, and then the deep learning Mobile-Net-V2 model was used for convolution operation. By deploying the experimental algorithm in the embedded computing device, the accurate detection and classification of impurities on the surface of cotton were successfully achieved. Experimental results show that the accuracy of this method for fine classification of multiple impurities is 90.7% in the training set and 89.5% in the test set. The 10 fold cross-validated method used in this paper is to ensure the efficiency of impurity detection of raw cotton while simplifying the equipment as much as possible. The effectiveness of the method also provides a reference for the intelligent detection and classification of cotton impurities. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Henan University of Science & Technology, Natural Science is the property of Editorial Office of Journal of Henan 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 Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics 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 problem that the existing super-resolution algorithm based on deep learning didn't make full use of the feature information of each level, resulting in low reconstruction accuracy and large parameter quantity, this paper proposed a double dense connection structure named densely channel attention skip connection network. In the inner structure of the network, it improved the original dense cascade block to generate a channel separable dense cascade block. The outer structure adopted a densely residual connection and attention mechanism to fuse the features extracted by the dense block to achieve the goal that less convolution layer and higher precision effect. This paper tested the network models on several benchmark datasets . The results show the proposed model has higher accuracy and fewer parameters than the other models. [ABSTRACT FROM AUTHOR]

Based on video action recognition, this paper firstly reviewed the traditional action recognition algorithms based on RGB, including traditional algorithms and deep learning algorithms. Because of the influence of background lighting, RGB-videobased action recognition is low accuracy, but it has rich color appearance information. Then this paper analyzed and summarized action recognition algorithms based on RGB-D, which was divided into depth sequence, bone and multi-feature fusion. RGB-D video has multiple modes and can provide more information for action recognition, which performs better than RGB video but also brings new challenges. Finally, this paper prospected the common data sets and the possible future development direction. [ABSTRACT FROM AUTHOR]

Loop closure detection is important for visual simultaneous localization and mapping (VSLAM) systems to reduce cumulative errors and perform re-localization. In order to shorten the loop closure detection online runtime while meeting the accuracy recall rate requirement, the paper proposed a fast loop closure detection algorithm using broad autoencoder (FLCD-BA). The algorithm improved broad learning architecture to learn feature representations autonomously from the input data in an unsupervised manner, and then accomplished loop closure detection tasks. Differing from the traditional deep learning methods, the network used a pseudo-inverse ridge regression algorithm to solve the weight matrix and achieved fast reconfiguration of the network by incremental learning without retraining the entire network. The proposed algorithm experimented on three public datasets without using GPU devices, and the training time of the network greatly reduced compared to bag-of-words models as well as deep learning methods. The experimental results show that the algorithm has high accuracy and recall in detecting loop closure, and the average running time per frame in the test is only 21 ms, which provides a new algorithm for loop closure detection in visual SLAM systems. [ABSTRACT FROM AUTHOR]

In order to meet the communication needs of the new era, the new generation communication network should have the characteristics of faster speed, larger capacity and wider coverage. By mixing different modulation formats in communica-tion networks, the requirements of new generation communication networks can be satisfied. Facing the situation that multiple modulation formats are mixed and variable in future communication networks, the correct identification of signal modulation formats can provide the basis for the subsequent use of digital technique to improve the performance. Machine learning has obvious advantages over traditional methods n feature extraction and classification. The application of machine earning techniques to realize modulation format identification has become a hot topic nowadays. This paper introduces several machine earning methods applied to feature extraction-based classification schemes, and try to summarize the find by analyzing and comparing their applications. [ABSTRACT FROM AUTHOR]

When the deep reinforcement learning algorithm is trained in a complex dynamic environment, it is difficult for the agent to obtain useful information due to the partial observability of the environment, which leads to typical problems such as failure to learn good strategies and slow algorithm convergence speed. This paper proposed an improved DDPG algorithm based on LSTM and asymmetric actor-critic network. This method introduced the LSTM structure into actor-critic network to learn the hidden states in partially observable Markov through memory reasoning. At the same time, when the actor network only used RGB images as partially observable inputs, the critic network used the complete state of the simulation environment to train, which formed an asymmetric network and speeded up the training convergence. The simulation experiment of manipulator grasping in ROS shows that the proposed algorithm has higher success rate and faster convergence speed compared with DDPG, PPO and LSTM-DDPG. [ABSTRACT FROM AUTHOR]

Aiming at the problems of complex background, variety of change types, missing detection and rough boundary recognition in high-resolution remote sensing image of the same region, this paper proposed a high-resolution remote sensing image building change detection algorithm based on Siam-UNet++network. The algorithm used UNet++as the backbone extraction network. In the encoder phase, it applied the Siam-diff structure to extract the change features of the two sequential images, and employed the attention mechanism after the up sampling and lateral jump path connection in the decoding stage to highlight the building change features and inhibit the network learning from other types of features. Meanwhile, it used the MSOF strategy to weight and fuse feature information of different semantic levels, which improved the accuracy of building change detection. Finally, it adopted a sliding window method to predict large-scale remote sensing images, reducing the hole pattern generated by the change result map during the splicing process. The experimental results demonstrate that proposed algorithm shows better performance than other models. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the recommendation model cannot mine the diversity of user interest and capture the order information between the sequence of user behavior, and the interaction occurs at the element level rather than between the feature vectors, etc., based on multiple attention mechanism and location information, this paper proposed the x DMAL FM. Firstly, it extracted the feature depth from different subspaces by using the multi-head attention mechanism, and then location information could capture the sequential relationship between user behavior sequences. Finally, it used three public datasets to conduct comparative experiments and the AUC index to evaluate the results. The experimental results show that the proposed algorithm has better recommendation performance than the xDeepFM model and show the effectiveness and feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In order to improve the information processing ability of the model in complex scenes, this paper proposed a dynamic selection mechanism based on attention, which selectively allocated weights according to the current information, effectively fused the feature outputs of deformation convolution and ordinary convolution, and adaptively balanced deformable convolution and standard convolution, so as to improve its representation ability. In addition, the learning ability of the model was further enhanced by means of iteration without additional parameters. It used seven different kinds of noises from Noise-X92 in TIMIT corpus, and carried out experiments in various SNR environments. The results show that the proposed algorithm outperforms other recent deep learning algorithms in terms of intelligibility and speech quality, regardless of SNR and whether noise appears in the training data set. [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.)

Faced with the problems encountered in fatigue detection under natural environment, such as low detection rate of the face, slow detection speed and single feature for judging, etc., this paper proposed a fatigue detection method combining receptive field with clustering algorithm. Firstly, it applied cluster on the face size and returned the cluster numbers which determined the number of detection layer. Then, it set the size of anchor boxes according to the face size. Secondly, the algorithm set the number of convolutional layers according to the principle that the receptive field should match the face size in the predicted feature map. Finally, new algorithm learnt a variety of fatigue characteristics by minimizing the loss function. Experiments show that this method based on clustering framework and local receptive field have improved the detection speed while maintaining the recognition accuracy. It can reach 125 fps by using GPU GeForce GTX TITAN, and satisfy the request of real time. [ABSTRACT FROM AUTHOR]

Most of the existing modeling methods of combining metadata are based on the same user I item attribute weights, so that the key relationships between users and items are not significant, and it is difficult to obtain better recommendation performance. To solve the above problems, this paper proposed a method of deep joint learning recommendation based on metadata and attention mechanism. It used double deep network joint learning, one of the networks implemented matrix nonlinear decomposition based on implicit feedback data to learn user/ project personalization relationship, and another network automatically captured influence the user/ item key attributes to recommend by using attention mechanism, through the user preference relation with weighted of different attributes modeling highlighted the extended model. Experimental results show that the proposed recommendation algorithm has better recommendation performance on two public datasets of MovieLens 100K and MovieLens lM. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics 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 Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural 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.)