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Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. 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.)

Rendering realistic virtual scenes from images has been a long-standing research goal in the fields of computer graphics and computer vision. NeRF (neural radiance fields) is an emerging method based on deep neural networks, which achieves realistic rendering by learning the radiance field of each point in the scene. By using neural radiance fields, not only realistic images but also realistic three-dimensional scenes can be generated, making it have a wide range of application prospects such as virtual reality, augmented reality and computer games. However, its basic model has shortcomings such as low training efficiency, poor generalization ability, insufficient interpretability, susceptible to lighting and material changes, inability to handle dynamic scenes, and other deficiencies that may result in suboptimal rendering results in certain situations. With the continuous popularity of this field, a large amount of research has been carried out, yielding impressive results in terms of efficiency and accuracy. In order to track the latest research in this field, this paper provides a review and summary of the key algorithms in recent years. This paper first outlines the background and principles of neural radiance fields, and briefly introduces the evaluation metrics and public datasets in this field. Then, a classification discussion is conducted on the key improvements to the model, mainly including: the optimization of basic NeRF model parameters, the improvement in rendering speed and inference ability, the enhancement of spatial representation and lighting ability, the improvement in camera pose and sparse view synthesis methods for static scene, and the development in dynamic scene modeling field. Subsequently, the speed and performance of various models are classified, compared and analyzed, and the main model evaluation indicators and open datasets in this field are briefly introduced. Finally, the future development trend of neural radiance field is prospected. [ABSTRACT FROM AUTHOR]

To overcome AMR s limited generalization and low classification accuracy in non-cooperative communication contexts with low signal-to-noise ratio, this paper proposed a model named CGDNN, which integrated CNN, GRU and deep neural networks. To mitigate noise impact on modulation detection, this paper initially denoised I/Q sampled signal using wavelet thresholding. Subsequently, this paper utilized CNN and GRU for extracting spatial and temporal features from signals before proceeding to classification through fully connected layers. Besides enhancing AMR performance, the CGDNN model significantly reduced computational complexity compared to competitors. Experiment results demonstrate an average recognition accuracy of 64.32% on the RML2016. 10b dataset, with an enhancement in signal classification accuracy from 12 dB to 0 dB. Moreover, the model substantially decreased confusion between 16QAM and 64QAM, achieving a peak recognition accuracy of 93.9% at 18 dB. CGDNN model effectively improved AMR detection accuracy under low signal-to-noise ratio conditions and enhanced model training efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. 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.)

As one of the basic activities of human beings, the analysis of walking is of great significance in clinical research. In this paper, we analyzed the published plantar pressure dataset Gait in Parkinson's Disease, designed a method to divide the gait period, and extracted the characteristic parameters of gait. A hybrid neural network (GRU-DNN) was applied to classify Parkinson's disease by combining the gated circulation unit (GRU) and the deep neural network (DNN). Data analysis provided more objective basis for clinical diagnosis, thereby assisting doctors in diagnosing the disease. To verify the effectiveness of the method, the network was used to classify and predict gait information with disease labels in the dataset. In Parkinson's disease diagnosis experiment, the recognition accuracy of this network was 98.7%. In the Parkinson's severity diagnosis experiment, the network achieved 100% recognition accuracy for severity level 2, and 98% for the rest of the severity level. [ABSTRACT FROM AUTHOR]

Deep learning models are highly susceptible to adversarial samples, and even minuscule image perturbations that are not perceptible to the naked eye can disable well-trained deep learning models. Recent research indicates that these perturbations can exist in the physical world. This paper provides insight into physical adversarial attacks on deep learning object detection models, clarifying the concept of physical adversarial attack and outlining the general process of such attacks on object detection. According to the different attack tasks, a series of physical adversarial attack methods against object detection networks in recent years are reviewed from vehicle detection and pedestrian detection. Other attacks against target detection models, other attack tasks and other attack methods are briefly introduced. The current challenges of physical adversarial attack are discussed, the limitations of adversarial training are leaded out, and future development directions and application prospect are suggested. [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 Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. 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 World Nuclear Geoscience is the property of World Nuclear Geoscience 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 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 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 Refrigeration Technology is the property of Shanghai Society of Refrigeration 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 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.)

Data-driven 3D digital clothing is a frontier research field of digital clothing with important theoretical significance and engineering value. In this paper, graph neural network was introduced into the representation of 3D clothing point cloud prototype. On the basis of human body feature recognition, the test sample of the graph neural network was obtained by adding ease to the human body feature point cloud. Then, the selected T-shirt clothing samples were divided into several surface models according to different parts, and the training samples of the graph neural network were sub-point cloud data obtained by using down-sampling operations on these surface models. The prototype representation model of clothing point cloud was generated by the feature training of all training samples through the graph neutral network. Finally, the distance value between the upsampling result and the reference point cloud was used to analyze the representation effect of the graph neural network model and the clothing prototype of the same structure. Compared with the point cloud of garment prototype constructed by other models, this method has better representation effect of three-dimension point cloud prototype. [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 Inorganic Analytical Chemistry / Zhongguo Wuji Fenxi Huaxue is the property of Beijing Research Institute of Mining & Metallurgy Technology Group 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 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.)

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 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.)

This paper focuses on a very challenging problem: few-shot class-incremental learning for deep neural networks, where the deep neural network model can gradually learn new knowledge from a small number of samples without forgetting the learned knowledge. To balance the model' s memory of old knowledge and learning of new knowledge, it proposes a few-shot class-incremental learning method based on feature distribution learning. First, it learns the model on the base classes to obtain a well-performing feature extractor and take the feature distribution information as the learned knowledge. Then, it maps the learned knowledge together with the features of the novel classes into a low-dimensional subspace to review old knowledge and learn new knowledge uniformly. Finally, within the subspace, it also generates classification weight initializations for each novel class to improve the adaptability of the model to novel classes. Extensive experiments show that the method can effectively alleviate the model's forgetting of learned knowledge and improve the model's adaptability to new knowledge. [ABSTRACT FROM AUTHOR]

As an important research topic, source code vulnerability detection has many problems in its traditional methods, such as high manual participation, weak detection ability of unknown vulnerabilities. Aiming at the above problems, based on the syntactic and semantic information of open source code and improved differential evolution algorithm, this paper proposed a cold start optimization method for source code vulnerability detection model. This paper used source code slicing technology, heuristic optimization algorithms and neural network models, which solved the problem that the hyperparameters couldn’t be correctly selected at the beginning of the vulnerability detection model. For the case of sample information redundancy and mixture of positive and negative sample distinctive features in the experiment, it proposed an idea of cross-exclusion of positive and negative sample distinctive features to reduce the false negative rate and false positive rate of the model. Experiments show that this method can effectively accelerate the convergence of the model, and making the model stable within 10 epochs. While improving the accuracy of the source code vulnerability detection model, its convergence speed is 2～3 times higher than other models. In the subsequent improvement experiments, the source code vulnerability detection model has improved the accuracy of each type of vulnerability by 1～3 percentage points, which fully proves the effectiveness of the improvement measures. The optimization strategies and improvement measures of this method are also applicable to other neural network classification models, and it can provide ideas for exploring new methods and models in the field of vulnerability detection. [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.)

Traditional neural networks have the disadvantages of over- reliance on hardware resources and high requirements for application equipment performance. Therefore, they cannot be deployed on edge devices and mobile terminals with limited computing power. The application development of artificial intelligence technology is limited to a certain extent. However, with the advent of the technological age, artificial intelligence, which is affected by user requirements, urgently needs to be able to successfully perform operations such as computer vision applications on portable devices. For this reason, this paper takes the convolution of popular lightweight neural networks in recent years as the research object. Firstly, by introducing the concept of lightweight neural network, the development status of lightweight neural networks and the problems faced by convolution in the network are introduced. Secondly, the convolution is divided into three aspects: lightweight of convolution structure, lightweight of convolution module and lightweight of convolution operation, specifically through the study of the convolution design in various lightweight neural network models, the lightweight effects of different convolutions are demonstrated, and the advantages and disadvantages of the optimization methods are explained. Finally, the main ideas and usage methods of all lightweight model convolutional design in this paper are summarized and analyzed, and their possible future development is prospected. [ABSTRACT FROM AUTHOR]

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 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 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.)

Copyright of Journal of Functional Polymers is the property of Journal of Functional Polymers 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 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 Policy & Complex Systems is the property of Policy Studies Organization 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.)

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.)

Graph neural networks (GNN), which extend deep neural networks to non-Euclidean data, have been proven to be powerful for numerous graph related tasks such as node classification, link prediction, and graph classification. On the task of graph classification, recent studies aim to learn graph-level representation through a hierarchical pooling procedure using the local and global structure information of the graph. After comparing and analyzing the current graph classification model, this paper proposes the structure and feature fusion pooling model (SAFPool) considering shortcomings of the current method and combining the advantages of different methods. SAFPool utilizes Graphtwo assignment matrix generation modules during the pooling process, which are structure-based cluster learning and feature- based cluster learning modules. Structure- based cluster learning module clusters nodes with similar structures based on graph structure information, and feature- based cluster learning clusters nodes with similar features based on graph node features. Then the two clustering assignment matrices are weighted and aggregated to obtain the clustering assignment matric which implements the clustering strategy to utilize graph structure and node feature information at the same time. Finally, comparative experiments and visualization on multiple graph classification datasets demonstrate the effectiveness of using graph node information and structure information to implement a clustering strategy in graph classification. [ABSTRACT FROM AUTHOR]

Developers in large software development environments rely on bug reports to complete fixes. Since reporters may use different representations to describe the same bug due to different expression habits, automated detection of duplicate bug reports can reduce development redundancy as well as maintenance costs. Recent detection of repetitive bug reports tends to use deep neural networks and considers both structured and unstructured information to generate hybrid representation features. In order to obtain the features of unstructured information of bug reports more effectively, this paper proposes a D＿BBAS(Doc2 Vec and BERT BiLSTM-Attention Similarity) method, which trains a feature extraction model based on a large-scale bug report library to generate a bug summary text representation set and a bug description text representation set that can reflect deep semantic information. Then, these two distributed representation sets compute the similarity of bug report pairs, resulting in two new similarity features. These two new features will participate in the detection of duplicate bug reports when combined with the traditional features generated based on structured information. In this paper, the effectiveness of the proposed approach is verified on the bug report repositories of well-known open-source projects Eclipse, NetBeans and Open Office, which contain more than 500, 000 bug reports. The experimental results show that compared with the representative methods, the method in this paper improves the F1 value by 1.7% on average, which proves the effectiveness of the method in this paper. [ABSTRACT FROM AUTHOR]

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 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.)

Aiming at the traditional ciphertext retrieval scheme in cloud environment, which generates file vectors and retrieval vectors based on statistical model, and does not consider the deep-seated semantic information of files and requests, this paper proposes a deep-seated semantic ciphertext retrieval model based on hybrid Cloud Architecture. The vector generation model is constructed through the private cloud federated learning neural network model, and the ciphertext data is stored through the public cloud. In addition, this paper proposes a secret inverted index table to store file vectors, so as to improve the efficiency of retrieval without ensuring that the retrieval information is not leaked in the retrieval process of public cloud. The analysis and experiments on real data sets show that this scheme is better than the current ciphertext retrieval schemes of the same type in terms of security and search efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. 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 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.)

Water inrush is the most frequent disaster in the process of railway tunnel construction. In order to effectively prevent water inrush accidents, reduce risk in tunnel construction and ensure the safety of construction personnel, this paper, on the basis of existing researches, selects 10 core indexes as the judgment basis for the occurrence of water inrush accident, collects 50 groups of typical tunnel water inrush cases data as the research samples of water inrush risk assessment, uses particle swarm optimization algorithm (PSO) to optimize radial basis function neural network (RBF), trains and tests the sample data, and establishes the PSO-RBF neural network railway tunnel water inrush risk assessment model. Finally, the model is applied to Jingjiashan Tunnel to verify its practicability. The case study shows that the PSO-RBF model can accurately determine the risk of water inrush in Jingjiashan Tunnel, and the PSO-RBF neural network model has higher accuracy and faster iterative speed in comparison with the RBF neural network improved by the gradient descent method. [ABSTRACT FROM AUTHOR]

In recent years, with the development of neural network technology and natural language processing technology, the research on dialogue generation based on deep neural networks has made breakthrough progress, making human-machine dialogue systems widely used in life to provide convenience, such as e-commerce customer service, voice assistant, etc. However, existing models tend to produce general answers and generally lack emotional factors. To solve this problem, this paper proposes an emotional dialogue content generation model based on a generative confrontation network-EC-GAN (emotional conversation generative adversarial network), which generates more meaningful and customizable emotional responses by combining multi- index rewards and emotional editing constraints. For the generator, the Seq2Seq model is used to generate responses, accept the rewards of the discriminator, and guide the generated sentence to improve diversity and emotional richness. For the discriminator, dual discriminator and the content discriminator is used to determine whether the reply belongs to general response, the emotion discriminator discriminates the consistency between the emotion of the generated sentence and the specified emotion category, and the discriminating result is fed back to the generator to guide the generation of the reply. The emotional changes between input and response are paid attention to and the directionality of interactive emotional resonance is verified. Experiments in NLPCC 2017 Shared Task 4-emotional conversation generation show that the model can not only improve the fluency and diversity of responses, but also significantly increase the emotional richness. [ABSTRACT FROM AUTHOR]

This paper proposed a new network framework for RGB-D salient object detection (SR-Net). In order to effectively integrate the complementarity of multi-model features, this paper took the depth feature extraction as an independent branch, use the Convolutional Block Attention Module (CBAM) to enhance the depth feature, and integrate the complementary information of the enhanced depth feature and RGB feature. Then, in order to remove feature redundancy and reduce the interference of background noise on the prediction results, it proposed a sequential refining network in the up-sampling network, that is, first, the primary global features are obtained by integrating the complementarity of multi-level and multiscale features, and used the Primary Global Feature Weight Matrix Acquisition Module (PFW) which based on the channel weight to obtains the weight matrix of the primary global feature, and then uses the obtained weight matrix to refine the features of each level to suppress the interference which caused by background noise. Finally, in order to better optimize the whole network, it proposed a new loss function. The experimental results on four public datasets show that the model is superior to nine advanced methods in different model evaluation indexes, and achieves more advanced performance. [ABSTRACT FROM AUTHOR]