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检索条件"任意字段=5th IEEE International Conference on Computer and Communication Engineering Technology, CCET 2022"

共 4257 条记录，以下是51-60 订阅

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Arbitrary-Shaped Text Detection with Gaussian Probability Distance Distribution 5

Arbitrary-Shaped Text Detection with Gaussian Probability Di...

引用

5th ieee international conference on computer and communication engineering technology, ccet 2022

作者： Guo, Li Chen, Zhongyue Chen, Xiaoping Soochow University School of Electronics and Information Engineering Suzhou China

ISBN: (数字)9781665467353

ISBN: (纸本)9781665467353

With the development of semantic segmentation, segmentation-based methods have yielded great success in detecting arbitrary-shaped texts. However, many existing text detection methods use binary discrete distributions to predict shrunk text instances, which cannot generate complete and accurate text bounding boxes. In this paper, we propose an arbitrary-shaped scene text detection method based on predicting Gaussian probability distance map of the complete text region, and this map can retain more text boundary information. then, the boundary pixels are clustered into high-confidence text centers by a learnable post-processing and false positives are filtered out by pixel-level score maps. We also propose an adaptive channel enhancement module to improve the pixel-level segmentation accuracy. Experiments on three standard datasets, including CTW1500, Total-Text, and MSRA-TD500, demonstrate that the proposed method achieves great robustness and performance. the method obtains an F-measure of S2.S% on CTW1500 and S3.0% on MSRA-TD500. © 2022 ieee.

关键词： Pixels

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Knowledge Graph Completion Based on Graph Attention Networks and Text Information 5

Knowledge Graph Completion Based on Graph Attention Networks...

引用

5th ieee international conference on computer and communication engineering technology, ccet 2022

作者： Hong, Shengyi Qian, Heng Gao, Yongchao Lyu, Hongli Qilu University of Technology School of Computer Science and Technology Jinan China Shandong Computer Science Center National Supercomputing Center in Jinan Jinan China Lakehead University Department of Electrical and Computer Engineering Thunder Bay Canada

ISBN: (数字)9781665467353

ISBN: (纸本)9781665467353

In knowledge graphs (KGs), there exist some unsolved problems such as incomplete data, hidden information with incomplete mining and so on. In the most completion models, the information of the triples in the KG is generally utilized, but the neighborhood information and rich entity description information are not included in the triples. In this paper, the knowledge graph completion (KGC) method is improved based on graph attention networks (GATs) with text information by using the neighborhood information of aggregated triples and entity description information. And the embedding capability of semantic information is enhanced in KGs. First, the feature vector of entity description information is extracted by the Bi-LSTM model and concatenated with the entity embedding in the triples. then the joint vectors are trained by GATs to aggregate the neighborhood information. Next, the KGC task is realized by a decoder. Finally, the effectiveness of the proposed method is verified by the link prediction experiments in the public datasets FB15K-237 and WNISRR and comparison is investigated with several other existing methods. the test results show that most of the indicators in the two datasets are improved. Furthermore, it is proved that the model combined with multi-source information has better representation ability for entities, which can further improve the accuracy and comprehensive performance of KGC tasks. © 2022 ieee.

关键词： Knowledge graph

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Construction of Narrative Text Component Recognition Corpus 5

Construction of Narrative Text Component Recognition Corpus

引用

5th ieee international conference on computer and communication engineering technology, ccet 2022

作者： Zhang, Feng Han, Yingqi Wang, Jiong Liu, Jie Information Science Research Institute China Electronics Technology Corporation Beijing China Wuhan University School of Cyber Science and Engineer Wuhan China Information Engineering College Capital Normal University Beijing China North China University of Technology School of Information Science Beijing China

ISBN: (数字)9781665467353

ISBN: (纸本)9781665467353

Textual structure analysis is an important part of Automatic Essay Score (AES), and is also one of the important research directions in Natural Language Processing. At present, there are still deficiencies in the research of narrative textual structure in China, one of the main reasons is the lack of data available for research. To solve this problem, this paper proposes and constructs a corpus for the textual component identification of narrative essay. this paper divides the text structure of narrative essay, and forms a corpus for the narrative essay component identification. the paper finally annotated 3024 articles with 21128 sentences in total. this paper combines manual annotation and the automatic annotation of the model to build corpus, and conducts statistical analysis on the distribution of the corpus content and the consistency of the corpus annotation. the experiment shows text component recognition performance achieves 80.75% F 1 score. the work provided basic data for the research of AES. © 2022 ieee.

关键词： Natural language processing systems

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A Modeling Method of Cyberspace Security Structure Based on Layer-Level Division 5

A Modeling Method of Cyberspace Security Structure Based on ...

引用

5th ieee international conference on computer and communication engineering technology, ccet 2022

作者： Zhu, Yuwen Yu, Lei State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Information Security Institute of Information Engineering Beijing100000 China

ISBN: (数字)9781665467353

ISBN: (纸本)9781665467353

As the cyberspace structure becomes more and more complex, the problems of dynamic network space topology, complex composition structure, large spanning space scale, and a high degree of self-organization are becoming more and more important. In this paper, we model the cyberspace elements and their dependencies by combining the knowledge of graph theory. Layer adopts a network space modeling method combining virtual and real, and level adopts a spatial iteration method. Combining the layer-level models into one, this paper proposes a fast modeling method for cyberspace security structure model with network connection relationship, hierarchical relationship, and vulnerability information as input. this method can not only clearly express the individual vulnerability constraints in the network space, but also clearly express the hierarchical relationship of the complex dependencies of network individuals. For independent network elements or independent network element groups, it has flexibility and can greatly reduce the computational complexity in later applications. © 2022 ieee.

关键词： Graph theory

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Research on Intelligent communication Scheduling System Based on Network Slices 5

Research on Intelligent Communication Scheduling System Base...

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5th ieee international conference on computer and communication engineering technology, ccet 2022

作者： Dou, Chenchen Wang, Xue Dai, Rui State Grid Gansu Electric Power Company Digital Business Department Lanzhou China

ISBN: (数字)9781665467353

ISBN: (纸本)9781665467353

the rapid development of smart grid has put forward higher requirements on communication technology. With the rapid development of power wireless communication network, the demand of power business becomes more and more complicated and diverse. However, the current single-standard network has poor coverage, poor power service carrying capacity, and weak transmission stability and security. To address the above problems, this paper explores the network slicing technology for intelligent power distribution services. Based on the analysis of existing network access selection methods, a network selection strategy for quality of service (QoS) assurance of power services is proposed for the characteristics of power distribution services. Combining its own QoS requirements and the characteristics of the network to be accessed, the power service can realize the access selection of the optimal adaptation network. In order to better meet various vertical industry applications, network slicing is required in both the core network and the radio access network, i.e., end-to-end network slicing is realized, and network slicing can be customized to tailor network functions and reasonably allocate network resources according to service scenario requirements. After receiving a request for network slicing, operators need to map the virtual network functions required for network slicing to the underlying physical network, a process that involves the optimal allocation of physical resources. © 2022 ieee.

关键词： Smart power grids

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Non-intrusive Load Monitoring for Consistent Shape Loads Based on Convolutional Neural Network 5

Non-intrusive Load Monitoring for Consistent Shape Loads Bas...

引用

5th ieee international conference on computer and communication engineering technology, ccet 2022

作者： Li, Xiang Guo, Yifan Yan, Meng Wu, Xin North China Electric Power University School of Electrical and Electronic Engineering Beijing China

ISBN: (数字)9781665467353

ISBN: (纸本)9781665467353

As the key method for demand-side management in power grid, non-intrusive load monitoring (NILM) keep up to the power consumption of various users in real time and provides data to support the formulation of relevant power policies. In order to achieve accurate resident load monitoring, this paper proposes a NILM architecture focus on consistent shape loads (CSL). Loads in CSL meet the following conditions: 1) current waveform images of different load individuals in the same type are highly similar. 2) different types of load waveform images are different in shapes which are distinguishable. Besides, a non-intrusive load monitoring method based on convolutional neural network (CNN) to identify CSL load is proposed and carried out on actual users. Power consumption data of CSL with different operating environments is taken as training samples. the outcome of our experiment shows the effectiveness of the method in accurately distinguishing CSL and high-precision identification which reaches 97.06%. the method ensures the real-time performance and accuracy of load monitoring. © 2022 ieee.

关键词： Electric power utilization

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A-pillar Blind Spot Display Algorithm Based on Line of Sight 5

A-pillar Blind Spot Display Algorithm Based on Line of Sight

引用

5th ieee international conference on computer and communication engineering technology, ccet 2022

作者： Sui, Shiyu Li, Tianjun Chen, Shengyong Tianjin University of Technology School of Computer Science and Engineering Tianjin China

ISBN: (数字)9781665467353

ISBN: (纸本)9781665467353

the closed load-bearing structure of a car is designed to decrease the injuries to drivers in traffic accidents, however, such a design also results in many blind spots in driving which are the main cause of a number of car crashes. A-pillar blind spots elimination can make for safer driving. therefore, this paper proposes an algorithm that utilizes a screen mounted on the A-pillar to display its blind spots in real time and uses a wide-angle camera to capture a panoramic view outside the car. We use a binocular vision system detecting facial features to calculate the driver's head pose and the 3D coordinates of the pupil center. After obtaining the pupil centers and coordinates of the four corners of the A-pillar display screen, the blind spot can be calculated through the angular relationship between the driver's line of sight and the outside wide-angle camera's angle of view. Considering the possibility that the pupil center detection may fail when the driver is not looking at the A-pillar, we propose a compensation method to indirectly calculate the pupil center by using the camera above the driver's head. We carried out a simulation experiment using a display screen replacing the A-pillar, which showed that our algorithm could display the blind spots occluded by the display screen well. © 2022 ieee.

关键词： Cameras

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Granularity Classification and Feature Fusion Methods in Traffic Sign Detection 5

Granularity Classification and Feature Fusion Methods in Tra...

引用

5th ieee international conference on computer and communication engineering technology, ccet 2022

作者： Huang, Wei Shi, Xiaohong Xu, Qi Li, Qingshu Yang, Peng Shanghai Maritime University College of Information Engineering Shanghai China

ISBN: (数字)9781665467353

ISBN: (纸本)9781665467353

Nowadays, deep learning based on detection algorithms have replaced the traditional manual feature extraction target algorithms and have achieved amazing results in many places with their powerful automatic feature extraction capabilities. However, the results are not ideal for the detection of small targets with low resolution and a lot of noise, such as traffic signs. To address the current problems of slow detection speed and low detection accuracy in small target detection, this paper adopts a feedback-driven mechanism to solve the image level imbalance of the input feature space under the original data distribution. At the same time, this paper designs a novel and flexible two-stage traffic sign recognition framework. the complex task of traffic sign detection and recognition is decomposed into two stages: 1) designing a superclass classifier to more accurately separate traffic signs in complex natural scene images;2) the idea of similarity metric learning is used to design fine-grained classifiers to recognize traffic signs. Finally, to verify the effectiveness of the model, the model was first compared with Faster R-CNN and found to possess higher detection accuracy;then the model was experimented with the R-FCN model on TTIOOK dataset and CCTSDB dataset respectively, and the comparison of the experimental results revealed that the model improved over the R-FCN model in most of the metrics in the traffic sign detection task. © 2022 ieee.

关键词： Traffic signs

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Tibetan Syllable Prediction with Pre-trained Cross-lingual Language Model 5

Tibetan Syllable Prediction with Pre-trained Cross-lingual L...

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5th ieee international conference on computer and communication engineering technology, ccet 2022

作者： Yi, Zibo Wu, Qingbo Yu, Jie Tang, Yongtao Liu, Xiaodong Peng, Long Ma, Jun Pla Academy of Military Science Information Research Center of Military Science Beijing100142 China National University of Defense Technology College of Computer Hunan Changsha410073 China

ISBN: (数字)9781665467353

ISBN: (纸本)9781665467353

In recent years, with the development of Tibetan language information technologies, the Internet Tibetan data is increasing year by year. Due to the need for the Tibetan input method and Tibetan error correction, Tibetan language prediction has become an urgent problem to be solved. At present, the challenges of Tibetan prediction are that the Tibetan syllable composition is complex, the vocabulary of Tibetan words which is composed of syllables is extremely large, and the Tibetan word separation technology is not mature. To solve the above problems, this paper proposes a Tibetan syllable prediction method based on a pre-trained cross-lingual language model using Tibetan syllables instead of Tibetan words as the token for prediction. the method uses the cross-lingual language model XLM-R and fine-tunes it using Tibetan news texts to make it more suitable for predicting Tibetan in the news domain. We conduct experiments on Tibetan syllable prediction for texts crawled on the Tibetan news website. the experiments show that the precision of our model for Tibetan text prediction is higher than that of the current n-gram methods. © 2022 ieee.

关键词： Forecasting

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A Small Target Detection Method Based on Feature Enhancement and Positioning Optimization 5

A Small Target Detection Method Based on Feature Enhancement...

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5th ieee international conference on computer and communication engineering technology, ccet 2022

作者： Li, Qingshu Shi, Xiaohong Xu, Qi Huang, Wei Yang, Peng Shanghai Maritime University College of Information Engineering Shanghai China

ISBN: (数字)9781665467353

ISBN: (纸本)9781665467353

Feature Pyramid Network (FPN) is a basic but important component in target detection system. Together with target detection algorithms, such as SSD, Faster R-CNN and YOLO series, they have achieved good detection results for large targets with high resolutions, but the performance is less effective when it comes to detect small targets that contain relatively little semantic information. And small target detection is quite common in daily life, such as face recognition at long distances, traffic sign detection in automatic driving, etc. that means it is significant to break the bottleneck of target detection and get better accuracy performance. In this article, based on the FPN, we propose an improved network structure (IMFPN) from two aspects to get a better accuracy result in small target detection task. In the first aspect, we improve the feature map pyramid structure for feature enhancement, reduce the problem of information loss during feature map fusion and get the semantic information of multiscale feature maps. In the second aspect, we concentrate on the problem of information loss in the pooling process of feature maps, we propose an improved version of the PRRoI pooling method that combines RoI Pooling and RoI Align Pooling. And we also optimize the positioning of the frame through a new IoU calculation standard. Based on these above ideas and methods, we propose a small target detection method based on feature enhancement and positioning optimization. © 2022 ieee.

关键词： Face recognition

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