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Hierarchical Multi-label Text Classification Method Based On Multi-level Decoupling 基于多级解耦的分层多标签文本分类方法
2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105736
Qingwu Fan, Changsheng Qiu
{"title":"Hierarchical Multi-label Text Classification Method Based On Multi-level Decoupling","authors":"Qingwu Fan, Changsheng Qiu","doi":"10.1109/NNICE58320.2023.10105736","DOIUrl":"https://doi.org/10.1109/NNICE58320.2023.10105736","url":null,"abstract":"In order to facilitate subsequent processing, the government hotline assigns hierarchical labels to the collected complaint and report texts. Classification of hierarchical multi-label text is a challenging task. Most previous studies regard hierarchical multi-label text classification as a flat multi-label classification problem, ignoring the constraints and connections between hierarchical labels. In this paper, we set hierarchical multi-label text classification as a sequence generation task, and propose a sequence-to-sequence-based hierarchical multi-label text classification model. Mainly use the method of multi-level decoupling to make better use of the connection between hierarchical tags, transform the constraints between tags into usable information, and help better classification. Compared with other models, the model proposed in this paper has a significant effect in the text classification of 12345 wading-related complaints and reports in Beijing.","PeriodicalId":263114,"journal":{"name":"2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123968806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Prediction of FPSO single point mooring line tension based on Bayesian optimization 基于贝叶斯优化的FPSO单点系泊索张力预测
2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105700
Zexin Zhang, Qixun Wang, Yazhou Zhu
{"title":"Prediction of FPSO single point mooring line tension based on Bayesian optimization","authors":"Zexin Zhang, Qixun Wang, Yazhou Zhu","doi":"10.1109/NNICE58320.2023.10105700","DOIUrl":"https://doi.org/10.1109/NNICE58320.2023.10105700","url":null,"abstract":"Oil exploitation requires offshore engineering equipment such as offshore platforms, positioning systems and drilling risers. As one of the positioning systems, the mechanical safety performance of mooring system is of great significance to offshore platforms. To ensure the safety of the mooring system under unknown sea conditions, predictive monitoring of mooring line tension magnitude is necessary. This paper uses business software Orcaflex to establish the single point mooring system model of FPSO, and then gives a mooring tension prediction method based on Training-data-set. The long short-term memory artificial neural network model is employed to establish the mooring line tension prediction model based on the parameters of the Bayesian optimization neural network model. The error analysis of the prediction results provides a valuable reference for the mooring system design.","PeriodicalId":263114,"journal":{"name":"2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116299190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Deep Learning-based Viewpoint Prediction Model and Influencing Attention Factors for Design Drawings 基于深度学习的设计图纸视点预测模型及影响注意因素
2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105774
Bai Liu
{"title":"Deep Learning-based Viewpoint Prediction Model and Influencing Attention Factors for Design Drawings","authors":"Bai Liu","doi":"10.1109/NNICE58320.2023.10105774","DOIUrl":"https://doi.org/10.1109/NNICE58320.2023.10105774","url":null,"abstract":"In order to meet the requirement of fine-grained viewpoint prediction for design drawings, a deep learning-based viewpoint prediction model is developed to achieve heat map generation, feature element recognition and interactive and instantaneous detection of design solutions, and the attentional influencing factors are discussed. A saliency map is introduced to model the visual attention allocation mechanism, and a fully convolutional networks-based Image Viewpoint Prediction Model (IVPM) is proposed to overcome the limitations of eye tracker tests. The model has excellent temporal performance after training on the Graphics Design Importance (GDI) dataset, and experiments validate that low-level attributes of images are the main influences on design attention. The IVPM can be applied to image creation, poster design, packaging design, product design and interface design, and is a useful reference for the creative design field.","PeriodicalId":263114,"journal":{"name":"2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126262823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Service Resilience Optimization Algorithm for Intelligent Space-based System with Minimum Delay 最小时延智能天基系统业务弹性优化算法
2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105810
J. Qin, Xuan Li, Xiaotian Ma, Jun Yang
{"title":"Service Resilience Optimization Algorithm for Intelligent Space-based System with Minimum Delay","authors":"J. Qin, Xuan Li, Xiaotian Ma, Jun Yang","doi":"10.1109/NNICE58320.2023.10105810","DOIUrl":"https://doi.org/10.1109/NNICE58320.2023.10105810","url":null,"abstract":"Space-based systems have enormous economic, social and military values that make them vulnerable to interference and even attack. With the gradual development of networked and intelligent space-based systems, intelligent space-based systems can still maintain a certain service performance without paralysis when confronted with interference or attack, that is, flexible services. However, when a traditional space-based system encounters node failure, the service is directly interrupted or needs ground re-planning, and it does not have the ability of flexible service. To solve this problem, this paper presents a service resilience recovery algorithm for minimum latency. Experiments show that in the service elasticity evaluation of smart space-based systems based on real networks, the more satellite nodes there are, the better the service elasticity, and the recovery time is 42.3% shorter than the traditional random algorithm, which is 3 times higher than the nearest strategy.","PeriodicalId":263114,"journal":{"name":"2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126535188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Design and Analysis of Software and Hardware Cooperative Mode 软硬件协同模式的设计与分析
2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105779
Yinglu Zhao, Xinyi Song, Yin Xiong, Siyu Wang
{"title":"Design and Analysis of Software and Hardware Cooperative Mode","authors":"Yinglu Zhao, Xinyi Song, Yin Xiong, Siyu Wang","doi":"10.1109/NNICE58320.2023.10105779","DOIUrl":"https://doi.org/10.1109/NNICE58320.2023.10105779","url":null,"abstract":"In daily work, there is a large number of remote document signing requirements. At present, the mainstream method is to mail the signed documents or transmit pictures of the signed documents. Obviously, mailing some confidential documents is not conducive to confidentiality, and the picture transmission of the signed documents is easy to leak and cannot guarantee the signature of the person. Combined with the above scenarios, a secure remote document signing solution is urgently needed in the current market. Based on the design idea of hardware and software collaboration,we introduce the domestic Hummingbird E203 processor with RISC- V architecture as the core controller, use FPGA (field-programmable gate array) to realize the convolutional neural network handwritten character recognition hardware accelerator and digital watermark embedding algorithm, and carry out functional simulation and experimental data collection.","PeriodicalId":263114,"journal":{"name":"2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128127959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Multi-scale Fusion Network with Transformer for Medical Image Segmentation 基于变压器的医学图像分割多尺度融合网络
2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105758
Guidi Lin, Lingna Chen
{"title":"A Multi-scale Fusion Network with Transformer for Medical Image Segmentation","authors":"Guidi Lin, Lingna Chen","doi":"10.1109/NNICE58320.2023.10105758","DOIUrl":"https://doi.org/10.1109/NNICE58320.2023.10105758","url":null,"abstract":"To further exploit the advantages of convolutional neural network (CNN) and Transformer, we introduce a new Multi-scale Fusion Network to this paper. With the U-shaped attention model, we introduce multi-scale blocks in the encoder phase to sufficiently exploit the multi-scale semantic information. We further invoke cross-fusion of the multi-scale channels with Transformer to reconstruct skip connections, which provides the decoder with different levels of long-range information. Moreover, we utilize a scale-aware pyramid fusion module built into the bottom of our framework for the dynamic fusion of multiscale contextual information from higher-level features. The results on two datasets indicate that the proposed approach obtains competitive performance and exceeds the comparison networks, which to some extent relieves the burden of physicians.","PeriodicalId":263114,"journal":{"name":"2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122014755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
PENet: Pre-Enhanced Network for Object Detection and Instance Segmentation PENet:用于对象检测和实例分割的预增强网络
2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105781
Yunda Shi, Jianming Hu, Li Li
{"title":"PENet: Pre-Enhanced Network for Object Detection and Instance Segmentation","authors":"Yunda Shi, Jianming Hu, Li Li","doi":"10.1109/NNICE58320.2023.10105781","DOIUrl":"https://doi.org/10.1109/NNICE58320.2023.10105781","url":null,"abstract":"Multi-level feature fusion has always been regarded as a powerful structure that can improve the performance of models for instance segmentation. In this work, we propose a novel proposal-based Pre-Enhanced Network (PENet) performing a bottom-up feature fusion between backbone and neck of the network, achieving a more adequate information propagation. Our main contribution is the new Pre-Enhanced Module (PEM) that utilizes feature maps generated by stages of backbone for multi-level fusion, which performs feature extraction and fusion on adjacent levels from bottom to top. Our PEM will equip feature maps with more sufficient information from the bottom, obtaining a more effective performance for object detection and instance segmentation. Evaluation and comparative experiments of models are conducted on Cityscapes dataset. Our PENet outperforms its baseline model obtaining absolute mAP gains of 1.3% for object detection and 0.9% for instance segmentation, and also achieves improvements compared with other corresponding models based on the same baseline. We hope our work will provide a new view of model structure modification for object detection and instance segmentation. Code will be made published.","PeriodicalId":263114,"journal":{"name":"2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115906710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
ViT-ReID: A Vehicle Re-identification Method Using Visual Transformer viti - reid:一种使用视觉变压器的车辆再识别方法
2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105738
Linshan Du, Kuilin Huang, He Yan
{"title":"ViT-ReID: A Vehicle Re-identification Method Using Visual Transformer","authors":"Linshan Du, Kuilin Huang, He Yan","doi":"10.1109/NNICE58320.2023.10105738","DOIUrl":"https://doi.org/10.1109/NNICE58320.2023.10105738","url":null,"abstract":"In the current vehicle re-identification task based on CNN method, due to the loss of detail information of image caused by convolution and downsampling operation, the ability to distinguish similar vehicles is affected. In this paper, a new vehicle Re-ID method ViT-ReID using visual Transformer was proposed. In order to extract robust discriminative features to enhance the ability of the model to distinguish similar vehicles, a SIE module based on ViT backbone network was designed to effectively reduce the deviation of learning features caused by camera perspective changes. At the same time, in order to improve the performance of the model, the latest VPT method for fine-tuning of large-scale Transformers was introduced. Finally, the ViT-ReID model proposed in this paper was compared with other methods in the VeRi-776 and VehicleID datasets. The experimental results showed that the proposed method can effectively extract robust discriminative features in vehicle re-identification and distinguish similar vehicles with high accuracy.","PeriodicalId":263114,"journal":{"name":"2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132015493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Dam crack detection based on deep learning cascade detection algorithm 基于深度学习级联检测算法的大坝裂缝检测
2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105763
Qinchuan Wang, Bin Li, Ziqian Deng, Wanting Zhao
{"title":"Dam crack detection based on deep learning cascade detection algorithm","authors":"Qinchuan Wang, Bin Li, Ziqian Deng, Wanting Zhao","doi":"10.1109/NNICE58320.2023.10105763","DOIUrl":"https://doi.org/10.1109/NNICE58320.2023.10105763","url":null,"abstract":"In order to improve the crack detection accuracy of concrete dam surface, a cascade crack detection algorithm based on deep learning and prior information reinforcement is proposed. In the algorithm, two neural networks are cascaded to detect cracks by a cascade detection method. Prior information strengthening includes standardization and edge strengthening. When making standardized crack slices, the crack is rotated to the horizontal trend and the center of mass is placed in the section center. Meanwhile, slices belonging to the general background are removed according to the gray gradient similarity. The slices were processed with the prior information of crack edges to artificially highlight the features and reduce the difficulty of network training. The network results without prior information reinforcement and cascade detection are compared respectively to evaluate the network detection performance. The experimental results show that the accuracy rate and recall rate of concrete dam surface crack detection have been improved significantly, which proves the effectiveness of the algorithm.","PeriodicalId":263114,"journal":{"name":"2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)","volume":"05 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130055209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Mask RFCT: Recursive Fully Convolutional Tracker for Video Instance Segmentation 掩码RFCT:用于视频实例分割的递归全卷积跟踪器
2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105756
Yunda Shi, Jianming Hu, Yan Zhang
{"title":"Mask RFCT: Recursive Fully Convolutional Tracker for Video Instance Segmentation","authors":"Yunda Shi, Jianming Hu, Yan Zhang","doi":"10.1109/NNICE58320.2023.10105756","DOIUrl":"https://doi.org/10.1109/NNICE58320.2023.10105756","url":null,"abstract":"Video instance segmentation has recently attracted a lot of attention due to the challenge of simultaneous segmentation and tracking in video clips. In this work, we propose a new video instance segmentation method, called Mask RFCT, which performs tracking via a precise feature vector matching by generating feature vectors from a recursive fully convolutional tracking branch. Our main contribution is the novel recursive fully convolutional tracker (RFCT) that generates corresponding feature vector for each instance within a bounding-box, which will be used for the following instance category assignment as tracking process. Model evaluation is conducted on YOUTUBE-VIS dataset. It shows that our Mask RFCT outperforms the baseline model, MaskTrack RCNN, obtaining an absolute gain of 2.8% for mean AP and 2.65% for mean AR. And we also conduct comparison experiments on different trackers we proposed and the baseline. We hope our work will provide a new method for architecture modification and instance tracking.","PeriodicalId":263114,"journal":{"name":"2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133580316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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