2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)最新文献_第3页

Missing Values Imputation Using Auto-associative Neural Network with Local Neighborhood Information 基于局部邻域信息的自关联神经网络缺失值插值

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105720

Xiaochen Lai, Hui Chen, Liyong Zhang

{"title":"Missing Values Imputation Using Auto-associative Neural Network with Local Neighborhood Information","authors":"Xiaochen Lai, Hui Chen, Liyong Zhang","doi":"10.1109/NNICE58320.2023.10105720","DOIUrl":"https://doi.org/10.1109/NNICE58320.2023.10105720","url":null,"abstract":"Missing values of real-world datasets bring challenges to data mining. Popular methods for handling missing values are to replace missing values in a dataset using predicted values, called imputation, which is a fundamental task in many research fields. In this paper, we propose an auto-associative neural network with local neighborhood information for missing values imputation. Specifically, we introduce local neighborhood information into auto-associative neural network to make the spatial relationships among samples work. With the spread of neighbor information, the similarity of samples is used to supplement the missing attributes of incomplete samples, which facilitates network analysis of attributes associations. Besides, we propose a training method to iteratively update estimation by treating missing values as variables, in which the imputation error and the incomplete model inputs are continuously optimized during training. The training method allows incomplete samples to participate in the network training as well, enabling all known attributes to be useful. Experiments based on several public datasets validate the effectiveness of the proposed method.","PeriodicalId":263114,"journal":{"name":"2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)","volume":"496 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":"115974873","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

Long Text Multi-label Classification 长文多标签分类

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105696

Miao Zang, Yang Gao, Siyi Niu, X. Chen

引用次数: 0

A hybrid prediction framework based on deep learning for wind power 基于深度学习的风电混合预测框架

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105749

Yifang Jin, Shunjiang Wang, Zhaowei Ling, Duo Wang, G. Wang

引用次数: 0

Semantic Segmentation of Traffic Scene Based on DeepLabv3+ and Attention Mechanism 基于DeepLabv3+和注意机制的交通场景语义分割

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105805

Yuan Zhang, Yuhao Zhang, Qianyi Zhang

{"title":"Semantic Segmentation of Traffic Scene Based on DeepLabv3+ and Attention Mechanism","authors":"Yuan Zhang, Yuhao Zhang, Qianyi Zhang","doi":"10.1109/NNICE58320.2023.10105805","DOIUrl":"https://doi.org/10.1109/NNICE58320.2023.10105805","url":null,"abstract":"In the increasingly mature autonomous driving technology, acquiring traffic scene information is a guarantee to improve driving safety. The emergence of semantic segmentation technology enables the segmentation of images at the pixel level into regions associated with semantic categories of scenes. This technology improves the vehicle perception and analysis of real-time traffic environment information, thus enhancing the safety of autonomous driving technology. The currently accepted semantic segmentation model DeepLabv3+ still suffers from small targets being ignored and similarly shaped objects being incorrectly segmented in the segmentation task, resulting in a need for further improvement in the accuracy of the result presentation. To address this problem, this paper proposes an improved DeepLabv3+ method for semantic segmentation of road scenes with a fused spatial attention mechanism based on the structure of DeepLabv3+ network, combined with the attention mechanism to increase the weights of segmented regions. After determining the use of Focal Loss, the ASPP structure is improved. The segmentation effects of the models with different ways of introducing the attention mechanism are compared and tested on the Cityscapes dataset. The experimental results show that the improved model segmentation accuracy is improved by 1.56% on average compared to DeepLabv3+, which is an improvement in cross-comparison.","PeriodicalId":263114,"journal":{"name":"2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)","volume":"20 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":"114676580","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

Research on Key Fragile Dependency Links Removal Strategies in Interdependent Power and Communication Networks under Cascading Failures 级联故障下电力通信网络关键脆弱依赖链路去除策略研究

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105753

Qian-Ying Zou, Lin Ding

{"title":"Research on Key Fragile Dependency Links Removal Strategies in Interdependent Power and Communication Networks under Cascading Failures","authors":"Qian-Ying Zou, Lin Ding","doi":"10.1109/NNICE58320.2023.10105753","DOIUrl":"https://doi.org/10.1109/NNICE58320.2023.10105753","url":null,"abstract":"In interdependent power and communication networks, the failure of a few nodes may cause large-scale cascading failures of the whole networks, and the fragile dependency links connecting central nodes at different layers are one of the important reasons for such cascading failures. Therefore, this paper aims to improve the robustness of the networks against cascading failures by removing key fragile dependency links. First, a realistic cascading model is constructed considering the heterogeneity of topology and load flow in the power and communication layers. Different parameters characterizing the node centrality are defined to assess the fragility of dependency links from the structural and robustness perspectives, and then different key fragile dependency links removal strategies are designed. Simulation results show that compared with the random dependency links removal strategy, key fragile dependency links removal strategies can remove fewer dependency links and make the networks more robust. Moreover, the key fragile dependency links removal strategy designed from the robustness perspective can perform better than the strategy designed from the structural perspective only.","PeriodicalId":263114,"journal":{"name":"2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)","volume":"225 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":"124480613","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 Novel Transmitarray Using Double-layer Hexagonal Element with Vias 一种新型带过孔的双层六角形元传输阵列

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105737

Hantao Zhou, Haogang Wang

引用次数: 0

Target Detection Approach for UAVs via Improved Unbiased Teacher 基于改进无偏教师的无人机目标检测方法

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105780

Zhendong Hua, Beibei Zhang

引用次数: 0

Session-based Recommendation with Temporal Graph Neural Network and Contrastive Learning 基于时间图神经网络和对比学习的会话推荐

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105727

Xiaolong Li, Xiaoru Wang, H. Zhang, Jiabin Zhang

{"title":"Session-based Recommendation with Temporal Graph Neural Network and Contrastive Learning","authors":"Xiaolong Li, Xiaoru Wang, H. Zhang, Jiabin Zhang","doi":"10.1109/NNICE58320.2023.10105727","DOIUrl":"https://doi.org/10.1109/NNICE58320.2023.10105727","url":null,"abstract":"Session-based recommendation aims to predict the next clicked item using the current ongoing session of an anonymous user. Since the user's information is unknown, the available information is limited. In recent years, due to the excellent performance of graph neural networks in many applications, many works have applied graph neural networks to session-based recommendation. However, we found that the conversion of session data into graph-structured data is a lossy graph encoding method, which leads to the loss of item order information in the session. In addition, only one session can be used for recommendation in session-based recommendation. Compared with other recommendations, the problem of data sparsity is more serious. Self-supervised learning can discover ground-truth samples and has great potential in solving the data sparsity problem. Therefore, we propose a session-based recommendation model based on graph neural network and contrastive learning to solve the problem of information loss and data sparsity in graph encoding. Extensive experiments on three benchmark datasets demonstrate that our model outperforms other methods.","PeriodicalId":263114,"journal":{"name":"2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)","volume":"316 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":"126254051","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

Passive Non-Line-of-Sight imaging reconstruction based on dual input U-Net 基于双输入U-Net的被动非视距成像重建

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105693

Xin He, Pengfei Wang, Hao Liu, Xinde Yu

引用次数: 0

Dynamic modelling of uncertain multiple coupled delays complex networks and its robust adaptive tracking control 不确定多耦合时滞复杂网络的动态建模及其鲁棒自适应跟踪控制

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105672

Housheng Xu

引用次数: 0