发布求助
文献互助 智能选刊 最新文献

2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)最新文献

筛选
英文 中文
A Target Detection Method Based on the Fusion Algorithm of Radar and Camera 一种基于雷达与相机融合算法的目标检测方法
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660407
Sheng Zhuang, Lin Cao, Zongmin Zhao, Dongfeng Wang
{"title":"A Target Detection Method Based on the Fusion Algorithm of Radar and Camera","authors":"Sheng Zhuang, Lin Cao, Zongmin Zhao, Dongfeng Wang","doi":"10.1109/IC-NIDC54101.2021.9660407","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660407","url":null,"abstract":"The method based on the fusion of radar and video in this paper is oriented to detecting surrounding objects while driving. This is usually a method of improving robustness and accuracy by using several senses, which makes sensor fusion a key part of the perception system. We propose a new fusion method called CT-EPNP, which uses radar and camera data for fast detection. Adding a central fusion algorithm on the basis of EPNP, and use the truncated cone method to compensate the radar information on the associated image when mapping. CT-EPNP returns to the object attributes depth, rotation, speed and other attributes. Based on this, simulation verification and related derivation of mathematical formulas are proved. We combined the improved algorithm with the RetinaNet model to ensure that the model is satisfied with the normal driving of the vehicle while gaining a certain increase in the detection rate. We have also made a certain improvement in ensuring repeated detection without using any additional time information.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129564232","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
Driving Fatigue Detection Combining Face Features with Physiological Information 结合人脸特征和生理信息的驾驶疲劳检测
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660529
Lingqiu Zeng, Yang Wang, Qingwen Han, Kun Zhou, L. Ye, Yang Long
{"title":"Driving Fatigue Detection Combining Face Features with Physiological Information","authors":"Lingqiu Zeng, Yang Wang, Qingwen Han, Kun Zhou, L. Ye, Yang Long","doi":"10.1109/IC-NIDC54101.2021.9660529","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660529","url":null,"abstract":"Fatigue driving is one of the main reasons that cause sever accidents. It's necessary to detect fatigue state and warn drivers to avoid life-threatening accidents. There are many related technologies to detect fatigue, some of which based on physiological information or face features. However, biological indicators are difficult to analyze in real-time and the signal sensor is invasive while image-based approaches have relatively strong subjective. Hence, in this paper, a method combined physiological information and face features is employed. We use near-infrared spectroscopy (fNIRS) on behalf of physical states and eye and mouth condition representing face states. Firstly, Multi-Task Convolutional Neural Network (MTCNN) was used to extract image features and then a lightly classifier was designed to recognize the state of face states. Finally, we use Long Short-Term Memory (LSTM) model to fuse these characters and predict fatigue. Experiment results show that the method proposed have a high accuracy about 95.8% and fast speed about 6.12ms to detect fatigue.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128404848","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
Full-Viewpoint Depth Recovery of Light Field Image via Deep Neural Network 基于深度神经网络的光场图像全视点深度恢复
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660403
Fan Zhang, Xueming Li, Qiang Fu
{"title":"Full-Viewpoint Depth Recovery of Light Field Image via Deep Neural Network","authors":"Fan Zhang, Xueming Li, Qiang Fu","doi":"10.1109/IC-NIDC54101.2021.9660403","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660403","url":null,"abstract":"Recovery depth from a lenslet light field image can facilitate lots of applications including super-resolution and 3D reconstruction. However, current works mainly focus on the central sub-aperture image but pay little attention to full-viewpoint light field images. In this paper, we propose a deep learning-based method to recovery full-viewpoint depth by estimating the disparity map from the given light field image. We employ the ResNet to extract multi-dimensional features from the given light field image which is encoded as a 3D epipolar plane image, establish dense connections to enable the neural network to calculate the cost volume from extracted features, and use an AutoEncoder to convert the cost volume to a disparity map of the given light field image. We show several experimental results and two comparisons with the related works to demonstrate the effect and performance of our method.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128699995","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
Decoupling Deep Domain Adaptation Method for Class-imbalanced Learning with Domain Discrepancy 具有域差异的类不平衡学习解耦深度域自适应方法
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660444
Juchuan Guo, Yichen Liu, Zhenyu Wu
{"title":"Decoupling Deep Domain Adaptation Method for Class-imbalanced Learning with Domain Discrepancy","authors":"Juchuan Guo, Yichen Liu, Zhenyu Wu","doi":"10.1109/IC-NIDC54101.2021.9660444","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660444","url":null,"abstract":"In a wide range of classification tasks, training data will produce class-imbalance due to collection difficulties in some classes, which leads to prediction biases on minority classes. For the class-imbalanced problem, existing researches are usually based on the assumption that the training dataset and the test dataset are from similar distributions. In reality, both of the datasets often come from domains with different distributions, which challenges generalization performances of models. In this paper, a decoupling deep domain adaptation method is proposed to overcome these problems. Based on the adversarial domain adaptation model, the method uses a two-stage training strategy which decouples representation learning and classifier adjustment. The results of experiments under scenarios of bearing fault diagnosis and digit images classification with class-imbalance and domain discrepancy show that the effect of the combination of domain adaptation method and specific decoupling strategy is better than that of one-stage training only using resampling or cost-sensitive methods in the domain adaptation model.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125349653","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
Zombie Hosts Identification Based on DNS Log 基于DNS日志识别僵尸主机
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660578
Renjie Wang, Yangsen Zhang, Ruixue Duan, Zhuofan Huang
{"title":"Zombie Hosts Identification Based on DNS Log","authors":"Renjie Wang, Yangsen Zhang, Ruixue Duan, Zhuofan Huang","doi":"10.1109/IC-NIDC54101.2021.9660578","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660578","url":null,"abstract":"Although the academia has done a lot of research on DNS abnormal behavior, whether from the perspective of traffic or irregular domain name recognition, the mechanism behind DNS is ignored in the pre-processing of DNS logs and other data. In addition, most studies focus on traffic anomaly detection and unconventional domain name recognition, and lack of systematic research on the combination of the two, so the proposed algorithm has no practical application. This paper proposes a clustering method based on DNS client IP address traffic characteristics, which divides DNS logs into five access modes. Then, a DNS log preprocessing algorithm is designed to preprocess the logs that may exist in zombie hosts. Finally, a two-layer GRU network detection algorithm based on domain name text features is proposed. Experimental results show that this method can effectively identify zombie hosts in DNS logs.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123986820","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
Embedding Methods or Link-based Similarity Measures, Which is Better for Link Prediction? 嵌入方法和基于链接的相似性度量，哪个更适合链接预测?
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660590
M. Hamedani, Sang-Wook Kim
{"title":"Embedding Methods or Link-based Similarity Measures, Which is Better for Link Prediction?","authors":"M. Hamedani, Sang-Wook Kim","doi":"10.1109/IC-NIDC54101.2021.9660590","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660590","url":null,"abstract":"The link prediction task has attracted significant attention in the literature. Link-based similarity measures (in short, similarity measures) are the conventional methods for this task, while recently graph embedding methods (in short, embedding methods) are widely employed as well. In this paper, we extensively investigate the effectiveness of embedding methods and similarity measures (i.e., both non-recursive and recursive ones) in link prediction. Our experimental results with three real-world datasets demonstrate that 1) recursive similarity measures are not beneficial in this task than non-recursive one,2) increasing the number of dimensions in vectors may not help improve the accuracy of embedding methods, and 3) in comparison with embedding methods, Adamic/Adar, a non-recursive similarity measure, can be a useful method for link prediction since it shows promising results while being parameter-free.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116917350","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
Combined Coverage, Attention and Pointer Networks for Improving Slot Filling in Spoken Language Understanding 综合覆盖、注意和指针网络提高口语理解槽填充
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660465
Yaping Wang, Huiqin Shao, Zhen Li, Yan Zhu, Zhe Liu
{"title":"Combined Coverage, Attention and Pointer Networks for Improving Slot Filling in Spoken Language Understanding","authors":"Yaping Wang, Huiqin Shao, Zhen Li, Yan Zhu, Zhe Liu","doi":"10.1109/IC-NIDC54101.2021.9660465","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660465","url":null,"abstract":"Sequence to sequence (Seq2Seq) model together with pointer network (Ptr-Net) has recently show promising results in slot filling task, in the situation where only sentence-level annotations are available, while the model's prediction contains repetition of slot values. In this paper, we add a coverage mechanism to alleviate issues of repeating prediction in slot filling task. We use a coverage vector to record attention history, and then add to the computation of attention, which can force model to consider more about un-predicted slot values. Experiments show that the proposed model significantly improves slot value prediction F1 with 8.5% relative improvement compare to the baseline models on benchmark DSTC2 (Dialog State Tracking Challenge 2) datasets.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132321008","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
Auto-Learning of Parameters for High Resolution Sparse Group Lasso SAR Imagery 高分辨率稀疏组Lasso SAR图像参数的自动学习
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660447
Wei Liu, Hanwen Xu, Cheng Fang, Lei Yang, Weidong Jiao
{"title":"Auto-Learning of Parameters for High Resolution Sparse Group Lasso SAR Imagery","authors":"Wei Liu, Hanwen Xu, Cheng Fang, Lei Yang, Weidong Jiao","doi":"10.1109/IC-NIDC54101.2021.9660447","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660447","url":null,"abstract":"Aiming at the problem of adjusting the penalty term coefficient of feature enhancement in high-resolution synthetic aperture radar (SAR) imaging, a marginal estimation Bayes (MEB) algorithm is proposed, so that the prior features of the target can be fitted properly to improve the accuracy of image feature extraction. Firstly, the alternating direction method of multipliers (ADMM) convex optimization framework is modeled based on the echoed data, and least absolute shrinkage and selection operator (Lasso) model and sparse group Lasso (SG-Lasso) model are introduced, then the maximum marginal likelihood distribution of the regularization parameters is derived. Moreover, the Moreau Yoshida unadjusted Langevin algorithm (MYULA) is used to realize target posteriori sampling solution. Because the posterior distribution is difficult to solve, the gradient projection method is introduced to estimate the regularization parameters. Finally, auto-learning parameters are used to optimize the imaging. The proposed algorithm can not only estimate the parameters of a single regularization term, but also estimate the parameters of multiple regularization terms. Aiming at non-differentiable part in the prior, MYULA is adopted to calculate the subgradient of the non-differentiable posterior distribution. Therefore, the proposed algorithm is capable of auto-leaning parameters even regularization function is non-differentiable. In the experimental part, compared with the optimal value of manual debugging, the error between the proposed method and the optimal value is within 15%, and the effectiveness of the algorithm are verified by phase transition diagram (PTD).","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"276 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134366693","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
Data-driven Differential Games for Affine Nonlinear Systems 仿射非线性系统的数据驱动微分对策
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660508
Conghui Ma, Bin Zhang, Lutao Yan, Haiyuan Li
{"title":"Data-driven Differential Games for Affine Nonlinear Systems","authors":"Conghui Ma, Bin Zhang, Lutao Yan, Haiyuan Li","doi":"10.1109/IC-NIDC54101.2021.9660508","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660508","url":null,"abstract":"This paper presents a data-driven optimal approach based on differential dynamic programming (DDP) for two-person differential game of nonlinear affine systems. Using test data, the Hamilton-Jacobi-Isaacs (HJI) equation is expanded into a set of high-order differential equations. Basis functions is adopted to approximate the unknown system function and value function. Based on the approximation, a data-driven optimal approach is proposed to obtain the unknown coefficients of the basis functions. A numerical example is proposed to demonstrate the effectiveness of this method.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131926997","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
Collaborative Multi-agent Reinforcement Learning for Intrusion Detection 入侵检测的协同多智能体强化学习
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660402
Guochen Shi, Gang He
{"title":"Collaborative Multi-agent Reinforcement Learning for Intrusion Detection","authors":"Guochen Shi, Gang He","doi":"10.1109/IC-NIDC54101.2021.9660402","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660402","url":null,"abstract":"Network intrusion detection system (NIDS) is the essential component of cyber security infrastructure to ensure the security of communication and information systems. In this paper, a collaborative multi-agent reinforcement learning, Major-Minor-RL, is proposed to make the detection more efficient. The model consists of one major agent and several minor agents. The role of major agent is to predict whether the traffic is normal or abnormal, while minor agents are auxiliary to the major agent and help it to correct errors. If the action of major agent is different fro m the behavior of most minor agents, the final action will be determined by minor agents, while in most cases, the final action is equal to the major one. In this paper, the model has been trained on NSL-KDD dataset and the results are boosted. After comparing with the existing models, we observed much better classification performance in Major-Minor-RL intrusion detection system.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123674683","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: 4
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信