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

Dynamic-YOLOv5: An improved aerial small object detector based on YOLOv5 动态-YOLOv5:基于YOLOv5改进的航空小目标探测器

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105776

Qiang Wei, Xiaoyang Hu, Qinxian Hou

引用次数: 1

Multi-terminal Service Access Method Based on Load Balancing 基于负载均衡的多终端业务接入方法

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105744

Xiao Ding, Zhibin Yin, H. Bai, Chao Huo

引用次数: 0

CMTR6D: Cross-Modal Transformer for 6D Pose Estimation CMTR6D: 6D姿态估计的跨模态变压器

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105785

Jiwei Hu, Zhiqiang Xu

引用次数: 0

Fast rare itemset mining in uncertain database 不确定数据库中稀有项集的快速挖掘

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105746

Weidong Qian, Han Li, Huang Huang, Maocai Yuan, Yuan Xu, Ganjiang Sun

{"title":"Fast rare itemset mining in uncertain database","authors":"Weidong Qian, Han Li, Huang Huang, Maocai Yuan, Yuan Xu, Ganjiang Sun","doi":"10.1109/NNICE58320.2023.10105746","DOIUrl":"https://doi.org/10.1109/NNICE58320.2023.10105746","url":null,"abstract":"Rare itemset mining in uncertain database finds itemsets which have their support no less than a given probability minimum threshold. Discovering probabilistic rare itemset from a uncertain database can help user in better decision making by focusing on information that it is important but rare to something. Usually, frequent itemset and its relevant researches have gotten a lot of attention in the previous works, and much more efficient and effective algorithms was proposed to obtain valued knowledge under various constrained scenarios. Compared with frequent itemset mining, especially probabilistic rare itemset mining are rarely studied. Therefore, we present the concept of mining rare itemsets in uncertain data based on rare probabilities, and propose two algorithms, i.e. the bottom-up algorithm and the top-down algorithm. On this basis, two pruning strategies are designed to reduce the computational cost by traversing from long itemsets to short itemsets. Finally, we validate and evaluate the correctness and effectiveness of the algorithms in several experiments.","PeriodicalId":263114,"journal":{"name":"2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)","volume":"53 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":"115551536","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

Low earth orbit satellite admission control scheme based on deep Q-learning 基于深度q -学习的近地轨道卫星准入控制方案

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105799

Debin Wei, Dongdong Zheng, Li Yang, Rui-yan Cai

引用次数: 0

Fine-grained Classification of Bone Scintigrams by Using Radiomics Features 利用放射组学特征对骨闪烁图进行细粒度分类

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105690

Xiaoqiang Ma, Yang He, Qiang Lin, Yongchun Cao, Zhengxing Man

引用次数: 0

Marine debris detection model based on the improved YOLOv5 基于改进型YOLOv5的海洋垃圾检测模型

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105682

Jiazhen Liu, Yi Zhou

引用次数: 0

A Novel Two-Stage Net for Human Body Region Segmentation via Centimeter-Wave Radar 一种基于厘米波雷达的人体区域分割新方法

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105808

Jiaren Xiao, Zhiguo Chen, Bing Luo

{"title":"A Novel Two-Stage Net for Human Body Region Segmentation via Centimeter-Wave Radar","authors":"Jiaren Xiao, Zhiguo Chen, Bing Luo","doi":"10.1109/NNICE58320.2023.10105808","DOIUrl":"https://doi.org/10.1109/NNICE58320.2023.10105808","url":null,"abstract":"Human body region segmentation is a sub-task in semantic segmentation. Due to the size, complexity, abstraction of radar signal and the lack of open source datasets of radar signal data, the radar signal is rarely used for human body segmentation in past. In recent years, with the development of deep learning, some radar signal data from unmanned drive has been used for research on classification, target detection and semantic segmentation. In this paper, a novel two-stage neural network model based on centimeter-wave radar sensor is designed for human body region segmentation. In the first stage, radar signals are encoded and compressed, in the second stage, decode the spatial feature information of the human body extracted by the encoder to output the segmentation results. The network adopts the design idea of teacher-student network, which use the teacher network to annotate radar signal data and provide cross-modal supervision for the student network. We collected human body data in different scenes, and input the radar signal into the model, performs human body region segmentation. The effectiveness of this human segmentation model is experimentally verified for single and multi-person scenes, especially for occlusion scenes.","PeriodicalId":263114,"journal":{"name":"2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)","volume":"14 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":"130518940","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

CAGAN: A Channel-aware Generative Adversarial Network for Passive Non-Line-of-Sight Imaging CAGAN:被动非视距成像的通道感知生成对抗网络

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105725

Xinde Yu, Pengfei Gao, Hao Liu, Xin He

引用次数: 0

Research on Robotic High-Precision Target Detection Technology for Transmission Lines Based on Convolutional Neural Networks 基于卷积神经网络的输电线路机器人高精度目标检测技术研究

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105809

Boyu Xu, Xiyi Gu, wenbin liu, Jianfeng Zhang, Dongdian Lin

{"title":"Research on Robotic High-Precision Target Detection Technology for Transmission Lines Based on Convolutional Neural Networks","authors":"Boyu Xu, Xiyi Gu, wenbin liu, Jianfeng Zhang, Dongdian Lin","doi":"10.1109/NNICE58320.2023.10105809","DOIUrl":"https://doi.org/10.1109/NNICE58320.2023.10105809","url":null,"abstract":"In recent years, machine vision detection technology has been widely used in various fields, and deep learning, as an emerging visual detection technology, has shown excellent detection performance. In this paper, aiming at the bolt fastening end operation device of the live working robot on the transmission line, the visual detection algorithm is studied to improve the efficiency of the robot operation. In view of the poor robustness and high false detection rate of the visual detection algorithm designed by Hough and SVM, it is proposed to use the Faster R-CNN network with leading performance in deep learning to design the bolt detection algorithm. The size of bolts appearing in the camera is analyzed, and suitable RPN parameters are proposed. Aiming at the problem that the network model cannot be fitted due to insufficient samples, a sample expansion method is proposed to increase the number of samples. Finally, the bolt detection network is obtained through joint training. Experiments prove that the designed Faster R-CNN detection algorithm can effectively detect bolts, greatly improving the efficiency of robot operations, and has broad application prospects.","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":"126918348","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