2022 10th International Conference on Intelligent Computing and Wireless Optical Communications (ICWOC)最新文献_第2页

DCN-ECPE: Dual-Channel Network for Emotion-Cause Pair Extraction DCN-ECPE:情感原因对提取的双通道网络

2022 10th International Conference on Intelligent Computing and Wireless Optical Communications (ICWOC) Pub Date : 2022-06-10 DOI: 10.1109/ICWOC55996.2022.9809891

Pei Qie, Kai Shuang

引用次数: 1

An Improved Gardner Feedback Timing Synchronization Loop 一种改进的Gardner反馈定时同步环路

2022 10th International Conference on Intelligent Computing and Wireless Optical Communications (ICWOC) Pub Date : 2022-06-10 DOI: 10.1109/ICWOC55996.2022.9809870

Qian Yu, Zhiping Huang, Junhao Ba

引用次数: 1

Landslide Recognition in High Resolution Remote Sensing Images Based on Semantic Segmentation 基于语义分割的高分辨率遥感图像滑坡识别

2022 10th International Conference on Intelligent Computing and Wireless Optical Communications (ICWOC) Pub Date : 2022-06-10 DOI: 10.1109/ICWOC55996.2022.9809850

Q. Zhang, Jie Zhang, Wencheng Sun, Zhangjian Qin

{"title":"Landslide Recognition in High Resolution Remote Sensing Images Based on Semantic Segmentation","authors":"Q. Zhang, Jie Zhang, Wencheng Sun, Zhangjian Qin","doi":"10.1109/ICWOC55996.2022.9809850","DOIUrl":"https://doi.org/10.1109/ICWOC55996.2022.9809850","url":null,"abstract":"In order to ensure the stable operation of high voltage transmission network, DeepLab V3+_SDF is proposed based on DeepLab V3+ for rapid and intelligent landslide detection from high resolution remote sensing images. Firstly, the backbone network is replaced by ResNet with squeeze-and-excitation (SE) attention mechanism to enhance the extraction of useful features. Secondly, astrous spatial pyramid pooling (ASPP) is reconstructed based on dense connection to expand the receptive field. More low-level features are then added to the decoder with feature pyramid networks plus (FPNP) to enhance detail recovery. Finally, a mixed loss function is proposed based on the pixel distribution to solve the sample imbalance problem. DeepLabV3+ _SDF is trained with self-made landslide remote sensing dataset. The experimental results show that the mean pixel accuracy(mPA) and mean intersection over union (mIoU) of DeepLab V3+_SDF on the landslide dataset reach 95.38 % and 85.27 %, which are 2.90 % and 7.76 % higher than those of DeepLabV3+. Finally, the trained DeepLab V3+_SDF is applied to Sichuan-Chongqing region in China, and the comparison results with manual interpretation show that the algorithm can be used for rapid identification of landslides in large-scale mountainous areas.","PeriodicalId":402416,"journal":{"name":"2022 10th International Conference on Intelligent Computing and Wireless Optical Communications (ICWOC)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116708526","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

Research on Edge Computing and Caching Resource Allocation Mechanism for Multi-view Video 多视点视频的边缘计算与缓存资源分配机制研究

2022 10th International Conference on Intelligent Computing and Wireless Optical Communications (ICWOC) Pub Date : 2022-06-10 DOI: 10.1109/ICWOC55996.2022.9809887

Dongyao Wang, Xiaobao Sun, Y. Liu, Yuan Chen

{"title":"Research on Edge Computing and Caching Resource Allocation Mechanism for Multi-view Video","authors":"Dongyao Wang, Xiaobao Sun, Y. Liu, Yuan Chen","doi":"10.1109/ICWOC55996.2022.9809887","DOIUrl":"https://doi.org/10.1109/ICWOC55996.2022.9809887","url":null,"abstract":"Multi-View Video (MVV) is an emerging video technology that allows users to freely change their viewing angle when watching. Compared with traditional video transmission, multi-view video transmission requires large bandwidth and high computing power, which brings great challenges to multi-view video transmission under wireless networks. With the rapid development of Mobile Edge Computing (MEC) technology, this technology has become one of the potential solutions to the problem of multi-view video transmission in wireless networks by using edge caching and computing technology. This paper firstly establishes a communication model for multi-view video transmission, models different transmission paths in the process of multi-view video transmission, and jointly optimizes the design of edge computing and storage resources to maximize the hit rate of edge caching and computing. Further, a deep reinforcement learning algorithm is designed for the resource allocation mechanism of edge computing and storage. Finally, the simulation results verify that the algorithm can significantly improve the hit rate of edge computing and storage.","PeriodicalId":402416,"journal":{"name":"2022 10th International Conference on Intelligent Computing and Wireless Optical Communications (ICWOC)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132029658","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

Research on Radar Wave Avoidance for UAV Swarm Based on Improved Artificial Potential Field 基于改进人工势场的无人机群雷达避波研究

2022 10th International Conference on Intelligent Computing and Wireless Optical Communications (ICWOC) Pub Date : 2022-06-10 DOI: 10.1109/ICWOC55996.2022.9809862

Yuefan Xie, Ying Wang, Jiahang Wei, Jiarui Wang

引用次数: 1

Unsupervised Item-Related Recommendation Method Combining BERT and Collaborative Filtering 结合BERT和协同过滤的无监督项目相关推荐方法

2022 10th International Conference on Intelligent Computing and Wireless Optical Communications (ICWOC) Pub Date : 2022-06-10 DOI: 10.1109/ICWOC55996.2022.9809848

Jing Yu, Jingjing Shi, Mingxing Zhou, Wenhai Liu, Yunwen Chen, Fan Xiong

{"title":"Unsupervised Item-Related Recommendation Method Combining BERT and Collaborative Filtering","authors":"Jing Yu, Jingjing Shi, Mingxing Zhou, Wenhai Liu, Yunwen Chen, Fan Xiong","doi":"10.1109/ICWOC55996.2022.9809848","DOIUrl":"https://doi.org/10.1109/ICWOC55996.2022.9809848","url":null,"abstract":"Item-related recommendation is widely used in e-commerce, news, video, and other business scenarios, but there are problems such as sparse data, a large amount of implicit feedback data, limited sample annotation, cold start of items, poor serendipity, insufficient real-time performance, and the recommendation effect needs to be continuously improved. An unsupervised recommendation method is proposed. The method included four recall strategies. The first was to use the search engine and BM25 for real-time text matching recommendation about multi fields, and the second was to combine pre-trained language model BERT and ANN algorithm for real-time semantic matching recommendation about multi fields, and the third was to calculate the similarity by reducing the influence of popular items and active users to optimize the item-based collaborative filtering recommendation algorithm, and the fourth was to introduce the heat index based on Wilson Confidence Intervals to assist the recommendation ranking. Finally, the four recall results were merged and sorted to generate the final recommendation result. Through multiple sets of comparative experiments by the AlB test in the online recommendation system, it is shown that the proposed unsupervised recommendation method is superior to the baseline method in multiple indicators and can effectively improve the recommendation effect and user satisfaction.","PeriodicalId":402416,"journal":{"name":"2022 10th International Conference on Intelligent Computing and Wireless Optical Communications (ICWOC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133277943","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

Machine Learning Based Channel Estimation Optimization for OFDM Communication Systems 基于机器学习的OFDM通信系统信道估计优化

2022 10th International Conference on Intelligent Computing and Wireless Optical Communications (ICWOC) Pub Date : 2022-06-10 DOI: 10.1109/ICWOC55996.2022.9809897

Li Wang, Hui Li

{"title":"Machine Learning Based Channel Estimation Optimization for OFDM Communication Systems","authors":"Li Wang, Hui Li","doi":"10.1109/ICWOC55996.2022.9809897","DOIUrl":"https://doi.org/10.1109/ICWOC55996.2022.9809897","url":null,"abstract":"Internet of things (IOT) networks aim for providing significantly higher data rates. Typical IOT applications like power IOT involves increasing volume of data, which requires high performance data transmission. Orthogonal Frequency Division Multiplexing (OFDM) is currently promising for IOT. Estimation of maximum doppler shift (MDS) is inevitable for the channel response estimation in OFDM systems. To improve the accuracy and efficiency of channel estimation, we propose machine learning (ML) based MDS estimation method in this paper. Our method is based on the fact that the distribution of the instantaneous frequency offset (IFO) is related to the MDS. The ML algorithm is used to learn the functional relationship between the statistic of the IFO and the MDS. To make our method feasible in the realtime communication process, we further propose MDS estimation architecture. The functional relationship is obtained through the offline training and can be directly used in the communication process, thus greatly decreasing the implementation complexity. Simulation results indicate that our method is effective in a wide range of MDS and signal to noise ratio (SNR), and greatly improves the communication performance.","PeriodicalId":402416,"journal":{"name":"2022 10th International Conference on Intelligent Computing and Wireless Optical Communications (ICWOC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115852485","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