2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE)最新文献

Research on End-to-End Programmable Transmission Technology for Ship Manufacturing 船舶制造端到端可编程传输技术研究

2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE) Pub Date : 2022-03-18 DOI: 10.1109/icicse55337.2022.9828883

Dongyao Wang, Y. Liu, Fan Yang, Fangmin Xu

引用次数: 0

Transformer-Based Deep Learning Method for the Prediction of Ventilator Pressure 基于变压器的呼吸机压力预测深度学习方法

2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE) Pub Date : 2022-03-18 DOI: 10.1109/icicse55337.2022.9828926

Rui Fan

{"title":"Transformer-Based Deep Learning Method for the Prediction of Ventilator Pressure","authors":"Rui Fan","doi":"10.1109/icicse55337.2022.9828926","DOIUrl":"https://doi.org/10.1109/icicse55337.2022.9828926","url":null,"abstract":"As patients infected with SARS-CoV-2 are more likely to have trouble breathing, a great demand for ventilators has been generated since the COVID-19 is continuing to spread around the world. However, the research and development of ventilator control suffer from high cost, slow efficiency, and lack of automation, especially regarding the estimation of ventilator pressure. In this paper, to address this challenge and help control the mechanical ventilators better, we develop a Transformer-based deep learning method for the prediction of ventilator pressure. Based on the dataset provided by Google Brain in a Kaggle competition, we connect the Transformer encoders by residual connections to extract features from the time-series ventilator data, and successfully achieve the goal to predict the ventilator pressure with excellent performance. After applying the K-Fold cross validation technique, our Transformer-based model reaches a mean absolute error 0.1311 on the private test set. This result ranks 67/2605 (top 2.6%) in the leaderboard of Google Brain - Ventilator Pressure Prediction competition, and can get a silver medal in this Kaggle competition. This work could accelerate the development of new methods to overcome the cost barrier of ventilator control.","PeriodicalId":177985,"journal":{"name":"2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122842367","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

A Rudimentary Proof on Goldbach Conjectures 哥德巴赫猜想的初步证明

2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE) Pub Date : 2022-03-18 DOI: 10.1109/icicse55337.2022.9828990

Talal Al-Ameen, Imad Muhi

引用次数: 1

Design and Implementation of a Perioperative Medical Data Quality Management Platform 围手术期医疗数据质量管理平台的设计与实现

2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE) Pub Date : 2022-03-18 DOI: 10.1109/icicse55337.2022.9828956

Jie Cao, Ju Zhang, Xiaoguang Lin, An Long Sun

引用次数: 0

Knowledge Graph Based Adversarial Radar Threat Assessment 基于知识图谱的对抗雷达威胁评估

2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE) Pub Date : 2022-03-18 DOI: 10.1109/icicse55337.2022.9828881

Chenyu Zhu, Yue Li, Xinyue Hou, Peng Wang, Xiaoyan Peng

引用次数: 0

Image Matching Based on Fast PCA-SIFT Descriptors with Automatic Determination of Dimensionality for PCA 基于快速PCA- sift描述子的PCA维数自动确定图像匹配

2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE) Pub Date : 2022-03-18 DOI: 10.1109/icicse55337.2022.9828915

Yi Zheng, Ping Zheng

引用次数: 0

Text Analysis Method Based on Multi-channel Parallel Classifier 基于多通道并行分类器的文本分析方法

2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE) Pub Date : 2022-03-18 DOI: 10.1109/icicse55337.2022.9828968

Bingliang Lu, Zhihao Lin, Xindong Zhang

引用次数: 0

Multi-view User Preference Learning with Knowledge Graph for Recommendation 基于知识图谱的多视图用户偏好学习推荐

2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE) Pub Date : 2022-03-18 DOI: 10.1109/icicse55337.2022.9828877

Yiming Zhang, Yitong Pang, Zhihua Wei

{"title":"Multi-view User Preference Learning with Knowledge Graph for Recommendation","authors":"Yiming Zhang, Yitong Pang, Zhihua Wei","doi":"10.1109/icicse55337.2022.9828877","DOIUrl":"https://doi.org/10.1109/icicse55337.2022.9828877","url":null,"abstract":"To learn more comprehensive user preference, existing works in recommendation propose to utilize side information, like Knowledge Graph (KG). However, the user representation can come from many views, including ID attributes of the user, collaborative signals of interaction history, and fine-grained preferences in KG, which has not been well studied in previous works. To address the limitation, in this work, we propose the Multi-view User Preference Learning with knowledge graph (MUPL) for recommendation to address the limitation. Specifically, we propose to employ Gate Recurrent Unit (GRU) to learn the user latent collaborative feature from interaction sequence. Besides, we design a Knowledge Graph Attention Network (KGANet) to capture user fine-grained preference for the entities related with the items. Then we fusion user ID attributes, the collaborative feature and fine-grained preference for entities into the user final representation. Similarly, we employ an item encoder to get the item final representation. Finally, a predictor is proposed for recommendation. Extensive experiments on three public datasets show that our model outperforms the state-of-the-art (SOTA) methods on effectiveness.","PeriodicalId":177985,"journal":{"name":"2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE)","volume":"311 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123459041","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

5G Wireless Intelligent Propagation Model and Application of Simulation Engine in Production 5G无线智能传播模型及仿真引擎在生产中的应用

2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE) Pub Date : 2022-03-18 DOI: 10.1109/icicse55337.2022.9828923

Zhong Xiang, Wen-Jin Niu, Pei Zhao, Feng Gao, Shumin Jiang, Nan Cheng, Xuemin Huang, Fan Chen, Bowei Pu, Li Peng, Xiaohui Zhang

引用次数: 1

AGC-UNet:A Global Context Feature Fusion Method Based On U-Net for Retinal Vessel Segmentation AGC-UNet:一种基于U-Net的全局上下文特征融合视网膜血管分割方法

2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE) Pub Date : 2022-03-18 DOI: 10.1109/icicse55337.2022.9828894

Xueyin Fu, Ning Zhao

{"title":"AGC-UNet:A Global Context Feature Fusion Method Based On U-Net for Retinal Vessel Segmentation","authors":"Xueyin Fu, Ning Zhao","doi":"10.1109/icicse55337.2022.9828894","DOIUrl":"https://doi.org/10.1109/icicse55337.2022.9828894","url":null,"abstract":"Computer-aided retinal vascular segmentation plays an irreplaceable role in the diagnosis of hypertension, retinal vascular occlusion, diabetic and other diseases. In this paper, we propose a global context feature fusion retinal vessel segmentation model based on U-Net, named AGC-UNet, which utilizes the encoding and decoding network, and uses Globle Context Block (GCB) in the encoding and decoding path to enhance the global context fusion of vascular features, and did not introduce a large amount of computation. In addition, Attention Gate Block(AGB) is added into the jump connection part to enhance the spatial extraction ability of vascular features, to weaken the ability of learning unrelated areas, and to improve the ability of vascular segmentation. AGC-UNet model is experienced respectively in the open datasets DRIVE and CHASE_DB1 and evaluating indicators of accuracy(Acc) in these two datasets are 0.9653 and 0.9646 respectively, 0.8347 and 0.8206 in sensitivity(Se), 0.9851 and 0.9791 in specificity(Sp) and 0.8639 and 0.8095 in F1-Score(F1). Compared with the newest existing methods, this method performs an outstanding performance in retinal vascular segmentation.","PeriodicalId":177985,"journal":{"name":"2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126493849","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: 2