2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA)最新文献

External validation of a deep learning prediction model for in-hospital mortality among ICU patients ICU患者住院死亡率深度学习预测模型的外部验证

2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA) Pub Date : 2022-01-21 DOI: 10.1109/ICPECA53709.2022.9718918

Shangping Zhao, Pan Liu, Guanxiu Tang, Yanming Guo, Guohui Li

{"title":"External validation of a deep learning prediction model for in-hospital mortality among ICU patients","authors":"Shangping Zhao, Pan Liu, Guanxiu Tang, Yanming Guo, Guohui Li","doi":"10.1109/ICPECA53709.2022.9718918","DOIUrl":"https://doi.org/10.1109/ICPECA53709.2022.9718918","url":null,"abstract":"With increasing hospital adoption of electronic health record (EHR) systems worldwide, a massive amount of EHR data are generated in intensive care practice, and deep learning models are increasingly applied in mortality prediction. However, due to the lack of external validation, it’s difficult to generalize the deep learning models in critical care settings. Our previous work proposed a routinely collected data based deep learning model for intensive care unit (ICU) mortality prediction. This study aimed to externally validate the model using a cohort from the published MIMIC III data set so as to examine the generalizability and feasibility of the model. With little changed in the modeling, the deep learning based model achieved a high accuracy (AUROC=0.90; AUPRC=0.70), and good calibration properties which was reflected by a brier score of 0.070, in the external validation database. The model’ excellent performance in our external validation cohort provides more evidence for the application of the deep learning model in clinical practice.","PeriodicalId":244448,"journal":{"name":"2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117180874","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 power equipment diagnosis based on big data 基于大数据的电力设备诊断研究

2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA) Pub Date : 2022-01-21 DOI: 10.1109/ICPECA53709.2022.9718959

Zichen Wang

{"title":"Research on power equipment diagnosis based on big data","authors":"Zichen Wang","doi":"10.1109/ICPECA53709.2022.9718959","DOIUrl":"https://doi.org/10.1109/ICPECA53709.2022.9718959","url":null,"abstract":"The continuous development of power system is an important guarantee for the rapid development of economy and science and technology in China. The emergence of smart grid has accelerated the development of power big data and artificial intelligence in power field. In smart grid, the state of power equipment determines the state of power system and is the decisive factor for the stable operation of power grid. spark distributed parallel processing system can meet the storage and calculation of big data, and provides a new research idea for realizing fault classification and diagnosis of power equipment under big data. The concept of deep learning provides a platform for the analysis and prediction of power equipment status under big data. This paper introduces the basic contents of power equipment handover test, analyzes the common fault types of power equipment in handover test, introduces the existing fault diagnosis methods, puts forward the scheme of power equipment state diagnosis-based on spark platform, and classifies the faults of power equipment by using the naive Bayesian network in spark, Taking the test data as sample input, the fault diagnosis of power equipment can be carried out. On this basis, through the research of deep learning network, the feasibility of power equipment fault diagnosis is analyzed. The test shows that the method used in this paper can diagnose and predict the state of power equipment effectively.","PeriodicalId":244448,"journal":{"name":"2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121339290","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

Homogeneous Charge Compression Ignition (HCCI) Engine Control Strategies and Fuel-air Mixture Preparation Research 均质压缩点火(HCCI)发动机控制策略及混合气制备研究

2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA) Pub Date : 2022-01-21 DOI: 10.1109/ICPECA53709.2022.9719253

X. Peng

引用次数: 0

Research on Risk Intelligent Assessment Method of IT Operation and Maintenance Based on Cloud Computing 基于云计算的IT运维风险智能评估方法研究

2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA) Pub Date : 2022-01-21 DOI: 10.1109/ICPECA53709.2022.9718883

Wanlin Yang

引用次数: 0

Algorithm Optimization Research on the Internet Value Distribution Based on Blockchain Technology 基于区块链技术的互联网价值分配算法优化研究

2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA) Pub Date : 2022-01-21 DOI: 10.1109/ICPECA53709.2022.9718970

Yongmei Sun, S. Lei

引用次数: 2

Research and Application of Deep Learning in Image Recognition 深度学习在图像识别中的研究与应用

2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA) Pub Date : 2022-01-21 DOI: 10.1109/ICPECA53709.2022.9718847

Yinglong Li

{"title":"Research and Application of Deep Learning in Image Recognition","authors":"Yinglong Li","doi":"10.1109/ICPECA53709.2022.9718847","DOIUrl":"https://doi.org/10.1109/ICPECA53709.2022.9718847","url":null,"abstract":"Deep learning is a technical tool with broad application prospects and has an important role in the field of image recognition. In view of the theoretical value and practical significance of image recognition technology in promoting the development of computer vision and artificial intelligence, this paper will review and study the application of deep learning in image recognition. This paper first outlines the development of icon recognition technology, and then introduces three main learning models in deep learning: convolutional neural networks, recurrent neural networks, and generative adversarial networks, and provides a comparative analysis of these three learning models. Finally, the research results of deep learning image recognition application fields, such as face recognition, medical image recognition, and remote sensing image classification, are analyzed and discussed. This paper also analyze the development trend of deep learning in the field of image recognition, and conclude that the future development direction is the effective recognition of video images and the theoretical strengthening of models.","PeriodicalId":244448,"journal":{"name":"2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115587496","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: 50

Research on uniform interface technology of radiotherapy equipment based on edge computing 基于边缘计算的放射治疗设备统一接口技术研究

2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA) Pub Date : 2022-01-21 DOI: 10.1109/ICPECA53709.2022.9719262

Yanjie Gong, Sufen Xie, Yanling Zhao

引用次数: 0

A Multilevel Graph Convolution Neural Network Model for Rumor Detection 基于多层图卷积神经网络的谣言检测模型

2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA) Pub Date : 2022-01-21 DOI: 10.1109/ICPECA53709.2022.9719043

Yuanyuan Ma, Shouzhi Xu, Fangmin Dong

引用次数: 1

Construction of English Aided Translation Learning System Based on Decision Tree Classification Algorithm 基于决策树分类算法的英语辅助翻译学习系统构建

2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA) Pub Date : 2022-01-21 DOI: 10.1109/ICPECA53709.2022.9719041

Yuanyuan Zhang

{"title":"Construction of English Aided Translation Learning System Based on Decision Tree Classification Algorithm","authors":"Yuanyuan Zhang","doi":"10.1109/ICPECA53709.2022.9719041","DOIUrl":"https://doi.org/10.1109/ICPECA53709.2022.9719041","url":null,"abstract":"English-assisted translation is one of the basic subjects for students to learn. Teachers are influenced by traditional teaching concepts in the process of English-assisted translation learning system. Using a single English-assisted translation learning system to guide students in English translation can not really expand students’ logical thinking consciousness and stimulate students’ enthusiasm for English translation. English-assisted translation learning system is never perfect. Bilingual sentence pairs are often wrongly arranged sentence by sentence, or due to human error, these sentences can not translate each other well. English translation-assisted learning system urgently needs an algorithm to optimize it. The decision tree classification algorithm is helpful for students to construct knowledge in English assisted translation. Through the decision tree classification algorithm, this paper can understand the relationship between the indicators of the construction of English assisted translation learning system, so as to guide students’ English assisted translation, so as to improve the construction of students’ English assisted translation learning system.","PeriodicalId":244448,"journal":{"name":"2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122551899","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

Damaged Insulator Detection Based on Matching Network 基于匹配网络的绝缘子破损检测

2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA) Pub Date : 2022-01-21 DOI: 10.1109/ICPECA53709.2022.9718880

Leiqing Ding, Jianjun Wang, Yunchu Mei

{"title":"Damaged Insulator Detection Based on Matching Network","authors":"Leiqing Ding, Jianjun Wang, Yunchu Mei","doi":"10.1109/ICPECA53709.2022.9718880","DOIUrl":"https://doi.org/10.1109/ICPECA53709.2022.9718880","url":null,"abstract":"Traditional transmission line inspection of the power system is mostly manual inspection, but with the emergence of higher voltage, higher power, longer distance transmission lines, and more complicated geographical environment which the line through, the application of helicopters or UAVs to complete the circuit inspection task has become the need of the development of the times. We use a neural network to process the images collected by the equipment and mark the transformers, circuit breakers, knife switches, transformers, power cables, insulators and other parts in the images. However, due to the unobvious rules of demaged parts, the hard-to-achieve manual labeling task, the lack of a large number of damaged parts of the image data, it is difficult to train an effective neural network for the screening of damaged parts. Moreover, gradients may disappear in high-level networks because of the scene is complexity and the components to be detected are likely to be occluded. In this paper, we use the idea of small sample learning matching network and match the semantic information of the image with the double attention model to propose a detection scheme that takes the detection of damaged insulators as an example.","PeriodicalId":244448,"journal":{"name":"2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122167205","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