2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)最新文献_第2页

A Practical Base Station Location Optimization Based On Four Networks Integration 基于四网融合的实用基站位置优化

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00091

Zhou Chunli, C. Zhijun

{"title":"A Practical Base Station Location Optimization Based On Four Networks Integration","authors":"Zhou Chunli, C. Zhijun","doi":"10.1109/ICCEAI52939.2021.00091","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00091","url":null,"abstract":"2G, 3G, 4G and WLAN (Wireless Local Area Networks) form the four network integration. The communication transmission rate and the related spectrum efficiency are limited in traditional mobile communication. Depending on key technology of four network integration, TD-LTE (Time Division Long Term), upward peak rate could be up to 50Mbps unit and descending peak rate reaches up to 100Mbps unit. But the relation between 2G, 3G, 4G and WLAN is complicated. The incorrect station location may increase the cost of network system, and even bring great difficulties to the network operation and maintenance. Because determining base station location appropriately is essential, we max the research between the genetic algorithm and the greedy algorithm to solve this issue. In this paper, relay wireless backhaul technology is assumed, and two types of base stations, RuralStar station and butterfly antenna station, are considered. In order to find out the best base station allocation to achieve the minimum cost and the maximum coverage, we make use of subpopulation initialization, fracture hybridization and mutation of genetic algorithm based on greedy algorithm.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131653030","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 License Plate Detection and Recognition Based on Deep Learning 基于深度学习的车牌检测与识别研究

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00081

Lei Gao, Weibin Zhang

引用次数: 2

Intelligent Modeling Method of Proton Exchange Membrane Fuel Cell Based on Grey Theory 基于灰色理论的质子交换膜燃料电池智能建模方法

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00068

JianFeng Zhao, Yifan Liang, Qianchao Liang, Mengjie Li

{"title":"Intelligent Modeling Method of Proton Exchange Membrane Fuel Cell Based on Grey Theory","authors":"JianFeng Zhao, Yifan Liang, Qianchao Liang, Mengjie Li","doi":"10.1109/ICCEAI52939.2021.00068","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00068","url":null,"abstract":"Numerical modeling is an important supplementary means for the study on fuel cell power system. Therefore, the research on modeling method has been a hot topic in academia from Lumped parameter modeling to three-dimensional modeling. But above modeling approaches share the common feature that the accuracy of the model is highly dependent on the key parameters' data veracity in the cell (e.g., conductivity, exchange current density, electrode area, etc.). However, these parameters are often difficult to determine for research work on commercial fuel cell applications and require extensive experimentation or even disassembly of the fuel cell for internal measurements. This paper proposes an intelligent modeling method for proton exchange membrane fuel cell (PEMFC) based on Grey Theory, and filter the optimal model by analyzing and comparing the simulation accuracy of several sub-models. The results show that the proposed intelligent modeling method can build a fuel cell model using limited experimental data and ensure the simulation accuracy, which can simplify the modeling of proton exchange membrane fuel cell work.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132775437","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

Target Locating of Robots Based on the Fusion of Binocular Vision and Laser Scanning 基于双目视觉与激光扫描融合的机器人目标定位

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00084

Ze Lv, Lecai Cai, Zhiming Wu, Kui Cheng, B. Chen, Keyuan Tang

引用次数: 0

Aspect oriented Sentiment classification of COVID-19 twitter data; an enhanced LDA based text analytic approach 面向方面的COVID-19 twitter数据情感分类一种基于LDA的增强文本分析方法

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00054

Junaid Abdul Wahid, S. Hussain, Hailing Wang, Zhaoyang Wu, Lei Shi, Yufei Gao

{"title":"Aspect oriented Sentiment classification of COVID-19 twitter data; an enhanced LDA based text analytic approach","authors":"Junaid Abdul Wahid, S. Hussain, Hailing Wang, Zhaoyang Wu, Lei Shi, Yufei Gao","doi":"10.1109/ICCEAI52939.2021.00054","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00054","url":null,"abstract":"Social media has become one of the most important sources of information dissemination during crisis and pandemics. The unknown nature of these disasters makes it hard to analyze the comprehensive situational awareness through different aspects and sentiments to support authorities. Current aspect detection and sentiment analysis system largely relies on labelled data and also categorize the aspects manually. So, in this research, we proposed a hybrid text analytical framework to do aspect level public sentiments analysis. Our approach consists of three layers, first we extracted and clustered the aspects from the data by utilizing the widely used Latent dirichlet allocation (LDA) topic modelling, then we extracted the sentiments and label the dataset by using the linguistic inquiry and word count (LIWC) lexicon, then in third layer of our framework we mapped the aspects into sentiments and sentiments are then classified with well-known machine learning classifiers. Experiments with real dataset gives us promising results as compared to existing aspect oriented sentiment analysis approaches and our method with different variant of classifiers outperforms existing methods with highest F1 scores of 91 %.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114886460","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: 3

Improve the interpretability by decision tree regression: exampled by an insurance dataset 通过决策树回归提高可解释性:以保险数据集为例

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00065

Shuyuan Dong, Dingzhou Fei

引用次数: 2

Bearing fault diagnosis based on attention mechanism and deep residual network 基于注意机制和深度残差网络的轴承故障诊断

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00057

Xinna Ma, Lin Qi, Meng Zhao

{"title":"Bearing fault diagnosis based on attention mechanism and deep residual network","authors":"Xinna Ma, Lin Qi, Meng Zhao","doi":"10.1109/ICCEAI52939.2021.00057","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00057","url":null,"abstract":"A bearing fault diagnosis model based on the deep residual network is proposed for the situation that the model recognition rate is low and the classification effect is poor due to the large difference of fault sample distribution in the actual working condition. Firstly the collected bearing fault signals are constructed as fault samples, reconstructs the one-dimensional time series signals into grayscale maps, and initially obtains the input data which is suitable for the deep residual network. To solve the situation of insufficient effective samples, data augmentation by sliding sampling is used to expand the bearing vibration dataset; the samples are further divided into training and testing sets as the input of ResNet101, and data normalization is used to make the training and testing sets learn the same distribution to shorten the training time; then a hybrid attention mechanism is introduced at the appropriate parts to effectively suppress the redundant features and enhance the feature extraction capability of the model. And then a softmax classifier is used for fault classification to achieve intelligent fault diagnosis of rolling bearings. Finally, the Western Reserve University bearing dataset (CWRU) is used to verify the effectiveness of the model. The experimental results show that the proposed bearing fault diagnosis method based on hybrid attention mechanism and residual network can achieve more than 99 % diagnostic accuracy, and it achieves good generalization performance on the high-speed rail wheel pair dataset with an accuracy above 94 %.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123716171","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

Development Survey of a Monitoring and Early Warning System for Banks and Dams of Expansive Soil 膨胀土坝坝监测预警系统研制概况

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00017

X. Shen, Chenghui Dong, Weiwei Liu, Yan Lan, M. Zhang

{"title":"Development Survey of a Monitoring and Early Warning System for Banks and Dams of Expansive Soil","authors":"X. Shen, Chenghui Dong, Weiwei Liu, Yan Lan, M. Zhang","doi":"10.1109/ICCEAI52939.2021.00017","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00017","url":null,"abstract":"Expansive soil is a kind of soil with special properties, widely distributed, difficult treatment technology, and affecting the engineering safety of banks and dams. It is particularly necessary to summarize the development status and trend of the monitoring and warning system of banks and dams for expansive soil based on the characteristics of expansion soil. Based on extensive literatures, the progress of the hardware and software were systematically analyzed for the monitoring and warning system of banks and dams for expansive soil. Specially, the hardware mainly includes: sensors, data acquisition, data transmission, and postprocessing platform and so on; The software mainly includes: multi-sensor integration, data sharing, post-processing, human-computer interface, and route planning of unmanned aerial vehicle and so on. Accordingly, the research directions of the hardware and software of monitoring and early warning system are put forward, and the results will be used for references in the development of monitoring and warning system, engineering construction, engineering management, and emergency management and so on for banks and dams of expansive soil.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125329213","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

Infrared and Low Light Image Registration from Coarse-to-Fine Matching 从粗到精匹配的红外和微光图像配准

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00024

Jiahui Wang, Zhengyou Wang, W. Lu, Shanna Zhuang

引用次数: 0

Application of Fuming Tablet Combined with Liraglutide in the Treatment of Diabetic Retinopathy 烟熏片联合利拉鲁肽治疗糖尿病视网膜病变的应用

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00093

Lijuan Gao, Lingling Wu

引用次数: 0