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2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)最新文献

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Analysis of sentiment optimization on social networks based on statistical data 基于统计数据的社交网络情感优化分析
2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543193
Yingshi Chen
{"title":"Analysis of sentiment optimization on social networks based on statistical data","authors":"Yingshi Chen","doi":"10.1109/CSAIEE54046.2021.9543193","DOIUrl":"https://doi.org/10.1109/CSAIEE54046.2021.9543193","url":null,"abstract":"As technology is much more developed, it has already merged with the daily life of people. Because of the characteristic of the network— no limitation, part of people believe that they are closer to their family and friends, while it makes people feel lonelier since companions are virtual. Therefore, it is easier to cause negative emotions and then lead to a more serious downside such as depression if those emotions cannot be alleviated or even eliminated on time. In this paper, the author focuses on the analysis of how to minimize the negative emotions so that to avoid more serious problems. All data are collected from COVID-19 Real World Worry Dataset which is related to Twitter. The author utilizes the comparison of P-value for each variable and Stepwise. Selection method to identify the most effective factor for causing negative emotions (anxiety, worry, fear, anger, disgust, and sadness). The author found that the frequency of participants on Twitter is the most influential variable. In other words, it is important to study ways to relieve negative emotions from Twitter emotions cannot be alleviated or even eliminated on time.","PeriodicalId":376014,"journal":{"name":"2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129178314","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
Momentum Method for Improving the Convergence of Newton-Raphson Method for Nonlinear Circuit Transient Simulations 改进非线性电路暂态仿真牛顿-拉夫逊法收敛性的动量法
2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543125
Wen Hu
{"title":"Momentum Method for Improving the Convergence of Newton-Raphson Method for Nonlinear Circuit Transient Simulations","authors":"Wen Hu","doi":"10.1109/CSAIEE54046.2021.9543125","DOIUrl":"https://doi.org/10.1109/CSAIEE54046.2021.9543125","url":null,"abstract":"With the growing scale of circuits designed nowadays, the computational efficiency of transient circuit simulations that verify the behavior of circuits becomes an important topic. The popular Newton-Raphson method applied in simulation programs like SPICE exhibit convergence issues when processing circuits with a mix of linear and nonlinear devices. Traditional methods proposed to tackle these issues are either effective to a limited group of circuits or entail more computation. This paper proposes the momentum method to improve convergence without requiring additional computation, demonstrates its effectiveness, and provides experimental results on an example.","PeriodicalId":376014,"journal":{"name":"2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)","volume":"503 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124455150","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 Countermeasures of Improving Competitiveness of Equipment Manufacturing Enterprises Based on Factor Analysis 基于因子分析的装备制造企业竞争力提升对策研究
2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543202
Z. Yin, Jin Yin
{"title":"Research on Countermeasures of Improving Competitiveness of Equipment Manufacturing Enterprises Based on Factor Analysis","authors":"Z. Yin, Jin Yin","doi":"10.1109/CSAIEE54046.2021.9543202","DOIUrl":"https://doi.org/10.1109/CSAIEE54046.2021.9543202","url":null,"abstract":"In order to comprehensively evaluate enterprise competitiveness and formulate sustainable development strategy of equipment manufacturing enterprise, according to operational characteristics of equipment manufacturing enterprise the paper establishes a evaluating system of enterprise competitiveness. Simultaneously the paper establishes factorial scoring model of debt paying capability, operating capability, profit capability, growth capability and market capacity of equipment manufacturing enterprise by applying modern factorial analysis of modern multivariate statistics. By computing composite score the paper realizes comprehensive evaluation of competitive capability of equipment manufacturing enterprise. By means of dynamic variation condition of competitive capability of equipment manufacturing enterprise the paper establishes strategic matrix diagram of enterprise competitive capability-sustainable development, so it will provide scientific evidence for formulating sustainable development strategy of equipment manufacturing enterprise.","PeriodicalId":376014,"journal":{"name":"2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131654636","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
ParallelMOT: Pay More Attention in Tracking ParallelMOT:更注重跟踪
2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543153
Changzhi Lv, Changdong Shu, Yingjun Lv, Chunsheng Song
{"title":"ParallelMOT: Pay More Attention in Tracking","authors":"Changzhi Lv, Changdong Shu, Yingjun Lv, Chunsheng Song","doi":"10.1109/CSAIEE54046.2021.9543153","DOIUrl":"https://doi.org/10.1109/CSAIEE54046.2021.9543153","url":null,"abstract":"Modern multiple object tracking has made great progress of the JDE model. Because the JDE model uses a shared model, its calculation speed and accuracy have been greatly improved. But using the same network to predict detection and re- ID will affect each other when the network feedback, thereby reducing the MOTA (Evaluation Measures for MOTChallenge) accuracy, and when the network detects the object and ID information separately, it will greatly increase the computing time. We propose a new MOT method named ParallelMOT, which uses two different branches to reduce the mutual influence of network feedback, and uses object information fusion to improve the feature extraction of the object, and uses a new network model to predict embedding for achieving better MOT accuracy.","PeriodicalId":376014,"journal":{"name":"2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115156148","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
Measuring Hilbert-Schmidt Independence Criterion with Different Kernels 用不同核测量Hilbert-Schmidt独立准则
2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543403
Chenge Hu, Huaqing Zhang, Yuyu Zhou, Ruixin Guan
{"title":"Measuring Hilbert-Schmidt Independence Criterion with Different Kernels","authors":"Chenge Hu, Huaqing Zhang, Yuyu Zhou, Ruixin Guan","doi":"10.1109/CSAIEE54046.2021.9543403","DOIUrl":"https://doi.org/10.1109/CSAIEE54046.2021.9543403","url":null,"abstract":"Hilbert-Schmidt independence criterion (HSIC) which is a kernel-based method for testing statistical dependence between two random variables. It is widely applied in a variety of areas. However, this approach comes with a question of the selection of kernel functions. In this paper, we conduct an experiment using the forest fire data from UCI in the context of independence test, contrasting four commonly used kernel functions: Linear kernels, Gaussian kernels, Brownian kernels, Matern kernels. Through comparing p-value and rejection rate of hypothesis test we constructed; it is shown that the different choices in associated kernel function of HSIC give comparable performance on results.","PeriodicalId":376014,"journal":{"name":"2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114766318","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
Improve Semantic Segmentation of Remote sensing Images with K-Mean Pixel Clustering: A semantic segmentation post-processing method based on k-means clustering 利用k-均值像素聚类改进遥感图像语义分割:一种基于k-均值聚类的语义分割后处理方法
2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543336
Xiaohui Zeng, Isabelle Chen, Pai Liu
{"title":"Improve Semantic Segmentation of Remote sensing Images with K-Mean Pixel Clustering: A semantic segmentation post-processing method based on k-means clustering","authors":"Xiaohui Zeng, Isabelle Chen, Pai Liu","doi":"10.1109/CSAIEE54046.2021.9543336","DOIUrl":"https://doi.org/10.1109/CSAIEE54046.2021.9543336","url":null,"abstract":"Semantic image segmentation has been used to detect objects and label pixels in images. It has been applied to high-resolution remote sensing images to detect different types of terrains and landforms. However, the accuracy of the existing methods is not always satisfactory. Here we propose a semantic segmentation post-processing method using K-mean clustering. Our method aggregates the predictions from network training algorithms such as Unet and HrNet [1], and then performs postprocessing using K-Mean clustering iteratively [2] [3]. The accuracy of our method improves as the number of iterations increases. Source code is at https://github.com/carlsummer/SSK.","PeriodicalId":376014,"journal":{"name":"2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133370780","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
Prediction of Development Prospect of Electric Vehicles in China by Using Natural Language Processing 基于自然语言处理的中国电动汽车发展前景预测
2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543175
S. Zheng, Qianhui Jin, Longtao Wang, Haoran Zhang
{"title":"Prediction of Development Prospect of Electric Vehicles in China by Using Natural Language Processing","authors":"S. Zheng, Qianhui Jin, Longtao Wang, Haoran Zhang","doi":"10.1109/CSAIEE54046.2021.9543175","DOIUrl":"https://doi.org/10.1109/CSAIEE54046.2021.9543175","url":null,"abstract":"It is believed that people's comments on a certain product may affect its sales condition. In this paper, we propose a method to predict the sales of electric vehicles by analyzing people's comments on social media. We scrap user comments from a Chinese social media “Weibo” and try to predict the electric vehicle sales in China by using Natural Language Processing (NLP). Sentiment score, number of comments and likes, and keyword existence are treated as input indicators. We test linear regression, random forest, and gradient boosting algorithm during the experiment. The result shows that the model which using gradient boosting algorithm to predict the market share of electric vehicles has the best performance.","PeriodicalId":376014,"journal":{"name":"2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116044066","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
Adaptive firefly algorithm based on reverse search strategy 基于反向搜索策略的自适应萤火虫算法
2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543395
H. Yin, Huipeng Meng, YuChen Zhang
{"title":"Adaptive firefly algorithm based on reverse search strategy","authors":"H. Yin, Huipeng Meng, YuChen Zhang","doi":"10.1109/CSAIEE54046.2021.9543395","DOIUrl":"https://doi.org/10.1109/CSAIEE54046.2021.9543395","url":null,"abstract":"Firefly algorithm is proposed by Prof. Yang Xinshe for solving global optimization problems, which uses the principle of mutual attraction of fireflies in nature. The firefly algorithm is a branch of evolutionary algorithms, which is often used to solve single-objective global optimization problems with fewer parameters, easy to implement and easy to understand. However, the traditional firefly algorithm uses the full-attraction model in updating, which is easy to fall into local optimum. Therefore, a firefly algorithm that performs backward search with Poisson distributed probabilities is proposed, which enables the firefly to search more widely in the solution space and easily jump out of the local optimum. Comparative experiments are conducted on 28 functions of the CEC2013 test set. The experimental results show that in 22 of the functions the firefly with the reverse search strategy performs more accurately than the other improved firefly algorithms and in 26 of the functions the firefly with the reverse search strategy converges faster than the other fireflies.","PeriodicalId":376014,"journal":{"name":"2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130180799","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
A Solution for Metropolis: Autonomous Transportation Hub System Using OpenCV Algorithm 基于OpenCV算法的大都市自主交通枢纽系统解决方案
2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543152
Kun Wang
{"title":"A Solution for Metropolis: Autonomous Transportation Hub System Using OpenCV Algorithm","authors":"Kun Wang","doi":"10.1109/CSAIEE54046.2021.9543152","DOIUrl":"https://doi.org/10.1109/CSAIEE54046.2021.9543152","url":null,"abstract":"The detector algorithm uses OpenCV as base with real-time image processing to detect useful pixels. The algorithm provides numerical result of the amount of vehicles in terminal. The method being elucidated in the article is sufficient for a traffic lighting system with simple principles that reduces the cost of producing the system integrated chips massively. The final objective after the installation of the system is to eliminate the redundant seconds that people have to spend in the crossings so that more seconds will be given to pathways with more vehicles.","PeriodicalId":376014,"journal":{"name":"2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130415720","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
Data Center Networks: Address Scheming, Traffic Distribution, and Load Balancing 数据中心网络:地址规划、流量分配和负载均衡
2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543259
Jinyang Jia, Gongxuan Liu
{"title":"Data Center Networks: Address Scheming, Traffic Distribution, and Load Balancing","authors":"Jinyang Jia, Gongxuan Liu","doi":"10.1109/CSAIEE54046.2021.9543259","DOIUrl":"https://doi.org/10.1109/CSAIEE54046.2021.9543259","url":null,"abstract":"Data center networks are a critical part in building a powerful data center, while tasks may start at any container and VMs may move around in physical machines as well. Not mentioning network traffic should be balanced while using network resources such as switches as efficient as possible. This paper summarizes two papers that talks about the data center networks. The first section summarizes a paper that describes efficient protocols in data centers, PortLand[1], where VMs could move to new physical machines without breaking TCP connection and achieve fast plug-and-play for new machine. The second section summarizes a survey paper which studies one of Facebook's data centers that provides web services. It mainly focuses on network traffic stability on server hosts and the traffic patterns on switches with different responsibilities.","PeriodicalId":376014,"journal":{"name":"2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129838502","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
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