2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)最新文献_第7页

The inverse problem study of the motion of charged ions in a uniform magnetic field 均匀磁场中带电离子运动的反问题研究

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00040

Lianqin Jia, Bingzhan Liu, Jun Wang

引用次数: 0

Spatial and Temporal Monocular Visual Odometry 时空单目视觉里程计

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00089

M. Cao

引用次数: 0

The Design and Implementation of Beidou Ground-based Augmentation System in Natural Resources Industry 自然资源行业北斗地基增强系统的设计与实现

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00090

Hongjuan Liu, Wei Huang, C. Xiao, Jingpeng Wen, Zheng Lian, Zhixuan Zhang

引用次数: 1

Visualization Analysis of Stock Data and Intelligent Time Series Stock Price Prediction Based on Extreme Gradient Boosting 基于极端梯度提升的股票数据可视化分析及智能时间序列股票价格预测

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00057

Xuanhe Er, Yunqi Sun

{"title":"Visualization Analysis of Stock Data and Intelligent Time Series Stock Price Prediction Based on Extreme Gradient Boosting","authors":"Xuanhe Er, Yunqi Sun","doi":"10.1109/MLISE54096.2021.00057","DOIUrl":"https://doi.org/10.1109/MLISE54096.2021.00057","url":null,"abstract":"This paper discusses the stock market with a complex nonlinear system and establishes a time series prediction model based on XGBoost by using the idea and theoretical basis of chaos theory. This paper analyzes the daily trend, weekly trend, and monthly trend of the three stocks and forecasts the future rise and fall of the price trend obtained from the data of different stocks. Through the rise and fall data to verify and analyze the cyclical changes of the stock and summarize the law. In the end, from the visualization results of the model, the trend and closing price fitted by the model are good. Therefore, according to the comparative analysis, it can be concluded that the XGBoost model can basically predict the general trend of stock prices. Also, not all stocks have a price reversal cycle phenomenon, and the influencing factors and variables of the stock rise and fall are chaotic. In the future, the turnover rate can reflect the liquidity of the stock in the market. Also, our line graph makes it more fitting. The accuracy of the model can be higher. The code is available at https://github.com/Fickle519Istockforecastingmodelbasedon_XGboost.git.","PeriodicalId":430495,"journal":{"name":"2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132288524","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

Forecasting Short-term Power Grid Load Based on Recurrent Neural Network 基于递归神经网络的短期电网负荷预测

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00018

Qiqi Zhang

{"title":"Forecasting Short-term Power Grid Load Based on Recurrent Neural Network","authors":"Qiqi Zhang","doi":"10.1109/MLISE54096.2021.00018","DOIUrl":"https://doi.org/10.1109/MLISE54096.2021.00018","url":null,"abstract":"Electric power is an important energy source for national development. Power resources are not only closely related to people's lives, but also related to a country's economy. In addition, the three main processes of power generation, power transmission, and power consumption must be highly coupled together. In recent years, domestic power consumption has gradually increased, causing huge power consumption, which may lead to unexpected power outages and other major accidents. The relevant part must strike a balance between power supply quality and economic benefits. The predicted quality directly affects the economic feasibility and reliability of each power company. In this paper, combining the real power load data of a power plant in Slovakia and the real power load data of a certain region of my country, the two-layer LSTM model, the LSTM model combined with the Attention mechanism, and the two-branch LSTM model are proposed and verified. Based on the experimental results of the two datasets, this paper finds that because the two-branch LSTM model has the highest model complexity, it has the best fit to the load data, and the prediction results are the most accurate.","PeriodicalId":430495,"journal":{"name":"2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"516 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116228254","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

Exploration of the Core Research on Functional Logic Development and Testing of Integrated Vehicle Controllers for Electric Vehicles 电动汽车集成车辆控制器功能逻辑开发与测试的核心研究探索

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00099

Chen Zhang

{"title":"Exploration of the Core Research on Functional Logic Development and Testing of Integrated Vehicle Controllers for Electric Vehicles","authors":"Chen Zhang","doi":"10.1109/MLISE54096.2021.00099","DOIUrl":"https://doi.org/10.1109/MLISE54096.2021.00099","url":null,"abstract":"At this stage, new energy vehicle technology has become the main technology type in the current development of the automotive industry. The number of electronic control units in automobiles is also increasing, and the rapid development of power electronics technology has promoted the improvement of automotive electronics technology. At present, the effective integration of vehicle controllers with other types of electronic controllers can form an integrated vehicle controller. This is the main development trend of new energy vehicle electronic control system technology in the future. In the specific research process, researchers need to base on the logical requirements of electric vehicle power system and vehicle energy management functions. The researcher should put forward the relevant points of the integrated design of the vehicle controller and the motor controller according to the specific situation of the electric vehicle controller. This can complete the integrated vehicle controller system design, functional requirements, and functional logic analysis. In addition, this can also ensure that the integrated vehicle controller can fully play its role and promote the stable and long-term development of China's new energy vehicle industry.","PeriodicalId":430495,"journal":{"name":"2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"217 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121620127","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 Mining research of bachelor's degree dissertation of College of Electronic and electrical engineering in a university in China 国内某大学电子电气工程学院学士学位论文数据挖掘研究

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00084

L. Xuemei, Maryli F. Rosas

{"title":"Data Mining research of bachelor's degree dissertation of College of Electronic and electrical engineering in a university in China","authors":"L. Xuemei, Maryli F. Rosas","doi":"10.1109/MLISE54096.2021.00084","DOIUrl":"https://doi.org/10.1109/MLISE54096.2021.00084","url":null,"abstract":"This study tries to find out some rules and characteristics through the data mining of students' dissertation topics, such as the College of Electronic and Electrical Engineering, dissertation of undergraduate students majoring in Electrical Engineering and Automation in the past three years. The study employs the quantitative method of research. In this study, we will use the process of K-DD(Knowledge Discovery in Database). Using logical regression, it is to predict whether the student can reach the average level of students. Linear multiple regression was used to study the final score of students' graduation project depending on what factors. Through the method of decision tree, according to the basic level of each student and the subjects that he is good at, determine the direction of the paper topic that the student is suitable for. The conclusion of this paper shows that the main topics of the dissertation of Electrical Engineering and Automation are control, system design and simulation. The main software used is matlab. The results of logical regression and linear regression show that there is a sexual relationship between the final score of the thesis and the evaluation and defense performance of fellow teachers and instructors, and the final decision tree can also provide constructive guidance for the selection of the topic of the thesis.","PeriodicalId":430495,"journal":{"name":"2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125401119","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 Novel High Performance 3300V IGBT with Wide RBSOA 一种新型高性能3300V宽RBSOA光源

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00086

Mingchao Gao, Rui Jin, Y. Wang, Jiang Liu, Li Li, Cui Li, Ruifen Nie, Wanru Sun, J. Wu, Wujie Chao

引用次数: 1

Research and Development of Entity Extraction Based on Information Extraction 基于信息抽取的实体抽取技术研究与发展

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00076

Qi Guo, Dengyun Zhu, Yuguang Wang, F. Wan

引用次数: 0

Research on the Design of High-viscosity Fluid Globe Valve and Its Piping System Dynamic Characteristics Analysis 高粘度流体截止阀设计及管路系统动态特性分析研究

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00096

Haiyan Liu

引用次数: 0