2023 Asia-Europe Conference on Electronics, Data Processing and Informatics (ACEDPI)最新文献

Prediction of PM2.5 Concentration Based on CNNLSTM Deep Learning Model 基于CNNLSTM深度学习模型的PM2.5浓度预测

2023 Asia-Europe Conference on Electronics, Data Processing and Informatics (ACEDPI) Pub Date : 2023-04-01 DOI: 10.1109/ACEDPI58926.2023.00051

Yuxuan Xie, Xinxin Chen, Lejun Zhang

{"title":"Prediction of PM2.5 Concentration Based on CNNLSTM Deep Learning Model","authors":"Yuxuan Xie, Xinxin Chen, Lejun Zhang","doi":"10.1109/ACEDPI58926.2023.00051","DOIUrl":"https://doi.org/10.1109/ACEDPI58926.2023.00051","url":null,"abstract":"Rational prediction of $PM_{2.5}$ concentration can effectively prevent and control atmospheric environmental pollution. To improve the accuracy of short-term $PM_{2.5}$ concentration prediction, the paper proposes a combined CNN-LSTM prediction model combining CNN and LSTM networks. The model first automatically extracts the spatial features of the dataset set using a CNN and a one-dimensional convolutional kernel function, and then uses a multilayer LSTM network to capture the time-dependent features of the sequence, then introduces a Dropout layer and trains the model with the Adam optimization algorithm mechanism to improve the operational efficiency. Finally, a deep neural network with a single hidden layer is used in the fully connected layer to fit and predict the data and output the predicted value. The paper predicts $PM_{2.5}$ concentrations using Beijing air pollutant concentration data and historical meteorological data from 2014-1-1 to 2022-7-5 to fully extract the spatial and temporal characteristics of multivariate nonlinear series. The results show that the optimization of the CNN-LSTM model on the LSTM model can provide a more accurate data basis, which is used in formulating air pollution prevention and control countermeasures.","PeriodicalId":124469,"journal":{"name":"2023 Asia-Europe Conference on Electronics, Data Processing and Informatics (ACEDPI)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126780752","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 Visualization of Multi-Source Heterogeneous Data Based on Statistical Maps and Knowledge Graph 基于统计图和知识图的多源异构数据可视化研究

2023 Asia-Europe Conference on Electronics, Data Processing and Informatics (ACEDPI) Pub Date : 2023-04-01 DOI: 10.1109/ACEDPI58926.2023.00055

Yibo Liu, Qinyun Zuo, Shenmin Zhang, Teng Zong

引用次数: 0

Oilfield Intelligent Production Optimization and System Simulation Based on Ant Colony Algorithm 基于蚁群算法的油田智能生产优化及系统仿真

2023 Asia-Europe Conference on Electronics, Data Processing and Informatics (ACEDPI) Pub Date : 2023-04-01 DOI: 10.1109/ACEDPI58926.2023.00050

Haochen Wang, Kaiwen Zhang, Chengcheng Liu

{"title":"Oilfield Intelligent Production Optimization and System Simulation Based on Ant Colony Algorithm","authors":"Haochen Wang, Kaiwen Zhang, Chengcheng Liu","doi":"10.1109/ACEDPI58926.2023.00050","DOIUrl":"https://doi.org/10.1109/ACEDPI58926.2023.00050","url":null,"abstract":"There are various forms of underground oil deposits in oil fields, complex surface processes, exploration and development, production and operation, involving many departments and complex processes. Many factors determine that the intelligent oilfield production optimization management system is a complex system engineering. The purpose of this paper is to study the intelligent oilfield production optimization and system simulation based on the ant colony algorithm. The algorithm involved in the neural network optimization model ACO-BP is briefly introduced, and the model building process is described by specific steps of model building. The system simulation environment is Python 3.7.0, and the parameters of the network model are executed. Finally, the results of the experiment are analyzed, and the prediction effect of each model is intuitively shown by the comparison diagram of the model prediction curve. From the experiment results, it can be seen that the ant colony algorithm based system is used to optimize intelligent production management in oilfield production enhancement measures. It has a good effect in effect prediction.","PeriodicalId":124469,"journal":{"name":"2023 Asia-Europe Conference on Electronics, Data Processing and Informatics (ACEDPI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128438100","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 and Demonstration of a Prediction Algorithm Based on GBDT-LightGBM Algorithm 基于GBDT-LightGBM算法的预测算法研究与论证

2023 Asia-Europe Conference on Electronics, Data Processing and Informatics (ACEDPI) Pub Date : 2023-04-01 DOI: 10.1109/ACEDPI58926.2023.00049

Houzhi Chen, Zichun Liu, Minyan Dai

{"title":"Research and Demonstration of a Prediction Algorithm Based on GBDT-LightGBM Algorithm","authors":"Houzhi Chen, Zichun Liu, Minyan Dai","doi":"10.1109/ACEDPI58926.2023.00049","DOIUrl":"https://doi.org/10.1109/ACEDPI58926.2023.00049","url":null,"abstract":"This paper aims to use the LightGBM algorithm to predict housing prices in Chinese municipalities. According to previous research experience, from the demand level, supply level, and regulation policy three main aspects as the main influencing factors of housing prices are analyzed and predicted. In the case analysis, the determination coefficient (R-Square) and the average absolute percentage error (MAPE) are used to test the accuracy of the model, and the Kendall tau-b (K) method in the Kendall coefficient is used for correlation analysis and consistency test. After eliminating the repeatability index, the Kendall coordination coefficient W of the model is 0.977. After selecting the appropriate influencing factors and data, the Light Gradient Boosting Machine is trained by using the gradient boosting decision tree GBDT as the base learner and SPSS-PRO software. It is found that the model has the highest accuracy when the number of base learners is 500. From the training results, the influence degree of demand and policy level is the largest, and the influence degree of supply level is small. The influence degree of the three is 47 %, 43 %, and 10 %. In the secondary indicators, the main business tax and additional, urbanization rate, and a loan amount of real estate development enterprises have a greater impact. The R-square of the training set and the test set are 0.905 and 0.902, respectively. The accuracy of the model is high, which provides an effective reference for housing price prediction.","PeriodicalId":124469,"journal":{"name":"2023 Asia-Europe Conference on Electronics, Data Processing and Informatics (ACEDPI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127048610","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

Trading strategy prediction model based on quadratic programming and XGBoost 基于二次规划和XGBoost的交易策略预测模型

2023 Asia-Europe Conference on Electronics, Data Processing and Informatics (ACEDPI) Pub Date : 2023-04-01 DOI: 10.1109/ACEDPI58926.2023.00040

Shuaikai Ding, Shaobo Ding, Tianshuo Ding

引用次数: 0

Information Security Integration Model of Computerized Accounting System Based on Deep Learning 基于深度学习的会计电算化系统信息安全集成模型

2023 Asia-Europe Conference on Electronics, Data Processing and Informatics (ACEDPI) Pub Date : 2023-04-01 DOI: 10.1109/ACEDPI58926.2023.00027

Jiong Guo

{"title":"Information Security Integration Model of Computerized Accounting System Based on Deep Learning","authors":"Jiong Guo","doi":"10.1109/ACEDPI58926.2023.00027","DOIUrl":"https://doi.org/10.1109/ACEDPI58926.2023.00027","url":null,"abstract":"With the further development of China’s capital market system, accounting computerization is in urgent need of injecting fresh blood. In view of the surge of financing needs of small and medium-sized growth and innovative enterprises, it serves as a further expansion of the service scope of the main board market. Growth Enterprise Market (GEM) was officially listed in 2009, with a history of more than ten years. As a typical representative of the real economy, once the listed companies have financial difficulties, they will not only suffer huge losses, but also seriously damage the interests of investors, and may even have a huge impact on the stable development of the whole economy and society. The openness and sharing characteristics of the network lead to huge network information security risks in the computerized accounting system. Only by scientifically researching the computerized accounting system of listed companies in China, can we find the inducement of financial distress in time and take effective preventive measures to avoid irreparable losses. In recent years, deep learning has achieved remarkable results in the field of computerized accounting. The development of deep learning has not only broken through many difficult problems that traditional algorithms can’t solve, but also improved the cognitive level of information security of computerized accounting system, and promoted the progress of information security integration model of computerized accounting system based on deep learning.","PeriodicalId":124469,"journal":{"name":"2023 Asia-Europe Conference on Electronics, Data Processing and Informatics (ACEDPI)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134296370","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

Building Trusted Artificial Intelligence with Cross-view: Cases Study 用交叉视角构建可信的人工智能:案例研究

2023 Asia-Europe Conference on Electronics, Data Processing and Informatics (ACEDPI) Pub Date : 2023-04-01 DOI: 10.1109/ACEDPI58926.2023.00056

Yuan Jinhui, Lin Shengsheng, Ke Zhipeng, Zhou Hongwei

引用次数: 0

Knowledge Base System of Electrical Equipment Management and Potential Risk Control Based on Natural Language Processing Technology 基于自然语言处理技术的电气设备管理与潜在风险控制知识库系统

2023 Asia-Europe Conference on Electronics, Data Processing and Informatics (ACEDPI) Pub Date : 2023-04-01 DOI: 10.1109/ACEDPI58926.2023.00090

Chen Huang, Yubin Feng, Yubo Zhang, Wei Zhang

引用次数: 0

Dynamic Integration and Analysis of Marine Environmental Monitoring Data Based on Support Vector Machine 基于支持向量机的海洋环境监测数据动态集成与分析

2023 Asia-Europe Conference on Electronics, Data Processing and Informatics (ACEDPI) Pub Date : 2023-04-01 DOI: 10.1109/ACEDPI58926.2023.00017

Huatang Xue

{"title":"Dynamic Integration and Analysis of Marine Environmental Monitoring Data Based on Support Vector Machine","authors":"Huatang Xue","doi":"10.1109/ACEDPI58926.2023.00017","DOIUrl":"https://doi.org/10.1109/ACEDPI58926.2023.00017","url":null,"abstract":"Environmental problems are worldwide problems. We must pay attention to the pollution problems facing the ocean today. It not only changes the quality of the ocean, but also has a great impact on the seafood products planted in the ocean. The basic purpose of marine environmental monitoring is to comprehensively, timely and accurately grasp the level, effect and trend of the impact of human activities on the marine environment. According to the actual needs of application services, the spatio-temporal analysis mode and related evaluation model suitable for the characteristics of the marine environment are discussed and studied, and the time and space analysis of monitoring data suitable for system construction and related evaluation techniques are applied and analyzed. By merging the data sets with different performance formats and using the unified monitoring database format as the blueprint, the processing technologies such as data format conversion, measurement unit conversion and monitoring parameter standardization are dynamically carried out. Finally, the same monitoring elements of different monitoring tasks are dynamically merged into a data set in batches, which provides a prerequisite for data quality control and warehousing. The emergence of support vector machine brings hope and convenience to the research. It has a set of perfect theoretical knowledge. On the basis of this set of perfect theory, it can achieve good learning effect. The data quality is mainly guaranteed through completeness inspection, quality control of station basic information and quality control of station monitoring parameter data.","PeriodicalId":124469,"journal":{"name":"2023 Asia-Europe Conference on Electronics, Data Processing and Informatics (ACEDPI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129876684","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

Application of Intelligent Algorithm in the Research of Logistics Distribution Positioning System 智能算法在物流配送定位系统研究中的应用

2023 Asia-Europe Conference on Electronics, Data Processing and Informatics (ACEDPI) Pub Date : 2023-04-01 DOI: 10.1109/ACEDPI58926.2023.00094

Xiaoming Li

引用次数: 0