Deterministic Artificial Intelligence最新文献

Random Forest-Based Ensemble Machine Learning Data-Optimization Approach for Smart Grid Impedance Prediction in the Powerline Narrowband Frequency Band 基于随机森林的电力线窄带智能电网阻抗预测集成机器学习数据优化方法

Deterministic Artificial Intelligence Pub Date : 2020-05-27 DOI: 10.5772/INTECHOPEN.91837

Emmanuel A. Oyekanlu, J. Uddin

{"title":"Random Forest-Based Ensemble Machine Learning Data-Optimization Approach for Smart Grid Impedance Prediction in the Powerline Narrowband Frequency Band","authors":"Emmanuel A. Oyekanlu, J. Uddin","doi":"10.5772/INTECHOPEN.91837","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.91837","url":null,"abstract":"In this chapter, the random forest-based ensemble regression method is used for the prediction of powerline impedance at the powerline communication (PLC) narrowband frequency range. It is discovered that while PLC load transfer function, phase, and frequency are crucial to powerline impedance estimation, the problem of data multicollinearity can adversely impact accurate prediction and lead to excessive mean square error (MSE). High MSE is obtained when multiple transfer functions corresponding to different PLC load transfer functions are used for random forest ensemble regression. Low MSE indicating more accurate impedance prediction is obtained when PLC load transfer function data is selectively used. Using data corresponding to 200, 400, 600, 800, and 1000 W PLC load transfer functions together led to poor impedance prediction, while using lesser amount of carefully selected data led to better impedance prediction. These results show that artificial intelligence (AI) methods such as random forest ensemble regression and deterministic data-optimization approach can be utilized for smart grid (SG) health monitoring applications using PLC-based sensors. Machine learning can also be applied to the design of better powerline communication signal transceivers and equalizers.","PeriodicalId":146389,"journal":{"name":"Deterministic Artificial Intelligence","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125604034","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

Application of Artificial Neural Networks for Accurate Prediction of Thermal and Rheological Properties of Nanofluids 人工神经网络在纳米流体热流变特性精确预测中的应用

Deterministic Artificial Intelligence Pub Date : 2020-05-27 DOI: 10.5772/INTECHOPEN.89101

B. Vaferi

{"title":"Application of Artificial Neural Networks for Accurate Prediction of Thermal and Rheological Properties of Nanofluids","authors":"B. Vaferi","doi":"10.5772/INTECHOPEN.89101","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.89101","url":null,"abstract":"Nanofluids have recently been considered as one of the most popular working fluid in heat transfer and fluid mechanics. Accurate estimation of thermophysical properties of nanofluids is required for the investigation of their heat transfer performance. Thermal conductivity coefficient, convective heat transfer coefficient, and viscosity are some the most important thermophysical properties that directly influence on the application of nanofluids. The aim of the present chapter is to develop and validate artificial neural networks (ANNs) to estimate these thermophysical properties with acceptable accuracy. Some simple and easy measurable parameters including type of nanoparticle and base fluid, temperature and pressure, size and concentration of nanoparticles, etc. are used as independent variables of the ANN approaches. The predictive performance of the developed ANN approaches is validated with both experimental data and available empirical correlations. Various statistical indices including mean square errors (MSE), root mean square errors (RMSE), average absolute relative deviation percent (AARD%), and regression coefficient (R2) are used for numerical evaluation of accuracy of the developed ANN models. Results confirm that the developed ANN models can be regarded as a practical tool for studying the behavior of those industrial applications, which have nanofluids as operating fluid.","PeriodicalId":146389,"journal":{"name":"Deterministic Artificial Intelligence","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122642432","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

Modern Control System Learning 现代控制系统学习

Deterministic Artificial Intelligence Pub Date : 2020-05-27 DOI: 10.5772/INTECHOPEN.90198

Brendon Smeresky, A. Rizzo

引用次数: 0

Stochastic Artificial Intelligence: Review Article 随机人工智能:综述文章

Deterministic Artificial Intelligence Pub Date : 2020-05-27 DOI: 10.5772/INTECHOPEN.90003

T. D. Raheni, P. Thirumoorthi

引用次数: 0

The Technique of Automated Design of Technological Objects with the Application of Artificial Intelligence Elements 应用人工智能元素的工艺对象自动化设计技术

Deterministic Artificial Intelligence Pub Date : 2020-05-27 DOI: 10.5772/INTECHOPEN.88295

T. Zubkova, M. Tokareva

引用次数: 0