scholarly journals Data Analysis and Symbolic Regression Models for Predicting CO and NOx Emissions from Gas Turbines

Computation ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 139
Author(s):  
Olga Kochueva ◽  
Kirill Nikolskii
Keyword(s):  
Machine Learning ◽  
Gas Turbines ◽  
Symbolic Regression ◽  
Monitoring Systems ◽  
Nox Emissions ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Model Based ◽  
Emission Monitoring ◽  
Electrical Generation

Predictive emission monitoring systems (PEMS) are software solutions for the validation and supplementation of costly continuous emission monitoring systems for natural gas electrical generation turbines. The basis of PEMS is that of predictive models trained on past data to estimate emission components. The gas turbine process dataset from the University of California at Irvine open data repository has initiated a challenge of sorts to investigate the quality of models of various machine learning methods to build a model for predicting CO and NOx emissions depending on ambient variables and the parameters of the technological process. The novelty and features of this paper are: (i) a contribution to the study of the features of the open dataset on CO and NOx emissions for gas turbines, which will enable one to more objectively compare different machine learning methods for further research; (ii) for the first time for the CO and NOx emissions, a model based on symbolic regression and a genetic algorithm is presented—the advantage of this being the transparency of the influence of factors and the interpretability of the model; (iii) a new classification model based on the symbolic regression model and fuzzy inference system is proposed. The coefficients of determination of the developed models are: R2=0.83 for NOx emissions, R2=0.89 for CO emissions.

Exploring energy-saving refrigerators through online e-commerce reviews: an augmented mining model based on machine learning methods

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yuyan Luo ◽  
Zheng Yang ◽  
Yuan Liang ◽  
Xiaoxu Zhang ◽  
Hong Xiao
Keyword(s):  
Machine Learning ◽  
Energy Saving ◽  
Online Reviews ◽  
Learning Methods ◽  
Content Type ◽  
Machine Learning Methods ◽  
Model Based ◽  
Review Mining ◽  
Strategy Model ◽  
Mining Model

PurposeBased on climate issues and carbon emissions, this study aims to promote low-carbon consumption and compel consumers to actively shift to energy-saving appliances. In this big data era, online reviews in social and electronic commerce (e-commerce) websites contain valuable product information, which can facilitate firm business strategies and consumer comparison shopping. This study is designed to advance existing research on energy-saving refrigerators by incorporating machine learning models in the analysis of online reviews to provide valuable suggestions to e-commerce platform managers and manufacturers to effectively understand the psychological cognition of consumers.Design/methodology/approachThis study proposes an online e-commerce review mining and management strategy model based on “data acquisition and cleaning, data mining and analysis and strategy formation” through multiple machine learning methods, namely, Bayes networks, support vector machine (SVM), latent Dirichlet allocation (LDA) and importance–performance analysis (IPA), to help managers.FindingsBased on a case study of one of the largest e-commerce platforms in China, this study linguistically analyzes 29,216 online reviews of energy-saving refrigerators. Results indicate that the energy-saving refrigerator features that consumers are generally satisfied with are, in sequential order, logistics, function, price, outlook, after-sales service, brand, quality and space. This study also identifies ten topics with 100 keywords by analyzing 18 different refrigerator models. Finally, based on the IPA, this study allocates different priorities to the features and provides suggestions from the perspective of consumers, the government and manufacturers.Research limitations/implicationsIn terms of limitations, future research may focus on the following points. First, the topics identified in this study derive from specific points in time and reviews; thus, the topics may change with the text data. A machine learning-based online review analysis platform could be developed in the future to dynamically improve consumer satisfaction. Moreover, given that consumers' needs may change over time, e-commerce platform types and consumer characteristics, such as user profiles, can be incorporated into the model to effectively analyze trends in consumers' perceived dimensions.Originality/valueThis study fills the gap in previous research in this field, which uses small-sample data for qualitative analysis, while integrating management ideas and proposes an online e-commerce review mining and management strategy model based on machine learning methods. Moreover, this study considers how consumers' emotional and thematic preferences for products affect their purchase decision-making from the perspective of their psychological perception and linguistically analyzes online reviews of energy-saving refrigerators using the proposed mining model. Through the improved IPA model, this study provides optimizing strategies to help e-commerce platform managers and manufacturers.

scholarly journals Laboratory Research on Polarized Optical Properties of Saline-Alkaline Soil Based on Semi-Empirical Models and Machine Learning Methods

2022 ◽  
Vol 14 (1) ◽  
pp. 226
Author(s):  
Qianyi Gu ◽  
Yang Han ◽  
Yaping Xu ◽  
Haiyan Yao ◽  
Haofang Niu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Empirical Models ◽  
Alkaline Soil ◽  
Alkaline Soils ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Model Based ◽  
Semi Empirical ◽  
Saline Alkaline Soils

Currently, soil salinization is a serious problem affecting agricultural production and human settlements. Remote sensing techniques have the advantages of a large monitoring range, rapid acquisition of information, implementation of dynamic monitoring, and low impact on the ground surface. Over the past two decades, many semi-empirical bidirectional polarized distribution function (BPDF) models have been proposed to accurately calculate the polarized reflectance (Rp) on the soil surface. Although there have been some studies on the BPDF model based on traditional machine learning methods, there is a lack of research on the BPDF model based on deep learning, especially using laboratory measurement spectrum data as the processing object, with limited research results. In this paper, we collected saline-alkaline soil in the field as the observation object and measured the Rp at multiple angles in the laboratory environment. We used semi-empirical models (the Nadal–Bréon model, Litvinov model, and Xie–Cheng model) and machine learning methods (support vector regression, random forest, and deep neural networks regression) to simulate and predict the surface Rp of saline-alkaline soils and compare them with experimental results. The measured values of the laboratory are compared and fitted, and the root mean squared error, R-squared, and correlation coefficient are calculated to express the prediction effect. The results show that the predictions of the BPDF model based on machine learning methods are generally better than those of the semi-empirical BPDF model, which is improved by 3.06% at 670 nm and 19.75% at 865 nm. The results of this study also provide new ideas and methods based on deep learning for the prediction of Rp on the surface of saline-alkaline soils.

scholarly journals Sequential Model Based Intrusion Detection System for IoT Servers Using Deep Learning Methods

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1113
Author(s):  
Ming Zhong ◽  
Yajin Zhou ◽  
Gang Chen
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Language Processing ◽  
Intrusion Detection System ◽  
Detection System ◽  
Learning Methods ◽  
Sequential Model ◽  
Machine Learning Methods ◽  
Model Based

IoT plays an important role in daily life; commands and data transfer rapidly between the servers and objects to provide services. However, cyber threats have become a critical factor, especially for IoT servers. There should be a vigorous way to protect the network infrastructures from various attacks. IDS (Intrusion Detection System) is the invisible guardian for IoT servers. Many machine learning methods have been applied in IDS. However, there is a need to improve the IDS system for both accuracy and performance. Deep learning is a promising technique that has been used in many areas, including pattern recognition, natural language processing, etc. The deep learning reveals more potential than traditional machine learning methods. In this paper, sequential model is the key point, and new methods are proposed by the features of the model. The model can collect features from the network layer via tcpdump packets and application layer via system routines. Text-CNN and GRU methods are chosen because the can treat sequential data as a language model. The advantage compared with the traditional methods is that they can extract more features from the data and the experiments show that the deep learning methods have higher F1-score. We conclude that the sequential model-based intrusion detection system using deep learning method can contribute to the security of the IoT servers.

Einzelteilüberwachung in Beschleunigungsphasen*/Single item processes containing acceleration phases – A comparison between conventional regression tools and machine learning

2019 ◽  
Vol 109 (05) ◽  
pp. 347-351
Author(s):  
B. Denkena ◽  
B. Bergmann ◽  
M. Witt ◽  
Keyword(s):  
Machine Learning ◽  
Process Monitoring ◽  
Input Signal ◽  
Regression Models ◽  
Learning Methods ◽  
Single Item ◽  
Machine Learning Methods ◽  
Model Based ◽  
Conventional Regression

Die modellbasierte Prozessüberwachung für die Einzelteilfertigung scheitert häufig an Prozessphasen, welche Beschleunigungsanteile enthalten. Der Beitrag widmet sich der Kompensation von Beschleunigungs- und Reibanteilen im Messsignal durch kennfeldbasierte Ansätze sowie Methoden des maschinellen Lernens. Für die anschließende Modellierung des Gesamtsignals zur Prozessüberwachung werden Regressionsansätze mit Methoden des maschinellen Lernens verglichen, welche sich als besonders geeignet zeigen.   Model-based process monitoring often fails to adequately assess process segments that exhibit acceleration. This work evaluates different compensation approaches for non-process-specific parts of the measured signals. For a subsequent modelling of the complete input signal enabling the monitoring of single item processes, conventional regression models are compared to machine learning methods. The latter prove to be very well suited for these requirements.

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