A Novel Isomap-SVR Soft Sensor Model and Its Application in Rotary Kiln Calcination Zone Temperature Prediction

Liu, Jialun; Wang, Yukun; Zhang, Yong

doi:10.3390/sym12010167

Cited by 8 publications

(2 citation statements)

References 23 publications

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“…That includes the principal component analysis (PCA), factor analysis, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and multiple discriminant analysis (MDA), isometric feature mapping (Isomap) (Liu et al. 2020 ), kernel principal component analysis (KPCA) (Abba et al. 2020 ), t-distributed stochastic neighbor embedding (t-SNE) (Zhan et al.…”

Section: Overview Of Ai-big Data Analytic Frameworkmentioning

confidence: 99%

AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

et al. 2022

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In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.g. evaluating buildings’ performance, detecting abnormal energy consumption, identifying the changes needed to improve efficiency, ensuring the security and privacy of end-users, etc. To that end, there has been a movement for developing artificial intelligence (AI) big data analytic tools as they offer various new and tailor-made solutions that are incredibly appropriate for practical buildings’ management. Typically, they can help the operator in (i) analyzing the tons of connected equipment data; and; (ii) making intelligent, efficient, and on-time decisions to improve the buildings’ performance. This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. It covers various AI-based tasks, e.g. load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. The first part of this paper adopts a well-designed taxonomy to overview existing frameworks. A comprehensive review is conducted about different aspects, including the learning process, building environment, computing platforms, and application scenario. Moving on, a critical discussion is performed to identify current challenges. The second part aims at providing the reader with insights into the real-world application of AI-big data analytics. Thus, three case studies that demonstrate the use of AI-big data analytics in BAMSs are presented, focusing on energy anomaly detection in residential and office buildings and energy and performance optimization in sports facilities. Lastly, future directions and valuable recommendations are identified to improve the performance and reliability of BAMSs in intelligent buildings.

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Section: Overview Of Ai-big Data Analytic Frameworkmentioning

confidence: 99%

AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

et al. 2022

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“…Therefore, past prominent capability is very worthy of being a starting point to a study and overview of the linear or nonlinear comparative studies for further research work. In view of these interesting facts for modeling classification works [57,58], they are used as the basis for the complete construction of the study framework. There are nine components (stages), with 11 detailed steps for raising the advantages and rationalities of this study.…”

Section: Background Of the Applied Study Frameworkmentioning

confidence: 99%

Applied Identification of Industry Data Science Using an Advanced Multi-Componential Discretization Model

et al. 2020

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Applied human large-scale data are collected from heterogeneous science or industry databases for the purposes of achieving data utilization in complex application environments, such as in financial applications. This has posed great opportunities and challenges to all kinds of scientific data researchers. Thus, finding an intelligent hybrid model that solves financial application problems of the stock market is an important issue for financial analysts. In practice, classification applications that focus on the earnings per share (EPS) with financial ratios from an industry database often demonstrate that the data meet the abovementioned standards and have particularly high application value. This study proposes several advanced multicomponential discretization models, named Models A–E, where each model identifies and presents a positive/negative diagnosis based on the experiences of the latest financial statements from six different industries. The varied components of the model test performance measurements comparatively by using data-preprocessing, data-discretization, feature-selection, two data split methods, machine learning, rule-based decision tree knowledge, time-lag effects, different times of running experiments, and two different class types. The experimental dataset had 24 condition features and a decision feature EPS that was used to classify the data into two and three classes for comparison. Empirically, the analytical results of this study showed that three main determinants were identified: total asset growth rate, operating income per share, and times interest earned. The core components of the following techniques are as follows: data-discretization and feature-selection, with some noted classifiers that had significantly better accuracy. Total solution results demonstrated the following key points: (1) The highest accuracy, 92.46%, occurred in Model C from the use of decision tree learning with a percentage-split method for two classes in one run; (2) the highest accuracy mean, 91.44%, occurred in Models D and E from the use of naïve Bayes learning for cross-validation and percentage-split methods for each class for 10 runs; (3) the highest average accuracy mean, 87.53%, occurred in Models D and E with a cross-validation method for each class; (4) the highest accuracy, 92.46%, occurred in Model C from the use of decision tree learning-C4.5 with the percentage-split method and no time-lag for each class. This study concludes that its contribution is regarded as managerial implication and technical direction for practical finance in which a multicomponential discretization model has limited use and is rarely seen as applied by scientific industry data due to various restrictions.

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Soft Sensor Modeling of Self-Organizing Interval Type-2 Fuzzy Neural Network Based on Adaptive Quantum-Behaved Particle Swarm Optimization Algorithm

Wang,

Zhao,

Cao

et al. 2024

Int. J. Fuzzy Syst.

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A Novel Isomap-SVR Soft Sensor Model and Its Application in Rotary Kiln Calcination Zone Temperature Prediction

Cited by 8 publications

References 23 publications

AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

Applied Identification of Industry Data Science Using an Advanced Multi-Componential Discretization Model

Soft Sensor Modeling of Self-Organizing Interval Type-2 Fuzzy Neural Network Based on Adaptive Quantum-Behaved Particle Swarm Optimization Algorithm

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