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A Deep Learning-Based Approach for Two-Phase Flow pattern classification Using Void Fraction Time Series Analysis

IEEE ACCESS 2025年 13卷 11778-11791页

作者： Ambrosio, Jefferson Dos Santos Jose da Silva, Marco Lazzaretti, Andre Eugenio Univ Tecnol Fed Parana UTFPR Grad Program Elect Engn & Ind Informat CPGEI BR-3165 Curitiba Brazil Johannes Kepler Univ Linz Inst Measurement Technol A-4040 Linz Austria

Flow regime classification is essential for analyzing and modeling two-phase flows, as it demarcates the flow behavior and influences the selection of appropriate predictive models. Machine learning-based approaches have gained relevance in flow regime classification research in the last few years. However, they are still solidly based on the construction and careful definition of hand-crafted features. Deep learning approaches, on the other hand, can provide more robust and end-to-end solutions. However, they are underexplored and have not evaluated the generalization of the models to other data or acquisition systems. Hence, this work proposes using end-to-end state-of-the-art (SOTA) time-series classification methods (ResNet, LSTM-FCN, and TSTPlus) for two-phase flow patterns (churn, bubbly, and slug). We also present the generalization analysis of the models with cross-dataset experiments, training the model with one dataset and testing it with another dataset collected in another system for two datasets: HZDR (from the Helmholtz-Zentrum Dresden-Rossendorf research laboratory) and TUD (from Technische Universit & auml;t Dresden). The results demonstrate that the approach chosen here presents superior classification metrics in all cases evaluated, particularly in cross-dataset experiments. With our proposed SOTA methods, all the evaluated metrics (accuracy and F1-Score) consistently surpass 85% in all cases, while the baseline method can decrease the performance under 75%. This demonstrates the relevance of the analysis proposed here for flow regime classification literature and opens up a new set of possibilities for research in this area, aiming at robust solutions that are viable for practical use.

关键词： Accuracy Feature extraction Time series analysis Support vector machines pattern classification Churn Analytical models Deep learning Convolutional neural networks Training Wire-mesh sensor deep learning gas-liquid two-phase flow transformer flow pattern

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An Enhanced Kernel Partial Least-Squares Fault Reconstruction Fused With pattern classification

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2025年第3期33卷 1119-1124页

作者： Liu, Meizhi Kong, Xiangyu Hu, Changhua Li, Hongzeng Wang, Ziwen High Tech Inst Xian Xian 710025 Peoples R China Shanxi Datong Univ Coll Phys & Elect Sci Datong 037009 Peoples R China

Partial least squares (PLS) is a well-known multivariate statistical process monitoring (MSPM) method. However, there are two key issues that restrict its application in reconstruction-based fault diagnosis, including weak fault representation ability and uncertainty caused by the overlap among different types of faults. To cope with these two issues, an enhanced kernel PLS (eKPLS) fault reconstruction approach fused with pattern classification is proposed in this study. For the first issue, a fine-grained fault subspace extraction method is developed. These fine-grained fault subspaces exhibit richer fault details, conferring upon the model heightened fault representation ability. For the second issue, the fault magnitude is supplemented into the paradigm of fault reconstruction, and a rediagnosis mechanism is designed to overcome the uncertainty in fault diagnosis. Thus, the ideas of fault reconstruction and pattern classification are fused into a consolidated framework, allowing us to complement their strengths together. Finally, several experiments on a hydraulic system and Tennessee Eastman process (TEP) are performed to validate the effectiveness of the proposed method.

关键词： Fault diagnosis fault magnitude fault reconstruction pattern classification fault magnitude fault reconstruction pattern classification

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A Generalized f-Divergence With Applications in pattern classification

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第4期37卷 1556-1570页

作者： Xiao, Fuyuan Ding, Weiping Pedrycz, Witold Chongqing Univ Sch Big Data & Software Engn Chongqing 401331 Peoples R China Nantong Univ Sch Artificial Intelligence & Comp Sci Nantong 226019 Peoples R China City Univ Macau Fac Data Sci Taipa 999078 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2R3 Canada Silesian Univ Technol SUT Fac Automatic Control Elect & Comp Sci PL-44100 Gliwice Poland Istinye Univ Res Ctr Performance & Prod Anal TR-34010 Istanbul Turkiye

In multisource information fusion (MSIF), Dempster- Shafer evidence (DSE) theory offers a useful framework for reasoning under uncertainty. However, measuring the divergence between belief functions within this theory remains an unresolved challenge, particularly in managing conflicts in MSIF, which is crucial for enhancing decision-making level. In this paper, several divergence and distance functions are proposed to quantitatively measure discrimination between belief functions in DSE theory, including the reverse evidential KullbackLeibler (REKL) divergence, evidential Jeffrey's (EJ) divergence, evidential JensenShannon (EJS) divergence, evidential chi(2) (E chi(2)) divergence, evidential symmetric chi(2) (ES chi(2)) divergence, evidential triangular (ET) discrimination, evidential Hellinger (EH) distance, and evidential total variation (ETV) distance. On this basis, a generalized f-divergence, also called the evidential f-divergence (Ef divergence), is proposed. Depending on different kernel functions, the Ef divergence degrades into several specific classes: EKL, REKL, EJ, EJS, E chi(2) and ES chi(2) divergences, ET discrimination, and EH and ETV distances. Notably, when basic belief assignments (BBAs) are transformed into probability distributions, these classes of Ef divergence revert to their classical counterparts in statistics and information theory. In addition, several Ef-MSIF algorithms are proposed for pattern classification based on the classes of Ef divergence. These Ef-MSIF algorithms are evaluated on real-world datasets to demonstrate their practical effectiveness in solving classification problems. In summary, this work represents the first attempt to extend classical f-divergence within the DSE framework, capitalizing on the distinct properties of BBA functions. Experimental results show that the proposed Ef-MSIF algorithms improve classification accuracy, with the best-performing Ef-MSIF algorithm achieving an overall performance difference appr

关键词： Dempster-Shafer evidence (DSE) theory evidential f-divergence belief divergence deng entropy multisource information fusion decision-making pattern classification Dempster-Shafer evidence (DSE) theory belief divergence deng entropy multisource information fusion decision-making pattern classification

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A Simplified Adaptive Fuzzy Min-Max Neural Network for pattern classification

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NEUROCOMPUTING 2025年 630卷

作者： Fu, Mingrui Chen, Shuai Wei, Xiaoxiao Du, Jinsong Wang, Wei Liu, Jinhai Chinese Acad Sci Shenyang Inst Automat Shenyang 110016 Peoples R China Key Lab Intelligent Detect & Equipment Technol Lia Shenyang 110169 Peoples R China Liaoning Liaohe Lab Shenyang 100169 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

neural network, as a prominent hyperbox classifier, has undergone significant research over the past three decades. Despite its effectiveness, the conventional FMM algorithm relies on a fixed expansion coefficient, which often leads to suboptimal performance because it cannot adapt to varying data distributions. Additionally, the membership function in FMM typically depends on sensitive parameters that need to be preset, further complicating its practical application. To address these limitations and improve the adaptability and classification accuracy of FMM, we propose a Simplified Adaptive Fuzzy Min-Max Neural Network (SAFMM) for pattern classification, where all parameters are adaptively chosen. Our contributions are twofold: Firstly, a novel FMM learning algorithm is introduced, where hyperboxes expand adaptively instead of using a fixed coefficient, and cluster and class-specific hyperboxes are simultaneously applied to avoid overlap. Secondly, considering uneven data distributions within hyperboxes, a new adaptive membership function based on data core is defined for classification. The proposed SAFMM model is evaluated on benchmark datasets and areal- world case of magnetic flux leakage (MFL) defect depth estimation in pipeline non-destructive testing (NDT). Experimental results demonstrate that SAFMM can generate fewer hyperboxes while ensuring classification accuracy and exhibits an absolute advantage for quick classification as the dataset size increases.

关键词： Fuzzy min-max neural network pattern classification MFL Defect depth estimation

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Performance Analysis of Radar Scan pattern classification using Visibility Graph

Performance Analysis of Radar Scan Pattern Classification us...

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International Conference on Information Networking

作者： Ji-Hyeon Kim Soon-Young Kwon Hyoung-Nam Kim Dept. Electronics Engineering Pusan National University Busan Republic of Korea

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

Accurate radar scan pattern classification is essential for identifying and responding to threats in electronic warfare systems. In this paper, we present an approach to enhance classification accuracy by leveraging visibility graphs to extract critical features from radar signals and applying the GoogLeNet deep neural network. By transforming radar scan patterns into visibility graphs, the method captures both global and local characteristics that are crucial for distinguishing between similar patterns. Simulation results demonstrate that the proposed approach achieves over $\mathbf{9 2 \%}$ in terms of accuracy, precision, and recall, highlighting its effectiveness in radar scan pattern classification.

关键词： Deep learning Electronic warfare Accuracy Simulation pattern classification Radar Radar imaging Feature extraction classification algorithms Signal analysis

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pattern classification driven enhancements for human-in-the-loop decision support systems

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DECISION SUPPORT SYSTEMS 2011年第2期50卷 460-468页

作者： Subramania, Halasya Siva Khare, Vineet R. Int Tech Pk Ltd GM Tech Ctr India Pvt Ltd Gen Motors Global Res & Dev Diag & Prognosis GrpIndia Sci Lab Bangalore 560066 Karnataka India

Data mining has been a key technology in the warranty sector for mass manufacturers to understand and improve product quality, reliability and durability. Cost savings is an important aspect of business which calls for processes that are error proof. pattern classification methods applied to the diagnostic data could help build error proof processes by improving the diagnostic technology. In this paper we present a case study from the automotive warranty and service domain involving a human-in-the-loop decision support system (HIL-DSS). The automotive manufacturers offer warranties on products, made of parts from different suppliers, and rely on a dealer network to assess warranty claims. The dealers use diagnostic equipment manufactured by third parties and also draw on their own expertise. In addition, a subject matter expert (SME) assesses these collective decisions to distinguish between inaccurate diagnoses by the dealers or an inadequate decision algorithm in the diagnostic equipment. Altogether this makes a comprehensive HIL-DSS. The proposed methodology continuously learns from collective decision making systems, enhances the diagnostic equipment, adds to the knowledge of dealers and minimizes the SME involvement in the review process of the overall system. Improving the diagnostic equipment helps in better warranty servicing, whereas improvements in the human expert knowledge help prevent field error and avoid customer dissatisfaction due to improper fault diagnosis. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Decision support systems Human expert pattern classification Decision trees Support vector machines Confusion matrix

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pattern classification by a neurofuzzy network: application to vibration monitoring

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ISA TRANSACTIONS 2000年第3期39卷 293-308页

作者： Meesad, P Yen, GG Oklahoma State Univ Sch Elect & Comp Engn Intelligent Syst & Control Lab Stillwater OK 74078 USA

An innovative neurofuzzy network is proposed herein for pattern classification applications, specifically for vibration monitoring. A fuzzy set interpretation is incorporated into the network design to handle imprecise information. A neural network architecture is used to automatically deduce fuzzy if-then rules based on a hybrid supervised learning scheme. The neurofuzzy classifier proposed is equipped with a one-pass, on-line, and incremental learning algorithm. This network can be considered a self-organized classifier with the ability to adaptively learn new information without forgetting old knowledge. The classification performance of the proposed neurofuzzy network is validated on the Fisher's Iris data, which is a well-known benchmark data set. For the generalization capability, the neurofuzzy network can achieve 97.33% correct classification. In addition, to demonstrate the efficiency and effectiveness of the proposed neurofuzzy paradigm, numerical simulations have been performed using the Westland data set. The Westland data set consists of vibration data collected from a US Navy CH-46E helicopter test stand. Using a simple fast Fourier transform technique for feature extraction, the proposed neurofuzzy network has shown promising results. Using various torque levels for training and testing, the network achieved 100% correct classification. (C) 2000 Elsevier Science Ltd. All rights reserved.

关键词： pattern classification neural networks fuzzy logic neurofuzzy classifier supervised-clustering learning membership functions rule bases hybrid architecture radial basis function

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pattern classification by linear goal programming and its extensions

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JOURNAL OF GLOBAL OPTIMIZATION 1998年第2期12卷 111-126页

作者： Nakayama, H Kagaku, N Konan Univ Dept Appl Math Kobe Hyogo 658 Japan

pattern classification is one of the main themes in pattern recognition, and has been tackled by several methods such as the statistic one, artificial neural networks, mathematical programming and so on. Among them, the multi-surface method proposed by Mangasarian is very attractive, because it can provide an exact discrimination function even for highly nonlinear problems without any assumption on the data distribution. However, the method often causes many slits on the discrimination curve. In other words, the piecewise linear discrimination curve is sometimes too complex resulting in a poor generalization ability. In this paper, several trials in order to overcome the difficulties of the multi-surface method are suggested. One of them is the utilization of goal programming in which the auxiliary linear programming problem is formulated as a goal programming in order to get as simple discrimination curves as possible. Another one is to apply fuzzy programming by which we can get fuzzy discrimination curves with gray zones. In addition, it will be shown that using the suggested methods, the additional learning can be easily made. These features of the methods make the discrimination more realistic. The effectiveness of the methods is shown on the basis of some applications.

关键词： multiobjective programming pattern classification goal programming portfolio selection

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pattern-classification USING NEURAL NETWORKS

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IEEE COMMUNICATIONS MAGAZINE 1989年第11期27卷 47-64页

作者： LIPPMANN, RP MIT Lincoln Lab. Lexington MA USA

This paper focuses on feed forward neural net classifiers for static patterns with continuous-valued inputs. A taxonomy of the different types of neural net classifiers is provided, ranging from probabilistic, to hyperplane, to kernel, to exemplar classifiers. Decision tree classifiers are also examined. The problem of matching classifier complexity to training data is studied. Other topics discussed include: GMDH networks and high-order nets, K-nearest neighbor classifiers, feature-map classifiers, hypersphere classifiers, and radial basis function classifiers.

关键词： pattern classification Neural networks Feedforward systems Taxonomy Kernel Training data Data handling

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pattern-classification IN DYNAMIC ENVIRONMENTS - TAGGED FEATURE-CLASS REPRESENTATION AND THE CLASSIFIERS

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS 1989年第5期19卷 1203-1210页

作者： ZHU, QM School of Engineering and Computer Science Oakland University Rochester MI USA

The author discusses: a tagged feature and class representation of the pattern recognition problem in a dynamic environment; univariate cooperative classifiers that are based on statistical feature evaluation and impose no constraint on the variations of the sets of classes and features; and inductive learning procedures that are used to create a class-feature space adaptive to the variations of the dynamic environment. The univariate classifier and the cooperative classifier apply a classify-by-rejection approach to a candidate class set. The classification is based on the individual evaluation of the features presented in the sample patterns and the classes. The tagged feature-class space permits convenient building of a hierarchical structure of the classifications A content-addressable data retrieved characteristic is possessed by both types of classifier. Experimental results on the classifiers are presented.< >

关键词： pattern classification pattern recognition Lungs Parameter estimation Nonlinear dynamical systems Heuristic algorithms Linear systems System identification Codes Convergence

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