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A data-driven study for evaluating the compressive strength of high-strength concrete

INTERNATIONAL JOURNAL OF MAcHINE LEARNING AND cYBERNETIcS 2021年第12期12卷 3585-3595页

作者： Wei, Yufeng Han, Aiguo Xue, Xinhua Chengdu Univ Technol State Key Lab Geohazard Prevent & Geoenvironm Pro Chengdu Peoples R China Sichuan Univ Coll Water Resource & Hydropower Chengdu 610065 Peoples R China

To estimate the compressive strength of high-strength concrete (HSc), a hybrid model integrating the firefly algorithm (FFA) and fuzzy c-means (FcM) clustering method into the adaptive neuro fuzzy inference system (ANFIS) was developed in this paper. The FFA and FcM techniques were utilized to improve the forecasting accuracy of the proposed ANFIS. To establish the hybrid ANFIS-FFA model, five main constituents of HSc, cement, water, fine and coarse aggregates, and superplasticizer, are considered the input variables, and the compressive strength of HSc is used as the output variable. A comparison was conducted among four artificial intelligence models, including the proposed ANFIS-FFA model, the traditional ANFIS, the back propagation neural network (BPNN) and the extreme learning machine (ELM), in terms of four statistical indices. In addition, a detailed parametric study was conducted to investigate the influence of each input variable on the compressive strength of HSc. The results showed that the developed ANFIS-FFA model exhibits greater accuracy than the other three models, with a higher correlation coefficient (R) and lower root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) values, and it has great potential to accurately estimate the compressive strength of HSc.

关键词： Firefly algorithm Adaptive neuro fuzzy inference system compressive strength fuzzy c-means clustering method High-strength concrete

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Research on spectral clustering algorithm for network communication big data based on wavelet analysis

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INTERNATIONAL JOURNAL OF AUTONOMOUS AND ADAPTIVE cOMMUNIcATIONS SYSTEMS 2022年第2期15卷 93-105页

作者： Dai, Xinjian Zeng, Zhichao Changsha Social Work Coll Basic Data Applicat Teaching & Res Off Changsha 410004 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China

In order to classify the features of big data in network communication, improve clustering efficiency and reduce error classification rate, a spectrum clustering algorithm based on wavelet analysis is proposed. The multi-scale, one-dimensional wavelet analysis method is used to sample the network communication big data, extract the spectral feature quantity of the network communication big data, and construct the channel model of big data transmission of network communication. combined with the fuzzy c-means clustering method, the spectral clustering is performed on network communication big data to mine association rules of the large data spectrum of network communication. combined with wavelet decomposition method, the time-frequency conversion and feature separation of network communication big data spectrum are carried out to complete the spectrum clustering of network communication big data. Simulation results show that this method is more accurate for spectrum clustering of communication big data and improves clustering efficiency.

关键词： wavelet analysis network communication big data spectral clustering feature extraction clustering efficiency fuzzy c-means clustering method wavelet decomposition method

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Applying a computational intelligence method to predict the rehabilitation treatment for females with lateral patellar displacement

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Performance Enhancement and Health 2018年第1期6卷 36-42页

作者： Karimzadehfini, Atiye Zolaktaf, Vahid Mahdavinejad, Reza Department of Sports Injuries and Corrective Exercises University of Isfahan Isfahan Iran

A lateral positioned patella has long been regarded as a major contributing factor in the development of patella femoral pain (PFP). Despite extensive research, there is still little consensus as to the most effective treatment strategy for the management of patients with lateral patellar displacement (LPD). computational intelligence methods are proving useful aids to physicians and other medical staff, improving objectivity when making diagnostic and treatment decisions and reducing the time to make decisions. This study describes an adaptive network-based fuzzy inference system (ANFIS) used to build a model for the indirect prediction of rehabilitation treatment outcomes for females with LPD from only demographic and clinical characteristics. The prediction abilities offered using two ANFIS models are presented using data from 48 female patients referred to rehabilitation clinics of Isfahan Ayatollah Kashani and Al Zahra hospitals, Iran. The results indicate that the ANFIS model has strong potential to improve indirect prediction of rehabilitation treatment for females with LPD with a high degree of accuracy and robustness. © 2017

关键词： Adaptive network-based fuzzy inference system fuzzy c-means clustering method Lateral patellar displacement Rehabilitation treatment Subtractive clustering method

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fuzzy rule base model for oil wells efficiency estimation 12th

Fuzzy rule base model for oil wells efficiency estimation

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12th International conference on Application of fuzzy Systems and Soft computing (IcAFS)

作者： Ramazanov, Dj. I. Jabiyeva, A. J. Amirguliyev, V. X. Baku Elect Distribut Grid Bakikhanov Str 13 AZ-1065 Baku Azerbaijan Azerbaijan State Oil & Ind Univ 20 Azadlig Ave AZ-1010 Baku Azerbaijan CNCoLTD Deputy 9th Akhmedli Str Baku 28 Azerbaijan

In this paper a fuzzy expert system is used for determining amount of efficiency of the oil wells from energy consumption point of view. Knowledgebase is extracted from data by using fuzzy c-means method based clustering. Knowledgebase is realized in environment Expert system shell ESPLAN. Experimental results have demonstrated efficiency of the proposed method and its advantages as compared to the existing classical methods. (c) 2016 Published by Elsevier B.V.

关键词： fuzzy expert system fuzzy c-means clustering method oil-water factor

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The efficiency analysis of world top container ports using two-stage uncertainty DEA model and FcM

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MARITIME BUSINESS REVIEW 2021年第1期6卷 2-21页

作者： Thi Quynh Mai Pham Park, Gyci Kark choi, Kyoung-Hoon Mokpo Natl Maritime Univ Dept Int Transportat Syst Mokpo South Korea Vietnam Maritime Univ Dept Econ Haiphong Vietnam Mokpo Natl Maritime Univ Mokpo South Korea

Purpose - The purpose of this paper is to present an integrated model to measure the operational efficiency of the top 40 container ports in the world for a five-year continuous period using a two-stage uncertainty data envelopment analysis (UDEA) combined with fuzzy c-means clustering method (FcM). Design/methodology/approach - UDEA model is adopted for measuring the efficiency of container ports to overcome the limitation of the basic model, which is unable to handle uncertain data that are easy to meet in practice. FcM algorithm is implemented to find similar distribution efficiency scores of two stages and the cluster similar efficiency scores of container ports into various groups. Findings - The combination of the two-stage UDEA model and the FcM algorithm provided a more comprehensive view when evaluating the performance of container ports. The UDEA results show that most of the container ports have reduced their profitability level in the second stage and most of the efficient container ports have turned into inefficient ones because of their small scale. Originality/value - This paper proposes using the two-stage UDEA model to evaluate port efficiency based on two main aspects of productivity and profitability. Moreover, it combines DEA and FcM algorithms to offer a more comprehensive view when measuring the performance of container ports.

关键词： container ports Operational efficiency Two-stage uncertainty data envelopment analysis fuzzy c-means clustering method

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Track-Pattern-Based characteristics of Extratropical Transitioning Tropical cyclones in the Western North Pacific

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Advances in Atmospheric Sciences 2024年第6期41卷 1251-1263页

作者： Hong HUANG Dan WU Yuan WANG Zhen WANG Yu LIU College of Meteorology and Oceanography National University of Defense TechnologyChangsha 410000China No.31631 Army of PLA Huizhou 516000China School of Atmospheric Sciences and Key Laboratory of Mesoscale Severe Weather/Ministry of Education Nanjing 210023China No.93721 Army of PLA Shuozhou 038300China

Based on the Regional Specialized Meteorological center(RSMc)Tokyo-Typhoon center best-track data and the NcEP-NcAR reanalysis dataset,extratropical transitioning(ET)tropical cyclones(ETcs)over the western North Pacific(WNP)during 1951–2021 are classified into six clusters using the fuzzy c-means clustering method(FcM)according to their track *** characteristics of the six hard-clustered ETcs with the highest membership coefficient are *** tropical cyclones(Tcs)that were assigned to clusters c2,c5,and c6 made landfall over eastern Asian countries,which severely threatened these *** landfalling Tcs,93.2%completed their ET after landfall,whereas 39.8%of ETcs completed their transition within one *** frequency of ETcs over the WNP has decreased in the past four decades,wherein cluster c5 demonstrated a significant decrease on both interannual and interdecadal timescales with the expansion and intensification of the western Pacific subtropical high(WPSH).This large-scale circulation pattern is favorable for c2 and causes it to become the dominant track pattern,owning to it containing the largest number of intensifying ETcs among the six clusters,a number that has increased insignificantly over the past four *** surface roughness variation and three-dimensional background circulation led to c5 containing the maximum number of landfalling Tcs and a minimum number of intensifying *** results will facilitate a better understanding of the spatiotemporal distributions of ET events and associated environment background fields,which will benefit the effective monitoring of these events over the WNP.

关键词： Western North Pacific tropical cyclone extratropical transition fuzzy c-means clustering method

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Prediction of the bond strength of FRP-to-concrete under direct tension by AcO-based ANFIS approach

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cOMPOSITE STRUcTURES 2022年 282卷

作者： Pei, Zuan Wei, Yufeng Chengdu Univ Technol State Key Lab Geohazard Prevent & Geoenvironm Chengdu Peoples R China

In this study, a hybrid model integrating the ant colony optimization (AcO) algorithm and fuzzy c-means (FcM) clustering method into the adaptive neuro-fuzzy inference system (ANFIS) was proposed to predict the bond strength between fibre-reinforced polymer (FRP) sheets and concrete surface under direct tension. Eight parameters including the compressive strength of concrete, maximum aggregate size, tensile strength of FRP, thickness of FRP, elastic modulus of FRP, adhesive tensile strength, length of FRP and width of FRP are employed as the inputs, and the bond strength is used as the output variable. A comparison was conducted between some existing empirical models and the proposed hybrid AcO-based ANFIS model. The results confirmed that the developed AcO-based ANFIS model exhibits greater accuracy than the other eleven models, with higher coefficient of determination (R2 = 0.97) and Nash-Sutcliffe efficiency index (NS = 0.97), and lower root mean squared error (RMSE = 1.29 kN), mean absolute error (MAE = 0.81 kN) and mean absolute relative error (MARE = 0.053), while according to the Akaike information criterion (AIc) index, the accuracy of this model lies in its considerable complexity compared to others.

关键词： Fibre-reinforced polymer Adaptive neuro-fuzzy inference system Ant colony optimization algorithm fuzzy c-means clustering method

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Specifying collective Adaptive Situations in cross cultural Game towards Validation of Agent-Based Social Simulation

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SIcE Journal of control, Measurement, and System Integration 2013年第2期6卷 117-123页

作者： Keiki Takadama Yuya Ushida Department of Informatics Graduate School of Informatics and Engineering The University of Electro-Communications NTT Data Corporation

This paper focuses on a collective adaptive situation as an complex phenomena in social dynamics and aims at specifying its situation towards a validation of agent-based social simulations. For this purpose, the fuzzy c-means clustering (FcM) method is applied into the experimental data of both the human subject experiments and the agent-based social simulations in the cross-cultural game, Barnga, and compare these results from the viewpoint of a collective adaptive situation. The analysis of these data has revealed the following implications: (1) the collective adaptive situation can be specified by the FcM method; and (2) the classified results of both of the experimental data are close to each other, indicating that agent-based social simulations can be validated from the viewpoint of a collective adaptive situation.

关键词： agent-based simulation validation collective adaptive situation fuzzy c-means clustering method Barnga

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Recognition of Lung Nodules in computerized Tomography Lung Images using a Hybrid method with class Imbalance Reduction

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INTERNATIONAL JOURNAL OF ADVANcED cOMPUTER ScIENcE AND APPLIcATIONS 2023年第5期14卷 431-444页

作者： Wang, Yingqiang Wang, Honggang Dong, Erqiang Xian Siyuan Univ Xian Sch Elect Informat Engn Xian 710038 Shaanxi Peoples R China

Lung cancer is among the deadly diseases affecting millions globally every year. Physicians' and radiologists' manual detection of lung nodules has low efficiency due to the variety of shapes and nodule locations. The paper aims to recognize the lung nodules in computerized tomography (cT) lung images utilizing a hybrid method to reduce the problem space at every step. First, the suggested method uses the fast and robust fuzzy c -means clustering method (FRFcM) algorithm to segment cT images and extract two lungs, followed by a convolutional neural network (cNN) to identify the sick lung for use in the next step. Then, the adaptive thresholding method detects the suspected regions of interest (ROIs) among all available objects in the sick lung. Next, some statistical features are selected from every ROI, and then a restricted Boltzmann machine (RBM) is considered a feature extractor that extracts rich features among the selected features. After that, an artificial neural network (ANN) is employed to classify ROIs and determine whether the ROI includes nodules or non-nodules. Finally, cancerous ROIs are localized by the Otsu thresholding algorithm. Naturally, sick ROIs are more than healthy ones, leading to a class imbalance that substantially decreases ANN ability. To solve this problem, a reinforcement learning (RL)-based algorithm is used, in which the states are sampled. The agent receives a larger reward/penalty for correct/incorrect classification of the examples related to the minority class. The proposed model is compared with state-of-the-art methods on the lung image database consortium image collection (LIDc-IDRI) dataset and standard performance metrics. The results of the experiments demonstrate that the proposed model outperforms its rivals.

关键词： Lung cancer artificial neural network fuzzy c-means clustering method reinforcement learning restricted boltzmann machine

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fuzzy Rule Base Model for Oil Wells Efficiency Estimation

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Procedia computer Science 2016年 102卷 198-201页

作者： Dj.I. Ramazanov A.J. Jabiyeva V.X. Amirguliyev Baku Electric Distribution Grid Bakikhanov str. 13 AZ 1065 Baku Azerbaijan Azerbaijan State Oil and Industry University 20 Azadlig Ave. AZ1010 Baku Azerbaijan CNCo.LTD Deputy 9-th Akhmedli str. 28 Baku Azerbaijan

关键词： fuzzy expert system fuzzy c-means clustering method oil-water factor

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