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A novel parameter estimation in dynamic model via fuzzy swarm intelligence and chaos theory for faults in wastewater treatment plant

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SOFT cOMPUTING 2020年第1期24卷 111-129页

作者： Anter, Ahmed M. Gupta, Deepak castillo, Oscar Beni Suef Univ Fac Comp & Informat Bani Suwayf 62511 Egypt Maharaja Agrsen Inst Technol Delhi India Tijuana Inst Technol Tijuana Mexico

Faults during a wastewater treatment for plant (WWTP) are critical issue for social and biological. Poorly treated wastewater may achieve dangerous effect for human as well as nature. This paper proposed a novel model based on a binary version of whale optimization algorithm (WOA), chaos theory and fuzzy logic, namely (cF-BWOA). cF-BWOA is applied in the application of WWTP to find out the more relevant attributes from the whole dataset, reducing cost and validation of decision rules, and helping to identify a non-well-structured domain. cF-BWOA attempts to reduce the whole feature set without loss of significant information to the classification process. Fast fuzzy c-means is used as a cost function to measure the fuzzification and uncertainty of data. Ten different chaos sequence maps are used to estimate and tune WOA parameters. Experiments are applied on a complex real-time dataset with various uncertainty features and missing values. The overall result indicates that the cWOA with the Sine chaos map shows the better performance, lower error, higher convergence speed and shorter execution time. In addition, the proposed model is capable of detecting sensor process faults in WWTP with high accuracy and can guide the operators of these systems to control decisions.

关键词： Fault detection Whale optimization algorithm Wastewater treatment chaos theory fuzzy c-means algorithm

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Multi-Objective Optimal Reactive Power Dispatch of Power Systems by combining classification-Based Multi-Objective Evolutionary algorithm and Integrated Decision Making

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IEEE AccESS 2020年 8卷 38198-38209页

作者： Zhang, Meng Li, Yang Northeast Elect Power Univ Sch Elect Engn Jilin 132012 Jilin Peoples R China

For the purpose of addressing the multi-objective optimal reactive power dispatch (MORPD) problem, a two-step approach is proposed in this paper. First of all, to ensure the economy and security of the power system, the MORPD model aiming to minimize active power loss and voltage deviation is formulated. And then the two-step approach integrating decision-making into optimization is proposed to solve the model. Specifically speaking, the first step aims to seek the Pareto optimal solutions (POSs) with good distribution by using a multi-objective optimization (MOO) algorithm named classification and Pareto domination based multi-objective evolutionary algorithm (cPSMOEA). Furthermore, the reference Pareto-optimal front is generated to validate the Pareto front obtained using cPSMOEA;in the second step, integrated decision-making by combining fuzzy c-means algorithm (FcM) with grey relation projection method (GRP) aims to extract the best compromise solutions which reflect the preferences of decision-makers from the POSs. Based on the test results on the IEEE 30-bus and IEEE 118-bus test systems, it is demonstrated that the proposed approach not only manages to address the MORPD issue but also outperforms other commonly-used MOO algorithms including multi-objective particle swarm optimization (MOPSO), preference-inspired coevolutionary algorithm (PIcEAg) and the third evolution step of generalized differential evolution (GDE3).

关键词： Optimization Decision making Reactive power Linear programming Evolutionary computation Security Optimal reactive power dispatch multi-objective evolutionary algorithm integrated decision-making best compromise solution fuzzy c-means algorithm grey relation projection

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A three-stage fuzzy neutrosophic decision support system for human resources decisions in organizations

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Decision Analytics Journal 2023年 7卷

作者： Yiğit, Fatih Altinbas University Department of Industrial Engineering Istanbul Turkey

This study proposes an integrated Decision Support System (DSS) with Multi-criteria Decision-Making (McDM) to evaluate trainers in organizations and choose the most suitable one(s) for a training program. The clustering stage determines the most appropriate number of trainees to be assigned to a trainer. The proposed model also considers the training budget and the constraint limiting the number of assignments. The proposed model has three stages: Delphi, the Interval-Valued Neutrosophic Analytic Hierarchy Process (IVN-AHP), and fuzzy c-means (FcM). The model's input is expert opinions on various criteria, the candidate assessment score, and the bi-comparison of agreed criteria. Outputs are weights, and the members of each cluster represent possible candidates. We present a case study to demonstrate the applicability of the proposed DSS in a training program assessment. Additional applications of the proposed system include recruitment and promotion. © 2023 The Author

关键词： Analytic hierarchy process Decision support system fuzzy c-means algorithm Human resources analytics Multi-criteria decision-making Neutrosophic sets

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A novel fuzzy c-means algorithm to generate diverse and desirable cluster solutions used by genetic-based clustering ensemble algorithms

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MEMETIc cOMPUTING 2012年第1期4卷 49-71页

作者： Ghaemi, Reza Sulaiman, Md Nasir Ibrahim, Hamidah Mustapha, Norwati Univ Putra Malaysia Dept Comp Sci Fac Comp Sci & Informat Technol Serdang Malaysia

One of the most significant discussions in the field of machine learning today is on the clustering ensemble. The clustering ensemble combines multiple partitions generated by different clustering algorithms into a single clustering solution. Genetic algorithms are known for their high ability to solve optimization problems, especially the problem of the clustering ensemble. To date, despite the major contributions to find consensus cluster partitions with application of genetic algorithms, there has been little discussion on population initialization through generative mechanisms in genetic-based clustering ensemble algorithms as well as the production of cluster partitions with favorable fitness values in first phase clustering ensembles. In this paper, a threshold fuzzy c-means algorithm, named TFcM, is proposed to solve the problem of diversity of clustering, one of the most common problems in clustering ensembles. Moreover, TFcM is able to increase the fitness of cluster partitions, such that it improves performance of genetic-based clustering ensemble algorithms. The fitness average of cluster partitions generated by TFcM are evaluated by three different objective functions and compared against other clustering algorithms. In this paper, a simple genetic-based clustering ensemble algorithm, named SGcE, is proposed, in which cluster partitions generated by the TFcM and other clustering algorithms are used as the initial population used by the SGcE. The performance of the SGcE is evaluated and compared based on the different initial populations used. The experimental results based on eleven real world datasets demonstrate that TFcM improves the fitness of cluster partitions and that the performance of the SGcE is enhanced using initial populations generated by the TFcM.

关键词： fuzzy c-means algorithm clustering ensemble Genetic-based clustering ensemble Genetic algorithms Diversity of clustering clustering accuracy

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Generalized rough fuzzy c-means algorithm for brain MR image segmentation

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cOMPUTER METHODS AND PROGRAMS IN BIOMEDIcINE 2012年第2期108卷 644-655页

作者： Ji, Zexuan Sun, Quansen Xia, Yong chen, Qiang Xia, Deshen Feng, Dagan Nanjing Univ Sci & Technol Sch Comp Sci & Technol Nanjing 210094 Jiangsu Peoples R China Univ Sydney Biomed & Multimedia Informat Technol BMIT Res Grp Sch Informat Technol Sydney NSW 2006 Australia Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Hong Kong Polytech Univ CMSP Dept Elect & Informat Engn Hong Kong Hong Kong Peoples R China Shanghai Jiao Tong Univ Med X Res Inst Shanghai 200025 Peoples R China

fuzzy sets and rough sets have been widely used in many clustering algorithms for medical image segmentation, and have recently been combined together to better deal with the uncertainty implied in observed image data. Despite of their wide spread applications, traditional hybrid approaches are sensitive to the empirical weighting parameters and random initialization, and hence may produce less accurate results. In this paper, a novel hybrid clustering approach, namely the generalized rough fuzzy c-means (GRFcM) algorithm is proposed for brain MR image segmentation. In this algorithm, each cluster is characterized by three automatically determined rough-fuzzy regions, and accordingly the membership of each pixel is estimated with respect to the region it locates. The importance of each region is balanced by a weighting parameter, and the bias field in MR images is modeled by a linear combination of orthogonal polynomials. The weighting parameter estimation and bias field correction have been incorporated into the iterative clustering process. Our algorithm has been compared to the existing rough c-means and hybrid clustering algorithms in both synthetic and clinical brain MR images. Experimental results demonstrate that the proposed algorithm is more robust to the initialization, noise, and bias field, and can produce more accurate and reliable segmentations. (c) 2011 Elsevier Ireland Ltd. All rights reserved.

关键词： Brain magnetic resonance image Image segmentation Intensity inhomogeneity fuzzy c-means algorithm Rough set Rough-fuzzy c-means algorithm

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coupling fuzzy clustering and cellular automata based on local maxima of development potential to model urban emergence and expansion in economic development zones

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INTERNATIONAL JOURNAL OF GEOGRAPHIcAL INFORMATION ScIENcE 2020年第10期34卷 1930-1952页

作者： Liang, Xun Liu, Xiaoping chen, Guangliang Leng, Jiye Wen, Youyue chen, Guangzhao Sun Yat Sen Univ Sch Geog & Planning Guangdong Key Lab Urbanizat & Geosimulat Guangzhou Peoples R China China Univ Geosci Sch Geog & Informat Engn Wuhan Hubei Peoples R China Guangzhou Landtu Geog Informat Technol Co Ltd Guangzhou Peoples R China Univ Toronto Dept Geog & Planning Toronto ON Canada South China Inst Environm Sci MEE Guangzhou Peoples R China

Modeling urban growth in Economic development zones (EDZs) can help planners determine appropriate land policies for these regions. However, sometimes EDZs are established in remote areas outside of central cities that have no historical urban areas. Existing models are unable to simulate the emergence of urban areas without historical urban land in EDZs. In this study, a cellular automaton (cA) model based on fuzzy clustering is developed to address this issue. This model is implemented by coupling an unsupervised classification method and a modified cA model with an urban emergence mechanism based on local maxima. Through an analysis of the planning policies and existing infrastructure, the proposed model can detect the potential start zones and simulate the trajectory of urban growth independent of the historical urban land use. The method is validated in the urban emergence simulation of the Taiping Bay development zone in Dalian, china from 2013 to 2019. The proposed model is applied to future simulation in 2019-2030. The results demonstrate that the proposed model can be used to predict urban emergence and generate the possible future urban form, which will assist planners in determining the urban layout and controlling urban growth in EDZs.

关键词： Economic development zone urban emergence simulation fuzzy c-means algorithm cellular automata

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The range of the value for the fuzzifier of the fuzzy c-means algorithm

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PATTERN REcOGNITION LETTERS 2012年第16期33卷 2280-2284页

作者： Huang, Ming Xia, Zhixun Wang, Hongbo Zeng, Qinghua Wang, Qian Natl Univ Def Technol Sci & Technol Scramjet Lab Changsha 410073 Hunan Peoples R China Xian Satellite Control Ctr Xian 710043 Peoples R China NW Inst Nucl Technol Xian 710024 Peoples R China

The fuzzy c-means algorithm (FcM) is a widely used clustering algorithm. It is well known that the fuzzifier, m, which is also called fuzzy weighting exponent, has a significant impact on the performance of the FcM. Most of the researches have shown that there exists an effective range of the value for m. However, since the method adopted by researchers is mainly experimental or empirical, it is still an open problem how to select an appropriate fuzzifier m in theory when implementing the FcM. In this paper, we propose a theoretical approach to determine the range of the value of m. This approach utilizes the behavior of membership function on two data points, based on which we reveal the partial relationship between the fuzzifier m and the dataset structure. (c) 2012 Elsevier B.V. All rights reserved.

关键词： fuzzy c-means algorithm Fuzzifier The range of the value The behavior of membership function

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fuzzy c-means clustering and criminisi algorithm Based Shadow Removal Scheme for Side Scan Sonar Images

Fuzzy C-Means Clustering and Criminisi Algorithm Based Shado...

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IEEE International conference on Innovative Mechanisms for Industry Applications (IcIMIA

作者： Vikas, A. Sreenivasan, A. Sudha, K. L. DSCE Bangalore 78 Karnataka India Dept ECE Bangalore Karnataka India

ISBN: (纸本)9781509059607

This paper proposes the fuzzy c-means and criminisi algorithm Based Shadow removal scheme for the Side Scan Sonar Images. Side Scan Sonar is widely used in the underwater ocean investigations like mining, pipelining, object detection, underwater communications etc. This paper make use of fuzzy c-means clustering algorithm for shadow Region segmentation and criminisi algorithm for filling the shadow region. Thus one can get clear view of detected object.

关键词： fuzzy c-means algorithm criminisi algorithm Side Scan Sonar Ocean investigations

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A robust fuzzy clustering algorithm using spatial information combined with local membership filtering for brain MR images

A robust fuzzy clustering algorithm using spatial informatio...

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IEEE International conference on Bioinformatics and Biomedicine (IEEE BIBM)

作者： Li, Lanting cao, Peng Yang, Jinzhu Zhao, Dazhe Zaiane, Osmar R. Northeastern Univ Key Lab Intelligent Comp Med Image Coll Comp Sci & Engn Shenyang Peoples R China Northeastern Univ Key Lab Intelligent Comp Med Image Coll Comp Sci & Engn Minist Educ Shenyang Peoples R China Northeastern Univ Coll Comp Sci & Engn Shenyang Peoples R China Univ Alberta Alberta Machine Intelligence Inst Edmonton AB Canada

ISBN: (纸本)9781728162157

MRI brain segmentation plays an important part in computer-aided diagnosis, which visually reveals the changes in brain structure for doctors to quickly and accurately discover and treat diseases related to brain tissue morphology. The fuzzy c-means (FcM) algorithm performs well when the segmenting images with no noise and with intensity uniformity. However, the MRI brain images are always defective in noise and intensity non-uniformity and thus we propose a novel FcM algorithm named adaptive FcM with neighborhood membership (FcM_anm). We design a filtering process with neighborhood membership to reduce the negative influence of noise and a novel objective function which further considers the spatial membership information adaptively. Finally, to verify the performance of our method, several experiments comparing among the Experimental results demonstrate the proposed method consistently outperforms the state-of-the-art FcM-based algorithms in synthetic images, simulated and real brain MR images with effects of the noise and intensity non-uniformity.

关键词： MRI brain segmentation fuzzy c-means algorithm Spatial information Local membership filtering Adaptive penalty item

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Analysis and Visualization of Air Quality Using Real Time Pollutant Data 6

Analysis and Visualization of Air Quality Using Real Time Po...

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6th International conference on Advanced computing and communication Systems (IcAccS)

作者： Grace, R. Kingsy Aishvarya, Karthika S. Monisha, B. Kaarthik, A. Sri Ramakrishna Engn Coll Dept Comp Sci & Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9781728151977;9781728151960

Since industrial revolution, the rate of industrialization and urbanization has increased dramatically. Most of the industry applications create pollution in the air and the vehicle emissions are also dangerous to the health of the people. In the developing countries, air pollution is severe in most of the areas. Air quality is the important factor to measure the quality of air. Most of the air quality measuring systems uses air quality index to tell the people about the air quality of their location. The primary objective of the system is to analyze and visualize air quality from the real time sensor data. The proposed system analyses six critical air pollutants which are, ozone (O-3), Particulate Matter (PM2.5), carbon monoxide (cO), Nitrogen dioxide (NO2) and Sulphur dioxide (SO2) are the most widespread health threats. The fuzzy c-means clustering is used to process the polluted air data from the sensors. From the results it is clear that the fuzzy c-means algorithm provides better results for the parameter accuracy while evaluating with the other algorithms in the literature.

关键词： Air Quality Index fuzzy c-means algorithm K-means algorithm

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