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11th IEEE International conference on Wireless communications and Signal Processing (WcSP)

作者： Wang, Gang Wang, cheng China Univ Min & Technol Natl Joint Engn Lab Internet Appl Technol Mines Xuzhou Jiangsu Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou Jiangsu Peoples R China

ISBN: (纸本)9781728135557

Due to the increasing deployment of Internet of Things in the mining industry, portable gas monitoring devices have been widely used. According to the character of time series of gas stream, the paper studies on mathematics analysis method of time series similarity based on pattern beam and pattern set. combining with short-time stationary of gas data, the feature selection method of short-time gas data based on sliding time window is proposed. On the basis of the fuzzy c-means clustering algorithm, short-time gas stream in the fuzzy c-means clustering algorithm is put forward to analyze the convergence effects of the data of gas stream based on binary statistic and multivariate statistic, which provides qualified data that available for analysis and calculation for data correction of gas sensors afterward.

关键词： underground coal mine time-series pattern beam sliding time window fuzzy c-means clustering algorithm

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A fuzzy classification approach for learning style prediction based on web mining technique in e-learning environments

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EDUcATION AND INFORMATION TEcHNOLOGIES 2019年第3期24卷 1943-1959页

作者： El Aissaoui, Ouafae El Madani, Yasser El Alami Oughdir, Lahcen El Allioui, Youssouf Sidi Mohamed Ben Abdellah Univ LSI FPT Fes Morocco Mohammed V Univ ENSIAS BP 713 Rabat Morocco Hassan First Univ LS3M FPK BP 145 Khouribga 25000 Morocco

Adaptive E-learning platforms provide personalized learning process relying mainly on learning styles. The traditional approach to find learning styles depends on asking learners to self-evaluate their own attitudes and behaviors through surveys and questionnaires. This approach presents several weaknesses including the lack of self-awareness of learners of their own preferences. Furthermore, the vast majority of learners experience boredom when they are asked to fill out the corresponding questionnaire. Besides that, traditional approach assumes that learning styles are fixed, and cannot change over time. In this paper, we propose a generic approach for detecting learning styles automatically according to a given learning styles model. In fact, our approach does not depend on a specific LSM. This work consists of two major steps. First, we extract learning sequences from learners log files using web usage mining techniques. Second, we classify the extracted learners' sequences according to a specific learning style model using clustering algorithms. To perform our approach we use Felder-Silverman Model as LSM and fuzzy c-means as a clustering algorithm. We have conducted an experimental study using a real-world dataset. The obtained results show that our approach outperforms traditional approach and provides promising results.

关键词： Web usage mining clustering algorithms fuzzy c-means clustering algorithm Adaptive E-learning systems Learning style Learning style prediction Learning object

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An Improved Dynamic Equivalent Method for the Dc Offshore Wind Farm 3

An Improved Dynamic Equivalent Method for the DC Offshore Wi...

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3rd IEEE International Electrical and Energy conference (cIEEc)

作者： Li, Wei Wang, Hui Natl Engn Res Ctr Energy Convers & Control Changsha Peoples R China

ISBN: (纸本)9781728116754

In order to analysis the output dynamic characteristics of dc offshore wind farm, a multimachine representation dynamic equivalent method based on an improved fuzzy c-means (IFcM) clustering algorithm is proposed. First, characteristic variables which can characterize the dynamic characteristics of the input and state performance are researched. Second, an IFcM clustering algorithm is first put forward. Third, the dc offshore wind generators (DcOWGs) are divided into many groups by analyzing the characteristic variables data with the IFcM. Finally, DcOWGs in the same group are equivalent as one DcOWG to analyze the dynamic characteristics. Simulation results from MATLAB/SIMULINK verify the theoretical analysis.

关键词： offshore wind generator fuzzy c-means clustering algorithm dynamic equivalent dc transmission

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A hybrid fuzzy c-means and Neutrosophic for jaw lesions segmentation

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AIN SHAMS ENGINEERING JOURNAL 2018年第4期9卷 697-706页

作者： Alsmadi, Mutasem K. Univ Dammam Coll Appl Studies & Community Serv Dept MIS Dammam Saudi Arabia

It is really important to diagnose jaw tumor in its early stages to improve its prognosis. A differential diagnosis could be performed using X-ray images;therefore, accurate and fully automatic jaw lesions image segmentation is a challenging and essential task. The aim of this work was to develop a novel, fully automatic and effective method for jaw lesions in panoramic X-ray image segmentation. The hybrid fuzzy c-means and Neutrosophic approach is used for segmenting jaw image and detecting the jaw lesion region in panoramic X-ray images which may help in diagnosing jaw lesions. Area error metrics are used to assess the performance and efficiency of the proposed approach from different aspects. Both efficiency and accuracy are analyzed. Specificity, sensitivity and similarity analyses are conducted to assess the robustness of the proposed approach. comparing the proposed approach with the Hybrid Firefly algorithm with the fuzzy c-means, and the Artificial Bee colony with the fuzzy c-means algorithm, the proposed approach produces the most identical lesion region to the manual delineation by the Oral Pathologist and shows better performance (FP rate is 6.1%, TP rate is 90%, specificity rate is 0.9412, sensitivity rate is 0.9592 and similarity rate is 0.9471). (c) 2016 Ain Shams University.

关键词： Automatic segmentation Jaw panoramic X-ray image Neutrosophy fuzzy c-means clustering algorithm

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A Novel clustering Validity Function of FcM clustering algorithm

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IEEE AccESS 2019年 7卷 152289-152315页

作者： Zhu, L. F. Wang, J. S. Wang, H. Y. Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Peoples R China Univ Sci & Technol Liaoning Natl Financial Secur & Syst Equipment Engn Res Ct Anshan 114051 Peoples R China

cluster analysis refers to the process of grouping a collection of physical or abstract objects into multiple classes of similar objects. Determining the optimal classification number of a data set is the key to the clustering problem, that is to say whether the data set can be effectively partitioned. cluster validity study is a process of establishing clustering effectiveness indicators, evaluating clustering quality and determining the optimal number of clusters. A validity function of fuzzy c-means (FcM) clustering algorithm is proposed by adopting the division of intra-class compactness and inter-class separation, whose minimum represents the best clustering. Then, the proposed validity function on FcM clustering algorithm is compared with the known typical validity functions by carrying out simulation experiments to compare the related clustering performance. Three data sets are adopted to carry out FcM clustering, which includes three classical data sets, two artificial data sets and six real data sets in UcI database. Simulation experimental results show that the proposed validity function can effectively partition the data set.

关键词： Polymers Production Artificial neural networks Optimization Whales Resins clustering analysis clustering validity index fuzzy c-means clustering algorithm

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Fractional gravitational search-radial basis neural network for bone marrow white blood cell classification

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IMAGING ScIENcE JOURNAL 2018年第2期66卷 106-124页

作者： Pergad, Namdev Devidas Hamde, Satish T. Shri Tulja Bhavani Coll Engn Dept ETC Engn Osmanabad Maharashtra India SGGS IE&T Dept Instrumentat Engn Nanded India

Automotive image segmentation systems are becoming an important tool in the medical field for disease diagnosis. The white blood cell (WBc) segmentation is crucial, because it plays an important role in the determination of the diseases and helps experts to diagnose the blood disease disorders. The precise segmentation of the WBcs is quite challenging because of the complex contents in the bone marrow smears. In this paper, a novel neural network (NN) classifier is proposed for the classification of the bone marrow WBcs. The proposed NN classifier integrates the fractional gravitation search (FGS) algorithm for updating the weight in the radial basis function mapping for the classification of the WBc based on the cell nucleus feature. The experimentation of the proposed FGS-RBNN classifier is carried on the images collected from the publically available dataset. The performance of the proposed methodology is evaluated over the existing classifier approaches using the measures accuracy, sensitivity, and specificity. The results show that the classification using the nucleus features alone can be utilized to achieve the classification with the better accuracy. Moreover, the classification performance of the proposed FGS-RBNN is better than the existing classifiers, and it is proved to be the efficacious classifier with a classification accuracy of 95%.

关键词： Radial basis network gravitational search algorithm bone marrow white blood cells fuzzy c-means clustering algorithm

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An Optimal Design Methodology of Adaptive Neuro-fuzzy Inference System for Energy Load Forecasting - Hail city case study (Saudi Arabia) 18

An Optimal Design Methodology of Adaptive Neuro-Fuzzy Infere...

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4th International conference on Engineering and MIS (IcEMIS)

作者： Hussein, Hisham Hail Univ Community Coll Dept Elect Engn Hail Saudi Arabia

ISBN: (纸本)9781450363921

In this paper, it is shown that accurate load forecasts are vital for short, medium and long-term operations. The energy load forecast has its impact on different outcomes and decisions for power generation companies. It also has its influence on electricity market prices. The purpose of this research is to develop an energy load forecasting model to predict future electricity loads for energy load management. The forecasting model is based on a straightforward sequential methodology by implementing subtractive clustering algorithm, fuzzy c-means clustering algorithm and eventually an adaptive Neuro-fuzzy inference system architecture for generating the best fuzzy inference system using historical energy load data. In addition, the influence of different weather factors on energy loads such as dry-bulb temperature is counted in.

关键词： Adaptive Neuro-fuzzy inference system Energy load forecast fuzzy c-means clustering algorithm fuzzy inference system Subtractive clustering algorithm

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Optimization of RBFneural network used in state recognition of coal flotation

Optimization of RBFneural network used in state recognition ...

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International conference on Telecommunication, Power Analysis and computing Techniques

作者： Tian Muling Tian Muqin Yang Jieming Li, Julie Taiyuan Univ Technol Coll Elect & Power Engn Taiyuan Shanxi Peoples R China Hong Kong Aircraft Engn Amer Co Ltd Charlotte NC USA

In order to make the RBF hidden layer centres being established more adaptively and avoid the blindness, this paper proposes a fusion algorithm in order to optimize the parameters of the RBF neural network used in recognizing the state of coal flotation. Firstly, in the optimization algorithm, the improved immune algorithm was used to determine the center position and the number of hidden layer of RBF neural network. Before this, the immune algorithm has been improved in several aspects, such as the initial population selection algorithm and the method for segment selection of affinity thresholds. In addition, the antibody removal mechanism, antibody immune mechanism and antibody concentration regulation principle had also been added in immune algorithm. Secondly, in virtue of combining a fuzzy c-means clustering algorithm, the centers of the hidden layer were optimized accurately. Through the sample verification, the RBF neural network obtained by the fusion algorithm was proved to have been improved significantly in the accuracy of identifying the coal flotation state and has better generalization ability.

关键词： RBF neural network centers of hidden layer froth image of coal flotation immune algorithm fuzzy c-means clustering algorithm state recognition

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common lecturers timetabling among departments based on funnel-shape clustering algorithm

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APPLIED INTELLIGENcE 2017年第2期46卷 386-408页

作者： Babaei, Hamed Karimpour, Jaber Hadidi, Amin Islamic Azad Univ Ahar Branch Dept Comp Engn Ahar Iran Univ Tabriz Dept Comp Sci Tabriz Iran Islamic Azad Univ Ahar Branch Dept Mech Engn Ahar Iran

The university course timetabling problem is considered one of the NP-problems which should be performed for each semester repeatedly, and it is an exhausting and time consuming task. The main technique of the proposed approach is focused on extension and scalability of common lecturers timetabling process across different departments of a university. In this paper the clustering algorithms, including K-means and fuzzy c-means, are used to schedule common lecturers between departments considering the constraints and their priorities offered, for the first time. For this purpose, a new clustering algorithm named funnel-shape clustering is proposed to schedule common lecturers between departments based on their constraints proposed. The main goals of this paper are to improve the satisfaction of common lecturers across departments and minimize the loss of resources within departments. In this method, all the departments perform their scheduling process locally;After that the clustering agent is applied to cluster common lecturers across departments by using the proposed funnel clustering algorithm and then the traversing agent is used to find the useless resources across departments. Following the clustering and traversing processes, mapping is performed based on the common lecturers' constraints in the excess resources in order to reach the problem goals. The applied dataset based on the real world requirements is across various departments of Islamic Azad University, Ahar Branch and the results' success would be based on uniform distribution and allocation of common lecturers on useless resources across different departments.

关键词： common lecturer timetabling problem (cLTTP) fuzzy c-means clustering algorithm University course timetabling problem (UcTTP) k- means clustering algorithm

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cloud manufacturing resources fuzzy classification based on genetic simulated annealing algorithm

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MATERIALS AND MANUFAcTURING PROcESSES 2017年第10期32卷 1109-1115页

作者： Hu, Yanjuan chang, Xingfu Wang, Yao Wang, Zhanli Shi, chao Wu, Lizhe Changchun Univ Technol Sch Mech Elect Engn Changchun Peoples R China Beihua Univ Coll Mech Engn Jilin Peoples R China

To solve the problem of fuzzy classification of manufacturing resources in a cloud manufacturing environment, a hybrid algorithm based on genetic algorithm (GA), simulated annealing (SA) and fuzzy c-means clustering algorithm (FcM) is proposed. In this hybrid algorithm, classification is based on the processing feature and attributes of the manufacturing resource;the inner and outer layers of the nested loops are solving it, GA obtains the best classification number in the outer layer;the fitness function is constructed by fuzzy clustering algorithm (FcM), carrying out the selection, crossover and mutation operation and SA cooling operation. The final classification results are obtained in the inner layer. Using the hybrid algorithm to solve 45 kinds of manufacturing resources, the optimal classification number is 9 and the corresponding classification results are obtained, proving that the algorithm is effective.

关键词： cloud manufacturing fuzzy c-means clustering algorithm fuzzy classification genetic algorithm hybrid algorithm manufacturing resources optimization simulated annealing

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