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Module division method of complex products for responding to user's requirements

ALEXANDRIA ENGINEERING JOURNAL 2023年 82卷 404-413页

作者： Hao, Juan Gao, Xinqin Liu, Yong Han, Zhoupeng Xian Univ Technol Sch Mech & Precis Instrument Engn Xian 710048 Peoples R China Xian Univ Technol Shaanxi Modern Equipment Green Mfg Collaborat Inn Xian 710048 Peoples R China

With the gradual diversification of personalized usage scenarios, user requirements play a direct role in product design decisions. Due to the problem of fuzzy demand caused by user cognitive bias, traditional design methods usually focus on realizing product functions and cannot effectively match user requirements. Therefore, this paper proposes a complex product module division method for user requirements. The method constitutes of three tasks, requirement analysis of module division, design mapping of module division and scheme implementation of module division. Firstly, based on the progressive architecture from initial requirements to precise requirements, the effective user requirements are obtained through similarity recommendation. Secondly, according to the four types of knowledge of function, geometry, physics and design, the design structure matrix is constructed to complete the Requirement-Function-Structure mapping. The improved fuzzy c-means algorithm is used to solve the mapping model, and finally a module division scheme that meets the user requirements is obtained. Taking the chip removal machine as an example, the rationality and effectiveness of the method are verified. The results show that the product modules divided by this method can effectively meet the multiple user requirements.

关键词： User requirements Modular division Design Structure Matrix fuzzy c-means algorithm

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Research on multi-source partial discharge localization based on improved FcM-LOF fuzzy clustering algorithm

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MEASUREMENT ScIENcE AND TEcHNOLOGY 2023年第1期34卷 015116-015116页

作者： Hu, Xiaoxu Wang, Jingang Xu, changjian Yan, Xiaojun Zhao, Pengcheng Liu, Ya Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing 40044 Peoples R China

At present, the ultra-high frequency method is widely used in the single-source partial discharge (PD) location of substation sites;however, there are issues with the positioning accuracy being too low, and it is difficult to meet the needs of multi-source PD positioning. Based on the local outlier factor (LOF) outlier idea and fuzzy c-means (FcM) clustering algorithm, this paper proposes the FcM-LOF algorithm to be applied to the research of multi-source PD positioning. This algorithm removes the discrete points of the original time difference data set based on the principle of the local density threshold, reduces the clustering center error, and improves the accuracy of multi-source positioning. The main research contents of this article are as follows. Firstly, using smoothing filter processing and the energy accumulation method, we collect the time difference of dual- and triple-source PD signals as the data set to be processed, and carry out the laboratory simulation experiment. Secondly, comparing the clustering effects of the k-means and FcM algorithms, it is found that the clustering accuracy of the FcM algorithm is significantly better than that of the k-means algorithm, and the positioning error is reduced by 27.3%. Then, using the neighborhood density and outlier factor to eliminate abnormal data, combined with FcM fuzzy clustering, an improved FcM-LOF algorithm is proposed. compared with the FcM algorithm, the positioning error of this algorithm is reduced by 11.6%. It is suitable for multi-source positioning and has a greater improvement in noisy environments. Finally, the improved algorithm is applied to a field simulation test, which verifies the accuracy of the algorithm. We also study the factors affecting the positioning accuracy of the improved algorithm. The research in this paper can provide a powerful reference for multi-source PD detection of power equipment.

关键词： multi-source partial discharge ultra-high frequency method fuzzy c-means algorithm local outlier factor simulation test positioning accuracy

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Selection of Minimum Rules from a fuzzy TSK Model Using a PSO-FcM combination

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JOURNAL OF cONTROL AUTOMATION AND ELEcTRIcAL SYSTEMS 2023年第2期34卷 384-393页

作者： Brikh, Lamine Guenounou, Ouahib Bakir, Toufik Univ Bejaia Fac Technol Lab Technol Ind & Informat LTII Bejaia 06000 Algeria Univ Bourgogne Lab ImViA EA 7535 Dijon France

This work proposes an efficient and promising method for linear and nonlinear dynamic systems modeling. Unlike previous methods, we combine fuzzy c-means (FcM) and modified Particle Swarm Optimization (PSO) algorithms. The FcM allows clustering large nonlinear data sets (good rule-base parameters initialization). The PSO algorithm is used to optimize the parameters of the fuzzy system rules (initialized by FcM). This combination allows reducing the rule-base of TSK fuzzy model considering the three goals: have a good initialization of the parameters to be optimized, handling complex systems with ensuring a high accuracy and a low complex algorithm structure. Indeed, from a given fuzzy rule-base, FcM-PSO selects a subset of important fuzzy rules based on the threshold criterion of each rule. The interaction between the particles improves iteratively the quality of each fuzzy rule. Simulation results using two well-known benchmark functions show the efficiency of the proposed approach when compared to previous works.

关键词： Evolutionary algorithm fuzzy c-means algorithm Optimization Particle swarm optimization TSK fuzzy logic

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Forecasting tourist arrivals using dual decomposition strategy and an improved fuzzy time series method

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NEURAL cOMPUTING & APPLIcATIONS 2023年第10期35卷 7161-7183页

作者： Liang, Xiaozhen Wu, Zhikun Shanghai Univ Sch Management Shanghai 200444 Peoples R China

Tourist arrivals forecasting has become an increasingly hot issue due to its important role in the tourism industry and hence the whole economy of a country. However, owing to the complex characteristics of tourist arrivals series, such as seasonality, randomness, and non-linearity, forecasting tourist arrivals remains a challenging task. In this paper, a hybrid model of dual decomposition and an improved fuzzy time series method is proposed for tourist arrivals forecasting. In the novel model, two stages are mainly involved, i.e., dual decomposition and integrated forecasting. In the first stage, a dual decomposition strategy, which can overcome the potential defects of individual decomposition approaches, is designed to fully extract the main features of the tourist arrivals series and reduce the data complexity. In the second stage, a fuzzy time series method with fuzzy c-means algorithm as the discretization method is developed for prediction. In the empirical study, the proposed model is implemented to predict the monthly tourist arrivals to Hong Kong from USA, UK, and Germany. The results show that our hybrid model can obtain more accurate and more robust prediction results than benchmark models. Relative to the benchmark fuzzy time series models, the hybrid models using traditional decomposition methods and strategies, as well as the traditional single prediction models, our proposed model shows a significant improvement, with the improvement percentages at about 80, 70, and 50%, respectively. Therefore, we can conclude that the proposed model is a very promising tool for forecasting future tourist arrivals or other related fields with complex time series.

关键词： Forecast of tourist arrivals Dual decomposition strategy fuzzy c-means algorithm fuzzy time series

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An improved FcM clustering algorithm with adaptive weights based on PSO-TVAc algorithm

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APPLIED INTELLIGENcE 2022年第8期52卷 9521-9536页

作者： Hu, Jianhua Yin, Huilin Wei, Guoliang Song, Yan Univ Shanghai Sci & Technol Coll Sci Shanghai 200093 Peoples R China Univ Shanghai Sci & Technol Dept Control Sci & Engn Shanghai 200093 Peoples R China

fuzzy c-means clustering algorithm (FcM), as the most widely used clustering algorithm, works by iteratively updating the membership degree and the cluster centers to improve the effectiveness of clustering. The performance of FcM algorithm is chiefly evaluated by intra-cluster compactness and inter-cluster separation. However, it has some defects such as high dependency on the initial cluster centers, sensitivity to the noise samples and outliers, difficulties in obtaining the optimization of hyperparameters, a fairly poor performance on datasets with the nonuniform distribution. The main purpose of this paper is to tackle these issues. The novelty of this paper is three-fold: 1) a new FcM clustering algorithm (i.e., cWAFcM) has been proposed, which has a good capability of performing the clustering on datasets with nonuniform distribution and reducing the high dependency on the initial cluster centers;2) considering the merits of AFcM-SP in removing noise samples and cWAFcM in performing clustering on datasets with nonuniform distribution, a combination of the objective functions of these two clustering algorithms is developed to construct the hybrid AFcM algorithm;and 3) during the parameter setting by means of the PSO algorithm with time-varying acceleration coefficients (PSO-TVAc), a new index, namely adaptive clustering validity index (AcVI), is presented to describe the intra-cluster compactness and the inter-cluster separation in a proper manner. Experiments on six data sets in UcI and one artificial data set have been carried out with a comparison of five well-known FcM algorithms. Experimental results have demonstrated that the proposed hybrid AFcM with adaptive weights can more effectively enhance the performance of FcM to increase the clustering effectiveness than the contrastive algorithms. The ranks for seven algorithms on seven datasets in terms of cVIXB, accuracy, and normalized mutual information(NMI), further verifying the superiority of the new al

关键词： fuzzy c-means algorithm Particle swarm optimization clustering validity index Adaptive weight

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Predicting the eddy current loss of a large nuclear power turbo generator using a fuzzy c-means deep Gaussian process regression model

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APPLIED SOFT cOMPUTING 2022年第0期116卷 108328-108328页

作者： Guo, Hai Guo, Hai Song, Yifan Song, Yifan Wang, Likun Wang, Likun Zhao, Jingying Zhao, Jingying Marignetti, Fabrizio Marignetti, Fabrizio Dalian Minzu Univ Coll Comp Sci & Technol Dalian 116600 Peoples R China Dalian Minzu Univ SEAC Key Lab Big Data Appl Technol Dalian 116600 Peoples R China Harbin Univ Sci & Technol Coll Elect & Elect Engn Harbin 150080 Peoples R China Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian 116650 Peoples R China Univ Cassino & South Lazio Dept Elect & Informat Engn I-03043 Cassino Italy

The distribution of the rotor slot wedge eddy current loss in large nuclear power turbo generators is complex and is influenced by many factors. Excessive eddy current loss leads to severe rotor heating, potentially leading to thermal accidents;therefore, the design precision of large generators must be improved. In this paper, a fuzzy c-means Deep Gaussian Process Regression (FcM-DGPR) method is proposed to predict the eddy current loss of a large generator in order to solve the problem of the insufficient accuracy of deep Gaussian process regression (DGPR) with increasing number of the data samples. First, the original dataset is obtained by the finite element method (FEM) and then normalized to construct the samples of the eddy current loss of a large nuclear power generator. Second, the training set is automatically clustered into different subsets by the fuzzy c-means algorithm, and each subset is used to train the DGPR model to obtain different sub models. The membership degree of each data point in the test set is calculated and used to evaluate the sub model of the data. Then, the sub model is used to predict the eddy current loss. Finally, the result is obtained by concatenating the results of each sub model. The results show that the goodness of fit (R-2) is 0.9809, the root mean square error (RMSE) is 0.0271, the prediction error is small, and the model exhibits good prediction performance. Further experimental results show that the FcM-DGPR method is superior to the existing DGPR models and other models and is more suitable for predicting the eddy current loss of large generators. (c) 2021 Published by Elsevier B.V.

关键词： Deep Gaussian process fuzzy c-means algorithm Regression prediction Eddy current Nuclear generator

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chaotic time series prediction based on selection of important input variables

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JOURNAL OF INTELLIGENT & fuzzy SYSTEMS 2022年第6期43卷 8289-8301页

作者： Ren, Yaxue Wen, Yintang Liu, Fucai Zhang, Yuyan Yanshan Univ Key Lab Measurement Technol & Instrumentat Hebei Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao Hebei Peoples R China

chaotic systems are dynamic systems with aperiodic and pseudo-random properties, and systems in many fields exhibit chaotic time-series properties. Aiming at the fuzzy modeling problem of chaotic time series, this paper proposes a new fuzzy identification method considering the selection of important input variables. The purpose is to achieve higher model modeling and prediction accuracy by constructing a model with a simple structure. The relevant input variable was swiftly chosen in accordance with the input variable index after the Two Stage fuzzy curves method was used to determine the weight of the correlation between each input variable and the output from a large number of selectable input variables. The center and width of the irregular Gaussian membership function were then optimized using the fuzzy c-means clustering algorithm and the particle swarm optimization technique, which led to the determination of the fuzzy model's underlying premise parameters. Finally, the fuzzy model's conclusion parameters were determined using the recursive least squares method. This model is used to simulate three chaotic time series, and the outcomes of the simulation are contrasted and examined. The outcomes demonstrate that the fuzzy identification system has higher prediction accuracy based on a simpler structure, demonstrating its validity.

关键词： fuzzy identification input variable selection chaotic time series fuzzy c-means algorithm irregular gaussian function

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A Hybrid algorithm for Location-Routing Sustainable Optimization Under fuzzy Demand

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IEEE AccESS 2022年 10卷 12191-12200页

作者： Li, Na Fan, Zhifeng Tianjin Chengjian Univ Sch Econ & Management Tianjin 300384 Peoples R China

With the globalization of the supply chain and the change of demand environment, designing an effective logistic system in the sustainable development of the supply chain becomes more critical. This study proposes a location-routing problem to determine an efficient integration of single factory and multi-distribution centers and multi-customers in uncertain demands. This problem can be regarded as an optimization integrating location, distribution decision, and routing management. The objective function is to minimize the total cost and satisfy all the requirements, which is a highly complex NP-hard problem, so a hybrid algorithm of genetic algorithm (GA) and tabu search (TS) algorithm is proposed. A fuzzy c-means clustering algorithm is used to produce an initial solution. fuzzy triangular number and confidence interval transformation are used to deal with fuzzy customer demand. The research findings concludes that (i) determine the numbers of facilities with locations that should be opened and (ii) minimize the total cost in supply chain. The experiments prove that the proposed hybrid algorithm of GA and TS algorithm overcomes the defect of local optimum in the literature viewpoint, and the optimization algorithms can effectively solve the location-routing problem.

关键词： costs clustering algorithms Supply chains Production facilities Linear programming Routing Genetic algorithms Location-routing problem fuzzy demand genetic algorithm tabu search fuzzy c-means algorithm

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Detection of hydrocephalus using machine learning in medical science - a review

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MULTIMEDIA TOOLS AND APPLIcATIONS 2022年第15期81卷 21199-21222页

作者： Baloni, Dev Verma, Shashi Kant Uttarakhand Tech Univ Comp Sci & Engn Sudhowala India Govind Ballabh Pant Inst Engn & Technol Pauri Garhwal India

Machine learning (ML) is the study of computer algorithms that expand spontaneously by knowledge. ML algorithms construct an analytical model centred on sample data, recognized as 'training data,' in order to make projections or conclusions without being specifically programmed to do so. Hydrocephalus is the generally known disease found in children of the central nervous system and requires neurosurgical treatment and that has been studied and imaged for years however, there is still no prevalent solution and effective method for precise detection and computable evaluation of this. This work suggests a modern form of Machine Learning (ML) for the early detection of hydrocephalus. ML is the fast growing and challenging field now days. For medical diagnosis, ML methods are used. Four phases are involved in the identification of hydrocephalus using image processing methods, namely image pre-processing, image segmentation, detection and classification of features.

关键词： Hydrocephalus Machine learning Image segmentation Mean shift clustering fuzzy c-means algorithm Feature extraction Pattern analysis conventional neural network

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An Accurate algorithm for Identifying Mutually Exclusive Patterns on Multiple Sets of Genomic Mutations 10th

An Accurate Algorithm for Identifying Mutually Exclusive Pat...

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10th International Work-conference on Bioinformatics and Biomedical Engineering (IWBBIO)

作者： He, Siyu Wang, Jiayin Zhao, Zhongmeng Zhang, Xuanping Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian 710049 Peoples R China

ISBN: (纸本)9783031349591;9783031349607

In cancer genomics, the mutually exclusive patterns of somatic mutations are important biomarkers that are suggested to be valuable in cancer diagnosis and treatment. However, detecting these patterns of mutation data is an NP-hard problem, which pose a great challenge for computational approaches. Existing approaches either limit themselves to pair-wise mutually exclusive patterns or largely rely on prior knowledge and complicated computational processes. Furthermore, the existing algorithms are often designed for genotype datasets, which may lose the information about tumor clonality, which is emphasized in tumor progression. In this paper, an algorithm for multiple sets with mutually exclusive patterns based on a fuzzy strategy to deal with real-type datasets is proposed. Different from the existing approaches, the algorithm focuses on both similarity within subsets and mutual exclusion among subsets, taking the mutual exclusion degree as the optimization objective rather than a constraint condition. fuzzy clustering of the is done mutations by method of membership degree, and a fuzzy strategy is used to iterate the clustering centers and membership degrees. Finally, the target subsets are obtained, which have the characteristics of high similarity within subsets and the largest number of mutations, and high mutual exclusion among subsets and the largest number of subsets. This paper conducted a series of experiments to verify the performance of the algorithm, including simulation datasets and truthful datasets from TcGA. According to the results, the algorithm shows good performance under different simulation configurations, and some of the mutually exclusive patterns detected from TcGA datasets were supported by published literatures. This paper compared the performance to MEGSA, which is the best and most widely used method at present. The purities and computational efficiencies on simulation datasets outperformed MEGSA.

关键词： Gene mutation Mutual exclusion clustering Membership degree fuzzy c-means algorithm

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