Fuzzy Clustering Analysis Method Research Articles

Performance Test, Index System Establishment, and Comprehensive Evaluation of Earthquake Rescue Robots

To effectively enhance the adaptability of earthquake rescue robots in dynamic environments and complex tasks, there is an urgent need for a comprehensive evaluation method that encompasses establishing an evaluation index system, testing performance indexes, and conducting performance evaluation. Firstly, four main criterion and twenty-three sub-criterion indexes are established by conducting a comprehensive review of existing assessment measures for rescue robots across diverse domains. These indexes are validated through test modules developed by the National Earthquake Response Support Service to obtain corresponding values for each criterion. Moreover, a method for establishing the index system is proposed based on the fuzzy clustering analysis and grey correlation analysis methods. This method effectively addresses issues related to excessive subjectivity, redundancy, and ambiguous stratification of indexes. Subsequently, the DEMATEL is employed to scrutinize the interrelationships and causal connections among each index within the established index system, leading to the identification of input and output indexes based on the analysis outcomes. Finally, as an empirical example, three earthquake rescue robots are comprehensively evaluated and ranked using the super efficiency DEA model. Alongside analyzing results regarding input redundancy and output deficiency, targeted improvement suggestions are provided for each earthquake rescue robot. Additionally, comparison analysis with the entropy weight method and VIKOR method verifies the effectiveness of our proposed method.

Factors Controlling Productivity – A Case Study on the Qixia Formation of the Shuangyushi Area, Sichuan Basin

The gas reservoir with high temperature and high pressure (HTHP) over 8,000 m is one of the carbonate gas reservoirs discovered in recent years in the Qixia Formation of the Shuangyushi area, NW Sichuan Basin, and the strong heterogeneity makes it difficult to deploy high-yield wells. The relationship between geology and productivity is given by using the research results of reservoir evaluation and statistical analysis of the dynamic analysis of trial production in the last 4 years in this paper. Then, the evaluation index system of high productivity controlling factors in the gas reservoir is given by using the fuzzy clustering analysis method. Finally, the relative importance of each index is quantitatively analyzed and evaluated by using the analytic hierarchy process to determine the key controlling factors affecting productivity. The evaluation results are highly consistent with the current characteristics of high-yield wells. The productivity of the newly completed Well 008 and Well 107 outside the trial mining area exceeded 1 million m3/day. The result shows that the high productivity of ultradeep gas wells in the Qixia Formation is mainly controlled by the degree of dolomitization, the distribution of high-energy shoal-mound complexes, and the degree of fracture development. It can be used to guide the deployment of productive wells in the southern area of the Shuangyushi-producing test area.

Shaanxi Red Cultural Resources into Party Building Education in Colleges and Universities under New Media Environment

Abstract Under the new media environment, exploring how Shaanxi red cultural resources can be integrated into party building education in colleges and universities significantly improves students’ political literacy and cultural cognition. The study adopts new media technology, combines K-means clustering algorithm and fuzzy cluster analysis method to extract students’ personalized characteristics, and uses them to optimize the content of party building education. The experimental data show that the intra-class average error of the K-means algorithm in the classification of excellent students is 2.56, while the overall average intra-class mistake of the fuzzy clustering algorithm is 10.28, indicating that the latter is more scientific and accurate in party assessment. Through the research and analysis of the student groups in 30 universities, it was found that 80% of the students believed that the universities paid attention to the development of intelligent party building, and 0.9198 of the students thought that the red cultural resources in Shaanxi could meet the needs of party building education. The study’s conclusion shows that combining technical means in the new media environment and Shaanxi red cultural resources can effectively improve the effect of party building education work in colleges and universities, and enhance students’ political literacy and cultural cognition.

Research on the construction of a visualization platform for customer demand analysis based on big data technology

Abstract In this paper, from the MC optimization oriented to customer demand, we use big data technology to optimize the model, and with the help of the fuzzy cluster analysis method, we convert the variable types of customer demand indexes into different clustering effects. Fuzzy cluster analysis is used to establish the mapping relationship between customer demand, functional requirements of the product, and design parameters. Use the idea of customer demand analysis and transformation and the module division method to build the framework system of product configuration design and complete the construction of a customer demand-oriented product configuration visualization platform. By dividing different customer requirements, the best classification of customer requirements is obtained, and the technical optimization design of washing machine products is taken as an example to analyze the practicability of the platform constructed in this paper. Among the 12 technical characteristics of the washing machine, the importance of EG 11 is 0.1395, the importance of EG 1 is 0.1116, and the importance of EG 5 is 0.1017, which indicates that customers are most concerned about the energy-saving function of the product, and thus the enterprise should design the product based on the customer needs to satisfy the customer’s demands.

Applying Fuzzy Cluster Analysis to Study the Efficiency of Sports Resource Allocation in Urban Communities

Abstract In the context of sports development, the demand for sports resources is increasing daily, and improving the efficiency of sports resource allocation is especially important. This paper first explores the principle of sports resource allocation, selects indicators according to input and output, and constructs an efficient evaluation system for sports resource allocation. Then, we use the fuzzy cluster analysis method to optimize the objective function and the FCM algorithm to select the cluster center and the number of clusters. To study the efficiency difference between urban and rural sports resource allocation, we analyzed the effectiveness and reliability of sports resource allocation in urban communities. The study shows that the average classification accuracy of the FCM algorithm is 97.87%, and the mean value of sports resource allocation efficiency in towns is 0.1348 higher than that in villages.

Suitability evaluation of urban human settlements based on fuzzy clustering analysis a case study of Hebei Province

The suitability evaluation of urban human settlements is an important foundation to promote the sustainable development of urban human settlements. This paper took 11 prefecture-level cities in Hebei Province as the research area, and selected 18 indicators from four aspects of ecological environment quality, socio-economic status, living conditions, infrastructure and public services to construct an evaluation index system for the suitability evaluation of the human settlement environment in Hebei Province. The fuzzy clustering analysis method was used to divide the suitability of the human settlement environment in Hebei Province in 2017. The results showed that there are obvious regional differences of human settlement environment suitability in Hebei Province, the findings provide experience for the sustainable development of human settlement environment in Hebei Province.

Application of fuzzy clustering analysis in Classification of dominant channel of steam flooding: A case study of S121 Layer, Member 1 of Shawan Formation, Block P612, CF Oilfield

In order to determine the dominant channel of steam flooding in heavy oil reservoir P612 block of CF oilfield, the geological evaluation parameters of five dominant channels of steam flooding, namely permeability, reservoir thickness, sedimentary microfacies, coefficient of variation and interlayer frequency, were determined by combining reservoir static description with development dynamic analysis. According to the development dynamic characteristics, the sand body of the dominant channel was determined and the sample data was extracted. The fuzzy clustering analysis method was used to divide the two categories of the dominant channel and determine the interval value of its parameters. Thus, the geological classification evaluation standard of the dominant channel was established.

Realization of Single-Phase Grounding Fault Location and Recovery Technology in Distribution Network

In order to locate the single-phase-to-ground fault in distribution network, a research method of single-phase-to-ground fault location and recovery technology in distribution network is proposed. According to the characteristics of single-phase-to-ground fault in distribution network, the fault characteristics of zero-sequence current and three-phase current transient records are extracted by generalized S transform of correlation coefficient, and multi-source redundant information is formed. Fuzzy C-means clustering analysis method is used to fuse multiple fault features online location strategy. The simulation results show that the fuzzy clustering based on FCM changes continuously with the accumulation of samples, and the sample center changes with the enrichment of the samples. When the arc suppression coil is grounded with different initial phase angle transition resistance and intermittent fault, the state of each measuring point can be correctly identified, and the accurate energization rate is 97%. The test results show that the system can achieve accurate positioning for all sections.

Fuzzy cluster analysis and decision-making algorithms for optimal water distribution network design

ABSTRACT Three objectives, maximization of resilience, minimization of cost, and minimization of leakages, were considered in the Water Distribution Network (WDN) framework for a benchmark problem of Hanoi WDN and a real-world problem, Pamapur WDN, Telangana, India. Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is employed to generate Non-dominated WDN Strategies (NWDNS). In order to simplify the decision-making process of engineers, Fuzzy Cluster Analysis (FCA) is employed to categorize NWDNS into groups. An optimum number of clusters are based on the Elbow method. Representative NWDNS, i.e. RNWDNS for each sub-cluster, is based on the membership values. Ranking of RNWDNS is performed with two decision-making algorithms, namely, Preference Ranking Organization METHod for Enrichment of Evaluations-2 (PROMETHEE-2) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). The additive ranking rule is also applied to obtain ranks in a group decision-making environment to arrive at the optimal WDN. It is observed that 1020 NWDNSH (H represents Hanoi) generated for the Hanoi are optimally classified into 18 clusters based on the Elbow method, and A13 representing RNWDNSH 37 (cost, resilience, and leakages, respectively, are 7.8818 × 106 $, 0.3194, 0.2024 × 10−3 m3/s) is preferable, respectively. Whereas 272 NWDNSP (P represents Pamapur) generated for the Pamapur are classified into 9 clusters where S6 representing RNWDNSP 203 (cost, resilience, and leakages, respectively, are 3.5159 × 106 Rs., 0.8367, 0.5317 × 10−3 m3/s) is preferred, respectively. The present study facilitated seamless integration of NSGA-II (generation of Pareto front), FCA (reducing to manageable set) and MCDM methods (to rank the reduced manageable set) in a robust manner.

Construction of Health Education Competency Model for Nurses Based on Fuzzy Comprehensive Evaluation Method and Fuzzy Cluster Analysis Method

Health education competency is essential for nurses to engage in clinical nursing practice. Nurses with a high level of health education competency have a promising future. However, at the present stage, health education of nurses suffers from insufficient effective communication skills, poor skillfulness, and lack of health education knowledge. The above problems seriously affect the improvement of nurses’ health education level and restrict the development of health education in health industry. Therefore, it is very important to construct a health education competency model on how to improve health education. This paper, by taking nurses as the research object, constructed a health education competency model for nurses using the fuzzy comprehensive evaluation method and fuzzy cluster analysis method. In this paper, we first applied the fuzzy cluster analysis method to classify the relevant factors affecting nurses’ health education competency. Then, we investigated 10 experts from four tertiary general hospitals in Heilongjiang Province of China and asked them to rate nurses’ health education competency through the fuzzy comprehensive evaluation method. According to the results of this research, nurses’ knowledge and experience, nurse-patient communication competency, and health education implementation competency are the key factors affecting nurses’ health education competency. But the influence from overall quality is relatively minimal. Among the indicators of knowledge and experience, nurses’ health education knowledge is the most important indicator because it can provide objective basis and expand ideas for the hospital health education management and nursing personnel training.

OPTIMAL LINEAR COMBINATIONS SELECTION BASED ON FUZZY CLUSTERING ANALYSIS FOR MULTI-FREQUENCY GNSS

Abstract. Currently, satellite navigation and positioning systems, such as BDS, GPS, and Galileo, can broadcast 3 or more navigation signals. In theory, multi-frequency signals can form more high-quality carrier-phase linear combinations, which is conducive to improving the performance of multi-frequency carrier ambiguity resolution (MCAR). In this paper, focusing on the selection of the optimal combinations in the multi-frequency GNSS carrier phase ambiguity resolution, according to the long wavelength of the combinations, the influence of weak ionospheric delay, and low noise, the fuzzy clustering analysis method is used to realize objective selection of multi-frequency optimal linear combinations (e.g., GPS, BDS-2, BDS-3, Galileo) and introduced the total noise level (TNL) for the analysis of the combinations characteristics under different baseline length scenarios. In order to verify the ambiguity resolution performance of the extra-wide lane combinations selected by the fuzzy clustering analysis method, the GF_MCAR model was used to solve the BDS-2, BDS-3 triple-frequency combination ambiguities and the BDS-3 five-frequency combination ambiguities of the baseline JNFG and WUH2. The results manifest that the number of high-quality linear combinations of multi-frequency GNSS increases sharply with the increase of the frequencies number, which is conducive to the formation of combinations with a lower total noise level, and the experiments show that the difference between adjacent epochs of the combination ambiguities of the extra-wide lane obtained by GF_MCAR is basically less than ±0.5 cycles, and the single epoch rounding can be used to directly fix the ambiguity of the extra-wide lane.

Component-based Access Design Method for Dynamic Spatial Database

In order to realize the optimal access of dynamic spatial database, a component-based optimal access method of dynamic spatial database is proposed. The statistical information distribution model for storing the characteristic data of association rules is constructed in the dynamic spatial database. The fuzzy information features are extracted by using the dynamic component fusion clustering analysis method. Combined with the distributed association feature quantity, the fusion scheduling is carried out to control the dynamic information clustering. Combined with fuzzy c-means clustering analysis method, dynamic attribute classification analysis is carried out. The dynamic component block matching model is used for update iterative optimization, and the optimal access to the dynamic spatial database is realized in the cluster center. Simulation results show that this method has strong adaptability to the optimal access of dynamic spatial database, and has high accuracy and good convergence for data information extraction in dynamic spatial database.

Useing the Hierarchical Cluster Analysis and Fuzzy Cluster Analysis Methods for Classification of Some Hospitals in Basra

In general, the importance of cluster analysis is that one can evaluate elements by clustering multiple homogeneous data; the main objective of this analysis is to collect the elements of a single, homogeneous group into different divisions, depending on many variables. This method of analysis is used to reduce data, generate hypotheses and test them, as well as predict and match models. The research aims to evaluate the fuzzy cluster analysis, which is a special case of cluster analysis, as well as to compare the two methods—classical and fuzzy cluster analysis. The research topic has been allocated to the government and private hospitals. The sampling for this research was comprised of 288 patients being treated in 10 hospitals. As the similarity between hospitals of the study sample was measured according to the standards of quality of health services under fuzzy conditions (a case of uncertainty of the opinions of patients who were in the evaluation of health services provided to them, which was represented by a set of criteria and was measured in the form of a Likert five-point scale). Moreover, those criteria were organized into a questionnaire containing 31 items. The research found a number of conclusions, the most important is that both methods of hierarchical cluster analysis and fuzzy cluster analysis, classify the hospitals of the research sample into two clusters, each cluster comprises a group of hospitals that depend on applying health quality service standards. The second important conclusion is that the fuzzy cluster analysis is more suitable for the classification of the research sample compared to hierarchical cluster analysis.

A New Medium and Long-Term Power Load Forecasting Method Considering Policy Factors

At present, China’s power load development is facing a new situation in which policies such as the new economic norm, industrial structure adjustment, energy conservation and emission reduction, etc. are being deeply promoted, load growth in some areas begin to ease, and volatility of load gradually become prominent, which increases the difficulty of medium and long-term load forecasting. In this context, in view of multi-correlation, uncertainty of influence of policy factors on power load, in order to improve the accuracy of load forecasting under the influence of policy factors, and solve the problem that policy factors are ambiguous, difficult to be quantified, and difficult to be integrated into load forecasting model, a medium and long-term load forecasting model considering policy factors is proposed. First, by analyzing the influence of various policies on power load, a hierarchical policy influencing factor index system that combines macro and micro levels is constructed to systematically reflect the influence of economy and policies on load under the new situation. Then, in view of the traditional grey relational analysis model’s insufficient consideration of the difference of historical data and future power development situation, by respectively weighting historical periods and factor indexes, a quantification analysis model of power load influencing factors based on two-way weighted grey relational analysis is proposed to quantify the influence of various policy factors on power load, achieve the combination of subjective weighting and objective weighting,and obtain quantification weights. Finally, the weighted fuzzy cluster analysis method combined with weights is used to predict load under the influence of policy factors. The proposed model can better solve the difficulty of medium and long-term load forecasting caused by the volatility of load under the influence of policy factors, and is suitable for medium and long-term load forecasting under the background of policy changes. The analysis of calculation examples shows that compared with traditional grey relational analysis model, the quantification results of proposed methods are more realistic, compared with traditional prediction methods such as time series extrapolation and elasticity coefficient, proposed method has better prediction accuracy and engineering application value.

Fuzzy clustering analysis method for optimal combinations of BDS triple-frequency signals

Fuzzy clustering analysis method for optimal combinations of BDS triple-frequency signals

Wave Height Interval Distribution Method Based on Nonlinear Shallow Water Wave Function

Li, X.; Zhao, Y., and Jin, H., 2020. Wave height interval distribution method based on nonlinear shallow water wave function. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 843–846. Coconut Creek (Florida), ISSN 0749-0208.In order to improve the ability to analyze and judge the interval distribution of wave height, it is necessary to estimate the interval distribution of wave height. An interval distribution estimation method of wave height based on nonlinear shallow water wave function is proposed. A statistical time series analysis model for estimating the interval distribution of sea wave height is constructed, large data mining and feature extraction for estimating the interval distribution of sea wave height are carried out by adopting a large data feature detection method, ordered clustering of statistical feature sequences of the interval distribution of sea wave height is carried out based on a nonlinear shallow water wave function estimation idea, information clustering and attribute merging in the process of estimating the interval distribution of sea wave height are carried out by combining a fuzzy C-means clustering analysis method. The feature quantity of association rules of statistical time series of wave height interval distribution is extracted, and the association rule set of statistical time series of wave height interval distribution is analyzed by using principal component analysis method, so as to realize accurate estimation of wave height interval distribution quantity in weighted Markov chain. The simulation results show that the method has high accuracy in estimating the interval distribution of wave height and improves the quantitative analysis capability of the interval distribution of wave height.

Application of Geophysical and Hydrogeochemical Methods to the Protection of Drinking Groundwater in Karst Regions.

To provide theoretical support for the protection of centralized drinking groundwater sources in karst areas, it is necessary to accurately identify the development of karst conduits and analyze the differences in hydrogeochemical characteristics of different karst systems. This provides a scientific basis for the accurate designation of risk zones that may cause drinking groundwater pollution. In this study, a geophysical survey, hydrogeological chemical process analysis and optimized fuzzy cluster analysis were used to gradually improve the understanding of karst water systems. AMT and HDR methods were used to calibrate the resistivity around the water-filling karst conduits, which ranged from 39 to 100 Ω·m. A total of seven karst systems were identified, including four karst systems in the north of the study area, one karst system in the west and two karst systems in the south. Analysis of the hydrochemical data showed that HCO3-Ca and HCO3-Mg-Ca types accounted for 90% of all samples. The δD and δ18O values of their main conduits were −51.70‰ to −38.30‰ and −7.99‰ to −5.96‰, respectively. The optimized fuzzy clustering analysis method based on the weight of variables assigned by AHP more accurately verified karst water systems. Based on these findings, the drinking groundwater source risk zone was designated with an area of 33.90 km2, accounting for 34.5% of the study area. This study effectively improved the rationality and accuracy of the designation of drinking groundwater source risk zones in karst areas, and provided a scientific basis for the identification of karst water systems and decision-making of drinking groundwater source protection in karst areas.

Dynamic Fuzzy Adjustment Algorithm for Web Information Acquisition and Data Transmission

In order to improve the transmission dynamic fuzzy adjustment ability of web information acquisition data, a dynamic fuzzy adjustment method of web information acquisition data transmission based on auto correlation feature matching is proposed. This paper constructs the key transfer protocol of the dynamic fuzzy adjustment of web information acquisition data transmission, uses a chaotic sequence structure reconstruction design method to carry out vector quantization and coding processing in the process of the dynamic fuzzy adjustment of web information acquisition data transmission, extracts nonlinear associated feature quantities of web information acquisition data, adopts a statistical feature detection method to select features in dynamic fuzzy adjustment process of web information acquisition data transmission, constructs a feature selection model of the dynamic fuzzy adjustment of web information acquisition data transmission, dynamically adjusts the fuzzy data with a fuzzy information clustering analysis method, and dynamically adjusts the fuzzy data transmission through fuzzy design and fuzzy encryption. The simulation results show that the dynamic fuzzy adjustment of web information acquisition data transmission when using this method is better, and the accurate transmission ability is stronger.

Fuzzy Preprocessing and Clustering Analysis Method of Underwater Multiple Targets in Forward Looking Sonar Image for AUV Tracking

Marine sonar image noise absorption under water can result in poor image quality, low image resolution and blurred target contour. This paper proposes a systematic framework including underwater image fuzzy preprocessing method, and clustering based on the principal component analysis (PCA) of underwater targets. Underwater images were denoised adopting fast median filtering algorithm with mean acceleration. The fuzzing mathematics were not only developed with the aim of improving the visual quality of underwater sonar images, but also involved in the sonar image segmentation for extracting the target area from the suspicious area. Both the geometric and statistical features were treated as features in PCA algorithm. As demonstrated by the experimental results, visual quality of the sonar image was improved, multi-target threshold segmentation was achieved and multiple targets could be analyzed and clustered for stably tracking AUV.

Prediction method of coal slagging property in layer-fired boiler

In this article, different coal samples are selected and their physicochemical properties are analyzed. Study and comprehensively analyze various factors affecting slagging. Prediction of coal slagging properties using 12 influencing factors, include: softening temperature, the difference between deformation temperature and flow temperature, characteristic of char residue, sodium oxide content, silicon ratio, silica-alumina ratio, iron-calcium ratio, acid-base oxide ratio, sulfur slagging index, contamination index, chlorine content, inorganic mineral content. The fuzzy clustering analysis method was used to classify and quantify all the influencing factors, and the coal slagging prediction method of layer fired boiler was proposed and verified by application.