Grey Clustering Research Articles

Clustering analysis using a novel locality-informed grey wolf-inspired clustering approach

Grey wolf optimizer (GWO) is known as one of the recent popular metaheuristic algorithms inspired from the social collaboration and team hunting activities of grey wolves in nature. This algorithm benefits from stochastic operators, but it is still prone to stagnation in local optima and premature convergence when solving problems with a large number of variables (e.g., clustering problems). To alleviate this shortcoming, the GWO algorithm is hybridized with the well-known tabu search (TS). To investigate the performance of the proposed hybrid GWO and TS (GWOTS), it is compared with well-regarded metaheuristics on various clustering datasets. The comprehensive experiments and analysis verify that the proposed GWOTS shows an improved performance compared to GWO and can be utilized for clustering applications.

Application of grey entropy weight cluster in clinical department sorting

Purpose The purpose of this paper is to explore the method of clinical department classification in comprehensive hospital to provide a scientific management reference in performance evaluation. Design/methodology/approach In order to perform the classification of clinical departments, a grey cluster model was constructed. In this model, the benefit index of each clinical department was analyzed using grey associative analysis, and a superior index was chosen to incorporate in this model. Through the relevancy degree of superior index, the authors confirmed the weight of index, and the quintile threshold was calculated by a quintile method and whitening weight functions were constructed. Findings This model, based on grey entropy weight cluster, can reasonably distinguish the merits of clinical departments. From the case we study, neurology?/Cardiovasology?/Integrated disease and Emergency were the optimum assessing department. Originality/value The results demonstrated that the index selection, weight determination, whitening weight functions and model construction in this study could provide technique’s reference and application for hospital administrators. It avoids the subjective discrimination and reduces the influence of sample noise, and it is easy to perform while conducting performance evaluation in a comprehensive hospital.

Hybrid model for security‐aware cluster head selection in wireless sensor networks

Wireless sensor network (WSN) is considered as the resource constraint network, in which the entire nodes have limited resources. In WSN, prolonging the lifetime of the network remains as the unsolved point. Accordingly, this study intends to propose a hybrid GGWSO (Grouped Grey Wolf Search Optimisation) algorithm to improve the performance of a cluster head selection in WSN, so that the network's lifetime can be extended. The proposed method concerns the main constraints associated with distance, delay, energy, and security. This study compares the performance of the proposed GGWSO with several traditional algorithms like artificial bee colony (ABC), fractional ABC, group search optimisation and Grey Wolf optimisation-based cluster head selection. During the performance analysis, the various ranges of risk, such as 20, 60, and 100% are added to validate the performance variations, by evaluating the number of alive nodes, and normalised network energy remained in the network. The simulation results have shown that there is a need for a hybrid model for attaining the superior results.

Study on Optimum Design of Outburst Prevention Measures in Weijiadi Coal Mine

Aiming at the problems of large amount of engineering, long construction period and high economic cost of regional outburst prevention measures in Dongyi Mining Area of Weijiadi Coal Mine, the grey clustering fuzzy evaluation method is used to investigate four regional outburst prevention measures from five indicators of Engineering quantity, construction period, capital input, cost of gas control and prevention of secondary disasters, and the best regional outburst prevention measures are selected. The results show that the best regional outburst prevention measures are drilling strips through strata and pre-draining coal seam gas by drilling along strata in floor rock roadway, with a quantified score of 0.9179, which is greater than the quantified results of the other three regional outburst prevention measures.

Two mathematical approaches to study the phosphorus eutrophication of a wetland in Puerto Rico

Introduction: Laguna Cartagena (LC), a wetland in Lajas, Puerto Rico, has been negatively impacted by nutrients, mainly phosphorus run-off from agricultural activities until the end of sugar cane cultivation in the late 1900s. This led to P concentration remain high at hypereutrophic state that was irremediable even after a 5-fold reduction in source water nutrient concentration.
 Objective: The main goal of this research paper is to apply two different mathematical approaches to assess the eutrophication level of a wetland in Puerto Rico.
 Method: Grey Cluster Method (GCM) was used to classify LC’s eutrophic state by applying the International and Chinese trophic standards and two parameters, total phosphorous (TP) and total nitrogen (TN). Mean TP and TN from LC consolidated bottom substrate and flocculence samples were used to classify LC. To address whether LC can recover, soluble reactive phosphorus (SRP) and TP from LC inlet, outlet and center water samples were used to model (differential equation) the input and loss of phosphorus in LC and determine whether an equilibrium point exists. GCM analysis classified LC as a eutrophic wetland using the International standard and hypereutrophic using the Chinese standard.
 Results: Trophic state classification did not vary with use of consolidated bottom substrate versus flocculence samples. The differential equation model showed that SRP and TP levels within LC were higher than levels of SRP and TP entering LC, which could be caused by a nutrient recycling process within LC that may predict failure of remediation efforts. An equilibrium point was found at the eutrophic level, which means that even if there is a reduction in phosphorus input, there will not be a change in LC’s eutrophic state.
 Conclusions: Chinese trophic standard indicated LC was in a hypertrophic state. Similar results were found using the international standard. The differential equation model showed that LC is irreversible.

Two-dimensional grey cloud clustering-fuzzy entropy comprehensive assessment model for river health evaluation

The river health evaluation is typically complex non-linear system with characteristics of fuzziness and randomness. However, conventional gray clustering method has difficult to effectively describe fuzzy and random information simultaneously. For this purpose, the cloud model and fuzzy entropy theory are introduced to establish 2D gray cloud clustering-fuzzy entropy comprehensive evaluation model. Different with health level models, it reflects river health situation from aspects of health level and corresponding water body complexity simultaneously. The health level is obtained by gray cloud whitened weight function (first sub-system) and fuzzy entropy represents complexity and fuzziness of river health situation (second sub-system). Moreover, multi-level river health evaluation indicator system is constructed with dividing indicators into common and distinct sections according to differences on river characteristics. Meanwhile, indicator weights are determined by renewed combined weighting method based on minimum deviation principle. Finally, we conduct health evaluation work for rivers in the Taihu basin. The evaluation health levels and fuzzy entropy for river A–G are H3 (0.4888, relatively significant); H2 (0.5476, relatively fuzzy); H2 (0.7526, fuzzy); H2 (0.4731, relatively significant); H2 (05138, relatively fuzzy); H3 (0.5822, relatively fuzzy), and H2 (0.4064, relatively significant), respectively. Results are consistent with current river health situation and more intuitive than compared models. Furthermore, evaluation results with four different weighting methods are compared to further demonstrate rationality of the weighting method and evaluation model. Hence, the model proposed is demonstrated to provide new insight for solving river health assessment problem effectively.

Predicting the Production of the World's Cephalopod Fisheries by Means of Differences in Level of Development and Production Trends

AbstractUsing cephalopod production data from the FishStatJ database of the Food and Agriculture Organization of the United Nations, this study (1) measured the degree of development of the cephalopod fisheries in 10 major cephalopod‐producing countries; (2) classified each of the 10 fisheries according to its development level; and (3) estimated cephalopod production by the world as a whole as well as by China and Japan individually in 2017, 2018, and 2019. For objectives (1) and (2), the grey constellation cluster model was used. The results of the analysis indicate that cephalopod fisheries in the major producing countries can be roughly divided into four classes: excellent (class I), good (class II), weak (class III), and poor (class IV), with Japan, China, and South Korea being in class I; Thailand, Peru, Viet Nam, India, and Morocco being in class II; and Indonesia and Chile being in classes III and IV, respectively. The grey interval forecasting model was used for objective (3). The model estimates that cephalopod production by the world as a whole will be in the range 5.44–5.76 million metric tons (t) over the period studied and that by China in the range 1.77–1.98 million t. Both areas show rising trends, with average annual growth rates of 2.88% and 5.90%, respectively. By contrast, cephalopod production by Japan is estimated to decline from 0.24 to 0.22 million t, with an average annual growth rate of −2.82%.

Operational Safety Risk Assessment of Civil Aviation based on Grey Clustering

To enhance the operational safety of civil aviation, based on the data of civil aviation unsafe incidents on 2006-2015, the assessment index was constructed with typical accident and incident events, and the operational safety risk assessment model based on grey whitening clustering was proposed in this paper. Then the weight of each index is determined by the principle of maximizing deviation. Finally, the safety risk in these 10 years was evaluated by actual operation data. According to the result, it was consistent with the actual operation of civil aviation, which verified the rationality of the safety risk assessment indexes taking into account of the typical accidents and incidents. It had changed the traditional safety assessment index and methods of civil aviation based on people, machines, environment and administration, which had positive significance to improve the safety production capacity of civil aviation.

Evaluation of ecological security and influencing factors analysis based on robustness analysis and the BP-DEMALTE model: A case study of the Pearl River Delta urban agglomeration

Over the past four decades, many Chinese cities have faced serious ecological security pressures with rapid urbanization, especially, in the economically developed areas such as the Pearl River Delta urban agglomeration (PRD). The question of how to accurately evaluate the ecological security status and determine the influencing factors has become a realistic problem for the sustainable development of these cities. However, this analysis is hard to do because of the variable results derived from different evaluation methods and index systems. Robustness analysis is a broad method that is used for stable screening of different results. In this study, robustness analysis was conducted for 20 evaluation methods and 20 index systems. The most stable method was screened, which was the complex correlation coefficient method for indicator weight calculation, associated with the grey clustering method for ecological security evaluation. Moreover, the robustness analysis suggested that the stability of various index systems was better than that of various evaluation methods. According to the final urban ecological security pattern, the BP-DEMATEL model analyzed the influencing factors. The effect and intensity of each indicator were analyzed, which suggested that the total value of ecosystem service was the fundamental influencing indicator on the urban ecological security of the PRD. Six other driving influencing indicators including GDP per capita, normalize difference vegetation index, gross product of primary industry per unit area and anthropogenic disturbance index, as well as five characteristic influencing indicators were determined. Therefore, ecological elements were the most important guarantee of urban security in the PRD urban agglomeration. However, socio-economic elements played a key role in promoting urban ecological recovery. A creative approach was developed in this study to evaluate the ecological security of a region by integrating numerous methods and index systems. The comprehensive results provide constructive suggestions for the sustainable development of cities in the PRD urban agglomeration.

Future Scenarios of Environmental Vulnerability Mapping Using Grey Analytic Hierarchy Process

Any sustainable resource utilization plan requires evaluation of the present and future environmental impact. The present research focuses on future scenario generation of environmental vulnerability zones based on grey analytic hierarchy process (grey-AHP). Grey-AHP combines the advantages of grey clustering method and the classical analytic hierarchy process (AHP). Environmental vulnerability index (EVI) considers twenty-five natural, environmental and anthropogenic parameters, e.g. soil, geology, aspect, elevation, slope, rainfall, maximum and minimum temperature, normalized difference vegetation index, drainage density, groundwater recharge, groundwater level, groundwater potential, water yield, evapotranspiration, land use/land cover, soil moisture, sediment yield, water stress, water quality, storage capacity, land suitability, population density, road density and normalized difference built-up index. Nine futuristic parameters were used for EVI calculation from the Dynamic Conversion of Land-Use and its Effects, Model for Interdisciplinary Research on Climate 5 and Soil and Water Assessment Tool. The resulting maps were classified into three classes: “high”, “moderate” and “low”. The result shows that the upstream portion of the river basin comes under the high vulnerability zone for the years 2010 and 2030, 2050. The effectiveness of zonation approach was between “better” and “common” classes. Sensitivity analysis was performed for EVI. Field-based soil moisture point data were utilized for validation purpose. The resulting maps provide a guideline for planning of detailed hydrogeological studies.

Grey clustering evaluation based on AHP and interval grey number

PurposeThe purpose of this paper is to propose a grey clustering evaluation model based on analytic hierarchy process (AHP) and interval grey number (IGN) to solve the clustering evaluation problem with IGNs.Design/methodology/approachFirst, the centre-point triangular whitenisation weight function with real numbers is built, and then by using interval mean function, the whitenisation weight function is extended to IGNs. The weights of evaluation indexes are determined by AHP. Finally, this model is used to evaluate the flight safety of a Chinese airline. The results indicate that the model is effective and reasonable.FindingsWhen IGN meets certain conditions, the centre-point triangular whitenisation weight function based on IGN is not multiple-cross and it is normative. It provides a certain standard and basis for obtaining the effective evaluation indexes and determining the scientific evaluation of the grey class.Originality/valueThe traditional grey clustering model is extended to the field of IGN. It can make full use of all the information of the IGN, so the result of the evaluation is more objective and reasonable, which provides supports for solving practical problems.

Performance ratio prediction of photovoltaic pumping system based on grey clustering and second curvelet neural network

Performance ratio is an important parameter of measuring the quality and efficiency of photovoltaic (PV) pumping system, which should be predicted correctly to provide guidance for regulating the measurements of reducing losses of every part, therefore this research proposed a prediction model of performance ratio of PV pumping system based on grey clustering and second curvelet neural network. The meaning of performance ratio is analyzed and the main affecting factors of the performance level for PV pumping system are also summarized. The second curvelet neural network is constructed combing the second curvelet transform and feed forward neural network, and the structure of second curvelet neural network is designed. The classification of training and testing samples are confirmed based on the improved grey clustering model, and the corresponding mathematical models are studied. The firefly algorithm is used to optimize the second curvelet neural network. The grey classifications of training samples are confirmed based on grey clustering, which are used to train the second curvelet neural network with different structure optimized by firefly with different parameters, and then the testing samples are used to carry out prediction analysis. Simulation results show that the second curvelet neural network has highest prediction precision and efficiency, which can correctly and efficiently predict the performance ratio of PV pumping system.

Evaluation of the development level of provinces by grey cluster analysis

Only economic indicators are not sufficient to determine the level of development in a country. In many countries with high levels of welfare, the failure to resolve social constraints has shown that for the indication of the development of that country indicators are needed. Various global indices are used to determine the level of development of the countries. One of the indices published by the United Nations Development Program is the Human Development Index (HDI). This index is used to measure development by taking into account income, education and health indicators. In this study, the provinces in Turkey were grouped according to their level of development using the HDI index. In this context, a grey clustering analysis was used to group similar provinces. The research data taken from TUIK for 81 provinces are divided into three clusters according to their level of development from high to weak and marked by the different colors on the map of Turkey. In addition, it is analyzed if there is a correlation between the life satisfaction of provinces and the obtained grey clusters. The results of the study can be used in determining plans and policies for the purposes of raising welfare, reducing development differences between the provinces, thus increasing the welfare of the population life level and conditions in the underdeveloped provinces.

Construction mechanism of whitenization weight function and its application in grey clustering evaluation

The clustering evaluation can be used to scientifically classify the objects to be evaluated according to the information aggregation of various evaluation rules. In grey weighted clustering evaluation, the index clustering rule relies on the construction of the whitenization weight function, while the existing construction method of the linear function lacks the construction mechanism analysis and validity explanation. A normative construction principle is put forward by analyzing the construction mechanism of the function. Through proving the normative principle of the function, the basic modal function (BMF) is proposed and characterized by different function forms. Then, a new type of the whitenization weight function and its grey clustering evaluation model algorithm are given by studying the mechanism and nature of the construction of different forms of the function. Finally, the comparative study for self-innovation capability of defense science and technology industry (DSTI) is taken as an example. The results show that the different construction ways of the function have an effect on the clustering result. The proposed construction mechanism can better explain the index clustering rules and evaluation effectiveness, which will perfect the theoretical system of grey clustering evaluation and be applied to practice effectively.

Gray Assessment of Equipment Maintenance Support Capability Based on the Structure Entropy Weight Method

The influencing factors which about equipment maintenance support abilities are analysed and the index system that is used to evaluate equipment support abilities are constructed in the paper. Then, the weight of the index is determined by the structure entropy weight method which is a combination of qualitative and quantitative. The comprehensive evaluation model of equipment maintenance support abilities are constructed by combining gray clustering evaluation theory. Finally, the feasibility and validity of the model are verified by an example.

Modeling and application for air quality evaluation based on improved grey clustering and neural networks classification

According to the air quality monitoring date of the six urban evaluation sites arranged in six main administrative districts in Beijing during the four seasons from 2013 to 2014, an evaluation system based on air quality indexes of Chinese was formulated. And, next, based on concentration limits of six main pollutants including PM10, PM2.5, O3, CO, SO2 and NO2, analysis of the improved grey clustering method and the neural networks classification method were used for evaluation by using the detailed evaluation procedures. An improved grey clustering method based on the whitening function of the exponential types was adopted to improve the calculation precision of the fuzzy factors. It could be seen that the method adopted could more fully reflect the comprehensive effects of all the pollutants on the air quality. Meanwhile, two classification methods based on neural networks were adopted to classify the pollutants into different levels by MATLAB tools. By these methods the comprehensive influence of the pollutants on ambient air could be determined accurately and quickly. The evaluation results demonstrated that the improved grey clustering method which modified weight and extend the whitening function scope could greatly improve information utilization. By considering the weights of whitening values and standard values in different grey classes, the cluster result was more comparable. The neural networks classification method effectively improved the network generalization by the massive data of training samples and testing samples constructed by LINSPACE function in MATLAB tools. The evaluation results obtained by BP and RBF neural network learning showed high accuracy. Among them, the evaluation results of RBF neural network classification and grey clustering modified were more similar that the air quality level of six main administrative districts in Beijing was generally in grade 2. According to the evaluation purposes of the ambient air quality and the practical evaluation demands, it was possible to combine these two methods from different perspectives to gain a more reasonable evaluation result. Thus, it could be concluded that the evaluation of the pollutant contents in the air was of great practical value for eventual heightening of the new ambient air quality standard.

Grey Clustering Evaluation of Water Resources Carrying Capacity Based on Triangle Whitening Weight Function

Water resources are an important component of the resource environment. The sustainable carrying capacity of water resources is an essential indicator for the comprehensive evaluation of regional sustainable development. This paper will select 17 water resources carrying capacity impact factors to establish a sustainable water resources assessment system. The grey clustering evaluation based on the triangle whitening weight function is used to obtain the objective evaluation results of water resources carrying capacity.

FUZZY INTEGRATED DESERTIFICATION VULNERABILITY MODEL

Abstract. Desertification and Land Degradation have constantly affected the global environment over the years. Land degradation is a reduction or loss of productivity over land due to natural processes, climate change and human activities. It involves complex set of processes, which interacts over space to decrease in land productivity. In the present study, regional desertification vulnerability assessment was carried out for Bhavnagar district of Gujarat, India. A vulnerability is a basic concept which explains the flaw in any structure of a system. A vulnerability may also refer to any type of shortcoming in systems, sub system parameters or processes, or in anything that leads the system to be exposed to a threat. Fuzzy Logic (FL) method combined with remote sensing (RS) and geographic information system (GIS) techniques has several advantages for assessing vulnerability. Technically FL combines the intricate classical analytic hierarchy process and grey clustering method for estimation of coefficients. Climate, soil, vegetation and land use play a significant role in desertification of any area, hence, in this work, several indices have been generated. However, man’s intervention leads to significant changes in the environment, making socio-economic factor as a major input to assess vulnerability for desertification. Thus in this study FL has been integrated with both natural and socioeconomic factors for understanding the vulnerability to desertification in Bhavnagar region.

Research on Class-Based Storage Strategies for Flood Control Materials Based on Grey Clustering

Scientific and rational class-based storage can effectively improve the management level of flood control materials, providing assurance for the material support that is essential in flood control. Based on an analysis of the three key factors—importance, cost, and reserve—of flood control materials and combined with the correlations of different classes of materials, a comprehensive evaluation index system and a class-based model for these materials are developed using the grey clustering method. An empirical analysis is also conducted based on the realistic requirements of a warehouse of the Jiangsu provincial water conservancy and flood control materials reserve centre, which results in the proposal of a class-based materials storage strategy for this warehouse.

PO-088 Effect of Aerobic Exercise on Myocardial Mitochondria Fusion-Fission in Chronic Intermittent Hypoxia Mice

Objective Training monitoring is an important part of scientific training, and also accumulated a large amount of data, but the analysis and evaluation of biochemical indicators are mostly concentrated on the level of experience and the general, phased and individualized research application of statistical methods. The data mining technology is applied to the analysis and evaluation of the biochemical indexes of competitive sports, the analysis of the data is carried out in the deep level, the potential, new and useful information and knowledge are extracted, and the new exploration ideas are carried out for the analysis of the biochemical indexes of competitive sports, and a more reliable and more powerful data branch is provided for the scientific and efficient training support.
 Methods Using the literature data method, logic analysis method and expert interview method, the application of the current data mining technology in the analysis of biochemical indicators is summarized.
 Results The scientific analysis and evaluation of athletes' physical function status has been the focus of domestic and foreign coaches and sports researchers. The application of data mining technology in sports biochemical indicators is also becoming more and more extensive. For example, Mao Jie and others applied the gray ART clustering model analysis method to the monitoring of competitive sports biochemical indicators. Through this data mining model, the coach can easily judge the athlete's competitive physical condition, and can provide a scientific basis for correct training according to the different competitive conditions of each athlete, using different training guidance programs and training methods. Ma Jing et al. explored the feasibility of applying decision tree algorithm and association rules in volleyball biochemical analysis. It was found that C5.0 decision tree and Apriori association rule algorithm can be used to predict and analyze the technical level of women's volleyball players. Li Guangjun and others successfully applied the association rule data mining to the biochemical data analysis of canoeists, and provided a basis for scientific decision-making and analysis of sports training and athlete selection. Zhang Hui designed a data mining system for sports biochemical index based on association rules. The results show that the system has fast data mining rate, short time consuming and high reliability. It provides a more scientific evaluation standard for the data mining of sports biochemical index, and also provides a basis for the future training program.
 Conclusions With the development of competitive sports, in order to achieve new heights, the application of data mining technology to vast biochemical data is of great significance for the establishment of scientific training evaluation methods and standards, and is also the inevitable development of future sports scientific research.