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.

An Application of Grey Clustering Method in the Sporting Clothing Style Evaluation

In this paper, a Grey clustering method is applied to the evaluation research of sporting clothing style, the result shows that the methods proposed in the paper is feasible and effective.

Grey Clustering Theory to Assess the Effect of Mineral Admixtures on the Cyclic Sulfate Resistance of Concrete

Concrete specimens made with ordinary portland cement or ordinary portland cement incorporating fly ash with the replacement of 10% or 20%, ground blast furnace slag with the replacement of 15% or 30%, or 15% fly ash and 15% ground blast furnace slag were made and exposed to a cyclic sulfate environment. Concrete properties including relative dynamic elastic modulus, chloride ion diffusion coefficient, compressive strength and flexural strength were measured. Effect of mineral admixtures on the cyclic sulfate resistance of concrete was assessed based on the grey clustering theory. The experimental results indicate that the cyclic sulfate resistance of concrete incorporating ground blast furnace slag belongs to the higher grey grade, which exhibits that it possesses excellent cyclic sulfate resistance. With increasing addition of fly ash, the cyclic sulfate resistance of concrete changes from the medium grey grade to the lower grey grade, which shows that incorporation of fly ash is disadvantageous for the cyclic sulfate resistance of concrete.

Grey incidence clustering method based on multidimensional dynamic time warping distance

The traditional grey incidence degree is mainly based on the distance analysis methods, which is measured by the displacement difference between corresponding points between sequences. When some data of sequences are missing (inconsistency in the length of the sequences), the only way is to delete the longer sequences or to fill the shorter sequences. Therefore, some uncertainty is introduced. To solve this problem, by introducing three-dimensional grey incidence degree (3D-GID), a novel GID based on the multidimensional dynamic time warping distance (MDDTW distance-GID) is proposed. On the basis of it, the corresponding grey incidence clustering (MDDTW distance-GIC) method is constructed. It not only has the simpler computation process, but also can be applied to the incidence comparison between uncertain multidimensional sequences directly. The experiment shows that MDDTW distance-GIC is more accurate when dealing with the uncertain sequences. Compared with the traditional GIC method, the precision of the MDDTW distance-GIC method has increased nearly 30%.

Study on Evaluation of the Rainstorm Disaster in Fujian Province Based on Spectral Clustering Model with Grey Correlation Analysis

[ Objective] The research aimed to study assessment index system of the rainstorm disaster in Fujian Province based on spectral cluste- ring model with grey correlation analysis. [Method] According to meteorological disaster yearbook in Fujian Province, by comprehensively consider- ing disaster-inducing factor, disaster-inducing environment, disaster-sustaining body and regional disaster-prevention level, evaluation index system of the regional rainstorm disaster in Fujian was established. By spectral clustering model based on grey correlation analysis, dsk zoning of the rain- storm disaster was conducted in each area of Fujian. Finally, effect and application of the clustering model were analyzed by case research. [ Re- sult] In order to dig immanent connection among regional characteristics and improve disaster-preventing linkage performance of the evaluation unit, a spectral clustering model based on grey correlation analysis was used to conduct risk zoning of the rainstorm disaster in Fujian Province. Moreo- ver, combined weight was introduced to judge each evaluation index, so as to adjust clustering model. By case study, rainstorm disaster levels in 67 counties were obtained. Internal characteristics of each type were analyzed, and main correlation factors of each type were extracted. It was compared with statistical result of the rainstorm disaster, verifying validity and feasibility of the model. [ Conclusion] The method was feasible, and its evaluated result had better differentiation and decision accuracv.

An Improvement of Grey Integrated Clustering Method

In the grey clustering assessment, the grey integrated clustering method can solve the problem that there are not distinguished differences of grey clustering coefficient. This paper proposes an improved grey integrated clustering method based on the existing problem that there are some deficiencies in the division of the value scope of the integrated cluster coefficients, and proves the effectiveness of the improved method through the empirical analysis.

Optimization Study of Outburst Prevention Measures for Tuzhu Coal Mine Based on Fixed Weight Clustering Analysis

Affected by many involved factors, different dimensions, data with large difference, incomplete information and so on, the most optimal selection of regional outburst prevention measures for outburst mine has become a complicated system project. The traditional way of outburst prevention measure selection belongs to qualitative method, which may cause high-cost of gas control, huge quantities of drilling work, long construction time and even secondary disaster. To solve the above-mentioned problems, in light of occurrence status of coal seam gas in No. 21 mining area of Jinzhushan Tuzhu Mine, through grey fixed weight clustering theory and a combination method of qualitative and quantitative analysis, the judging model with multi-objective classification for optimization of outburst prevention measures was established. The three weight coefficients of outburst prevention technology scheme are sorted, in order to determine the advantages and disadvantages of each outburst prevention technology scheme under the comprehensive evaluation of multi-target. Finally, the problem of quantitative selection for regional outburst prevention technology scheme is solved under the situation of multi-factor mode and incomplete information, which provides reasonable and effective technical measures for prevention of coal and gas outburst disaster.

Residential Electricity Consumption Behavior Mining Based on System Cluster and Grey Relational Degree

In order to improve the utilization of the residential electricity consumption data which contains the information on the user’s electricity consumption habits, a residential electricity consumption behaviors mining algorithm model is constructed. Firstly, according to the attribute, the collected data can be divided into the global data and the phase data, then the appropriate global variables are selected to mine the user’s electricity consumption patterns in the near future on the system clustering algorithm. Based on the theory of grey relational analysis, combing phase data with the power modes to analyze the potential characteristics of residential electricity consumption behaviors deeply that verify the ability of latest power mode to predict household electricity consumption situation in the coming few days and the effect of dominant phase variables on the peak load shifting. Finally, from the actual data of a certain family, the proposed data mining algorithm is testified that it can effectively explore the electricity consumption behavior habits and characteristics of the family.

Evaluation of Comprehensive Performance of Agronomic Traits of Anmai 1241 by Grey Correlation Analysis and Cluster Analysis Methods

The agronomic traits of the new wheat variety Anmai 1241 were comprehensively evaluated,in order to provide comprehensive and objective theoretical basis for further improvement and production utilization of this variety.The winter water production test results of Anmai 1241 in 14 pilot sites of Henan Seed Management Station from 2016 to 2017 were summarized.The comprehensive performance of 11 agronomic traits of Anmai 1241 in different tests sites in Henan Province was evaluated by the grey correlation analysis and clustering analysis methods.The results showed that among the observed values of 11 traits,the variation coefficient,correlation degree and weight of black embryo rate were 181.64%,0.6679 and 0.1051,respectively.The clustering analysis showed that the 11 traits could be divided into 3 groups.The first type of traits(yield,number of grains per ear and 1000-grain weight)and the third group of traits(percentage of earbearing tillers,number of productive tillers and volume weight)belonged to the yield factor traits,and the sum of their weights was 0.5242.Yield and its related factors played an important role in the variety evaluation of Anmai 1241,and the effect of black embryo on yield should be eliminated in variety improvement.

The Cooperation of Industrial Clusters and Regional Logistics of Shaanxi Province: An Evaluation Research

As a middle organization between enterprise organization which gains the competition advantage and eration of industrial clusters and regional logistics refers development of modern regional logistics. On the basis and market, industrial cluster is now the space industrial innovation advantage for a nation or a region. The coop- to improvement of industrial clusters＇ competitiveness and of reviewing the recent years＇ situation of Shaanxi Prov- ince＇s regional logistics and its industrial clusters, this paper analyzes positively about the supporting role of the regional logistics, builds gray relational model by choosing corresponding indicators, and carries out test of signif- icance. Finally it brings out strategic recommendations to and regional logistics enhance the level of cooperation of industrial clusters

Employment Quality EvaluationModel Based on Hybrid Intelligent Algorithm

In order to solve the defect of large error in current employment quality evaluation,an employment quality evaluation model based on grey correlation degree method and fuzzy C-means(FCM)is proposed.Firstly,it analyzes the related research work of employment quality evaluation,establishes the employment quality evaluation index system,collects the index data,and normalizes the index data;Then,the weight value of employment quality evaluation index is determined by Grey relational analysis method,and some unimportant indexes are removed;Finally,the employment quality evaluation model is established by using fuzzy cluster analysis algorithm,and compared with other employment quality evaluation models.The test results show that the employment quality evaluation accuracy of the design model exceeds 93%,the employment quality evaluation error can meet the requirements of practical application,and the employment quality evaluation effect is much better than the comparison model.The comparison test verifies the superiority of the model.

Grey clustering evaluation based on AHP and interval grey number

Purpose–The 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/approach–First,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.Findings–When 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/value–The 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.

Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment.Designing the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the research.Sensor node clustering is a popular approach for WSN.Moreover,the sensor nodes are grouped to form clusters in a cluster-based WSN environment.The battery performance of the sensor nodes is likewise constrained.As a result,the energy efficiency of WSNs is critical.In specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station(BS).Therefore,energy efficiency and load balancing are very essential in WSN.In the proposed method,a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques(GW-IPSO-TS)was used.The selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed method.It provides the best routing path and increases the lifetime and energy efficiency of the network.End-to-end delay and packet loss rate have also been improved.The proposed GW-IPSO-TS method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor nodes.Compared to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.

The Establishment of Mathematical Models for the Composition Analysis and Identification of Ancient Glass Products

Glass is the precious material evidence of the trade of the early Silk Road. The ancient glass was easily affected by the environmental impact and weathering, and the change of composition ratios affected the correct judgment of its category. In this paper, mathematical models and methods such as Chi-square test, weighted average method, principal component analysis, cluster analysis, binary classification model and grey correlation analysis were used comprehensively to analyze the data of sample glass products combined with their categories. The results showed that the weathered high-potassium glass could be divided into 12, 9, 10 and 27, 7, 22 and so on.

Bat-Grey Wolf Optimizer and kernel mapping for automatic incremental clustering

The technical advancement in information systems contributes towards the massive availability of the documents stored in the electronic databases such as e-mails,internet and web pages.Therefore,it becomes a complex task for arranging and browsing the required document.This paper proposes an approach for incremental clustering using the BatGrey Wolf Optimizer(BAGWO).The input documents are initially subjected to the pre-processing module to obtain useful keywords,and then the feature extraction is performed based on wordnet features.After feature extraction,feature selection is carried out using entropy function.Subsequently,the clustering is done using the proposed BAGWO algorithm.The BAGWO algorithm is designed by integrating the Bat Algorithm(BA)and Grey Wolf Optimizer(GWO)for generating the different clusters of text documents.Hence,the clustering is determined using the BAGWO algorithm,yielding the group of clusters.On the other side,upon the arrival of a new document,the same steps of pre-processing and feature extraction are performed.Based on the features of the test document,the mapping is done between the features of the test document,and the clusters obtained by the proposed BAGWO approach.The mapping is performed using the kernel-based deep point distance and once the mapping terminated,the representatives are updated based on the fuzzy-based representative update.The performance of the developed BAGWO outperformed the existing techniques in terms of clustering accuracy,Jaccard coefficient,and rand coefficient with maximal values 0.948,0.968,and 0.969,respectively.

基于灰色聚类法的绿色建筑全寿命周期评价(英文)

To promote and develop green buildings,a standardized,applicable and easily operable index system for the assessment of such buildings was established on the basis of life cycle cost effectiveness.From the perspectives of environment-friendly materials,water resource environment,energy and environment,quality of indoor and outdoor environment,operation and management,and economical efficiency of life cycle,a modified index system was built,AHP was applied to obtain weights of indexes,evaluation methods of the grey system were used to evaluate green buildings,case study was adopted to verify the practicability and scientificity of the method.The results showed that Grey Clustering Method was an objective and reliable tool to evaluate green buildings,the calculation was simple,practical and easily operable,and moreover,the assessment process could be optimized by computer programming to improve its efficiency and precision.

Shaping the handicraft cluster through innovation capability

There are several enablers to the innovation capability in the exisiting literature,but they all are considered with equal importance.Researchers believe ranking will advance the understanding of academicians and practitioners further.Hence,we started by recognizing the enablers from the available literature and exploring the possible causal relationship among them.A framework based on the causal relationship among the enablers is proposed.Grey DEMATEL(Decision-making trial and evaluation laboratory)has been used to establish this causal relationship.This study identifies nine of the fifteen enablers as causal factors.They are as follows:Knowledge Exploration,Ideation and Organizational Structure,Organizational Climate,Risk Taking Ability,Collaboration and Networking,Institutional Support,Rejuvenation and Upgrading,Leadership Practices,Technological Adaptation.Practitioners,academicians,and policymakers will have a better understanding of this causal relationship among enablers.Knowledge of these enablers will help in fostering a more conducive environment for escalating the process of innovation in the handicraft cluster.

Association discovery and outlier detection of air pollution emissions from industrial enterprises driven by big data

Air pollution is a major issue related to national economy and people's livelihood.At present,the researches on air pollution mostly focus on the pollutant emissions in a specific industry or region as a whole,and is a lack of attention to enterprise pollutant emissions from the micro level.Limited by the amount and time granularity of data from enterprises,enterprise pollutant emissions are stll understudied.Driven by big data of air pollution emissions of industrial enterprises monitored in Beijing-Tianjin-Hebei,the data mining of enterprises pollution emissions is carried out in the paper,including the association analysis between different features based on grey association,the association mining between different data based on association rule and the outlier detection based on clustering.The results show that:(1)The industries affecting NOx and SO2 mainly are electric power,heat production and supply industry,metal smelting and processing industries in Beijing-Tianjin-Hebei;(2)These districts nearby Hengshui and Shijiazhuang city in Hebei province form strong association rules;(3)The industrial enterprises in Beijing-Tianjin-Hebei are divided into six clusters,of which three categories belong to outliers with excessive emissions of total vOCs,PM and NH3 respectively.

Multi-scale analysis of schizophrenia risk genes, brain structure, and clinical symptoms reveals integrative dues for subtyping schizophrenia patients

Analysis Unking directly genomics, neuroimaging phenotypes and clinical measurements is crucial for understanding psychiatric disorders, but remains rare.Here, we describe a multi-scale analysis using genome-wide SNPs, gene expression, grey matter volume (GMV), and the positive and negative syndrome scale scores (PANSS) to explore the etiology of schizophrenia. With 72 drug-naive schizophrenic first episode patients (FEPs) and 73 matched heathy controls, we identified 108 genes, from schizophrenia risk genes, that correlated significantly with GMV, which are highly co-expressed in the brain during development. Among these 108 candidates, 19 distinct genes were found associated with 16 brain regions referred to as hot clusters (HCs), primarily in the frontal cortex, sensory-motor regions and temporal and parietal regions.The patients were subtyped into three groups with distinguishable PANSS scores by the GMV of the identified HCs. Furthermore, we found that HCs with common GMV among patient groups are related to genes that mostly mapped to pathways relevant to neural signaling, which are associated with the risk for schizophrenia.Our results provide an integrated view of how genetic variants may affect brain structures that lead to distinct disease phenotypes.The method of multi-scale analysis that was described in this research, may help to advance the understanding of the etiology of schizophrenia.

Comprehensive evaluation of multiple cropping systems on upland red soil

According to the principles and methods of ecology and system engineering,we set up an evaluation indicator system for multi-component and multiple crop-ping systems,evaluated the comprehensive benefits of multi-component and multiple cropping systems using grey relation clustering analysis and screened out the optimized model based on research done in the upland red soil in Jiangxi Agricultural University from 1984 to 2004.The results show that the grey relation degree of"cabbage/potato/maize-sesame"was the highest among 23 multi-component and multiple cropping systems and was clustered into the optimized system.This indicates that"cabbage/potato/maize-sesame"can bring the best social,economic and ecological benefits,increase product yield and farmers’income and promote sustainable development of agricultural production.Therefore,it is suitable for promotion on upland red soil.The grey relation degree of"canola/Chinese milk vetch/maize/mung bean/maize"was second,which is suitable for imple-mentation at the city outskirts.In conclusion,these two planting patterns are expected to play important roles in the reconstruction of the planting structure and optimization of the planting patterns on upland red soil.