Grey Relational Degree Research Articles

A combination of FDM, EWM, and GRA to measure performance in higher vocational college: An empirical case from China

Given the problems of unclear division of evaluation factors, inadequate utilization of objective data and unreasonable distribution of weight in college performance measurement, this study extracts relevant indicators and quantitative data from the survey report of administrational institutions, and constructs a comprehensive performance measurement model based on the integration of fuzzy Delphi method (FDM), Entropy weight method (EWM), and grey relational analysis (GRA). The study seeks to identify differences in measurement analysis by comparing the weights, the performance and the rankings of the alternatives about higher vocational colleges in Zhejiang province. Finally, the results show the following: (1) by FDM, 33 indicators about colleges' performance are selected to form the evaluation system of the college’s performance, (2) the indicators' weights are obtained through EWM and GRA, and a nonparametric test shows that there is no significant difference between the two types of weights, (3) the grey relational degrees of the alternatives are obtained and ranked on the basis of comprehensive evaluation model. By nonparametric test, there is a significant difference between the two types of relational degrees. On the contrary, no significant difference is found in the ranking of relational degrees, (4) based on the analysis results, this study further compares the performance of alternatives in different forms. There are significant differences between the performance of public colleges and private colleges, while no significant differences are observed between the performance of vocational colleges in Hangzhou and non-Hangzhou. Given the reliability and validity of the model, the comprehensive measurement model provides a relatively objective reference for college governance and administrational institutions, and also becomes an effective tool of colleges' evaluation to assist and improve the management practice in educational institutions.

Research on high-quality economic development in Tianjin

ABSTRACT This paper selects development indicators of primary industry, secondary industry, and tertiary industry, as well as indicators of public lifestyle, to measure the impact of economic development and general public lifestyle on the environment. A grey relational degree model is used to analyse Tianjin’s evolving characteristics from 2000 to 2020 on the balanced development of the economy and environment. The results suggest that: Firstly, Tianjin’s overall coupling degree between economic growth and environmental change varies from 0.56 to 0.75, which is at a moderate coordination level, and the degree of coordination between tertiary industry and environment is high. Secondly, taking time as the axis, the relational degree is significant between economic growth and environmental change. From 2015 to 2020, the synergy between the development of the secondary industry and the environment declined, indicating that the development of the secondary sector has caused some environmental destruction. On these grounds, Tianjin should give full play to its traditional advantages and comparative advantages, lay emphasis on the optimisation and upgrading of its industrial structure in the process of continuously deepening the integration of Beijing-Tianjin-Hebei, promote the deep integration of manufacturing and service industry, and boost the quality its economic development.

An evaluation method of teaching reform effect based on grey relational degree

An evaluation method of teaching reform effect based on grey relational degree

Egg Freshness Indexes Correlations with Ovomucin Concentration during Storage

The relationship between protein changes and egg quality during storage is a critical area of investigation in both basic science and egg product preservation. Viscosity changes in albumen are a sensitive sign of the deterioration process. Ovomucin, which is present in albumen, plays an important role in the gelation of the albumen. Egg freshness indices and ovomucin concentration will alter in response to storage time. This paper analyzed the correlation and gray relational degree between the Haugh unit (HU), yolk index, albumen pH, and ovomucin concentration. We studied the differences in the ovomucin concentration at different levels of HU, yolk index, and pH during storage. We established an equivalent egg age prediction model using ovomucin concentration as the independent variable. The findings indicated a correlation between the freshness indices and the ovomucin concentration. There was a good, significantly positive relationship between HU and ovomucin concentration (r = 0.713, P < 0.01 ), a positive correlation between the yolk index and ovomucin concentration (r = 0.699, P < 0.01 ), and a negative correlation between albumen pH and ovomucin concentration (r = −0.683, P < 0.01 ). The highest Pearson correlation coefficient (r = 0.970, P < 0.01 ) was obtained between the albumen and ovomucin concentration. Significant differences in ovomucin concentration were observed when HU, yolk index, and pH were varied. The gray relational degree between each freshness parameter and ovomucin concentration was greater than 0.8. Between the HU and ovomucin concentration, there was a gray correlation degree of 0.885, indicating that the HU was the primary factor affecting ovomucin concentration variation during storage. During storage, at 22°C, the ovomucin concentration in albumen was significantly and negatively related to storage time (r = −0.926, P < 0.05 ). The coefficient of determination for the equivalent egg age prediction model with ovomucin concentration as the independent variable was 0.985 ( P < 0.05 ), indicating strong reliability. The study’s findings show the possibility of nondestructive prediction of an egg’s internal microscopic protein composition using its freshness index value.

Simultaneous optimization of working fluid and temperature matching for heat pump assisted geothermal cascade heating system

The heat pump assisted geothermal cascade heating system (GCHS) has a promising prospect in space heating. In this paper, considering thermodynamic, economic and environmental performance, the optimal working fluid from four hydrocarbon candidates and temperature-matching for the GCHS are determined simultaneously based on multi-objective optimization and grey relational analysis. The results show that according to the optimal compromise solutions, the GCHS with R600 has the highest thermodynamic performance (COPtot) of 22.58 and the least annual carbon emission (ACE) of 2.80 × 103 ton while R290 has the largest net present value (NPV) about 45.83 × 106 CNY. Additionally, all temperature variables considered generally tend to reach their extreme value except for the temperature lift of middle circulation water in the secondary plate heat exchanger. The optimal working fluid based on comprehensive performance is considered to be R600 with the largest grey relational degree of 0.752. Besides, the global sensitivity analysis on the GCHS with R600 indicates that the pinch point temperature difference of condenser is the most important influential parameter for COPtot with total Sobol index about 0.918 and the geothermal tail water temperature plays a significant role for both NPV and ACE with total Sobol index up to 0.806 and 0.992, respectively.

Spatial–Temporal Multivariate Correlation Analysis of Ecosystem Services and Ecological Risk in Areas of Overlapped Cropland and Coal Resources in the Eastern Plains, China

The overlapped areas of cropland and coal resources play a fundamental role in promoting economic and social progress. However, intensive mining operations in high water-level areas have brought significant spatial–temporal heterogeneity and ecological problems. From the dual dimensions of the ecosystem service value (ESV) and ecological risk (ER), it is of great significance to explore the influence characteristics of underground mining on the landscape, such as above-ground cultivated land, which is valuable to achieving regional governance and coordinated development. In this study, taking Peixian as the research area, a multiple-dimensional correlation framework was constructed based on the revised ESV and ER, integrating the grey relational degree, spatial–temporal heterogeneity, disequilibrium, and inconsistency index to explore the ESV and ER assessment and correlation characteristics from 2010 to 2020. The results show that (1) the ESV showed a high agglomerated distribution pattern in the east, with a net decrease of 13.61%. (2) The ER decreased by 78.18 and was concentrated in the western and southern regions, with overall contiguous and local agglomeration characteristics. This indicates that the ecological security of the region has improved. (3) The comprehensive grey correlation between the cultural service value and the ecological risk index was the highest. Furthermore, the spatial–temporal heterogeneity of the ESV and ER weakened, and the disequilibrium rose and then fell, indicating that the ecosystem gradually tended to be stable. The study is crucial for overlapped cropland and coal resource areas to maintain stability and sustainable development. The multivariate correlation framework provides practical value for ecosystem management and risk control.

Exploring the Influences of Stream Network Structure and Connectivity on Water Environment Risk in China

Stream networks are the transportation channels of pollutants that can significantly influence water environment risk (WER). However, the influences of stream network structure and connectivity (SC) on WER at the national scale and its regional variability have been rarely investigated in China. In this study, the WER was assessed from the grey water footprint of nitrogen and phosphorus in 214 catchments in China. The relationship between WER and SC and its regional variability were analyzed using correlation and grey relational analysis. Results showed that the water environment risk index (RI) in some catchments located in the Hai and Liao River Basins was the highest in China (RI > 0.8). On national scale, longitudinal connectivity (Cl) and cyclical connectivity (Cc) had the strongest influences on WER with grey relational degree index (GRAI) of 0.68 and 0.67, respectively. The average slope (Sr) was the most important in humid zones, whereas Cl and water surface ratio (Rw) had a stronger influence in arid zones. In zones with intensive human activities, Cc, river density (Rd), and the node connection ratio (Rnc) mostly affected WER. The main influenced factors varied significantly among nationwide and different zones, which indicated that climate and human activities played an important role in the spatial variation of the relationship between WER and SC. This study highlights the important role of SC on WER and that the relationship between WER and SC varies with climate and human activities.

Social network group decision-making for probabilistic linguistic information based on GRA

In group decision-making problems, decision-makers typically use probabilistic linguistic term sets (PLTSs) to express their evaluation opinions. This paper focuses on the social network group decision-making method for probabilistic linguistic information. First, we propose a new consensus judgment mechanism by computing the absolute grey relation degree between the most probable optimal vectors of individual decision makers and collective opinion. Furthermore, in order to reduce the calculated amount in the decision-making process, we propose a model to transform the score value into a PLTS. This technique uses stochastic multicriteria acceptability analysis to determine the criteria weights. The preferences of the decision-makers can be accurately portrayed by this approach. In addition, we put forward a model to transform the score value into a PLTS and propose a new way to obtain the criteria weights using stochastic multicriteria acceptability analysis. Moreover, we develop an advice generation method with two steps for the PLTS in a feedback adjustment process. Finally, we use a case study and comparative analysis to illustrate the effectiveness of our method. Our proposed method can be applied to address many group decision-making problems involving multiple interest groups, such as social policy, facility placement, and other issues.

Usability Evaluation of in-Vehicle AR-HUD Interface Applying AHP-GRA

Usability is regarded to be a fundamental requirement for in-vehicle HMIs, and usability evaluation reflects the impact of the interface and the acceptance from the users. This study introduced a usability evaluation model of AR-HUD interface by applying grey analytic hierarchy process (AHP). First, based on the ameliorated PSSUQ (Post-Study System Usability Questionnaire), the usability evaluation system was modified and optimized according to the characteristics of AR-HUD. On this basis, the preference weights for evaluation indexes were calculated by AHP and the idea of group decision. Finally, the criteria of usability were integrated into grey relational degree by applying grey relational analysis (GRA) to obtain optimal design. A case study was conducted to demonstrate the applicability of the developed model to the usability evaluation of AR-HUD interface design. According to the existing AR-HUD interface design, 7 dimensions of design elements (A-G) and 18 interface prototypes (S1-S18) were selected by Taguchi orthogonal array test (TOAT). As the results indicated, the grey relational degree of S5 was 0.923, signifying that it was the optimal sample; and the results were also compared with entropy-TOPSIS to verify the feasibility of the proposed method. The grey-based AHP evaluation model can be used to evaluate the usability level of AR-HUD interface effectively, which may help designers achieve insights for design process and samples decision-making.

State Monitoring of Gas Regulator Station Based on Feature Selection of Improved Grey Relational Analysis

The in-depth application of the IoT technology in the gas industry has improved the intelligence of the gas system. As an important part of the gas system, the optimization of state monitoring of regulator stations is of great significance. The efficiency of monitoring can be improved by feature selection, but the reduction of important features will reduce the accuracy of identification. Therefore, a feature selection method based on gray relational analysis is proposed. First, the distance between the comparison matrix and the reference matrix is transformed using the geometric probability distribution, which solves the problem of data initial transformation. Then, an adaptive value method of resolution coefficient based on the cuckoo algorithm is proposed. Finally, a weight coefficient is constructed by combining the redundancy and the correlation to improve the gray relational degree applied to nonsequential systems. The maximum classification accuracy and the number of selected features are used to compare the performance of feature selection methods for several different types of data sets. Simulation analysis shows that the proposed method has higher maximum classification accuracy and smaller selected feature set. Finally, the proposed method is applied to the state monitoring of gas regulator stations. Seven feature selection methods, including the proposed method, the classical methods, and the advanced methods are combined with a support vector machine, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -nearest neighbor, and decision tree, respectively. The experimental results indicate that the comprehensive performance of the proposed method combined with the three classifiers is good. It enables the subset containing the most identifiable features to be obtained quickly, which is beneficial to improve the efficiency of state monitoring.

Study on Influencing Factors of Ethanol Coupling to Prepare C4 Olefins

In this paper, we study the data analysis and calculation of the factors influencing the preparation of C4 olefins by ethanol coupling. Specifically, by combining each catalyst, the relationship between temperature and ethanol conversion and C4 olefin selectivity was analyzed by regression analysis and correlation analysis, respectively. In addition, the grey relational degree analysis was used to solve the grey relational degree between the influencing factors of the experiment. The nonlinear correspondence between ethanol conversion and C4 olefin selectivity with different catalyst combinations and temperatures was evaluated by means of random forest model and BP neural network model. Finally, we build a support vector machine regression (SVMR) model and optimize the cost and gamma parameters of this model using a genetic algorithm.

Comprehensive Evaluation of Paddy Quality by Different Drying Methods, Based on Gray Relational Analysis

If paddy is not dried in time after harvesting, it very easily becomes moldy, which causes substantial losses. Inappropriate drying methods also affect the quality and safety of paddy when it enters the drying process. In order to select the optimal drying method, paddy with different moisture content was treated with sun drying, mechanical drying, and late harvesting, and the quality indexes of paddy were tested for safe moisture content. The effects of different drying methods on the quality of paddy were analyzed in terms of burst rate, taste value, appearance quality, and pasting characteristics. A paddy quality index system was constructed, and gray relational analysis (GRA) was used to evaluate comprehensively the paddy quality. The results showed that when the moisture content of paddy was 24.4%, the best quality of paddy was obtained after mechanical drying, when the gray relational degree was 0.996. Timely harvesting and mechanical drying can not only reduce the loss of paddy, but can also ensure the quality and safety of paddy.

Air Conditioning Load Prediction of an Office Building Based on Long Short Term Memory Neural Network

Background: Energy conservation has always been a major issue in our country, and the air conditioning energy consumption of buildings accounts for the majority of the energy consumption of buildings. If the building load can be predicted and the air conditioning equipment can respond in advance, it can not only save energy but also extend the life of the equipment. Introduction: The neural network proposed in this paper can deeply analyze the load characteristics through three gate structures, which helps improving the prediction accuracy. Combined with the grey relational degree method, the prediction speed can be accelerated. Method: This paper introduces a grey relational degree method to analyze the factors related to air conditioning load and selects the best ones. A Long Short-Term Memory Neural Network (LSTMNN) prediction model was established. In this paper, grey relational analysis and LSTMNN are combined to predict the air conditioning load of an office building, and the predicted results are compared with the real values. Results: Compared with the Back Propagation Neural Network (BPNN) prediction model and Support Vector Machine (SVM) prediction model, the simulation results show that this method has a better effect on air conditioning load prediction. Conclusion: Grey relational degree analysis can extract the main factors from the numerous data, which is more convenient and quicker without repeated trial and error. LSTMNN prediction model not only considers the relation of air conditioning load on time series but also considers the nonlinear relation between load and other factors. This model has higher prediction accuracy, shorter prediction time, and great application potential.

Performance evaluation of the practical application of cleaner production management system: A case study of steel enterprise

Recently, cleaner production (CP) has attracted more attention from developing countries because of its various advantages. However, due to the lack of management constraints, CP activities often cannot keep efficient for long. To alleviate this problem, we put forward the cleaner production management (CPM) system and applied it in China. Based on our previous research, this paper constructed a comprehensive performance evaluation system to effectively supervise the implementation effect of CPM system. By analysing the flows of material, energy and information, a multi-level index system integrating the CPM requirements and sustainable development needs was established. And the quantification of performance was realized by combining the analytic hierarchy process and grey relational degree operation, which reliability was confirmed by sensitivity analysis. The case study in Chinese steel enterprise showed that after implementing CPM system for three years, the overall performance level increased by about 25%. It has confirmed that the application of CPM mode could obviously promote the sustainable green development of the steel enterprise and provide the directions for carrying out CP plans in the next step, such as strengthening innovation of management system and improving employees' awareness of CP activities. In the context of enterprises seeking sustainable development, CPM system and performance evaluation possess broad application value.

Interval-Valued Intuitionistic Fuzzy Decision With Graph Pattern in Big Graph

Graph pattern matching (GPM) in big graph has been widely used in decision making, such as expert finding, social group discovery, etc. However, these existing works consider neither the preference of the decision maker (DM), nor the subjectivity of constraints during the process of GPM. Therefore, this paper proposes an interval-valued intuitionistic fuzzy decision (IVIFD) with graph pattern in big graph. As traditionally IVIFD can maximally reduce the uncertainty of decision making and it is only valid for small datasets, which makes it impossible to be applied in big graph. In this paper, GPM is adopted to prune the searching space, which makes it possible to process IVIFD under the preference of the DM later. Technically, firstly, each DM selects the preferred vertices and/or edges in big graph, and the interval-valued intuitionistic fuzzy preference (IVIFP) is calculated and used to form the contextual constraints to conduct GPM. Secondly, a probability-certainty density function is introduced to capture the subjective probability of the contextual preference of subgraphs via the bijection from rating space of the context to preference space of the context, which leads to an interval-valued intuitionistic fuzzy set (IVIFS). In addition to the IVIFS, the IVIFD is made through interval-valued intuitionistic fuzzy cross entropy and grey relation degree. Moreover, the weight problem between different contexts is taken into account and handled respectively as three cases. Finally, numerical experiments and perturbation analysis validate the effectiveness and stability of our proposed method, and verify its necessity and efficiency through ablation experiments.

Novel multi-attribute decision-making method based on Z-number grey relational degree

Novel multi-attribute decision-making method based on Z-number grey relational degree

OPTIMIZATION OF 3D PROCESSING PARAMETERS USED FDM METHOD IN THE PRODUCTION OF ABS BASED SAMPLES

3D yazıcılar ile imalat birçok girdi parametresi olmasından dolayı oldukça karmaşık bir süreçtir. Bu durum optimum üretim parametrelerin belirlenmesini zorlaştırmaktadır. Bu çalışmada; ABS malzemelerin 3D yazıcılarda ergiyik yığma modelleme (FDM) ile üretilmesinde doluluk oranı (%50, %70 ve %90), dolgu deseni (doğrusal, sekizgen ve bal peteği) ve katman kalınlığı (0,19 mm, 0,29mm ve 0,39 mm) işlem parametrelerinin; yüzey pürüzlülüğü, çekme dayanımı ve imalat sürelerine etkileri varyans analizi (ANOVA) incelenmiştir. Çıktı parametreleri üzerinde en etkili işlem parametresi katman kalınlığıdır. Azalan katman kalınlığının yüzey pürüzlülüğü ve çekme dayanımı üzerinde olumlu etkisi olurken, imalat süresi üzerinde olumsuz etkisi olmuştur. Taguchi L27 (33) deneysel tasarımına göre geçekleştirilen deneylerde optimazyon işleminde üç farklı çıktı parametresi değerlendiği için çoklu performans optimizasyonunda değerlendirme kriteri olarak gri ilişkisel derece kullanılmıştır. Optimum işlem parametresi doğrusal dolgu deseni, %90 dolgu oranı ve 0,19 mm katman kalınlığı olarak belirlenmiştir.

Multi-Response Optimisation of Automotive Door Using Grey Relational Analysis with Entropy Weights

Tail-welded blanks (TWBs) are widely used in automotive bodies to improve the structural performance and reduce weight. The stiffness and modal lightweight design optimisation of TWBs for automotive doors was performed in this study. The finite element model was validated through physical experiments. An L27 (312) Taguchi orthogonal array was used to collect the sample points. The multi-objective optimisation problem was transformed into a single-objective optimisation problem based on the grey relational degree. The optimal combination of structural design parameters was obtained for a tail-welded door using the proposed method, and the weight of the door structure was reduced by 2.83 kg. The proposed optimisation method has fewer iterations and a lower computational cost, enabling the design of lightweight TWBs.

A New Fault Diagnosis Method for Quality Control of Electromagnet Based on T–S Fault Tree and Grey Relation

Product fault diagnosis has been a focus of research in the field of quality control. Many studies have been made to ensure the accuracy of fault diagnosis in electromagnet quality control direction. However, in the pursuit of perfect identification method, the fault diagnosis process is extremely complex in quality control management. A new fault diagnosis method for quality control of electromagnet machining processes based on [Formula: see text]–[Formula: see text] fault tree and grey relation degree analysis is proposed in this paper. First, [Formula: see text]–[Formula: see text] fault tree is used to analyze and describe the dynamic fault relationship of the electromagnet machining processes. In order to make the fault mode more intuitive and specific, [Formula: see text]–[Formula: see text] fault tree logic diagram is used to describe the actual fault form of machining processes. Second, according to the fault output algorithm of [Formula: see text]–[Formula: see text] fault tree, the fault rate of basic event in each cycle is calculated and the fault rate of intermediate event and top event in each cycle is obtained. Then, the grey relation method is applied to calculate the relation between the basic event and the trend of top event failure rates in electromagnet machining processes. Based on the obtained relation degree, the faulty system of electromagnet machining processes can be repaired pertinently and achieve the purpose of quality control. Finally, an example of electromagnet quality control’s fault diagnosis is used to verify the objectivity and simplicity of the new method.

Intuitionistic fuzzy decision support based on EDAS and grey relational degree for historic bridges reconstruction priority

Bridge management includes all actions in the life cycle of the bridge, to ensure its safety, stability, and functionality. Numerous problems have been identified that are primarily related to the organization of planning and the role of decision-making in the reconstruction of the historic pedestrian bridges. The planning process for the reconstruction of these bridges is crucial due to increased traffic load, poor condition, or damage to bridges. Some of these bridges are part of the cultural heritage, while some are unfairly neglected. The motivation for this research arose from the need to establish the priority for the reconstruction of historic pedestrian bridges to achieve their safety, stability, functionality, and cultural preservation. For this reason, a new decision support model based on intuitionistic fuzzy group decision-making to the multi-criteria analysis is created. The model combines multi-criteria method Evaluation Based on Distance from Average Solution and grey relational degree (GRD) with intuitionistic fuzzy theory. Three relevant decision groups of experts are formed, with the knowledge and expertise in the area of research problematic, establishing criteria for the evaluation. A new approach to the consistency of criteria weights is proposed. The intuitionistic fuzzy likelihood function is developed for the aggregation of bridge evaluations. Furthermore, GRD values are calculated to determine the reconstruction priority ranking of bridge for each decision group. The final ranking is defined by integrating Integer Linear Programming (ILP) and Ant Colony Optimization (ACO), determining spatial-functional, time, and financial constraints.